OpenRaiser/Intern-Atlas huggingface dataset:
A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
Venue: arXiv cs.LG 2016
Year: 2016

The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) algorithm, which considers removing the hierarchical redundancy during the classifier learning process, when coping with data containing hierarchically structured features. The experiments showed that HRE-TAN obtains significantly better predictive performance than the conventional Tree Augmented Naive Bayes classifier, and enhanced the robustness against imbalanced class distributions, in aging-related gene datasets with Gene Ontology terms used as features.
Landauer Bound for Analog Computing Systems
Venue: arXiv cs.IT 2016
Year: 2016

By establishing a relation between information erasure and continuous phase transitions we generalise the Landauer bound to analog computing systems. The entropy production per degree of freedom during erasure of an analog variable (reset to standard value) is given by the logarithm of the configurational volume measured in units of its minimal quantum. As a consequence every computation has to be carried on with a finite number of bits and infinite precision is forbidden by the fundamental laws of physics, since it would require an infinite amount of energy.
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
Venue: arXiv cs.NE 2016
Year: 2016

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an equivalent isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) in which the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, widely regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.
CrowdCafe - Mobile Crowdsourcing Platform
Venue: arXiv cs.HC 2016
Year: 2016

In this paper we present a mobile crowdsourcing platform CrowdCafe, where people can perform microtasks using their smartphones while they ride a bus, travel by train, stand in a queue or wait for an appointment. These microtasks are executed in exchange for rewards provided by local stores, such as coffee, desserts and bus tickets. We present the concept, the implementation and the evaluation by conducting a study with 52 participants, having 1108 tasks completed.
Neural Name Translation Improves Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered unks to learn the correspondence between source and target rare words. However, testing words unseen in the training corpus cannot be handled by this method. And it also suffers from the noisy word alignment. In this paper, we focus on a major type of rare words -- named entity (NE), and propose to translate them with character level sequence to sequence model. The NE translation model is further used to derive high quality NE alignment in the bilingual training corpus. With the integration of NE translation and alignment modules, our NMT system is able to surpass the baseline system by 2.9 BLEU points on the Chinese to English task.
A First Look at User Activity on Tinder
Venue: arXiv cs.SI 2016
Year: 2016

Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Venue: arXiv cs.CV 2016
Year: 2016

Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution. The proposed deep depth super-resolution method learns the mapping from a low-resolution depth image to a high resolution one in an end-to-end style. Furthermore, to better regularize the learned depth map, we propose to exploit the depth field statistics and the local correlation between depth image and color image. These priors are integrated in an energy minimization formulation, where the deep neural network learns the unary term, the depth field statistics works as global model constraint and the color-depth correlation is utilized to enforce the local structure in depth images. Extensive experiments on various depth super-resolution benchmark datasets show that our method outperforms the state-of-the-art depth image super-resolution methods with a margin.
Synchronization Detection in Networks of Coupled Oscillators for Pattern Recognition
Venue: arXiv cs.ET 2016
Year: 2016

Coupled oscillator-based networks are an attractive approach for implementing hardware neural networks based on emerging nanotechnologies. However, the readout of the state of a coupled oscillator network is a difficult challenge in hardware implementations, as it necessitates complex signal processing to evaluate the degree of synchronization between oscillators, possibly more complicated than the coupled oscillator network itself. In this work, we focus on a coupled oscillator network particularly adapted to emerging technologies, and evaluate two schemes for reading synchronization patterns that can be readily implemented with basic CMOS circuits. Through simulation of a simple generic coupled oscillator network, we compare the operation of these readout techniques with a previously proposed full statistics evaluation scheme. Our approaches provide results nearly identical to the mathematical method, but also show better resilience to moderate noise, which is a major concern for hardware implementations. These results open the door to widespread realization of hardware coupled oscillator-based neural systems.
Demand-Flow of Agents with Gross-Substitute Valuations
Venue: arXiv cs.GT 2016
Year: 2016

We consider the class of valuations on indivisible items called gross-substitute (GS). This class was introduced by Kelso and Crawford (1982) and is widely used in studies of markets with indivisibilities. GS is a condition on the demand-flow in a specific scenario: some items become more expensive while other items retain their price. We prove that GS implies a much stronger condition, describing the demand-flow in the general scenario in which all prices may change. We prove that the demand of GS agents always flows (weakly) downwards, i.e, from items with higher price-increase to items with lower price-increase. We show that this property is equivalent to GS and is not true when there are complementarities.
Biabduction (and Related Problems) in Array Separation Logic
Venue: arXiv cs.LO 2016
Year: 2016

We investigate array separation logic (ASL), a variant of symbolic-heap separation logic in which the data structures are either pointers or arrays, i.e., contiguous blocks of allocated memory. This logic provides a language for compositional memory safety proofs of imperative array programs. We focus on the biabduction problem for this logic, which has been established as the key to automatic specification inference at the industrial scale. We present an NP decision procedure for biabduction in ASL that produces solutions of reasonable quality, and we also show that the problem of finding a consistent solution is NP-hard. Along the way, we study satisfiability and entailment in our logic, giving decision procedures and complexity bounds for both problems. We show satisfiability to be NP-complete, and entailment to be decidable with high complexity. The somewhat surprising fact that biabduction is much simpler than entailment is explained by the fact that, as we show, the element of choice over biabduction solutions enables us to dramatically reduce the search space.
Improper signaling and symbol extensions: How far can we go with Gaussian P2P codebooks in the interfering MAC with TIN?
Venue: arXiv cs.IT 2016
Year: 2016

Meeting the challenges of 5G demands better exploitation of the available spectrum by allowing multiple parties to share resources. For instance, a secondary unlicensed system can share resources with the cellular uplink of a primary licensed system for an improved spectral efficiency. This induces interference which has to be taken into account when designing such a system. A simple yet robust strategy is treating interference as noise (TIN), which is widely adapted in practice. It is thus important to study the capabilities and limitations of TIN in such scenarios. In this paper, we study this scenario modelled as Multiple Access Channel (MAC) interfered by a Point-to-Point (P2P) channel. Here, we focus on rate maximization and power minimization problems separately. We use improper Gaussian signaling (instead of proper) at the transmitters to increase the design flexibility, which offers the freedom of optimizing the transmit signal pseudo-variance in addition to its variance. Furthermore, we allow correlation among the transmitted signals over orthogonal resource basis (i.e., time or frequency) for the purpose of optimal signaling design over the extended channel. We formulate the rate maximization problem as a semidefinite program, and use semidefinite relaxation (SDR) to obtain a near-optimal solution. Numerical optimizations show that, by improper Gaussian signaling the achievable rates can be improved upto three times depending on the strength of the interfering links. Furthermore, we observe significant benefits in power consumption by improper Gaussian signaling with symbol extensions compared to the traditional proper Gaussian signaling. Interestingly, by minimizing sum power given the solution of the rate maximization problem improves the energy efficiency significantly.
Numeric Deduction in Symbolic Computation. Application to Normalizing Transformations
Venue: arXiv cs.SC 2016
Year: 2016

Algorithms of numeric (in exact arithmetic) deduction of analytical expressions, proposed and described by Shevchenko and Vasiliev (1993), are developed and implemented in a computer algebra code. This code is built as a superstructure for the computer algebra package by Shevchenko and Sokolsky (1993a) for normalization of Hamiltonian systems of ordinary differential equations, in order that high complexity problems of normalization could be solved. As an example, a resonant normal form of a Hamiltonian describing the hyperboloidal precession of a dynamically symmetric satellite is derived by means of the numeric deduction technique. The technique provides a considerable economy, about 30 times in this particular application, in computer memory consumption. It is naturally parallelizable. Thus the economy of memory consumption is convertible into a gain in computation speed.
Artificial neural networks and fuzzy logic for recognizing alphabet characters and mathematical symbols
Venue: arXiv cs.NE 2016
Year: 2016

Optical Character Recognition software (OCR) are important tools for obtaining accessible texts. We propose the use of artificial neural networks (ANN) in order to develop pattern recognition algorithms capable of recognizing both normal texts and formulae. We present an original improvement of the backpropagation algorithm. Moreover, we describe a novel image segmentation algorithm that exploits fuzzy logic for separating touching characters.
Detecting Communities under Differential Privacy
Venue: arXiv cs.SI 2016
Year: 2016

Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified their outperformance over the state-of-the-art.
Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies
Venue: arXiv cs.CV 2016
Year: 2016

The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets. In this paper we apply persistent homology to the characterization of gas plumes in time dependent sequences of hyperspectral cubes, i.e. the analysis of 4-way arrays. We investigate hyperspectral movies of Long-Wavelength Infrared data monitoring an experimental release of chemical simulant into the air. Our approach models regions of interest within the hyperspectral data cubes as points on the real Grassmann manifold $G(k, n)$ (whose points parameterize the $k$-dimensional subspaces of $\mathbb{R}^n$), contrasting our approach with the more standard framework in Euclidean space. An advantage of this approach is that it allows a sequence of time slices in a hyperspectral movie to be collapsed to a sequence of points in such a way that some of the key structure within and between the slices is encoded by the points on the Grassmann manifold. This motivates the search for topological features, associated with the evolution of the frames of a hyperspectral movie, within the corresponding points on the Grassmann manifold. The proposed mathematical model affords the processing of large data sets while retaining valuable discriminatory information. In this paper, we discuss how embedding our data in the Grassmann manifold, together with topological data analysis, captures dynamical events that occur as the chemical plume is released and evolves.
On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract
Venue: arXiv cs.LO 2016
Year: 2016

In this extended abstract a view on the role of Formal Methods in System Engineering is briefly presented. Then two examples of useful analysis techniques based on solid mathematical theories are discussed as well as the software tools which have been built for supporting such techniques. The first technique is Scalable Approximated Population DTMC Model-checking. The second one is Spatial Model-checking for Closure Spaces. Both techniques have been developed in the context of the EU funded project QUANTICOL.
Stochastic and Spatial Equivalences for PALOMA
Venue: arXiv cs.LO 2016
Year: 2016

We concentrate our study on a recent process algebra - PALOMA - intended to capture interactions between spatially distributed agents, for example in collective adaptive systems. New agent-based semantic rules for deriving the underlying continuous time Markov chain are given in terms of State to Function Labelled Transition Systems. Furthermore we define a bisimulation with respect to an isometric transformation of space allowing us to compare PALOMA models with respect to their relative rather than absolute locations.
Low-Complexity Sub-band Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access
Venue: arXiv cs.IT 2016
Year: 2016

Noncontiguous transmission schemes combined with high power-efficiency requirements pose big challenges for radio transmitter and power amplifier (PA) design and implementation. Due to the nonlinear nature of the PA, severe unwanted emissions can occur, which can potentially interfere with neighboring channel signals or even desensitize the own receiver in frequency division duplexing (FDD) transceivers. In this article, to suppress such unwanted emissions, a low-complexity sub-band DPD solution, specifically tailored for spectrally noncontiguous transmission schemes in low-cost devices, is proposed. The proposed technique aims at mitigating only the selected spurious intermodulation distortion components at the PA output, hence allowing for substantially reduced processing complexity compared to classical linearization solutions. Furthermore, novel decorrelation based parameter learning solutions are also proposed and formulated, which offer reduced computing complexity in parameter estimation as well as the ability to track time-varying features adaptively. Comprehensive simulation and RF measurement results are provided, using a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed solution in real world scenarios. The obtained results demonstrate that highly efficient spurious component suppression can be obtained using the proposed solutions.
On the Optimal Boolean Function for Prediction under Quadratic Loss
Venue: arXiv cs.IT 2016
Year: 2016

Suppose $Y^{n}$ is obtained by observing a uniform Bernoulli random vector $X^{n}$ through a binary symmetric channel. Courtade and Kumar asked how large the mutual information between $Y^{n}$ and a Boolean function $\mathsf{b}(X^{n})$ could be, and conjectured that the maximum is attained by a dictator function. An equivalent formulation of this conjecture is that dictator minimizes the prediction cost in a sequential prediction of $Y^{n}$ under logarithmic loss, given $\mathsf{b}(X^{n})$. In this paper, we study the question of minimizing the sequential prediction cost under a different (proper) loss function - the quadratic loss. In the noiseless case, we show that majority asymptotically minimizes this prediction cost among all Boolean functions. We further show that for weak noise, majority is better than dictator, and that for strong noise dictator outperforms majority. We conjecture that for quadratic loss, there is no single sequence of Boolean functions that is simultaneously (asymptotically) optimal at all noise levels.
Finite Length Performance of Random Slotted ALOHA Strategies
Venue: arXiv cs.IT 2016
Year: 2016

Multiple connected devices sharing common wireless resources might create interference if they access the channel simultaneously. Medium access control (MAC) protocols gener- ally regulate the access of the devices to the shared channel to limit signal interference. In particular, irregular repetition slotted ALOHA (IRSA) techniques can achieve high-throughput performance when interference cancellation methods are adopted to recover from collisions. In this work, we study the finite length performance for IRSA schemes by building on the analogy between successive interference cancellation and iterative belief- propagation on erasure channels. We use a novel combinatorial derivation based on the matrix-occupancy theory to compute the error probability and we validate our method with simulation results.
Enlightening Deep Neural Networks with Knowledge of Confounding Factors
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning techniques have demonstrated significant capacity in modeling some of the most challenging real world problems of high complexity. Despite the popularity of deep models, we still strive to better understand the underlying mechanism that drives their success. Motivated by observations that neurons in trained deep nets predict attributes indirectly related to the training tasks, we recognize that a deep network learns representations more general than the task at hand to disentangle impacts of multiple confounding factors governing the data, in order to isolate the effects of the concerning factors and optimize a given objective. Consequently, we propose a general framework to augment training of deep models with information on auxiliary explanatory data variables, in an effort to boost this disentanglement and train deep networks that comprehend the data interactions and distributions more accurately, and thus improve their generalizability. We incorporate information on prominent auxiliary explanatory factors of the data population into existing architectures as secondary objective/loss blocks that take inputs from hidden layers during training. Once trained, these secondary circuits can be removed to leave a model with the same architecture as the original, but more generalizable and discerning thanks to its comprehension of data interactions. Since pose is one of the most dominant confounding factors for object recognition, we apply this principle to instantiate a pose-aware deep convolutional neural network and demonstrate that auxiliary pose information indeed improves the classification accuracy in our experiments on SAR target classification tasks.