OpenRaiser/Intern-Atlas huggingface dataset:
Vortices Instead of Equilibria in MinMax Optimization: Chaos and Butterfly Effects of Online Learning in Zero-Sum Games
Venue: arXiv cs.GT 2019
Year: 2019

We establish that algorithmic experiments in zero-sum games "fail miserably" to confirm the unique, sharp prediction of maxmin equilibration. Contradicting nearly a century of economic thought that treats zero-sum games nearly axiomatically as the exemplar symbol of economic stability, we prove that no meaningful prediction can be made about the day-to-day behavior of online learning dynamics in zero-sum games. Concretely, Multiplicative Weights Updates (MWU) with constant step-size is Lyapunov chaotic in the dual (payoff) space. Simply put, let's assume that an observer asks the agents playing Matching-Pennies whether they prefer Heads or Tails (and by how much in terms of aggregate payoff so far). The range of possible answers consistent with any arbitrary small set of initial conditions blows up exponentially with time everywhere in the payoff space. This result is robust both algorithmically as well as game theoretically: 1) Algorithmic robustness: Chaos is robust to agents using any of a general sub-family of Follow-the-Regularized-Leader (FTRL) algorithms, the well known regret-minimizing dynamics, even when agents mix-and-match dynamics, use different or slowly decreasing step-sizes. 2) Game theoretic robustness: Chaos is robust to all affine variants of zero-sum games (strictly competitive games), network variants with arbitrary large number of agents and even to competitive settings beyond these. Our result is in stark contrast with the time-average convergence of online learning to (approximate) Nash equilibrium, a result widely reported as "(weak) convergence to equilibrium".
Exploring Bias in GAN-based Data Augmentation for Small Samples
Venue: arXiv cs.LG 2019
Year: 2019

For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversarial networks (GAN) had been proposed, we see the hope of small samples data augmentation (DA) with realistic fake data, and many works validated the viability of GAN-based DA. Although most of the works pointed out higher accuracy can be achieved using GAN-based DA, some researchers stressed that the fake data generated from GAN has inherent bias, and in this paper, we explored when the bias is so low that it cannot hurt the performance, we set experiments to depict the bias in different GAN-based DA setting, and from the results, we design a pipeline to inspect specific dataset is efficiently-augmentable with GAN-based DA or not. And finally, depending on our trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based DA application.
PDH : Probabilistic deep hashing based on MAP estimation of Hamming distance
Venue: arXiv cs.CV 2019
Year: 2019

With the growth of image on the web, research on hashing which enables high-speed image retrieval has been actively studied. In recent years, various hashing methods based on deep neural networks have been proposed and achieved higher precision than the other hashing methods. In these methods, multiple losses for hash codes and the parameters of neural networks are defined. They generate hash codes that minimize the weighted sum of the losses. Therefore, an expert has to tune the weights for the losses heuristically, and the probabilistic optimality of the loss function cannot be explained. In order to generate explainable hash codes without weight tuning, we theoretically derive a single loss function with no hyperparameters for the hash code from the probability distribution of the images. By generating hash codes that minimize this loss function, highly accurate image retrieval with probabilistic optimality is performed. We evaluate the performance of hashing using MNIST, CIFAR-10, SVHN and show that the proposed method outperforms the state-of-the-art hashing methods.
Mesh-based Camera Pairs Selection and Occlusion-Aware Masking for Mesh Refinement
Venue: arXiv cs.CV 2019
Year: 2019

Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, in this paper, to increase the quality of pairs selection, we exploit the 3D model (before the refinement) to compute five metrics: scene coverage, mutual image overlap, image resolution, camera parallax, and a new symmetry term. To improve the refinement robustness, we also propose an explicit method to manage occlusions, which may negatively affect the computation of the photometric error. The proposed method takes into account the depth of the model while computing the similarity measure and its gradient. We quantitatively and qualitatively validated our approach on publicly available datasets against state of the art reconstruction methods.
Blockchain in the Government Technology Fabric
Venue: arXiv cs.CY 2019
Year: 2019

Fuelled by the success (and hype) around cryptocurrencies, distributed ledger technologies (DLT), particularly blockchains, have gained a lot of attention from a wide spectrum of audience who perceive blockchains as a key to carry out business processes that have hitherto been cumbersome in a cost and time effective manner. Governments across the globe have responded to this promising but nascent technology differently - from being apathetic or adopting a wait-and-watch approach: letting the systems shape themselves, to creating regulatory sandboxes and sponsoring capacity building, or in some instances (arguably) over-regulating and attempting to put the blockchain genie back in the bottle. Possible government role spans across a spectrum: regulating crypto-currencies and initial coin offerings (ICO), formulating regulatory frameworks for managing the adoption of blockchains, particularly in critical infrastructure industries, facilitating capacity building, and finally, embracing blockchain technology in conducting the activities of the government itself - be it internally, or in using them to deliver public services. In this paper we survey the last, namely, the use of blockchain and associated distributed ledger technologies in the government technology (GovTech) stack, and discuss the merits and concerns associated with the existing initiatives and approaches.
Online Signature Verification Based on Writer Specific Feature Selection and Fuzzy Similarity Measure
Venue: arXiv cs.CV 2019
Year: 2019

Online Signature Verification (OSV) is a widely used biometric attribute for user behavioral characteristic verification in digital forensics. In this manuscript, owing to large intra-individual variability, a novel method for OSV based on an interval symbolic representation and a fuzzy similarity measure grounded on writer specific parameter selection is proposed. The two parameters, namely, writer specific acceptance threshold and optimal feature set to be used for authenticating the writer are selected based on minimum equal error rate (EER) attained during parameter fixation phase using the training signature samples. This is in variation to current techniques for OSV, which are primarily writer independent, in which a common set of features and acceptance threshold are chosen. To prove the robustness of our system, we have exhaustively assessed our system with four standard datasets i.e. MCYT-100 (DB1), MCYT-330 (DB2), SUSIG-Visual corpus and SVC-2004- Task2. Experimental outcome confirms the effectiveness of fuzzy similarity metric-based writer dependent parameter selection for OSV by achieving a lower error rate as compared to many recent and state-of-the art OSV models.
Une ou deux composantes ? La réponse de la diffusion en ondelettes
Venue: arXiv cs.SD 2019
Year: 2019

With the aim of constructing a biologically plausible model of machine listening, we study the representation of a multicomponent stationary signal by a wavelet scattering network. First, we show that renormalizing second-order nodes by their first-order parents gives a simple numerical criterion to establish whether two neighboring components will interfere psychoacoustically. Secondly, we generalize the `one or two components' framework to three sine waves or more, and show that a network of depth $M = \log_2 N$ suffices to characterize the relative amplitudes of the first $N$ terms in a Fourier series, while enjoying properties of invariance to frequency transposition and component-wise phase shifts.
3D Dense Separated Convolution Module for Volumetric Image Analysis
Venue: arXiv cs.CV 2019
Year: 2019

With the thriving of deep learning, 3D Convolutional Neural Networks have become a popular choice in volumetric image analysis due to their impressive 3D contexts mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data is often limited in biomedical tasks, a tradeoff has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of over-fitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network's representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.
Vehicle Shape and Color Classification Using Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a module of vehicle reidentification based on make/model and color classification. It could be used by the Automated Vehicular Surveillance (AVS) or by the fast analysis of video data. Many of problems, that are related to this topic, had to be addressed. In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set. We used deeper neural networks in our training. They showed a good classification accuracy. We show the results of make/model and color classification on controlled and video data set. We demonstrate with the help of a developed application the re-identification of vehicles on video images based on make/model and color classification. This work was partially funded under the grant.
Automatic Long-Term Deception Detection in Group Interaction Videos
Venue: arXiv cs.CV 2019
Year: 2019

Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/
Joint embedding of structure and features via graph convolutional networks
Venue: arXiv cs.LG 2019
Year: 2019

The creation of social ties is largely determined by the entangled effects of people's similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of the emergent network structure. We propose \emph{AN2VEC}, a node embedding method which ultimately aims at disentangling the information shared by the structure of a network and the features of its nodes. Building on the recent developments of Graph Convolutional Networks (GCN), we develop a multitask GCN Variational Autoencoder where different dimensions of the generated embeddings can be dedicated to encoding feature information, network structure, and shared feature-network information. We explore the interaction between these disentangled characters by comparing the embedding reconstruction performance to a baseline case where no shared information is extracted. We use synthetic datasets with different levels of interdependency between feature and network characters and show (i) that shallow embeddings relying on shared information perform better than the corresponding reference with unshared information, (ii) that this performance gap increases with the correlation between network and feature structure, and (iii) that our embedding is able to capture joint information of structure and features. Our method can be relevant for the analysis and prediction of any featured network structure ranging from online social systems to network medicine.
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
Venue: arXiv cs.CV 2019
Year: 2019

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.
GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
Venue: arXiv cs.CV 2019
Year: 2019

Exploiting fine-grained semantic features on point cloud is still challenging due to its irregular and sparse structure in a non-Euclidean space. Among existing studies, PointNet provides an efficient and promising approach to learn shape features directly on unordered 3D point cloud and has achieved competitive performance. However, local feature that is helpful towards better contextual learning is not considered. Meanwhile, attention mechanism shows efficiency in capturing node representation on graph-based data by attending over neighboring nodes. In this paper, we propose a novel neural network for point cloud, dubbed GAPNet, to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers. Firstly, we introduce a GAPLayer to learn attention features for each point by highlighting different attention weights on neighborhood. Secondly, in order to exploit sufficient features, a multi-head mechanism is employed to allow GAPLayer to aggregate different features from independent heads. Thirdly, we propose an attention pooling layer over neighbors to capture local signature aimed at enhancing network robustness. Finally, GAPNet applies stacked MLP layers to attention features and local signature to fully extract local geometric structures. The proposed GAPNet architecture is tested on the ModelNet40 and ShapeNet part datasets, and achieves state-of-the-art performance in both shape classification and part segmentation tasks.
Lightweight Network Architecture for Real-Time Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

In this work we present a new efficient approach to Human Action Recognition called Video Transformer Network (VTN). It leverages the latest advances in Computer Vision and Natural Language Processing and applies them to video understanding. The proposed method allows us to create lightweight CNN models that achieve high accuracy and real-time speed using just an RGB mono camera and general purpose CPU. Furthermore, we explain how to improve accuracy by distilling from multiple models with different modalities into a single model. We conduct a comparison with state-of-the-art methods and show that our approach performs on par with most of them on famous Action Recognition datasets. We benchmark the inference time of the models using the modern inference framework and argue that our approach compares favorably with other methods in terms of speed/accuracy trade-off, running at 56 FPS on CPU. The models and the training code are available.
Adaptation and learning over networks under subspace constraints -- Part I: Stability Analysis
Venue: arXiv cs.MA 2019
Year: 2019

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus optimization as a special case, and allows for more general task relatedness models such as smoothness. While such formulations can be solved via projected gradient descent, the resulting algorithm is not distributed. Starting from the centralized solution, we propose an iterative and distributed implementation of the projection step, which runs in parallel with the stochastic gradient descent update. We establish in this Part I of the work that, for small step-sizes $μ$, the proposed distributed adaptive strategy leads to small estimation errors on the order of $μ$. We examine in the accompanying Part II [2] the steady-state performance. The results will reveal explicitly the influence of the gradient noise, data characteristics, and subspace constraints, on the network performance. The results will also show that in the small step-size regime, the iterates generated by the distributed algorithm achieve the centralized steady-state performance.
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Venue: arXiv cs.LG 2019
Year: 2019

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility.
A Comparative Analysis of Distributional Term Representations for Author Profiling in Social Media
Venue: arXiv cs.CL 2019
Year: 2019

Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability.
Revisiting hard thresholding for DNN pruning
Venue: arXiv cs.LG 2019
Year: 2019

The most common method for DNN pruning is hard thresholding of network weights, followed by retraining to recover any lost accuracy. Recently developed smart pruning algorithms use the DNN response over the training set for a variety of cost functions to determine redundant network weights, leading to less accuracy degradation and possibly less retraining time. For experiments on the total pruning time (pruning time + retraining time) we show that hard thresholding followed by retraining remains the most efficient way of reducing the number of network parameters. However smart pruning algorithms still have advantages when retraining is not possible. In this context we propose a novel smart pruning algorithm based on difference of convex functions optimisation and show that it is often orders of magnitude faster than competing approaches while achieving the lowest classification accuracy degradation. Furthermore we investigate theoretically the effect of hard thresholding on DNN accuracy. We show that accuracy degradation increases with remaining network depth from the pruned layer. We also discover a link between the latent dimensionality of the training data manifold and network robustness to hard thresholding.
Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management
Venue: arXiv cs.CY 2019
Year: 2019

Data cooperatives with fiduciary obligations to members provide a promising direction for the empowerment of individuals through their own personal data. A data cooperative can manage, curate and protect access to the personal data of citizen members. Furthermore, the data cooperative can run internal analytics in order to obtain insights regarding the well-being of its members. Armed with these insights, the data cooperative would be in a good position to negotiate better services and discounts for its members. Credit Unions and similar institutions can provide a suitable realization of data cooperatives.