pwc-archive/papers-with-abstracts huggingface dataset:
Wearable Technologies for Monitoring Upper Extremity Functions During Daily Life in Neurologically Impaired Individuals
Authors: Tommaso Proietti, Andrea Bandini
Date: 2023-11-21 00:00:00

Neurological disorders, including stroke, spinal cord injuries, multiple sclerosis, and Parkinson's disease, generally lead to diminished upper extremity (UE) function, impacting individuals' independence and quality of life. Traditional assessments predominantly focus on standardized clinical tasks, offering limited insights into real-life UE performance. In this context, this review focuses on wearable technologies as a promising solution to monitor UE function in neurologically impaired individuals during daily life activities. Our primary objective is to categorize the different sensors, review the data collection and understand the employed data processing approaches. After screening over 1500 papers and including 21 studies, what comes to light is that the majority of them involved stroke survivors, and predominantly employed accelerometers or inertial measurement units to collect kinematics. Most analyses in these studies were performed offline, focusing on activity duration and frequency as key metrics. Although wearable technology shows potential in monitoring UE function in real-life scenarios, it also appears that a solution combining non-intrusiveness, lightweight design, detailed hand and finger movement capture, contextual information, extended recording duration, ease of use, and privacy protection remains an elusive goal. These are critical characteristics for a monitoring solution and researchers in the field should try to integrate the most in future developments. Last but not least, it stands out a growing necessity for a multimodal approach in capturing comprehensive data on UE function during real-life activities to enhance the personalization of rehabilitation strategies and ultimately improve outcomes for these individuals.
A study on Satellite-to-Ground Propagation in Urban Environment
Authors: N. Cenni, V. Degli-Esposti, E. M. Vitucci, F. Fuschini, M. Barbiroli
Date: 2023-11-21 00:00:00
Tasks: Position

Non-Terrestrial Networks are going to play an important role in future 6G wireless networks to enhance global connectivity a performance in cooperation with terrestrial networks. In order to properly design and deploy non-terrestrial networks, the satellite-to-ground channel must be properly characterized, with particular focus on the urban environment. This paper uses a Ray-Tracing simulation tool to analyze the primary propagation mechanisms and the behaviour of the Rician K-factor as a function of satellite position in a reference urban environment. Non-specular reflection due to surface irregularities emerges as a primary propagation mechanism in non-line-of-sight cases. Additionally, the Rician K-factor shows a slightly increasing trend with elevation angle, in contrast to previous studies.
A Hybrid Frame Structure Design of OTFS for Multi-tasks Communications
Authors: Pu Yuan, Jin Liu, Dajie Jiang, Fei Qin
Date: 2023-11-21 00:00:00
Tasks: Diversity

Orthogonal time frequency space (OTFS) is a promising waveform in high mobility scenarios for it fully exploits the time-frequency diversity using a discrete Fourier transform (DFT) based two dimensional spreading. However, it trades off the processing latency for performance and may not fulfill the stringent latency requirements in some services. This fact motivates us to design a hybrid frame structure where the OTFS and Orthogonal Frequency Division Multiplexing (OFDM) are orthogonally multiplexed in the time domain, which can adapt to both diversity-preferred and latency-preferred tasks. As we identify that this orthogonality is disrupted after channel coupling, we provide practical algorithms to mitigate the inter symbol interference between (ISI) the OTFS and OFDM, and the numerical results ensure the effectiveness of the hybrid frame structure.
Optimal Portfolio with Ratio Type Periodic Evaluation under Short-Selling Prohibition
Authors: Wenyuan Wang, Kaixin Yan, Xiang Yu
Date: 2023-11-21 00:00:00

This paper studies some unconventional utility maximization problems when the ratio type relative portfolio performance is periodically evaluated over an infinite horizon. Meanwhile, the agent is prohibited from short-selling stocks. Our goal is to understand the impact of the periodic reward structure on the long-run constrained portfolio strategy. For power and logarithmic utilities, we can reformulate the original problem into an auxiliary one-period optimization problem. To cope with the auxiliary problem with no short-selling, the dual control problem is introduced and studied, which gives the characterization of the candidate optimal portfolio within one period. With the help of the results from the auxiliary problem, the value function and the optimal constrained portfolio for the original problem with periodic evaluation can be derived and verified, allowing us to discuss some financial implications under the new performance paradigm.
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets
Authors: Kevin Li, Danko Nikolić, Vjekoslav Nikolić, Davor Andrić, Lauren M. Sanders, Sylvain V. Costes
Date: 2023-11-17 00:00:00
Tasks: AI Agent, Few-Shot Learning, Transfer Learning

Given the increasing availability of RNA-seq data and its complex and heterogeneous nature, there has been growing interest in applying AI/machine learning methodologies to work with such data modalities. However, because omics data is characterized by high dimensionality and low sample size (HDLSS), current attempts at integrating AI in this domain require significant human guidance and expertise to mitigate overfitting. In this work we look at how transfer learning can be improved to learn from small RNA-seq sample sizes without significant human interference. The strategy is to gain general prior knowledge about a particular domain of data (e.g. RNA-seq data) by pre-training on a general task with a large aggregate of data, then fine-tuning to various specific, downstream target tasks in the same domain. Because previous attempts have shown traditional transfer learning failing on HLDSS, we propose to improve performance by using Guided Transfer Learning (GTL). Collaborating with Robots Go Mental, the AI we deploy here not only learns good initial parameters during pre-training, but also learns inductive biases that affect how the AI learns downstream tasks. In this approach, we first pre-trained on recount3 data, a collection of over 400,000 mouse RNA-seq samples sourced from thousands of individual studies. With such a large collection, patterns of expression between the ~30,000 genes in mammalian systems were pre-determined. Such patterns were sufficient for the pre-trained AI agent to efficiently learn new downstream tasks involving RNA-seq datasets with very low sample sizes and performed notably better on few-shot learning tasks compared to the same model without pre-training.
Exploring the Relationship Between COVID-19 Induced Economic Downturn and Women's Nutritional Health Disparities
Authors: Alaa M. Sadeq
Date: 2023-11-20 00:00:00
Tasks: Nutrition

This study explores how the COVID-19 pandemic's economic impact has exacerbated nutritional health disparities among women. It sought to understand the effects of economic challenges on women's dietary choices and access to nutritious food across different socioeconomic groups. Using a mixed-methods approach, the research combined quantitative data from health and economic records with qualitative insights from interviews with diverse women. The study analyzed trends in nutritional health and economic factors before and after the pandemic and gathered personal accounts regarding nutrition and economic difficulties during this period. Findings showed a clear link between the economic downturn and deteriorating nutritional health, particularly in low-income and marginalized groups. These women reported decreased access to healthy foods and an increased dependence on less nutritious options due to budget constraints, leading to a decline in dietary quality. This trend was less evident in higher-income groups, highlighting stark disparities. The pandemic intensified pre-existing nutritional inequalities, with the most vulnerable groups facing greater adverse effects. However, community support and public health measures provided some relief. In summary, the pandemic's economic repercussions have indirectly impaired women's nutritional health, especially among the socioeconomically disadvantaged. This highlights the necessity for tailored nutritional interventions and economic policies focused on safeguarding women's health.
Effect of some plant extracts on hardwood cuttings of Bottlebrush (Callistemon viminalis)
Authors: Hemn Abdalla Mustafa, Tariq Abubakr Ahmad, Aram Akram Mohammed, Zainab Sabah Lazim, Chopi Omer Ibrahim, Roshna Faeq Kak bra, Shvan Ramzi Salih
Date: 2023-09-09 00:00:00
Tasks: Sand

The study was conducted at the Collage of Agricultural Engineering Sciences, University of Sulaimani, Kurdistan Region-Iraq so as to investigate response hardwood cuttings of Callistemon viminalis to some plant extracts. The hardwood cuttings were taken on 11 March 2021 and soaked separately in 3 and 6 g/L aqueous extracts of moringa leaf, licorice root, willow shoot, fenugreek seed and cinnamon bark for 1 hour. They were compared to the cuttings dipped in 3000 ppm IBA for 10s and control cuttings which were soaked in distilled water for 1 hour. The experiment laid out in CRD with three replications in a greenhouse, and each replication included six cuttings which planted in a mixture of sand and rice husk medium. The results showed that the highest (86.66%) rooting was achieved in the cuttings treated with 6 g/L licorice extract and they were significantly different with control cuttings (53.33%), but they were not significantly different with 3000 ppm IBA (66.66%). Cinnamon 3g/L and fenugreek 3g/L extracts gave the lowest (6.66% and 33.33%, respectively) rooting and other studied parameters. The cuttings dipped in 3000 ppm IBA gave the highest (18.91) root number and the highest (66.66%) survival cuttings after transplanting. The longest root (15.54 cm) was found in cuttings were treated with 6 g/L moringa extract. The longest (5.83 cm) shoot was observed in treated cuttings with 3 g/L willow extract. The highest chlorophyll a and b (10.08 and 4.62 mg/L, respectively) were observed in cuttings treated with 6 g/L willow extract. Moreover, 3000 ppm IBA gave the highest (20.23%) total carbohydrate and (1.77 mg/g) IAA content along with 6 g/L licorice, moringa and fenugreek extracts, after 30 days from planting of the cuttings. Licorice root extract at 6 g/L fairly improved the measurements similar to 3000 ppm IBA throughout the study.
Phylogenetic least squares estimation without genetic distances
Authors: Peter B. Chi, Volodymyr M. Minin
Date: 2023-11-21 00:00:00

Least squares estimation of phylogenies is an established family of methods with good statistical properties. State-of-the-art least squares phylogenetic estimation proceeds by first estimating a distance matrix, which is then used to determine the phylogeny by minimizing a squared-error loss function. Here, we develop a method for least squares phylogenetic inference that does not rely on a pre-estimated distance matrix. Our approach allows us to circumvent the typical need to first estimate a distance matrix by forming a new loss function inspired by the phylogenetic likelihood score function; in this manner, inference is not based on a summary statistic of the sequence data, but directly on the sequence data itself. We use a Jukes-Cantor substitution model to show that our method leads to improvements over ordinary least squares phylogenetic inference, and is even observed to rival maximum likelihood estimation in terms of topology estimation efficiency. Using a Kimura 2-parameter model, we show that our method also allows for estimation of the global transition/transversion ratio simultaneously with the phylogeny and its branch lengths. This is impossible to accomplish with any other distance-based method as far as we know. Our developments pave the way for more optimal phylogenetic inference under the least squares framework, particularly in settings under which likelihood-based inference is infeasible, including when one desires to build a phylogeny based on information provided by only a subset of all possible nucleotide substitutions such as synonymous or non-synonymous substitutions.
Fast calculation of Counterparty Credit exposures and associated sensitivities using fourier series expansion
Authors: Gijs Mast, Xiaoyu Shen, Fang Fang
Date: 2023-11-21 00:00:00

This paper introduces a novel approach for computing netting--set level and counterparty level exposures, such as Potential Future Exposure (PFE) and Expected Exposure (EE), along with associated sensitivities. The method is essentially an extension of the Fourier-cosine series expansion (COS) method, originally proposed for option pricing. This method can accommodate a broad range of models where the joint distribution of involved risk factors is analytically or semi-analytically tractable. This inclusivity encompasses nearly all CCR models commonly employed in practice. A notable advantage of the COS method is its sustained efficiency, particularly when handling large portfolios. A theoretical error analysis is also provided to justify the method's theoretical stability and accuracy. Various numerical tests are conducted using real-sized portfolios, and the results underscore its potential as a significantly more efficient alternative to the Monte Carlo method for practical usage, particularly applicable to portfolios involving a relatively modest number of risk factors. Furthermore, the observed error convergence rates align closely with the theoretical error analysis.
Optimal Transport Divergences induced by Scoring Functions
Authors: Silvana M. Pesenti, Steven Vanduffel
Date: 2023-11-20 00:00:00

We employ scoring functions, used in statistics for eliciting risk functionals, as cost functions in the Monge-Kantorovich (MK) optimal transport problem. This gives raise to a rich variety of novel asymmetric MK divergences, which subsume the family of Bregman-Wasserstein divergences. We show that for distributions on the real line, the comonotonic coupling is optimal for the majority of the new divergences. Specifically, we derive the optimal coupling of the MK divergences induced by functionals including the mean, generalised quantiles, expectiles, and shortfall measures. Furthermore, we show that while any elicitable law-invariant coherent risk measure gives raise to infinitely many MK divergences, the comonotonic coupling is simultaneously optimal. The novel MK divergences, which can be efficiently calculated, open an array of applications in robust stochastic optimisation. We derive sharp bounds on distortion risk measures under a Bregman-Wasserstein divergence constraint, and solve for cost-efficient payoffs under benchmark constraints.
Hedging carbon risk with a network approach
Authors: Michele Azzone, Maria Chiara Pocelli, Davide Stocco
Date: 2023-11-21 00:00:00
Tasks: Network Embedding

Sustainable investing refers to the integration of environmental and social aspects in investors' decisions. We propose a novel methodology based on the Triangulated Maximally Filtered Graph and node2vec algorithms to construct an hedging portfolio for climate risk, represented by various risk factors, among which the CO2 and the ESG ones. The CO2 factor is strongly correlated consistently over time with the Utility sector, which is the most carbon intensive in the S&P 500 index. Conversely, identifying a group of sectors linked to the ESG factor proves challenging. As a consequence, while it is possible to obtain an efficient hedging portfolio strategy with our methodology for the carbon factor, the same cannot be achieved for the ESG one. The ESG scores appears to be an indicator too broadly defined for market applications. These results support the idea that bank capital requirements should take into account carbon risk.
Measure of Dependence for Financial Time-Series
Authors: Martin Winistörfer, Ivan Zhdankin
Date: 2023-11-15 00:00:00
Tasks: Time Series

Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the groundwork for a comprehensive analysis of shaping time-series data and evaluating various measures of dependence. Lastly, we present a method, framework, and a concrete example for selecting and evaluating a suitable measure of dependence.
A simulated electronic market with speculative behaviour and bubble formation
Authors: Nicolas Cofre, Magdalena Mosionek-Schweda
Date: 2023-11-21 00:00:00

This paper presents an agent based model of an electronic market with two types of trading agents. One type follows a mean reverting strategy and the other, the speculative trader, tracks the maximum realised return over recent trades. The speculators have a distribution of returns concentrated on negative returns, with a small fraction making profits. The market experiences an increased volatility and prices that greatly depart from the fundamental value of the asset. Our research provides synthetic datasets of the order book to study its dynamics under different levels of speculation
Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
Authors: Xuanle Zhao, Yue Sun, Tielin Zhang, Bo Xu
Date: 2023-11-21 00:00:00

Neural operators improve conventional neural networks by expanding their capabilities of functional mappings between different function spaces to solve partial differential equations (PDEs). One of the most notable methods is the Fourier Neural Operator (FNO), which draws inspiration from Green's function method and directly approximates operator kernels in the frequency domain. However, after empirical observation followed by theoretical validation, we demonstrate that the FNO approximates kernels primarily in a relatively low-frequency domain. This suggests a limited capability in solving complex PDEs, particularly those characterized by rapid coefficient changes and oscillations in the solution space. Such cases are crucial in specific scenarios, like atmospheric convection and ocean circulation. To address this challenge, inspired by the translation equivariant of the convolution kernel, we propose a novel hierarchical Fourier neural operator along with convolution-residual layers and attention mechanisms to make them complementary in the frequency domain to solve complex PDEs. We perform experiments on forward and reverse problems of multiscale elliptic equations, Navier-Stokes equations, and other physical scenarios, and find that the proposed method achieves superior performance in these PDE benchmarks, especially for equations characterized by rapid coefficient variations.
Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods
Authors: Saman Yazdannik, Morteza Tayefi, Mojtaba Farrokh
Date: 2023-11-21 00:00:00

This study designs and evaluates multiple nonlinear system identification techniques for modeling the UAV swarm system in planar space. learning methods such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is to forecast future swarm trajectories by accurately approximating the nonlinear dynamics of the swarm model. The modeling process is performed using both transient and steady-state data from swarm simulations. Results show that the combination of Neural ODE with a well-trained model using transient data is robust for varying initial conditions and outperforms other learning methods in accurately predicting swarm stability.
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics
Authors: Honghui Du, Qizhi He
Date: 2023-11-21 00:00:00
Tasks: Form, Physics-informed machine learning, Unity

We present the neural-integrated meshfree (NIM) method, a differentiable programming-based hybrid meshfree approach within the field of computational mechanics. NIM seamlessly integrates traditional physics-based meshfree discretization techniques with deep learning architectures. It employs a hybrid approximation scheme, NeuroPU, to effectively represent the solution by combining continuous DNN representations with partition of unity (PU) basis functions associated with the underlying spatial discretization. This neural-numerical hybridization not only enhances the solution representation through functional space decomposition but also reduces both the size of DNN model and the need for spatial gradient computations based on automatic differentiation, leading to a significant improvement in training efficiency. Under the NIM framework, we propose two truly meshfree solvers: the strong form-based NIM (S-NIM) and the local variational form-based NIM (V-NIM). In the S-NIM solver, the strong-form governing equation is directly considered in the loss function, while the V-NIM solver employs a local Petrov-Galerkin approach that allows the construction of variational residuals based on arbitrary overlapping subdomains. This ensures both the satisfaction of underlying physics and the preservation of meshfree property. We perform extensive numerical experiments on both stationary and transient benchmark problems to assess the effectiveness of the proposed NIM methods in terms of accuracy, scalability, generalizability, and convergence properties. Moreover, comparative analysis with other physics-informed machine learning methods demonstrates that NIM, especially V-NIM, significantly enhances both accuracy and efficiency in end-to-end predictive capabilities.
Clustered Policy Decision Ranking
Authors: Mark Levin, Hana Chockler
Date: 2023-11-21 00:00:00
Tasks: Fault localization, Reinforcement Learning (RL)

Policies trained via reinforcement learning (RL) are often very complex even for simple tasks. In an episode with n time steps, a policy will make n decisions on actions to take, many of which may appear non-intuitive to the observer. Moreover, it is not clear which of these decisions directly contribute towards achieving the reward and how significant their contribution is. Given a trained policy, we propose a black-box method based on statistical covariance estimation that clusters the states of the environment and ranks each cluster according to the importance of decisions made in its states. We compare our measure against a previous statistical fault localization based ranking procedure.