Query: Optimal Target Assignment and Path Finding for Teams of Agents

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Planning optimal paths for multiple robots on graphs <=SEP=> In this paper, we study the problem of optimal multi-robot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that compute minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resulting algorithms from these ILP models are complete and guaranteed to yield true optimal solutions. In addition, our flexible framework can easily accommodate other variants of the MPP problem. Focusing on the time optimal algorithm, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. Computational results confirm the effectiveness of our method.

Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses <=SEP=> The Kiva warehouse management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.

Multi-agent Path Planning and Network Flow <=SEP=> This paper connects multi-agent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the latter, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multi-agent path planning problems on graphs. Exploiting this connecti on, we show that when the goals are permutation invariant, the problem always has a feasible solution path set with a longes t finish time of no more than n+V − 1 steps, in which n is the number of agents andV is the number of vertices of the underlying graph. We then give a complete algorithm that finds such a solution in O(nVE) time, with E being the number of edges of the graph. Taking a further step, we study time and distance optimality of the feasible solutions, show that th ey have a pairwise Pareto optimal structure, and again provide efficient algorithms for optimizing two of these practical objectives.

Experiences with an Interactive Museum Tour-Guide Robot <=SEP=> This article describes the software architecture of an auto nomous, interactive tour-guide robot. It presents a modular and distributed software archi te ture, which integrates localization, mapping, collision avoidance, planning, and vari ous modules concerned with user interaction and Web-based telepresence. At its heart, the s oftware approach relies on probabilistic computation, on-line learning, and any-time alg orithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot ’s peration. Special emphasis is placed on the design of interactive capabilities that appeal to people’s intuition. The interface provides new means for human-robot interaction w ith crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approac h, results are reported obtained in mid-1997, when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate relia bl operation in public environments. The robot successfully raised the museum’s atten dance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much large r range of application domains for service robots.

Principles of Robot Motion: Theory, Algorithms, and Implementations [Book Review] <=SEP=> introduction to autonomous mobile robots intelligent robotics and autonomous agents series PDF disaster robotics intelligent robotics and autonomous agents series PDF probabilistic robotics intelligent robotics and autonomous agents series PDF self-reconfigurable robots an introduction intelligent robotics and autonomous agents series PDF mechanics of robotic manipulation intelligent robotics and autonomous agents PDF strategic negotiation in multiagent environments intelligent robotics and autonomous agents PDF random finite sets for robot mapping & slam new concepts in autonomous robotic map representations springer tracts in advanced robotics PDF

What If You Can't Trust Your Network Card? <=SEP=> In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.

A Scalable Multiphysics Network Simulation Using PETSc DMNetwork <=SEP=> A scientific framework for simulations of large-scale networks, such as is required for the analysis of critical infrastructure interaction and interdependencies, is needed for applications on exascale computers. Such a framework must be able to manage heterogeneous physics and unstructured topology, and must be reusable. To this end we have developed DMNetwork, a class in PETSc that provides data and topology management and migration for network problems, along with multiphysics solvers to exploit the problem structure. It eases the application development cycle by providing the necessary infrastructure through simple abstractions to define and query the network. This paper presents the design of the DMNetwork, illustrates its user interface, and demonstrates its ability to solve large network problems through the numerical simulation of a water pipe network with more than 2 billion variables on extreme-scale computers using up to 30,000 processor cores.

3D Integration technology: Status and application development <=SEP=> As predicted by the ITRS roadmap, semiconductor industry development dominated by shrinking transistor gate dimensions alone will not be able to overcome the performance and cost problems of future IC fabrication. Today 3D integration based on through silicon vias (TSV) is a well-accepted approach to overcome the performance bottleneck and simultaneously shrink the form factor. Several full 3D process flows have been demonstrated, however there are still no microelectronic products based on 3D TSV technologies in the market — except CMOS image sensors. 3D chip stacking of memory and logic devices without TSVs is already widely introduced in the market. Applying TSV technology for memory on logic will increase the performance of these advanced products and simultaneously shrink the form factor. In addition to the enabling of further improvement of transistor integration densities, 3D integration is a key technology for integration of heterogeneous technologies. Miniaturized MEMS/IC products represent a typical example for such heterogeneous systems demanding for smart system integration rather than extremely high transistor integration densities. The European 3D technology platform that has been established within the EC funded e-CUBES project is focusing on the requirements coming from heterogeneous systems. The selected 3D integration technologies are optimized concerning the availability of devices (packaged dies, bare dies or wafers) and the requirements of performance and form factor. There are specific technology requirements for the integration of MEMS/NEMS devices which differ from 3D integrated ICs (3D-IC). While 3D-ICs typically show a need for high interconnect densities and conductivities, TSV technologies for the integration of MEMS to ICs may result in lower electrical performance but have to fulfill other requirements, e. g. mechanical stability issues. 3D integration of multiple MEMS/IC stacks was successfully demonstrated for the fabrication of miniaturized sensor systems (e-CUBES), as for automotive, health & fitness and aeronautic applications.

Taxonomy of attacks on industrial control protocols <=SEP=> Industrial control systems (ICS) are highly distributed information systems used to control and monitor critical infrastructures such as nuclear plants, power generation and distribution plants, Oil and Gas and many other facilities. The main architecture principles of ICS are; real time response, high availability and reliability. For these specific purposes, several protocols has been designed to ensure the control and supervision operations. Modbus and DNP3 are the most used protocols in the ICS world due to their compliance with real time needs. With the increasing of the connectivity to the internet world for business reasons, ICS adopted Internet based technologies and most of communication protocols are redesigned to work over IP. This openness exposed the ICS components as well as communication protocols to cyber-attacks with a higher risk than attacks on traditional IT systems. In order to facilitate the risk assessment of cyber-attacks on ICS protocols we propose a taxonomy model of different identified attacks on Modbus and DNP3.the model is based on the threat origin, threat type, attack type, attack scenario, vulnerability type and the impact of the attack. We populate this Taxonomy model with identified attacks on Modbus and DNP3 from previous academic and industrial works.

Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties <=SEP=> A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.

Some mathematical notes on three-mode factor analysis. <=SEP=> 

Ee364a: Convex Optimization I Final Exam <=SEP=> This is a 24 hour take-home final. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs, but you may not discuss the exam with anyone until March 16, after everyone has taken the exam. The only exception is that you can ask us for clarification, via the course staff email address. We've tried pretty hard to make the exam unambiguous and clear, so we're unlikely to say much. Please make a copy of your exam, or scan it, before handing it in. Please attach the cover page to the front of your exam. Assemble your solutions in order (problem 1, problem 2, problem 3,. . .), starting a new page for each problem. Put everything associated with each problem (e.g., text, code, plots) together; do not attach code or plots at the end of the final. We will deduct points from long needlessly complex solutions, even if they are correct. Our solutions are not long, so if you find that your solution to a problem goes on and on for many pages, you should try to figure out a simpler one. We expect neat, legible exams from everyone, including those enrolled Cr/N. When a problem involves computation you must give all of the following: a clear discussion and justification of exactly what you did, the source code that produces the result, and the final numerical results or plots. Files containing problem data can be found in the usual place, Please respect the honor code. Although we allow you to work on homework assignments in small groups, you cannot discuss the final with anyone, at least until everyone has taken it. All problems have equal weight. Some are easy. Others, not so much. Be sure you are using the most recent version of CVX, CVXPY, or Convex,jl. Check your email often during the exam, just in case we need to send out an important announcement. Some problems involve applications. But you do not need to know anything about the problem area to solve the problem; the problem statement contains everything you need.

Classical Statistics and Statistical Learning in Imaging Neuroscience <=SEP=> Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques.

A Mining and Visualizing System for Large-Scale Chinese Technical Standards <=SEP=> A technical standard is an established norm or requirement in regard to technical systems. It is usually a formal document that establishes uniform engineering or technical criteria, methods, processes and practices. Every year, more than 10K standards are released by standardization organizations, such as International Standardization Organization (ISO), American National Standards Institute (ANSI), and China National Institute of Standardization (CNIS). Thereinto, China publishes more than 4K national and industrial standards per year. Confronted with the large amount of standards, how to manage and make full use of these standards efficiently and effectively has become a major issue to be addressed. In this paper, we introduce a mining and visualizing system for large-scale Chinese technical standards from spatial and temporal perspectives. Specifically, we propose an s-index metric to measure the contribution of a drafting unit. Moreover, we develop a multiple dimensional mining and visualizing analysis system for standardization experts and users to explore regional differences and temporal trends of standards in China. Our system has run on 95K Chinese standards spanning from 2001 to 2016 and provided online service. The extensive experiments and case study demonstrate the effectiveness of our proposed system.

Foot pressure distribution and contact duration pattern during walking at self-selected speed in young adults <=SEP=> Foot load observed as pressure distribution is examined in relation to a foot and ankle functions within the gait cycle. This load defines the modes of healthy and pathological gait. Determination of the patterns of healthy, i.e. “normal” walk is the basis for classification of pathological modes. Eleven healthy participants were examined in this initial study. Participants walked over pressure plate barefoot at their self - selected speed. Maximal values of the pressure were recorded in the heel, the first, the second and the third metatarsal joints and in the hallux region. Largest contact duration is recorded in the metatarsal region.

SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases <=SEP=> A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage operator for exact computation of queries on tuple-independent probabilistic databases. We consider the conjunctive queries without self-joins that are known to be tractable on any tuple-independent database, and queries that are not tractable in general but become tractable on probabilistic databases restricted by functional dependencies. Our operator is semantically equivalent to a sequence of aggregations and can be naturally integrated into existing relational query plans. As a proof of concept, we developed an extension of the PostgreSQL 8.3.3 query engine called SPROUT. We study optimizations that push or pull our operator or parts thereof past joins. The operator employs static information, such as the query structure and functional dependencies, to decide which constituent aggregations can be evaluated together in one scan and how many scans are needed for the overall confidence computation task. A case study on the TPC-H benchmark reveals that most TPC-H queries obtained by removing aggregations can be evaluated efficiently using our operator. Experimental evaluation on probabilistic TPC-H data shows substantial efficiency improvements when compared to the state of the art.

R-ADMAD: high reliability provision for large-scale de-duplication archival storage systems <=SEP=> Data de-duplication has become a commodity component in data-intensive systems and it is required that these systems provide high reliability comparable to others. Unfortunately, by storing duplicate data chunks just once, de-duped system improves storage utilization at cost of error resilience or reliability. In this paper, R-ADMAD, a high reliability provision mechanism is proposed. It packs variable-length data chunks into fixed sized objects, and exploits ECC codes to encode the objects and distributes them among the storage nodes in a redundancy group, which is dynamically generated according to current status and actual failure domains. Upon failures, R-ADMAD proposes a distributed and dynamic recovery process. Experimental results show that R-ADMAD can provide the same storage utilization as RAID-like schemes, but comparable reliability to replication based schemes with much more redundancy. The average recovery time of R-ADMAD based configurations is about 2-6 times less than RAID-like schemes. Moreover, R-ADMAD can provide dynamic load balancing even without the involvement of the overloaded storage nodes.

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning <=SEP=> Model selection is one of the most computationally expensive tasks in a machine learning application. When dealing with kernel methods for structures, the choice with the largest impact on the overall performance is the selection of the feature bias, i.e. the choice of the concrete kernel for structures. Each kernel in turn exposes several hyper-parameters which also need to be fine tuned. Multiple Kernel Learning offers a way to approach this computational bottleneck by generating a combination of different kernels under different parametric settings. However, this solution still requires the computation of many large kernel matrices. In this paper we propose a method to efficiently select a small number of kernels on a subset of the original data, gaining a dramatic reduction in the runtime without a significant loss of predictive performance.

Houston, we have a problem...: a survey of actual problems in computer games development <=SEP=> This paper presents a survey of problems found in the development process of electronic games. These problems were collected mainly from game postmortems and specialized litterature on game development, allowing a comparison with respect to well-known problems in the traditional software industry.

Deep Kernelized Autoencoders <=SEP=> In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework <=SEP=> 

Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer <=SEP=> Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.

An Innovative Approach to Investigate Various Software Testing Techniques and Strategies <=SEP=> Software testing is a way of finding errors from the system. It helps us to identify and debug mistakes, errors, faults and failures of a system. There are many techniques and strategies emerged since the concept of software development emerged. The aim of testing is to make the quality of software as efficient as possible.in this paper we discuss most widely used techniques and strategies. Where they can be used and how they can be used. How they work and how they differ (from each other).They are the following. Techniques: Black Box Testing, White Box Testing, And Grey Box Testing. Strategies: Unit Testing, System Testing, And Acceptance Testing.

Online Dominant and Anomalous Behavior Detection in Videos <=SEP=> We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.

Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors <=SEP=> Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.

Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data <=SEP=> This letter presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term three-dimensional (3-D) Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3-D refinement of semantic maps (i.e. fusing semantic observations). The most widely used approach for the 3-D semantic map refinement is “Bayes update,” which fuses the consecutive predictive probabilities following a Markov-chain model. Instead, we propose a learning approach to fuse the semantic features, rather than simply fusing predictions from a classifier. In our approach, we represent and maintain our 3-D map as an OctoMap, and model each cell as a recurrent neural network, to obtain a Recurrent-OctoMap. In this case, the semantic mapping process can be formulated as a sequence-to-sequence encoding–decoding problem. Moreover, in order to extend the duration of observations in our Recurrent-OctoMap, we developed a robust 3-D localization and mapping system for successively mapping a dynamic environment using more than two weeks of data, and the system can be trained and deployed with arbitrary memory length. We validate our approach on the ETH long-term 3-D Lidar dataset. The experimental results show that our proposed approach outperforms the conventional “Bayes update” approach.

Learning to generate chairs with convolutional neural networks <=SEP=> We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.

Designing augmented reality interfaces <=SEP=> Most interactive computer graphics appear on a screen separate from the real world and the user's surroundings. However this does not always have to be the case. In augmented reality (AR) interfaces, three-dimensional virtual images appear superimposed over real objects. AR applications typically use head-mounted or handheld displays to make computer graphics appear in the user's environment.

Comics on the Brain: Structure and Meaning in Sequential Image Comprehension <=SEP=> Just as syntax differentiates coherent sentences from scrambled word strings, sequential images must also use a cognitive system to distinguish coherent narratives from random strings of images. We conducted experiments analogous to two classic psycholinguistic studies to examine structure and semantics in sequential images. We compared Normal comic strips with both structure and meaning to sequences with Semantics Only, Structure Only, or randomly Scrambled panels. Experiment 1 used a target-monitoring paradigm, and found that RTs were slowest to target panels in Scrambled sequences, intermediate in Structural Only and Semantic Only sequences, and fastest in Normal sequences. Experiment 2 measured ERPs to the same strips. The largest N400 appeared in Scrambled and Structural Only sequences, intermediate in Semantic Only sequences and smallest in Normal sequences. Together, these findings suggest that sequential image comprehension is guided by an interaction between a structure and meaning, broadly analogous to syntax and semantics in language.

Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework <=SEP=> In recent years, the shared bicycle project has developed rapidly. In use of shared bicycles, a great deal of user riding information is recorded. How to extract effective knowledge from these vast amounts of information, how to use this knowledge to improve the shared bicycle system, and how to improve the user experience, are problems to solve. Citi Bike is selected as the research target. Data on Citi Bike’s user historical behavior, weather information, and holiday information are collected from three different sources, and converted into appropriate formats for model training. Spark MLlib is used to construct three different predictive models, advantages and disadvantages of different forecasting models are compared. Some techniques are used to enhance the accuracy of random forests model. The experimental results show that the root mean square error RMSE of the final model is reduced from 305.458 to 243.346.