OpenRaiser/Intern-Atlas huggingface dataset:
Linear Degrees of Freedom for $K $-user MISO Interference Channels with Blind Interference Alignment
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we characterize the degrees of freedom (DoF) for $K $-user $M \times 1 $ multiple-input single-output interference channels with reconfigurable antennas which have multiple preset modes at the receivers, assuming linear coding strategies in the absence of channel state information at the transmitters, i.e., blind interference alignment. Our linear DoF converse builds on the lemma that if a set of transmit symbols is aligned at their common unintended receivers, those symbols must have independent signal subspace at their corresponding receivers. This lemma arises from the inherent feature that channel state's changing patterns of the links towards the same receiver are always identical, assuming that the coherence time of the channel is long enough. We derive an upper bound for the linear sum DoF, and propose an achievable scheme that exactly achieves the linear sum DoF upper-bound when both of the $\frac{n^{*}}{M}=R_{1} $ and $\frac{MK}{n^{*}}=R_{2} $ are integers. For the other cases, where either $R_1 $ or $R_2 $ is not an integer, we only give some guidelines how the interfering signals are aligned at the receivers to achieve the upper-bound. As an extension, we also show the linear sum DoF upper-bound for downlink/uplink cellular networks.
Grokya: a Privacy-Friendly Framework for Ubiquitous Computing
Venue: arXiv cs.CY 2016
Year: 2016

In a world where for-profit enterprises are increasingly looking to maximize profits by engaging in privacy invading consumer-profiling techniques, the rise of ubiquitous computing and the Internet of Things (IoT) poses a major problem. If not acted upon quickly, the combination of Big Data with IoT will explode into a dystopian world that even George Orwell could not have predicted. The proposed project aims to fill a gap that no other solution is addressing, which is to reach a win-win scenario that works for both the enterprises and the consumers. It aims to do this by creating the building blocks for a consumer owned infrastructure that can provide both privacy for the user, and still enable the enterprises to achieve their high-level goals.
An Empirical Study on Academic Commentary and Its Implications on Reading and Writing
Venue: arXiv cs.CY 2016
Year: 2016

The relationship between reading and writing (RRW) is one of the major themes in learning science. One of its obstacles is that it is difficult to define or measure the latent background knowledge of the individual. However, in an academic research setting, scholars are required to explicitly list their background knowledge in the citation sections of their manuscripts. This unique opportunity was taken advantage of to observe RRW, especially in the published academic commentary scenario. RRW was visualized under a proposed topic process model by using a state of the art version of latent Dirichlet allocation (LDA). The empirical study showed that the academic commentary is modulated both by its target paper and the author's background knowledge. Although this conclusion was obtained in a unique environment, we suggest its implications can also shed light on other similar interesting areas, such as dialog and conversation, group discussion, and social media.
Convolutional Radio Modulation Recognition Networks
Venue: arXiv cs.LG 2016
Year: 2016

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
Consistent and fault-tolerant SDN with unmodified switches
Venue: arXiv cs.NI 2016
Year: 2016

In a reliable SDN environment, different controllers coordinate different switches and backup controllers can be set in place to tolerate faults. This approach increases the challenge to maintain a consistent network view. If this global view is not consistent with the actual network state, applications will operate on a stale state and potentially lead to incorrect behavior. Faced with this problem, we propose a fault-tolerant SDN controller that is able to maintain a consistent network view by using transactional semantics on both control and data plane state. Different from previous proposals, our solution does not require changes to OpenFlow or to switches, increasing the chances of quicker adoption.
Haplotype Inference for Pedigrees with Few Recombinations
Venue: arXiv cs.DS 2016
Year: 2016

Pedigrees, or family trees, are graphs of family relationships that are used to study inheritance. A fundamental problem in computational biology is to find, for a pedigree with $n$ individuals genotyped at every site, a set of Mendelian-consistent haplotypes that have the minimum number of recombinations. This is an NP-hard problem and some pedigrees can have thousands of individuals and hundreds of thousands of sites. This paper formulates this problem as a optimization on a graph and introduces a tailored algorithm with a running time of O(n^{(k+2)}m^{6k}) for n individuals, m sites, and k recombinations. Since there are generally only 1-2 recombinations per chromosome in each meiosis, k is small enough to make this algorithm practically relevant.
Designing Intelligent Instruments
Venue: arXiv cs.AI 2016
Year: 2016

Remote science operations require automated systems that can both act and react with minimal human intervention. One such vision is that of an intelligent instrument that collects data in an automated fashion, and based on what it learns, decides which new measurements to take. This innovation implements experimental design and unites it with data analysis in such a way that it completes the cycle of learning. This cycle is the basis of the Scientific Method. The three basic steps of this cycle are hypothesis generation, inquiry, and inference. Hypothesis generation is implemented by artificially supplying the instrument with a parameterized set of possible hypotheses that might be used to describe the physical system. The act of inquiry is handled by an inquiry engine that relies on Bayesian adaptive exploration where the optimal experiment is chosen as the one which maximizes the expected information gain. The inference engine is implemented using the nested sampling algorithm, which provides the inquiry engine with a set of posterior samples from which the expected information gain can be estimated. With these computational structures in place, the instrument will refine its hypotheses, and repeat the learning cycle by taking measurements until the system under study is described within a pre-specified tolerance. We will demonstrate our first attempts toward achieving this goal with an intelligent instrument constructed using the LEGO MINDSTORMS NXT robotics platform.
Time Window Temporal Logic
Venue: arXiv cs.FL 2016
Year: 2016

This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are typically seen in robotics and control applications. This paper also discusses the relaxation of TWTL formulae with respect to deadlines of tasks. Efficient automata-based frameworks to solve synthesis, verification and learning problems are also presented. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the specification. Case studies illustrating the expressivity of the logic and the proposed algorithms are included.
Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach
Venue: arXiv cs.IT 2016
Year: 2016

We consider a downlink multiuser MISO system with bounded errors in the Channel State Information at the Transmitter (CSIT). We first look at the robust design problem of achieving max-min fairness amongst users (in the worst-case sense). Contrary to the conventional approach adopted in literature, we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy. Each user's message is split into two parts, a common part and a private part. All common parts are packed into one super common message encoded using a public codebook, while private parts are independently encoded. The resulting symbol streams are linearly precoded and simultaneously transmitted, and each receiver retrieves its intended message by decoding both the common stream and its corresponding private stream. For CSIT uncertainty regions that scale with SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to conventional designs (NoRS). For the special case of non-scaling CSIT (e.g. fixed number of feedback bits), and contrary to NoRS, RS can achieve a non-saturating max-min rate. We propose a robust algorithm based on the cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE) approach, and we demonstrate its performance gains over state-of-the art designs. Finally, we extend the RS strategy to address the Quality of Service (QoS) constrained power minimization problem, and we demonstrate significant gains over NoRS-based designs.
Convex Optimization For Non-Convex Problems via Column Generation
Venue: arXiv cs.LG 2016
Year: 2016

We apply column generation to approximating complex structured objects via a set of primitive structured objects under either the cross entropy or L2 loss. We use L1 regularization to encourage the use of few structured primitive objects. We attack approximation using convex optimization over an infinite number of variables each corresponding to a primitive structured object that are generated on demand by easy inference in the Lagrangian dual. We apply our approach to producing low rank approximations to large 3-way tensors.
Autoregressive Moving Average Graph Filtering
Venue: arXiv cs.LG 2016
Year: 2016

One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design philosophy, which allows us to design the ARMA coefficients independently from the underlying graph, renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph are changing over time. We show that in case of a time-varying graph signal our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domains. We also derive sufficient conditions for filter stability when the graph and signal are time-varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, as predicted by theoretical derivations.
A new scheme for maximizing the lifetime of heterogeneous wireless sensor networks
Venue: arXiv cs.NI 2016
Year: 2016

Heterogeneous wireless sensor network consists of wireless sensor nodes with different abilities, such as different computing power and different initial energy. We present in this paper a new scheme for maximizing heterogeneous WSN lifetime. The proposed scheme employs two types of sensor nodes that are named (consistent with IEEE 802.15.4 standard) Full Function Device (FFD) and Reduced Function Device (RFD). The FFDs are the expensive sensor nodes with high power and computational capabilities compared to the RFDs which are cheap sensors with a limited power supply. The scheme divides the network into smaller sub-networks (regions) that are built from sectors and tracks. The objective of this research is to balance and reduce the communication load on RFDs, reduce the delay, and increase the connectivity and lifetime, by using a limited number of FFDs. We investigate the performance of our scheme via numerical simulation and compare it to other related schemes that are presented for homogeneous WSNs with chain topology, such as Pegasis, Epegasis and Chiron. In addition to extending the lifetime, our scheme also results in reducing the data transmission delay compared to the related schemes. Furthermore, the scheme increases the network security and reduces the RFDs power consumption by preventing the direct communication with the base station (BS). The FFD is the communication bridge between RFDs and BS. The FFD communicate with the BS using the one-hop approach or multi-hop approach through other FFDs.
Bounds on the Maximal Minimum Distance of Linear Locally Repairable Codes
Venue: arXiv cs.IT 2016
Year: 2016

Locally repairable codes (LRCs) are error correcting codes used in distributed data storage. Besides a global level, they enable errors to be corrected locally, reducing the need for communication between storage nodes. There is a close connection between almost affine LRCs and matroid theory which can be utilized to construct good LRCs and derive bounds on their performance. A generalized Singleton bound for linear LRCs with parameters $(n,k,d,r,δ)$ was given in [N. Prakash et al., "Optimal Linear Codes with a Local-Error-Correction Property", IEEE Int. Symp. Inf. Theory]. In this paper, a LRC achieving this bound is called perfect. Results on the existence and nonexistence of linear perfect $(n,k,d,r,δ)$-LRCs were given in [W. Song et al., "Optimal locally repairable codes", IEEE J. Sel. Areas Comm.]. Using matroid theory, these existence and nonexistence results were later strengthened in [T. Westerbäck et al., "On the Combinatorics of Locally Repairable Codes", Arxiv: 1501.00153], which also provided a general lower bound on the maximal achievable minimum distance $d_{\rm{max}}(n,k,r,δ)$ that a linear LRC with parameters $(n,k,r,δ)$ can have. This article expands the class of parameters $(n,k,d,r,δ)$ for which there exist perfect linear LRCs and improves the lower bound for $d_{\rm{max}}(n,k,r,δ)$. Further, this bound is proved to be optimal for the class of matroids that is used to derive the existence bounds of linear LRCs.
Benefits of depth in neural networks
Venue: arXiv cs.LG 2016
Year: 2016

For any positive integer $k$, there exist neural networks with $Θ(k^3)$ layers, $Θ(1)$ nodes per layer, and $Θ(1)$ distinct parameters which can not be approximated by networks with $\mathcal{O}(k)$ layers unless they are exponentially large --- they must possess $Ω(2^k)$ nodes. This result is proved here for a class of nodes termed "semi-algebraic gates" which includes the common choices of ReLU, maximum, indicator, and piecewise polynomial functions, therefore establishing benefits of depth against not just standard networks with ReLU gates, but also convolutional networks with ReLU and maximization gates, sum-product networks, and boosted decision trees (in this last case with a stronger separation: $Ω(2^{k^3})$ total tree nodes are required).
Secure Data Storage Structure and Privacy-Preserving Mobile Search Scheme for Public Safety Networks
Venue: arXiv cs.CR 2016
Year: 2016

In a Public Safety (PS) situation, agents may require critical and personally identifiable information. Therefore, not only does context and location-aware information need to be available, but also the privacy of such information should be preserved. Existing solutions do not address such a problem in a PS environment. This paper proposes a framework in which anonymized Personal Information (PI) is accessible to authorized public safety agents under a PS circumstance. In particular, we propose a secure data storage structure along with privacy-preserving mobile search framework, suitable for Public Safety Networks (PSNs). As a result, availability and privacy of PI are achieved simultaneously. However, the design of such a framework encounters substantial challenges, including scalability, reliability of the data, computation and communication and storage efficiency, etc. We leverage Secure Indexing (SI) methods and modify Bloom Filters (BFs) to create a secure data storage structure to store encrypted meta-data. As a result, our construction enables secure and privacy-preserving multi-keyword search capability. In addition, our system scales very well, maintains availability of data, imposes minimum delay, and has affordable storage overhead. We provide extensive security analysis, simulation studies, and performance comparison with the state-of-the-art solutions to demonstrate the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this work is the first to address such issues in the context of PSNs.
Minimum Storage Regenerating Codes For All Parameters
Venue: arXiv cs.IT 2016
Year: 2016

Regenerating codes for distributed storage have attracted much research interest in the past decade. Such codes trade the bandwidth needed to repair a failed node with the overall amount of data stored in the network. Minimum storage regenerating (MSR) codes are an important class of optimal regenerating codes that minimize (first) the amount of data stored per node and (then) the repair bandwidth. Specifically, an $[n,k,d]$-$(α)$ MSR code $\mathbb{C}$ over $\mathbb{F}_q$ is defined as follows. Using such a code $\mathbb{C}$, a file $\cal{F}$ consisting of $αk$ symbols over $\mathbb{F}_q$ can be distributed among $n$ nodes, each storing $α$ symbols, in such a way that: The file $\cal{F}$ can be recovered by downloading the content of any $k$ of the $n$ nodes; and the content of any failed node can be reconstructed by accessing any $d$ of the remaining $n-1$ nodes and downloading $α/(d-k+1)$ symbols from each of these nodes. Unfortunately, explicit constructions of $[n,k,d]$ MSR codes are known only for certain special cases: either low rate, namely $k/n<0.5$, or high repair connectivity, namely $d = n-1$. Although setting $d = n-1$ minimizes the repair bandwidth, it may be impractical to connect to all the remaining nodes in order to repair a single failed node. Our main result in this paper is an explicit construction of systematic-repair $[n,k,d]$ MSR codes for all possible values of parameters $n,k,d$. In particular, we construct systematic-repair MSR codes of high rate $k/n>0.5$ and low repair connectivity $k< d<n-1$. Such codes were not previously known to exist. In order to construct these codes, we solve simultaneously several repair scenarios, each of which is expressible as an interference alignment problem. Extension of our results beyond systematic repair remains an open problem.
Do We Need Binary Features for 3D Reconstruction?
Venue: arXiv cs.CV 2016
Year: 2016

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time applications, e.g., SLAM, where the matching operations are relative few. However, in computer vision, there are many applications such as 3D reconstruction requiring lots of matching operations between local features. Therefore, a natural question is that is the binary feature still a promising solution to this kind of applications? To get the answer, this paper conducts a comparative study of binary features and their matching methods on the context of 3D reconstruction in a recently proposed large scale mutliview stereo dataset. Our evaluations reveal that not all binary features are capable of this task. Most of them are inferior to the classical SIFT based method in terms of reconstruction accuracy and completeness with a not significant better computational performance.
Quasi Linear Codes: Application to Point-to-Point and Multi-Terminal Source Coding
Venue: arXiv cs.IT 2016
Year: 2016

A new ensemble of structured codes is introduced. These codes are called Quasi Linear Codes (QLC). The QLC's are constructed by taking subsets of linear codes. They have a looser structure compared to linear codes and are not closed under addition. We argue that these codes provide gains in terms of achievable Rate-Distortions (RD) in different multi-terminal source coding problems. We derive the necessary covering bounds for analyzing the performance of QLC's. We then consider the Multiple-Descriptions (MD) problem, and prove through an example that the application of QLC's gives an improved achievable RD region for this problem. Finally, we derive an inner bound to the achievable RD region for the general MD problem which strictly contains all of the previous known achievable regions.
Circuit Evaluation for Finite Semirings
Venue: arXiv cs.CC 2016
Year: 2016

The computational complexity of the circuit evaluation problem for finite semirings is considered, where semirings are not assumed to have an additive or multiplicative identity. The following dichotomy is shown: If a finite semiring is such that (i) the multiplicative semigroup is solvable and (ii) it does not contain a subsemiring with an additive identity $0$ and a multiplicative identity $1 \neq 0$, then the circuit evaluation problem for the semiring is in $\mathsf{DET} \subseteq \mathsf{NC}^2$. In all other cases, the circuit evaluation problem is $\mathsf{P}$-complete.
Personalized Expertise Search at LinkedIn
Venue: arXiv cs.IR 2016
Year: 2016

LinkedIn is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the problem of personalized expertise search on LinkedIn, particularly for exploratory search queries containing {\it skills}. In the offline phase, we introduce a collaborative filtering approach based on matrix factorization. Our approach estimates expertise scores for both the skills that members list on their profiles as well as the skills they are likely to have but do not explicitly list. In the online phase (at query time) we use expertise scores on these skills as a feature in combination with other features to rank the results. To learn the personalized ranking function, we propose a heuristic to extract training data from search logs while handling position and sample selection biases. We tested our models on two products - LinkedIn homepage and LinkedIn recruiter. A/B tests showed significant improvements in click through rates - 31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages sent from search - 37% for recruiter (20% for homepage). As of writing this paper, these models serve nearly all live traffic for skills search on LinkedIn homepage as well as LinkedIn recruiter.