OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Mechanism Design with Flexible Consumers and Costly Supply
Venue: arXiv cs.GT 2017
Year: 2017

The problem of designing a profit-maximizing, Bayesian incentive compatible and individually rational mechanism with flexible consumers and costly heterogeneous supply is considered. In our setup, each consumer is associated with a flexibility set that describes the subset of goods the consumer is equally interested in. Each consumer wants to consume one good from its flexibility set. The flexibility set of a consumer and the utility it gets from consuming a good from its flexibility set are its private information. We adopt the flexibility model of [1] and focus on the case of nested flexibility sets -- each consumer's flexibility set can be one of k nested sets. Examples of settings with this inherent nested structure are provided. On the supply side, we assume that the seller has an initial stock of free supply but it can purchase more goods for each of the nested sets at fixed exogenous prices. We characterize the allocation and purchase rules for a profit-maximizing, Bayesian incentive compatible and individually rational mechanism as the solution to an integer program. The optimal payment function is pinned down by the optimal allocation rule in the form of an integral equation. We show that the nestedness of flexibility sets can be exploited to obtain a simple description of the optimal allocations, purchases and payments in terms of thresholds that can be computed through a straightforward iterative procedure.
Barriers for Rank Methods in Arithmetic Complexity
Venue: arXiv cs.CC 2017
Year: 2017

Arithmetic complexity is considered simpler to understand than Boolean complexity, namely computing Boolean functions via logical gates. And indeed, we seem to have significantly more lower bound techniques and results in arithmetic complexity than in Boolean complexity. Despite many successes and rapid progress, however, challenges like proving super-polynomial lower bounds on circuit or formula size for explicit polynomials, or super-linear lower bounds on explicit 3-dimensional tensors, remain elusive. At the same time, we have plenty more "barrier results" for failing to prove basic lower bounds in Boolean complexity than in arithmetic complexity. Finding barriers to arithmetic lower bound techniques seem harder, and despite some attempts we have no excuses of similar quality for these failures in arithmetic complexity. This paper aims to add to this study. We address rank methods, which were long recognized as encompassing and abstracting almost all known arithmetic lower bounds to-date, including the most recent impressive successes. Rank methods (or flattenings) are also in wide use in algebraic geometry for proving tensor rank and symmetric tensor rank lower bounds. Our main results are barriers to these methods. In particular, 1. Rank methods cannot prove better than $Ω_d (n^{\lfloor d/2 \rfloor})$ lower bound on the tensor rank of any $d$-dimensional tensor of side $n$. (In particular, they cannot prove super-linear, indeed even $>8n$ tensor rank lower bounds for any 3-dimensional tensors.) 2. Rank methods cannot prove $Ω_d (n^{\lfloor d/2 \rfloor})$ on the Waring rank of any $n$-variate polynomial of degree $d$. (In particular, they cannot prove such lower bounds on stronger models, including depth-3 circuits.)
CODA: Enabling Co-location of Computation and Data for Near-Data Processing
Venue: arXiv cs.AR 2017
Year: 2017

Recent studies have demonstrated that near-data processing (NDP) is an effective technique for improving performance and energy efficiency of data-intensive workloads. However, leveraging NDP in realistic systems with multiple memory modules introduces a new challenge. In today's systems, where no computation occurs in memory modules, the physical address space is interleaved at a fine granularity among all memory modules to help improve the utilization of processor-memory interfaces by distributing the memory traffic. However, this is at odds with efficient use of NDP, which requires careful placement of data in memory modules such that near-data computations and their exclusively used data can be localized in individual memory modules, while distributing shared data among memory modules to reduce hotspots. In order to address this new challenge, we propose a set of techniques that (1) enable collections of OS pages to either be fine-grain interleaved among memory modules (as is done today) or to be placed contiguously on individual memory modules (as is desirable for NDP private data), and (2) decide whether to localize or distribute each memory object based on its anticipated access pattern and steer computations to the memory where the data they access is located. Our evaluations across a wide range of workloads show that the proposed mechanism improves performance by 31% and reduces 38% remote data accesses over a baseline system that cannot exploit computate-data affinity characteristics.
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
Venue: arXiv cs.CL 2017
Year: 2017

We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components. Our models are trained to predict either phonemes or graphemes as subword units, thus allowing us to detect arbitrary keyword phrases, without any out-of-vocabulary words. In order to adapt the models to the requirements of keyword spotting, we propose a novel technique which biases the RNN-T system towards a specific keyword of interest. Our systems are compared against a strong sequence-trained, connectionist temporal classification (CTC) based "keyword-filler" baseline, which is augmented with a separate phoneme language model. Overall, our RNN-T system with the proposed biasing technique significantly improves performance over the baseline system.
Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators
Venue: arXiv cs.SY 2017
Year: 2017

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.
Situational Awareness based Risk-Adapatable Access Control in Enterprise Networks
Venue: arXiv cs.CR 2017
Year: 2017

As the computing landscape evolves towards distributed architectures such as Internet of Things (IoT),enterprises are moving away from traditional perimeter based security models toward so called zero trust networking (ZTN) models that treat both the intranet and Internet as equally untrustworthy. Such security models incorporate risk arising from dynamic and situational factors, such as device location and security risk level risk, into the access control decision. Researchers have developed a number of risk models such as RAdAC (Risk Adaptable Access Control) to handle dynamic contexts and these have been applied to medical and other scenarios. In this position paper we describe our ongoing work to apply RAdAC to ZTN. We develop a policy management framework, FURZE, to facilitate fuzzy risk evaluation that also defines how to adapt to dynamically changing contexts. We also consider how enterprise security situational awareness (SSA) - which describes the potential impact to an organisations mission based on the current threats and the relative importance of the information asset under threat - can be incorporated into a RAdAC scheme
Meta Learning Shared Hierarchies
Venue: arXiv cs.LG 2017
Year: 2017

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.
Joint Screening Tests for LASSO
Venue: arXiv cs.LG 2017
Year: 2017

This paper focusses on "safe" screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint" screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respectively expressed as sphere and dome regions. After presenting the mathematical derivations of the tests, we elaborate on their relative effectiveness and discuss the practical use of such procedures.
Gaussian Process Regression for Generalized Frequency Response Function Estimation
Venue: arXiv cs.SY 2017
Year: 2017

Kernel-based modeling of dynamic systems has garnered a significant amount of attention in the system identification literature since its introduction to the field. While the method was originally applied to linear impulse response estimation in the time domain, the concepts have since been extended to the frequency domain for estimation of frequency response functions (FRFs), as well as to the estimation of the Volterra series in time domain. In the latter case, smoothness and exponential decay was imposed along the hypersurfaces of the multidimensional impulse responses, allowing lower variance estimates than could be obtained in a simple least squares framework. The Volterra series can also be expressed in a frequency domain context, however there are several competing representations which all possess some unique advantages. Perhaps the most natural representation is the generalized frequency response function (GFRF), which is defined as the multidimensional Fourier transform of the corresponding Volterra kernel in the time-domain series. The representation leads to a series of frequency domain functions with increasing dimension.
Alone Together: Compositional Reasoning and Inference for Weak Isolation
Venue: arXiv cs.PL 2017
Year: 2017

Serializability is a well-understood correctness criterion that simplifies reasoning about the behavior of concurrent transactions by ensuring they are isolated from each other while they execute. However, enforcing serializable isolation comes at a steep cost in performance and hence database systems in practice support, and often encourage, developers to implement transactions using weaker alternatives. Unfortunately, the semantics of weak isolation is poorly understood, and usually explained only informally in terms of low-level implementation artifacts. Consequently, verifying high-level correctness properties in such environments remains a challenging problem. To address this issue, we present a novel program logic that enables compositional reasoning about the behavior of concurrently executing weakly-isolated transactions. Recognizing that the proof burden necessary to use this logic may dissuade application developers, we also describe an inference procedure based on this foundation that ascertains the weakest isolation level that still guarantees the safety of high-level consistency invariants associated with such transactions. The key to effective inference is the observation that weakly-isolated transactions can be viewed as functional (monadic) computations over an abstract database state, allowing us to treat their operations as state transformers over the database. This interpretation enables automated verification using off-the-shelf SMT solvers. Case studies and experiments of real-world applications (written in an embedded DSL in OCaml) demonstrate the utility of our approach.
Gradient Sparsification for Communication-Efficient Distributed Optimization
Venue: arXiv cs.LG 2017
Year: 2017

Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost we propose a convex optimization formulation to minimize the coding length of stochastic gradients. To solve the optimal sparsification efficiently, several simple and fast algorithms are proposed for approximate solution, with theoretical guaranteed for sparseness. Experiments on $\ell_2$ regularized logistic regression, support vector machines, and convolutional neural networks validate our sparsification approaches.
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

Neural networks are analogous in many ways to spin glasses, systems which are known for their rich set of dynamics and equally complex phase diagrams. We apply well-known techniques in the study of spin glasses to a convolutional sparsely encoding neural network and observe power law finite-size scaling behavior in the sparsity and reconstruction error as the network denoises 32$\times$32 RGB CIFAR-10 images. This finite-size scaling indicates the presence of a continuous phase transition at a critical value of this sparsity. By using the power law scaling relations inherent to finite-size scaling, we can determine the optimal value of sparsity for any network size by tuning the system to the critical point and operate the system at the minimum denoising error.
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
Venue: arXiv cs.CV 2017
Year: 2017

The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.
Online Learning of Power Transmission Dynamics
Venue: arXiv cs.SY 2017
Year: 2017

We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.
Rapidly Mixing Markov Chain Monte Carlo Technique for Matching Problems with Global Utility Function
Venue: arXiv cs.DM 2017
Year: 2017

This paper deals with a complete bipartite matching problem with the objective of finding an optimal matching that maximizes a certain generic predefined utility function on the set of all matchings. After proving the NP-hardness of the problem using reduction from the 3-SAT problem, we propose a randomized algorithm based on Markov Chain Monte Carlo (MCMC) technique for solving this. We sample from Gibb's distribution and construct a reversible positive recurrent discrete time Markov chain (DTMC) that has the steady state distribution same as the Gibb's distribution. In one of our key contributions, we show that the constructed chain is `rapid mixing', i.e., the convergence time to reach within a specified distance to the desired distribution is polynomial in the problem size. The rapid mixing property is established by obtaining a lower bound on the conductance of the DTMC graph and this result is of independent interest.
Distributional Reinforcement Learning with Quantile Regression
Venue: arXiv cs.AI 2017
Year: 2017

In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean. That is, we examine methods of learning the value distribution instead of the value function. We give results that close a number of gaps between the theoretical and algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we extend existing results to the approximate distribution setting. Second, we present a novel distributional reinforcement learning algorithm consistent with our theoretical formulation. Finally, we evaluate this new algorithm on the Atari 2600 games, observing that it significantly outperforms many of the recent improvements on DQN, including the related distributional algorithm C51.
Edge-as-a-Service: Towards Distributed Cloud Architectures
Venue: arXiv cs.DC 2017
Year: 2017

We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery protocol that identifies edge nodes and make them publicly accessible in a computing environment, and (ii) a scalable resource provisioning mechanism for offloading workloads from the cloud on to the edge for servicing multiple user requests. We validate the feasibility of EaaS on an online game use-case to highlight the improvement in the QoS of the application hosted on our cloud-edge platform. On this platform we demonstrate (i) low overheads of less than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii) minimised application latency between 40%-60%.
Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing
Venue: arXiv cs.CV 2017
Year: 2017

The ability to modulate vocal sounds and generate speech is one of the features which set humans apart from other living beings. The human voice can be characterized by several attributes such as pitch, timbre, loudness, and vocal tone. It has often been observed that humans express their emotions by varying different vocal attributes during speech generation. Hence, deduction of human emotions through voice and speech analysis has a practical plausibility and could potentially be beneficial for improving human conversational and persuasion skills. This paper presents an algorithmic approach for detection and analysis of human emotions with the help of voice and speech processing. The proposed approach has been developed with the objective of incorporation with futuristic artificial intelligence systems for improving human-computer interactions.
New Methods for Metadata Extraction from Scientific Literature
Venue: arXiv cs.DL 2017
Year: 2017

Within the past few decades we have witnessed digital revolution, which moved scholarly communication to electronic media and also resulted in a substantial increase in its volume. Nowadays keeping track with the latest scientific achievements poses a major challenge for the researchers. Scientific information overload is a severe problem that slows down scholarly communication and knowledge propagation across the academia. Modern research infrastructures facilitate studying scientific literature by providing intelligent search tools, proposing similar and related documents, visualizing citation and author networks, assessing the quality and impact of the articles, and so on. In order to provide such high quality services the system requires the access not only to the text content of stored documents, but also to their machine-readable metadata. Since in practice good quality metadata is not always available, there is a strong demand for a reliable automatic method of extracting machine-readable metadata directly from source documents. This research addresses these problems by proposing an automatic, accurate and flexible algorithm for extracting wide range of metadata directly from scientific articles in born-digital form. Extracted information includes basic document metadata, structured full text and bibliography section. Designed as a universal solution, proposed algorithm is able to handle a vast variety of publication layouts with high precision and thus is well-suited for analyzing heterogeneous document collections. This was achieved by employing supervised and unsupervised machine-learning algorithms trained on large, diverse datasets. The evaluation we conducted showed good performance of proposed metadata extraction algorithm. The comparison with other similar solutions also proved our algorithm performs better than competition for most metadata types.
Tensor network language model
Venue: arXiv cs.CL 2017
Year: 2017

We propose a new statistical model suitable for machine learning of systems with long distance correlations such as natural languages. The model is based on directed acyclic graph decorated by multi-linear tensor maps in the vertices and vector spaces in the edges, called tensor network. Such tensor networks have been previously employed for effective numerical computation of the renormalization group flow on the space of effective quantum field theories and lattice models of statistical mechanics. We provide explicit algebro-geometric analysis of the parameter moduli space for tree graphs, discuss model properties and applications such as statistical translation.