pwc-archive/papers-with-abstracts huggingface dataset:
Situational Coverage and Rate Distribution Maps for 5G V2X Systems Using Ray-Tracing
Authors: Elyes Balti
Date: 2021-05-03 00:00:00
Tasks: Autonomous Vehicles

Millimeter wave (mmWave) is a practical solution to provide high data rate for vehicle-to-everything (V2X) communications. This enables the future autonomous vehicles to exchange big data with the base stations (BSs) such as the velocity, and the location to enhance the safety for the advanced driving assistance system (ADAS). To achieve this goal, we propose to develop a situational rate map to characterize the distribution of the rates achieved between the BSs and a uniform grid of vehicles. In this context, we consider a mmWave 5G cellular system with two physical structures which are the analog-only beamforming and hybrid precoding with limited feedback to investigate the rate distribution for single and multiuser scenarios. We will use the Ray-Tracing tool to construct the simulation environment and generate the channels between the BSs and the grid of vehicles. Finally, we will study the effects of the carrier frequency, the bandwidth, the BSs deployment, the blockage, the codebook, the physical architectures and the number of served users on the rate distribution maps. Moreover, we will present the rate statistics to evaluate the coverage of served users for certain services requirements and for various road shapes such as corners, intersections and straightways.
Linear correlation between active and resistive stresses informs on force generation and stress transmission in adherent cells
Authors: Hélène Delanoë-Ayari, Nicolas Bouchonville, Marie Courçon, Alice Nicolas
Date: 2021-05-12 00:00:00

Animal cells are active, contractile objects. While bioassays address the molecular characterization of cell contractility, the mechanical characterization of the active forces in cells remains challenging. Here by confronting theoretical analysis and experiments, we calculated both the resistive and the active components of the intracellular stresses that build up following cell adhesion. We obtained a linear relationship between the divergence of the resistive stress and the traction forces, which we show is the consequence of the cell adhering and applying forces on the surface only through very localized adhesion points (whose size is inferior to our best resolution, of 400 nm). This entails that there is no measurable forces outside of these active point sources, and also that the resistive and active stresses inside cells are proportional.
MomentClosure.jl: automated moment closure approximations in Julia
Authors: Augustinas Sukys, Ramon Grima
Date: 2021-05-12 00:00:00

MomentClosure.jl is a Julia package providing automated derivation of the time-evolution equations of the moments of molecule numbers for virtually any chemical reaction network using a wide range of moment closure approximations. It extends the capabilities of modelling stochastic biochemical systems in Julia and can be particularly useful when exact analytic solutions of the chemical master equation are unavailable and when Monte Carlo simulations are computationally expensive. MomentClosure.jl is freely accessible under the MIT license. Source code and documentation are available at https://github.com/augustinas1/MomentClosure.jl
Coupling from the Past for the Stochastic Simulation of Chemical Reaction Networks
Authors: J. N. Mueller, J. N. Corcoran
Date: 2021-05-11 00:00:00

Chemical reaction networks (CRNs) are fundamental computational models used to study the behavior of chemical reactions in well-mixed solutions. They have been used extensively to model a broad range of biological systems, and are primarily used when the more traditional model of deterministic continuous mass action kinetics is invalid due to small molecular counts. We present a perfect sampling algorithm to draw error-free samples from the stationary distributions of stochastic models for coupled, linear chemical reaction networks. The state spaces of such networks are given by all permissible combinations of molecular counts for each chemical species, and thereby grow exponentially with the numbers of species in the network. To avoid simulations involving large numbers of states, we propose a subset of chemical species such that coupling of paths started from these states guarantee coupling of paths started from all states in the state space and we show for the well-known Reversible Michaelis-Menten model that the subset does in fact guarantee perfect draws from the stationary distribution of interest. We compare solutions computed in two ways with this algorithm to those found analytically using the chemical master equation and we compare the distribution of coupling times for the two simulation approaches.
Assessing ecosystem services for evidence-based nature-based solutions
Authors: Mario V Balzan
Date: 2021-05-12 00:00:00
Tasks: Decision Making

The term nature-based solutions has often been used to refer to adequate green infrastructure, which is cost-effective and simultaneously provides environmental, social and economic benefits, through the delivery of ecosystem services, and contributes to build resilience. This paper provides an overview of the recent work mapping and assessing ecosystem services in Malta and the implications for decision-making. Research has focused on the identification and mapping of ecosystems, and ecosystem condition, the capacity to deliver key ecosystem services and the actual use (flow) of these services by local communities leading to benefits to human well-being. The integration of results from these different assessments demonstrates several significant synergies between ecosystem services, indicating multifunctionality in the provision of ecosystem services leading to human well-being. This is considered as key criterion in the identification of green infrastructure in the Maltese Islands. A gradient in green infrastructure cover and ecosystem services capacity is observed between rural and urban areas but ecosystem services flow per unit area was in some cases higher in urban environments. These results indicate a potential mismatch between ecosystem service demand and capacity but also provide a scientific baseline for evidence-based policy which fosters the development of green infrastructure through nature-based innovation promoting more specific and novel solutions for landscape and urban planning.
EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation
Authors: Hannah R. Biegel, Joceline Lega
Date: 2021-05-12 00:00:00
Tasks: parameter estimation

We introduce a minimalist outbreak forecasting model that combines data-driven parameter estimation with variational data assimilation. By focusing on the fundamental components of nonlinear disease transmission and representing data in a domain where model stochasticity simplifies into a process with independent increments, we design an approach that only requires four core parameters to be estimated. We illustrate this novel methodology on COVID-19 forecasts. Results include case count and deaths predictions for the US and all of its 50 states, the District of Columbia, and Puerto Rico. The method is computationally efficient and is not disease- or location-specific. It may therefore be applied to other outbreaks or other countries, provided case counts and/or deaths data are available.
Multilevel Monte Carlo simulation for VIX options in the rough Bergomi model
Authors: Florian Bourgey, Stefano De Marco
Date: 2021-05-11 00:00:00

We consider the pricing of VIX options in the rough Bergomi model. In this setting, the VIX random variable is defined by the one-dimensional integral of the exponential of a Gaussian process with correlated increments, hence approximate samples of the VIX can be constructed via discretization of the integral and simulation of a correlated Gaussian vector. A Monte-Carlo estimator of VIX options based on a rectangle discretization scheme and exact Gaussian sampling via the Cholesky method has a computational complexity of order $\mathcal{O}(\varepsilon^{-4})$ when the mean-squared error is set to $\varepsilon^2$. We demonstrate that this cost can be reduced to $\mathcal{O}(\varepsilon^{-2} \log^2(\varepsilon))$ combining the scheme above with the multilevel method, and further reduced to the asymptotically optimal cost $\mathcal{O}(\varepsilon^{-2})$ when using a trapezoidal discretization. We provide numerical experiments highlighting the efficiency of the multilevel approach in the pricing of VIX options in such a rough forward variance setting.
A rough SABR formula
Authors: Masaaki Fukasawa, Jim Gatheral
Date: 2021-05-11 00:00:00

Following an approach originally suggested by Balland in the context of the SABR model, we derive an ODE that is satisfied by normalized volatility smiles for short maturities under a rough volatility extension of the SABR model that extends also the rough Bergomi model. We solve this ODE numerically and further present a very accurate approximation to the numerical solution that we dub the rough SABR formula.
Development of an expected possession value model to analyse team attacking performances in rugby league
Authors: Thomas Sawczuk, Anna Palczewska, Ben Jones
Date: 2021-05-05 00:00:00

This study aimed to provide a framework to evaluate team attacking performances in rugby league using 59,233 plays from 180 Super League matches via expected possession value (EPV) models. The EPV-308 split the pitch into 308 5m x 5m zones, the EPV-77 split the pitch into 77 10m x 10m zones and the EPV-19 split the pitch in 19 zones of variable size dependent on the total zone value generated during a match. Attacking possessions were considered as Markov Chains, allowing the value of each zone visited to be estimated based on the outcome of the possession. The Kullback-Leibler Divergence was used to evaluate the reproducibility of the value generated from each zone (the reward distribution) by teams between matches. The EPV-308 had the greatest variability and lowest reproducibility, compared to EPV-77 and EPV-19. When six previous matches were considered, the team's subsequent match attacking performances had a similar reward distribution for EPV-19, EPV-77 and EPV-308 on 95 +/- 4%, 51 +/- 12% and 0 +/- 0% of occasions. This study supports the use of EPV-19 to evaluate team attacking performance in rugby league and provides a simple framework through which attacking performances can be compared between teams.
Forecast Combinations in the Presence of Structural Breaks Evidence from U.S. Equity Markets
Authors: Davide De Gaetano
Date: 2018-03-01 00:00:00

Realized volatility, building on the theory of a simple continuous time process, has recently
received attention as a nonparametric ex-post estimate of the return variation. This paper addresses
the problem of parameter instability due to the presence of structural breaks in realized volatility in
the context of three HAR-type models. The analysis is conducted on four major U.S. equity indices.
More specifically, a recursive testing methodology is performed to evaluate the null hypothesis of
constant parameters, and then, the performance of several forecast combinations based on different
weighting schemes is compared in an out-of-sample variance forecasting exercise. The main findings
are the following: (i) the hypothesis of constant model parameters is rejected for all markets under
consideration; (ii) in all cases, the recursive forecasting approach, which is appropriate in the absence
of structural changes, is outperformed by forecast combination schemes; and (iii) weighting schemes
that assign more weight in most recent observations are superior in the majority of cases.
Archetypal Case Study of Pupil Service learning in the Paddock of Challenging and destitute Community Based Organizations
Authors: Dr BE, Vasudendra HK
Date: 2021-01-28 00:00:00

In sight of mechanical engineering aspects, here our students have undergone for two weeks industrial training in a well-regarded industry; there they have applied their class room learning skills in needy community organizations. In which they have resolved the evils which are being associated with machine tools and worker’s environment. There they found the satisfactory results after applying class room learning. The acceptable discussions have recounted in impending section of this paper.
IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada
Authors: Nikhil Ghanghor, Parameswari Krishnamurthy, Sajeetha Thavareesan, Ruba Priyadharshini, Bharathi Raja Chakravarthi
Date: 2021-04-17 00:00:00
Tasks: Classification, Language Identification, Meme Classification

This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021. Our best configuration for Tamil troll meme classification achieved 0.55 weighted average F1 score, and for offensive language identification, our system achieved weighted F1 scores of 0.75 for Tamil, 0.95 for Malayalam, and 0.71 for Kannada. Our rank on Tamil troll meme classification is 2, and offensive language identification in Tamil, Malayalam and Kannada are 3, 3 and 4 respectively.
IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and English
Authors: Nikhil Ghanghor, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi
Date: 2021-04-19 00:00:00
Tasks: Diversity, Hope Speech Detection

This paper describes the IIITK’s team submissions to the hope speech detection for equality, diversity and inclusion in Dravidian languages shared task organized by LT-EDI 2021 workshop@EACL 2021. Our best configurations for the shared tasks achieve weighted F1 scores of 0.60 for Tamil, 0.83 for Malayalam, and 0.93 for English. We have secured ranks of 4, 3, 2 in Tamil, Malayalam and English respectively.
3D Spatial Recognition without Spatially Labeled 3D
Authors: Zhongzheng Ren, Ishan Misra, Alexander G. Schwing, Rohit Girdhar
Date: 2021-05-13 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Object Detection, Multiple Instance Learning, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation, Weakly supervised segmentation

We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. We show that in conjunction with standard multiple-instance learning objectives, WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time. We demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming prior state of the art on weakly-supervised segmentation by more than 6% mIoU. In addition, we set up the first benchmark for weakly-supervised 3D object detection on both datasets, where WyPR outperforms standard approaches and establishes strong baselines for future work.
Episodic Transformer for Vision-and-Language Navigation
Authors: Alexander Pashevich, Cordelia Schmid, Chen Sun
Date: 2021-05-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: Vision and Language Navigation

Interaction and navigation defined by natural language instructions in dynamic environments pose significant challenges for neural agents. This paper focuses on addressing two challenges: handling long sequence of subtasks, and understanding complex human instructions. We propose Episodic Transformer (E.T.), a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions. To improve training, we leverage synthetic instructions as an intermediate representation that decouples understanding the visual appearance of an environment from the variations of natural language instructions. We demonstrate that encoding the history with a transformer is critical to solve compositional tasks, and that pretraining and joint training with synthetic instructions further improve the performance. Our approach sets a new state of the art on the challenging ALFRED benchmark, achieving 38.4% and 8.5% task success rates on seen and unseen test splits.
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization
Authors: Safa Messaoud, Ismini Lourentzou, Assma Boughoula, Mona Zehni, Zhizhen Zhao, ChengXiang Zhai, Alexander G. Schwing
Date: 2021-05-13 00:00:00
Tasks: Diversity, Video Summarization

The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
Authors: Wenqi Shao, Hang Yu, Zhaoyang Zhang, Hang Xu, Zhenguo Li, Ping Luo
Date: 2021-05-13 00:00:00

This work presents a probabilistic channel pruning method to accelerate Convolutional Neural Networks (CNNs). Previous pruning methods often zero out unimportant channels in training in a deterministic manner, which reduces CNN's learning capacity and results in suboptimal performance. To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated. BWCP has several merits. (1) It simultaneously trains and prunes CNNs from scratch in a probabilistic way, exploring larger network space than deterministic methods. (2) BWCP is empowered by the proposed batch whitening tool, which is able to empirically and theoretically increase the activation probability of useful channels while keeping unimportant channels unchanged without adding any extra parameters and computational cost in inference. (3) Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet with various network architectures show that BWCP outperforms its counterparts by achieving better accuracy given limited computational budgets. For example, ResNet50 pruned by BWCP has only 0.70\% Top-1 accuracy drop on ImageNet, while reducing 43.1\% FLOPs of the plain ResNet50.
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation
Authors: Mahdi Pourmirzaei, Gholam Ali Montazer, Farzaneh Esmaili
Date: 2021-05-13 00:00:00
Tasks: Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition (FER), Head Pose Estimation, Multi-Task Learning, Pose Estimation, Self-Supervised Learning

In this paper, at first, the impact of ImageNet pre-training on fine-grained Facial Emotion Recognition (FER) is investigated which shows that when enough augmentations on images are applied, training from scratch provides better result than fine-tuning on ImageNet pre-training. Next, we propose a method to improve fine-grained and in-the-wild FER, called Hybrid Multi-Task Learning (HMTL). HMTL uses Self-Supervised Learning (SSL) as an auxiliary task during classical Supervised Learning (SL) in the form of Multi-Task Learning (MTL). Leveraging SSL during training can gain additional information from images for the primary fine-grained SL task. We investigate how proposed HMTL can be used in the FER domain by designing two customized version of common pre-text task techniques, puzzling and in-painting. We achieve state-of-the-art results on the AffectNet benchmark via two types of HMTL, without utilizing pre-training on additional data. Experimental results on the common SSL pre-training and proposed HMTL demonstrate the difference and superiority of our work. However, HMTL is not only limited to FER domain. Experiments on two types of fine-grained facial tasks, i.e., head pose estimation and gender recognition, reveals the potential of using HMTL to improve fine-grained facial representation.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
Authors: Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang
Date: 2021-05-13 00:00:00
Tasks: Diversity

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem. In this paper, to enhance the diversity of relabeled goals, we develop FGI (Foresight Goal Inference), a new relabeling strategy that relabels the goals by looking into the future with a learned dynamics model. Besides, to improve sample efficiency, we propose to use the dynamics model to generate simulated trajectories for policy training. By integrating these two improvements, we introduce the MapGo framework (Model-Assisted Policy Optimization for Goal-oriented tasks). In our experiments, we first show the effectiveness of the FGI strategy compared with the hindsight one, and then show that the MapGo framework achieves higher sample efficiency when compared to model-free baselines on a set of complicated tasks.
Graph Learning based Recommender Systems: A Review
Authors: Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu
Date: 2021-05-13 00:00:00
Tasks: Collaborative Filtering, Graph Learning, Recommendation Systems

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area. Finally, we share some new research directions in this vibrant area.
Explainable Machine Learning for Fraud Detection
Authors: Ismini Psychoula, Andreas Gutmann, Pradip Mainali, S. H. Lee, Paul Dunphy, Fabien A. P. Petitcolas
Date: 2021-05-13 00:00:00
Tasks: BIG-bench Machine Learning, Fraud Detection

The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.