OpenRaiser/Intern-Atlas huggingface dataset:
Maximal cliques structure for cocomparability graphs and applications
Venue: arXiv cs.DM 2016
Year: 2016

A cocomparability graph is a graph whose complement admits a transitive orientation. An interval graph is the intersection graph of a family of intervals on the real line. In this paper we investigate the relationships between interval and cocomparability graphs. This study is motivated by recent results Corneil,Dalton, Habib (2013) and Dusart, Habib (2016) and that show that for some problems, the algorithm used on interval graphs can also be used with small modifications on cocomparability graphs. Many of these algorithms are based on graph searches that preserve cocomparability orderings. First we propose a characterization of cocomparability graphs via a lattice structure on the set of their maximal cliques. Using this characterization we can prove that every maximal interval subgraph of a cocomparability graph $G$ is also a maximal chordal subgraph of $G$. Although the size of this lattice of maximal cliques can be exponential in the size of the graph, it can be used as a framework to design and prove algorithms on cocomparability graphs. In particular we show that a new graph search, namely Local Maximal Neighborhood Search (LocalMNS) leads to an $O(n+mlogn)$ time algorithm to find a maximal interval subgraph of a cocomparability graph. Similarly we propose a linear time algorithm to compute all simplicial vertices in a cocomparability graph. In both cases we improve on the current state of knowledge.
A Markov Decision Process Model to Guide Treatment of Abdominal Aortic Aneurysms
Venue: arXiv cs.SY 2016
Year: 2016

An abdominal aortic aneurysm (AAA) is an enlargement of the abdominal aorta which, if left untreated, can progressively widen and may rupture with fatal consequences. In this paper, we determine an optimal treatment policy using Markov decision process modeling. The policy is optimal with respect to the number of quality adjusted life-years (QALYs) that are expected to be accumulated during the remaining life of a patient. The new policy takes into account factors that are ignored by the current clinical policy (e.g. the life-expectancy and the age-dependent surgical mortality). The resulting optimal policy is structurally different from the current policy. In particular, the policy suggests that young patients with small aneurysms should undergo surgery. The robustness of the policy structure is demonstrated using simulations. A gain in the number of expected QALYs is shown, which indicates a possibility of improved care for patients with AAAs.
Unsupervised Cross-Domain Image Generation
Venue: arXiv cs.CV 2016
Year: 2016

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.
Multiround Private Information Retrieval: Capacity and Storage Overhead
Venue: arXiv cs.IT 2016
Year: 2016

The capacity has recently been characterized for the private information retrieval (PIR) problem as well as several of its variants. In every case it is assumed that all the queries are generated by the user simultaneously. Here we consider multiround PIR, where the queries in each round are allowed to depend on the answers received in previous rounds. We show that the capacity of multiround PIR is the same as the capacity of single-round PIR (the result is generalized to also include $T$-privacy constraints). Combined with previous results, this shows that there is no capacity advantage from multiround over single-round schemes, non-linear over linear schemes or from $ε$-error over zero-error schemes. However, we show through an example that there is an advantage in terms of storage overhead. We provide an example of a multiround, non-linear, $ε$-error PIR scheme that requires a strictly smaller storage overhead than the best possible with single-round, linear, zero-error PIR schemes.
Meat adulteration detection through digital image analysis of histological cuts using LBP
Venue: arXiv cs.CV 2016
Year: 2016

Food fraud has been an area of great concern due to its risk to public health, reduction of food quality or nutritional value and for its economic consequences. For this reason, it's been object of regulation in many countries (e.g. [1], [2]). One type of food that has been frequently object of fraud through the addition of water or an aqueous solution is bovine meat. The traditional methods used to detect this kind of fraud are expensive, time-consuming and depend on physicochemical analysis that require complex laboratory techniques, specific for each added substance. In this paper, based on digital images of histological cuts of adulterated and not-adulterated (normal) bovine meat, we evaluate the of digital image analysis methods to identify the aforementioned kind of fraud, with focus on the Local Binary Pattern (LBP) algorithm.
Automated Application Offloading through Ant-inspired Decision-Making
Venue: arXiv cs.DC 2016
Year: 2016

-The explosive trend of smartphone usage as the most effective and convenient communication tools of human life in recent years make developers build ever more complex smartphone applications. Gaming, navigation, video editing, augmented reality, and speech recognition applications require considerable computational power and energy. Although smart- phones have a wide range of capabilities - GPS, WiFi, cameras - their inherent limitations - frequent disconnections, mobility - and significant constraints - size, lower weights, longer battery life - make difficult to exploiting their full potential to run complex applications. Several research works have proposed solutions in application offloading domain, but few ones concerning the highly changing properties of the environment. To address these issues, we realize an automated application offloading middleware, ACOMMA, with dynamic and re-adaptable decision-making engine. The decision engine of ACOMMA is based on an ant- inspired algorithm.
Low-Complexity QoS-Aware Coordinated Scheduling for Heterogenous Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider a heterogenous network (HetNet), where low-power indoor femtocells are deployed in the coverage area of the existing macro base station (MBS). This paper proposes a novel coordinated random beamforming and user scheduling strategy to improve the throughput of users served by the femtocell access point (FAP) while satisfying the quality-of-service (QoS) requirements of users served by both MBS and FAP. The strategy, termed as QoS-Aware Coodinated Scheduling (QACS), requires limited coordination between the MBS and FAP, i.e., only the indexes of the qualified beams are shared. Exact statistical analysis for the ergodic achievable rate of both FAP and MBS with the proposed strategy are presented. Scheduling fairness is also addressed for the proposed QACS.
Policy Optimization for Content Push via Energy Harvesting Small Cells in Heterogeneous Networks
Venue: arXiv cs.IT 2016
Year: 2016

Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS (MBS) instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS. With finite battery capacity, Markov decision process based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based (POTB) policy and the energy-efficient threshold-based (EETB) policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based (GOTB) policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high.
Complementing Model Learning with Mutation-Based Fuzzing
Venue: arXiv cs.SE 2016
Year: 2016

An ongoing challenge for learning algorithms formulated in the Minimally Adequate Teacher framework is to efficiently obtain counterexamples. In this paper we compare and combine conformance testing and mutation-based fuzzing methods for obtaining counterexamples when learning finite state machine models for the reactive software systems of the Rigorous Exampination of Reactive Systems (RERS) challenge. We have found that for the LTL problems of the challenge the fuzzer provided an independent confirmation that the learning process had been successful, since no additional counterexamples were found. For the reachability problems of the challenge, however, the fuzzer discovered more reachable error states than the learner and tester, albeit in some cases the learner and tester found some that were not discovered by the fuzzer. This leads us to believe that these orthogonal approaches are complementary in the context of model learning.
A Greedy Approach to Answer Reachability Queries on DAGs
Venue: arXiv cs.DS 2016
Year: 2016

Several modern applications involve huge graphs and require fast answers to reachability queries. In more than two decades since first proposals, several approaches have been presented adopting on-line searches, hop labelling or transitive closure compression. Transitive closure compression techniques usually construct a graph reachability index, for example by decomposing the graph into disjoint chains. As memory consumption is proportional to the number of chains, the target of those algorithms is to decompose the graph into an optimal number \width\ of chains. However, commonly used techniques fail to meet general expectations, are exceedingly complex, and their application on large graphs is impractical. The main contribution of this paper is a novel approach to construct such reachability indexes. The proposed method decomposes the graph into a sub-optimal number $\widehat{c}$ of chains by following a greedy strategy. We show that, given a vertex topological order, such a decomposition is obtained in $\mathcal{O}(\widehat{c} m)$ time, and requires $\mathcal{O}(\widehat{c} n)$ space, with $\widehat{c}$ bounded by $[c \log(\frac{n}{c})]$. We provide experimental evidence suggesting that, on different categories of automatically generated benchmarks as well as on graphs arising from the field of logic synthesis and formal verification, the proposed method produces a number of chains very close to the optimum, while significantly reducing computation time.
An Optimal Ancestry Labeling Scheme with Applications to XML Trees and Universal Posets
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we solve the ancestry-labeling scheme problem which aims at assigning the shortest possible labels (bit strings) to nodes of rooted trees, so that ancestry queries between any two nodes can be answered by inspecting their assigned labels only. This problem was introduced more than twenty years ago by Kannan et al. [STOC '88], and is among the most well-studied problems in the field of informative labeling schemes. We construct an ancestry-labeling scheme for $n$-node trees with label size $\log_2 n + O(\log \log n)$ bits, thus matching the $\log_2 n + O(\log \log n)$ bits lower bound given by Alstrup et al. [SODA '03]. Our scheme is based on a simplified ancestry scheme that operates extremely well on a restricted set of trees. In particular, for the set of n-node trees with depth at most d, the simplified ancestry scheme enjoys label size of $\log_2 n + 2 \log_2 d + O(1)$ bits. Since the depth of most XML trees is at most some small constant, such an ancestry scheme may be of practical use. In addition, we also obtain an adjacency-labeling scheme that labels n-node trees of depth d with labels of size $\log_2 n + 3 \log_2 d + O(1)$ bits. All our schemes assign the labels in linear time, and guarantee that any query can be answered in constant time. Finally, our ancestry scheme finds applications to the construction of small universal partially ordered sets (posets). Specifically, for any fixed integer k, it enables the construction of a universal poset of size $\tilde O(n^k)$ for the family of $n$-element posets with tree-dimension at most $k$. Up to lower order terms, this bound is tight thanks to a lower bound of $n^{k-o(1)}$ due to Alon and Scheinerman [Order '88].
RSB Decoupling Property of MAP Estimators
Venue: arXiv cs.IT 2016
Year: 2016

The large-system decoupling property of a MAP estimator is studied when it estimates the i.i.d. vector $\boldsymbol{x}$ from the observation $\boldsymbol{y}=\mathbf{A}\boldsymbol{x}+\boldsymbol{z}$ with $\mathbf{A}$ being chosen from a wide range of matrix ensembles, and the noise vector $\boldsymbol{z}$ being i.i.d. and Gaussian. Using the replica method, we show that the marginal joint distribution of any two corresponding input and output symbols converges to a deterministic distribution which describes the input-output distribution of a single user system followed by a MAP estimator. Under the $b$RSB assumption, the single user system is a scalar channel with additive noise where the noise term is given by the sum of an independent Gaussian random variable and $b$ correlated interference terms. As the $b$RSB assumption reduces to RS, the interference terms vanish which results in the formerly studied RS decoupling principle.
Gradients of Counterfactuals
Venue: arXiv cs.LG 2016
Year: 2016

Gradients have been used to quantify feature importance in machine learning models. Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient. We study various networks, and observe that this phenomena is indeed widespread, across many inputs. We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. We apply our method to the GoogleNet architecture for object recognition in images, as well as a ligand-based virtual screening network with categorical features and an LSTM based language model for the Penn Treebank dataset. We visualize how interior gradients better capture feature importance. Furthermore, interior gradients are applicable to a wide variety of deep networks, and have the attribution property that the feature importance scores sum to the the prediction score. Best of all, interior gradients can be computed just as easily as gradients. In contrast, previous methods are complex to implement, which hinders practical adoption.
$k$-connectivity of inhomogeneous random key graphs with unreliable links
Venue: arXiv cs.CR 2016
Year: 2016

We consider secure and reliable connectivity in wireless sensor networks that utilize a heterogeneous random key predistribution scheme. We model the unreliability of wireless links by an on-off channel model that induces an Erdős-Rényi graph, while the heterogeneous scheme induces an inhomogeneous random key graph. The overall network can thus be modeled by the intersection of both graphs. We present conditions (in the form of zero-one laws) on how to scale the parameters of the intersection model so that with high probability i) all of its nodes are connected to at least $k$ other nodes; i.e., the minimum node degree of the graph is no less than $k$ and ii) the graph is $k$-connected, i.e., the graph remains connected even if any $k-1$ nodes leave the network. We also present numerical results to support these conditions in the finite-node regime. Our results are shown to complement and generalize several previous work in the literature.
Recursive Decomposition for Nonconvex Optimization
Venue: arXiv cs.AI 2016
Year: 2016

Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the objective function have combinatorial structure, and thus ideas from combinatorial optimization can be brought to bear. Based on this, we propose a problem-decomposition approach to nonconvex optimization. Similarly to DPLL-style SAT solvers and recursive conditioning in probabilistic inference, our algorithm, RDIS, recursively sets variables so as to simplify and decompose the objective function into approximately independent sub-functions, until the remaining functions are simple enough to be optimized by standard techniques like gradient descent. The variables to set are chosen by graph partitioning, ensuring decomposition whenever possible. We show analytically that RDIS can solve a broad class of nonconvex optimization problems exponentially faster than gradient descent with random restarts. Experimentally, RDIS outperforms standard techniques on problems like structure from motion and protein folding.
Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling
Venue: arXiv cs.AR 2016
Year: 2016

Hierarchical Temporal Memory (HTM) is a biomimetic machine learning algorithm imbibing the structural and algorithmic properties of the neocortex. Two main functional components of HTM that enable spatio-temporal processing are the spatial pooler and temporal memory. In this research, we explore a scalable hardware realization of the spatial pooler closely coupled with the mathematical formulation of spatial pooler. This class of neuromorphic algorithms are advantageous in solving a subset of the future engineering problems by extracting nonintuitive patterns in complex data. The proposed architecture, Non-volatile HTM (NVHTM), leverages large-scale solid state flash memory to realize a optimal memory organization, area and power envelope. A behavioral model of NVHTM is evaluated against the MNIST dataset, yielding 91.98% classification accuracy. A full custom layout is developed to validate the design in a TSMC 180nm process. The area and power profile of the spatial pooler are 30.538mm2 and 64.394mW, respectively. This design is a proof-of-concept that storage processing is a viable platform for large scale HTM network models.
Online Learning for Wireless Distributed Computing
Venue: arXiv cs.LG 2016
Year: 2016

There has been a growing interest for Wireless Distributed Computing (WDC), which leverages collaborative computing over multiple wireless devices. WDC enables complex applications that a single device cannot support individually. However, the problem of assigning tasks over multiple devices becomes challenging in the dynamic environments encountered in real-world settings, considering that the resource availability and channel conditions change over time in unpredictable ways due to mobility and other factors. In this paper, we formulate a task assignment problem as an online learning problem using an adversarial multi-armed bandit framework. We propose MABSTA, a novel online learning algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge. For maximal adaptability, MABSTA is designed to make no stochastic assumption about the environment. We analyze it mathematically and provide a worst-case performance guarantee for any dynamic environment. We also compare it with the optimal offline policy as well as other baselines via emulations on trace-data obtained from a wireless IoT testbed, and show that it offers competitive and robust performance in all cases. To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.
Policy-Compliant Path Diversity and Bisection Bandwidth
Venue: arXiv cs.NI 2016
Year: 2016

How many links can be cut before a network is bisected? What is the maximal bandwidth that can be pushed between two nodes of a network? These questions are closely related to network resilience, path choice for multipath routing or bisection bandwidth estimations in data centers. The answer is quantified using metrics such as the number of edge-disjoint paths between two network nodes and the cumulative bandwidth that can flow over these paths. In practice though, such calculations are far from simple due to the restrictive effect of network policies on path selection. Policies are set by network administrators to conform to service level agreements, protect valuable resources or optimize network performance. In this work, we introduce a general methodology for estimating lower and upper bounds for the policy-compliant path diversity and bisection bandwidth between two nodes of a network, effectively quantifying the effect of policies on these metrics. Exact values can be obtained if certain conditions hold. The approach is based on regular languages and can be applied in a variety of use cases.
Energy Efficient Barring Factor Enabled Extended Access Barring for IoT Devices in LTE-Advanced
Venue: arXiv cs.NI 2016
Year: 2016

Synchronized Random Access Channel (RACH) attempts by Internet of Things (IoT) devices could result in Radio Access Network (RAN) overload in LTE-A. 3GPP adopted Barring Bitmap Enabled-Extended Access Barring (EAB-BB) mechanism that announces the EAB information (i.e., a list of barred Access Classes) through a barring bitmap as the baseline solution to mitigate the RAN overload. EAB-BB was analyzed for its optimal performance in a recent work. However, there has been no work that analyzes Barring Factor Enabled-Extended Access Barring (EAB-BF), an alternative mechanism that was considered during the standardization process. Due to the modeling complexity involved, not only has it been difficult to analyze EAB-BF, but also, a much more far-reaching issue, like the effect of these schemes on key network performance parameter, like eNodeB energy consumption, has been overlooked. In this regard, for the first time, we develop a novel analytical model for EAB-BF to obtain its performance metrics. Results obtained from our analysis and simulation are seen to match very well. Furthermore, we also build an eNodeB energy consumption model to serve the IoT RACH requests. We then show that our analytical and energy consumption models can be combined to obtain EAB-BF settings that can minimize eNodeB energy consumption, while simultaneously providing optimal Quality of Service (QoS) performance. Results obtained reveal that the optimal performance of EAB-BF is better than that of EAB-BB. Furthermore, we also show that not only all the three 3GPP-proposed EAB-BF settings considered during standardization provide sub-optimal QoS to devices, but also result in excessive eNodeB energy consumption, thereby acutely penalizing the network. Finally, we provide corrections to these 3GPP-settings that can lead to significant gains in EAB-BF performance.