pwc-archive/papers-with-abstracts huggingface dataset:
Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis
Authors: Ivan Ezhov, Luca Giannoni, Suprosanna Shit, Frederic Lange, Florian Kofler, Bjoern Menze, Ilias Tachtsidis, Daniel Rueckert
Date: 2023-01-12 00:00:00

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery.
Singapore's Role for ASEAN's Portfolio Investment
Authors: Tomoo Kikuchi, Satoshi Tobe
Date: 2023-01-13 00:00:00
Tasks: model

We investigate the elasticity of portfolio investment of ASEAN and OECD members to geographical distance in a gravity model utilizing a bilateral panel of 86 reporting and 241 counterparty countries/territories for 2007-2017. We find that the elasticity is more negative for ASEAN than OECD members. The difference is larger if we exclude Singapore. This indicates that Singapore's behavior is distinct from other ASEAN members. While Singapore tends to invest in distant OECD countries, other ASEAN members tend to invest in nearby countries. Our study sheds light on the role of a regional financial center in global finance.
Filtering Down to Size: A Theory of Consideration
Authors: Tonna Emenuga
Date: 2023-01-13 00:00:00

The standard rational choice model describes individuals as making choices by selecting the best option from a menu. A wealth of evidence instead suggests that individuals often filter menus into smaller sets - consideration sets - from which choices are then made. I provide a theoretical foundation for this phenomenon, developing a formal language of axioms to characterize how consideration sets are formed from menus. I posit that consideration filters - mappings that translate a menu into one of its subsets - capture this process, and I introduce several properties that consideration filters can have. I then extend this core model to provide linkages with the sequential choice and rational attention literatures. Finally, I explore whether utility representation is feasible under this consideration model, conjecturing necessary and sufficient conditions for consideration-mediated choices to be rationalizable.
Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network
Authors: Cory Hilton, Steve Bush, Faiz Sherman, Matt Barker, Aditya Deshpande, Steve Willeke, Jeffrey A. Nanzer
Date: 2023-01-13 00:00:00
Tasks: TAG

We demonstrate the classification of common motions of held objects using the harmonic micro-Doppler signatures scattered from harmonic radio-frequency tags. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged handheld objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We developed narrow-band harmonic tags at 2.4/4.8 GHz that support frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-label classifier was developed and obtained a real time classification accuracy of 94.24%, with a response time of 2 seconds per sample with a data processing latency of less than 0.5 seconds.
Random Telegraph Noise based True Random Number Generator for Fully Integrated Systems
Authors: Gilson Wirth, Pedro A B Alves, Roberto da Silva
Date: 2023-01-13 00:00:00

Generating streams of true random numbers is a critical component of many embedded systems. The design of fully integrated, area and power efficient True Random Number Generators is a challenge. We propose a fully integrated, lightweight implementation, that uses the random telegraph noise (RTN) of standard MOSFET as entropy source. It is not analog-intensive, and without traditional post-processing algorithms, the generated random bit sequence passes the National Institute of Standards and Technology (NIST) tests.
Reconfigurable Intelligent Surface Empowered Rate-Splitting Multiple Access for Simultaneous Wireless Information and Power Transfer
Authors: Chengzhong Tian, Yijie Mao, Kangchun Zhao, Yuanming Shi, Bruno Clerckx
Date: 2023-01-13 00:00:00

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) have been both recognized as promising techniques for 6G. The benefits of combining the two techniques to enhance the spectral and energy efficiency have been recently exploited in communication-only networks. Inspired by the recent advances on RIS empowered RSMA, in this work we investigate the use of RIS empowered RSMA for simultaneous wireless information and power transfer (SWIPT) with one transmitter concurrently sending information to multiple information receivers (IRs) and transferring energy to multiple energy receivers (ERs). Specifically, we jointly optimize the transmit beamformers and the RIS reflection coefficients to maximize the weighted sum-rate (WSR) of IRs under the harvested energy constraint of ERs and the transmit power constraint. An alternating optimization and successive convex approximation (SCA)-based optimization framework is then proposed to solve the problem. Numerical results show that by marrying the benefits of RSMA and RIS, the proposed RIS empowered RSMA achieves a better tradeoff between the WSR of IRs and energy harvested at ERs. Therefore, we conclude that RIS empowered RSMA is a promising strategy for SWIPT.
An Electromagnetic-Information-Theory Based Model for Efficient Characterization of MIMO Systems in Complex Space
Authors: Ruifeng Li, Da Li, Jinyan Ma, Zhaoyang Feng, Ling Zhang, Shurun Tan, Wei E. I. Sha, Hongsheng Chen, Er-Ping Li
Date: 2023-01-13 00:00:00

It is the pursuit of a multiple-input-multiple-output (MIMO) system to approach and even break the limit of channel capacity. However, it is always a big challenge to efficiently characterize the MIMO systems in complex space and get better propagation performance than the conventional MIMO systems considering only free space, which is important for guiding the power and phase allocation of antenna units. In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space. The group-T-matrix-based multiple scattering fast algorithm, the mode-decomposition-based characterization method, and their joint theoretical framework in complex space are discussed. Firstly, key informatics parameters in free electromagnetic space based on a dyadic Green's function are derived. Next, a novel group-T-matrix-based multiple scattering fast algorithm is developed to describe a representative inhomogeneous electromagnetic space. All the analytical results are validated by simulations. In addition, the complete form of the EMIT-based model is proposed to derive the informatics parameters frequently used in electromagnetic propagation, through integrating the mode analysis method with the dyadic Green's function matrix. Finally, as a proof-or-concept, microwave anechoic chamber measurements of a cylindrical array is performed, demonstrating the effectiveness of the EMIT-based model. Meanwhile, a case of image transmission with limited power is presented to illustrate how to use this EMIT-based model to guide the power and phase allocation of antenna units for real MIMO applications.
Average Communication Rate for Event-Triggered Stochastic Control Systems
Authors: Zengjie Zhang, Qingchen Liu, Mohammad H. Mamduhi, Sandra Hirche
Date: 2023-01-13 00:00:00

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the nonlinear statistics propagation of the network communication status brought up by deterministic thresholds makes the precise computation of ACR difficult. Previous work used to over-simplify the computation using a Gaussian distribution without incorporating this nonlinearity, leading to sacrificed precision. This paper proposes both analytical and numerical approaches to predict the exact ACR for a NET-SCS using a recursive model. We use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation.
Analysis of LGM Model for sEMG Signals related to Weight Training
Authors: Durgesh Kusuru, Anish C. Turlapaty, Mainak Thakur
Date: 2023-01-13 00:00:00

Statistical models of Surface electromyography (sEMG) signals have several applications such as better understanding of sEMG signal generation, improved pattern recognition based control of wearable exoskeletons and prostheses, improving training strategies in sports activities, and EMG simulation studies. Most of the existing studies analysed the statistical model of sEMG signals acquired under isometric contractions. However, there is no study that addresses the statistical model under isotonic contractions. In this work, a new dataset, electromyography analysis of human activities - database 2 (EMAHA-DB2) is developed. It consists of two experiments based on both isometric and isotonic activities during weight training. Previously, a novel Laplacian-Gaussian Mixture (LGM) model was demonstrated for a few benchmark datasets consisting of basic movements and gestures. In this work, the model suitability analysis is extended to the EMAHA-DB2 dataset. Further, the LGM model is compared with three existing statistical models including the recent scale-mixture model. According to qualitative and quantitative analyses, the LGM model has a better fit to the empirical pdf of the recorded sEMG signals compared with the scale mixture model and the other standard models. The variance and mixing weight of the Laplacian component of the signal are analyzed with respect to the type of muscle, type of muscle contraction, dumb-bell weight and training experience of the subjects. The sEMG variance (the Laplacian component) increases with respect to the weights, is greater for isotonic activity especially for the biceps. For isotonic activity, the signal variance increases with training experience. Importantly, the ratio of the variances from the two muscle sites is observed to be nearly independent of the lifted weight and consistently increases with the training experience.
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO with Hardware Impairments
Authors: Alexander James Fernandes, Ioannis Psaromiligkos
Date: 2023-01-12 00:00:00

We consider the problem of channel estimation in a multiple-input-multiple-output (MIMO) full-duplex (FD) wireless communication system assisted by a reconfigurable intelligent surface (RIS) with hardware impairments (HI) occurring at the transceivers and RIS elements. We propose an unbiased channel estimator that requires knowledge of only the first and second order statistics of the HI, for which we derive closed form expressions. The proposed estimator reduces to the maximum likelihood estimator (MLE) in the case of ideal hardware. We also describe FD and HD orthogonal pilot schemes that minimize the mean square error of the MLE in the case of ideal hardware. We verify the performance of the estimator under varying conditions of transceiver and RIS HI via numerical simulations.
EWF : simulating exact paths of the Wright--Fisher diffusion
Authors: Jaromir Sant, Paul A. Jenkins, Jere Koskela, Dario Spanò
Date: 2023-01-13 00:00:00

The Wright--Fisher diffusion is important in population genetics in modelling the evolution of allele frequencies over time subject to the influence of biological phenomena such as selection, mutation, and genetic drift. Simulating paths of the process is challenging due to the form of the transition density. We present EWF, a robust and efficient sampler which returns exact draws for the diffusion and diffusion bridge processes, accounting for general models of selection including those with frequency-dependence. Given a configuration of selection, mutation, and endpoints, EWF returns draws at the requested sampling times from the law of the corresponding Wright--Fisher process. Output was validated by comparison to approximations of the transition density via the Kolmogorov--Smirnov test and QQ plots. All software is available at https://github.com/JaroSant/EWF
A Levy-driven Ornstein-Uhlenbeck process for the valuation of credit index swaptions
Authors: Yoshihiro Shirai
Date: 2023-01-12 00:00:00

A Levy-driven Ornstein-Uhlenbeck process is proposed to model the evolution of the risk-free rate and default intensities for the purpose of evaluating option contracts on a credit index. Time evolution in credit markets is assumed to follow a gamma process in order to reflect the different pace at which credit products are exchanged with respect to that of risk-free debt. Formulas for the characteristic function, zero coupon bonds, moments of the process and its stationary distribution are derived. Numerical experiments showing convergence of standard numerical methods for the valuation PIDE to analytical and Montecarlo solutions are shown. Calibration to market prices of options on a credit index is performed, and model and market implied summary statistics for the underlying credit spreads are estimated and compared.
Evolutionary mismatch and the role of GxE interactions in human disease
Authors: Amanda J. Lea, Andrew G. Clark, Andrew W. Dahl, Orrin Devinsky, Angela R. Garcia, Christopher D. Golden, Joseph Kamau, Thomas S. Kraft, Yvonne A. L. Lim, Dino Martins, Donald Mogoi, Paivi Pajukanta, George Perry, Herman Pontzer, Benjamin C. Trumble, Samuel S. Urlacher, Vivek V. Venkataraman, Ian J. Wallace, Michael Gurven, Daniel Lieberman, Julien F. Ayroles
Date: 2023-01-12 00:00:00

Globally, we are witnessing the rise of complex, non-communicable diseases (NCDs) related to changes in our daily environments. Obesity, asthma, cardiovascular disease, and type 2 diabetes are part of a long list of "lifestyle" diseases that were rare throughout human history but are now common. A key idea from anthropology and evolutionary biology--the evolutionary mismatch hypothesis--seeks to explain this phenomenon. It posits that humans evolved in environments that radically differ from the ones experienced by most people today, and thus traits that were advantageous in past environments may now be "mismatched" and disease-causing. This hypothesis is, at its core, a genetic one: it predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions and have differential health effects in ancestral versus modern environments. Here, we discuss how this concept could be leveraged to uncover the genetic architecture of NCDs in a principled way. Specifically, we advocate for partnering with small-scale, subsistence-level groups that are currently transitioning from environments that are arguably more "matched" with their recent evolutionary history to those that are more "mismatched". These populations provide diverse genetic backgrounds as well as the needed levels and types of environmental variation necessary for mapping GxE interactions in an explicit mismatch framework. Such work would make important contributions to our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and sociocultural contexts.
Homeostatic regulation of renewing tissue cell populations via crowding control
Authors: Cristina Parigini, Philip Greulich
Date: 2023-01-12 00:00:00

To maintain renewing epithelial tissues in a healthy, homeostatic state, (stem) cell divisions and differentiation need to be tightly regulated. Mechanisms of homeostatic control often rely on crowding control: cells are able to sense the cell density in their environment (via various molecular and mechanosensing pathways) and respond by adjusting division, differentiation, and cell state transitions appropriately. Here we determine, via a mathematically rigorous framework, which general conditions for the crowding feedback regulation (i) must be minimally met, and (ii) are sufficient, to allow the maintenance of homeostasis in renewing tissues. We show that those conditions naturally allow for a degree of robustness toward disruption of regulation. Furthermore, intrinsic to this feedback regulation is that stem cell identity is established collectively by the cell population, not by individual cells, which implies the possibility of `quasi-dedifferentiation', in which cells committed to differentiation may reacquire stem cell properties upon depletion of the stem cell pool. These findings can guide future experimental campaigns to identify specific crowding feedback mechanisms.
Deep Reinforcement Learning for Asset Allocation: Reward Clipping
Authors: Jiwon Kim, Moon-Ju Kang, KangHun Lee, HyungJun Moon, Bo-Kwan Jeon
Date: 2023-01-02 00:00:00
Tasks: Deep Reinforcement Learning, Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.
Acceptable Bilateral Gamma Parameters
Authors: Yoshihiro Shirai
Date: 2023-01-13 00:00:00

The purpose of this paper is to utilize statistical methodologies to infer from market prices of assets and their derivatives the magnitude of the set of a measure M that defines acceptance sets of risky future cash flows. Specifically, we estimate upper and lower boundaries of the compensation needed for a given bilateral gamma distributed future cash flow to be acceptable. We show that prospects theory provides a natural interpretation of the behaviors implied by such boundaries, which are not compatible with expected utility theory over terminal wealth. Boundaries for bilateral gamma risk neutral scale parameters for given speed parameters are also estimated and tested against market data and, in particular, comparisons are made with known empirical facts about the magnitude of the acceptance set of a common class of risk measures.
Stock market forecasting using DRAGAN and feature matching
Authors: Fateme Shahabi Nejad, Mohammad Mehdi Ebadzadeh
Date: 2023-01-06 00:00:00

Applying machine learning methods to forecast stock prices has been one of the research topics of interest in recent years. Almost few studies have been reported based on generative adversarial networks (GANs) in this area, but their results are promising. GANs are powerful generative models successfully applied in different areas but suffer from inherent challenges such as training instability and mode collapse. Also, a primary concern is capturing correlations in stock prices. Therefore, our challenges fall into two main categories: capturing correlations and inherent problems of GANs. In this paper, we have introduced a novel framework based on DRAGAN and feature matching for stock price forecasting, which improves training stability and alleviates mode collapse. We have employed windowing to acquire temporal correlations by the generator. Also, we have exploited conditioning on discriminator inputs to capture temporal correlations and correlations between prices and features. Experimental results on data from several stocks indicate that our proposed method outperformed long short-term memory (LSTM) as a baseline method, also basic GANs and WGAN-GP as two different variants of GANs.
MaNLP@SMM4H22: BERT for Classification of Twitter Posts
Authors: Keshav Kapur, Rajitha Harikrishnan
Date: 2022-12-12 00:00:00
Tasks: Binary Classification, Classification

The reported work is our straightforward approach for the shared task Classification of tweets self-reporting age organized by the Social Media Mining for Health Applications (SMM4H) workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.
The problem with AI consciousness: A neurogenetic case against synthetic sentience
Authors: Yoshija Walter, Lukas Zbinden
Date: 2022-12-07 00:00:00

Ever since the creation of the first artificial intelligence (AI) machinery built on machine learning (ML), public society has entertained the idea that eventually computers could become sentient and develop a consciousness of their own. As these models now get increasingly better and convincingly more anthropomorphic, even some engineers have started to believe that AI might become conscious, which would result in serious social consequences. The present paper argues against the plausibility of sentient AI primarily based on the theory of neurogenetic structuralism, which claims that the physiology of biological neurons and their structural organization into complex brains are necessary prerequisites for true consciousness to emerge.