OpenRaiser/Intern-Atlas huggingface dataset:
On the Characteristic and Permanent Polynomials of a Matrix
Venue: arXiv cs.DM 2017
Year: 2017

There is a digraph corresponding to every square matrix over $\mathbb{C}$. We generate a recurrence relation using the Laplace expansion to calculate the characteristic, and permanent polynomials of a square matrix. Solving this recurrence relation, we found that the characteristic, and permanent polynomials can be calculated in terms of characteristic, and permanent polynomials of some specific induced subdigraphs of blocks in the digraph, respectively. Interestingly, these induced subdigraphs are vertex-disjoint and they partition the digraph. Similar to the characteristic, and permanent polynomials; the determinant, and permanent can also be calculated. Therefore, this article provides a combinatorial meaning of these useful quantities of the matrix theory. We conclude this article with a number of open problems which may be attempted for further research in this direction.
Dandelion: Redesigning the Bitcoin Network for Anonymity
Venue: arXiv cs.CR 2017
Year: 2017

Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of being a `privacy-preserving' financial system. In reality, cryptocurrencies publish users' entire transaction histories in plaintext, albeit under a pseudonym; this is required for transaction validation. Therefore, if a user's pseudonym can be linked to their human identity, the privacy fallout can be significant. Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to deanonymize users. In this work, we redesign the P2P network from first principles with the goal of providing strong, provable anonymity guarantees. We propose a simple networking policy called Dandelion, which achieves nearly-optimal anonymity guarantees at minimal cost to the network's utility. We also provide a practical implementation of Dandelion.
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning
Venue: arXiv cs.NE 2017
Year: 2017

How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks? And how much can these fundamentals help while devising new pruning techniques? A lot, it turns out. Neural network pruning has become a topic of great interest in recent years, and many different techniques have been proposed to address this problem. The decision of what to prune and when to prune necessarily forces us to confront our assumptions about how neural networks actually learn to represent patterns in data. In this work, we set out to test several long-held hypotheses about neural network learning representations, approaches to pruning and the relevance of one in the context of the other. To accomplish this, we argue in favor of pruning whole neurons as opposed to the traditional method of pruning weights from optimally trained networks. We first review the historical literature, point out some common assumptions it makes, and propose methods to demonstrate the inherent flaws in these assumptions. We then propose our novel approach to pruning and set about analyzing the quality of the decisions it makes. Our analysis led us to question the validity of many widely-held assumptions behind pruning algorithms and the trade-offs we often make in the interest of reducing computational complexity. We discovered that there is a straightforward way, however expensive, to serially prune 40-70% of the neurons in a trained network with minimal effect on the learning representation and without any re-training. It is to be noted here that the motivation behind this work is not to propose an algorithm that would outperform all existing methods, but to shed light on what some inherent flaws in these methods can teach us about learning representations and how this can lead us to superior pruning techniques.
A New Backpressure Algorithm for Joint Rate Control and Routing with Vanishing Utility Optimality Gaps and Finite Queue Lengths
Venue: arXiv cs.NI 2017
Year: 2017

The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling a parameter $V$ in the algorithm, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is $[O(1/V), O(V)]$ and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, the existing backpressure algorithms necessarily yield an arbitrarily large queue length. However, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.
Systematic study of color spaces and components for the segmentation of sky/cloud images
Venue: arXiv cs.CV 2017
Year: 2017

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation risks not being able to benefit from this due to the difficulty in obtaining data sets of considerable size. In this work we propose a combination of hand-crafted and deep-learned features that makes the most of deep learning techniques in small sample settings. Encoding shape, appearance, and dynamics, our method significantly outperforms the current state of the art, attaining a RMSE error of less than 1 point on a 16-level pain scale, whilst simultaneously scoring a 67.3% Pearson correlation coefficient between our predicted pain level time series and the ground truth.
Modeling and Performance Studies of Data Communication Networks using Dynamic Complex Networks
Venue: arXiv cs.NI 2017
Year: 2017

All the existing real world networks are evolving, hence, study of traffic dynamics in these enlarged networks is a challenging task. The critical issue is to optimize the network structure to improve network capacity and avoid traffic congestion. We are interested in taking user's routes such that it is least congested with optimal network capacity. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time varying data communication networks (TVCN) based on the dynamics of in-flowing links. Newly appeared node prefers to attach with most influential node present in the network. In this paper, influence is termed as \textit{reputation} and is applied for computing overall congestion at any node. User path with least betweenness centrality and most reputation is preferred for routing. Kelly's optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user's path with lowest betweenness centrality and highest reputation will always give maximum rate at stable point.
Multi-Gbps HTTP Traffic Analysis in Commodity Hardware Based on Local Knowledge of TCP Streams
Venue: arXiv cs.NI 2017
Year: 2017

In this paper we propose and implement novel techniques for performance evaluation of web traffic (response time, response code, etc.), with no reassembly of the underlying TCP connection, which severely restricts the traffic analysis throughput. Furthermore, our proposed software for HTTP traffic analysis runs in standard hardware, which is very cost-effective. Besides, we present sub-TCP connection load balancing techniques that significantly increase throughput at the expense of losing very few HTTP transactions. Such techniques provide performance evaluation statistics which are indistinguishable from the single-threaded alternative with full TCP connection reassembly.
Circuit Treewidth, Sentential Decision, and Query Compilation
Venue: arXiv cs.LO 2017
Year: 2017

The evaluation of a query over a probabilistic database boils down to computing the probability of a suitable Boolean function, the lineage of the query over the database. The method of query compilation approaches the task in two stages: first, the query lineage is implemented (compiled) in a circuit form where probability computation is tractable; and second, the desired probability is computed over the compiled circuit. A basic theoretical quest in query compilation is that of identifying pertinent classes of queries whose lineages admit compact representations over increasingly succinct, tractable circuit classes. Fostering previous work by Jha and Suciu (2012) and Petke and Razgon (2013), we focus on queries whose lineages admit circuit implementations with small treewidth, and investigate their compilability within tame classes of decision diagrams. In perfect analogy with the characterization of bounded circuit pathwidth by bounded OBDD width, we show that a class of Boolean functions has bounded circuit treewidth if and only if it has bounded SDD width. Sentential decision diagrams (SDDs) are central in knowledge compilation, being essentially as tractable as OBDDs but exponentially more succinct. By incorporating constant width SDDs and polynomial size SDDs, we refine the panorama of query compilation for unions of conjunctive queries with and without inequalities.
Polynomial-time Algorithms for the Subset Feedback Vertex Set Problem on Interval Graphs and Permutation Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Given a vertex-weighted graph $G=(V,E)$ and a set $S \subseteq V$, a subset feedback vertex set $X$ is a set of the vertices of $G$ such that the graph induced by $V \setminus X$ has no cycle containing a vertex of $S$. The \textsc{Subset Feedback Vertex Set} problem takes as input $G$ and $S$ and asks for the subset feedback vertex set of minimum total weight. In contrast to the classical \textsc{Feedback Vertex Set} problem which is obtained from the \textsc{Subset Feedback Vertex Set} problem for $S=V$, restricted to graph classes the \textsc{Subset Feedback Vertex Set} problem is known to be NP-complete on split graphs and, consequently, on chordal graphs. However as \textsc{Feedback Vertex Set} is polynomially solvable for AT-free graphs, no such result is known for the \textsc{Subset Feedback Vertex Set} problem on any subclass of AT-free graphs. Here we give the first polynomial-time algorithms for the problem on two unrelated subclasses of AT-free graphs: interval graphs and permutation graphs. As a byproduct we show that there exists a polynomial-time algorithm for circular-arc graphs by suitably applying our algorithm for interval graphs. Moreover towards the unknown complexity of the problem for AT-free graphs, we give a polynomial-time algorithm for co-bipartite graphs. Thus we contribute to the first positive results of the \textsc{Subset Feedback Vertex Set} problem when restricted to graph classes for which \textsc{Feedback Vertex Set} is solved in polynomial time.
HARE: Supporting efficient uplink multi-hop communications in self-organizing LPWANs
Venue: arXiv cs.NI 2017
Year: 2017

The emergence of low-power wide area networks (LPWANs) as a new agent in the Internet of Things (IoT) will result in the incorporation into the digital world of low-automated processes from a wide variety of sectors. The single-hop conception of typical LPWAN deployments, though simple and robust, overlooks the self-organization capabilities of network devices, suffers from lack of scalability in crowded scenarios, and pays little attention to energy consumption. Aimed to take the most out of devices' capabilities, the HARE protocol stack is proposed in this paper as a new LPWAN technology flexible enough to adopt uplink multi-hop communications when proving energetically more efficient. In this way, results from a real testbed show energy savings of up to 15% when using a multi-hop approach while keeping the same network reliability. System's self-organizing capability and resilience have been also validated after performing numerous iterations of the association mechanism and deliberately switching off network devices.
Optimal Reduction without Oracle?
Venue: arXiv cs.LO 2017
Year: 2017

We suggest an approach to solve the problem of matching fans in interaction net implementations of optimal reduction for the pure untyped lambda calculus without use of any additional agent types. Our implementation supports a wider class of lambda terms than the abstract version of Lamping's algorithm and beats the interaction net implementation of closed reduction by the total number of interactions.
A Resistive CAM Processing-in-Storage Architecture for DNA Sequence Alignment
Venue: arXiv cs.ET 2017
Year: 2017

A novel processing-in-storage (PRinS) architecture based on Resistive CAM (ReCAM) is described and proposed for Smith-Waterman (S-W) sequence alignment. The ReCAM massively-parallel compare operation finds matching base-pairs in a fixed number of cycles, regardless of sequence length. The ReCAM PRinS S-W algorithm is simulated and compared to FPGA, Xeon Phi and GPU-based implementations, showing at least 4.7x higher throughput and at least 15x lower power dissipation.
A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Venue: arXiv cs.DC 2017
Year: 2017

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers.
Performance of Step Network Using Simulation Tool
Venue: arXiv cs.NI 2017
Year: 2017

Nowadays distributed computing approach has become very popular due to several advantages over the centralized computing approach as it also offers high performance computing at a very low cost. Each router implements some queuing mechanism for resources allocation in a best possible optimize manner and governs with packet transmission and buffer mechanism. In this paper, different types of queuing disciplines have been implemented for packet transmission when the bandwidth is allocated as well as packet dropping occurs due to buffer overflow. This gives result in latency in packet transmission, as the packet has to wait in a queue which is to be transmitted again. Some common queuing mechanisms are first in first out, priority queue and weighted fair queuing, etc. This targets simulation in heterogeneous environment through simulator tool to improve the quality of services by evaluating the performance of said queuing disciplines. This is demonstrated by interconnecting heterogeneous devices through step topology. In this paper, authors compared data packet, voice and video traffic by analyzing the performance based on packet dropped rate, delay variation, end to end delay and queuing delay and how the different queuing discipline effects the applications and utilization of network resources at the routers. Before evaluating the performance of the connected devices, a Unified Modeling Language class diagram is designed to represent the static model for evaluating the performance of step topology. Results are described by taking the various case studies.
3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds
Venue: arXiv cs.LG 2017
Year: 2017

We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding low-dimensional coordinates. A discriminant adjacency matrix is constructed to maximize the separation between progressive and non-progressive groups of patients diagnosed with scoliosis, while minimizing the distance in latent variables belonging to the same class. To predict the evolution of deformation, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. Rate of progression is modulated from the spine flexibility and curve magnitude of the 3D spine deformation. The method was tested on 745 reconstructions from 133 subjects using longitudinal 3D reconstructions of the spine, with results demonstrating the discriminatory framework can identify between progressive and non-progressive of scoliotic patients with a classification rate of 81% and prediction differences of 2.1$^{o}$ in main curve angulation, outperforming other manifold learning methods. Our method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in highly deformed spines compared to other learning methods.
A Proof Theory for Model Checking: An Extended Abstract
Venue: arXiv cs.LO 2017
Year: 2017

While model checking has often been considered as a practical alternative to building formal proofs, we argue here that the theory of sequent calculus proofs can be used to provide an appealing foundation for model checking. Since the emphasis of model checking is on establishing the truth of a property in a model, we rely on the proof theoretic notion of additive inference rules, since such rules allow provability to directly describe truth conditions. Unfortunately, the additive treatment of quantifiers requires inference rules to have infinite sets of premises and the additive treatment of model descriptions provides no natural notion of state exploration. By employing a focused proof system, it is possible to construct large scale, synthetic rules that also qualify as additive but contain elements of multiplicative inference. These additive synthetic rules -- essentially rules built from the description of a model -- allow a direct treatment of state exploration. This proof theoretic framework provides a natural treatment of reachability and non-reachability problems, as well as tabled deduction, bisimulation, and winning strategies.
Linear $β$-reduction
Venue: arXiv cs.LO 2017
Year: 2017

Linear head reduction is a key tool for the analysis of reduction machines for lambda-calculus and for game semantics. Its definition requires a notion of redex at a distance named primary redex in the literature. Nevertheless, a clear and complete syntactic analysis of this rule is missing. We present here a general notion of beta-reduction at a distance and of linear reduction (i.e., not restricted to the head variable), and we analyse their relations and properties. This analysis rests on a variant of the so-called sigma-equivalence that is more suitable for the analysis of reduction machines, since the position along the spine of primary redexes is not permuted. We finally show that, in the simply typed case, the proof of strong normalisation of linear reduction can be obtained by a trivial tuning of Gandy's proof for strong normalisation of beta-reduction.
Ensemble Control of Cycling Energy Loads: Markov Decision Approach
Venue: arXiv cs.SY 2017
Year: 2017

A Markov decision process (MDP) framework is adopted to represent ensemble control of devices with cyclic energy consumption patterns, e.g., thermostatically controlled loads. Specifically we utilize and develop the class of MDP models previously coined linearly solvable MDPs, that describe optimal dynamics of the probability distribution of an ensemble of many cycling devices. Two principally different settings are discussed. First, we consider optimal strategy of the ensemble aggregator balancing between minimization of the cost of operations and minimization of the ensemble welfare penalty, where the latter is represented as a KL-divergence between actual and normal probability distributions of the ensemble. Then, second, we shift to the demand response setting modeling the aggregator's task to minimize the welfare penalty under the condition that the aggregated consumption matches the targeted time-varying consumption requested by the system operator. We discuss a modification of both settings aimed at encouraging or constraining the transitions between different states. The dynamic programming feature of the resulting modified MDPs is always preserved; however, `linear solvability' is lost fully or partially, depending on the type of modification. We also conducted some (limited in scope) numerical experimentation using the formulations of the first setting. We conclude by discussing future generalizations and applications.
A Generalized Short Circuit Ratio for Multi-Infeed LCC-HVDC System
Venue: arXiv cs.SY 2017
Year: 2017

The relationship between the short circuit ratio (SCR) and static voltage stability is analyzed in this paper. According to eigenvalue decomposition method, a novel concept named generalized short circuit ratio (gSCR) has been proposed for multi-infeed LCC-HVDC (MIDC) systems to mathematically measure the connect-ed AC strength from the point view of voltage stability, which can overcome the rule-of-thumb basis of existing multi-infeed short circuit ratio (MISCR) concept. In gSCR, two indices, the critical gSCR (CgSCR) and the boundary gSCR (BgSCR) are developed to quantitatively evaluate if the connected AC system is strong or weak, in which CgSCR=2 and BgSCR=3 are two critical values for strength evaluation. Finally, numerical simulations are conducted to validate the effectiveness of the proposed gSCR concept.