OpenRaiser/Intern-Atlas huggingface dataset:
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present YoTube-a novel network fusion framework for searching action proposals in untrimmed videos, where each action proposal corresponds to a spatialtemporal video tube that potentially locates one human action. Our method consists of a recurrent YoTube detector and a static YoTube detector, where the recurrent YoTube explores the regression capability of RNN for candidate bounding boxes predictions using learnt temporal dynamics and the static YoTube produces the bounding boxes using rich appearance cues in a single frame. Both networks are trained using rgb and optical flow in order to fully exploit the rich appearance, motion and temporal context, and their outputs are fused to produce accurate and robust proposal boxes. Action proposals are finally constructed by linking these boxes using dynamic programming with a novel trimming method to handle the untrimmed video effectively and efficiently. Extensive experiments on the challenging UCF-101 and UCF-Sports datasets show that our proposed technique obtains superior performance compared with the state-of-the-art.
Multi-level SVM Based CAD Tool for Classifying Structural MRIs
Venue: arXiv cs.CV 2017
Year: 2017

The revolutionary developments in the field of supervised machine learning have paved way to the development of CAD tools for assisting doctors in diagnosis. Recently, the former has been employed in the prediction of neurological disorders such as Alzheimer's disease. We propose a CAD (Computer Aided Diagnosis tool for differentiating neural lesions caused by CVA (Cerebrovascular Accident) from the lesions caused by other neural disorders by using Non-negative Matrix Factorisation (NMF) and Haralick features for feature extraction and SVM (Support Vector Machine) for pattern recognition. We also introduce a multi-level classification system that has better classification efficiency, sensitivity and specificity when compared to systems using NMF or Haralick features alone as features for classification. Cross-validation was performed using LOOCV (Leave-One-Out Cross Validation) method and our proposed system has a classification accuracy of over 86%.
Deep Semantics-Aware Photo Adjustment
Venue: arXiv cs.CV 2017
Year: 2017

Automatic photo adjustment is to mimic the photo retouching style of professional photographers and automatically adjust photos to the learned style. There have been many attempts to model the tone and the color adjustment globally with low-level color statistics. Also, spatially varying photo adjustment methods have been studied by exploiting high-level features and semantic label maps. Those methods are semantics-aware since the color mapping is dependent on the high-level semantic context. However, their performance is limited to the pre-computed hand-crafted features and it is hard to reflect user's preference to the adjustment. In this paper, we propose a deep neural network that models the semantics-aware photo adjustment. The proposed network exploits bilinear models that are the multiplicative interaction of the color and the contexual features. As the contextual features we propose the semantic adjustment map, which discovers the inherent photo retouching presets that are applied according to the scene context. The proposed method is trained using a robust loss with a scene parsing task. The experimental results show that the proposed method outperforms the existing method both quantitatively and qualitatively. The proposed method also provides users a way to retouch the photo by their own likings by giving customized adjustment maps.
Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks
Venue: arXiv cs.AI 2017
Year: 2017

Temporal landmarks have been proved to be a helpful mechanism to deal with temporal planning problems, specifically to improve planners performance and handle problems with deadline constraints. In this paper, we show the strength of using temporal landmarks to handle the state trajectory constraints of PDDL3.0. We analyze the formalism of TempLM, a temporal planner particularly aimed at solving planning problems with deadlines, and we present a detailed study that exploits the underlying temporal landmark-based mechanism of TempLM for representing and reasoning with trajectory constraints.
Sequential Composition in the Presence of Intermediate Termination
Venue: arXiv cs.LO 2017
Year: 2017

The standard operational semantics of the sequential composition operator gives rise to unbounded branching and forgetfulness when transparent process expressions are put in sequence. Due to transparency, the correspondence between context-free and pushdown processes fails modulo bisimilarity, and it is not clear how to specify an always terminating half counter. We propose a revised operational semantics for the sequential composition operator in the context of intermediate termination. With the revised operational semantics, we eliminate transparency. As a consequence, we establish a correspondence between context-free processes and pushdown processes. Moreover, we prove the reactive Turing powerfulness of TCP with iteration and nesting with the revised operational semantics for sequential composition.
Counting Restricted Homomorphisms via Möbius Inversion over Matroid Lattices
Venue: arXiv cs.CC 2017
Year: 2017

We present a framework for the complexity classification of parameterized counting problems that can be formulated as the summation over the numbers of homomorphisms from small pattern graphs H_1,...,H_l to a big host graph G with the restriction that the coefficients correspond to evaluations of the Möbius function over the lattice of a graphic matroid. This generalizes the idea of Curticapean, Dell and Marx [STOC 17] who used a result of Lovász stating that the number of subgraph embeddings from a graph H to a graph G can be expressed as such a sum over the lattice of partitions of H. In the first step we introduce what we call graphically restricted homomorphisms that, inter alia, generalize subgraph embeddings as well as locally injective homomorphisms. We provide a complete parameterized complexity dichotomy for counting such homomorphisms, that is, we identify classes of patterns for which the problem is fixed-parameter tractable (FPT), including an algorithm, and prove that all other pattern classes lead to #W[1]-hard problems. The main ingredients of the proof are the complexity classification of linear combinations of homomorphisms due to Curticapean, Dell and Marx [STOC 17] as well as a corollary of Rota's NBC Theorem which states that the sign of the Möbius function over a geometric lattice only depends on the rank of its arguments. We use the general theorem to classify the complexity of counting locally injective homomorphisms as well as homomorphisms that are injective in the r-neighborhood for constant r. Furthermore, we show that the former has "real" FPT cases by considering the subgraph counting problem restricted to trees on both sides. Finally we show that the dichotomy for counting graphically restricted homomorphisms readily extends to so-called linear combinations.
Bounds on the Satisfiability Threshold for Power Law Distributed Random SAT
Venue: arXiv cs.DM 2017
Year: 2017

Propositional satisfiability (SAT) is one of the most fundamental problems in computer science. The worst-case hardness of SAT lies at the core of computational complexity theory. The average-case analysis of SAT has triggered the development of sophisticated rigorous and non-rigorous techniques for analyzing random structures. Despite a long line of research and substantial progress, nearly all theoretical work on random SAT assumes a uniform distribution on the variables. In contrast, real-world instances often exhibit large fluctuations in variable occurrence. This can be modeled by a scale-free distribution of the variables, which results in distributions closer to industrial SAT instances. We study random k-SAT on n variables, $m=Θ(n)$ clauses, and a power law distribution on the variable occurrences with exponent $β$. We observe a satisfiability threshold at $β=(2k-1)/(k-1)$. This threshold is tight in the sense that instances with $β\le(2k-1)/(k-1)-\varepsilon$ for any constant $\varepsilon>0$ are unsatisfiable with high probability (w.h.p.). For $β\geq(2k-1)/(k-1)+\varepsilon$, the picture is reminiscent of the uniform case: instances are satisfiable w.h.p. for sufficiently small constant clause-variable ratios $m/n$; they are unsatisfiable above a ratio $m/n$ that depends on $β$.
A Simulator for Hedonic Games
Venue: arXiv cs.MA 2017
Year: 2017

Hedonic games are meant to model how coalitions of people form and break apart in the real world. However, it is difficult to run simulations when everything must be done by hand on paper. We present an online software that allows fast and visual simulation of several types of hedonic games. http://lukemiles.org/hedonic-games/
White, Man, and Highly Followed: Gender and Race Inequalities in Twitter
Venue: arXiv cs.SI 2017
Year: 2017

Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.
SUNNY-CP and the MiniZinc Challenge
Venue: arXiv cs.AI 2017
Year: 2017

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge---the annual international competition for CP solvers---where it won two gold medals in 2015 and 2016. Under consideration in Theory and Practice of Logic Programming (TPLP)
Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a simple spatial-temporal smoothness based method for solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the temporal smoothness and demonstrate that it can be extended to dense case directly. Second, we propose to exploit the spatial smoothness by resorting to the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and outliers in measurements, we robustify the data term by using the $L_1$ norm. In this way, our method could robustly exploit both spatial and temporal smoothness effectively and make dense non-rigid reconstruction easy. Our method is very easy to implement, which involves solving a series of least squares problems. Experimental results on both synthetic and real image dense NRSfM tasks show that the proposed method outperforms state-of-the-art dense non-rigid reconstruction methods.
Using text analysis to quantify the similarity and evolution of scientific disciplines
Venue: arXiv cs.DL 2017
Year: 2017

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g., computer science) are becoming increasingly central, but that on average the similarity between pairs has not changed in the last decades. This suggests that tendencies of convergence (e.g., multi-disciplinarity) and divergence (e.g., specialization) of disciplines are in balance.
Fast and robust tensor decomposition with applications to dictionary learning
Venue: arXiv cs.LG 2017
Year: 2017

We develop fast spectral algorithms for tensor decomposition that match the robustness guarantees of the best known polynomial-time algorithms for this problem based on the sum-of-squares (SOS) semidefinite programming hierarchy. Our algorithms can decompose a 4-tensor with $n$-dimensional orthonormal components in the presence of error with constant spectral norm (when viewed as an $n^2$-by-$n^2$ matrix). The running time is $n^5$ which is close to linear in the input size $n^4$. We also obtain algorithms with similar running time to learn sparsely-used orthogonal dictionaries even when feature representations have constant relative sparsity and non-independent coordinates. The only previous polynomial-time algorithms to solve these problem are based on solving large semidefinite programs. In contrast, our algorithms are easy to implement directly and are based on spectral projections and tensor-mode rearrangements. Or work is inspired by recent of Hopkins, Schramm, Shi, and Steurer (STOC'16) that shows how fast spectral algorithms can achieve the guarantees of SOS for average-case problems. In this work, we introduce general techniques to capture the guarantees of SOS for worst-case problems.
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction.
Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstrained multi-view and partially occluded faces. The faces have many variations in color and grayscale, image quality, image resolution, image illumination, image background, image illusion, human face, cartoon face, facial expression, light and severe partial facial occlusion, make up, gender, age, and race. Many of these faces are partially occluded with accessories such as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves, microphones, or other objects or persons. The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of labeled images and the number of individuals compared to other existing labeled face image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces image datasets. The crowedFaces and CrowedNonFaces datasets include faces and non-faces images from crowed scenes. These datasets essentially aim for researchers to provide a large number of training examples with many variations for large scale face learning and face recognition tasks.
Independent Motion Detection with Event-driven Cameras
Venue: arXiv cs.CV 2017
Year: 2017

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.
Spectral shaping with low resolution signals
Venue: arXiv cs.IT 2017
Year: 2017

We aim at investigating the impact of low resolution digital-to-analog converters (DACs) at the transmitter and low resolution analog-to-digital converters (ADCs) at the receiver on the required bandwidth and the required signalto- noise ratio (SNR). In particular, we consider the extreme case of only 1-bit resolution (with oversampling), where we propose a single carrier system architecture for minimizing the spectral occupation and the required SNR of 1-bit signals. In addition, the receiver is optimized to take into account the effects of quantization at both ends. Through simulations, we show that despite of the coarse quantization, sufficient spectral confinement is still achievable.
An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport
Venue: arXiv cs.NA 2017
Year: 2017

We present an efficient algorithm for recent generalizations of optimal mass transport theory to matrix-valued and vector-valued densities. These generalizations lead to several applications including diffusion tensor imaging, color images processing, and multi-modality imaging. The algorithm is based on sequential quadratic programming (SQP). By approximating the Hessian of the cost and solving each iteration in an inexact manner, we are able to solve each iteration with relatively low cost while still maintaining a fast convergent rate. The core of the algorithm is solving a weighted Poisson equation, where different efficient preconditioners may be employed. We utilize incomplete Cholesky factorization, which yields an efficient and straightforward solver for our problem. Several illustrative examples are presented for both the matrix and vector-valued cases.
Decidable Weighted Expressions with Presburger Combinators
Venue: arXiv cs.FL 2017
Year: 2017

In this paper, we investigate the expressive power and the algorithmic properties of weighted expressions, which define functions from finite words to integers. First, we consider a slight extension of an expression formalism, introduced by Chatterjee. et. al. in the context of infinite words, by which to combine values given by unambiguous (max,+)-automata, using Presburger arithmetic. We show that important decision problems such as emptiness, universality and comparison are PSpace-c for these expressions. We then investigate the extension of these expressions with Kleene star. This allows to iterate an expression over smaller fragments of the input word, and to combine the results by taking their iterated sum. The decision problems turn out to be undecidable, but we introduce the decidable and still expressive class of synchronised expressions.
Constant composition codes derived from linear codes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a class of linear codes and obtain their weight distribution. Some of these codes are almost optimal. Moreover, several classes of constant composition codes(CCCs) are constructed as subcodes of linear codes.
Invariant components of synergy, redundancy, and unique information among three variables
Venue: arXiv cs.IT 2017
Year: 2017

In a system of three stochastic variables, the Partial Information Decomposition (PID) of Williams and Beer dissects the information that two variables (sources) carry about a third variable (target) into nonnegative information atoms that describe redundant, unique, and synergistic modes of dependencies among the variables. However, the classification of the three variables into two sources and one target limits the dependency modes that can be quantitatively resolved, and does not naturally suit all systems. Here, we extend the PID to describe trivariate modes of dependencies in full generality, without introducing additional decomposition axioms or making assumptions about the target/source nature of the variables. By comparing different PID lattices of the same system, we unveil a finer PID structure made of seven nonnegative information subatoms that are invariant to different target/source classifications and that are sufficient to construct any PID lattice. This finer structure naturally splits redundant information into two nonnegative components: the source redundancy, which arises from the pairwise correlations between the source variables, and the non-source redundancy, which does not, and relates to the synergistic information the sources carry about the target. The invariant structure is also sufficient to construct the system's entropy, hence it characterizes completely all the interdependencies in the system.
UAV Assisted Public Safety Communications with LTE-Advanced HetNets and FeICIC
Venue: arXiv cs.NI 2017
Year: 2017

Establishing a reliable communication infrastructure at an emergency site is a crucial task for mission-critical and real-time public safety communications (PSC). To this end, use of the unmanned aerial vehicles (UAVs) has recently received extensive interest for PSC to establish reliable connectivity in a heterogeneous network (HetNet) environment. These UAVs can be deployed as unmanned aerial base stations (UABSs) as a part of HetNet infrastructure. In this article, we explore the role of agile UABSs in LTE-Advanced HetNets by applying 3GPP Release 11 further-enhanced inter-cell interference coordination (FeICIC) and cell range expansion (CRE) techniques. Through simulations, we compare the system-wide 5th percentile spectral efficiency (SE) when UABSs are deployed in a hexagonal grid and when their locations are optimized using a genetic algorithm, while also jointly optimizing the CRE and the FeICIC parameters. Our simulation results show that at optimized UABS locations, the 3GPP Release 11 FeICIC with reduced power subframes can provide considerably better 5th percentile SE than the 3GPP Release~10 with almost blank subframes.