OpenRaiser/Intern-Atlas huggingface dataset:
A Metamodel of the Telemac Errors
Venue: arXiv cs.CE 2019
Year: 2019

A Telemac study is a computationally intensive application for the real cases and in the context of quantifying or optimizing uncertainties, the running times can be too long. This paper is an example of an approximation of the Telemac results by a more abstract but significantly faster model. It shows how a metamodel can be easily built with low computational costs, and how it can help to understand and improve some global results of Telemac. I. INTRODUCTION Many sources of uncertainty lie in the real-world problems. Telemac as any model (i.e. approximation of reality) is error prone since uncertainties appear in the initial or boundary conditions, the system parameters, the modelling simplification or the numerical calculations themselves. Therefore, it is difficult to say with confidence if the design of a Telemac model has met all the requirements to be optimal. Calibration consists of tuning the model parameters so that the results are in better agreement with a set of observations. This phase is crucial before any further study can be conducted by avoiding a meaningless analysis or prediction based on false or too inaccurate results. This paper presents a statistical calibration of a Telemac 2D model (Gironde Estuary in France) with the learning of Telemac errors by a metamodel (i.e. a model of the simulation errors) to make the best use of limited observations data over a short time period. The metamodel here is a simplified version of Telemac behaving the same for all the locations where observation points are available. If the metamodel is correct, it will be able to compute as Telemac would do but with a highly reduced computational cost.
Impact-Aware Online Motion Planning for Fully-Actuated Bipedal Robot Walking
Venue: arXiv cs.RO 2019
Year: 2019

The ability to track a general walking path with specific timing is crucial to the operational safety and reliability of bipedal robots for avoiding dynamic obstacles, such as pedestrians, in complex environments. This paper introduces an online, full-body motion planner that generates the desired impact-aware motion for fully-actuated bipedal robotic walking. The main novelty of the proposed planner lies in its capability of producing desired motions in real-time that respect the discrete impact dynamics and the desired impact timing. To derive the proposed planner, a full-order hybrid dynamic model of fully-actuated bipedal robotic walking is presented, including both continuous dynamics and discrete lading impacts. Next, the proposed impact-aware online motion planner is introduced. Finally, simulation results of a 3-D bipedal robot are provided to confirm the effectiveness of the proposed online impact-aware planner. The online planner is capable of generating full-body motion of one walking step within 0.6 second, which is shorter than a typical bipedal walking step.
Fast Independent Vector Extraction by Iterative SINR Maximization
Venue: arXiv cs.SD 2019
Year: 2019

We propose fast independent vector extraction (FIVE), a new algorithm that blindly extracts a single non-Gaussian source from a Gaussian background. The algorithm iteratively computes beamforming weights maximizing the signal-to-interference-and-noise ratio for an approximate noise covariance matrix. We demonstrate that this procedure minimizes the negative log-likelihood of the input data according to a well-defined probabilistic model. The minimization is carried out via the auxiliary function technique whereas, unlike related methods, the auxiliary function is globally minimized at every iteration. Numerical experiments are carried out to assess the performance of FIVE. We find that it is vastly superior to competing methods in terms of convergence speed, and has high potential for real-time applications.
A Useful Taxonomy for Adversarial Robustness of Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial attacks and defenses are currently active areas of research for the deep learning community. A recent review paper divided the defense approaches into three categories; gradient masking, robust optimization, and adversarial example detection. We divide gradient masking and robust optimization differently: (1) increasing intra-class compactness and inter-class separation of the feature vectors improves adversarial robustness, and (2) marginalization or removal of non-robust image features also improves adversarial robustness. By reframing these topics differently, we provide a fresh perspective that provides insight into the underlying factors that enable training more robust networks and can help inspire novel solutions. In addition, there are several papers in the literature of adversarial defenses that claim there is a cost for adversarial robustness, or a trade-off between robustness and accuracy but, under this proposed taxonomy, we hypothesis that this is not universal. We follow up on our taxonomy with several challenges to the deep learning research community that builds on the connections and insights in this paper.
On decentralized oracles for data availability
Venue: arXiv cs.CR 2019
Year: 2019

Nakamoto consensus, the protocol underlying Bitcoin, has the potential to secure a new class of systems which agree on non-mathematical truths. As an example of this capability, we propose a design for a trustless, data availability oracle. This exposition reduces the problem of determining whether or not a registered datum is publicly available to the problem of constructing a network in which either almost all nodes can download a given datum, or almost none of them can.
KnowIT VQA: Answering Knowledge-Based Questions about Videos
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel video understanding task by fusing knowledge-based and video question answering. First, we introduce KnowIT VQA, a video dataset with 24,282 human-generated question-answer pairs about a popular sitcom. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered. Second, we propose a video understanding model by combining the visual and textual video content with specific knowledge about the show. Our main findings are: (i) the incorporation of knowledge produces outstanding improvements for VQA in video, and (ii) the performance on KnowIT VQA still lags well behind human accuracy, indicating its usefulness for studying current video modelling limitations.
A Novel Approach for Automatic Bengali Question Answering System using Semantic Similarity Analysis
Venue: arXiv cs.IR 2019
Year: 2019

Finding the semantically accurate answer is one of the key challenges in advanced searching. In contrast to keyword-based searching, the meaning of a question or query is important here and answers are ranked according to relevance. It is very natural that there is almost no common word between the question sentence and the answer sentence. In this paper, an approach is described to find out the semantically relevant answers in the Bengali dataset. In the first part of the algorithm, a set of statistical parameters like frequency, index, part-of-speech (POS), etc. is matched between a question and the probable answers. In the second phase, entropy and similarity are calculated in different modules. Finally, a sense score is generated to rank the answers. The algorithm is tested on a repository containing a total of 275000 sentences. This Bengali repository is a product of Technology Development for Indian Languages (TDIL) project sponsored by Govt. of India and provided by the Language Research Unit of Indian Statistical Institute, Kolkata. The shallow parser, developed by the LTRC group of IIIT Hyderabad is used for POS tagging. The actual answer is ranked as 1st in 82.3% cases. The actual answer is ranked within 1st to 5th in 90.0% cases. The accuracy of the system is coming as 97.32% and precision of the system is coming as 98.14% using confusion matrix. The challenges and pitfalls of the work are reported at last in this paper.
Diversifying Database Activity Monitoring with Bandits
Venue: arXiv cs.LG 2019
Year: 2019

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity streams and operating costs, such systems are restricted to examining only a sample of the activity. Current solutions use policies, manually crafted by experts, to decide which transactions to monitor and log. This limits the diversity of the data collected. Bandit algorithms, which use reward functions as the basis for optimization while adding diversity to the recommended set, have gained increased attention in recommendation systems for improving diversity. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit (MAB) problem and present a novel algorithm, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection tasks using a simulated dataset. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task and maximizing population coverage without decreasing the quality in terms of issuing alerts about events.
Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems
Venue: arXiv cs.LG 2019
Year: 2019

Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks. However, very few works have considered how to interpolate between these no- to high-data regimes. In particular, how can one use the availability of a small amount of data (even $5-25$ examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well? In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest. Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization. Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than $1 \%$ of the data needed to train a generative model.
Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links
Venue: arXiv cs.IT 2019
Year: 2019

Millimeter wave (mmWave) vehicular channels are highly dynamic, and the communication link needs to be reconfigured frequently. In this work, we propose to use a passive radar receiver at the roadside unit to reduce the training overhead of establishing an mmWave communication link. Specifically, the passive radar will tap the transmissions from the automotive radars of the vehicles on the road. The spatial covariance of the received radar signals will be estimated and used to establish the communication link. We propose a simplified radar receiver that does not require the transmitted waveform as a reference. To leverage the radar information for beamforming, the radar azimuth power spectrum (APS) and the communication APS should be similar. We outline a radar covariance correction strategy to increase the similarity between the radar and communication APS. We also propose a metric to compare the similarity of the radar and communication APS that has a connection with the achievable rate. We present simulation results based on ray-tracing data. The results show that: (i) covariance correction improves the similarity of radar and communication APS, and (ii) the radar-assisted strategy significantly reduces the training overhead, being particularly useful in non-line-of-sight scenarios.
Variational Predictive Information Bottleneck
Venue: arXiv cs.LG 2019
Year: 2019

In classic papers, Zellner demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most modern inference procedures and suggests novel ones.
Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing
Venue: arXiv cs.GR 2019
Year: 2019

High fidelity reproductions of paintings provide new opportunities to museums in preserving and providing access to cultural heritage. This paper presents an integrated system which is able to capture and fabricate color, topography and gloss of a painting, of which gloss capturing forms the most important contribution. A 3D imaging system, using fringe-encoded stereo imaging, is extended to capture spatially-varying gloss, utilizing specular reflectance polarization. The gloss is measured by sampling the specular reflection around Brewster's angle, where these reflections are effectively polarized, and can be separated from the unpolarized, diffuse reflectance. Off-center gloss measurements are calibrated relative to the center measurement. Off-specular gloss measurements, following from local variation of the surface normal, are masked based on the height map and corrected. Shadowed regions, caused by the 3D relief, are treated similarly. The area of a single capture is approximately 180x90mm at a resolution of 25x25micron. Aligned color, height, and gloss tiles are stitched together, registering overlapping color areas. These maps are inputs for a 3D printer. Two paintings were reproduced to verify the effectiveness and efficiency of the proposed system. One painting was scanned four times, consecutively rotated by 90 degrees, to evaluate the influence of the scanning system geometric configuration on the gloss measurement. Experimental results show that the method is sufficiently fast for practical application. The results can well be used for the purpose of physical reproduction and other applications needing first-order estimates of the appearance. Our method to extend appearance scanning with gloss measurements is a valuable addition in the quest for realistic reproductions, in terms of its practical applicability and its perceptual added value, when added to color and topography.
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
Venue: arXiv cs.LG 2019
Year: 2019

We propose improving the privacy properties of a dataset by publishing only a strategically chosen "core-set" of the data containing a subset of the instances. The core-set allows strong performance on primary tasks, but forces poor performance on unwanted tasks. We give methods for both linear models and neural networks and demonstrate their efficacy on data.
On the Complexity of Asynchronous Freezing Cellular Automata
Venue: arXiv cs.CC 2019
Year: 2019

In this paper we study the family of freezing cellular automata (FCA) in the context of asynchronous updating schemes. A cellular automaton is called freezing if there exists an order of its states, and the transitions are only allowed to go from a lower to a higher state. A cellular automaton is asynchronous if at each time-step only one cell is updated. Given configuration, we say that a cell is unstable if there exists a sequential updating scheme that changes its state. In this context, we define the problem AsyncUnstability, which consists in deciding if a cell is unstable or not. In general AsyncUnstability is in NP, and we study in which cases we can solve the problem by a more efficient algorithm. We begin showing that AsyncUnstability is in NL for any one-dimensional FCA. Then we focus on the family of life-like freezing CA (LFCA), which is a family of two-dimensional two-state FCA that generalize the freezing version of the game of life, known as life without death. We study the complexity of AsyncUnstability for all LFCA in the triangular and square grids, showing that almost all of them can be solved in NC, except for one rule for which the problem is NP-complete.
User Association and Path Planning for UAV-Aided Mobile Edge Computing with Energy Restriction
Venue: arXiv cs.IT 2019
Year: 2019

Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.
ROBO: Robust, Fully Neural Object Detection for Robot Soccer
Venue: arXiv cs.CV 2019
Year: 2019

Deep Learning has become exceptionally popular in the last few years due to its success in computer vision and other fields of AI. However, deep neural networks are computationally expensive, which limits their application in low power embedded systems, such as mobile robots. In this paper, an efficient neural network architecture is proposed for the problem of detecting relevant objects in robot soccer environments. The ROBO model's increase in efficiency is achieved by exploiting the peculiarities of the environment. Compared to the state-of-the-art Tiny YOLO model, the proposed network provides approximately 35 times decrease in run time, while achieving superior average precision, although at the cost of slightly worse localization accuracy.
Learning Hierarchical Control for Robust In-Hand Manipulation
Venue: arXiv cs.RO 2019
Year: 2019

Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. To address these challenges, the majority of prior work has either focused on model-based, low-level controllers or on model-free deep reinforcement learning that each have their own limitations. We propose a hierarchical method that relies on traditional, model-based controllers on the low-level and learned policies on the mid-level. The low-level controllers can robustly execute different manipulation primitives (reposing, sliding, flipping). The mid-level policy orchestrates these primitives. We extensively evaluate our approach in simulation with a 3-fingered hand that controls three degrees of freedom of elongated objects. We show that our approach can move objects between almost all the possible poses in the workspace while keeping them firmly grasped. We also show that our approach is robust to inaccuracies in the object models and to observation noise. Finally, we show how our approach generalizes to objects of other shapes.
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Venue: arXiv cs.LG 2019
Year: 2019

Human beings are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed as \emph{Catastrophic Forgetting}, is one of the major roadblocks that prevent deep neural networks from achieving human-level artificial intelligence. Several research efforts, e.g. \emph{Lifelong} or \emph{Continual} learning algorithms, have been proposed to tackle this problem. However, they either suffer from an accumulating drop in performance as the task sequence grows longer, or require to store an excessive amount of model parameters for historical memory, or cannot obtain competitive performance on the new tasks. In this paper, we focus on the incremental multi-task image classification scenario. Inspired by the learning process of human students, where they usually decompose complex tasks into easier goals, we propose an adversarial feature alignment method to avoid catastrophic forgetting. In our design, both the low-level visual features and high-level semantic features serve as soft targets and guide the training process in multiple stages, which provide sufficient supervised information of the old tasks and help to reduce forgetting. Due to the knowledge distillation and regularization phenomenons, the proposed method gains even better performance than finetuning on the new tasks, which makes it stand out from other methods. Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
Venue: arXiv cs.CV 2019
Year: 2019

Zero-shot learning (ZSL) has received extensive attention recently especially in areas of fine-grained object recognition, retrieval, and image captioning. Due to the complete lack of training samples and high requirement of defense transferability, the ZSL model learned is particularly vulnerable against adversarial attacks. Recent work also showed adversarially robust generalization requires more data. This may significantly affect the robustness of ZSL. However, very few efforts have been devoted towards this direction. In this paper, we take an initial attempt, and propose a generic formulation to provide a systematical solution (named ATZSL) for learning a robust ZSL model. It is capable of achieving better generalization on various adversarial objects recognition while only losing a negligible performance on clean images for unseen classes, by casting ZSL into a min-max optimization problem. To address it, we design a defensive relation prediction network, which can bridge the seen and unseen class domains via attributes to generalize prediction and defense strategy. Additionally, our framework can be extended to deal with the poisoned scenario of unseen class attributes. An extensive group of experiments are then presented, demonstrating that ATZSL obtains remarkably more favorable trade-off between model transferability and robustness, over currently available alternatives under various settings.
Wasserstein distances for evaluating cross-lingual embeddings
Venue: arXiv cs.CL 2019
Year: 2019

Word embeddings are high dimensional vector representations of words that capture their semantic similarity in the vector space. There exist several algorithms for learning such embeddings both for a single language as well as for several languages jointly. In this work we propose to evaluate collections of embeddings by adapting downstream natural language tasks to the optimal transport framework. We show how the family of Wasserstein distances can be used to solve cross-lingual document retrieval and the cross-lingual document classification problems. We argue on the advantages of this approach compared to more traditional evaluation methods of embeddings like bilingual lexical induction. Our experimental results suggest that using Wasserstein distances on these problems out-performs several strong baselines and performs on par with state-of-the-art models.