OpenRaiser/Intern-Atlas huggingface dataset:
Minimizing Data Distortion of Periodically Reporting IoT Devices with Energy Harvesting
Venue: arXiv cs.IT 2017
Year: 2017

Energy harvesting is a promising technology for the Internet of Things (IoT) towards the goal of self-sustainability of the involved devices. However, the intermittent and unreliable nature of the harvested energy demands an intelligent management of devices' operation in order to ensure a sustained performance of the IoT application. In this work, we address the problem of maximizing the quality of the reported data under the constraints of energy harvesting, energy consumption and communication channel impairments. Specifically, we propose an energy-aware joint source-channel coding scheme that minimizes the expected data distortion, for realistic models of energy generation and of the energy spent by the device to process the data, when the communication is performed over a Rayleigh fading channel. The performance of the scheme is optimized by means of a Markov Decision Process framework.
Efficient, High-Quality Stack Rearrangement
Venue: arXiv cs.RO 2017
Year: 2017

This work studies rearrangement problems involving the sorting of robots or objects in stack-like containers, which can be accessed only from one side. Two scenarios are considered: one where every robot or object needs to reach a particular stack, and a setting in which each robot has a distinct position within a stack. In both cases, the goal is to minimize the number of stack removals that need to be performed. Stack rearrangement is shown to be intimately connected to pebble motion problems, a useful abstraction in multi-robot path planning. Through this connection, feasibility of stack rearrangement can be readily addressed. The paper continues to establish lower and upper bounds on optimality, which differ only by a logarithmic factor, in terms of stack removals. An algorithmic solution is then developed that produces suboptimal paths much quicker than a pebble motion solver. Furthermore, informed search-based methods are proposed for finding high-quality solutions. The efficiency and desirable scalability of the methods is demonstrated in simulation.
On the Solution of Linear Programming Problems in the Age of Big Data
Venue: arXiv cs.DS 2017
Year: 2017

The Big Data phenomenon has spawned large-scale linear programming problems. In many cases, these problems are non-stationary. In this paper, we describe a new scalable algorithm called NSLP for solving high-dimensional, non-stationary linear programming problems on modern cluster computing systems. The algorithm consists of two phases: Quest and Targeting. The Quest phase calculates a solution of the system of inequalities defining the constraint system of the linear programming problem under the condition of dynamic changes in input data. To this end, the apparatus of Fejer mappings is used. The Targeting phase forms a special system of points having the shape of an n-dimensional axisymmetric cross. The cross moves in the n-dimensional space in such a way that the solution of the linear programming problem is located all the time in an "-vicinity of the central point of the cross.
Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library
Venue: arXiv cs.DC 2017
Year: 2017

We present an analysis on optimizing performance of a single C++11 source code using the Alpaka hardware abstraction library. For this we use the general matrix multiplication (GEMM) algorithm in order to show that compilers can optimize Alpaka code effectively when tuning key parameters of the algorithm. We do not intend to rival existing, highly optimized DGEMM versions, but merely choose this example to prove that Alpaka allows for platform-specific tuning with a single source code. In addition we analyze the optimization potential available with vendor-specific compilers when confronted with the heavily templated abstractions of Alpaka. We specifically test the code for bleeding edge architectures such as Nvidia's Tesla P100, Intel's Knights Landing (KNL) and Haswell architecture as well as IBM's Power8 system. On some of these we are able to reach almost 50\% of the peak floating point operation performance using the aforementioned means. When adding compiler-specific #pragmas we are able to reach 5 TFLOPS/s on a P100 and over 1 TFLOPS/s on a KNL system.
Evaluation of No Reference Bitstream-based Video Quality Assessment Methods
Venue: arXiv cs.MM 2017
Year: 2017

Many different parametric models for video quality assessment have been proposed in the past few years. This paper presents a review of nine recent models which cover a wide range of methodologies and have been validated for estimating video quality due to different degradation factors. Each model is briefly described with key algorithms and relevant parametric formulas. The generalization capability of each model to estimate video quality in real-application scenarios is evaluated and compared with other models, using a dataset created with video sequences from practical applications. These video sequences cover a wide range of possible realistic encoding parameters, labeled with mean opinion scores (MOS) via subjective test. The weakness and strength of each model are remarked. Finally, future work towards a more general parametric model that could apply for a wider range of applications is discussed.
Codebook Based Hybrid Precoding for Millimeter Wave Multiuser Systems
Venue: arXiv cs.IT 2017
Year: 2017

In millimeter wave (mmWave) systems, antenna architecture limitations make it difficult to apply conventional fully digital precoding techniques but call for low cost analog radio-frequency (RF) and digital baseband hybrid precoding methods. This paper investigates joint RF-baseband hybrid precoding for the downlink of multiuser multi-antenna mmWave systems with a limited number of RF chains. Two performance measures, maximizing the spectral efficiency and the energy efficiency of the system, are considered. We propose a codebook based RF precoding design and obtain the channel state information via a beam sweep procedure. Via the codebook based design, the original system is transformed into a virtual multiuser downlink system with the RF chain constraint. Consequently, we are able to simplify the complicated hybrid precoding optimization problems to joint codeword selection and precoder design (JWSPD) problems. Then, we propose efficient methods to address the JWSPD problems and jointly optimize the RF and baseband precoders under the two performance measures. Finally, extensive numerical results are provided to validate the effectiveness of the proposed hybrid precoders.
Modern Random Access for Satellite Communications
Venue: arXiv cs.IT 2017
Year: 2017

The present PhD dissertation focuses on modern random access (RA) techniques. In the first part an slot- and frame-asynchronous RA scheme adopting replicas, successive interference cancellation and combining techniques is presented and its performance analysed. The comparison of both slot-synchronous and asynchronous RA at higher layer, follows. Next, the optimization procedure, for slot-synchronous RA with irregular repetitions, is extended to the Rayleigh block fading channel. Finally, random access with multiple receivers is considered.
Selfish Network Creation with Non-Uniform Edge Cost
Venue: arXiv cs.GT 2017
Year: 2017

Network creation games investigate complex networks from a game-theoretic point of view. Based on the original model by Fabrikant et al. [PODC'03] many variants have been introduced. However, almost all versions have the drawback that edges are treated uniformly, i.e. every edge has the same cost and that this common parameter heavily influences the outcomes and the analysis of these games. We propose and analyze simple and natural parameter-free network creation games with non-uniform edge cost. Our models are inspired by social networks where the cost of forming a link is proportional to the popularity of the targeted node. Besides results on the complexity of computing a best response and on various properties of the sequential versions, we show that the most general version of our model has constant Price of Anarchy. To the best of our knowledge, this is the first proof of a constant Price of Anarchy for any network creation game.
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Venue: arXiv cs.CV 2017
Year: 2017

Generally, the performance of a generic detector decreases significantly when it is tested on a specific scene due to the large variation between the source training dataset and the samples from the target scene. To solve this problem, we propose a new formalism of transfer learning based on the theory of a Sequential Monte Carlo (SMC) filter to automatically specialize a scene-specific Faster R-CNN detector. The suggested framework uses different strategies based on the SMC filter steps to approximate iteratively the target distribution as a set of samples in order to specialize the Faster R-CNN detector towards a target scene. Moreover, we put forward a likelihood function that combines spatio-temporal information extracted from the target video sequence and the confidence-score given by the output layer of the Faster R-CNN, to favor the selection of target samples associated with the right label. The effectiveness of the suggested framework is demonstrated through experiments on several public traffic datasets. Compared with the state-of-the-art specialization frameworks, the proposed framework presents encouraging results for both single and multi-traffic object detections.
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
Venue: arXiv cs.IR 2017
Year: 2017

Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.
Is it ethical to avoid error analysis?
Venue: arXiv cs.CY 2017
Year: 2017

Machine learning algorithms tend to create more accurate models with the availability of large datasets. In some cases, highly accurate models can hide the presence of bias in the data. There are several studies published that tackle the development of discriminatory-aware machine learning algorithms. We center on the further evaluation of machine learning models by doing error analysis, to understand under what conditions the model is not working as expected. We focus on the ethical implications of avoiding error analysis, from a falsification of results and discrimination perspective. Finally, we show different ways to approach error analysis in non-interpretable machine learning algorithms such as deep learning.
Lifelong Learning in Costly Feature Spaces
Venue: arXiv cs.LG 2017
Year: 2017

An important long-term goal in machine learning systems is to build learning agents that, like humans, can learn many tasks over their lifetime, and moreover use information from these tasks to improve their ability to do so efficiently. In this work, our goal is to provide new theoretical insights into the potential of this paradigm. In particular, we propose a lifelong learning framework that adheres to a novel notion of resource efficiency that is critical in many real-world domains where feature evaluations are costly. That is, our learner aims to reuse information from previously learned related tasks to learn future tasks in a feature-efficient manner. Furthermore, we consider novel combinatorial ways in which learning tasks can relate. Specifically, we design lifelong learning algorithms for two structurally different and widely used families of target functions: decision trees/lists and monomials/polynomials. We also provide strong feature-efficiency guarantees for these algorithms; in fact, we show that in order to learn future targets, we need only slightly more feature evaluations per training example than what is needed to predict on an arbitrary example using those targets. We also provide algorithms with guarantees in an agnostic model where not all the targets are related to each other. Finally, we also provide lower bounds on the performance of a lifelong learner in these models, which are in fact tight under some conditions.
Onions in the Crosshairs: When The Man really is out to get you
Venue: arXiv cs.CR 2017
Year: 2017

We introduce and investigate *targeting adversaries* who selectively attack users of Tor or other secure-communication networks. We argue that attacks by such adversaries are more realistic and more significant threats to those most relying on Tor's protection than are attacks in prior analyses of Tor security. Previous research and Tor design decisions have focused on protecting against adversaries who are equally interested in any user of the network. Our adversaries selectively target users---e.g., those who visit a particular website or chat on a particular private channel---and essentially disregard Tor users other than these. We present a model of such adversaries and investigate three example cases where particular users might be targeted: a cabal conducting meetings using MTor, a published Tor multicast protocol; a cabal meeting on a private IRC channel; and users visiting a particular .onion website. In general for our adversaries, compromise is much faster and provides more feedback and possibilities for adaptation than do attacks examined in prior work. We also discuss selection of websites for targeting of their users based on the distribution across users of site activity. We describe adversaries both attempting to learn the size of a cabal meeting online or of a set of sufficiently active visitors to a targeted site and attempting to identify guards of each targeted user. We compare the threat of targeting adversaries versus previously considered adversaries, and we briefly sketch possible countermeasures for resisting targeting adversaries.
Latency Analysis of Coded Computation Schemes over Wireless Networks
Venue: arXiv cs.IT 2017
Year: 2017

Large-scale distributed computing systems face two major bottlenecks that limit their scalability: straggler delay caused by the variability of computation times at different worker nodes and communication bottlenecks caused by shuffling data across many nodes in the network. Recently, it has been shown that codes can provide significant gains in overcoming these bottlenecks. In particular, optimal coding schemes for minimizing latency in distributed computation of linear functions and mitigating the effect of stragglers was proposed for a wired network, where the workers can simultaneously transmit messages to a master node without interference. In this paper, we focus on the problem of coded computation over a wireless master-worker setup with straggling workers, where only one worker can transmit the result of its local computation back to the master at a time. We consider 3 asymptotic regimes (determined by how the communication and computation times are scaled with the number of workers) and precisely characterize the total run-time of the distributed algorithm and optimum coding strategy in each regime. In particular, for the regime of practical interest where the computation and communication times of the distributed computing algorithm are comparable, we show that the total run-time approaches a simple lower bound that decouples computation and communication, and demonstrate that coded schemes are $Θ(\log(n))$ times faster than uncoded schemes.
Adversarial Image Alignment and Interpolation
Venue: arXiv cs.CV 2017
Year: 2017

Volumetric (3d) images are acquired for many scientific and biomedical purposes using imaging methods such as serial section microscopy, CT scans, and MRI. A frequent step in the analysis and reconstruction of such data is the alignment and registration of images that were acquired in succession along a spatial or temporal dimension. For example, in serial section electron microscopy, individual 2d sections are imaged via electron microscopy and then must be aligned to one another in order to produce a coherent 3d volume. State of the art approaches find image correspondences derived from patch matching and invariant feature detectors, and then solve optimization problems that rigidly or elastically deform series of images into an aligned volume. Here we show how fully convolutional neural networks trained with an adversarial loss function can be used for two tasks: (1) synthesis of missing or damaged image data from adjacent sections, and (2) fine-scale alignment of block-face electron microscopy data. Finally, we show how these two capabilities can be combined in order to produce artificial isotropic volumes from anisotropic image volumes using a super-resolution adversarial alignment and interpolation approach.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
Venue: arXiv cs.LG 2017
Year: 2017

How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group? How can we train such a classifier when data on the protected group is difficult to attain? In many settings, finding out the sensitive input attribute can be prohibitively expensive even during model training, and sometimes impossible during model serving. For example, in recommender systems, if we want to predict if a user will click on a given recommendation, we often do not know many attributes of the user, e.g., race or age, and many attributes of the content are hard to determine, e.g., the language or topic. Thus, it is not feasible to use a different classifier calibrated based on knowledge of the sensitive attribute. Here, we use an adversarial training procedure to remove information about the sensitive attribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training effects the resulting fairness properties. We find two interesting results: a small amount of data is needed to train these adversarial models, and the data distribution empirically drives the adversary's notion of fairness.
Multisided Fairness for Recommendation
Venue: arXiv cs.CY 2017
Year: 2017

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.
An Empirical Study on Collaborative Architecture Decision Making in Software Teams
Venue: arXiv cs.SE 2017
Year: 2017

Architecture decision making is considered one of the most challenging cognitive tasks in software development. The objective of this study is to explore the state of the practice of architecture decision making in software teams, including the role of the architect and the associated challenges. An exploratory case study was conducted in a large software company in Europe and fifteen software architects were interviewed as the primary method of data collection. The results reveal that the majority of software teams make architecture decisions collaboratively. Especially, the consultative decision- making style is preferred as it helps to make decisions efficiently while taking the opinions of the team members into consideration. It is observed that most of the software architects maintain a close relationship with the software teams. Several organisational, process and human related challenges and their impact on architecture decision-making are also identified.
Joint Time Allocation and Power Control in Multi-Cell Networks with Load Coupling: Energy Saving and Rate Improvement
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider the problems of minimizing sum power and maximizing sum rate for multi-cell networks with load coupling, where coupling relation occurs among cells due to inter-cell interference. This coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model with cell load vector and cell power vector as the variables. Due to the nonlinear SINR coupling model, the optimization problems for multi-cell networks with load coupling is nonconvex. To solve these nonconvex problems, we first consider the optimization problems for single-cell networks. Through variable transformations, the optimization problems can be equivalently transformed into convex problems. By solving the Karush-Kuhn-Tucker (KKT), the optimal solutions to power minimization and rate maximization problems can be obtained in closed form. Based on the theoretical findings of optimization problems for single-cell networks, we develop a distributed time allocation and power control algorithm with low complexity for sum power minimization in multi-cell networks. This algorithm is proved to be convergent and globally optimal by using the properties of standard interference function. For sum rate optimization in multi-cell networks, we also provide a distributed algorithm which yields suboptimal solution. Besides, the convergence for this distributed algorithm is proved. Numerical results illustrate the theoretical findings, showing the superiority of our solutions compared to the conventional solution of allocating uniform power for users in the same cell.
Geometry-Oblivious FMM for Compressing Dense SPD Matrices
Venue: arXiv cs.NA 2017
Year: 2017

We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in $N \log N$ or even $N$ time, where $N$ is the matrix size. Compression requires $N \log N$ storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme for hierarchical matrix computations that reduces synchronization barriers. We present results on the Intel Knights Landing and Haswell architectures, and on the NVIDIA Pascal architecture for a variety of matrices.
Sequential Experimentation to Efficiently Test Automated Vehicles
Venue: arXiv cs.SY 2017
Year: 2017

Automated vehicles have been under heavy developments in major auto and tech companies and are expected to release into market in the foreseeable future. However, the road safety of these vehicles remains a concern. One approach to evaluate their safety is via on-track experimentation, but this requires gigantic costs and time investments. This paper discusses a sequential learning approach based on kriging models to reduce the experimental runs and economize on-track experimentation. The approach relies on a heuristic simulation-based gradient descent procedure to search for the best next test scenario. We demonstrate our approach with some numerical test cases.