pwc-archive/papers-with-abstracts huggingface dataset:
Using a quantitative assessment of propulsion biomechanics in wheelchair racing to guide the design of personalized gloves: a case study
Authors: Félix Chénier, Gerald Parent, Mikaël Leblanc, Colombe Bélaise, Mathieu Andrieux
Date: 2023-09-15 00:00:00

This study with a T-52 class wheelchair racing athlete aimed to combine quantitative biomechanical measurements to the athlete's perception to design and test different prototypes of a new kind of rigid gloves. Three personalized rigid gloves with various, fixed wrist extension angles were prototyped and tested on a treadmill in a biomechanics laboratory. The prototype with 45{\deg} wrist extension was the athlete's favourite as it reduced his perception of effort. Biomechanical assessment and user-experience data indicated that his favourite prototype increased wrist stability throughout the propulsion cycle while maintaining a very similar propulsion technique to the athlete's prior soft gloves. Moreover, the inclusion of an innovative attachment system on the new gloves allowed the athlete to put his gloves on by himself, eliminating the need for external assistance and thus significantly increasing his autonomy. This multidisciplinary approach helped to prototype and develop a new rigid personalized gloves concept and is clearly a promising avenue to tailor adaptive sports equipment to an athlete's needs.
Mathematical modeling of heterogeneous stem cell regeneration: from cell division to Waddington's epigenetic landscape
Authors: Jinzhi Lei
Date: 2023-09-14 00:00:00

Stem cell regeneration is a crucial biological process for most self-renewing tissues during the development and maintenance of tissue homeostasis. In developing the mathematical models of stem cell regeneration and tissue development, cell division is the core process connecting different scale biological processes and leading to changes in cell population number and the epigenetic state of cells. This chapter focuses on the primary strategies for modeling cell division in biological systems. The Lagrange coordinate modeling approach considers gene network dynamics within each cell and random changes in cell states and model parameters during cell division. In contrast, the Euler coordinate modeling approach formulates the evolution of cell population numbers with the same epigenetic state via a differential-integral equation. These strategies focus on different scale dynamics, respectively, and result in two methods of modeling Waddington's epigenetic landscape: the Fokker-Planck equation and the differential-integral equation approaches. The differential-integral equation approach formulates the evolution of cell population density based on simple assumptions in cell proliferation, apoptosis, differentiation, and epigenetic state transitions during cell division. Moreover, machine learning methods can establish low-dimensional macroscopic measurements of a cell based on single-cell RNA sequencing data. The low dimensional measurements can quantify the epigenetic state of cells and become connections between static single-cell RNA sequencing data with dynamic equations for tissue development processes. The differential-integral equation presented in this chapter provides a reasonable approach to understanding the complex biological processes of tissue development and tumor progression.
Neuromuscular and Metabolic Responses during Repeated Bouts of Loaded Downhill Walking
Authors: Emeric Chalchat, Julien Siracusa, Luis Peñailillo, Alexandra Malgoyre, Cyprien Bourrilhon, Keyne Charlot, Vincent Martin, Sebastian Garcia-Vicencio
Date: 2023-09-18 00:00:00

The aim of this study was to compare vastus lateralis (VL) and rectus femoris (RF) muscles for their nervous and mechanical adaptations during two bouts of downhill walking (DW) with load carriage performed two weeks apart. Moreover, we investigated cardio-metabolic and perceived exertion responses during both DW bouts. Methods Seventeen participants performed two 45-min sessions of loaded DW (30% of body mass; slope: -25 %; speed: 4.5 km$\times$h -1 ) separated by two weeks. Rating of perceived exertion (RPE), cost of walking (C w ), heart rate (HR), and EMG activity of thigh muscles were assessed during the DW. Muscle shear elastic modulus ($\mu$) of RF and VL were assessed before each exercise bout. Maximal voluntary contraction (MVC) torque was assessed before (PRE), immediately after (POST), 24 and 48 h after the two exercise bouts. Results MVC torque decreased from POST (-23.7 $\pm$ 9.2%) to 48 h (-19.2 $\pm$ 11.9%) after the first exercise (Ex1), whereas it was significantly reduced only at POST (-14.6 $\pm$ 11.0%) after the second exercise (Ex2) (p < 0.001). RPE (Ex1: 12.3 $\pm$ 1.9; Ex2: 10.8 $\pm$ 2.0), HR (Ex1: 156 $\pm$ 23 bpm; Ex2: 145 $\pm$ 25 bpm), C w (Ex1: 4.5 $\pm$ 0.9 J$\times$m -1 $\times$kg -1 ; Ex2: 4.1 $\pm$ 0.7 J$\times$m -1 $\times$kg -1 ) and RF EMG activity (Ex1: 0.071 $\pm$ 0.028 mV; Ex2: 0.041 $\pm$ 0.014 mV) were significantly decreased during Ex2 compared to Ex1 (p < 0.01). RF $\mu$ was significantly greater in Ex2 (0.44 $\pm$ 0.18) compared to Ex1 (0.56 $\pm$ 0.27; p < 0.001). Conclusions The RF muscle displayed specific mechanical and nervous adaptations to repeated DW bouts as compared to VL. Moreover, the muscle adaptations conferred by the first bout of DW could have induced greater exercise efficiency, inducing lesser perceived exertion and cardio-metabolic demand when the same exercise was repeated two weeks later.
Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis
Authors: S. M. Masrur Ahmed
Date: 2023-09-16 00:00:00
Tasks: Algorithmic Trading

Backtest is a way of financial risk evaluation which helps to analyze how our trading algorithm would work in markets with past time frame. The high volatility situation has always been a critical situation which creates challenges for algorithmic traders. The paper investigates different models of sizing in financial trading and backtest to high volatility situations to understand how sizing models can lower the models of VaR during crisis events. Hence it tries to show that how crisis events with high volatility can be controlled using short and long positional size. The paper also investigates stocks with AR, ARIMA, LSTM, GARCH with ETF data.
Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision
Authors: Álvaro Cartea, Fayçal Drissi, Marcello Monga
Date: 2023-09-15 00:00:00

Constant product markets with concentrated liquidity (CL) are the most popular type of automated market makers. In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools. Their wealth results from fee income, the value of their holdings in the pool, and rebalancing costs. Next, we derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP's liquidity range is determined by the profitability of the pool (provision fees minus gas fees), the predictable losses (PL) of the LP's position, and concentration risk. Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP's range of liquidity. When the drift in the marginal rate is stochastic, we show how to optimally skew the range of liquidity to increase fee revenue and profit from the expected changes in the marginal rate. Finally, we use Uniswap v3 data to show that, on average, LPs have traded at a significant loss, and to show that the out-of-sample performance of our strategy is superior to the historical performance of LPs in the pool we consider.
Closed-form solutions for VIX derivatives in a Legendre empirical model
Authors: Ying-Li Wang, Cheng-Long Xu, Ping He
Date: 2023-09-15 00:00:00
Tasks: Form, Management

In this paper, we introduce a data-driven, single-parameter Markov diffusion model for the VIX. The volatility factor evolves in $(-1,1)$ with a uniform invariant distribution ensured by Legendre polynomials, mapped to the empirical distribution. We derive analytical series solutions for VIX futures and options using separation of variables to solve the Feynman-Kac PDE. Compared to the 3/2 model, our approach offers equal or superior accuracy and flexibility, providing an efficient, robust alternative for VIX pricing and risk management. Code and data are available at github.com/gagawjbytw/empirical-VIX.
Pragmatic Comparison Analysis of Alternative Option Pricing Models
Authors: Natasha Latif, Shafqat Ali Shad, Muhammad Usman, Chandan Kumar, Bahman B Motii, MD Mahfuzer Rahman, Khuram Shafi, Zahra Idrees
Date: 2023-08-23 00:00:00

In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
Authors: Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren
Date: 2023-09-15 00:00:00
Tasks: Dynamic Time Warping, Time Series

In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationships is valuable in downstream tasks, such as control, forecasting, and clustering. By understanding the temporal dependencies between different time series, one can better comprehend the complex interactions and patterns within the system. We develop a cluster-driven methodology based on dynamic time warping for robust detection of lead-lag relationships in lagged multi-factor models. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our algorithm is able to robustly detect lead-lag relationships in financial markets, which can be subsequently leveraged in trading strategies with significant economic benefits.
Variance Reduction of Resampling for Sequential Monte Carlo
Authors: Xiongming Dai, Gerald Baumgartner
Date: 2023-09-10 00:00:00

A resampling scheme provides a way to switch low-weight particles for sequential Monte Carlo with higher-weight particles representing the objective distribution. The less the variance of the weight distribution is, the more concentrated the effective particles are, and the quicker and more accurate it is to approximate the hidden Markov model, especially for the nonlinear case. We propose a repetitive deterministic domain with median ergodicity for resampling and have achieved the lowest variances compared to the other resampling methods. As the size of the deterministic domain $M\ll N$ (the size of population), given a feasible size of particles, our algorithm is faster than the state of the art, which is verified by theoretical deduction and experiments of a hidden Markov model in both the linear and non-linear cases.
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup
Authors: Hannes Fassold
Date: 2023-08-28 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

The standard recipe applied in transfer learning is to finetune a pretrained model on the task-specific dataset with different hyperparameter settings and pick the model with the highest accuracy on the validation dataset. Unfortunately, this leads to models which do not perform well under distribution shifts, e.g. when the model is given graphical sketches of the object as input instead of photos. In order to address this, we propose the manifold mixing model soup, an algorithm which mixes together the latent space manifolds of multiple finetuned models in an optimal way in order to generate a fused model. We show that the fused model gives significantly better out-of-distribution performance (+3.5 % compared to best individual model) when finetuning a CLIP model for image classification. In addition, it provides also better accuracy on the original dataset where the finetuning has been done.
Media of Langue: The Interface for Exploring Word Translation Network/Space
Authors: Goki Muramoto, Atsuki Sato, Takayoshi Koyama
Date: 2023-08-25 00:00:00
Tasks: Translation, Word Translation

In the human activity of word translation, two languages face each other, mutually searching their own language system for the semantic place of words in the other language. We discover the huge network formed by the chain of these mutual translations as Word Translation Network, a network where words are nodes, and translation volume is represented as edges, and propose Media of Langue, a novel interface for exploring this network. Media of Langue points to the semantic configurations of many words in multiple languages at once, containing the information of existing dictionaries such as bilingual and synonym dictionaries. We have also implemented and published this interface as a web application, focusing on seven language pairs. This paper first defines the Word Translation Network and describes how to actually construct the network from bilingual corpora, followed by an analysis of the properties of the network. Next, we explain how to design a Media of Langue using the Word Translation Network, and finally, we analyze the features of the Media of Langue as a dictionary. Our website is https://www.media-of-langue.org .
Explaining Vision and Language through Graphs of Events in Space and Time
Authors: Mihai Masala, Nicolae Cudlenco, Traian Rebedea, Marius Leordeanu
Date: 2023-08-29 00:00:00
Tasks: Graph Matching, Video Generation

Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two domains. In this work we come to address this limitation and propose the Graph of Events in Space and Time (GEST), by which we can represent, create and explain, both visual and linguistic stories. We provide a theoretical justification of our model and an experimental validation, which proves that GEST can bring a solid complementary value along powerful deep learning models. In particular, GEST can help improve at the content-level the generation of videos from text, by being easily incorporated into our novel video generation engine. Additionally, by using efficient graph matching techniques, the GEST graphs can also improve the comparisons between texts at the semantic level.
Maneuver Decision-Making Through Proximal Policy Optimization And Monte Carlo Tree Search
Authors: Zhang Hong-Peng
Date: 2023-08-28 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning

Maneuver decision-making can be regarded as a Markov decision process and can be address by reinforcement learning. However, original reinforcement learning algorithms can hardly solve the maneuvering decision-making problem. One reason is that agents use random actions in the early stages of training, which makes it difficult to get rewards and learn how to make effective decisions. To address this issue, a method based on proximal policy optimization and Monte Carlo tree search is proposed. The method uses proximal policy optimization to train the agent, and regards the results of air combat as targets to train the value network. Then, based on the value network and the visit count of each node, Monte Carlo tree search is used to find the actions with more expected returns than random actions, which can improve the training performance. The ablation studies and simulation experiments indicate that agents trained by the proposed method can make different decisions according to different states, which demonstrates that the method can solve the maneuvering decision problem that the original reinforcement learning algorithm cannot solve.
Artificial Intelligence for Web 3.0: A Comprehensive Survey
Authors: Meng Shen, Zhehui Tan, Dusit Niyato, Yuzhi Liu, Jiawen Kang, Zehui Xiong, Liehuang Zhu, Wei Wang, Xuemin, Shen
Date: 2023-08-17 00:00:00
Tasks: Management, Survey

Web 3.0 is the new generation of the Internet that is reconstructed with distributed technology, which focuses on data ownership and value expression. Also, it operates under the principle that data and digital assets should be owned and controlled by users rather than large corporations. In this survey, we explore the current development state of Web 3.0 and the application of AI Technology in Web 3.0. Through investigating the existing applications and components of Web 3.0, we propose an architectural framework for Web 3.0 from the perspective of ecological application scenarios. We outline and divide the ecology of Web 3.0 into four layers. The main functions of each layer are data management, value circulation, ecological governance, and application scenarios. Our investigation delves into the major challenges and issues present in each of these layers. In this context, AI has shown its strong potential to solve existing problems of Web 3.0. We illustrate the crucial role of AI in the foundation and growth of Web 3.0. We begin by providing an overview of AI, including machine learning algorithms and deep learning techniques. Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3.0 and offer some insights into its potential future development direction.
Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc
Authors: Doug Lenat, Gary Marcus
Date: 2023-07-31 00:00:00
Tasks: Knowledge Graphs

Generative AI, the most popular current approach to AI, consists of large language models (LLMs) that are trained to produce outputs that are plausible, but not necessarily correct. Although their abilities are often uncanny, they are lacking in aspects of reasoning, leading LLMs to be less than completely trustworthy. Furthermore, their results tend to be both unpredictable and uninterpretable. We lay out 16 desiderata for future AI, and discuss an alternative approach to AI which could theoretically address many of the limitations associated with current approaches: AI educated with curated pieces of explicit knowledge and rules of thumb, enabling an inference engine to automatically deduce the logical entailments of all that knowledge. Even long arguments produced this way can be both trustworthy and interpretable, since the full step-by-step line of reasoning is always available, and for each step the provenance of the knowledge used can be documented and audited. There is however a catch: if the logical language is expressive enough to fully represent the meaning of anything we can say in English, then the inference engine runs much too slowly. That's why symbolic AI systems typically settle for some fast but much less expressive logic, such as knowledge graphs. We describe how one AI system, Cyc, has developed ways to overcome that tradeoff and is able to reason in higher order logic in real time. We suggest that any trustworthy general AI will need to hybridize the approaches, the LLM approach and more formal approach, and lay out a path to realizing that dream.
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
Authors: Ziqian Zhang, Nana Bao, Xingting Yan, Aokai Zhu, Chenyang Li, MingYu Liu
Date: 2023-09-19 00:00:00
Tasks: Time Series, Time Series Forecasting

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world.
NoisyNN: Exploring the Impact of Information Entropy Change in Learning Systems
Authors: Xiaowei Yu, Zhe Huang, Minheng Chen, Yao Xue, Tianming Liu, Dajiang Zhu
Date: 2023-09-19 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

We investigate the impact of entropy change in deep learning systems by noise injection at different levels, including the embedding space and the image. The series of models that employ our methodology are collectively known as Noisy Neural Networks (NoisyNN), with examples such as NoisyViT and NoisyCNN. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this work shows noise can be an effective way to change the entropy of the learning system. We demonstrate that specific noise can boost the performance of various deep models under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the task complexity. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of 95$\%$ on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise, can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.