pwc-archive/papers-with-abstracts huggingface dataset:
Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification
Authors: Changwei Gong, Changhong Jing, Junren Pan, Shuqiang Wang
Date: 2022-06-29 00:00:00
Tasks: feature selection

Functional alterations in the relevant neural circuits occur from drug addiction over a certain period. And these significant alterations are also revealed by analyzing fMRI. However, because of fMRI's high dimensionality and poor signal-to-noise ratio, it is challenging to encode efficient and robust brain regional embeddings for both graph-level identification and region-level biomarkers detection tasks between nicotine addiction (NA) and healthy control (HC) groups. In this work, we represent the fMRI of the rat brain as a graph with biological attributes and propose a novel feature-selected graph spatial attention network(FGSAN) to extract the biomarkers of addiction and identify from these brain networks. Specially, a graph spatial attention encoder is employed to capture the features of spatiotemporal brain networks with spatial information. The method simultaneously adopts a Bayesian feature selection strategy to optimize the model and improve classification task by constraining features. Experiments on an addiction-related neural imaging dataset show that the proposed model can obtain superior performance and detect interpretable biomarkers associated with addiction-relevant neural circuits.
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG
Authors: Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Shengquan Xie, Zhiqiang Zhang
Date: 2022-07-04 00:00:00

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moment) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods has emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.
Particle Flow Gaussian Particle Filter
Authors: Karthik Comandur, Yunpeng Li, Santosh Nannuru
Date: 2022-07-04 00:00:00
Tasks: State Estimation

State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in high dimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples.
A Novel Low Complexity High Resolution Spectrum Hole Detection Technique for Cognitive Radio
Authors: Sushmitha Sajeevu, Sakthivel Vellaisamy
Date: 2022-07-03 00:00:00

Cognitive radio is a potential solution to meet the upcoming spectrum crunch issue. In a cognitive radio, spectrum holes can be identified using spectrum sensing techniques. A high resolution spectrum hole detection can ensure even the smallest inactive portion in the spectrum is efficiently utilized. In this paper, a spectrum hole detection technique is proposed in which coarse sensing is done initially so as to detect occupied channels simultaneously. Spectrum holes in the occupied band can be efficiently detected using a fine sensing method. A two stage Frequency Response Masking (FRM) filter sandwiched between two Pascal structure based sampling rate converters results in arbitrary variation of bandwidth. This arbitrary variation of bandwidth can be utilized for fine sensing the spectrum such that the spectrum holes can be detected with high resolution. In the proposed method, high resolution in spectrum hole detection can be achieved without increasing the hardware complexity of the design. The hardware complexity of the proposed method is compared with the state of the art and is found to be significantly less
Environment Sensing Considering the Occlusion Effect: A Multi-View Approach
Authors: Xin Tong, Zhaoyang Zhang, Yihan Zhang, Zhaohui Yang, Chongwen Huang, Kai-Kit Wong, Merouane Debbah
Date: 2022-07-02 00:00:00

In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account the occlusion effect that is common in the wireless context, we make full use of the different views of the environment from different users and/or BS(s), and propose an effective sensing algorithm called GAMP-MVSVR (generalized-approximate-message-passing-based multi-view sparse vector reconstruction). In the proposed algorithm, a multi-layer factor graph is constructed to iteratively estimate the scattering coefficients of the cloud points and their occlusion relationship. In each iteration, the occlusion relationship between the cloud points of the sparse environment is recalculated according to a simple occlusion detection rule, and in turn, used to estimate the scattering coefficients of the cloud points. Our proposed algorithm can achieve improved sensing performance with multi-BS collaboration in addition to the multi-views from the UEs. The simulation results verify its convergence and effectiveness.
FSE Compensated Motion Correction for MRI Using Data Driven Methods
Authors: Brett Levac, Sidharth Kumar, Sofia Kardonik, Jonathan I. Tamir
Date: 2022-07-01 00:00:00

Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality boasting great soft tissue contrast without ionizing radiation, but unfortunately suffers from long acquisition times. Long scan times can lead to motion artifacts, for example due to bulk patient motion such as head movement and periodic motion produced by the heart or lungs. Motion artifacts can degrade image quality and in some cases render the scans nondiagnostic. To combat this problem, prospective and retrospective motion correction techniques have been introduced. More recently, data driven methods using deep neural networks have been proposed. As a large number of publicly available MRI datasets are based on Fast Spin Echo (FSE) sequences, methods that use them for training should incorporate the correct FSE acquisition dynamics. Unfortunately, when simulating training data, many approaches fail to generate accurate motion-corrupt images by neglecting the effects of the temporal ordering of the k-space lines as well as neglecting the signal decay throughout the FSE echo train. In this work, we highlight this consequence and demonstrate a training method which correctly simulates the data acquisition process of FSE sequences with higher fidelity by including sample ordering and signal decay dynamics. Through numerical experiments, we show that accounting for the FSE acquisition leads to better motion correction performance during inference.
Time-consistent pension policy with minimum guarantee and sustainability constraint
Authors: Caroline Hillairet, Sarah Kaakai, Mohamed Mrad
Date: 2022-07-04 00:00:00
Tasks: Fairness

This paper proposes and investigates an optimal pair investment/pension policy for a pay-as-you-go (PAYG) pension scheme. The social planner can invest in a buffer fund in order to guarantee a minimal pension amount. The model aims at taking into account complex dynamic phenomena such as the demographic risk and its evolution over time, the time and age dependence of agents preferences, and financial risks. The preference criterion of the social planner is modeled by a consistent dynamic utility defined on a stochastic domain, which incorporates the heterogeneity of overlapping generations and its evolution over time. The preference criterion and the optimization problem also incorporate sustainability, adequacy and fairness constraints. The paper designs and solves the social planner's dynamic decision criterion, and computes the optimal investment/pension policy in a general framework. A detailed analysis for the case of dynamic power utilities is provided.
How far is my network from being edge-based? Proximity measures for edge-basedness of unrooted phylogenetic networks
Authors: Mareike Fischer, Tom Niklas Hamann, Kristina Wicke
Date: 2022-07-04 00:00:00

Phylogenetic networks which are, as opposed to trees, suitable to describe processes like hybridization and horizontal gene transfer, play a substantial role in evolutionary research. However, while non-treelike events need to be taken into account, they are relatively rare, which implies that biologically relevant networks are often assumed to be similar to trees in the sense that they can be obtained by taking a tree and adding some additional edges. This observation led to the concept of so-called tree-based networks, which recently gained substantial interest in the literature. Unfortunately, though, identifying such networks in the unrooted case is an NP-complete problem. Therefore, classes of networks for which tree-basedness can be guaranteed are of the utmost interest. The most prominent such class is formed by so-called edge-based networks, which have a close relationship to generalized series-parallel graphs known from graph theory. They can be identified in linear time and are in some regards biologically more plausible than general tree-based networks. While concerning the latter proximity measures for general networks have already been introduced, such measures are not yet available for edge-basedness. This means that for an arbitrary unrooted network, the "distance" to the nearest edge-based network could so far not be determined. The present manuscript fills this gap by introducing two classes of proximity measures for edge-basedness, one based on the given network itself and one based on its so-called leaf shrink graph (LS graph). Both classes contain four different proximity measures, whose similarities and differences we study subsequently.
Stochastic arbitrage with market index options
Authors: Brendan K. Beare, Juwon Seo, Zhongxi Zheng
Date: 2022-07-03 00:00:00

Opportunities for stochastic arbitrage in an options market arise when it is possible to construct a portfolio of options which provides a positive option premium and which, when combined with a direct investment in the underlying asset, generates a payoff which stochastically dominates the payoff from the direct investment in the underlying asset. We provide linear and mixed-integer linear programs for computing the stochastic arbitrage opportunity providing the maximum option premium to an investor. We apply our programs to 18 years of data on monthly put and call options on the Standard & Poors 500 index, finding no evidence that stochastic arbitrage opportunities are systematically present. A skewed specification of the underlying market return distribution with a constant market risk premium and constant multiplicative variance risk premium is broadly consistent with the pricing of market index options at moderate strikes.
Deep Bellman Hedging
Authors: Hans Buehler, Phillip Murray, Ben Wood
Date: 2022-07-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The key characteristics of our approach are: the ability to hedge with derivatives such as forwards, swaps, futures, options; incorporation of trading frictions such as trading cost and liquidity constraints; applicability for any reasonable portfolio of financial instruments; realistic, continuous state and action spaces; and formal risk-adjusted return objectives. Most importantly, the trained model provides an optimal hedge for arbitrary initial portfolios and market states without the need for re-training. We also prove existence of finite solutions to our Bellman equation, and show the relation to our vanilla Deep Hedging approach
A Study on the Impact of Human Resource Accounting on Firms Value with Respect to Companies Listed in National Stock Exchange
Authors: Anil S, Sudharani R, Suresh N
Date: 2022-07-02 00:00:00

The study focuses on the Impact of Employment Benefit Cots on the Profitability of Companies listed in the National Stock Exchange. The study has considered the Amount spent on Employment Benefit Cots as an Independent variable and Profit after tax, Total Assets, Return on Equity, and Return on Asset and Debt equity Ration as the Dependent variable. The present study is to analyses the relationship between Employment Benefit Cots and Profit after tax, Total Assets, Return on Equity, Return on Asset, and Debt equity Ration. The data is collected from 20 companies listed on the National Stock Exchange for 10 years from the Annual reports of companies. The data collected were analyzed using Panel data Regression in E-Views. Results revealed that there is a significant Relationship between Employment Benefit Cots and Profit after tax, Total Assets, Return on Equity, Return on Asset, and Debt equity Ration. The study shows that Employment Benefit Cots impact positively on Firms profitability.
A Study on Impact of Capital Structure on Profitability of Companies Listed in Indian Stock Exchange with respect to Automobile Industry
Authors: P. Aishwarya, Sudharani R, Suresh N
Date: 2022-07-02 00:00:00

Current research helps in understanding both positive and negative impacts of capital structure on profits of Indian automobile companies by using variables like Return on Capital Employed, Return on Long Term Funds, Return on Net Worth, Gross Profit Margin, and Operating Profit, and Return on Asset. The study hypothesized that RoCE, RoLT, and RoNW have a positive effect and GP, OP and ROA have a negative impact on debt-equity and interest coverage ratios i.e capital structure of the companies. Also, the study proves that the relationship between profitability and capital structure variables is strongly significant. The hypothesis was tested by using fixed effect and random effect models by considering 10 years of data (from 2010-2019) from 17 automobile companies. The result of the study recommends that the firms can improve their performance by using an optimal capital structure. Also, a fair mix of debt and equity should be established to ensure that the firm maintains capital adequacy. Firms can thus be able to meet their financial compulsions and investments that can promise attractive returns.
A Study on Impact of Environmental Accounting on Profitability of Companies listed in Bombay Stock Exchange
Authors: Nandini E. S, Sudharani R, Suresh N
Date: 2022-07-02 00:00:00

The study focuses on the Impact of Environmental Accounting on the Profitability of Companies listed on the Bombay Stock Exchange. The study has considered the Amount spent on Environmental protection as an Independent variable and Return on Capital Employed, Return on Assets, Return on Net worth/equity, Net Profit Margin, and Dividend per Share as the Dependent variable. The present study is to analyses the relationship between Amounts spent on Environmental protection costs and Return on Capital Employed, Return on Assets, Return on Net worth/equity, Net Profit Margin, and Dividend per Share. The data is collected from 18 companies listed on the Bombay Stock Exchange for 10 years from the Annual reports of companies. The data collected were analysed using Panel data Regression in E-Views. Results revealed that there is a significant Relationship between Environmental protection Cost and Return on Capital Employed, Return on Assets, Return on Net worth/equity, Net Profit Margin, and Dividend per Share. The study shows that Environmental accounting impact positively on Firms profitability.
A study on Determinants of Dividend Policy and its Impact on Financial Performances: A Panel Data Analysis for Indian Listed Firms
Authors: Suresh N, Pooja M
Date: 2022-07-02 00:00:00
Tasks: Management

Determination of the correct mix of dividend and retained earnings and its effect on profitability has been a subject of controversy in financial management literature. This paper seeks to contribute to the ongoing debate by examining the relationship between dividend payout policy and the financial performance of 60 firms listed on the National Stock Exchange between 2009-2018. The Return on Assets (ROA) served as a surrogate for the dependent variable, profitability, while the Dividend Pay-out ratio proxied for dividend policy and was the only explanatory variable. Control variables include firm size, asset tangibility, and leverage. Regression result reveals a positive and significant relationship between dividend payout policy (DPO) and firm performance (ROA). It is recommended that companies should endeavor to put in place a robust dividend payout policy that would encourage investment in projects that give positive Net Present Value.
Static Hedging of Freight Rate Risk in the Shipping Market under Model Uncertainty
Authors: Georgios I. Papayiannis
Date: 2022-07-02 00:00:00
Tasks: Management, model

Freight rate derivatives constitute a very popular financial tool in shipping industry, that allows to the market participants and the individuals operating in the field, to reassure their financial positions against the risk occurred by the volatility of the freight rates. The special structure of the shipping market attracted the interest of both academics and practitioners, since pricing of the related traded options which are written on non-storable assets (i.e. the freight service) is not a trivial task. Management of freight risk is of major importance to preserve the viability of shipping operations, especially in periods where shocks appear in the world economy, which introduces uncertainty in the freight rate prices. In practice, the reduction of freight risk is almost exclusively performed by constructing hedging portfolios relying on freight rate options. These portfolios needs to be robust to the market uncertainties, i.e. to choose the portfolio which returns will be as less as it gets affected by the market changes. Especially, for time periods where the future states of the market (even in short term) are extremely ambiguous, i.e. there are a number of different scenarios that can occur, it is of great importance for the firms to decide robustly to these uncertainties. In this work, a framework for the robust treatment of model uncertainty in (a) modeling the freight rates dynamics employing the notion of Wasserstein barycenter and (b) in choosing the optimal hedging strategy for freight risk management, is proposed. A carefully designed simulation study in the discussed hedging problem, employing standard modelling approaches in freight rates literature, illustrates the capabilities of the proposed method with very satisfactory results in approximating the optimal strategy even in high noise cases.
Consistency of Neural Networks with Regularization
Authors: Xiaoxi Shen, Jinghang Lin
Date: 2022-06-22 00:00:00

Neural networks have attracted a lot of attention due to its success in applications such as natural language processing and computer vision. For large scale data, due to the tremendous number of parameters in neural networks, overfitting is an issue in training neural networks. To avoid overfitting, one common approach is to penalize the parameters especially the weights in neural networks. Although neural networks has demonstrated its advantages in many applications, the theoretical foundation of penalized neural networks has not been well-established. Our goal of this paper is to propose the general framework of neural networks with regularization and prove its consistency. Under certain conditions, the estimated neural network will converge to true underlying function as the sample size increases. The method of sieves and the theory on minimal neural networks are used to overcome the issue of unidentifiability for the parameters. Two types of activation functions: hyperbolic tangent function(Tanh) and rectified linear unit(ReLU) have been taken into consideration. Simulations have been conducted to verify the validation of theorem of consistency.
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
Authors: Madeline C. Schiappa, Shruti Vyas, Hamid Palangi, Yogesh S. Rawat, Vibhav Vineet
Date: 2022-07-05 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval, Text to Video Retrieval, Video Retrieval

Joint visual and language modeling on large-scale datasets has recently shown good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In this work, we perform the first extensive robustness study of video-language models against various real-world perturbations. We focus on text-to-video retrieval and propose two large-scale benchmark datasets, MSRVTT-P and YouCook2-P, which utilize 90 different visual and 35 different text perturbations. The study reveals some interesting initial findings from the studied models: 1) models are generally more susceptible when only video is perturbed as opposed to when only text is perturbed, 2) models that are pre-trained are more robust than those trained from scratch, 3) models attend more to scene and objects rather than motion and action. We hope this study will serve as a benchmark and guide future research in robust video-language learning. The benchmark introduced in this study along with the code and datasets is available at https://bit.ly/3CNOly4.