OpenRaiser/Intern-Atlas huggingface dataset:
Unitary Query for the $M \times L \times N$ MIMO Backscatter RFID Channel
Venue: arXiv cs.IT 2014
Year: 2014

A MIMO backscatter RFID system consists of three operational ends: the query end (with $M$ reader transmitting antennas), the tag end (with $L$ tag antennas) and the receiving end (with $N$ reader receiving antennas). Such an $M \times L \times N$ setting in RFID can bring spatial diversity and has been studied for STC at the tag end. Current understanding of the query end is that it is only an energy provider for the tag and query signal designs cannot improve the performance. However, we propose a novel \textit{unitary query} scheme, which creates time diversity \emph{within channel coherent time} and can yield \emph{significant} performance improvements. To overcome the difficulty of evaluating the performance when the unitary query is employed at the query end and STC is employed at the tag end, we derive a new measure based on the ranks of certain carefully constructed matrices. The measure implies that the unitary query has superior performance. Simulations show that the unitary query can bring $5-10$ dB gain in mid SNR regimes. In addition, the unitary query can also improve the performance of single-antenna tags significantly, allowing employing low complex and small-size single-antenna tags for high performance. This improvement is unachievable for single-antenna tags when the conventional uniform query is employed.
Decoding Delay Controlled Reduction of Completion Time in Instantly Decodable Network Coding
Venue: arXiv cs.IT 2014
Year: 2014

For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in persistent erasure channels. We first derive the decoding-delay-dependent expressions of the users' and overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we design two novel heuristics that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. We, then, extend our study to the limited feedback scenario. Simulation results show that our new algorithms achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.
The substantive and practical significance of citation impact differences between institutions: Guidelines for the analysis of percentiles using effect sizes and confidence intervals
Venue: arXiv cs.DL 2014
Year: 2014

In our chapter we address the statistical analysis of percentiles: How should the citation impact of institutions be compared? In educational and psychological testing, percentiles are already used widely as a standard to evaluate an individual's test scores - intelligence tests for example - by comparing them with the percentiles of a calibrated sample. Percentiles, or percentile rank classes, are also a very suitable method for bibliometrics to normalize citations of publications in terms of the subject category and the publication year and, unlike the mean-based indicators (the relative citation rates), percentiles are scarcely affected by skewed distributions of citations. The percentile of a certain publication provides information about the citation impact this publication has achieved in comparison to other similar publications in the same subject category and publication year. Analyses of percentiles, however, have not always been presented in the most effective and meaningful way. New APA guidelines (American Psychological Association, 2010) suggest a lesser emphasis on significance tests and a greater emphasis on the substantive and practical significance of findings. Drawing on work by Cumming (2012) we show how examinations of effect sizes (e.g. Cohen's d statistic) and confidence intervals can lead to a clear understanding of citation impact differences.
Distributed Approximate Message Passing for Compressed Sensing
Venue: arXiv cs.DC 2014
Year: 2014

In this paper, an efficient distributed approach for implementing the approximate message passing (AMP) algorithm, named distributed AMP (DAMP), is developed for compressed sensing (CS) recovery in sensor networks with the sparsity K unknown. In the proposed DAMP, distributed sensors do not have to use or know the entire global sensing matrix, and the burden of computation and storage for each sensor is reduced. To reduce communications among the sensors, a new data query algorithm, called global computation for AMP (GCAMP), is proposed. The proposed GCAMP based DAMP approach has exactly the same recovery solution as the centralized AMP algorithm, which is proved theoretically in the paper. The performance of the DAMP approach is evaluated in terms of the communication cost saved by using GCAMP. For comparison purpose, thresholding algorithm (TA), a well known distributed Top-K algorithm, is modified so that it also leads to the same recovery solution as the centralized AMP. Numerical results demonstrate that the GCAMP based DAMP outperforms the Modified TA based DAMP, and reduces the communication cost significantly.
Data Modeling with Large Random Matrices in a Cognitive Radio Network Testbed: Initial Experimental Demonstrations with 70 Nodes
Venue: arXiv cs.IT 2014
Year: 2014

This short paper reports some initial experimental demonstrations of the theoretical framework: the massive amount of data in the large-scale cognitive radio network can be naturally modeled as (large) random matrices. In particular, using experimental data we will demonstrate that the empirical spectral distribution of the large sample covariance matrix---a Hermitian random matrix---agree with its theoretical distribution (Marchenko-Pastur law). On the other hand, the eigenvalues of the large data matrix ---a non-Hermitian random matrix---are experimentally found to follow the single ring law, a theoretical result that has been discovered relatively recently. To our best knowledge, our paper is the first such attempt, in the context of large-scale wireless network, to compare theoretical predictions with experimental findings.
Robust Dynamic State Feedback Guaranteed Cost Control of Nonlinear Systems using Copies of Plant Nonlinearities
Venue: arXiv cs.SY 2014
Year: 2014

This paper presents a systematic approach to the design of a robust dynamic state feedback controller using copies of the plant nonlinearities, which is based on the use of IQCs and minimax LQR control. The approach combines a linear state feedback guaranteed cost controller and copies of the plant nonlinearities to form a robust nonlinear controller.
Lossless Coding of Correlated Sources with Actions
Venue: arXiv cs.IT 2014
Year: 2014

This work studies the problem of distributed compression of correlated sources with an action-dependent joint distribution. This class of problems is, in fact, an extension of the Slepian-Wolf model, but where cost-constrained actions taken by the encoder or the decoder affect the generation of one of the sources. The purpose of this work is to study the implications of actions on the achievable rates. In particular, two cases where transmission occurs over a rate-limited link are studied; case A for actions taken at the decoder and case B where actions are taken at the encoder. A complete single-letter characterization of the set of achievable rates is given in both cases. Furthermore, a network coding setup is investigated for the case where actions are taken at the encoder. The sources are generated at different nodes of the network and are required at a set of terminal nodes, yet transmission occurs over a general, acyclic, directed network. For this setup, generalized cut-set bounds are derived, and a full characterization of the set of achievable rates using single-letter expressions is provided. For this scenario, random linear network coding is proved to be optimal, even though this is not a classical multicast problem. Additionally, two binary examples are investigated and demonstrate how actions taken at different nodes of the system have a significant affect on the achievable rate region in comparison to a naive time-sharing strategy.
Representation as a Service
Venue: arXiv cs.LG 2014
Year: 2014

Consider a Machine Learning Service Provider (MLSP) designed to rapidly create highly accurate learners for a never-ending stream of new tasks. The challenge is to produce task-specific learners that can be trained from few labeled samples, even if tasks are not uniquely identified, and the number of tasks and input dimensionality are large. In this paper, we argue that the MLSP should exploit knowledge from previous tasks to build a good representation of the environment it is in, and more precisely, that useful representations for such a service are ones that minimize generalization error for a new hypothesis trained on a new task. We formalize this intuition with a novel method that minimizes an empirical proxy of the intra-task small-sample generalization error. We present several empirical results showing state-of-the art performance on single-task transfer, multitask learning, and the full lifelong learning problem.
Phase Precoding for the Compute-and-Forward Protocol
Venue: arXiv cs.IT 2014
Year: 2014

The compute-and-forward (CoF) is a relaying protocol, which uses algebraic structured codes to harness the interference and remove the noise in wireless networks. We propose the use of phase precoders at the transmitters of a network, where relays apply CoF strategy. We define the {\em phase precoded computation rate} and show that it is greater than the original computation rate of CoF protocol. We further give a new low-complexity method for finding network equations. We finally show that the proposed precoding scheme increases the degrees-of-freedom (DoF) of CoF protocol. This overcomes the limitations on the DoF of the CoF protocol, recently presented by Niesen and Whiting. Using tools from Diophantine approximation and algebraic geometry, we prove the existence of a phase precoder that approaches the maximum DoF when the number of transmitters tends to infinity.
The Dynamics of Emotional Chats with Bots: Experiment and Agent-Based Simulations
Venue: arXiv cs.SI 2014
Year: 2014

Quantitative research of emotions in psychology and machine-learning methods for extracting emotion components from text messages open an avenue for physical science to explore the nature of stochastic processes in which emotions play a role, e.g., in human dynamics online. Here, we investigate the occurrence of collective behavior of users that is induced by chats with emotional Bots. The Bots, designed in an experimental environment, are considered. Furthermore, using the agent-based modeling approach, the activity of these experimental Bots is simulated within a social network of interacting emotional agents. Quantitative analysis of time series carrying emotional messages by agents suggests temporal correlations and persistent fluctuations with clustering according to emotion similarity. {All data used in this study are fully anonymized.}
On Level persistence (Relevant level persistence numbers)
Venue: arXiv cs.CG 2014
Year: 2014

The purpose of this note is to describe of a new set of numerical invariants, the relevant level persistence numbers, and make explicit their relationship with the four types of bar codes, a more familiar set of complete invariants for level persistence. The paper provides the opportunity to compare level persistence with the persistence introduced by Edelsbrunner- Letscher-Zomorodian called in this paper, as sub-level persistence.
Generic Object Detection With Dense Neural Patterns and Regionlets
Venue: arXiv cs.CV 2014
Year: 2014

This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns, short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with the Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the original Regionlets approach without DNPs.
Open Question Answering with Weakly Supervised Embedding Models
Venue: arXiv cs.CL 2014
Year: 2014

Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.
A novel interactive OBE approach in SCM pedagogy using beer game simulation theory
Venue: arXiv cs.CY 2014
Year: 2014

The primary challenge in SCM pedagogy is the learners interaction with the dynamic nature of supply chain transactions. Once achieved, it is also required to evaluate learners learning experience based on their performance. In this paper, a combination of outcome-based education (OBE) and simulation-based education is proposed focusing on beer game theory. The analysis is based on 336 runs of beer game simulation within a target group of 56 participants divided into 14 subgroups (SG1-SG14).The purpose of the study is mainly to investigate the effect of mutual interactions on students learning process using supply chain total cost and ordering fluctuations as critical measurement criteria.
Low-power Distance Bounding
Venue: arXiv cs.CR 2014
Year: 2014

A distance bounding system guarantees an upper bound on the physical distance between a verifier and a prover. However, in contrast to a conventional wireless communication system, distance bounding systems introduce tight requirements on the processing delay at the prover and require high distance measurement precision making their practical realization challenging. Prior proposals of distance bounding systems focused primarily on building provers with minimal processing delays but did not consider the power limitations of provers and verifiers. However, in a wide range of applications (e.g., physical access control), provers are expected to be fully or semi-passive introducing additional constraints on the design and implementation of distance bounding systems. In this work, we propose a new physical layer scheme for distance bounding and leverage this scheme to implement a distance bounding system with a low-power prover. Our physical layer combines frequency modulated continuous wave (FMCW) and backscatter communication. The use of backscatter communication enables low power consumption at the prover which is critical for a number of distance bounding applications. By using the FMCW-based physical layer, we further decouple the physical distance estimation from the processing delay at the prover, thereby enabling the realization of the majority of distance bounding protocols developed in prior art. We evaluate our system under various attack scenarios and show that it offers strong security guarantees against distance, mafia and terrorist frauds. Additionally, we validate the communication and distance measurement characteristics of our system through simulations and experiments and show that it is well suited for short-range physical access control and payment applications.
3-D Channel Characteristics for Molecular Communications with an Absorbing Receiver
Venue: arXiv cs.IT 2014
Year: 2014

Within the domain of molecular communications, researchers mimic the techniques in nature to come up with alternative communication methods for collaborating nanomachines. This work investigates the channel transfer function for molecular communication via diffusion. In nature, information-carrying molecules are generally absorbed by the target node via receptors. Using the concentration function, without considering the absorption process, as the channel transfer function implicitly assumes that the receiver node does not affect the system. In this letter, we propose a solid analytical formulation and analyze the signal metrics (attenuation and propagation delay) for molecular communication via diffusion channel with an absorbing receiver in a 3-D environment. The proposed model and the formulation match well with the simulations without any normalization.
Approximation Algorithms for Hypergraph Small Set Expansion and Small Set Vertex Expansion
Venue: arXiv cs.DS 2014
Year: 2014

The expansion of a hypergraph, a natural extension of the notion of expansion in graphs, is defined as the minimum over all cuts in the hypergraph of the ratio of the number of the hyperedges cut to the size of the smaller side of the cut. We study the Hypergraph Small Set Expansion problem, which, for a parameter $δ\in (0,1/2]$, asks to compute the cut having the least expansion while having at most $δ$ fraction of the vertices on the smaller side of the cut. We present two algorithms. Our first algorithm gives an $\tilde O(δ^{-1} \sqrt{\log n})$ approximation. The second algorithm finds a set with expansion $\tilde O(δ^{-1}(\sqrt{d_{\text{max}}r^{-1}\log r\, ϕ^*} + ϕ^*))$ in a $r$--uniform hypergraph with maximum degree $d_{\text{max}}$ (where $ϕ^*$ is the expansion of the optimal solution). Using these results, we also obtain algorithms for the Small Set Vertex Expansion problem: we get an $\tilde O(δ^{-1} \sqrt{\log n})$ approximation algorithm and an algorithm that finds a set with vertex expansion $O\left(δ^{-1}\sqrt{ϕ^V \log d_{\text{max}} } + δ^{-1} ϕ^V\right)$ (where $ϕ^V$ is the vertex expansion of the optimal solution). For $δ=1/2$, Hypergraph Small Set Expansion is equivalent to the hypergraph expansion problem. In this case, our approximation factor of $O(\sqrt{\log n})$ for expansion in hypergraphs matches the corresponding approximation factor for expansion in graphs due to ARV.
Asymptotically-Optimal Incentive-Based En-Route Caching Scheme
Venue: arXiv cs.NI 2014
Year: 2014

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than $Ω(\log n)$, where $n$ is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study an extension to the basic algorithm and show its effectiveness through extensive simulations.
Graph-based Anomaly Detection and Description: A Survey
Venue: arXiv cs.SI 2014
Year: 2014

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured {\em graph} data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we provide a comprehensive exploration of both data mining and machine learning algorithms for these {\em detection} tasks. we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly {\em attribution} and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.
Radical-Enhanced Chinese Character Embedding
Venue: arXiv cs.CL 2014
Year: 2014

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.
Challenges in Persian Electronic Text Analysis
Venue: arXiv cs.CL 2014
Year: 2014

Farsi, also known as Persian, is the official language of Iran and Tajikistan and one of the two main languages spoken in Afghanistan. Farsi enjoys a unified Arabic script as its writing system. In this paper we briefly introduce the writing standards of Farsi and highlight problems one would face when analyzing Farsi electronic texts, especially during development of Farsi corpora regarding to transcription and encoding of Farsi e-texts. The pointes mentioned may sounds easy but they are crucial when developing and processing written corpora of Farsi.