OpenRaiser/Intern-Atlas huggingface dataset:
Linear Programming Approaches for Power Savings in Software-defined Networks (The Extended Version)
Venue: arXiv cs.NI 2016
Year: 2016

Software-defined networks have been proposed as a viable solution to decrease the power consumption of the networking component in data center networks. Still the question remains on which scheduling algorithms are most suited to achieve this goal. We propose 4 different linear programming approaches that schedule requested traffic flows on SDN switches according to different objectives. Depending on pre-defined software quality requirements such as delay and performance, a single variation or a combination of variations can be selected to optimize the power saving and the performance metrics. Our simulation results demonstrate that all our algorithm variations outperform the shortest path scheduling algorithm, our baseline on power savings, less or more strongly depending on the power model chosen. We show that in FatTree networks, where switches can save up to 60% of power in sleeping mode, we can achieve 15% minimum improvement assuming a one-to-one traffic scenario. Two of our algorithm variations privilege performance over power saving and still provide around 45% of the maximum achievable savings.
An optimal algorithm for bandit convex optimization
Venue: arXiv cs.LG 2016
Year: 2016

We consider the problem of online convex optimization against an arbitrary adversary with bandit feedback, known as bandit convex optimization. We give the first $\tilde{O}(\sqrt{T})$-regret algorithm for this setting based on a novel application of the ellipsoid method to online learning. This bound is known to be tight up to logarithmic factors. Our analysis introduces new tools in discrete convex geometry.
Academic Torrents: Scalable Data Distribution
Venue: arXiv cs.NI 2016
Year: 2016

As competitions get more popular, transferring ever-larger data sets becomes infeasible and costly. For example, downloading the 157.3 GB 2012 ImageNet data set incurs about $4.33 in bandwidth costs per download. Downloading the full ImageNet data set takes 33 days. ImageNet has since become popular beyond the competition, and many papers and models now revolve around this data set. For sharing such an important resource to the machine learning community, the sharers of ImageNet must shoulder a large bandwidth burden. Academic Torrents reduces this burden for disseminating competition data, and also increases download speeds for end users. Academic Torrents is run by a pending nonprofit.. By augmenting an existing HTTP server with a peer-to-peer swarm, requests get re-routed to get data from downloaders. While existing systems slow down with more users, the benefits of Academic Torrents grow, with noticeable effects even when only one other person is downloading.
Controlling Search in Very large Commonsense Knowledge Bases: A Machine Learning Approach
Venue: arXiv cs.AI 2016
Year: 2016

Very large commonsense knowledge bases (KBs) often have thousands to millions of axioms, of which relatively few are relevant for answering any given query. A large number of irrelevant axioms can easily overwhelm resolution-based theorem provers. Therefore, methods that help the reasoner identify useful inference paths form an essential part of large-scale reasoning systems. In this paper, we describe two ordering heuristics for optimization of reasoning in such systems. First, we discuss how decision trees can be used to select inference steps that are more likely to succeed. Second, we identify a small set of problem instance features that suffice to guide searches away from intractable regions of the search space. We show the efficacy of these techniques via experiments on thousands of queries from the Cyc KB. Results show that these methods lead to an order of magnitude reduction in inference time.
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
Venue: arXiv cs.SI 2016
Year: 2016

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.
Conformal Predictors for Compound Activity Prediction
Venue: arXiv cs.LG 2016
Year: 2016

The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures (NCM) extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. Keywords: Conformal Prediction, Confidence Estimation, Chemoinformatics, Non-Conformity Measure.
Pushing the Limits of Deep CNNs for Pedestrian Detection
Venue: arXiv cs.CV 2016
Year: 2016

Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophisticated deep CNN models, with a number of hand-crafted features, or explicit occlusion handling mechanism. In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional neural network (DCNN) model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labelling may be simply combined with a detector to boost the detection performance. By adding complementary hand-crafted features such as optical flow, the DCNN based detector can be further improved. We set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from $11.7\%$ to $8.9\%$, a relative improvement of $24\%$. We also achieve a comparable result to the state-of-the-art approaches on the KITTI dataset.
Threshold Saturation of Spatially Coupled Sparse Superposition Codes for All Memoryless Channels
Venue: arXiv cs.IT 2016
Year: 2016

We recently proved threshold saturation for spatially coupled sparse superposition codes on the additive white Gaussian noise channel. Here we generalize our analysis to a much broader setting. We show for any memoryless channel that spatial coupling allows generalized approximate message-passing (GAMP) decoding to reach the potential (or Bayes optimal) threshold of the code ensemble. Moreover in the large input alphabet size limit: i) the GAMP algorithmic threshold of the underlying (or uncoupled) code ensemble is simply expressed as a Fisher information; ii) the potential threshold tends to Shannon's capacity. Although we focus on coding for sake of coherence with our previous results, the framework and methods are very general and hold for a wide class of generalized estimation problems with random linear mixing.
Modified Micropipline Architecture for Synthesizable Asynchronous FIR Filter Design
Venue: arXiv cs.AR 2016
Year: 2016

The use of asynchronous design approaches to construct digital signal processing (DSP) systems is a rapidly growing research area driven by a wide range of emerging energy constrained applications such as wireless sensor network, portable medical devices and brain implants. The asynchronous design techniques allow the construction of systems which are samples driven, which means they only dissipate dynamic energy when there processing data and idle otherwise. This inherent advantage of asynchronous design over conventional synchronous circuits allows them to be energy efficient. However the implementation flow of asynchronous systems is still difficult due to its lack of compatibility with industry-standard synchronous design tools and modelling languages. This paper devises a novel asynchronous design for a finite impulse response (FIR) filter, an essential building block of DSP systems, which is synthesizable and suitable for implementation using conventional synchronous systems design flow and tools. The proposed design is based on a modified version of the micropipline architecture and it is constructed using four phase bundled data protocol. A hardware prototype of the proposed filter has been developed on an FPGA, and systematically verified. The results prove correct functionality of the novel design and a superior performance compared to a synchronous FIR implementation. The findings of this work will allow a wider adoption of asynchronous circuits by DSP designers to harness their energy and performance benefits.
Beyond representing orthology relations by trees
Venue: arXiv cs.DM 2016
Year: 2016

Reconstructing the evolutionary past of a family of genes is an important aspect of many genomic studies. To help with this, simple operations on a set of sequences called orthology relations may be employed. In addition to being interesting from a practical point of view they are also attractive from a theoretical perspective in that e. g. a characterization is known for when such a relation is representable by a certain type of phylogenetic tree. For an orthology relation inferred from real biological data it is however generally too much to hope for that it satisfies that characterization. Rather than trying to correct the data in some way or another which has its own drawbacks, as an alternative, we propose to represent an orthology relation $δ$ in terms of a structure more general than a phylogenetic tree called a phylogenetic network. To compute such a network in the form of a level-1 representation for $δ$, we introduce the novel {\sc Network-Popping} algorithm which has several attractive properties. In addition, we characterize orthology relations $δ$ on some set $X$ that have a level-1 representation in terms of eight natural properties for $δ$ as well as in terms for level-1 representations of orthology relations on certain subsets of $X$.
A 2.542-Approximation for Precedence Constrained Single Machine Scheduling with Release Dates and Total Weighted Completion Time Objective
Venue: arXiv cs.DM 2016
Year: 2016

We present a $\sqrt{e}/(\sqrt{e}-1)$-approximation algorithm for the nonpreemptive scheduling problem to minimize the total weighted completion time of jobs on a single machine subject to release dates and precedence constraints. The previously best known approximation algorithm dates back to 1997; its performance guarantee can be made arbitrarily close to the Euler constant $e$.
The SPURS Algorithm for Resampling an Irregularly Sampled Signal onto a Cartesian Grid
Venue: arXiv cs.IT 2016
Year: 2016

We present an algorithm for resampling a function from its values on a non-Cartesian grid onto a Cartesian grid. This problem arises in many applications such as MRI, CT, radio astronomy and geophysics. Our algorithm, termed SParse Uniform ReSampling (SPURS), employs methods from modern sampling theory to achieve a small approximation error while maintaining low computational cost. The given non-Cartesian samples are projected onto a selected intermediate subspace, spanned by integer translations of a compactly supported kernel function. This produces a sparse system of equations describing the relation between the nonuniformly spaced samples and a vector of coefficients representing the projection of the signal onto the chosen subspace. This sparse system of equations can be solved efficiently using available sparse equation solvers. The result is then projected onto the subspace in which the sampled signal is known to reside. The second projection is implemented efficiently using a digital linear shift invariant (LSI) filter and produces uniformly spaced values of the signal on a Cartesian grid. The method can be iterated to improve the reconstruction results. We then apply SPURS to reconstruction of MRI data from nonuniformly spaced k-space samples. Simulations demonstrate that SPURS outperforms other reconstruction methods while maintaining a similar computational complexity over a range of sampling densities and trajectories as well as various input SNR levels.
Topic Modeling Using Distributed Word Embeddings
Venue: arXiv cs.CL 2016
Year: 2016

We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused across the corpus. We also find that Vec2Topic works equally well for non-user generated content, such as papers, reports, etc., and for small corpora such as a single-document.
Abstractions of Varying Decentralization Degree for Coupled Multi-Agent Systems
Venue: arXiv cs.SY 2016
Year: 2016

In this report, we aim at the development of a decentralized abstraction framework for multi-agent systems under coupled constraints, with the possibility for a varying degree of decentralization. The methodology is based on the analysis employed in our recent work, where decentralized abstractions based exclusively on the information of each agent's neighbors were derived. In the first part of this report, we define the notion each agent's m-neighbor set, which constitutes a measure for the employed degree of decentralization. Then, sufficient conditions are provided on the space and time discretization that provides the abstract system's model, which guarantee the extraction of a transition system with quantifiable transition possibilities.
Distributed Newest Vertex Bisection
Venue: arXiv cs.DC 2016
Year: 2016

Distributed adaptive conforming refinement requires multiple iterations of the serial refinement algorithm and global communication as the refinement can be propagated over several processor boundaries. We show bounds on the maximum number of iterations. The algorithm is implemented within the software package DUNE-ALUGrid.
Scaled stochastic gradient descent for low-rank matrix completion
Venue: arXiv cs.LG 2016
Year: 2016

The paper looks at a scaled variant of the stochastic gradient descent algorithm for the matrix completion problem. Specifically, we propose a novel matrix-scaling of the partial derivatives that acts as an efficient preconditioning for the standard stochastic gradient descent algorithm. This proposed matrix-scaling provides a trade-off between local and global second order information. It also resolves the issue of scale invariance that exists in matrix factorization models. The overall computational complexity is linear with the number of known entries, thereby extending to a large-scale setup. Numerical comparisons show that the proposed algorithm competes favorably with state-of-the-art algorithms on various different benchmarks.
Hardware Software Co-design for Automotive CPS using Architecture Analysis and Design Language
Venue: arXiv cs.SE 2016
Year: 2016

Modern cyber-physical systems (CPS) have a close inter-dependence between software and physical components. Automotive embedded systems are typical CPS, as physical chips, sensors and actuators are physical components and software embedded within are the cyber components. The current stage of embedded systems is highly complex in architecture design for both software and hardware. It is common in industrial practice that high level control algorithm development and low level code implementation on hardware platforms are developed separately with limited shared information. However, software code and hardware architecture become closely related with the increasing complexity. Correlated requirements and dependencies between hardware and software are emerging problems of industrial practice. We demonstrate in this paper a method to link model based system design with real-time simulations and analysis of the architecture model. This allows hardware software co-design and thus early selection of hardware architecture.
Reconciling Rationality and Stochasticity: Rich Behavioral Models in Two-Player Games
Venue: arXiv cs.GT 2016
Year: 2016

Two traditional paradigms are often used to describe the behavior of agents in multi-agent complex systems. In the first one, agents are considered to be fully rational and systems are seen as multi-player games. In the second one, agents are considered to be fully stochastic processes and the system itself is seen as a large stochastic process. From the standpoint of a particular agent - having to choose a strategy, the choice of the paradigm is crucial: the most adequate strategy depends on the assumptions made on the other agents. In this paper, we focus on two-player games and their application to the automated synthesis of reliable controllers for reactive systems - a field at the crossroads between computer science and mathematics. In this setting, the reactive system to control is a player, and its environment is its opponent, usually assumed to be fully antagonistic or fully stochastic. We illustrate several recent developments aiming to breach this narrow taxonomy by providing formal concepts and mathematical frameworks to reason about richer behavioral models. The interest of such models is not limited to reactive system synthesis but extends to other application fields of game theory. The goal of our contribution is to give a high-level presentation of key concepts and applications, aimed at a broad audience. To achieve this goal, we illustrate those rich behavioral models on a classical challenge of the everyday life: planning a journey in an uncertain environment.
A linear complementarity based characterization of the weighted independence number and the independent domination number in graphs
Venue: arXiv cs.DM 2016
Year: 2016

The linear complementarity problem is a continuous optimization problem that generalizes convex quadratic programming, Nash equilibria of bimatrix games and several such problems. This paper presents a continuous optimization formulation for the weighted independence number of a graph by characterizing it as the maximum weighted $\ell_1$ norm over the solution set of a linear complementarity problem (LCP). The minimum $\ell_1$ norm of solutions of this LCP is a lower bound on the independent domination number of the graph. Unlike the case of the maximum $\ell_1$ norm, this lower bound is in general weak, but we show it to be tight if the graph is a forest. Using methods from the theory of LCPs, we obtain a few graph theoretic results. In particular, we provide a stronger variant of the Lovász theta of a graph. We then provide sufficient conditions for a graph to be well-covered, i.e., for all maximal independent sets to also be maximum. This condition is also shown to be necessary for well-coveredness if the graph is a forest. Finally, the reduction of the maximum independent set problem to a linear program with (linear) complementarity constraints (LPCC) shows that LPCCs are hard to approximate.
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
Venue: arXiv cs.CV 2016
Year: 2016

Many deep Convolutional Neural Networks (CNN) make incorrect predictions on adversarial samples obtained by imperceptible perturbations of clean samples. We hypothesize that this is caused by a failure to suppress unusual signals within network layers. As remedy we propose the use of Symmetric Activation Functions (SAF) in non-linear signal transducer units. These units suppress signals of exceptional magnitude. We prove that SAF networks can perform classification tasks to arbitrary precision in a simplified situation. In practice, rather than use SAFs alone, we add them into CNNs to improve their robustness. The modified CNNs can be easily trained using popular strategies with the moderate training load. Our experiments on MNIST and CIFAR-10 show that the modified CNNs perform similarly to plain ones on clean samples, and are remarkably more robust against adversarial and nonsense samples.
A (Basis for a) Philosophy of a Theory of Fuzzy Computation
Venue: arXiv cs.OH 2016
Year: 2016

Vagueness is a linguistic phenomenon as well as a property of physical objects. Fuzzy set theory is a mathematical model of vagueness that has been used to define vague models of computation. The prominent model of vague computation is the fuzzy Turing machine. This conceptual computing device gives an idea of what computing under vagueness means, nevertheless, it is not the most natural model. Based on the properties of this and other models of vague computing, it is aimed to formulate a basis for a philosophy of a theory of fuzzy computation.
Explicit Non-Malleable Extractors, Multi-Source Extractors and Almost Optimal Privacy Amplification Protocols
Venue: arXiv cs.CR 2016
Year: 2016

We make progress in the following three problems: 1. Constructing optimal seeded non-malleable extractors; 2. Constructing optimal privacy amplification protocols with an active adversary, for any security parameter; 3. Constructing extractors for independent weak random sources, when the min-entropy is extremely small (i.e., near logarithmic). For the first two problems, the best known non-malleable extractors by Chattopadhyay, Goyal and Li [CGL16], and by Cohen [Coh16a,Coh16b] all require seed length and min-entropy at least $\log^2 (1/ε)$, where $ε$ is the error of the extractor. As a result, the best known explicit privacy amplification protocols with an active adversary, which achieve 2 rounds of communication and optimal entropy loss in [Li15c,CGL16], can only handle security parameter up to $s=Ω(\sqrt{k})$, where $k$ is the min-entropy of the shared secret weak random source. For larger $s$ the best known protocol with optimal entropy loss in [Li15c] requires $O(s/\sqrt{k})$ rounds of communication. In this paper we give an explicit non-malleable extractor that only requires seed length and min-entropy $\log^{1+o(1)} (n/ε)$, which also yields a 2-round privacy amplification protocol with optimal entropy loss for security parameter up to $s=k^{1-α}$ for any constant $α>0$. For the third problem, previously the best known extractor which supports the smallest min-entropy due to Li [Li13a], requires min-entropy $\log^{2+δ} n$ and uses $O(1/δ)$ sources, for any constant $δ>0$. A very recent result by Cohen and Schulman [CS16] improves this, and constructed explicit extractors that use $O(1/δ)$ sources for min-entropy $\log^{1+δ} n$, any constant $δ>0$. In this paper we further improve their result, and give an explicit extractor that uses $O(1)$ (an absolute constant) sources for min-entropy $\log^{1+o(1)} n$.