OpenRaiser/Intern-Atlas huggingface dataset:
The Case for Data Plane Timestamping in SDN
Venue: arXiv cs.NI 2016
Year: 2016

This paper presents the case for Data Plane Timestamping (DPT). We argue that in the unique environment of Software-Defined Networks (SDN), attaching a timestamp to the header of all packets is a powerful feature that can be leveraged by various diverse SDN applications. We analyze three key use cases that demonstrate the advantages of using DPT, and show that SDN applications can benefit even from using as little as one bit for the timestamp field.
Random Distances Associated with Arbitrary Polygons: An Algorithmic Approach between Two Random Points
Venue: arXiv cs.CG 2016
Year: 2016

This report presents a new, algorithmic approach to the distributions of the distance between two points distributed uniformly at random in various polygons, based on the extended Kinematic Measure (KM) from integral geometry. We first obtain such random Point Distance Distributions (PDDs) associated with arbitrary triangles (i.e., triangle-PDDs), including the PDD within a triangle, and that between two triangles sharing either a common side or a common vertex. For each case, we provide an algorithmic procedure showing the mathematical derivation process, based on which either the closed-form expressions or the algorithmic results can be obtained. The obtained triangle-PDDs can be utilized for modeling and analyzing the wireless communication networks associated with triangle geometries, such as sensor networks with triangle-shaped clusters and triangle-shaped cellular systems with highly directional antennas. Furthermore, based on the obtained triangle-PDDs, we then show how to obtain the PDDs associated with arbitrary polygons through the decomposition and recursion approach, since any polygons can be triangulated, and any geometry shapes can be approximated by polygons with a needed precision. Finally, we give the PDDs associated with ring geometries. The results shown in this report can enrich and expand the theory and application of the probabilistic distance models for the analysis of wireless communication networks.
Using session types as an effect system
Venue: arXiv cs.PL 2016
Year: 2016

Side effects are a core part of practical programming. However, they are often hard to reason about, particularly in a concurrent setting. We propose a foundation for reasoning about concurrent side effects using sessions. Primarily, we show that session types are expressive enough to encode an effect system for stateful processes. This is formalised via an effect-preserving encoding of a simple imperative language with an effect system into the pi-calculus with session primitives and session types (into which we encode effect specifications). This result goes towards showing a connection between the expressivity of session types and effect systems. We briefly discuss how the encoding could be extended and applied to reason about and control concurrent side effects.
Precise subtyping for synchronous multiparty sessions
Venue: arXiv cs.LO 2016
Year: 2016

The notion of subtyping has gained an important role both in theoretical and applicative domains: in lambda and concurrent calculi as well as in programming languages. The soundness and the completeness, together referred to as the preciseness of subtyping, can be considered from two different points of view: operational and denotational. The former preciseness has been recently developed with respect to type safety, i.e. the safe replacement of a term of a smaller type when a term of a bigger type is expected. The latter preciseness is based on the denotation of a type which is a mathematical object that describes the meaning of the type in accordance with the denotations of other expressions from the language. The result of this paper is the operational and denotational preciseness of the subtyping for a synchronous multiparty session calculus. The novelty of this paper is the introduction of characteristic global types to prove the operational completeness.
Variations of the Similarity Function of TextRank for Automated Summarization
Venue: arXiv cs.CL 2016
Year: 2016

This article presents new alternatives to the similarity function for the TextRank algorithm for automatic summarization of texts. We describe the generalities of the algorithm and the different functions we propose. Some of these variants achieve a significative improvement using the same metrics and dataset as the original publication.
Link prediction in Foursquare network
Venue: arXiv cs.SI 2016
Year: 2016

Foursquare is an online social network and can be represented with a bipartite network of users and venues. A user-venue pair is connected if a user has checked-in at that venue. In the case of Foursquare, network analysis techniques can be used to enhance the user experience. One such technique is link prediction, which can be used to build a personalized recommendation system of venues. Recommendation systems in bipartite networks are very often designed using the global ranking method and collaborative filtering. A less known method- network based inference is also a feasible choice for link prediction in bipartite networks and sometimes performs better than the previous two. In this paper we test these techniques on the Foursquare network. The best technique proves to be the network based inference. We also show that taking into account the available metadata can be beneficial.
SDxVPN: A Software-Defined Solution for VPN Service Providers
Venue: arXiv cs.NI 2016
Year: 2016

BGP/MPLS IP VPN and VPLS services are considered to be widely used in IP/MPLS networks for connecting customers' remote sites. However, service providers struggle with many challenges to provide these services. Management complexity, equipment costs, and last but not least, scalability issues emerging as the customers increase in number, are just some of these problems. Software-defined networking (SDN) is an emerging paradigm that can solve aforementioned issues using a logically centralized controller for network devices. In this paper, we propose a SDN-based solution called SDxVPN which considerably lowers the complexity of VPN service definition and management. Our method eliminates complex and costly device interactions that used to be done through several control plane protocols and enables customers to determine their service specifications, define restriction policies and even interconnect with other customers automatically without operator's intervention. We describe our prototype implementation of SDxVPN and its scalability evaluations under several representative scenarios. The results indicate the effectiveness of the proposed solution for deployment to provide large scale VPN services.
HMM and DTW for evaluation of therapeutical gestures using kinect
Venue: arXiv cs.HC 2016
Year: 2016

Automatic recognition of the quality of movement in human beings is a challenging task, given the difficulty both in defining the constraints that make a movement correct, and the difficulty in using noisy data to determine if these constraints were satisfied. This paper presents a method for the detection of deviations from the correct form in movements from physical therapy routines based on Hidden Markov Models, which is compared to Dynamic Time Warping. The activities studied include upper an lower limbs movements, the data used comes from a Kinect sensor. Correct repetitions of the activities of interest were recorded, as well as deviations from these correct forms. The ability of the proposed approach to detect these deviations was studied. Results show that a system based on HMM is much more likely to determine if a certain movement has deviated from the specification.
Finding overlapping communities in multiplex networks
Venue: arXiv cs.SI 2016
Year: 2016

We define an approach to identify overlapping communities in multiplex networks, extending the popular clique percolation method for simple graphs. The extension requires to rethink the basic concepts on which the clique percolation algorithm is based, including cliques and clique adjacency, to allow the presence of multiple types of edges.
MISO Networks with Imperfect CSIT: A Topological Rate-Splitting Approach
Venue: arXiv cs.IT 2016
Year: 2016

Recently, the Degrees-of-Freedom (DoF) region of multiple-input-single-output (MISO) networks with imperfect channel state information at the transmitter (CSIT) has attracted significant attentions. An achievable scheme is known as rate-splitting (RS) that integrates common-message-multicasting and private-message-unicasting. In this paper, focusing on the general $K$-cell MISO IC where the CSIT of each interference link has an arbitrary quality of imperfectness, we firstly identify the DoF region achieved by RS. Secondly, we introduce a novel scheme, so called Topological RS (TRS), whose novelties compared to RS lie in a multi-layer structure and transmitting multiple common messages to be decoded by groups of users rather than all users. The design of TRS is motivated by a novel interpretation of the $K$-cell IC with imperfect CSIT as a weighted-sum of a series of partially connected networks. We show that the DoF region achieved by TRS covers that achieved by RS. Also, we find the maximal sum DoF achieved by TRS via hypergraph fractional packing, which yields the best sum DoF so far. Lastly, for a realistic scenario where each user is connected to three dominant transmitters, we identify the sufficient condition where TRS strictly outperforms conventional schemes.
Semi-supervised Learning with Explicit Relationship Regularization
Venue: arXiv cs.CV 2016
Year: 2016

In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Existing approaches attempt to exploit this relationship information implicitly by enforcing smoothness on function evaluations only. However, what happens if we explicitly regularize the relationships between function evaluations? Inspired by homophily, we regularize based on a smooth relationship function, either defined from the data or with labels. In experiments, we demonstrate that this significantly improves the performance of state-of-the-art algorithms in semi-supervised classification and in spectral data embedding for constrained clustering and dimensionality reduction.
Estimating the unconfined compressive strength of carbonate rocks using gene expression programming
Venue: arXiv cs.CE 2016
Year: 2016

Conventionally, many researchers have used both regression and black box techniques to estimate the unconfined compressive strength (UCS) of different rocks. The advantage of the regression approach is that it can be used to render a functional relationship between the predictive rock indices and its UCS. The advantage of the black box techniques is in rendering more accurate predictions. Gene expression programming (GEP) is proposed, in this study, as a robust mathematical alternative for predicting the UCS of carbonate rocks. The two parameters of total porosity and P-wave speed were selected as predictive indices. The proposed GEP model had the advantage of the both traditionally used approaches by proposing a mathematical model, similar to a regression, while keeping the prediction errors as low as the black box methods. The GEP outperformed both artificial neural networks and support vector machines in terms of yielding more accurate estimates of UCS. Both the porosity and the P-wave velocity were sufficient predictive indices for estimating the UCS of the carbonate rocks in this study. Nearly, 95% of the observed variation in the UCS values was explained by these two parameters (i.e., R2 =95%).
Budget-Constrained Multi-Battle Contests: A New Perspective and Analysis
Venue: arXiv cs.GT 2016
Year: 2016

In a multi-battle contest, each time a player competes by investing some of her budgets or resources in a component battle to collect a value if winning the battle. There are multiple battles to fight, and the budgets get consumed over time. The final winner in the overall contest is the one who first reaches some amount of total value. Examples include R & D races, sports competition, elections, and many more. A player needs to make adequate sequential actions to win the contest against dynamic competition over time from the others. We are interested in how much budgets the players would need and what actions they should take in order to perform well. We model and study such budget-constrained multi-battle contests where each component battle is a first-price or all-pay auction. We focus on analyzing the 2-player budget ratio that guarantees a player's winning (or falling behind in just a bounded amount of collected value) against the other omnipotent player. In the settings considered, we give efficient dynamic programs to find the optimal budget ratios and the corresponding bidding strategies. Our definition of game, budget constraints, and emphasis on budget analyses provide a new perspective and analysis in the related context.
Pursuits in Structured Non-Convex Matrix Factorizations
Venue: arXiv cs.LG 2016
Year: 2016

Efficiently representing real world data in a succinct and parsimonious manner is of central importance in many fields. We present a generalized greedy pursuit framework, allowing us to efficiently solve structured matrix factorization problems, where the factors are allowed to be from arbitrary sets of structured vectors. Such structure may include sparsity, non-negativeness, order, or a combination thereof. The algorithm approximates a given matrix by a linear combination of few rank-1 matrices, each factorized into an outer product of two vector atoms of the desired structure. For the non-convex subproblems of obtaining good rank-1 structured matrix atoms, we employ and analyze a general atomic power method. In addition to the above applications, we prove linear convergence for generalized pursuit variants in Hilbert spaces - for the task of approximation over the linear span of arbitrary dictionaries - which generalizes OMP and is useful beyond matrix problems. Our experiments on real datasets confirm both the efficiency and also the broad applicability of our framework in practice.
Energy efficient OFDMA networks maintaining statistical QoS guarantees for delay-sensitive traffic
Venue: arXiv cs.IT 2016
Year: 2016

An energy-efficient design is proposed under specific statistical quality-of-service (QoS) guarantees for delay-sensitive traffic in the downlink orthogonal frequency-division multiple-access (OFDMA) networks. This design is based on Wu's $\textit{effective capacity}$ (EC) concept [1], which characterizes the maximum throughput of a system subject to statistical delay-QoS requirements at the data-link layer. In the particular context considered, our main contributions consist of quantifying the $\textit{effective energy-efficiency}$ (EEE)-versus-EC tradeoff and characterizing the delay-sensitive traffic as a function of the QoS-exponent $θ$, which expresses the exponential decay rate of the delay-QoS violation probabilities. Upon exploiting the properties of fractional programming, the originally quasi-concave EEE optimization problem having a fractional form is transformed into a subtractive optimization problem by applying Dinkelbach's method. As a result, an iterative inner-outer loop based resource allocation algorithm is conceived for efficiently solving the transformed EEE optimization problem. Our simulation results demonstrate that the proposed scheme converges within a few Dinkelbach algorithm's iterations to the desired solution accuracy. Furthermore, the impact of the circuitry power, of the QoS-exponent and of the power amplifier inefficiency is characterized numerically. These results reveal that the optimally allocated power maximizing the EEE decays exponentially with respect to both the circuitry power and the QoS-exponent, whilst decaying linearly with respect to the power amplifier inefficiency.
Identifying Diabetic Patients with High Risk of Readmission
Venue: arXiv cs.AI 2016
Year: 2016

Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a high risk of readmission can enable healthcare providers to to conduct additional investigations and possibly prevent future readmissions. This not only improves the quality of care but also reduces the medical expenses on readmission. Machine learning methods have been leveraged on public health data to build a system for identifying diabetic patients facing a high risk of future readmission. Number of inpatient visits, discharge disposition and admission type were identified as strong predictors of readmission. Further, it was found that the number of laboratory tests and discharge disposition together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. <30 days) or after a longer period of time (i.e. >30 days). These insights can help healthcare providers to improve inpatient diabetic care. Finally, the cost analysis suggests that \$252.76 million can be saved across 98,053 diabetic patient encounters by incorporating the proposed cost sensitive analysis model.
Short-term Predictable Power of a Wind Farm via Distributed Control of Wind Generators with Integrated Storage
Venue: arXiv cs.SY 2016
Year: 2016

In this paper we introduce a Leader-follower consensus protocol and study its stability properties with and without communication delays. On the practical side, we explore its application on coordinating a group of wind Double-Fed Induction Generators (DFIGs) with integrated storage. To begin with, we establish asymptotic stability of the consensus protocol by employing singular perturbation theory. Subsequently, we establish asymptotic stability of the protocol under communication delays using a Lyapunov-Krasovskii functional. Lastly, we use the proposed protocol to design a methodology that can be adopted by a fleet of state-of-the-art wind generators (WGs). The objective is that the WGs selforganize and control their storage devices such that WF total power output is tracking a reference while equal contribution from each storage device is attained i.e equal power output from each storage. We demonstrate the effectiveness of our results and the corresponding approach via simulations on the IEEE 24-bus RT system.
Deep Learning on FPGAs: Past, Present, and Future
Venue: arXiv cs.DC 2016
Year: 2016

The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.
Learning Over Long Time Lags
Venue: arXiv cs.NE 2016
Year: 2016

The advantage of recurrent neural networks (RNNs) in learning dependencies between time-series data has distinguished RNNs from other deep learning models. Recently, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on memory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short term memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancements and learning techniques for capturing long term dependencies in RNNs.
Fast Computation of the Kinship Coefficients
Venue: arXiv cs.DS 2016
Year: 2016

For families, kinship coefficients are quantifications of the amount of genetic sharing between a pair of individuals. These coefficients are critical for understanding the breeding habits and genetic diversity of diploid populations. Historically, computations of the inbreeding coefficient were used to prohibit inbred marriages and prohibit breeding of some pairs of pedigree animals. Such prohibitions foster genetic diversity and help prevent recessive Mendelian disease at a population level. This paper gives the fastest known algorithms for computing the kinship coefficient of a set of individuals with a known pedigree. The algorithms given here consider the possibility that the founders of the known pedigree may themselves be inbred, and they compute the appropriate inbreeding-adjusted kinship coefficients. The exact kinship algorithm has running-time $O(n^2)$ for an $n$-individual pedigree. The recursive-cut exact kinship algorithm has running time $O(s^2m)$ where $s$ is the number of individuals in the largest segment of the pedigree and $m$ is the number of cuts. The approximate algorithm has running-time $O(n)$ for an $n$-individual pedigree on which to estimate the kinship coefficients of $\sqrt{n}$ individuals from $\sqrt{n}$ founder kinship coefficients.