pwc-archive/papers-with-abstracts huggingface dataset:
LS-CAT: A Large-Scale CUDA AutoTuning Dataset
Authors: Lars Bjertnes, Jacob O. Tørring, Anne C. Elster
Date: 2021-03-26 00:00:00
Tasks: GPU

The effectiveness of Machine Learning (ML) methods depend on access to large suitable datasets. In this article, we present how we build the LS-CAT (Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of training NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused on linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5 028 536 associated runtimes, with different combinations of kernels, block sizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980 and Nvidia T4 systems. This information creates a foundation upon which NLP-based models can find correlations between source-code features and optimal choice of thread block sizes. There are several results that can be drawn out of our LS-CAT database. E.g., our experimental results show that an optimal choice in thread block size can gain an average of 6% for the average case. We thus also analyze how much performance increase can be achieved in general, finding that in 10% of the cases more than 20% performance increase can be achieved by using the optimal block. A description of current and future work is also included.
Invertible Grayscale via Dual Features Ensemble
Authors: TAIZHONG YE, Yong Du, JUNJIE DENG, AND SHENGFENG HE
Date: 2020-05-22 00:00:00
Tasks: Colorization, Image Colorization

Grayscale image colorization is known as an ill-posed problem because of the imbalanced
matching between intensity and color values. Even given prior hints about the original color image, existing
colorization methods cannot recover the original color image from grayscale faithfully. In this paper,
we propose to embed color information into an invertible grayscale, such that it can be easily recovered
to the original color. However, a vanilla encoding-decoding network cannot produce rich representations
of color information and thus the reconstruction quality is limited. Moreover, due to the neglect of the
discrimination of color information, it cannot embed color information into visually inconspicuous patterns
located in the grayscale. In this paper, we propose a novel color-encoding schema, dual features ensemble
network (DFENet), for the effective embedding and faithfully reconstruction. In particular, we complement
the residual representations with dense representations, to integrate the ability of local residual learning
and local feature fusion. Furthermore, we propose an element-wise self-attention mechanism that highlights
the discriminative features and suppresses the redundant ones generated from the dual path module.
Extensive experiments demonstrate the proposed method outperforms state-of-the-art methods in terms of
reconstruction quality as well as the similarity between the generated invertible grayscale and its groundtruth.
Pose-Guided Knowledge Transfer for Object Part Segmentation
Authors: Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, David J. Crandall
Date: 2020-04-01 00:00:00
Tasks: Object, Segmentation, Semantic Part Detection, Transfer Learning

Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive. Recently, Fang et al. augmented object part segmentation datasets by using keypoint locations as weak supervision to transfer a source object instance’s part annotations to an unlabeled target object. We show that while their approach works well when the source and target objects have clearly visible keypoints, it often fails for severely articulated poses. Also, their model does not generalize well across multiple object classes, even if they are very similar. In this paper, we propose and evaluate a new model for transferring part segmentations using keypoints, even for complex object poses and across different object classes.
Spatio-Temporal Stability Analysis in Satellite Image Times Series
Authors: Mohamed Chelali, Camille Kurtz, Anne Puissant, Nicole Vincent
Date: 2020-10-09 00:00:00
Tasks: Feature Engineering, Time Series, Time Series Analysis, Video Summarization

Satellite Image Time Series (SITS) provide valuable information for the study of the Earth’s surface. In particular, this information may improve the comprehension, the understanding and the mapping of many phenomenons such as earthquake monitoring, urban sprawling or agricultural practices. In this article, we propose a method to define new spatio-temporal features from SITS based on the measure of the temporal stability. The proposed method is based on a compression algorithm named Run Length Encoding leading to a novel image representation from which stability features can be measured. Such features can then be used in several applications such as SITS summarizing, to make easier the interpretation of such a data-cube, or the classification of spatio-temporal patterns. The preliminary results obtained from a series of 50 Sentinel-2 optical images highlight the interest of our approach in a remote sensing application.
Parallel Model Exploration for Tumor Treatment Simulations
Authors: Charilaos Akasiadis, Miguel Ponce-de-Leon, Arnau Montagud, Evangelos Michelioudakis, Alexia Atsidakou, Elias Alevizos, Alexander Artikis, Alfonso Valencia, Georgios Paliouras
Date: 2021-03-25 00:00:00
Tasks: Computational Efficiency, Drug Discovery, Heuristic Search, model

Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.
Mesh Denoising with Facet Graph Convolutions
Authors: Matthieu Armando, Jean-Sébastien Franco, Edmond Boyer
Date: 2020-12-15 00:00:00
Tasks: Denoising

We examine the problem of mesh denoising, which consists of removing noise from corrupted 3D meshes while preserving existing geometric features. Most mesh denoising methods require a lot of mesh-specific parameter fine-tuning, to account for specific features and noise types. In recent years, data-driven methods have demonstrated their robustness and effectiveness with respect to noise and feature properties on a wide variety of geometry and image problems. Most existing mesh denoising methods still use hand-crafted features, and locally denoise facets rather than examine the mesh globally. In this work, we propose the use of a fully end-to-end learning strategy based on graph convolutions, where meaningful features are learned directly by our network. It operates on a graph of facets, directly on the existing topology of the mesh, without resampling, and follows a multi-scale design to extract geometric features at different resolution levels. Similar to most recent pipelines, given a noisy mesh, we first denoise face normals with our novel approach, then update vertex positions accordingly. Our method performs significantly better than the current state-of-the-art learning-based methods. Additionally, we show that it can be trained on noisy data, without explicit correspondence between noisy and ground-truth facets. We also propose a multi-scale denoising strategy, better suited to correct noise with a low spatial frequency.
Mesh Denoising via Cascaded Normal Regression
Authors: Peng-Shuai Wang, Yang Liu, Xin Tong
Date: 2016-11-15 00:00:00
Tasks: Denoising, regression

We present a data-driven approach for mesh denoising. Our key idea is to formulate the denoising process with cascaded non-linear regression functions and learn them from a set of noisy meshes and their ground-truth counterparts. Each regression function infers the normal of a denoised output mesh facet from geometry features extracted from its neighborhood facets on the input mesh and sends the result as the input of the next regression function. Specifically, we develop a filtered facet normal descriptor (FND) for modeling the geometry features around each facet on the noisy mesh and model a regression function with neural networks for mapping the FNDs to the facet normals of the denoised mesh. To handle meshes with different geometry features and reduce the training difficulty, we cluster the input mesh facets according to their FNDs and train neural networks for each cluster separately in an offline learning stage. At runtime, our method applies the learned cascaded regression functions to a noisy input mesh and reconstructs the denoised mesh from the output facet normals. Our method learns the non-linear denoising process from the training data and makes no specific assumptions about the noise distribution and geometry features in the input. The runtime denoising process is fully automatic for different input meshes. Our method can be easily adapted to meshes with arbitrary noise patterns by training a dedicated regression scheme with mesh data and the particular noise pattern. We evaluate our method on meshes with both synthetic and real scanned noise, and compare it to other mesh denoising algorithms. Results demonstrate that our method outperforms the state-of-the-art mesh denoising methods and successfully removes different kinds of noise for meshes with various geometry features.
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Authors: Curtis G. Northcutt, Anish Athalye, Jonas Mueller
Date: 2021-03-26 00:00:00
Tasks: BIG-bench Machine Learning

We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results. Errors in test sets are numerous and widespread: we estimate an average of at least 3.3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set. Putative label errors are identified using confident learning algorithms and then human-validated via crowdsourcing (51% of the algorithmically-flagged candidates are indeed erroneously labeled, on average across the datasets). Traditionally, machine learning practitioners choose which model to deploy based on test accuracy - our findings advise caution here, proposing that judging models over correctly labeled test sets may be more useful, especially for noisy real-world datasets. Surprisingly, we find that lower capacity models may be practically more useful than higher capacity models in real-world datasets with high proportions of erroneously labeled data. For example, on ImageNet with corrected labels: ResNet-18 outperforms ResNet-50 if the prevalence of originally mislabeled test examples increases by just 6%. On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5%. Test set errors across the 10 datasets can be viewed at https://labelerrors.com and all label errors can be reproduced by https://github.com/cleanlab/label-errors.
Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum
Authors: Pablo Krupa, Jose Camara, Ignacio Alvarado, Daniel Limon, Teodoro Alamo
Date: 2021-03-26 00:00:00
Tasks: Model Predictive Control

This article presents the real-time implementation of the model predictive control for tracking formulation to control a two-wheeled inverted pendulum robot. This formulation offers several advantages over standard MPC formulations at the expense of the addition of a small number of decision variables, which complicates the inner structure of the matrices of the optimization problem. We implement a sparse solver, based on an extension of the alternating direction method of multipliers, in the system's embedded hardware. The results indicate that the solver is suitable for controlling a real system with sample times in the range of milliseconds using current, readily-available hardware.
A cloud-IoT platform for passive radio sensing: challenges and application case studies
Authors: Sanaz Kianoush, Muneeba Raja, Stefano Savazzi, Stephan Sigg
Date: 2021-03-26 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation (ontology models). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smart-space environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.
A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments
Authors: Sanaz Kianoush, Stefano Savazzi, Manuel Beschi, Stephan Sigg, Vittorio Rampa
Date: 2021-03-26 00:00:00

Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the paper targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal pre-processing, feature extraction, data distribution and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely a multi-antenna WiFi installation (2.4-5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8-13 {\mu}m). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human--robot distance must be guaranteed considering latency and detection uncertainties.
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
Authors: Luca Bortolussi, Francesca Cairoli, Ginevra Carbone, Francesco Franchina, Enrico Regolin
Date: 2020-09-04 00:00:00

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.
Asset Selection via Correlation Blockmodel Clustering
Authors: Wenpin Tang, Xiao Xu, Xun Yu Zhou
Date: 2021-03-26 00:00:00
Tasks: Clustering

We aim to cluster financial assets in order to identify a small set of stocks to approximate the level of diversification of the whole universe of stocks. We develop a data-driven approach to clustering based on a correlation blockmodel in which assets in the same cluster are highly correlated with each other and, at the same time, have the same correlations with all other assets. We devise an algorithm to detect the clusters, with theoretical analysis and practical guidance. Finally, we conduct an empirical analysis to verify the performance of the algorithm.
The Effect of Super-spreader Events in Epidemics
Authors: Harisankar Ramaswamy, Assad A Oberai, Mitul Luhar, Yannis C Yortsos
Date: 2021-03-03 00:00:00

The spread of infectious epidemics is often accelerated by super-spreader events. Understanding their effect is important, particularly in the context of standard epidemiological models, which require estimates for parameters such as $R_0$. In this letter, we show that the effective value of $R_0$ in super-spreader situations is significantly large, of the order of hundreds, suggesting a delta-function-like behavior during the event. Use of a well-mixed room model supports these findings. They elucidate infection kinetic modeling in enclosed environments, which differ from the standard SIR model, and provide expressions for $R_0$ in terms of physical and operational parameters. The overall impact of super-spreader events can be significant, depending on the state of the epidemic and how the infections generated by the event subsequently spread in the community.
Legendre transformation and information geometry for the maximum entropy theory of ecology
Authors: Pedro Pessoa
Date: 2021-03-20 00:00:00

Here I investigate some mathematical aspects of the maximum entropy theory of ecology (METE). In particular I address the geometrical structure of METE endowed by information geometry. As novel results, the macrostate entropy is calculated analytically by the Legendre transformation of the log-normalizer in METE. This result allows for the calculation of the metric terms in the information geometry arising from METE and, by consequence, the covariance matrix between METE variables.
Graph Transformation for Enzymatic Mechanisms
Authors: Jakob L. Andersen, Rolf Fagerberg, Christoph Flamm, Walter Fontana, Juraj Kolčák, Christophe V. F. P. Laurent, Daniel Merkle, Nikolai Nøjaard
Date: 2021-02-05 00:00:00

Motivation: The design of enzymes is as challenging as it is consequential for making chemical synthesis in medical and industrial applications more efficient, cost-effective and environmentally friendly. While several aspects of this complex problem are computationally assisted, the drafting of catalytic mechanisms, i.e. the specification of the chemical steps-and hence intermediate states-that the enzyme is meant to implement, is largely left to human expertise. The ability to capture specific chemistries of multi-step catalysis in a fashion that enables its computational construction and design is therefore highly desirable and would equally impact the elucidation of existing enzymatic reactions whose mechanisms are unknown. Results: We use the mathematical framework of graph transformation to express the distinction between rules and reactions in chemistry. We derive about 1000 rules for amino acid side chain chemistry from the M-CSA database, a curated repository of enzymatic mechanisms. Using graph transformation we are able to propose hundreds of hypothetical catalytic mechanisms for a large number of unrelated reactions in the Rhea database. We analyze these mechanisms to find that they combine in chemically sound fashion individual steps from a variety of known multi-step mechanisms, showing that plausible novel mechanisms for catalysis can be constructed computationally.
A novel S-shape based NURBS interpolation with acc-jerk- Continuity and round-off error elimination
Authors: Yifei Hu, Xin Jiang, Guanying Huo, Cheng Su, Bolun Wang, Hexiong Li, Zhiming Zheng
Date: 2021-03-26 00:00:00
Tasks: Scheduling

Feedrate scheduling is a key step in computer numerical control (CNC) machining, as it has a close relationship with machining time and surface quality, and has now become a hot issue in industry and academia. To reduce high chord errors and round-off errors, and generate continuous velocity, acceleration, and jerk profile of parametric interpolation, a novel and complete S-shape based feedrate scheduling algorithm is presented in this paper. The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module. The bidirectional scanning module with the limitations of chord error, normal acceleration/jerk and command feedrate aims to guarantee the continuity of the feed rate at the junctions between successive NURBS sub-curves. After the NURBS sub-curves have been classified into two cases, the velocity scheduling module firstly calculates the actual maximum federate, and then generates the feed rate profiles of all NURBS sub-curves according to our velocity scheduling function. Later, the round-off error elimination module is proposed to make the total interpolating time become an integer multiple of the interpolation period, which leads to the elimination of round-off errors. Finally, benchmarks are conducted to verify the applicability of the proposed method compared with some other methods.