pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Tree Model-based DNN to Achieve a High-Resolution DOA Estimation via Massive MIMO receive array
Authors: YiFan Li, Feng Shu, Jun Zou, Wei Gao, Yaoliang Song, Jiangzhou Wang
Date: 2023-11-15 00:00:00

To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative, where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root-MUSIC at extremely low signal-to-noise ratio (SNR), and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.
Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach
Authors: Yuchen Zhang, Haiyang Zhang, Sa Xiao, Wanbin Tang, Yonina C. Eldar
Date: 2023-11-15 00:00:00

In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation and wideband beamsplit, an uncharted domain in existing literature. Our approach to maximizing secrecy rates involves formulating an optimization problem for joint power allocation and analog beamformer design, employing a two-stage process encompassing a semi-digital solution and analog approximation. This problem is efficiently solved through a combination of alternating optimization, fractional programming, and block successive upper-bound minimization techniques. Additionally, we present a low-complexity beamsplit-aware beamfocusing strategy, capitalizing on geometric insights from near-field wideband propagation, which can also serve as a robust initial value for the optimization-based approach. Numerical results substantiate the efficacy of the proposed methods, clearly demonstrating their superiority over TTD-free approaches in fortifying wideband PLS, as well as the advantageous secrecy energy efficiency achieved by leveraging low-cost analog devices.
Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces
Authors: Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Kai-Kit Wong
Date: 2023-11-15 00:00:00

Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting elements to enhance the passive beam gain, improve Wireless Energy Transfer (WET) efficiency, and enable its deployment for numerous Internet of Things (IoT) devices. However, the increasing number of IRS elements presents considerable channel estimation challenges. This is due to the lack of active Radio Frequency (RF) chains in an IRS, while pilot overhead becomes intolerable. To address this issue, we propose a Channel State Information (CSI)-free scheme that maximizes received energy in a specific direction and covers the entire space through phased beam rotation. Furthermore, we take into account the impact of an imperfect IRS and meticulously design the active precoder and IRS reflecting phase shift to mitigate its effects. Our proposed technique does not alter the existing IRS hardware architecture, allowing for easy implementation in the current system, and enabling access or removal of any Energy Receivers (ERs) without additional cost. Numerical results illustrate the efficacy of our CSI-free scheme in facilitating large-scale IRS without compromising performance due to excessive pilot overhead. Furthermore, our scheme outperforms the CSI-based counterpart in scenarios involving large-scale ERs, making it a promising solution in the era of IoT.
A Category of Genes
Authors: Yanying Wu
Date: 2023-11-14 00:00:00
Tasks: Miscellaneous

Understanding how genes interact and relate to each other is a fundamental question in biology. However, current practices for describing these relationships, such as drawing diagrams or graphs in a somewhat arbitrary manner, limit our ability to integrate various aspects of the gene functions and view the genome holistically. To overcome these limitations, we need a more appropriate way to describe the intricate relationships between genes. Interestingly, category theory, an abstract field of mathematics seemingly unrelated to biology, has emerged as a powerful language for describing relations in general. We propose that category theory could provide a framework for unifying our knowledge of genes and their relationships. As a starting point, we construct a category of genes, with its morphisms abstracting various aspects of the relationships betweens genes. These relationships include, but not limited to, the order of genes on the chromosomes, the physical or genetic interactions, the signalling pathways, the gene ontology causal activity models (GO-CAM) and gene groups. Previously, they were encoded by miscellaneous networks or graphs, while our work unifies them in a consistent manner as a category. By doing so, we hope to view the relationships between genes systematically. In the long run, this paves a promising way for us to understand the fundamental principles that govern gene regulation and function.
Optimal control of oscillatory neuronal models with applications to communication through coherence
Authors: Michael Orieux, Antoni Guillamon, Gemma Huguet
Date: 2023-11-15 00:00:00

Macroscopic oscillations in the brain are involved in various cognitive and physiological processes, yet their precise function is not not completely understood. Communication Through Coherence (CTC) theory proposes that these rhythmic electrical patterns might serve to regulate the information flow between neural populations. Thus, to communicate effectively, neural populations must synchronize their oscillatory activity, ensuring that input volleys from the presynaptic population reach the postsynaptic one at its maximum phase of excitability. We consider an Excitatory-Inhibitory (E-I) network whose macroscopic activity is described by an exact mean-field model. The E-I network receives periodic inputs from either one or two external sources, for which effective communication will not be achieved in the absence of control. We explore strategies based on optimal control theory for phase-amplitude dynamics to design a control that sets the target population in the optimal phase to synchronize its activity with a specific presynaptic input signal and establish communication. The control mechanism resembles the role of a higher cortical area in the context of selective attention. To design the control, we use the phase-amplitude reduction of a limit cycle and leverage recent developments in this field in order to find the most effective control strategy regarding a defined cost function. Furthermore, we present results that guarantee the local controllability of the system close to the limit cycle.
A short note on super-hedging an arbitrary number of European options with integer-valued strategies
Authors: Dorsaf Cherif, Meriam El Mansour, Emmanuel Lepinette
Date: 2023-11-15 00:00:00

The usual theory of asset pricing in finance assumes that the financial strategies, i.e. the quantity of risky assets to invest, are real-valued so that they are not integer-valued in general, see the Black and Scholes model for instance. This is clearly contrary to what it is possible to do in the real world. Surprisingly, it seems that there is no many contributions in that direction in the literature, except for a finite number of states. In this paper, for arbitrary {\Omega}, we show that, in discrete-time, it is possible to evaluate the minimal super-hedging price when we restrict ourselves to integer-valued strategies. To do so, we only consider terminal claims that are continuous piecewise affine functions of the underlying asset. We formulate a dynamic programming principle that can be directly implemented on an historical data and which also provides the optimal integer-valued strategy. The problem with general payoffs remains open but should be solved with the same approach.
Robust discrete-time super-hedging strategies under AIP condition and under price uncertainty
Authors: Meriam El Mansour, Emmanuel Lepinette
Date: 2023-11-15 00:00:00

We solve the problem of super-hedging European or Asian options for discrete-time financial market models where executable prices are uncertain. The risky asset prices are not described by single-valued processes but measurable selections of random sets that allows to consider a large variety of models including bid-ask models with order books, but also models with a delay in the execution of the orders. We provide a numerical procedure to compute the infimum price under a weak no-arbitrage condition, the so-called AIP condition, under which the prices of the non negative European options are non negative. This condition is weaker than the existence of a risk-neutral martingale measure but it is sufficient to numerically solve the super-hedging problem. We illustrate our method by a numerical example.
Quantification of cell contractile behavior based on non-destructive macroscopic measurement of tension forces on bioprinted hydrogel
Authors: Sarah Pragnere, Naima El Kholti, Leslie Gudimard, Lucie Essayan, Christophe Marquette, Emma Petiot, Cyril Pailler-Mattei
Date: 2023-11-15 00:00:00
Tasks: Descriptive

Contraction assay based on surface measurement have been widely used to evaluate cell contractility in 3D models. This method is straightforward and requires no specific equipment, but it does not provide quantitative data about contraction forces generated by cells. We expanded this method with a new biomechanical model, based on the work-energy theorem, to provide non-destructive longitudinal monitoring of contraction forces generated by cells in 3D.We applied this method on hydrogels seeded with either fibroblasts or osteoblasts. Hydrogel mechanical characteristics were modulated to enhance (condition HCA$_{High}$: hydrogel contraction assay high contraction) or limit (condition HCA$_{Low}$: hydrogel contraction assay low contraction) cell contractile behaviors. Macroscopic measures were further correlated with cell contractile behavior and descriptive analysis of their physiology in response to different mechanical environments. Fibroblasts and osteoblasts contracted their matrix up to 47% and 77% respectively. Contraction stress peaked at day 5 with 1.1 10$^{-14}$Pa for fibroblasts and 3.5 10$^{-14}$Pa for osteoblasts, which correlated with cell attachment and spreading. Negligible contraction was seen in HCA$_{Low}$. Both fibroblasts and osteoblasts expressed $\alpha$-SMA contractile fibers in HCA$_{High}$ and HCA$_{Low}$. Failure to contract HCA$_{Low}$ was attributed to increased cross-linking and resistance to proteolytic degradation of the hydrogel.
Physiological complexity of EEG as a proxy for dementia risk prediction: a review and preliminary cross-section analysis
Authors: Milena Čukić Radenković, Simon Annaheim, Patrick Eggenberger, René Michel Rossi
Date: 2023-11-15 00:00:00
Tasks: EEG

The aim of this work is to give the readers a review (perspective) of prior work on this kind of complexity-based detection from resting-state EEG and present our preliminary cross-section analysis results on how EEG complexity of supposedly healthy senior persons can serve as an early warning to clinicians. Together with the use of wearables for health, this approach to early detection can be done out of clinical setting improving the chances of increasing the quality of life in seniors.
Practical Use of ChatGPT in Psychiatry for Treatment Plan and Psychoeducation
Authors: Farzan Vahedifard, Atieh Sadeghniiat Haghighi, Tirth Dave, Mohammad Tolouei, Fateme Hoshyar Zare
Date: 2023-11-15 00:00:00
Tasks: Management

Artificial Intelligence (AI) has revolutionized various fields, including medicine and mental health support. One promising application is ChatGPT, an advanced conversational AI model that uses deep learning techniques to provide human-like responses. This review paper explores the potential impact of ChatGPT in psychiatry and its various applications, highlighting its role in therapy and counseling techniques, self-help and coping strategies, mindfulness and relaxation techniques, screening and monitoring, education and information dissemination, specialized support, group and family support, learning and training, expressive and artistic therapies, telepsychiatry and online support, and crisis management and prevention. While ChatGPT offers personalized, accessible, and scalable support, it is essential to emphasize that it should not replace the expertise and guidance of qualified mental health professionals. Ethical considerations, such as user privacy, data security, and human oversight, are also discussed. By examining the potential and challenges, this paper sheds light on the responsible integration of ChatGPT in psychiatric research and practice, fostering improved mental health outcomes.
Golden Ratio Information for Neural Spike Code
Authors: Bo Deng
Date: 2023-11-14 00:00:00

Spike bursting is a ubiquitous feature of all neuronal systems. Assuming the spiking states form an alphabet for a communication system, what is the optimal information processing rate? and what is the channel capacity? Here we demonstrate that the quaternary alphabet of spike number code gives the maximal processing rate, and that a binary source in Golden Ratio distribution gives rise to the channel capacity. A multi-time scaled neural circuit is shown to satisfy the hypotheses of this neural communication system.
Illness-death model with renewal
Authors: Ralph Brinks
Date: 2023-11-15 00:00:00
Tasks: model

The illness-death model for chronic conditions is combined with a renewal equation for the number of newborns taking into account possibly different fertility rates in the healthy and diseased parts of the population. The resulting boundary value problem consists of a system of partial differential equations with an integral boundary condition. As an application, the boundary value problem is applied to an example about type 2 diabetes.
Emergence of a Novel Phase in Population and Community Dynamics Due to Fat-Tailed Environmental Correlations
Authors: Immanuel Meyer, Ami Taitelbaum, Michael Assaf, Nadav M. Shnerb
Date: 2023-11-15 00:00:00

Temporal environmental noise (EN) is a prevalent natural phenomenon that controls population and community dynamics, shaping the destiny of biological species and genetic types. Conventional theoretical models often depict EN as a Markovian process with an exponential distribution of correlation times, resulting in two distinct qualitative dynamical categories: quenched (pertaining to short demographic timescales) and annealed (pertaining to long timescales). However, numerous empirical studies demonstrate a fat-tailed decay of correlation times. Here, we study the consequences of power-law correlated EN on the dynamics of isolated and competing populations. We reveal the emergence of a novel intermediate phase that lies between the quenched and annealed regimes. Within this phase, dynamics are primarily driven by rare, yet not exceedingly rare, long periods of almost-steady environmental conditions. For an isolated population, the time to extinction in this phase exhibits a novel scaling with the abundance, and also a non-monotonic dependence on the spectral exponent.
Alternatives to the ROC Curve AUC and C-statistic for Risk Prediction Models
Authors: Ralph H. Stern
Date: 2023-11-14 00:00:00

Assessment of risk prediction models has primarily utilized measures of discrimination, the ROC curve AUC and C-statistic. These derive from the risk distributions of patients and nonpatients, which in turn are derived from a population risk distribution. As greater dispersion of the population risk distribution produces greater separation of patient and nonpatient risks (discrimination), its parameters can be used as alternatives to the ROC curve AUC and C-statistic. Here continuous probability distributions are employed to develop insight into the relationship between their parameters and the ROC curve AUC and C-statistic derived from them. The ROC curve AUC and C-statistic are shown to have a straight-line relationship with the SD for uniform, half-sine, and symmetric triangular probability distributions, with slight differences in the slope: AUC approx 1/2+0.28 SD/(mean(1-mean)). This also characterizes the beta distribution over the same range of SD's. But at larger beta distribution SD's the plot of AUC versus SD deviates downward from this straight-line relationship, approaching the ROC curve AUC and SD of a perfect model (AUC=1, SD= $\sqrt{\rm mean(1-mean)}$). A simpler and more intuitive discrimination metric is the coefficient of discrimination, the difference between the mean risk in patients and nonpatients. This is SD2/(mean(1-mean)), which is also the same for any distribution. Since estimating parameters or metrics discards information, the population risk distribution should always be presented. As the ROC curve AUC and C-statistic are functions of this distribution's parameters, the parameters represent simpler, intuitive alternatives to these discrimination metrics. Among discrimination metrics, the coefficient of discrimination provides a simple, intuitive alternative to the ROC curve AUC and C-statistic.
The Role of Public Health in the Fight Against Cancer: Awareness, Prevention, and Early Detection
Authors: Narges Ramezani, Erfan Mohammadi
Date: 2023-11-15 00:00:00
Tasks: Diagnostic

Cancer, as a complex and devastating sickness, poses a great public health challenge on a global scale. Its far-attaining impact necessitates a devoted branch of medicine referred to as oncology, which makes a specialty in the prevention, diagnosis, and treatment of cancer. In this paper, we aim to offer a complete evaluation of the oncology sector, delving into its rich history, numerous forms of most cancers, diagnostic methods, and treatment alternatives. By exploring recent advances in oncology, which include precision remedy, immunotherapy, and the integration of era, we shed mild on the promising traits of the subject. However, it is miles essential to know the continual challenges that we face, such as the high costs related to remedy and the emergence of drug resistance. Despite these challenges, the final goal of oncology remains unwavering - to provide exceptional feasible outcomes for sufferers of most cancers, using both healing and palliative remedy strategies. As our knowledge of this complicated ailment continues to adapt, we must prioritize prevention and early detection and deal with disparities in access to care. By fostering collaboration and operating collectively, we can seriously improve the lives of tens of millions of individuals affected by cancer around the arena.
Predicting risk/reward ratio in financial markets for asset management using machine learning
Authors: Reza Yarbakhsh, Mahdieh Soleymani Baghshah, Hamidreza Karimaghaie
Date: 2023-11-15 00:00:00
Tasks: Algorithmic Trading, Asset Management, Management

Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions.
Bank Performance Determinants: State of the Art and Future Research Avenues
Authors: Anas Azzabi, Younes Lahrichi
Date: 2023-11-15 00:00:00

Banks' performance is an important topic for both professionals and researchers. Given the important literature on this subject, this paper aims to bring an up-to-date and organized review of literature on the determinants of banks performance. This paper discusses the main approaches that molded the debate on banks performance and their main determinants. An in-depth understanding of these latter may allow on the one hand, bank managers and regulators to improve the sector efficiency and to deal with the new trends shaping the future of their industry and on the other hand, academicians to enrich research and knowledge on this field. Through the analysis of 54 studies published in 42 peer-reviewed journals, we show that despite the importance of the existent literature, the subject of bank performance factors did not reveal all its secrets and still constitute a fertile field for critical debates, especially since the COVID-19 and the increasingly pressing rise in power of digital transformation and artificial intelligence in general and FinTechs in particular. The study concludes by suggesting new promising research avenues.
Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario
Authors: Wenjin Li
Date: 2023-10-17 00:00:00

The autonomous valet parking (AVP) functionality in self-driving vehicles is currently capable of handling most simple parking tasks. However, further training is necessary to enable the AVP algorithm to adapt to complex scenarios and complete parking tasks in any given situation. Training algorithms with real-world data is time-consuming and labour-intensive, and the current state of constructing simulation environments is predominantly manual. This paper introduces an approach to automatically generate 3D underground garage simulation scenarios of varying difficulty levels based on pre-input 2D underground parking structure plans.
Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots
Authors: Felix Ocker, Jörg Deigmöller, Julian Eggert
Date: 2023-10-19 00:00:00
Tasks: Common Sense Reasoning

Service robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontologies face a challenge because common-sense knowledge is often implicit, i.e., it is obvious to humans but not explicitly stated. This paper investigates if Large Language Models (LLMs) can fill this gap. Our experiments reveal limited effectiveness in the selective extraction of contextual action knowledge, suggesting that LLMs may not be sufficient on their own. However, the large-scale extraction of general, actionable knowledge shows potential, indicating that LLMs can be a suitable tool for efficiently creating ontologies for robots. This paper shows that the technique used for knowledge extraction can be applied to populate a minimalist ontology, showcasing the potential of LLMs in synergy with formal knowledge representation.