OpenRaiser/Intern-Atlas huggingface dataset:
Can a CNN Recognize Catalan Diet?
Venue: arXiv cs.CV 2016
Year: 2016

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient's behaviour, allowing specialists to discover unhealthy food patterns and understand the user's lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediterranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29\%, and top-5 of 97.07\%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07\%, and top-5 of 89.53\%, for a total of 115+101 food classes.
Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low-rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Lastly, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.
The DLVHEX System for Knowledge Representation: Recent Advances (System Description)
Venue: arXiv cs.CL 2016
Year: 2016

The DLVHEX system implements the HEX-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top of DLVHEX. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
Optimal Learning of Specifications from Examples
Venue: arXiv cs.PL 2016
Year: 2016

A fundamental challenge in synthesis from examples is designing a learning algorithm that poses the minimal number of questions to an end user while guaranteeing that the target hypothesis is discovered. Such guarantees are practically important because they ensure that end users will not be overburdened with unnecessary questions. We present SPEX -- a learning algorithm that addresses the above challenge. SPEX considers the hypothesis space of formulas over first-order predicates and learns the correct hypothesis by only asking the user simple membership queries for concrete examples. Thus, SPEX is directly applicable to any learning problem that fits its hypothesis space and uses membership queries. SPEX works by iteratively eliminating candidate hypotheses from the space until converging to the target hypothesis. The main idea is to use the implication order between hypotheses to guarantee that in each step the question presented to the user obtains maximal pruning of the space. This problem is particularly challenging when predicates are potentially correlated. To show that SPEX is practically useful, we expressed two rather different applications domains in its framework: learning programs for the domain of technical analysts (stock trading) and learning data structure specifications. The experimental results show that SPEX's optimality guarantee is effective: it drastically reduces the number of questions posed to the user while successfully learning the exact hypothesis.
Online Learning of Event Definitions
Venue: arXiv cs.LG 2016
Year: 2016

Systems for symbolic event recognition infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). We present an ILP system for online learning of Event Calculus theories. To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream. We employ a decoupling scheme of the Event Calculus axioms during the learning process, that allows to learn each clause in isolation. Moreover, we use abductive-inductive logic programming techniques to handle unobserved target predicates. We evaluate our approach on an activity recognition application and compare it to a number of batch learning techniques. We obtain results of comparable predicative accuracy with significant speed-ups in training time. We also outperform hand-crafted rules and match the performance of a sound incremental learner that can only operate on noise-free datasets. This paper is under consideration for acceptance in TPLP.
Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy
Venue: arXiv cs.CV 2016
Year: 2016

Multi-task learning (MTL) is useful for domains in which data originates from multiple sources that are individually under-sampled. MTL methods are able to learn classification models that have higher performance as compared to learning a single model by aggregating all the data together or learning a separate model for each data source. The performance of these methods relies on label accuracy. We address the problem of simultaneously learning multiple classifiers in the MTL framework when the training data has imprecise labels. We assume that there is an additional source of information that provides a score for each instance which reflects the certainty about its label. Modeling this score as being generated by an underlying ranking function, we augment the MTL framework with an added layer of supervision. This results in new MTL methods that are able to learn accurate classifiers while preserving the domain structure provided through the rank information. We apply these methods to the task of detecting abnormal cortical regions in the MRIs of patients suffering from focal epilepsy whose MRI were read as normal by expert neuroradiologists. In addition to the noisy labels provided by the results of surgical resection, we employ the results of an invasive intracranial-EEG exam as an additional source of label information. Our proposed methods are able to successfully detect abnormal regions for all patients in our dataset and achieve a higher performance as compared to baseline methods.
Local Testing for Membership in Lattices
Venue: arXiv cs.DS 2016
Year: 2016

Motivated by the structural analogies between point lattices and linear error-correcting codes, and by the mature theory on locally testable codes, we initiate a systematic study of local testing for membership in lattices. Testing membership in lattices is also motivated in practice, by applications to integer programming, error detection in lattice-based communication, and cryptography. Apart from establishing the conceptual foundations of lattice testing, our results include the following: 1. We demonstrate upper and lower bounds on the query complexity of local testing for the well-known family of code formula lattices. Furthermore, we instantiate our results with code formula lattices constructed from Reed-Muller codes, and obtain nearly-tight bounds. 2. We show that in order to achieve low query complexity, it is sufficient to design one-sided non-adaptive canonical tests. This result is akin to, and based on an analogous result for error-correcting codes due to Ben-Sasson et al. (SIAM J. Computing 35(1) pp1-21).
Deep FisherNet for Object Classification
Venue: arXiv cs.CV 2016
Year: 2016

Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN's representation power is still somewhat limited in dealing with object images that have large variation in size and clutter, where Fisher Vector (FV) has shown to be an effective encoding strategy. FV encodes an image by aggregating local descriptors with a universal generative Gaussian Mixture Model (GMM). FV however has limited learning capability and its parameters are mostly fixed after constructing the codebook. To combine together the best of the two worlds, we propose in this paper a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using backpropagation. Our proposed FisherNet combines convolutional neural network training and Fisher Vector encoding in a single end-to-end structure. We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL VOC object classification task.
Fuzzy thresholding in wavelet domain for speckle reduction in Synthetic Aperture Radar images
Venue: arXiv cs.CV 2016
Year: 2016

The application of wavelet transforms to Synthetic Aperture Radar (SAR) imagery has improved despeckling performance. To deduce the problem of filtering the multiplicative noise to the case of an additive noise, the wavelet decomposition is performed on the logarithm of the image gray levels. The detail coefficients produced by the bidimensional discrete wavelet transform (DWT-2D) needs to be thresholded to extract out the speckle in highest subbands. An initial threshold value is estimated according to the noise variance. In this paper, an additional fuzzy thresholding approach for automatic determination of the rate threshold level around the traditional wavelet noise thresholding (initial threshold) is applied, and used for the soft or hard-threshold performed on all the high frequency subimages. The filtered logarithmic image is then obtained by reconstruction from the thresholded coefficients. This process is applied a single time, and exclusively to the first level of decomposition. The exponential function of this reconstructed image gives the final filtered image. Experimental results on test images have demonstrated the effectiveness of this method compared to the most of methods in use at the moment.
Collaborative Smartphone Sensing using Overlapping Coalition Formation Games
Venue: arXiv cs.DC 2016
Year: 2016

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to improve the quality of smartphone applications. However, due to time-varying wireless channels and lack of incentives for the users to participate, the quality and quantity of the data uploaded by the smartphone users are not always satisfying. In this paper, we consider a smartphone sensing system in which a platform publicizes multiple tasks, and the smartphone users choose a set of tasks to participate in. In the traditional non-cooperative approach with incentives, each smartphone user gets rewards from the platform as an independent individual and the limit of the wireless channel resources is often omitted. To tackle this problem, we introduce a novel cooperative approach with an overlapping coalition formation game (OCF-game) model, in which the smartphone users can cooperate with each other to form the overlapping coalitions for different sensing tasks. We also utilize a centralized case to describe the upper bound of the system sensing performance. Simulation results show that the cooperative approach achieves a better performance than the non-cooperative one in various situations.
DOOMED: Direct Online Optimization of Modeling Errors in Dynamics
Venue: arXiv cs.RO 2016
Year: 2016

It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather then the desired accelerations. This work takes a different approach. We define a class of gradient-based online learning algorithms we term Direct Online Optimization for Modeling Errors in Dynamics (DOOMED) that directly minimize an objective measuring the divergence between actual and desired accelerations. Our objective is defined in terms of the true system's unknown dynamics and is therefore impossible to evaluate. However, we show that its gradient is measurable online from system data. We develop a novel adaptive control approach based on running online learning to directly correct (inverse) dynamics errors in real time using the data stream from the robot to accurately achieve desired accelerations during execution.
Accelerated Kaczmarz Algorithms using History Information
Venue: arXiv cs.NA 2016
Year: 2016

The Kaczmarz algorithm is a well known iterative method for solving overdetermined linear systems. Its randomized version yields provably exponential convergence in expectation. In this paper, we propose two new methods to speed up the randomized Kaczmarz algorithm by utilizing the past estimates in the iterations. The first one utilize the past estimates to get a preconditioner. The second one combines the stochastic average gradient (SAG) method with the randomized Kaczmarz algorithm. It takes advantage of past gradients to improve the convergence speed. Numerical experiments indicate that the new algorithms can dramatically outperform the standard randomized Kaczmarz algorithm.
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
Venue: arXiv cs.CV 2016
Year: 2016

One of the key challenges in machine learning is to design a computationally efficient multi-class classifier while maintaining the output accuracy and performance. In this paper, we present a tree-based classifier: Attention Tree (ATree) for large-scale image classification that uses recursive Adaboost training to construct a visual attention hierarchy. The proposed attention model is inspired from the biological 'selective tuning mechanism for cortical visual processing'. We exploit the inherent feature similarity across images in datasets to identify the input variability and use recursive optimization procedure, to determine data partitioning at each node, thereby, learning the attention hierarchy. A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity. The attention model maximizes the margins for the binary classifiers for optimal decision boundary modelling, leading to better performance at minimal complexity. The proposed framework has been evaluated on both Caltech-256 and SUN datasets and achieves accuracy improvement over state-of-the-art tree-based methods at significantly lower computational cost.
Two-Buffer Simulation Games
Venue: arXiv cs.FL 2016
Year: 2016

We consider simulation games played between Spoiler and Duplicator on two Buchi automata in which the choices made by Spoiler can be buffered by Duplicator in two different buffers before she executes them on her structure. Previous work on such games using a single buffer has shown that they are useful to approximate language inclusion problems. We study the decidability and complexity and show that games with two buffers can be used to approximate corresponding problems on finite transducers, i.e. the inclusion problem for rational relations over infinite words.
Parametric, Probabilistic, Timed Resource Discovery System
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents a fully distributed resource discovery and reservation system. Verification of such a system is important to ensure the execution of distributed applications on a set of resources in appropriate conditions. A semi-formal model for his system is presented using probabilistic timed automata. This model is timed, parametric and probabilistic, making it a challenge to the parameter synthesis community.
Rate Reduction for State-labelled Markov Chains with Upper Time-bounded CSL Requirements
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents algorithms for identifying and reducing a dedicated set of controllable transition rates of a state-labelled continuous-time Markov chain model. The purpose of the reduction is to make states to satisfy a given requirement, specified as a CSL upper time-bounded Until formula. We distinguish two different cases, depending on the type of probability bound. A natural partitioning of the state space allows us to develop possible solutions, leading to simple algorithms for both cases.
Transmit Optimization for Symbol-Level Spoofing
Venue: arXiv cs.IT 2016
Year: 2016

With recent developments of wireless communication technologies, malicious users can use them to commit crimes or launch terror attacks, thus imposing new threats on the public security. To quickly respond to defend these attacks, authorized parities (e.g., the National Security Agency of the USA) need to intervene in the malicious communication links over the air. This paper investigates this emerging wireless communication intervention problem at the physical layer. Unlike prior studies using jamming to disrupt or disable the targeted wireless communications, we propose a new physical-layer spoofing approach to change their communicated information. Consider a fundamental three-node system over additive white Gaussian noise (AWGN) channels, in which an intermediary legitimate spoofer aims to spoof a malicious communication link from Alice to Bob, such that the received message at Bob is changed from Alice's originally sent message to the one desired by the spoofer. We propose a new symbol-level spoofing scheme, where the spoofer designs the spoofing signal via exploiting the symbol-level relationship between each original constellation point of Alice and the desirable one of the spoofer. In particular, the spoofer aims to minimize the average spoofing-symbol-error-rate (SSER), which is defined as the average probability that the symbols decoded by Bob fail to be changed or spoofed, by designing its spoofing signals over symbols subject to the average transmit power constraint. By considering two cases when Alice employs the widely-used binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulations, we obtain the respective optimal solutions to the two average SSER minimization problems. Numerical results show that the symbol-level spoofing scheme with optimized transmission achieves a much lower average SSER, as compared to other benchmark schemes.
Infinite Unlimited Churn
Venue: arXiv cs.DC 2016
Year: 2016

We study unlimited infinite churn in peer-to-peer overlay networks. Under this churn, arbitrary many peers may concurrently request to join or leave the overlay network; moreover these requests may never stop coming. We prove that unlimited adversarial churn, where processes may just exit the overlay network, is unsolvable. We focus on cooperative churn where exiting processes participate in the churn handling algorithm. We define the problem of unlimited infinite churn in this setting. We distinguish the fair version of the problem, where each request is eventually satisfied, from the unfair version that just guarantees progress. We focus on local solutions to the problem, and prove that a local solution to the Fair Infinite Unlimited Churn is impossible. We then present and prove correct an algorithm UIUC that solves the Unfair Infinite Unlimited Churn Problem for a linearized peer-to-peer overlay network. We extend this solution to skip lists and skip graphs.
Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval
Venue: arXiv cs.IR 2016
Year: 2016

Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37]. The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.