pwc-archive/papers-with-abstracts huggingface dataset:
Energy landscape reveals the underlying mechanism of cancer-adipose conversion with gene network models
Authors: Zihao Chen, Jia Lu, Xing-Ming Zhao, Haiyang Yu, Chunhe Li
Date: 2023-05-22 00:00:00

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments proposed that cancer cells can be transformed into adipocytes with combination drugs. However, the underlying mechanisms for how these drugs work from molecular network perspective remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), we adopt a systems biology approach by combing mathematical modeling and molecular experiments based on the underlying molecular regulatory network. We identified four types of attractors which correspond to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that the intermediate states play critical roles in cancer to adipose transition. Through a landscape control strategy, we identified two new therapeutic strategies for drug combinations to promote CAC. We further verified these predictions by molecular experiments in different cell lines. Our combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. Our results revealed the underlying mechanism for intermediate cell states governing the CAC, and identified new potential drug combinations to induce cancer adipogenesis.
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions
Authors: Zijun Zhang, Adam R. Lamson, Michael Shelley, Olga Troyanskaya
Date: 2023-05-18 00:00:00
Tasks: Neural Architecture Search, Transfer Learning

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework which addresses these challenges with data-driven and biophysically interpretable models for determining the kinetics of biochemical systems. First, it uses in vitro kinetic assays to rapidly hypothesize an ensemble of high-quality Kinetically Interpretable Neural Networks (KINNs) that predict reaction rates. It then employs a novel transfer learning step, where the KINNs are inserted as intermediary layers into deeper convolutional neural networks, fine-tuning the predictions for reaction-dependent in vivo outcomes. Elektrum makes effective use of the limited, but clean in vitro data and the complex, yet plentiful in vivo data that captures cellular context. We apply Elektrum to predict CRISPR-Cas9 off-target editing probabilities and demonstrate that Elektrum achieves state-of-the-art performance, regularizes neural network architectures, and maintains physical interpretability.
Topological data analysis suggests human brain networks reconfiguration in the transition from a resting state to cognitive load
Authors: Ilya Ernston, Arsenii Onuchin, Timofey Adamovich
Date: 2023-05-21 00:00:00
Tasks: Functional Connectivity, Topological Data Analysis

The functional network of the brain continually adapts to changing environmental demands. The environmental changes closely connect with changes of active cognitive processes. In recent years, the network approach has emerged as a promising method for analyzing the neurophysiological mechanisms that underlie psychological functions. The present study examines topological characteristics of functional brain networks in resting state and in cognitive load, provided by the execution of the Sternberg Item Recognition Paradigm (SIRP) based on electroencephalographic data. We propose that the topological properties of the functional networks in the human brain are distinct between cognitive load and resting state with higher integration in the networks during cognitive load. It was shown that topological features of functional connectomes strongly depend on the type of cognitive process performed by the subject and change in accordance with task change. The analysis also demonstrated that functional connectivity during working memory tasks showed a faster emergence of homology groups generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role.
Mapping Biological Neuron Dynamics into an Interpretable Two-layer Artificial Neural Network
Authors: Jingyang Ma, Songting Li, Douglas Zhou
Date: 2023-05-21 00:00:00
Tasks: image-classification, Image Classification

Dendrites are crucial structures for computation of an individual neuron. It has been shown that the dynamics of a biological neuron with dendrites can be approximated by artificial neural networks (ANN) with deep structure. However, it remains unclear whether a neuron can be further captured by a simple, biologically plausible ANN. In this work, we develop a two-layer ANN, named as dendritic bilinear neural network (DBNN), to accurately predict both the sub-threshold voltage and spike time at the soma of biological neuron models with dendritic structure. Our DBNN is found to be interpretable and well captures the dendritic integration process of biological neurons including a bilinear rule revealed in previous works. In addition, we show DBNN is capable of performing diverse tasks including direction selectivity, coincidence detection, and image classification. Our work proposes a biologically interpretable ANN that characterizes the computation of biological neurons, which can be potentially implemented in the deep learning framework to improve computational ability.
Quantifying stimulus-relevant representational drift using cross-modality contrastive learning
Authors: Siwei Wang, Elizabeth A de Laittre, Jason MacLean, Stephanie E Palmer
Date: 2023-05-19 00:00:00
Tasks: Contrastive Learning, Navigate

Previous works investigating representational drift from sensory to central nervous systems converged to show that neural coding, especially at the population level, readily overcomes these session-to-session fluctuations. However, representational drift in the primary visual cortex is more prominent when presenting naturalistic stimuli than artificial stimuli. Animals continuously navigate natural environments during the evolutionary timescale. Why did evolution not get rid of representational drift if it was just an inconvenience? Here, we investigate how representational drift simultaneously influences the encoding of multiple behaviorally relevant features in a natural movie stimulus. Because natural environments contain multiple interacting spatio-temporal features, previous works only provided incomplete understanding of representational drift because of such simplification. Here, we use cross modality contrastive learning to learn an embedding of neural activity that retains only those relevant components of the natural movie stimulus. We also observe that our learned embedding is near-optimal in decoding a whole suite of natural features (scene, optic flow, complex spatio-temporal features, and time) and generalizable to decode those features from single-trial or novel hold-out data. Using this embedding as a surrogate model, we observe that representational drift perturbs the local geometry of the embedding, and this results in various changes in performance when we decode from a different session (90 min later) even at the population level. Our work further suggests that a separate compensation mechanism may be necessary for the optic flow features, as their autocorrelation scale is shorter than the minimum time needed to discriminate scene texture features. Thus, representational drift may encourage neural processing flexibility rather than be a mere nuisance.
Enhancing biodiversity through intraspecific suppression in large ecosystems
Authors: Seong-Gyu Yang, Hye Jin Park
Date: 2023-05-21 00:00:00
Tasks: Diversity, Unity

The competitive exclusion principle (CEP) is a fundamental concept in the niche theory, which posits that the number of available resources constrains the coexistence of species. While the CEP offers an intuitive explanation on coexistence, it has been challenged by counterexamples observed in nature. One prominent counterexample is the phytoplankton community, known as the paradox of the plankton. Diverse phytoplankton species coexist in the ocean even though they demand a limited number of resources. To shed light on this remarkable biodiversity in large ecosystems quantitatively, we consider \textit{intraspecific suppression} into the generalized MacArthur's consumer-resource model and study the relative diversity, the number ratio between coexisting consumers and resource kinds. By employing the cavity method and generating functional analysis, we demonstrate that, under intraspecific suppression, the number of consumer species can surpass the available resources. This phenomenon stems from the fact that intraspecific suppression prevents the emergence of dominant species, thereby fostering high biodiversity. Furthermore, our study highlights that the impact of this competition on biodiversity is contingent upon environmental conditions. Our work presents a comprehensive framework that encompasses the CEP and its counterexamples by introducing intraspecific suppression.
Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples
Authors: Lucy D'Agostino McGowan, Shirlee Wohl, Justin Lessler
Date: 2023-05-20 00:00:00

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. Here we present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.
Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market
Authors: Peyman Alipour, Ali Foroush Bastani
Date: 2023-05-21 00:00:00
Tasks: Benchmarking, Financial Analysis

Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [Jiang C., Ma Y. and An Y. "The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis" , International Review of Financial Analysis 18(4) (2009): 185-197]. In their study, the risky asset follows a geometric Brownian motion with constant drift and diffusion coefficients. In this paper, we first extend their idea to a regime-switching framework in which the expected return of the risky asset and its volatility depend on an unobservable Markovian term which describes the cyclical nature of asset returns in modern financial markets. We then analyze and compare the resulting VaR-based portfolio insurance (VBPI) strategy with the well-known constant proportion portfolio insurance (CPPI) strategy. In this respect, we employ a variety of performance evaluation criteria such as Sharpe, Omega and Kappa ratios to compare the two methods. Our results indicate that the CPPI strategy has a better risk-return tradeoff in most of the scenarios analyzed and maintains a relatively stable return profile for the resulting portfolio at the maturity.
Efficient Learning of Nested Deep Hedging using Multiple Options
Authors: Masanori Hirano, Kentaro Imajo, Kentaro Minami, Takuya Shimada
Date: 2023-05-20 00:00:00

Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple options. To extend the deep hedging framework to this setting, the options used as hedging instruments also have to be priced during training. While one might use classical pricing model such as the Black-Scholes formula, ignoring frictions can offer arbitrage opportunities which are undesirable for deep hedging learning. The goal of this study is to develop a nested deep hedging method. That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions. However, since the prices of hedging instruments have to be calculated under many different conditions, the entire learning process can be computationally intractable. To overcome this problem, we propose an efficient learning method for nested deep hedging. Our method consists of three techniques to circumvent computational intractability, each of which reduces redundant computations during training. We show through experiments that the Black-Scholes pricing of hedge instruments can admit significant arbitrage opportunities, which are not observed when the pricing is performed by deep hedging. We also demonstrate that our proposed method successfully reduces the hedging risks compared to a baseline method that does not use options as hedging instruments.
The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis
Authors: Aman Saggu, Lennart Ante
Date: 2023-05-22 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The introduction of OpenAI's large language model, ChatGPT, catalyzed investor attention towards artificial intelligence (AI) technologies, including AI-related crypto assets not directly related to ChatGPT. Utilizing the synthetic difference-in-difference methodology, we identify significant 'ChatGPT effects' with returns of AI-related crypto assets experiencing average returns ranging between 10.7% and 15.6% (35.5% to 41.3%) in the one-month (two-month) period after the ChatGPT launch. Furthermore, Google search volumes, a proxy for attention to AI, emerged as critical pricing indicators for AI-related crypto post-launch. We conclude that investors perceived AI-assets as possessing heightened potential or value after the launch, resulting in higher market valuations.
A Simulation Package in VBA to Support Finance Students for Constructing Optimal Portfolios
Authors: Abdulnasser Hatemi-J, Alan Mustafa
Date: 2023-05-22 00:00:00

This paper introduces a software component created in Visual Basic for Applications (VBA) that can be applied for creating an optimal portfolio using two different methods. The first method is the seminal approach of Markowitz that is based on finding budget shares via the minimization of the variance of the underlying portfolio. The second method is developed by El-Khatib and Hatemi-J, which combines risk and return directly in the optimization problem and yields budget shares that lead to maximizing the risk adjusted return of the portfolio. This approach is consistent with the expectation of rational investors since these investors consider both risk and return as the fundamental basis for selection of the investment assets. Our package offers another advantage that is usually neglected in the literature, which is the number of assets that should be included in the portfolio. The common practice is to assume that the number of assets is given exogenously when the portfolio is constructed. However, the current software component constructs all possible combinations and thus the investor can figure out empirically which portfolio is the best one among all portfolios considered. The software is consumer friendly via a graphical user interface. An application is also provided to demonstrate how the software can be used using real-time series data for several assets.
Study on Intelligent Forecasting of Credit Bond Default Risk
Authors: Kai Ren
Date: 2023-05-20 00:00:00
Tasks: Time Series

Credit risk in the China's bond market has become increasingly evident, creating a progressively escalating risk of default for credit bond investors. Given the current incomplete and inaccurate bond information disclosure, timely tracking and forecasting the individual credit bond default risks have become essential to maintain market stability and ensure healthy development. This paper proposes an Intelligent Forecasting Framework for Default Risk that provides precise day-by-day default risk prediction. In this framework, we first summarize the factors that impact credit bond defaults and construct a risk index system. Then, we employ a combined default probability annotation method based on the evolutionary characteristics of bond default risk. The method considers the weighted average of Variational Bayesian Gaussian Mixture estimation, Market Index estimation, and Default Trend Backward estimation for daily default risk annotation of matured or defaulted bonds according to the risk index system. Moreover, to mine time-series correlation and cross-sectional index correlation features efficiently, an intelligent prediction model for Chinese credit bond default risk is designed using the ConvLSTM neural network and trained with structured feature data. The experiments demonstrate that the predicted individual bond risk is slightly higher and substantially more responsive to fluctuations than the risk indicated by authoritative ratings, thereby improving on the inadequacies of inflated and untimely bond ratings. Consequently, this study's findings offer multiple insights for regulators, issuers, and investors.
Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis
Authors: Matthieu Garcin
Date: 2023-05-22 00:00:00
Tasks: Density Estimation

We are interested in the nonparametric estimation of the probability density of price returns, using the kernel approach. The output of the method heavily relies on the selection of a bandwidth parameter. Many selection methods have been proposed in the statistical literature. We put forward an alternative selection method based on a criterion coming from information theory and from the physics of complex systems: the bandwidth to be selected maximizes a new measure of complexity, with the aim of avoiding both overfitting and underfitting. We review existing methods of bandwidth selection and show that they lead to contradictory conclusions regarding the complexity of the probability distribution of price returns. This has also some striking consequences in the evaluation of the relevance of the efficient market hypothesis. We apply these methods to real financial data, focusing on the Bitcoin.
Neural information coding for efficient spike-based image denoising
Authors: Andrea Castagnetti, Alain Pegatoquet, Benoît Miramond
Date: 2023-05-15 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising, Image Restoration

In recent years, Deep Convolutional Neural Networks (DCNNs) have outreached the performance of classical algorithms for image restoration tasks. However most of these methods are not suited for computational efficiency and are therefore too expensive to be executed on embedded and mobile devices. In this work we investigate Spiking Neural Networks (SNNs) for Gaussian denoising, with the goal of approaching the performance of conventional DCNN while reducing the computational load. We propose a formal analysis of the information conversion processing carried out by the Leaky Integrate and Fire (LIF) neurons and we compare its performance with the classical rate-coding mechanism. The neural coding schemes are then evaluated through experiments in terms of denoising performance and computation efficiency for a state-of-the-art deep convolutional neural network. Our results show that SNNs with LIF neurons can provide competitive denoising performance but at a reduced computational cost.
Iterative Adversarial Attack on Image-guided Story Ending Generation
Authors: Youze Wang, WenBo Hu, Richang Hong
Date: 2023-05-16 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness, Adversarial Text, Image-guided Story Ending Generation, Machine Translation, Multimodal Machine Translation, Question Answering, Text Generation

Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial aspect that involves processing data from multiple modalities and outputting text. The image-guided story ending generation (IgSEG) is a particularly significant task, targeting on an understanding of complex relationships between text and image data with a complete story text ending. Unfortunately, deep neural networks, which are the backbone of recent IgSEG models, are vulnerable to adversarial samples. Current adversarial attack methods mainly focus on single-modality data and do not analyze adversarial attacks for multimodal text generation tasks that use cross-modal information. To this end, we propose an iterative adversarial attack method (Iterative-attack) that fuses image and text modality attacks, allowing for an attack search for adversarial text and image in an more effective iterative way. Experimental results demonstrate that the proposed method outperforms existing single-modal and non-iterative multimodal attack methods, indicating the potential for improving the adversarial robustness of multimodal text generation models, such as multimodal machine translation, multimodal question answering, etc.
FR-Net:A Light-weight FFT Residual Net For Gaze Estimation
Authors: Tao Xu, Bo Wu, Ruilong Fan, Yun Zhou, Di Huang
Date: 2023-05-04 00:00:00
Tasks: Gaze Estimation

Gaze estimation is a crucial task in computer vision, however, existing methods suffer from high computational costs, which limit their practical deployment in resource-limited environments. In this paper, we propose a novel lightweight model, FR-Net, for accurate gaze angle estimation while significantly reducing computational complexity. FR-Net utilizes the Fast Fourier Transform (FFT) to extract gaze-relevant features in frequency domains while reducing the number of parameters. Additionally, we introduce a shortcut component that focuses on the spatial domain to further improve the accuracy of our model. Our experimental results demonstrate that our approach achieves substantially lower gaze error angles (3.86 on MPII and 4.51 on EYEDIAP) compared to state-of-the-art gaze estimation methods, while utilizing 17 times fewer parameters (0.67M) and only 12\% of FLOPs (0.22B). Furthermore, our method outperforms existing lightweight methods in terms of accuracy and efficiency for the gaze estimation task. These results suggest that our proposed approach has significant potential applications in areas such as human-computer interaction and driver assistance systems.