OpenRaiser/Intern-Atlas huggingface dataset:
Massive MIMO-Enabled Full-Duplex Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. To facilitate bi-directional wireless functionality, we adopt (i) a downlink (DL) linear zero-forcing with self-interference-nulling (ZF-SIN) precoding scheme at the FD base stations (BSs), and (ii) an uplink (UL) self-interference-aware (SIA) fractional power control mechanism at the FD user equipments (UEs). Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the baseline FD single-antenna system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency (SE) gain of FD massive MIMO over its half-duplex (HD) counterpart largely depends on the SI cancellation capability of the UEs. In addition, the anticipated two-fold increase in SE is shown to be only achievable with an infinitely large number of antennas.
Optimal incentives for collective intelligence
Venue: arXiv cs.GT 2017
Year: 2017

Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one's peers. We investigate the role incentives play in maintaining useful diversity through an evolutionary game-theoretic model of collective prediction. We show that market-based incentive systems produce herding effects, reduce information available to the group and suppress collective intelligence. In response, we propose a new incentive scheme that rewards accurate minority predictions, and show that this produces optimal diversity and collective predictive accuracy. We conclude that real-world systems should reward those who have demonstrated accuracy when majority opinion has been in error.
Energy-Based Adaptive Multiple Access in LPWAN IoT Systems with Energy Harvesting
Venue: arXiv cs.IT 2017
Year: 2017

This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. The objective is to adapt their transmission strategy to the state of the network, including the energy available to the individual nodes. In order to reduce the complexity of control, an optimization framework is proposed where energy storage dynamics are replaced by dynamic average power constraints induced by the time correlated energy supply, thus enabling lightweight and flexible network control. Specifically, the BS adapts the packet transmission probability of the "active" nodes (those currently under a favorable energy harvesting state) so as to maximize the average long-term throughput, under these dynamic average power constraints. The resulting policy takes the form of the packet transmission probability as a function of the energy harvesting state and number of active nodes. The structure of the throughput-optimal genie-aided policy, in which the number of active nodes is known non-causally at the BS, is proved. Inspired by the genie-aided policy, a Bayesian estimation approach is presented to address the case where the BS estimates the number of active nodes based on the observed network transmission pattern. It is shown that the proposed scheme outperforms by 20% a scheme in which the nodes operate based on local state information only, and performs well even when energy storage dynamics are taken into account.
Towards Information Privacy for the Internet of Things
Venue: arXiv cs.IT 2017
Year: 2017

In an Internet of Things network, multiple sensors send information to a fusion center for it to infer a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sensor makes a private observation and utilizes a local sensor decision rule or privacy mapping to summarize that observation independently of the other sensors. The local decision made by a sensor is then sent to the fusion center. Without assuming knowledge of the joint distribution of the sensor observations and hypotheses, we adopt a nonparametric learning approach to design local privacy mappings. We introduce the concept of an empirical normalized risk, which provides a theoretical guarantee for the network to achieve information privacy for the private hypothesis with high probability when the number of training samples is large. We develop iterative optimization algorithms to determine an appropriate privacy threshold and the best sensor privacy mappings, and show that they converge. Finally, we extend our approach to the case of a private multiple hypothesis. Numerical results on both synthetic and real data sets suggest that our proposed approach yields low error rates for inferring the public hypothesis, but high error rates for detecting the private hypothesis.
Bounds and Constructions for $\overline{3}$-Strongly Separable Codes with Length $3$
Venue: arXiv cs.DM 2017
Year: 2017

As separable code (SC, IEEE Trans Inf Theory 57:4843-4851, 2011) and frameproof code (FPC, IEEE Trans Inf Theory 44:1897-1905, 1998) do in multimedia fingerprinting, strongly separable code (SSC, Des. Codes and Cryptogr.79:303-318, 2016) can be also used to construct anti-collusion codes. Furthermore, SSC is better than FPC and SC in the applications for multimedia fingerprinting since SSC has lower tracing complexity than that of SC (the same complexity as FPC) and weaker structure than that of FPC. In this paper, we first derive several upper bounds on the number of codewords of $\overline{t}$-SSC. Then we focus on $\overline{3}$-SSC with codeword length $3$, and obtain the following two main results: (1) An equivalence between an SSC and an SC. %Consequently a more tighter upper bound $(3q^2/4)$ and lower bound $(q^{3/2})$ on the number of codewords are obtained; (2) An improved lower bound $Ω(q^{5/3}+q^{4/3}-q)$ on the size of a $q$-ary SSC when $q=q_1^6$ for any prime power $q_1\equiv\ 1 \pmod 6$, better than the before known bound $\lfloor\sqrt{q}\rfloor^{3}$, which is obtained by means of difference matrix and the known result on the subset of $\mathbb{F}^{n}_q$ containing no three points on a line.
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
Venue: arXiv cs.CV 2017
Year: 2017

We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.
Can fully convolutional networks perform well for general image restoration problems?
Venue: arXiv cs.CV 2017
Year: 2017

We present a fully convolutional network(FCN) based approach for color image restoration. FCNs have recently shown remarkable performance for high-level vision problem like semantic segmentation. In this paper, we investigate if FCN models can show promising performance for low-level problems like image restoration as well. We propose a fully convolutional model, that learns a direct end-to-end mapping between the corrupted images as input and the desired clean images as output. Our proposed method takes inspiration from domain transformation techniques but presents a data-driven task specific approach where filters for novel basis projection, task dependent coefficient alterations, and image reconstruction are represented as convolutional networks. Experimental results show that our FCN model outperforms traditional sparse coding based methods and demonstrates competitive performance compared to the state-of-the-art methods for image denoising. We further show that our proposed model can solve the difficult problem of blind image inpainting and can produce reconstructed images of impressive visual quality.
SimDoc: Topic Sequence Alignment based Document Similarity Framework
Venue: arXiv cs.CL 2017
Year: 2017

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.
Heavy-Hitter Detection Entirely in the Data Plane
Venue: arXiv cs.NI 2017
Year: 2017

Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.
Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data
Venue: arXiv cs.LG 2017
Year: 2017

In this paper we propose a novel learning framework called Supervised and Weakly Supervised Learning where the goal is to learn simultaneously from weakly and strongly labeled data. Strongly labeled data can be simply understood as fully supervised data where all labeled instances are available. In weakly supervised learning only data is weakly labeled which prevents one from directly applying supervised learning methods. Our proposed framework is motivated by the fact that a small amount of strongly labeled data can give considerable improvement over only weakly supervised learning. The primary problem domain focus of this paper is acoustic event and scene detection in audio recordings. We first propose a naive formulation for leveraging labeled data in both forms. We then propose a more general framework for Supervised and Weakly Supervised Learning (SWSL). Based on this general framework, we propose a graph based approach for SWSL. Our main method is based on manifold regularization on graphs in which we show that the unified learning can be formulated as a constraint optimization problem which can be solved by iterative concave-convex procedure (CCCP). Our experiments show that our proposed framework can address several concerns of audio content analysis using weakly labeled data.
Undecidability of Asynchronous Session Subtyping
Venue: arXiv cs.PL 2017
Year: 2017

Session types are used to describe communication protocols in distributed systems and, as usual in type theories, session subtyping characterizes substitutability of the communicating processes. We investigate the (un)decidability of subtyping for session types in asynchronously communicating systems. We first devise a core undecidable subtyping relation that is obtained by imposing limitations on the structure of types. Then, as a consequence of this initial undecidability result, we show that (differently from what stated or conjectured in the literature) the three notions of asynchronous subtyping defined so far for session types are all undecidable. Namely, we consider the asynchronous session subtyping by Mostrous and Yoshida for binary sessions, the relation by Chen et al. for binary sessions under the assumption that every message emitted is eventually consumed, and the one by Mostrous et al. for multiparty session types. Finally, by showing that two fragments of the core subtyping relation are decidable, we evince that further restrictions on the structure of types make our core subtyping relation decidable.
Learning To Score Olympic Events
Venue: arXiv cs.CV 2017
Year: 2017

Estimating action quality, the process of assigning a "score" to the execution of an action, is crucial in areas such as sports and health care. Unlike action recognition, which has millions of examples to learn from, the action quality datasets that are currently available are small -- typically comprised of only a few hundred samples. This work presents three frameworks for evaluating Olympic sports which utilize spatiotemporal features learned using 3D convolutional neural networks (C3D) and perform score regression with i) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training mechanism for the limited data scenarios is presented for clip-based training with LSTM. The proposed systems show significant improvement over existing quality assessment approaches on the task of predicting scores of Olympic events {diving, vault, figure skating}. While the SVR-based frameworks yield better results, LSTM-based frameworks are more natural for describing an action and can be used for improvement feedback.
The emergence and evolution of the research fronts in HIV/AIDS research
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we have identified and analyzed the emergence, structure and dynamics of the paradigmatic research fronts that established the fundamentals of the biomedical knowledge on HIV/AIDS. A search of papers with the identifiers "HIV/AIDS", "Human Immunodeficiency Virus", "HIV-1" and "Acquired Immunodeficiency Syndrome" in the Web of Science (Thomson Reuters), was carried out. A citation network of those papers was constructed. Then, a sub-network of the papers with the highest number of inter-citations (with a minimal in-degree of 28) was selected to perform a combination of network clustering and text mining to identify the paradigmatic research fronts and analyze their dynamics. Thirteen research fronts were identified in this sub-network. The biggest and oldest front is related to the clinical knowledge on the disease in the patient. Nine of the fronts are related to the study of specific molecular structures and mechanisms and two of these fronts are related to the development of drugs. The rest of the fronts are related to the study of the disease at the cellular level. Interestingly, the emergence of these fronts occurred in successive "waves" over the time which suggest a transition in the paradigmatic focus. The emergence and evolution of the biomedical fronts in HIV/AIDS research is explained not just by the partition of the problem in elements and interactions leading to increasingly specialized communities, but also by changes in the technological context of this health problem and the dramatic changes in the epidemiological reality of HIV/AIDS that occurred between 1993 and 1995.
Optimizing DF Cognitive Radio Networks with Full-Duplex-Enabled Energy Access Points
Venue: arXiv cs.IT 2017
Year: 2017

With the recent advances in radio frequency (RF) energy harvesting (EH) technologies, wireless powered cooperative cognitive radio network (CCRN) has drawn an upsurge of interest for improving the spectrum utilization with incentive to motivate joint information and energy cooperation between the primary and secondary systems. Dedicated energy beamforming (EB) is aimed for remedying the low efficiency of wireless power transfer (WPT), which nevertheless arouses out-of-band EH phases and thus low cooperation efficiency. To address this issue, in this paper, we consider a novel RF EH CCRN aided by full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to wireless charge the secondary transmitter (ST) while concurrently receiving primary transmitter (PT)'s signal in the first transmission phase, and to perform decode-and-forward (DF) relaying in the second transmission phase. We investigate a weighted sum-rate maximization problem subject to the transmitting power constraints as well as a total cost constraint using successive convex approximation (SCA) techniques. A zero-forcing (ZF) based suboptimal scheme that requires only local CSIs for the EAPs to obtain their optimum receive beamforming is also derived. Various tradeoffs between the weighted sum-rate and other system parameters are provided in numerical results to corroborate the effectiveness of the proposed solutions against the benchmark ones.
Towards Interconnected Virtual Reality: Opportunities, Challenges and Enablers
Venue: arXiv cs.NI 2017
Year: 2017

Just recently, the concept of augmented and virtual reality (AR/VR) over wireless has taken the entire 5G ecosystem by storm spurring an unprecedented interest from both academia, industry and others. Yet, the success of an immersive VR experience hinges on solving a plethora of grand challenges cutting across multiple disciplines. This article underscores the importance of VR technology as a disruptive use case of 5G (and beyond) harnessing the latest development of storage/memory, fog/edge computing, computer vision, artificial intelligence and others. In particular, the main requirements of wireless interconnected VR are described followed by a selection of key enablers, then, research avenues and their underlying grand challenges are presented. Furthermore, we examine three VR case studies and provide numerical results under various storage, computing and network configurations. Finally, this article exposes the limitations of current networks and makes the case for more theory, and innovations to spearhead VR for the masses.
VisualBackProp: efficient visualization of CNNs
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.
Upscaledb: Efficient Integer-Key Compression in a Key-Value Store using SIMD Instructions
Venue: arXiv cs.DB 2017
Year: 2017

Compression can sometimes improve performance by making more of the data available to the processors faster. We consider the compression of integer keys in a B+-tree index. For this purpose, systems such as IBM DB2 use variable-byte compression over differentially coded keys. We revisit this problem with various compression alternatives such as Google's VarIntGB, Binary Packing and Frame-of-Reference. In all cases, we describe algorithms that can operate directly on compressed data. Many of our alternatives exploit the single-instruction-multiple-data (SIMD) instructions supported by modern CPUs. We evaluate our techniques in a database environment provided by Upscaledb, a production-quality key-value database. Our best techniques are SIMD accelerated: they simultaneously reduce memory usage while improving single-threaded speeds. In particular, a differentially coded SIMD binary-packing techniques (BP128) can offer a superior query speed (e.g., 40% better than an uncompressed database) while providing the best compression (e.g., by a factor of ten). For analytic workloads, our fast compression techniques offer compelling benefits. Our software is available as open source.
A Discriminatively Learned CNN Embedding for Person Re-identification
Venue: arXiv cs.CV 2017
Year: 2017

We revisit two popular convolutional neural networks (CNN) in person re-identification (re-ID), i.e, verification and classification models. The two models have their respective advantages and limitations due to different loss functions. In this paper, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a new siamese network that simultaneously computes identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the annotations. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show our architecture can also be applied in image retrieval.
Learning to reinforcement learn
Venue: arXiv cs.LG 2017
Year: 2017

In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.