pwc-archive/papers-with-abstracts huggingface dataset:
Optical Satellite Eavesdropping
Authors: Olfa Ben Yahia, Eylem Erdogan, Gunes Karabulut Kurt, Ibrahim Altunbas, Halim Yanikomeroglu
Date: 2021-12-09 00:00:00

In recent years, satellite communication (SatCom) systems have been widely used for navigation, broadcasting application, disaster recovery, weather sensing, and even spying on the Earth. As the number of satellites is highly increasing and with the radical revolution in wireless technology, eavesdropping on SatCom will be possible in next-generation networks. In this context, we introduce the satellite eavesdropping approach, where an eavesdropping spacecraft can intercept optical communications established between a low Earth orbit satellite and a high altitude platform station (HAPS). Specifically, we propose two practical eavesdropping scenarios for satellite-to-HAPS (downlink) and HAPS-to-satellite (uplink) optical communications, where the attacker spacecraft can eavesdrop on the transmitted signal or the received signal. To quantify the secrecy performance of the scenarios, the average secrecy capacity and secrecy outage probability expressions are derived and validated with Monte Carlo simulations. Moreover, secrecy throughput of the proposed models is investigated. We observe that turbulence-induced fading significantly impacts the secrecy performance of free-space optical communication.
Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems
Authors: Boyu Teng, Xiaojun Yuan, Rui Wang, Shi Jin
Date: 2021-12-09 00:00:00

In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate and track the user position and the angle-of-arrival (AoAs) at the user in an online fashion. We also derive Bayesian Cram\'er Rao bound (BCRB) to characterize the fundamental performance limit of the considered tracking problem. To improve the tracking performance, we optimize the beamforming design at the BS and the RISs to minimize the derived BCRB. Simulation results show that our BULT algorithm can perform close to the derived BCRB, and significantly outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.
Emergent Regulatory Response and Shift of Half induction point under Resource Competition in Genetic circuits
Authors: Priya Chakraborty, Sayantari Ghosh
Date: 2021-12-09 00:00:00
Tasks: Translation

Synthetic genetic circuits are implemented in living cells for their operation. During gene expression, proteins are produced from the respective genes, by formation of complexes through the process of transcription and translation. In transcription the circuit uses RNAP, etc. as resource from the host cell and in translation, ribosome, tRNA and other cellular resources are supplied to the operating circuit. As the cell contains these resources in limited number, the circuit can suffer from unprecedented resource competition which might destroy the circuit functionality, or introduce some emergent responses. In this paper, we have studied a three-gene motif under resource competition where interesting behaviour, similar to regulatory responses occur due to limited supply of necessary resources. The system of interest exhibits prominent changes in behaviour which can be observed experimentally. We focus on two specific aspects, namely, dynamic range and half-induction point, which inherently describe the circuit functionalities, and can be affected by corresponding resource affinity and availability.
Network mechanisms of working memory: the role of neuronal nonlinearities
Authors: Alex Suarez-Perez, Omri Harish, David Hansel
Date: 2021-12-09 00:00:00

The oculomotor delayed-response (ODR) task is a common experimental paradigm of working memory (WM) study, in which a monkey must fixate its gaze on the center of a screen and, following a brief cue that flashes on the screen, keep fixating for several more seconds before shifting its gaze to the location where the cue flashed. Consequently, in the delay period between the cue and the response the monkey must maintain a memory of cue location. Electrophysiological recordings from the prefrontal area of the cortex (PFC) revealed neurons that display selective persistent activity: their firing rate change induced by the cue persists through delay period, but only in response to a confined range of cue locations. This suggests that the representation of the cue is maintained in the network by a change in network activity profile. In this work, we study a network of rate-model neurons that is capable of preserving information about a past input, owing to structured connectivity and nonlinearities in the neuronal transfer function (TFs). Particularly, we focus on the acceleration of the TF close to firing threshold and the concavity around TF saturation. Any memory mechanism which exploits TF saturation means that some neurons must fire close to their saturation rates; with our model, however, we show that a certain relation between the excitatory and inhibitory neurons' TFs can cause an effective saturation in the network without forcing the neurons into the saturating parts of their TFs. In addition, this mechanism enables the erasure of memory at the end of the delay by a global excitatory signal. Finally, we demonstrate the mechanism in a model network of spiking neurons which describes with more detail the oscillatory dynamics in the state transition due to the interaction of membrane and synaptic time constants which is neglected in the rate model.
What Omicron Does, and How It Does It
Authors: J. C. Phillips
Date: 2021-12-08 00:00:00

Improvement of protein function by evolution (natural selection) is expected on general grounds, but even with the modern database positive proof has remained a difficult problem for theory. Here we extend our recent analysis of the evolution of CoV-1 to much more contagious CoV-2, to Omicron, which appears to be qualitatively different from other recent strains like Delta. Overall the synchronized dynamics of Omicron is more elaborate than CoV-2 or its variants like Delta. The surprising result is that while Omicron could be more contagious than even Delta, it is probably much less dangerous
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
Authors: Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai
Date: 2021-11-28 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: molecular representation, Representation Learning

Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules. PhysChem is composed of a physicist network (PhysNet) and a chemist network (ChemNet). PhysNet is a neural physical engine that learns molecular conformations through simulating molecular dynamics with parameterized forces; ChemNet implements geometry-aware deep message-passing to learn chemical / biomedical properties of molecules. Two networks specialize in their own tasks and cooperate by providing expertise to each other. By fusing physical and chemical information, PhysChem achieved state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. The effectiveness of PhysChem was further corroborated on cutting-edge datasets of SARS-CoV-2.
Adaptive calibration of Heston Model using PCRLB based switching Filter
Authors: Kumar Yashaswi
Date: 2021-12-08 00:00:00
Tasks: parameter estimation, State Estimation

Stochastic volatility models have existed in Option pricing theory ever since the crash of 1987 which violated the Black-Scholes model assumption of constant volatility. Heston model is one such stochastic volatility model that is widely used for volatility estimation and option pricing. In this paper, we design a novel method to estimate parameters of Heston model under state-space representation using Bayesian filtering theory and Posterior Cramer-Rao Lower Bound (PCRLB), integrating it with Normal Maximum Likelihood Estimation (NMLE) proposed in [1]. Several Bayesian filters like Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF) are used for latent state and parameter estimation. We employ a switching strategy proposed in [2] for adaptive state estimation of the non-linear, discrete-time state-space model (SSM) like Heston model. We use a particle filter approximated PCRLB [3] based performance measure to judge the best filter at each time step. We test our proposed framework on pricing data from S&P 500 and NSE Index, estimating the underlying volatility and parameters from the index. Our proposed method is compared with the VIX measure and historical volatility for both the indexes. The results indicate an effective framework for estimating volatility adaptively with changing market dynamics.
Cross-ownership as a structural explanation for rising correlations in crisis times
Authors: Nils Bertschinger, Axel A. Araneda
Date: 2021-12-09 00:00:00
Tasks: Relation

In this paper, we examine the interlinkages among firms through a financial network where cross-holdings on both equity and debt are allowed. We relate mathematically the correlation among equities with the unconditional correlation of the assets, the values of their business assets and the sensitivity of the network, particularly the $\Delta$-Greek. We noticed also this relation is independent of the Equities level. Besides, for the two-firms case, we analytically demonstrate that the equities correlation is always higher than the correlation of the assets; showing this issue by numerical illustrations. Finally, we study the relation between equity correlations and asset prices, where the model arrives to an increase in the former due to a fall in the assets.
Real-World Dexterous Object Manipulation based Deep Reinforcement Learning
Authors: Qingfeng Yao, Jilong Wang, Shuyu Yang
Date: 2021-11-22 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has shown its advantages in real-time decision-making based on the state of the agent. In this stage, we solved the task of using a real robot to manipulate the cube to a given trajectory. The task is broken down into different procedures and we propose a hierarchical structure, the high-level deep reinforcement learning model selects appropriate contact positions and the low-level control module performs the position control under the corresponding trajectory. Our framework reduces the disadvantage of low sample efficiency of deep reinforcement learning and lacking adaptability of traditional robot control methods. Our algorithm is trained in simulation and migrated to reality without fine-tuning. The experimental results show the effectiveness of our method both simulation and reality. Our code and video can be found at https://github.com/42jaylonw/RRC2021ThreeWolves and https://youtu.be/Jr176xsn9wg.
VINE -- A numerical code for simulating astrophysical systems using particles II: Implementation and performance characteristics
Authors: Andrew F. Nelson, M. Wetzstein, T. Naab, .
Date: 2008-02-28 00:00:00

We continue our presentation of VINE. We begin with a description of relevant architectural properties of the serial and shared memory parallel computers on which VINE is intended to run, and describe their influences on the design of the code itself. We continue with a detailed description of a number of optimizations made to the layout of the particle data in memory and to our implementation of a binary tree used to access that data for use in gravitational force calculations and searches for SPH neighbor particles. We describe modifications to the code necessary to obtain forces efficiently from special purpose `GRAPE' hardware. We conclude with an extensive series of performance tests, which demonstrate that the code can be run efficiently and without modification in serial on small workstations or in parallel using OpenMP compiler directives on large scale, shared memory parallel machines. We analyze the effects of the code optimizations and estimate that they improve its overall performance by more than an order of magnitude over that obtained by many other tree codes. Scaled parallel performance of the gravity and SPH calculations, together the most costly components of most simulations, is nearly linear up to maximum machine sizes available to us (120 processors on an Origin~3000). At similar accuracy, performance of VINE, used in GRAPE-tree mode, is approximately a factor two slower than that of VINE, used in host-only mode. Optimizations of the GRAPE/host communications could improve the speed by as much as a factor of three, but have not yet been implemented in VINE.
Temporal Convolutional Attention Neural Networks for Time Series Forecasting
Authors: Yang Lin, Irena Koprinska, Mashud Rana
Date: 2021-09-23 00:00:00
Proceeding: International Joint Conference on Neural Networks (IJCNN) 2021 9
Tasks: Multivariate Time Series Forecasting, Probabilistic Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Temporal Convolutional Neural Networks (TCNNs) have been applied for various sequence modelling tasks including time series forecasting. However, TCNNs may require many convolutional layers if the input sequence is long and are not able to provide interpretable results. In this paper, we present TCAN, a novel deep learning approach that employs attention mechanism with temporal convolutions for probabilistic forecasting, and demonstrate its performance in a case study for solar power forecasting. TCAN uses the hierarchical convolutional structure of TCNN to extract temporal dependencies and then uses sparse attention to focus on the important timesteps. The sparse attention layer of TCAN enables an extended receptive field without requiring a deeper architecture and allows for interpretability of the forecasting results. An evaluation using three large solar power data sets demonstrates that TCAN outperforms several state-of-the-art deep learning forecasting models including TCNN in terms of accuracy. TCAN requires less number of convolutional layers than TCNN for an extended receptive field, is faster to train and is able to visualize the most important timesteps for the prediction.
Information-Theoretic Measures for Objective Evaluation of Classifications
Authors: Bao-Gang Hu, Ran He, Xiaotong Yuan
Date: 2011-07-10 00:00:00

This work presents a systematic study of objective evaluations of abstaining classifications using Information-Theoretic Measures (ITMs). First, we define objective measures for which they do not depend on any free parameter. This definition provides technical simplicity for examining "objectivity" or "subjectivity" directly to classification evaluations. Second, we propose twenty four normalized ITMs, derived from either mutual information, divergence, or cross-entropy, for investigation. Contrary to conventional performance measures that apply empirical formulas based on users' intuitions or preferences, the ITMs are theoretically more sound for realizing objective evaluations of classifications. We apply them to distinguish "error types" and "reject types" in binary classifications without the need for input data of cost terms. Third, to better understand and select the ITMs, we suggest three desirable features for classification assessment measures, which appear more crucial and appealing from the viewpoint of classification applications. Using these features as "meta-measures", we can reveal the advantages and limitations of ITMs from a higher level of evaluation knowledge. Numerical examples are given to corroborate our claims and compare the differences among the proposed measures. The best measure is selected in terms of the meta-measures, and its specific properties regarding error types and reject types are analytically derived.
Derivations of Normalized Mutual Information in Binary Classifications
Authors: Yong Wang, Bao-Gang Hu
Date: 2007-11-23 00:00:00
Tasks: Form

This correspondence studies the basic problem of classifications - how to evaluate different classifiers. Although the conventional performance indexes, such as accuracy, are commonly used in classifier selection or evaluation, information-based criteria, such as mutual information, are becoming popular in feature/model selections. In this work, we propose to assess classifiers in terms of normalized mutual information (NI), which is novel and well defined in a compact range for classifier evaluation. We derive close-form relations of normalized mutual information with respect to accuracy, precision, and recall in binary classifications. By exploring the relations among them, we reveal that NI is actually a set of nonlinear functions, with a concordant power-exponent form, to each performance index. The relations can also be expressed with respect to precision and recall, or to false alarm and hitting rate (recall).
Squarified Treemaps
Authors: Mark Bruls, Kees Huizing, Jarke J. van Wijk
Date: 1999-10-01 00:00:00
Proceeding: IEEE TCVG Symposium on Visualization 1999 10
Tasks: Tree Map Layout

An extension to the treemap method for the visualization of hierarchical information, such as directory structures and organization structures, is presented. The standard treemap method often gives thin, elongated rectangles. As a result, rectangles are difficult to compare and to select. A new method is presented to generate lay-outs in which the rectangles approximate squares. To strenghten the visualization of the structure, shaded frames are used around groups of related nodes.
TSFEL: Time Series Feature Extraction Library
Authors: Marília Barandas, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreu, Patrícia Bota, Hui Liu, Tanja Schultz, Hugo Gamboa
Date: 2020-03-21 00:00:00
Proceeding: SoftwareX 2020 3
Tasks: Feature Engineering, Time Series, Time Series Analysis

Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scientists must consider a combination between a multitude of domain knowledge factors and coding implementation. We present in this paper a Python package entitled Time Series Feature Extraction Library (TSFEL), which computes over 60 different features extracted across temporal, statistical and spectral domains. User customisation is achieved using either an online interface or a conventional Python package for more flexibility and integration into real deployment scenarios. TSFEL is designed to support the process of fast exploratory data analysis and feature extraction on time series with computational cost evaluation.
Ouroboros: Early identification of at-risk students without models based on legacy data
Authors: Martin Hlosta, Zdenek Zdrahal, Jaroslav Zendulka
Date: 2017-03-17 00:00:00
Proceeding: ACM 2017 3

This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learn- ing models. This situation typically occurs in new courses. We present the concept of a ”self-learner” that builds the machine learning models from the data generated during the current course. The approach utilises information about al- ready submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models.
There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk stu- dents using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the chal- lenge of imbalanced data, which appears both in training and testing data.
The results show the comparison with the traditional ap- proach of learning the models from the legacy course data, validating the proposed concept.
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Authors: Zhiwei Chen, Changan Wang, Yabiao Wang, Guannan Jiang, Yunhang Shen, Ying Tai, Chengjie Wang, Wei zhang, Liujuan Cao
Date: 2021-12-10 00:00:00
Tasks: Inductive Bias, Object, Object Localization, Weakly-Supervised Object Localization

Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.
Learning-based personal speech enhancement for teleconferencing by exploiting spatial-spectral features
Authors: Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Date: 2021-12-10 00:00:00
Tasks: Speech Enhancement, Speech Extraction

Teleconferencing is becoming essential during the COVID-19 pandemic. However, in real-world applications, speech quality can deteriorate due to, for example, background interference, noise, or reverberation. To solve this problem, target speech extraction from the mixture signals can be performed with the aid of the user's vocal features. Various features are accounted for in this study's proposed system, including speaker embeddings derived from user enrollment and a novel long-short-term spatial coherence feature pertaining to the target speaker activity. As a learning-based approach, a target speech sifting network was employed to extract the relevant features. The network trained with LSTSC in the proposed approach is robust to microphone array geometries and the number of microphones. Furthermore, the proposed enhancement system was compared with a baseline system with speaker embeddings and interchannel phase difference. The results demonstrated the superior performance of the proposed system over the baseline in enhancement performance and robustness.