pwc-archive/papers-with-abstracts huggingface dataset:
Exploring novel prognostic biomarkers and biologic processes involved in NASH, cirrhosis and HCC based on survival analysis using systems biology approach
Authors: Sedigheh Behrouzifar
Date: 2023-06-28 00:00:00
Tasks: Survival Analysis

There is an unmet need to develop medications or drug combinations which can stop advancement of NASH to liver cirrhosis and HCC. Therefore, identifying key biomarkers based on overall survival and the exploring biological processes involved in the pathogenesis and progression of NASH toward cirrhosis and HCC to improve therapeutic interventions is necessary. The microarray dataset from the GPL13667 platform was downloaded from the Gene Expression Omnibus (GEO). The inclusion criteria for the DEGs included an adjusted p-value <0.05 and a log(2) fold change >1. In the first step, protein-protein interaction (PPI) network of differentially expressed genes (DEGs) in every three groups (NASH, Cirrhosis and HCC) was constructed using STRING online database. In the second step, the DEGs of each group were imported to Cytoscape software separately, and the genes with Degree >3 were selected. In the third step, the genes with Degree >3 were imported to Gephi software (0.9.2 version) and the genes with betweenness centrality >0 were selected. Venn diagram of these genes was depicted for the NASH, cirrhotic and HCC groups. According to venn diagram, 96 (NASH), 30 (cirrhotic) and 213 (HCC) genes were specifically upregulated (based on inclusion criteria). Among specific genes, 22 (NASH), 5 (cirrhotic) and 82 (HCC) genes were with poor overall survival. The overlap among the 3 groups (NASH, Cirrhosis and HCC) contained 4 upregulated genes HLA-F, HLA-DPA1, TPM1 and YWHAZ. From 4 upregulated genes, only YWHAZ gene was with poor overall survival. The present study detected new candidate genes and key biological processes in NASH, cirrhosis and HCC based on overall survival and using in silico analysis. Therefore, performing in vitro and in vivo researches to verify the results is necessary.
Identifying downregulated hub genes and key pathways in HBV-related hepatocellular carcinoma using systems biology approach
Authors: Sedigheh Behrouzifar
Date: 2023-06-28 00:00:00
Tasks: Diagnostic

Chronic Hepatitis B (CHB) is an independent risk factor for hepatocellular carcinoma (HCC) initiation without cirrhosis occurrence. Apart from the favorable effects of some antiviral drugs following tumor resection on the survival of HCC patients, the use of these agents is essential lifelong. Thus, designing the target-oriented therapeutic strategies to increase life expectancy in HCC patients would be very important. The present study aimed to identify downregulated hub genes and enriched pathways in HB-related HCC using a systems biology-based approach. Microarray data of GSE121248 were downloaded from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) with the cut-off criteria of adjusted p < 0.05 and Log Fold-change (FC) < -1.5 were selected. Then, the genes with the highest centrality were detected. Finally, the prognostic values of the hub genes were assessed. Six under-expressed hub genes with the highest interaction degree, Betweenness and Eigenvector centrality were including IGF-1, PTGS2, PLG, HGF, ESR-1 and CYP2B6. Among genes with high centrality, several genes including CYP2C9, ESR-1, CXCL2, CYP2C8, IGF-1, CYP3A4, CYP2E1, CERPINE-1 and PXR were prognostic in HCC. The important repressed pathways were including metabolic pathways and PI3K-Akt and chemokine signaling pathways. The under-expression of several genes implicated in metabolism, differentiation and chemotaxis might be a hallmark of the progression of HCC that can be considered as diagnostic and therapeutic targets.
Geomagnetic field influences probabilistic abstract decision-making in humans
Authors: Kwon-Seok Chae, In-Taek Oh, Soo Hyun Jeong, Yong-Hwan Kim, Soo-Chan Kim, Yongkuk Kim
Date: 2023-06-28 00:00:00
Tasks: Decision Making, Time Series

To resolve disputes or determine the order of things, people commonly use binary choices such as tossing a coin, even though it is obscure whether the empirical probability equals to the theoretical probability. The geomagnetic field (GMF) is broadly applied as a sensory cue for various movements in many organisms including humans, although our understanding is limited. Here we reveal a GMF-modulated probabilistic abstract decision-making in humans and the underlying mechanism, exploiting the zero-sum binary stone choice of Go game as a proof-of-principle. The large-scale data analyses of professional Go matches and in situ stone choice games showed that the empirical probabilities of the stone selections were remarkably different from the theoretical probability. In laboratory experiments, experimental probability in the decision-making was significantly influenced by GMF conditions and specific magnetic resonance frequency. Time series and stepwise systematic analyses pinpointed the intentionally uncontrollable decision-making as a primary modulating target. Notably, the continuum of GMF lines and anisotropic magnetic interplay between players were crucial to influence the magnetic field resonance-mediated abstract decision-making. Our findings provide unique insights into the impact of sensing GMF in decision-makings at tipping points and the quantum mechanical mechanism for manifesting the gap between theoretical and empirical probability in 3-dimensional living space.
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neurons
Authors: Matthew G. Thomas
Date: 2023-06-28 00:00:00
Tasks: Model Selection

Inferring the mechanisms underlying physiological and pathological processes in the brain from recorded electrical activity is challenging. Bayesian model selection and dynamic causal modelling aim to identify likely biophysical models to explain data and to fit the model parameters. Here, we use data generated by simulations to investigate the effectiveness of Bayesian model selection and dynamic causal modelling when applied at steady state in the frequency domain to identify and fit Jansen-Rit models. We first investigate the impact of the necessary assumption of linearity on the dynamics of the Jansen-Rit model. We then apply dynamic causal modelling and Bayesian model selection to data generated from simulations of linear neural mass models, non-linear neural mass models, and networks of discrete spiking neurons. Action potentials are a characteristic feature of neuronal dynamics but have not previously been explicitly included in simulations used to test Bayesian model selection or dynamic causal modelling. We find that the assumption of linearity abolishes the qualitative transitions seen as a function of the connectivity parameter in the original Jansen-Rit model. As with previous work, we find that the recovery procedures are effective when applied to data from linear Jansen-Rit neural mass models, however, when applying them to non-linear neural mass models and networks of discrete spiking neurons we find that their effectiveness is significantly reduced, suggesting caution is required when applying these methods.
New Perspectives on Sensitivity and Identifiability Analysis using the Unscented Kalman Filter
Authors: Harry Saxton, Xu Xu, Ian Halliday, Torsten Schenkel
Date: 2023-06-27 00:00:00
Tasks: Sensitivity

Detailed dynamical systems' models used in the life sciences may include hundreds of state variables and many input parameters, often with physical meaning. Therefore, efficient and unique input parameter identification, from experimental data, is an essential but challenging task for this class of model. To clarify our understating of the process (which within a clinical context amounts to a personalisation), we utilise the computational methods of Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We have applied these three techniques to a test-bench model of a single ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC) Windkessel electrical analogue model of the systemic circulation, chosen in view of its relative simplicity, interpretability and prior art. Utilising an efficient, novel and real-time implementation of the UKF (Code available at https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input parameters are efficiently recovered from experimental data \emph{even if they are not sensitive parameters in the currently accepted sense}. This result (i) exposes potential limitations in the standard interpretation of what it means for an input parameter to be designated identifiable and (ii) suggests that the concepts of sensitivity and identifiability may have a weaker relationship than commonly thought - at least in the presence of an appropriate data set. We rationalise these observations. Practically, we present results which show the UKF to be an efficient method for assigning personalised input parameters from experimental data in real-time, which enhances the clinical significance of our approach.
Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity
Authors: Qi Deng, Zhong-guo Zhou
Date: 2023-06-27 00:00:00

We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models. We demonstrate that these models produce superior predictability at extreme liquidity to their traditional counterparts. We provide empirical support by comparing the performances of a series of Mean Variance portfolios.
Expectile Quadrangle and Applications
Authors: Viktor Kuzmenko, Anton Malandii, Stan Uryasev
Date: 2023-06-28 00:00:00
Tasks: Management

The paper explores the concept of the \emph{expectile risk measure} within the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ theory, a quadrangle comprises four stochastic functions associated with a random variable: ``error'', ``regret'', ``risk'', and ``deviation''. These functions are interconnected through a stochastic function known as the ``statistic''. Expectile is a risk measure that, similar to VaR (quantile) and CVaR (superquantile), can be employed in risk management. While quadrangles based on VaR and CVaR statistics are well-established and widely used, the paper focuses on the recently proposed quadrangles based on expectile. The aim of this paper is to rigorously examine the properties of these Expectile Quadrangles, with particular emphasis on a quadrangle that encompasses expectile as both a statistic and a measure of risk.
A closed form model-free approximation for the Initial Margin of option portfolios
Authors: Claude Martini, Arianna Mingone
Date: 2023-06-28 00:00:00
Tasks: Form

Central clearing counterparty houses (CCPs) play a fundamental role in mitigating the counterparty risk for exchange traded options. CCPs cover for possible losses during the liquidation of a defaulting member's portfolio by collecting initial margins from their members. In this article we analyze the current state of the art in the industry for computing initial margins for options, whose core component is generally based on a VaR or Expected Shortfall risk measure. We derive an approximation formula for the VaR at short horizons in a model-free setting. This innovating formula has promising features and behaves in a much more satisfactory way than the classical Filtered Historical Simulation-based VaR in our numerical experiments. In addition, we consider the neural-SDE model for normalized call prices proposed by [Cohen et al., arXiv:2202.07148, 2022] and obtain a quasi-explicit formula for the VaR and a closed formula for the short term VaR in this model, due to its conditional affine structure.
Analysis of Indian foreign exchange markets: A Multifractal Detrended Fluctuation Analysis (MFDFA) approach
Authors: R. P. Datta
Date: 2023-06-28 00:00:00
Tasks: Time Series

The multifractal spectra of daily foreign exchange rates for US dollar (USD), the British Pound (GBP), the Euro (Euro) and the Japanese Yen (Yen) with respect to the Indian Rupee are analysed for the period 6th January 1999 to 24th July 2018. We observe that the time series of logarithmic returns of all the four exchange rates exhibit features of multifractality. Next, we research the source of the observed multifractality. For this, we transform the return series in two ways: a) We randomly shuffle the original time series of logarithmic returns and b) We apply the process of phase randomisation on the unchanged series. Our results indicate in the case of the US dollar the source of multifractality is mainly the fat tail. For the British Pound and the Euro, we see the long-range correlations between the observations and the thick tails of the probability distribution give rise to the observed multifractal features, while in the case of the Japanese Yen, the origin of the multifractal nature of the return series is mostly due to the broad tail.
ChatGPT may excel in States Medical Licensing Examination but falters in basic Linear Algebra
Authors: Eli Bagno, Thierry Dana-Picard, Shulamit Reches
Date: 2023-06-23 00:00:00

The emergence of ChatGPT has been rapid, and although it has demonstrated positive impacts in certain domains, its influence is not universally advantageous. Our analysis focuses on ChatGPT's capabilities in Mathematics Education, particularly in teaching basic Linear Algebra. While there are instances where ChatGPT delivers accurate and well-motivated answers, it is crucial to recognize numerous cases where it makes significant mathematical errors and fails in logical inference. These occurrences raise concerns regarding the system's genuine understanding of mathematics, as it appears to rely more on visual patterns rather than true comprehension. Additionally, the suitability of ChatGPT as a teacher for students also warrants consideration.
Effective Hierarchical Information Threading Using Network Community Detection
Authors: Hitarth Narvala, Graham McDonald, Iadh Ounis
Date: 2023-03-17 00:00:00
Proceeding: European Conference on Information Retrieval 2023 3
Tasks: Articles, Community Detection, Event Extraction, Information Threading

With the tremendous growth in the volume of information produced online every day (e.g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i.e., information threads). In this work, we propose a novel unsupervised approach, called HINT, which identifies coherent Hierarchical Information Threads. These threads can enable users to easily interpret a hierarchical association of diverse evolving information about an event or discussion. In particular, HINT deploys a scalable architecture based on network community detection to effectively identify hierarchical links between documents based on their chronological relatedness and answers to the 5W1H questions (i.e., who, what, where, when, why & how). On the NewSHead collection, we show that HINT markedly outperforms existing state-of-the-art approaches in terms of the quality of the identified threads. We also conducted a user study that shows that our proposed network-based hierarchical threads are significantly (p<0.05) preferred by users compared to cluster-based sequential threads.
Identifying chronological and coherent information threads using 5W1H questions and temporal relationships
Authors: Hitarth Narvala, Graham McDonald, Iadh Ounis
Date: 2023-01-18 00:00:00
Proceeding: Information Processing & Management 2023 1
Tasks: Articles, Event Extraction, Information Threading

Due to the massive volume of articles produced online every day, it is challenging for online platforms (e.g., news agencies) to present the information about an event, activity or discussion to their users in an easily digestible format. Therefore, there is a need for automatic methods to extract related and time-ordered information about events (i.e., information threads) from large unstructured collections of documents. In this work, we propose a novel unsupervised hierarchical agglomerative clustering (HAC) based information threading approach to generate chronological and coherent threads of information in a collection. Unlike, the well-known tasks of topic detection and tracking or event threading that focus on grouping information by important keywords and/or entities, our proposed approach identifies threads based on temporal relations and diverse information about an event, i.e., who did what, why, where, when and how (aka the 5W1H questions). In particular, our proposed approach, deploys a tailored similarity function for HAC by leveraging extracted answers to 5W1H questions along with time decay between documents. We evaluate our proposed HAC 5W1H information threading approach on two large expert-annotated collections of news articles, i.e., NewSHead and Multi-News (over 112k and 32k articles, respectively). Our experiments show that HAC 5W1H markedly improves the number of, and quality of, threads that are generated compared to existing state-of-the-art approaches from the literature, e.g., 100.98% more threads and +213.39% improvement in Normalised Mutual Information compared to the best evaluated baseline on the larger NewSHead collection. We also conducted a user study that shows that our proposed HAC 5W1H information threading approach is significantly (p < 0.05) preferred by users in terms of coherence, diversity and chronological correctness compared to the existing state-of-the-art approaches.
Data Driven computational mechanics
Authors: T. Kirchdoerfer, M. Ortiz
Date: 2016-02-08 00:00:00
Proceeding: Graduate Aerospace Laboratories, California Institute of Technology 2016 2

we develop a new computing paradigm, which we refer to as data-driven computing, according to which calculations are carried out directly from experimental material data and pertinent constraints and conservation laws, such as compatability and equilibrium, thus bypassing the empirical material modelling step of conventional computing altogether. Data-driven solvers seek to assign to each material point the state from a prescribed dataset that is closest to satisfying the conservation laws.
Deep Learning Methods for Device Identification Using Symbols Trace Plot
Authors: Da Huang, Akram Al-Hourani, Kandeepan Sithamparanathan, Wayne S. T. Rowe
Date: 2023-06-29 00:00:00
Tasks: Deep Learning

Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs).
Accurate PET Reconstruction from Reduced Set of Measurements based on GMM
Authors: Tomislav Matulić, Damir Seršić
Date: 2023-06-29 00:00:00

In this paper, we provide a novel method for the estimation of unknown parameters of the Gaussian Mixture Model (GMM) in Positron Emission Tomography (PET). A vast majority of PET imaging methods are based on reconstruction model that is defined by values on some pixel/voxel grid. Instead, we propose a continuous parametric GMM model. Usually, Expectation-Maximization (EM) iterations are used to obtain the GMM model parameters from some set of point-wise measurements. The challenge of PET reconstruction is that the measurement is represented by the so called lines of response (LoR), instead of points. The goal is to estimate the unknown parameters of the Gaussian mixture directly from a relatively small set of LoR-s. Estimation of unknown parameters relies on two facts: the marginal distribution theorem of the multivariate normal distribution; and the properties of the marginal distribution of LoR-s. We propose an iterative algorithm that resembles the maximum-likelihood method to determine the unknown parameters. Results show that the estimated parameters follow the correct ones with a great accuracy. The result is promising, since the high-quality parametric reconstruction model can be obtained from lower dose measurements, and is directly suitable for further processing.
ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles
Authors: Haoqin Tu, Bowen Yang, Xianfeng Zhao
Date: 2023-06-29 00:00:00
Tasks: News Generation, Sentence, Text Generation

Automatically generating textual content with desired attributes is an ambitious task that people have pursued long. Existing works have made a series of progress in incorporating unimodal controls into language models (LMs), whereas how to generate controllable sentences with multimodal signals and high efficiency remains an open question. To tackle the puzzle, we propose a new paradigm of zero-shot controllable text generation with multimodal signals (\textsc{ZeroGen}). Specifically, \textsc{ZeroGen} leverages controls of text and image successively from token-level to sentence-level and maps them into a unified probability space at decoding, which customizes the LM outputs by weighted addition without extra training. To achieve better inter-modal trade-offs, we further introduce an effective dynamic weighting mechanism to regulate all control weights. Moreover, we conduct substantial experiments to probe the relationship of being in-depth or in-width between signals from distinct modalities. Encouraging empirical results on three downstream tasks show that \textsc{ZeroGen} not only outperforms its counterparts on captioning tasks by a large margin but also shows great potential in multimodal news generation with a higher degree of control. Our code will be released at https://github.com/ImKeTT/ZeroGen.
Hyena Neural Operator for Partial Differential Equations
Authors: Saurabh Patil, Zijie Li, Amir Barati Farimani
Date: 2023-06-28 00:00:00

Numerically solving partial differential equations typically requires fine discretization to resolve necessary spatiotemporal scales, which can be computationally expensive. Recent advances in deep learning have provided a new approach to solving partial differential equations that involves the use of neural operators. Neural operators are neural network architectures that learn mappings between function spaces and have the capability to solve partial differential equations based on data. This study utilizes a novel neural operator called Hyena, which employs a long convolutional filter that is parameterized by a multilayer perceptron. The Hyena operator is an operation that enjoys sub-quadratic complexity and state space model to parameterize long convolution that enjoys a global receptive field. This mechanism enhances the model's comprehension of the input's context and enables data-dependent weight for different partial differential equations instances. To measure how effective the layers are in solving partial differential equations, we conduct experiments on Diffusion-Reaction equation and Navier Stokes equation. Our findings indicate Hyena Neural operator can serve as an efficient and accurate model for learning partial differential equations solution operator. The data and code used can be found at: https://github.com/Saupatil07/Hyena-Neural-Operator