OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults
Venue: arXiv cs.AR 2019
Year: 2019

Voltage underscaling below the nominal level is an effective solution for improving energy efficiency in digital circuits, e.g., Field Programmable Gate Arrays (FPGAs). However, further undervolting below a safe voltage level and without accompanying frequency scaling leads to timing related faults, potentially undermining the energy savings. Through experimental voltage underscaling studies on commercial FPGAs, we observed that the rate of these faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To mitigate these faults, we evaluated the efficiency of the built-in Error-Correction Code (ECC) and observed that more than 90% of the faults are correctable and further 7% are detectable (but not correctable). This efficiency is the result of the single-bit type of these faults, which are then effectively covered by the Single-Error Correction and Double-Error Detection (SECDED) design of the built-in ECC. Finally, motivated by the above experimental observations, we evaluated an FPGA-based Neural Network (NN) accelerator under low-voltage operations, while built-in ECC is leveraged to mitigate undervolting faults and thus, prevent NN significant accuracy loss. In consequence, we achieve 40% of the BRAM power saving through undervolting below the minimum safe voltage level, with a negligible NN accuracy loss, thanks to the substantial fault coverage by the built-in ECC.
Shed More Light on Bloom Filter's Variants
Venue: arXiv cs.DS 2019
Year: 2019

Bloom Filter is a probabilistic membership data structure and it is excessively used data structure for membership query. Bloom Filter becomes the predominant data structure in approximate membership filtering. Bloom Filter extremely enhances the query response time, and the response time is very fast. Bloom filter (BF) is used to detect whether an element belongs to a given set or not. The Bloom Filter returns True Positive (TP), False Positive (FP), or True Negative (TN). The Bloom Filter is widely adapted in numerous areas to enhance the performance of a system. In this paper, we present a) in-depth insight on the Bloom Filter,and b) the prominent variants of the Bloom Filters.
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
Venue: arXiv cs.LG 2019
Year: 2019

Time series forecasting is one of the challenging problems for humankind. Traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses as well as to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M) where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.
Alternating Weak Automata from Universal Trees
Venue: arXiv cs.FL 2019
Year: 2019

An improved translation from alternating parity automata on infinite words to alternating weak automata is given. The blow-up of the number of states is related to the size of the smallest universal ordered trees and hence it is quasi-polynomial, and only polynomial if the asymptotic number of priorities is logarithmic in the number of states. This is an exponential improvement on the translation of Kupferman and Vardi (2001) and a quasi-polynomial improvement on the translation of Boker and Lehtinen (2018). Any slightly better such translation would (if---like all presently known such translations---it is efficiently constructive) lead to algorithms for solving parity games that are asymptotically faster in the worst case than the current state of the art (Calude, Jain, Khoussainov, Li, and Stephan, 2017; Jurdziński and Lazić, 2017; and Fearnley, Jain, Schewe, Stephan, and Wojtczak, 2017), and hence it would yield a significant breakthrough.
Autonomous Highway Driving using Deep Reinforcement Learning
Venue: arXiv cs.RO 2019
Year: 2019

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, formulating a rule based decision maker for selecting maneuvers may not be ideal. Similarly, it may not be effective to design an a-priori cost function and then solve the optimal control problem in real-time. In order to address these issues and to avoid peculiar behaviors when encountering unforeseen scenario, we propose a reinforcement learning (RL) based method, where the ego car, i.e., an autonomous vehicle, learns to make decisions by directly interacting with simulated traffic. The decision maker for AV is implemented as a deep neural network providing an action choice for a given system state. In a critical application such as driving, an RL agent without explicit notion of safety may not converge or it may need extremely large number of samples before finding a reliable policy. To best address the issue, this paper incorporates reinforcement learning with an additional short horizon safety check (SC). In a critical scenario, the safety check will also provide an alternate safe action to the agent provided if it exists. This leads to two novel contributions. First, it generalizes the states that could lead to undesirable "near-misses" or "collisions ". Second, inclusion of safety check can provide a safe and stable training environment. This significantly enhances learning efficiency without inhibiting meaningful exploration to ensure safe and optimal learned behavior. We demonstrate the performance of the developed algorithm in highway driving scenario where the trained AV encounters varying traffic density in a highway setting.
Joining Sound Event Detection and Localization Through Spatial Segregation
Venue: arXiv cs.SD 2019
Year: 2019

Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This work presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions.
Analysis of Distributed Congestion Control in Cellular Vehicle-to-everything Networks
Venue: arXiv cs.NI 2019
Year: 2019

Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in order to maintain the network performance required for safety-related applications. With the aid of our high-fidelity link-level network simulator, we investigate the feasibility of implementing the distributed congestion control algorithm specified in SAE J2945/1 standard on top of the C-V2X stack. We describe our implementation and evaluate the performance of transmission rate and range control mechanisms using relevant metrics. Additionally, we identify areas for potential design enhancements and further investigation.
3D Organ Shape Reconstruction from Topogram Images
Venue: arXiv cs.CV 2019
Year: 2019

Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up. However, addressing this problem typically requires performing computed tomography (CT) scanning and complicated postprocessing of the resulting scans using slice-by-slice techniques. In this paper, we show that 3D organ shape can be automatically predicted directly from topogram images, which are easier to acquire and have limited exposure to radiation during acquisition, compared to CT scans. We evaluate our approach on the challenging task of predicting liver shape using a generative model. We also demonstrate that our method can be combined with user annotations, such as a 2D mask, for improved prediction accuracy. We show compelling results on 3D liver shape reconstruction and volume estimation on 2129 CT scans.
Scalable Robust Adaptive Control from the System Level Perspective
Venue: arXiv cs.SY 2019
Year: 2019

We will present a new general framework for robust and adaptive control that allows for distributed and scalable learning and control of large systems of interconnected linear subsystems. The control method is demonstrated for a linear time-invariant system with bounded parameter uncertainties, disturbances and noise. The presented scheme continuously collects measurements to reduce the uncertainty about the system parameters and adapts dynamic robust controllers online in a stable and performance-improving way. A key enabler for our approach is choosing a time-varying dynamic controller implementation, inspired by recent work on System Level Synthesis. We leverage a new robustness result for this implementation to propose a general robust adaptive control algorithm. In particular, the algorithm allows us to impose communication and delay constraints on the controller implementation and is formulated as a sequence of robust optimization problems that can be solved in a distributed manner. The proposed control methodology performs particularly well when the interconnection between systems is sparse and the dynamics of local regions of subsystems depend only on a small number of parameters. As we will show on a five-dimensional exemplary chain-system, the algorithm can utilize system structure to efficiently learn and control the entire system while respecting communication and implementation constraints. Moreover, although current theoretical results require the assumption of small initial uncertainties to guarantee robustness, we will present simulations that show good closed-loop performance even in the case of large uncertainties, which suggests that this assumption is not critical for the presented technique and future work will focus on providing less conservative guarantees.
Group-centered framework towards a positive design of digital collaboration in global settings
Venue: arXiv cs.HC 2019
Year: 2019

Globally distributed groups require collaborative systems to support their work. Besides being able to support the teamwork, these systems also should promote well-being and maximize the human potential that leads to an engaging system and joyful experience. Designing such system is a significant challenge and requires a thorough understanding of group work. We used the field theory as a lens to view the essential aspects of group motivation and then utilized collaboration personas to analyze the elements of group work. We integrated well-being determinants as engagement factors to develop a group-centered framework for digital collaboration in a global setting. Based on the outcomes, we proposed a conceptual framework to design an engaging collaborative system and recommend system values that can be used to evaluate the system further
Enabling decentral collaborative innovation processes - a web based real time collaboration platform
Venue: arXiv cs.CY 2019
Year: 2019

The main goal of this paper is to define a collaborative innovation process as well as a supporting tool. It is motivated through the increasing competition on global markets and the resultant propagation of decentralized projects with a high demand of innovative collaboration in global contexts. It bases on a project accomplished by the author group. A detailed literature review and the action design research methodology of the project led to an enhanced process model for decentral collaborative innovation processes and a basic realization of a browser based real time tool to enable these processes. The initial evaluation in a practical distributed setting has shown that the created tool is a useful way to support collaborative innovation processes.
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
Venue: arXiv cs.LG 2019
Year: 2019

We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a "manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
Venue: arXiv cs.CV 2019
Year: 2019

We present a self-supervised task on point clouds, in order to learn meaningful point-wise features that encode local structure around each point. Our self-supervised network, named MortonNet, operates directly on unstructured/unordered point clouds. Using a multi-layer RNN, MortonNet predicts the next point in a point sequence created by a popular and fast Space Filling Curve, the Morton-order curve. The final RNN state (coined Morton feature) is versatile and can be used in generic 3D tasks on point clouds. In fact, we show how Morton features can be used to significantly improve performance (+3% for 2 popular semantic segmentation algorithms) in the task of semantic segmentation of point clouds on the challenging and large-scale S3DIS dataset. We also show how MortonNet trained on S3DIS transfers well to another large-scale dataset, vKITTI, leading to an improvement over state-of-the-art of 3.8%. Finally, we use Morton features to train a much simpler and more stable model for part segmentation in ShapeNet. Our results show how our self-supervised task results in features that are useful for 3D segmentation tasks, and generalize well to other datasets.
On the Estimation and Use of Statistical Modelling in Information Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

Several tasks in information retrieval (IR) rely on assumptions regarding the distribution of some property (such as term frequency) in the data being processed. This thesis argues that such distributional assumptions can lead to incorrect conclusions and proposes a statistically principled method for determining the "true" distribution. This thesis further applies this method to derive a new family of ranking models that adapt their computations to the statistics of the data being processed. Experimental evaluation shows results on par or better than multiple strong baselines on several TREC collections. Overall, this thesis concludes that distributional assumptions can be replaced with an effective, efficient and principled method for determining the "true" distribution and that using the "true" distribution can lead to improved retrieval performance.
Learning Semantic Embedding Spaces for Slicing Vegetables
Venue: arXiv cs.RO 2019
Year: 2019

In this work, we present an interaction-based approach to learn semantically rich representations for the task of slicing vegetables. Unlike previous approaches, we focus on object-centric representations and use auxiliary tasks to learn rich representations using a two-step process. First, we use simple auxiliary tasks, such as predicting the thickness of a cut slice, to learn an embedding space which captures object properties that are important for the task of slicing vegetables. In the second step, we use these learned latent embeddings to learn a forward model. Learning a forward model affords us to plan online in the latent embedding space and forces our model to improve its representations while performing the slicing task. To show the efficacy of our approach we perform experiments on two different vegetables: cucumbers and tomatoes. Our experimental evaluation shows that our method is able to capture important semantic properties for the slicing task, such as the thickness of the vegetable being cut. We further show that by using our learned forward model, we can plan for the task of vegetable slicing.
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches.In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.
A Comparative Study of Asynchronous Many-Tasking Runtimes: Cilk, Charm++, ParalleX and AM++
Venue: arXiv cs.DC 2019
Year: 2019

We evaluate and compare four contemporary and emerging runtimes for high-performance computing(HPC) applications: Cilk, Charm++, ParalleX and AM++. We compare along three bases: programming model, execution model and the implementation on an underlying machine model. The comparison study includes a survey of each runtime system's programming models, their corresponding execution models, their stated features, and performance and productivity goals. We first qualitatively compare these runtimes with programmability in mind. The differences in expressivity and programmability arising from their syntax and semantics are clearly enunciated through examples common to all runtimes. Then, the execution model of each runtime, which acts as a bridge between the programming model and the underlying machine model, is compared and contrasted to that of the others. We also evaluate four mature implementations of these runtimes, namely: Intel Cilk++, Charm++ 6.5.1, AM++ and HPX, that embody the principles dictated by these models. With the emergence of the next generation of supercomputers, it is imperative for parallel programming models to evolve and address the integral challenges introduced by the increasing scale. Rather than picking a winner out of these four models under consideration, we end with a discussion on lessons learned, and how such a study is instructive in the evolution of parallel programming frameworks to address the said challenges.
Palmprint image registration using convolutional neural networks and Hough transform
Venue: arXiv cs.CV 2019
Year: 2019

Minutia-based palmprint recognition systems has got lots of interest in last two decades. Due to the large number of minutiae in a palmprint, approximately 1000 minutiae, the matching process is time consuming which makes it unpractical for real time applications. One way to address this issue is aligning all palmprint images to a reference image and bringing them to a same coordinate system. Bringing all palmprint images to a same coordinate system, results in fewer computations during minutia matching. In this paper, using convolutional neural network (CNN) and generalized Hough transform (GHT), we propose a new method to register palmprint images accurately. This method, finds the corresponding rotation and displacement (in both x and y direction) between the palmprint and a reference image. Exact palmprint registration can enhance the speed and the accuracy of matching process. Proposed method is capable of distinguishing between left and right palmprint automatically which helps to speed up the matching process. Furthermore, designed structure of CNN in registration stage, gives us the segmented palmprint image from background which is a pre-processing step for minutia extraction. The proposed registration method followed by minutia-cylinder code (MCC) matching algorithm has been evaluated on the THUPALMLAB database, and the results show the superiority of our algorithm over most of the state-of-the-art algorithms.
Passive Head-Mounted Display Music-Listening EEG dataset
Venue: arXiv cs.HC 2019
Year: 2019

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2617084 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (Cattan and al, 2018). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.PHMDML.EEG.2017-GIPSA. The ID of this dataset is PHMDML.EEG.2017-GIPSA.