Question: Presenting MVTec ITODD - A dataset designed for 3D object recognition in industrial settings.

Introducing T-LESS: A Comprehensive RGB-D Dataset for 6D Pose Estimation of Texture-Poor Objects

We present T-LESS, a novel and publicly accessible dataset designed for the estimation of 6D poses, encompassing both translation and rotation, of rigid objects that lack discernible texture. The dataset comprises thirty objects of industrial significance, characterized by their lack of distinct color or reflectance patterns and minimal texture. Notably, these objects share similarities in shape and size, some even serving as components of others, a distinctive feature not commonly found in other datasets. The dataset includes training and testing images captured using three synchronized sensors: a structured-light RGB-D sensor, a time-of-flight RGB-D sensor, and a high-resolution RGB camera. Approximately 39,000 training and 10,000 testing images are available from each sensor type.

Two 3D models are provided for every object: a manually crafted CAD model and one reconstructed semi-automatically. Training images feature single objects set against a black background, while test images are derived from twenty scenarios of varying complexity. These scenarios progress from straightforward setups with a few isolated objects to more challenging environments with multiple instances of various objects, high levels of clutter, and significant occlusion. The images are captured from systematically sampled viewpoints around the object or scene and are annotated with precise ground truth 6D poses for all modeled objects.

Preliminary evaluation outcomes suggest that the current state of the art in 6D object pose estimation has significant potential for enhancement, particularly in scenarios with substantial occlusion. The T-LESS dataset is available online for researchers to access and utilize in their work.

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

The 3-D reconstruction involves the construction of a 3-D model from a set of images. The 3-D reconstruction has varied uses that include 3-D printing, the generation of 3-D models that can be shared through social media, and more. The 3-D reconstruction involves complex computations in mobile phones that must determine the pose estimation. The pose estimation involves the process of transforming a 2-D object into 3-D space. Once the pose estimation is done, then the mesh generation is performed using the graphics processing unit. This helps render the 3-D object. The competitive advantages of using hardware processors are to accelerate the intensive computation using graphics processors and digital signal processors. The stated problem that this technical paper addresses is the need for a reliable automated test for the 3-D reconstruction feature. The solution to this problem involved the design and development of an automated test system using a programmable robotic arm and rotor for precisely testing the quality of 3-D reconstruction features. The 3-D reconstruction testing involves using a robotic arm lab to accurately test the algorithmic integrity and end-to-end validation of the generated 3-D models. The robotic arm can move the hardware at different panning speeds, specific angles, fixed distances from the object, and more. The ability to reproduce the scanning at a fixed distance and the same panning speed helps to generate test results that can be benchmarked by different software builds. The 3-D reconstruction also requires a depth sensor to be mounted onto the device under examination. We use this robotic arm lab for functional, high performance, and stable validation of the 3-D reconstruction feature. This paper addresses the computer vision use case testing for 3-D reconstruction features and how we have used the robotic arm lab for automating these use cases.

We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes. This is especially useful for enhancing volumetric ConvNets and classifying 3D scans with insufficient surface details. Experimental results on CAD and real-world scan datasets showed that our technique outperforms previous approaches.

In this work, we describe a novel method for creating 3D models of persons freely moving in front of a consumer depth sensor and we show how they can be used for long-term person re-identification. For overcoming the problem of the different poses a person can assume, we exploit the information provided by skeletal tracking algorithms for warping every point cloud frame to a standard pose in real time. Then, the warped point clouds are merged together to compose the model. Re-identification is performed by matching body shapes in terms of whole point clouds warped to a standard pose with the described method. We compare this technique with a classification method based on a descriptor of skeleton features and with a mixed approach which exploits both skeleton and shape features. We report experiments on two datasets we acquired for RGB-D re-identification which use different skeletal tracking algorithms and which are made publicly available to foster research in this new research branch.

We present a survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, it is rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on structured light concept, projecting a given pattern on the measuring surfaces. However, some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the needed information to solve the correspondence problem. ( 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Pattern projection Correspondence problem Active stereo Depth perception Range data Computer vision.

• Lighting. When an object is in bright light, it looks brighter than when it’s in shadow, so a program can’t just look at image intensity values. • Within-class variation. Different instances of the same kind of object can look quite different to one another. For example, a green station wagon and a red convertible are both cars, so a program can’t simply compare a picture to one example. • Aspect. The same object can look very different when viewed at from different directions—pick up a book and compare its cover and its spine to see this effect. Again, this means that a program might need to have many examples of each type of object. • Deformation. Many objects can change their appearance significantly without their identity changing. For example, you can move your limbs around, change clothes, paint your face, or have your hair cut. You will look very different indeed, but you will still be a person.

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the birds eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 14.9% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.

Understanding 3D environments is a vital element of modern computer vision research due to paramount relevance in many vision systems, spanning a wide field of application scenarios from self-driving cars to autonomous robots [Qi et al. 2016]. At the present time, object recognition mainly employs two methods: volumetric CNNs [Wu Z 2015] and multi-view CNNs [Xu et al. 2015] [Xu et al. 2016]. In this paper, we propose a volumetric spatial transformer network for object recognition. It fills the gap between 3D CNN and 2D CNN for the first time, and provides an end-to-end training fashion. Given a 3D shape, the network can automatically select the best view that maximizes the accuracy of object recognition.

The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.


Question: A method for distributed learning in unreliable network environments.

This paper explores the efficacy of large minibatch Stochastic Gradient Descent (SGD) in accelerating the training of deep learning models, particularly focusing on the ImageNet dataset. Deep learning models, known for their reliance on extensive neural networks and vast datasets, often suffer from prolonged training times, which can hinder the pace of research and development. Distributed synchronous SGD presents a promising solution by distributing the SGD minibatch workload across a cluster of parallel workers. However, for this strategy to be effective, the per-worker workload must be substantial, necessitating a significant increase in the SGD minibatch size. Our empirical findings reveal that large minibatches introduce optimization challenges on the ImageNet dataset, but these can be mitigated without compromising the model's generalization ability. Specifically, we demonstrate that accuracy is not adversely affected when training with minibatch sizes as large as 8192 images. To achieve this, we employ a linear scaling rule to adjust learning rates in accordance with the minibatch size and introduce a novel warmup scheme to tackle early training optimization issues. Utilizing this approach, our Caffe2-based system trains the ResNet50 model with a minibatch size of 8192 on 256 GPUs within a single hour, achieving comparable accuracy to smaller minibatch training. Furthermore, our implementation on commodity hardware exhibits approximately 90% scaling efficiency when expanding from 8 to 256 GPUs. This system enables efficient training of visual recognition models on large-scale internet datasets.

We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/fF’,and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes nr.inimaflyas the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.

The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen

This article introduces a class of incremental learning procedures specialized for prediction-that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.

The paper reviews the past and present results in the area of decentralized control of large-scale complex systems. An emphasis is laid on decentralization, decomposition, and robustness. These methodologies serve as effective tools to overcome specific difficulties arising in largescale complex systems such as high dimensionality, information structure constraints, uncertainty, and delays. Several prospective topics for future research are introduced in this contents. The overview is focused on recent decomposition approaches in interconnected dynamic systems due to their potential in providing the extension of decentralized control into networked control systems. # 2008 Elsevier Ltd. All rights reserved.

As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Effective use of clusters for ML programs requires considerable expertise in writing distributed code, but existing highlyabstracted frameworks like Hadoop that pose low bar-ed frameworks like Hadoop that pose low barriers to distributed-programming have not, in practice, matched the performance seen in highly specialized and advanced ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML programs into distributed ones, while maintaining high throughput through relaxed “consistency models” that allow asynchronous (and, hence, inconsistent) parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoreticallymotivated but undiscovered opportunities to maximize computational throughput. Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an “eager” PS communication mechanism, and implement it as a new PS system that enables ML programs to reach their solution more quickly.

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning. “Poor is the pupil who does not surpass his master.” – Leonardo da Vinci

Sender-initiated reliable multicast protocols, based on the use of positive acknowledgments (ACKs), lead to an ACK implosion problem at the sender as the number of receivers increases. Briefly, the ACK implosion problem refers to the significant overhead incurred by the sending host due to the processing of ACKs from each receiver. A potential solution to this problem is to shift the burden of providing reliable data transfer to the receivers—thus resulting in a receiver-initiated multicast error control protocol based on the use of negative acknowledgments (NAKs). In this paper we determine the maximum throughputs of the sending and receiving hosts for generic sender-initiated and receiver-initiated protocols. We show that the receiver-initiated error control protocols provide substantially higher throughputs than their sender-initiated counterparts. We further demonstrate that the introduction of random delays prior to generating NAKs coupled with the multicasting of NAKs to all receivers has the potential for an additional substantial increase in the throughput of receiver-initiated error control protocols over sender-initiated protocols.

Multi-user applications are commonly implemented using a centralized server. This paper presents a new design for multi-user chat applications (Chronos) that works in a distributed, serverless fashion over Named Data Networking. In Chronos, all participants share their views by exchanging the cryptographic digests of the chat room data set. A newly generated message causes a change of the digest at the message originator, which leads to retrieving the new data by all other participants in an efficient way and resynchronization of chat room views. Chronos does not have a single point of failure and eliminates traffic concentration problem of server-based implementations. We use simulations to evaluate and compare Chronos with a traditional serverbased chat room implementation. Our results demonstrate Chronos’ robustness and efficiency in data dissemination. Chronos’ approach of replacing centralized servers by distributed data synchronization can be applied to a variety of distributed applications to simplify design and ease deployment.

In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead. Introduction Consider the distributed training of deep neural networks over multiple workers (Dean et al. 2012), where all workers can access all or partial training data and aim to find a common model that yields the minimum training loss. Such a scenario can be modeled as the following distributed parallel non-convex optimization min x∈Rm f(x) ∆ = 1 N N ∑

Asynchronous execution of processes and unpredictable communication delays create nondeterminism in distributed systems that complicates the design, verification, and analysis of distributed programs. To simplify the design and development of distributed applications, the idea of “causal message ordering” (CO) was introduced by Birman and Joseph [1]. If for any two messages M and M’ sent to the same destination d, Send(M) + Send(M’), then CO ensures that Delivered + Deliver-yd(lf’) at d. Existing algorithms that provide CO have high message overhead and use more storage than is required by an optimal algorithm, or make simplifying assumptions by assuming certain topolog y/communicat ion pat t ems and inbuilt synchronize ation. We present an optimal CO algorithm [2] under the following framework:


Question: A decentralized learning method for unreliable network environments.

In the realm of distributed machine learning, the robustness of Stochastic Gradient Descent (SGD) against Byzantine failures has gained significant attention. Prior to our study, distributed machine learning systems often overlooked the potential for failures, particularly those that are arbitrary or Byzantine in nature. Such failures can stem from a variety of sources, including software errors, network asynchrony, data biases, or even malicious attacks aimed at disrupting the system. With a scenario where n workers are involved, up to f of which may exhibit Byzantine behavior, we explore the extent to which SGD can maintain resilience, without restricting the dimensionality or the size of the parameter space.

Our investigation reveals that no existing gradient aggregation method, which typically involves a linear combination of vectors proposed by the workers (as is standard practice in current approaches), can withstand even a single Byzantine failure. To address this, we define a resilience criterion for the aggregation rule that encapsulates the essential requirements for ensuring convergence in the presence of f Byzantine workers. In response, we introduce "Krum," an innovative aggregation rule that meets our resilience criterion. To the best of our knowledge, Krum is the first algorithm proven to be robust against Byzantine adversaries in the context of distributed SGD. Our findings are supported by empirical evaluations of Krum's performance.

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

We present decentralized rollout sampling policy iteration (DecRSPI) — a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses MonteCarlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. Experimental results confirm the effectiveness and scalability of the approach.

In this paper, we consider a robust network control problem. We consider linear unstable and uncertain discrete time plants with a network between the sensors and controller and the controller and plant. We investigate two defining characteristics of network controlled systems and the impact of uncertainty on these. Namely, the minimum data rates required for the two networks and the tolerable data drop out in the form of packet losses. We are able to derive sufficient conditions in terms of the minimum data rate and minimum packet arrival rate to ensure stability of the closed loop system. I. I NTRODUCTION Recently, networked control systems (NCS) have gained great attention from both the control community and the network and communication community. When compared with classical feedback control system, networked control systems have many advantages. For example, they can reduce the system wiring, make the system easy to operate and maintain and later diagnose in case of malfunctioning, and increase system agility [16]. In spite of the great advantages that the networked control architecture brings, inserting a network in between the plant and the controller introduces many problems as well. For instance, zero-delayed sensing and actuation, perfect information and synchronization are no longer guaranteed in the new system architecture as only finite bandwidth is available and packet drops and delays may occur due to network traffic conditions. These must be revisited and analyzed before any practical networked control systems are built. In the past decade, many researchers have spent effort on those issues and a number of significant results were obtained and many are in progress. Many of the aforementioned issues are studied separately. Tatikonda [15] and Sahai [11] have presented some interesting results in the area of control under communication constraints. Specifically, Tatikonda gave a necessary and sufficient condition on the channel data rate such that a noiseless LTI system in the closed loop is asymptotically stable. He also gave rate results for stabilizing a noisy LTI system over the digital channel. Sahai proposed the notion of anytime capacity to deal with real time estimation and control for a networked control system. In our companion paper [13], the authors have considered various rate issues under finite bandwidth, packet drops and finite controls. The effect of pacekt loss and delay on state Control and Dynamical Systems, California Institute of Technology; Pasadena, CA 91125. Email:{shiling, epstein, murray }@cds.caltech.edu; Tel: (626) 395-2313, Fax: (626) 796-8914. Work supported in part by NSF ITR grant CCR-0326554. The authors are equally contributed to this work. estimation was studied by the work of Sinopoli, et. al. in [2]. It has further been investigated by many researchers including the present authors in [12] and [5]. One of the hallmarks of a good control system design is that the closed loop remain stable in the presence of uncertainty [3], [4]. While the researchers in [7] studied the problem of LQG control across a packet dropping networks, not many have considered the norm bounded uncertainty investigated in the present paper. We examine the impact of a norm bounded uncertainty on the network control system and provide sufficient conditions for stability in terms of the minimum data rates and packet arrival rates for the networks. The paper is organized as follows. In Section II, we present the mathematical model of the closed loop system and state our assumptions. In Section III, we state the sufficient conditions for closed loop stability for the case where a network connects the sensors to the controller. In Section IV, we state the sufficient stability conditions where in addition there is a network between the controller and the plant. For both sections we obtain results for scalar and general vector cases. Conclusions and future work are given in the last section. II. PROBLEM SET UP We consider linear discrete time systems with a norm bounded uncertainty in the A matrix. We will investigate two NCS that we will define by the type of networks embedded in the control loop. The first NCS considered has a network between the measurement sensors and the controller, with the controller then directly connected to the actuators/plant. The second NCS will also include a network between the controller and the actuators/plant. These two network types and depicted in Figures 1 and 2. The networks are defined in terms of their data rates and probability of dropping packets. We would consider any packet delays as losses, i. ., we do not use delayed packets for estimation or control. The following equations represent the closed loop system for NCS I (Figure 1). xk+1 = (A + ∆k)xk + Buk (1) yk = λkCxk (2) wherexk ∈ IR is the state of the system, uk ∈ IR is the control input,yk ∈ IR is the output of the system, and λk are Bernoulli i.i.d random variable with parameter λ, i.e., E[λk] = λ for all k. ∆k satisfies∆k ∆k ≤ KI for all k. We also assume the initial condition x0 ∈ IR is bounded. The matrix A is assumed to be unstable without loss of generality as for any matrix A, we can always do some state transformation to decompose the states into stable ones and

Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable clustering solutions that offer accurate clustering models at a reasonable pace. In this paper, we propose GoScan, a fully decentralized density-based clustering algorithm which is capable of clustering dynamic and distributed datasets without requiring central control or message flooding. We identify two major tasks: finding the core data points, and forming the actual clusters, which we execute in parallel employing gossip-based communication. This approach is very efficient, as it offers each peer enough authority to discover the clusters it is interested in. Our algorithm poses no extra burden of overlay formation in the network, while providing high levels of scalability. We also offer several optimizations to the basic clustering algorithm for improving communication overhead and processing costs. Coping with dynamic data is made possible by introducing an age factor, which gradually detects data-set changes and enables clustering updates. In our experimental evaluation, we will show that GoSCAN can discover the clusters efficiently with scalable transmission cost.

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).

We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.

Our growing reliance on online services accessible on the Internet demands highly-available systems that provide correct service without interruptions. Byzantine faults such as software bugs, operator mistakes, and malicious attacks are the major cause of service interruptions. This thesis describes a new replication algorithm, BFT, that can be used to build highly-available systems that tolerate Byzantine faults. It shows, for the first time, how to build Byzantine-fault-tolerant systems that can be used in practice to implement real services because they do not rely on unrealistic assumptions and they perform well. BFT works in asynchronous environments like the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. The window may increase under a denial-of-service attack but the algorithm can detect and respond to such attacks and it can also detect when the state of a replica is corrupted by an attacker. BFT has been implemented as a generic program library with a simple interface. The BFT library provides a complete solution to the problem of building real services that tolerate Byzantine faults. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations. The most important optimization is the use of symmetric cryptography to authenticate messages. Public-key cryptography, which was the major bottleneck in previous systems, is used only to exchange the symmetric keys. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. Therefore, we believe that the BFT library can be used to build practical systems that tolerate Byzantine faults. Thesis Supervisor: Barbara H. Liskov Title: Ford Professor of Engineering

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages.

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.


Question: A method for distributed learning in unreliable network environments.

This paper explores stochastic optimization for deep learning in a parallel computing environment, focusing on scenarios with communication constraints. We introduce a novel algorithm designed to optimize work distribution and communication among concurrent processes (local workers) in relation to a central variable managed by a parameter server (master). The proposed method employs an elastic force concept that connects the parameters calculated by the local workers to the center variable, facilitating increased exploration by allowing the local parameters to fluctuate more widely from the center. This is achieved by minimizing the communication between local workers and the master, thereby enabling greater flexibility and exploration in the search for optimal solutions, particularly in the presence of multiple local optima common in deep learning tasks. The paper presents both synchronous and asynchronous versions of the algorithm. A stability analysis of the asynchronous variant under a round-robin scheme is provided, contrasting it with the widely used Alternating Direction Method of Multipliers (ADMM). Our findings reveal that the stability of the proposed Elastic Averaging Stochastic Gradient Descent (EASGD) is assured under a straightforward stability condition, a feature lacking in ADMM. Additionally, we propose a momentum-enhanced version of the algorithm, applicable in both synchronous and asynchronous contexts. The asynchronous EASGD is tested on training convolutional neural networks for image classification tasks using the CIFAR and ImageNet datasets. Experimental results confirm that the new algorithm expedites the training of deep neural architectures compared to DOWNPOUR and other conventional baseline methods, while also showcasing superior communication efficiency.

Agent-based systems are increasingly being applied in a wide range of areas including telecommunications, business process modelling, computer games, control of mobile robots and military simulations. Such systems are typically extremely complex and it is often useful to be able to simulate an agent-based system to learn more about its behaviour or investigate the implications of alternative architectures. In this paper, we discuss the application of distributed discrete-event simulation techniques to the simulation of multi-agent systems. We identify the efficient distribution of the agents’ environment as a key problem in the simulation of agent-based systems, and present an approach to the decomposition of the environment which facilitates load balancing. Keywords— agents, distributed simulation, interest management, load balancing.

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called metadata. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPIN protocols perform close to the theoretical optimum.

Since its introduction nearly 20 years ago, the Byzantine Generals Problem has been the subject of many papers having the scrutiny of the fault tolerance community. Numerous Byzantine tolerant algorithms and architectures have been proposed. However, this problem is not yet sufficiently understood by those who design, build, and maintain systems with high dependability requirements. Today, there are still many misconceptions relating to Byzantine failure, what makes a system vulnerable, and indeed the very nature and reality of Byzantine faults. This paper revisits the Byzantine problem from a practitioner’s perspective. It has the intention to provide the reader with a working appreciation of the Byzantine failure from a practical as well as a theoretical perspective. A discussion of typical failure properties and the difficulties in preventing the associated failure propagation is presented. These are illustrated with real Byzantine failure observations. Finally, various architectural solutions to the Byzantine problem are presented. 1 What You Thought Could Never Happen In English, the phrase “one in a million” is popularly used to describe the highly improbable. The ratio itself is difficult to comprehend. The easiest way to give it reason is to equate it to real-world expectations. For example, the probability of winning the U.K. National Lottery is around one in fourteen million; the probability of getting struck by lightning in the U.S. is around one in six hundred thousand [1]. It is not safe to rely on intuition for reasoning about unfathomably small probabilities (for example, the 1-in-1,000,000,000 maximum failure probability for critical aerospace systems1). It is problematic in two ways: (1) real-world parallels are beyond typical human experience and comprehension; (2) faults that are not recognized, such as Byzantine faults, are incorrectly assumed to occur with zero or very low probability. The lack of recognition causes additional issues in that it allows the manifestation of such faults to pass unnoticed or be otherwise misclassified, reinforcing the misconception of low probability of occurrence. 1 Usually written as a failure rate of 10/hr 236 K. Driscoll et al. The lack of recognition leads to repeating the “Legionnaire’s Disease” phenomenon. After its “discovery” in 1976, a search of medical records found that the disease had seldom occurred for many decades. The lack of shared knowledge and the disease’s rarity made each occurrence appear to be unique. Only after 1976 was it realized that all these “unique” occurrences had a common cause. Similarly, an observation of a Byzantine failure will not be recognized as being an instance of a known class of failure by those who are not intimately familiar with Byzantine failures. The intent of this paper is to redress this situation. Drawing from the authors’ experiences with Byzantine failures in real-world systems, this paper shows that Byzantine problems are real, have nasty properties, and are likely to increase in frequency with emerging technology trends. Some of the myths with respect to the containment of Byzantine faults are dispelled and suitable mitigation strategies and architectures are discussed. 2 The Byzantine Army Is Growing The microprocessor revolution has seen electronic and software technologies proliferate into almost every domain of life, including an increasing responsibility in safety-critical domains. High assurance processes (e.g. DO-254 [2]) have been matured to manage the development of high integrity systems. The cost of using high assurance processes is relatively high in comparison to equivalent levels of functionality in commercial counterparts. In recent years, there has been a push to adopt commercial off-the-shelf (COTS) technology into high integrity systems. The timing of the COTS push is alarming, considering the decreasing reliability and dependability trends emerging within the COTS integrated circuit (IC) arena. With increasing clock frequencies, decreasing process geometries, and decreasing power supply voltages, studies [3] conclude that the dependability of modern ICs is decreasing. This development reverses the historical trend of increasing IC dependability. Bounding the failure behaviors of emerging COTS ICs will become increasingly more difficult. The expected lifetime of ICs is also decreasing, as modes of “silicon wear-out” (i.e. time-dependent dielectric breakdown, hot carrier aging, and electro-migration) become more significant. The anticipated working life of current ICs may be on the order of 5-10 years. Although viable in the commercial arena, this is a world away from the requirements for high integrity systems, which have traditionally had deployment lifetimes ranging over multiple decades. Strategies to mitigate the problems are in development [4]. It is safe to assume that the anticipated rate of IC failure will increase and the modes of failure will become ever more difficult to characterize. Another significant trend is the move towards more distributed, safety-critical processing system topologies. These distributed architectures appear to be favored for the emerging automotive “by-wire” control systems [5], where a new breed of safety critical communications protocols and associated COTS are being developed [6]. Such technologies, if developed in compliance with the traditional high assurance processes, show promise to answer the cost challenges of high integrity applications. However, it is imperative that such technologies are developed with full knowledge of all possible failure modes. Distributed control systems, by their very nature, require Byzantine Fault Tolerance, from Theory to Reality 237 consensus among their constituent elements. The required consensus might be lowlevel (e.g. in the form of mutual synchronization) or at a higher level (e.g. in the form of some coordinated system action such as controlling a braking force). Addressing Byzantine faults that can disrupt consensus is thus a crucial system requirement. We expect Byzantine faults to be of increasing concern given the two major trends described in this section: (1) Byzantine faults are more likely to occur due to trends in device physics; (2) systems are becoming more vulnerable due to increasing emphasis on safety-critical distributed topologies. It is therefore imperative that Byzantine faults and failure mechanisms become widely understood and that the design of safety-critical systems includes mitigation strategies. 3 Parables from the Classical Byzantine Age The initial definition of Byzantine failure was given in a landmark paper by Lamport et al [7]. They present the scenario of a group of Byzantine Generals whose divisions surround an enemy camp. After observing the enemy, the Generals must communicate amongst themselves and come to consensus on a plan of action— whether to attack or retreat. If they all attack, they win; if none attack, they live to fight another day. If only some of the generals attack, then the generals will die. The generals communicate via messages. The problem is that one or more of the generals may be traitors who send inconsistent messages to disrupt the loyal generals from reaching consensus. The original paper discusses the problem in the context of oral messages and written signed messages that are attributed different properties. Oral messages are characterized as follows: A1. Every message that is sent is delivered correctly (messages are not lost). A2. The receiver of a message knows who sent it. A3. The absence of a message can be detected. For messages with these properties, it has been proven that consensus cannot be achieved with three generals, if one of the generals is assumed to be a traitor. A solution is presented in which each of four general exchanges information with his peers and a majority vote makes selections over all of the data exchanged. This solution is generalized to accommodate multiple traitors, concluding: to tolerate m traitorous generals, requires 3 m + 1 generals utilizing m + 1 rounds of information exchange. Written, signed messages assume all of the properties (A1-A3) of the oral messages, and are further characterized by the properties below: A4. A loyal general’s signature cannot be forged. A5. Anyone can verify the authenticity of a signature. Assuming the signed message properties above, it is shown that consensus is possible with just three generals, using a simple majority voting function. The solution is further generalized to address multiple fault scenarios, concluding: to tolerate m traitorous generals requires 2m + 1 loyal generals and m + 1 rounds of information exchange. The initial proofs presented in the paper assume that all generals communicate directly with one another. The assumptions are later relaxed to address topologies of 238 K. Driscoll et al. less connectivity. It is proven that for oral messages, consensus is possible if the generals are connected in a p regular graph, where p > 3m 1. For signed (authenticated) messages, it is proven that consensus is possible if the loyal generals are connected to each other. However, this solution requires additional rounds of information exchange Since its initial presentation, nearly two decades ago, the Byzantine Generals problem has been the subject of intense academic study, leading to the development and formal validation of numerous Byzantine-tolerant algorithms and architectures. As stated previously, industry’s recognition and treatment of the problem has been far less formal and rigorous. A reason for this might be the anthropomorphic tone and presentation of the problem definition. Although the authors warned against adopting too literal an interpretation, much of the related literature that has followed the original text has reinforced the “traitorous” anthropomorphic failure model. Such treatment has resulted in the work being ignored by a large segment of the commu

We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective method based on weighted voting is introduced for constructing a compound algorithm in such a circumstance We call this method the Weighted Majority Algorithm We show that this algorithm is robust in the presence of errors in the data We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool For example given a sequence of trials if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c log jAj m mistakes on that sequence where c is xed constant

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.

Recent archaeological discoveries on the island of Paxos reveal that the parliament functioned despite the peripatetic propensity of its part-time legislators. The legislators maintained consistent copies of the parliamentary record, despite their frequent forays from the chamber and the forgetfulness of their messengers. The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems.

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.

Summary form only given. As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.

We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of carrying specific values (e.g., the number offree parking places in a given area), our aggregates contain a modified Flajolet-Martin sketch as a probabilistic approximation. The main advantage of this approach is that the aggregates are duplicate insensitive. This overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher level aggregates, regardless if it has already been previously - directly or indirectly - added. As a result of those characteristics the quality of the aggregates is high, while their construction is very flexible. We demonstrate these traits of our approach by a simulation study.

Mission-critical applications that use computer networks require guaranteed communication with bounds on the time needed to deliver packets, even if hardware along a path fails or congestion occurs. Current networking technologies follow a reactive approach in which retransmission mechanisms and routing update protocols respond once packet loss occurs. We present a proactive approach that pre-builds and monitors redundant paths to ensure timely delivery with no recovery latency, even in the case of a single-link failure. The paper outlines the approach, describes a new mechanism we call a redundancy controller, and explains the use of SDN to establish and change paths. The paper also reports on a prototype system and gives experimental measurements.


Question: A method for distributed learning in unreliable network environments.

This paper introduces a cutting-edge algorithm designed to factorize large matrices with an immense scale, encompassing millions of rows and columns, and billions of non-zero elements. The methodology we propose is rooted in stochastic gradient descent (SGD), a powerful iterative optimization technique. To address the specific challenges of large-scale matrix factorization, we have developed a novel variant of SGD, termed Stratified Stochastic Gradient Descent (SSGD). This algorithm is uniquely suited to problems where the overall loss function can be decomposed into a weighted sum of individual "stratum losses." By leveraging theoretical frameworks from stochastic approximation and regenerative process theory, we establish rigorous conditions under which SSGD ensures convergence. We then further specialize SSGD to create Distributed Stochastic Gradient Descent (DSGD), an algorithm specifically tailored for matrix factorization tasks that can be fully distributed and executed on web-scale datasets, potentially utilizing frameworks like MapReduce. DSGD is versatile, capable of handling various matrix factorization models. We detail the practical optimizations incorporated into our DSGD implementation to enhance performance. Empirical evaluations demonstrate that DSGD exhibits superior convergence rates and scalability compared to competing algorithms, making it a compelling choice for large-scale matrix factorization challenges over unreliable networks.

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany

An algorithm is proposed that creates mutual exclusion in a computer network whose nodes communicate only by messages and do not share memory. The algorithm sends only 2*(N 1) messages, where N is the number of nodes in the network per critical section invocation. This number of messages is at a minimum if parallel, distributed, symmetric control is used; hence, the algorithm is optimal in this respect. The time needed to achieve mutual exclusion is also minimal under some general assumptions. As in Lamport's "bakery algorithm," unbounded sequence numbers are used to provide first-come firstserved priority into the critical section. It is shown that the number can be contained in a fixed amount of memory by storing it as the residue of a modulus. The number of messages required to implement the exclusion can be reduced by using sequential node-by-node processing, by using broadcast message techniques, or by sending information through timing channels. The "readers and writers" problem is solved by a simple modification of the algorithm and the modifications necessary to make the algorithm robust are described.

We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of carrying specific values (e.g., the number offree parking places in a given area), our aggregates contain a modified Flajolet-Martin sketch as a probabilistic approximation. The main advantage of this approach is that the aggregates are duplicate insensitive. This overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher level aggregates, regardless if it has already been previously - directly or indirectly - added. As a result of those characteristics the quality of the aggregates is high, while their construction is very flexible. We demonstrate these traits of our approach by a simulation study.

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.

The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example.<<ETX>>

The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.

Implementing E cient, Portable Computations for Machine Learning

Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to learn the communication strategy and the control policy for multiple agents. However, when implementing the communication for real-world multi-agent applications, there is a more practical limited-bandwidth restriction, which has been largely ignored by the existing DRL-based methods. Specifically, agents trained by most previous methods keep sending messages incessantly in every control cycle; due to emitting too many messages, these methods are unsuitable to be applied to the real-world systems that have a limited bandwidth to transmit the messages. To handle this problem, we propose a gating mechanism to adaptively prune unprofitable messages. Results show that the gating mechanism can prune more than 80% messages with little damage to the performance. Moreover, our method outperforms several state-of-the-art DRL-based and rule-based methods by a large margin in both the real-world packet routing tasks and four benchmark tasks.

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called metadata. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPIN protocols perform close to the theoretical optimum.

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.


Question: A decentralized learning method for unreliable network environments.

This paper outlines an innovative framework for integrating classification, localization, and detection using Convolutional Networks (ConvNets). The framework demonstrates the efficient implementation of a multiscale and sliding window strategy within a ConvNet. A distinctive feature is the introduction of a deep learning technique for localization, where the network learns to predict object boundaries. Contrary to traditional methods, bounding boxes are accumulated rather than suppressed, enhancing detection confidence. The framework's versatility is highlighted by its ability to learn multiple tasks simultaneously using a single shared network. This approach not only won the localization task in the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) but also achieved highly competitive results in detection and classification tasks. In subsequent research, the framework set a new benchmark for detection tasks. Additionally, a feature extractor derived from the model, named OverFeat, is made available for use.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This paper introduces a novel hybrid algorithm combining the strengths of genetic algorithms (GA) and particle swarm optimization (PSO) to address the challenges in designing recurrent neural networks. The proposed method, termed Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), integrates both crossover and mutation operations from GA and the dynamic exploration capabilities of PSO to create a robust evolutionary learning algorithm. The algorithm features an elite strategy, where the top-performing individuals from the current generation are enhanced through PSO, simulating a maturation process, before contributing to the next generation. This dual approach ensures that the population evolves efficiently, leading to superior network designs. The efficacy of HGAPSO is demonstrated in the design of fully connected recurrent neural networks for temporal sequence production and Takagi-Sugeno-Kang-type recurrent fuzzy networks for dynamic plant control, showcasing its superiority over traditional GA and PSO methods.

We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multiagent Hierarchies (FMH). In this framework, a ‘manager’ agent, which is tasked with maximising the environmentally-determined reward function, learns to communicate subgoals to multiple, simultaneously-operating, ‘worker’ agents. Workers, which are rewarded for achieving managerial subgoals, take concurrent actions in the world. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We find that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use a shared reward function.

Multi-agent learning provides a potential framework for learning and simulating traffic behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a traffic scenario. The proposed architecture can learn multiple behaviors independently as well as simultaneously. We take advantage of the homogeneity of agents and learn in a parameter sharing paradigm. To further speed up the training process asynchronous updates are employed into the architecture. While learning different behaviors simultaneously, the given framework was also able to learn cooperation between the agents, without any explicit communication. We applied this framework to learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking. Results indicate faster convergence and learning of a more generic behavior, that is scalable to any number of agents. When compared the results with existing approaches, our results indicate equal and even better performance in some cases.

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.

Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.

Summary form only given. A novel method called dynamic node creation (DNC) that attacks issues of training large networks and of testing networks with different numbers of hidden layer units is presented. DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The procedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal backpropagation (BP) networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried. BP applied to the same large networks with randomized initial weights was unable, after repeated attempts, to replicate some minimum solutions found by DNC.<<ETX>>

Sensors in a data fusion environment over hostile territory are geographically dispersed and change location with time. In order to collect and process data from these sensors an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.

Reinforcement Learning (RL) is learning through direct experimentation. It does not assume the existence of a teacher that provides examples upon which learning of a task takes place. Instead, in RL experience is the only teacher. With historical roots on the study of conditioned reflexes, RL soon attracted the interest of Engineers and Computer Scientists because of its theoretical relevance and potential applications in fields as diverse as Operational Research and Robotics. Computationally, RL is intended to operate in a learning environment composed by two subjects: the learner and a dynamic process. At successive time steps, the learner makes an observation of the process state, selects an action and applies it back to the process. The goal of the learner is to find out an action policy that controls the behavior of this dynamic process, guided by signals (reinforcements) that indicate how well it is performing the required task. These signals are usually associated to some dramatic condition — e.g., accomplishment of a subtask (reward) or complete failure (punishment), and the learner’s goal is to optimize its behavior based on some performance measure (a function of the received reinforcements). The crucial point is that in order to do that, the learner must evaluate the conditions (associations between observed states and chosen actions) that lead to rewards or punishments. In other words, it must learn how to assign credit to past actions and states by correctly estimating costs associated to these events. Starting from basic concepts, this tutorial presents the many flavors of RL algorithms, develops the corresponding mathematical tools, assess their practical limitations and discusses alternatives that have been proposed for applying RL to realistic tasks, such as those involving large state spaces or partial observability. It relies on examples and diagrams to illustrate the main points, and provides many references to the specialized literature and to Internet sites where relevant demos and additional information can be obtained.

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a di cult task. The bene t of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.