pwc-archive/papers-with-abstracts huggingface dataset:
Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems
Authors: Ana Lokmer, Charanraj Goud Alladi, Réjane Troudet, Delphine Bacq-Daian, Anne Boland-Auge, Violaine Latapie, Jean-François Deleuze, Ravi Philip Rajkumar, Deepak Gopal Shewade, Frank Bélivier, Cynthia Marie-Claire, Stéphane Jamain
Date: 2023-04-06 00:00:00

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material \& methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.
Computational role of sleep in memory reorganization
Authors: Kensuke Yoshida, Taro Toyoizumi
Date: 2023-04-06 00:00:00

Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models did not focus on some sleep characteristics. Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, and rapid eye movement (REM) sleep reorganizes the representations of memories. We first review the possibility that slow waves during NREM sleep contribute to memory selection by using sequential firing patterns and the existence of up and down states. Second, we discuss the role of dreaming during REM sleep in developing neuronal representations. We finally discuss how to develop these points further, emphasizing the connections to experimental neuroscience and machine learning.
Nonequilibrium antigen recognition during infections and vaccinations
Authors: Roberto Morán-Tovar, Michael Lässig
Date: 2023-04-05 00:00:00

The immune response to an acute primary infection is a coupled process of antigen proliferation, molecular recognition by naive B cells, and their subsequent proliferation and antibody shedding. This process contains a fundamental problem: the recognition of an exponentially time-dependent antigen signal. Here we show that B cells can efficiently recognise new antigens by a tuned kinetic proofreading mechanism, where the molecular recognition machinery is adapted to the complexity of the immune repertoire. This process produces potent, specific and fast recognition of antigens, maintaining a spectrum of genetically distinct B cell lineages as input for affinity maturation. We show that the proliferation-recognition dynamics of a primary infection is a generalised Luria-Delbr\"uck process, akin to the dynamics of the classic fluctuation experiment. This map establishes a link between signal recognition dynamics and evolution. We derive the resulting statistics of the activated immune repertoire: antigen binding affinity, expected size, and frequency of active B cell clones are related by power laws, which define the class of generalised Luria-Delbr\"uck processes. Their exponents depend on the antigen and B cell proliferation rate, the number of proofreading steps, and the lineage density of the naive repertoire. We extend the model to include spatio-temporal processes, including the diffusion-recognition dynamics of a vaccination. Empirical data of activated mouse immune repertoires are found to be consistent with activation involving about three proofreading steps. The model predicts key clinical characteristics of acute infections and vaccinations, including the emergence of elite neutralisers and the effects of immune ageing. More broadly, our results establish infections and vaccinations as a new probe into the global architecture and functional principles of immune repertoires.
The Expected Sample Allele Frequencies from Populations of Changing Size via Orthogonal Polynomials
Authors: Lynette Caitlin Mikula, Claus Vogl
Date: 2023-04-05 00:00:00

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for step-wise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.
Constructing Phylogenetic Networks via Cherry Picking and Machine Learning
Authors: Giulia Bernardini, Leo van Iersel, Esther Julien, Leen Stougie
Date: 2023-03-31 00:00:00

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. Existing methods are computationally expensive and can either handle only small numbers of phylogenetic trees or are limited to severely restricted classes of networks. In this paper, we apply the recently-introduced theoretical framework of cherry picking to design a class of efficient heuristics that are guaranteed to produce a network containing each of the input trees, for datasets consisting of binary trees. Some of the heuristics in this framework are based on the design and training of a machine learning model that captures essential information on the structure of the input trees and guides the algorithms towards better solutions. We also propose simple and fast randomised heuristics that prove to be very effective when run multiple times. Unlike the existing exact methods, our heuristics are applicable to datasets of practical size, and the experimental study we conducted on both simulated and real data shows that these solutions are qualitatively good, always within some small constant factor from the optimum. Moreover, our machine-learned heuristics are one of the first applications of machine learning to phylogenetics and show its promise.
Transition paths in Potts-like energy landscapes: general properties and application to protein sequence models
Authors: Eugenio Mauri, Simona Cocco, Rémi Monasson
Date: 2023-04-06 00:00:00

We study transition paths in energy landscapes over multi-categorical Potts configurations using the mean-field approach introduced by Mauri et al., {\em Phys Rev Lett 130, 158402 (2023)}. Paths interpolate between two fixed configurations or are anchored at one extremity only. We characterize the properties of `good' transition paths realizing a trade-off between exploring low-energy regions in the landscape and being not too long, such as their entropy or the probability of escape from a region of the landscape. We unveil the existence of a phase transition separating a regime in which paths are stretched in between their anchors, from another regime, where paths can explore the energy landscape more globally to minimize the energy. This phase transition is first illustrated and studied in detail on a mathematically tractable Hopfield-Potts toy model, then studied in energy landscapes inferred from protein-sequence data.
Unifying Market Microstructure and Dynamic Asset Pricing
Authors: Davide Lauria, W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu
Date: 2023-04-05 00:00:00

We introduce a discrete binary tree for pricing contingent claims with the underlying security prices exhibiting history dependence characteristic of that induced by market microstructure phenomena. Example dependencies considered include moving average or autoregressive behavior. Our model is market-complete, arbitrage-free, and preserves all of the parameters governing the historical (natural world) price dynamics when passing to an equivalent martingale (risk-neutral) measure. Specifically, this includes the instantaneous mean and variance of the asset return and the instantaneous probabilities for the direction of asset price movement. We believe this is the first paper to demonstrate the ability to include market microstructure effects in dynamic asset/option pricing in a market-complete, no-arbitrage, format.
TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data
Authors: Faraz Sasani, Ramin Mousa, Ali Karkehabadi, Samin Dehbashi, Ali Mohammadi
Date: 2023-03-13 00:00:00

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.
Optimal Trading in Automatic Market Makers with Deep Learning
Authors: Sebastian Jaimungal, Yuri F. Saporito, Max O. Souza, Yuri Thamsten
Date: 2023-04-05 00:00:00
Tasks: Deep Learning

This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms na\"ive strategies.
Abstraction-based Probabilistic Stability Analysis of Polyhedral Probabilistic Hybrid Systems
Authors: Spandan Das, Pavithra Prabhakar
Date: 2023-03-29 00:00:00

In this paper, we consider the problem of probabilistic stability analysis of a subclass of Stochastic Hybrid Systems, namely, Polyhedral Probabilistic Hybrid Systems (PPHS), where the flow dynamics is given by a polyhedral inclusion, the discrete switching between modes happens probabilistically at the boundaries of their invariant regions and the continuous state is not reset during switching. We present an abstraction-based analysis framework that consists of constructing a finite Markov Decision Processes (MDP) such that verification of certain property on the finite MDP ensures the satisfaction of probabilistic stability on the PPHS. Further, we present a polynomial-time algorithm for verifying the corresponding property on the MDP. Our experimental analysis demonstrates the feasibility of the approach in successfully verifying probabilistic stability on PPHS of various dimensions and sizes.
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
Authors: Hanyao Huang, Ou Zheng, Dongdong Wang, Jiayi Yin, Zijin Wang, Shengxuan Ding, Heng Yin, Chuan Xu, Renjie Yang, Qian Zheng, Bing Shi
Date: 2023-03-23 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Authors: Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan
Date: 2023-03-27 00:00:00
Tasks: Deformable Object Manipulation, Object

In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory optimization approaches with differentiable physics for deformable manipulation would suffer from local optima caused by the explosion of contact modes from hand-object interactions. To address these challenges, we propose DexDeform, a principled framework that abstracts dexterous manipulation skills from human demonstration and refines the learned skills with differentiable physics. Concretely, we first collect a small set of human demonstrations using teleoperation. And we then train a skill model using demonstrations for planning over action abstractions in imagination. To explore the goal space, we further apply augmentations to the existing deformable shapes in demonstrations and use a gradient optimizer to refine the actions planned by the skill model. Finally, we adopt the refined trajectories as new demonstrations for finetuning the skill model. To evaluate the effectiveness of our approach, we introduce a suite of six challenging dexterous deformable object manipulation tasks. Compared with baselines, DexDeform is able to better explore and generalize across novel goals unseen in the initial human demonstrations.
Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia
Authors: Mukhlis Amien
Date: 2023-03-28 00:00:00
Tasks: Information Retrieval, Machine Translation, Part-Of-Speech Tagging, Retrieval, Sentiment Analysis

This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods; practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis; and methods and techniques used in Indonesian language NLP research, such as machine learning, statistics-based machine translation, and conflict-based approaches. This study also explores the application of NLP in Indonesian language industry and research and identifies challenges and opportunities in Indonesian language NLP research and development. Recommendations for future Indonesian language NLP research and development include developing more efficient methods and technologies, expanding NLP applications, increasing sustainability, further research into the potential of NLP, and promoting interdisciplinary collaboration. It is hoped that this review will help researchers, practitioners, and the government to understand the development of Indonesian language NLP and identify opportunities for further research and development.
End-to-End Learning of User Equilibrium with Implicit Neural Networks
Authors: Zhichen Liu, Yafeng Yin, Bai fan, and Donald K Grimm
Date: 2022-08-18 00:00:00
Proceeding: semantic scholar 2022 8

This paper intends to transform the transportation network equilibrium modeling paradigm via an ”end-to-end” framework that directly learns travel choice preferences and the equilibrium state from multi-day link flow observations. The centerpiece of the proposed framework is to use deep neural networks to represent travelers’ route choice preferences and then encapsulate the neural networks in a variational inequality that prescribes the user equilibrium flow distribution. The proposed neural network architecture ensures the existence of equilibrium and accommodates future changes in road network structures. The variational inequality is then embedded as an implicit layer in a learning framework, which takes the context features (e.g., road network and traveler characteristics) as input and outputs the user equilibrium flow distribution. By comparing computed equilibrium flows with observed flows, the neural networks can be trained. The proposed end-to-end framework is demonstrated and validated using synthesized data for the Sioux Falls network.
An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting
Authors: E. Chondrodima, N. Pelekis, A. Pikrakis, Y. Theodoridis
Date: 2023-03-16 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2023 3
Tasks: Data Augmentation

Forecasting vessel locations is of major importance in the maritime domain, with applications in safety, logistics, etc. Nowadays, vessel tracking has become possible largely due to the increased GPS-based data availability. This paper introduces a novel Vessel Location Forecasting (VLF) framework, based on Long-Short Term Memory (LSTM) Neural Networks, aiming to perform effective location forecasting in time horizons up to 60 minutes, even for vessels not recorded in the past. The proposed VLF framework is specially designed for handling vessel data by addressing some major GPS-related obstacles including variable sampling rate, sparse trajectories, and noise contained in such data. Our framework also learns by incorporating a novel trajectory data augmentation method to improve its predictive power. We validate VLF framework using three real-word datasets of vessels moving in different sea areas, comparing with various methods, and examining several aspects. Results prove VLF framework’s generic nature, robustness regarding parameter changes, and superiority against state of the art in terms of prediction accuracy (higher than 30%) and computational effort.
Two-Stream Transformer for Multi-Label Image Classification
Authors: Xuelin Zhu, Jiuxin Cao, Jiawei Ge, Weijia Liu, Bo Liu
Date: 2022-10-01 00:00:00
Proceeding: ACMMM 2022 10
Tasks: Classification, image-classification, Image Classification, Multi-Label Image Classification, Vocal Bursts Valence Prediction

Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal interactions between label semantics and high-level visual representations via an attention operation. However, these one-shot attention based approaches generally perform poorly in establishing accurate and robust alignments between vision and text due to the acknowledged semantic gap. In this paper, we propose a two-stream transformer (TSFormer) learning framework, in which the spatial stream focuses on extracting patch features with a global perception, while the semantic stream aims to learn vision-aware label semantics as well as their correlations via a multi-shot attention mechanism. Specifically, in each layer of TSFormer, a cross-modal attention module is developed to aggregate visual features from spatial stream into semantic stream and update label semantics via a residual connection. In this way, the semantic gap between two streams gradually narrows as the procedure progresses layer by layer, allowing the semantic stream to produce sophisticated visual representations for each label towards accurate label recognition. Extensive experiments on three visual benchmarks, including Pascal VOC 2007, Microsoft COCO and NUS-WIDE, consistently demonstrate that our proposed TSFormer achieves state-of-the-art performance on the multi-label image classification task.
DetFusion: A Detection-driven Infrared and Visible Image Fusion Network
Authors: Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu
Date: 2022-10-01 00:00:00
Proceeding: ACMMM 2022 10
Tasks: Infrared And Visible Image Fusion, Object, object-detection, Object Detection

Infrared and visible image fusion aims to utilize the complementary information between the two modalities to synthesize a new image containing richer information. Most existing works have focused on how to better fuse the pixel-level details from both modalities in terms of contrast and texture, yet ignoring the fact that the significance of image fusion is to better serve downstream tasks. For object detection tasks, object-related information in images is often more valuable than focusing on the pixel-level details of images alone. To fill this gap, we propose a detection-driven infrared and visible image fusion network, termed DetFusion, which utilizes object-related information learned in the object detection networks to guide multimodal image fusion. We cascade the image fusion network with the detection networks of both modalities and use the detection loss of the fused images to provide guidance on task-related information for the optimization of the image fusion network. Considering that the object locations provide a priori information for image fusion, we propose an object-aware content loss that motivates the fusion model to better learn the pixel-level information in infrared and visible images. Moreover, we design a shared attention module to motivate the fusion network to learn object-specific information from the object detection networks. Extensive experiments show that our DetFusion outperforms state-of-the-art methods in maintaining pixel intensity distribution and preserving texture details. More notably, the performance comparison with state-of-the-art image fusion methods in task-driven evaluation also demonstrates the superiority of the proposed method. Our code will be available: https://github.com/SunYM2020/DetFusion.