pwc-archive/papers-with-abstracts huggingface dataset:
Joint Network Topology Inference in the Presence of Hidden Nodes
Authors: Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
Date: 2023-06-30 00:00:00

We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are available at all nodes, we consider the realistic and more difficult scenario where a subset of nodes are hidden and cannot be measured. Under the assumptions that the partially observed nodal signals are graph stationary and the networks have similar connectivity patterns, we derive structural characteristics of the connectivity between hidden and observed nodes. This allows us to formulate an optimization problem for estimating networks while accounting for the influence of hidden nodes. We identify conditions under which a convex relaxation yields the sparsest solution, and we formalize the performance of our proposed optimization problem with respect to the effect of the hidden nodes. Finally, synthetic and real-world simulations provide evaluations of our method in comparison with other baselines.
Toward Trusted and Swift UAV Communication: ISAC-Enabled Dual Identity Mapping
Authors: Yanpeng Cui, Zhiyong Feng, Qixun Zhang, Zhiqing Wei, Chenlong Xu, Ping Zhang
Date: 2023-06-30 00:00:00
Tasks: Integrated sensing and communication, Intelligent Communication, ISAC, Management

The UAV network has recently emerged as a capable carrier for ubiquitous wireless intelligent communication in the B5G/6G era. Nevertheless, the separation of dual identity raises challenges from the perspective of communication efficiency and security, including tedious communication feedback and malicious Sybil attacks. Meanwhile, thanks to the emerging integrated sensing and communication (ISAC) technology, the sensing ability incorporated in communication advances crucial opportunities for accurately and efficiently mapping identity from dual domains. This tutorial discusses the exciting intersection of ISAC and the future intelligent and efficient UAV network. We first describe the motivation scenario and present the framework of the proposed novel ISAC-enabled dual identity solution. The detailed modules of identity production, mapping, management, and authentication are discussed. By endowing UAVs with an advanced capability: opening their eyes when communicating with each other, we detail three typical applications and the advantages of our proposal. Finally, a series of key enabling techniques, open challenges, and potential solutions for ISAC-enabled dual-domain identity are discussed. This tutorial for the intelligent and efficient UAV network brings new insight on providing dual-domain identity via ISAC technology, with an eye on trusted and swift communication research tailored for the 6G UAV network.
The fundamental theorem of asset pricing with and without transaction costs
Authors: Christoph Kühn
Date: 2023-07-02 00:00:00

We prove a version of the fundamental theorem of asset pricing (FTAP) in continuous time that is based on the strict no-arbitrage condition and that is applicable to both frictionless markets and markets with proportional transaction costs. We consider a market with a single risky asset whose ask price process is higher than or equal to its bid price process. Neither the concatenation property of the set of wealth processes, that is used in the proof of the frictionless FTAP, nor some boundedness property of the trading volume of admissible strategies usually argued with in models with a nonvanishing bid-ask spread need to be satisfied in our model.
Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns
Authors: Eugene W. Park
Date: 2023-07-02 00:00:00

This paper presents a method for predicting stock returns using principal component analysis (PCA) and the hidden Markov model (HMM) and tests the results of trading stocks based on this approach. Principal component analysis is applied to the covariance matrix of stock returns for companies listed in the S&P 500 index, and interpreting principal components as factor returns, we apply the HMM model on them. Then we use the transition probability matrix and state conditional means to forecast the factors returns. Reverting the factor returns forecasts to stock returns using eigenvectors, we obtain forecasts for the stock returns. We find that, with the right hyperparameters, our model yields a strategy that outperforms the buy-and-hold strategy in terms of the annualized Sharpe ratio.
Computation of random time-shift distributions for stochastic population models
Authors: Dylan Morris, John Maclean, Andrew J. Black
Date: 2023-06-29 00:00:00

Even in large systems, the effect of noise arising from when populations are initially small can persist to be measurable on the macroscale. A deterministic approximation to a stochastic model will fail to capture this effect, but it can be accurately approximated by including an additional random time-shift to the initial conditions. We present a efficient numerical method to compute this time-shift distribution for a large class of stochastic models. The method relies on differentiation of certain functional equations, which we show can be effectively automated by deriving rules for different types of model rates that arise commonly when mass-action mixing is assumed. Explicit computation of the time-shift distribution can be used to build a practical tool for the efficient generation of macroscopic trajectories of stochastic population models, without the need for costly stochastic simulations. Full code is provided to implement this and we demonstrate our method on an epidemic model and a model of within-host viral dynamics.
Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement
Authors: Ruoyang Zhao, Feng Gao, Maoyu Li, Xingkun Niu, Shihao Liu, Xinmin Zhao, Liping Wang, Jun Guo, Feng Zhang
Date: 2023-07-03 00:00:00

Hydrophobic domains provide specific microenvironment for essential functional activities in life. Herein, we studied how the coassembling of peptides with catecholamines regulate the hydrophobic domain-containing nanostructures for fluorescence enhancement. By peptide encoding and coassembling with catecholamines of different hydrophilicities, a series of hierarchical assembling systems were constructed. In combination with molecular dynamics simulation, we experimentally discovered the hydrophobic domain of chromophore microenvironment regulates the fluorescence of coassembled nanostructures. Our results shed light on the rational design of fluorescent bio-coassembled nanoprobes for biomedical applications.
The power of motifs as inductive bias for learning molecular distributions
Authors: Johanna Sommer, Leon Hetzel, David Lüdke, Fabian Theis, Stephan Günnemann
Date: 2023-04-04 00:00:00
Tasks: Drug Discovery, Inductive Bias

Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules. Deep generative models have shown promising results for molecule generation, but the benefits of specific inductive biases for learning distributions over small graphs are unclear. Our study aims to investigate the impact of subgraph structures and vocabulary design on distribution learning, using small drug molecules as a case study. To this end, we introduce Subcover, a new subgraph-based fragmentation scheme, and evaluate it through a two-step variational auto-encoder. Our results show that Subcover's improved identification of chemically meaningful subgraphs leads to a relative improvement of the FCD score by 30%, outperforming previous methods. Our findings highlight the potential of Subcover to enhance the performance and scalability of existing methods, contributing to the advancement of drug discovery.
Analyzing Lack of Concordance Between the Proteome and Transcriptome in Paired scRNA-Seq and Multiplexed Spatial Proteomics
Authors: Jai Prakash Veerla, Jillur Rahman Saurav, Michael Robben, Jacob M Luber
Date: 2023-07-02 00:00:00

In this study, we analyze discordance between the transcriptome and proteome using paired scRNA-Seq and multiplexed spatial proteomics data from HuBMAP. Our findings highlight persistent transcripts in key immune markers, including CD45-RO, Ki67, CD45, CD20, and HLA-DR. CD45-RO is consistently expressed in memory T cells, while Ki67, associated with cell proliferation, also displays sustained expression. Furthermore, HLA-DR, part of the MHC class II molecules, demonstrates continuous expression, possibly crucial for APCs to trigger an effective immune response. This investigation provides novel insights into the complexity of gene expression regulation and protein function.
Comparative study of under-expressed prognostic biomarkers and pivotal signaling pathways in colon cancer and ulcerative colitis using integrated bioinformatics approach
Authors: Sedigheh Behrouzifar
Date: 2023-07-01 00:00:00

Colon cancer is a prevalent gastrointestinal malignancy arising in the colon. Ulcerative colitis(UC) is one of the risk factors of colorectal cancer. The detection of under-expressed biomarkers and molecular mechanisms in UC and colon cancer can lead to effective management of colitis-associated cancer. A total of two mRNA expression datasets (GSE87473 and GSE44076) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to screen differentially expressed genes (DEGs) between extensive ulcerative colitis samples and healthy samples, limited ulcerative colitis samples and healthy samples, and colon cancer samples and healthy samples. In extensive ulcerative colitis, limited ulcerative colitis and colon cancer groups, 95,69 and 635 under-expressed genes with adjusted p-value<0.05 and log(2) fold change<-2 were detected respectively. Using Cytoscape software, the genes with degree> 15 including CLCA1, SLC26A3, SI, KIT, HPGDS, NR1H4, ADIPOQ, PPARGC1A, GCG, MS4A12, GUCA2A and FABP1 were screened as hub under-expressed genes in colon cancer. In extensive ulcerative colitis, the genes with degree>5 including ABCB1, ABCG2, UGT1A6, CYP2B6 and AQP8 were identified as hub genes. Moreover, the genes including NR1H4, CYP2B6, ABCB1, ABCG2, UGT2A3 and PLA2G12B were detected as hub genes with degree>5 in limited ulcerative colitis. According to inclusion criteria and venn diagram, the downregulated gene NR1H4 was common gene in limited ulcerative colitis and colon cancer. The current in silico study showed that downregulation of CLCA1, PPARGC1A and AQP8 genes may increase cancer cell invasion and metastasis ability.
Application of Time-Aware PC algorithm to compute Causal Functional Connectivity in Alzheimer's Disease from fMRI data
Authors: Rahul Biswas, SuryaNarayana Sripada
Date: 2023-07-01 00:00:00
Tasks: Functional Connectivity, Time Series

Functional Connectivity between brain regions is known to be altered in Alzheimer's disease, and promises to be a biomarker for early diagnosis of the disease. While several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions, association does not necessarily imply causation. In contrast, Causal Functional Connectivity is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from recordings of resting-state functional magnetic resonance imaging (rs-fMRI) for subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain causal functional connectivity based on directed graphical modeling in a time series setting. We then perform an exploratory analysis to identify the causal brain connections between brain regions which have altered strengths between pairs of subject groups, and over the three subject groups, based on edge-wise p-values from statistical tests. We used the altered causal brain connections thus obtained to compile a comprehensive list of brain regions impacted by Alzheimer's disease according to the current data set. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
Imputing phylogenetic trees using tropical polytopes over the space of phylogenetic trees
Authors: Ruriko Yoshida
Date: 2023-06-30 00:00:00

When we apply comparative phylogenetic analyses to genome data, it is a well-known problem and challenge that some of given species (or taxa) often have missing genes. In such a case, we have to impute a missing part of a gene tree from a sample of gene trees. In this short paper we propose a novel method to infer a missing part of a phylogenetic tree using an analogue of a classical linear regression in the setting of tropical geometry. In our approach, we consider a tropical polytope, a convex hull with respect to the tropical metric closest to the data points. We show a condition that we can guarantee that an estimated tree from our method has at most four Robinson-Foulds (RF) distance from the ground truth and computational experiments with simulated data show our method works well.
Infomap Bioregions 2 - Exploring the interplay between biogeography and evolution
Authors: Daniel Edler, Anton Holmgren, Alexis Rojas, Joaquín Calatayud, Martin Rosvall, Alexandre Antonelli
Date: 2023-06-29 00:00:00

Identifying and understanding the large-scale biodiversity patterns in time and space is vital for conservation and addressing fundamental ecological and evolutionary questions. Network-based methods have proven useful for simplifying and highlighting important structures in species distribution data. However, current network-based biogeography approaches cannot exploit the evolutionary information available in phylogenetic data. We introduce a method for incorporating evolutionary relationships into species occurrence networks to produce more biologically informative and robust bioregions. To keep the bipartite network structure where bioregions are grid cells indirectly connected through shared species, we incorporate the phylogenetic tree by connecting ancestral nodes to the grid cells where their descendant species occur. To incorporate the whole tree without destroying the spatial signal of narrowly distributed species or ancestral nodes, we weigh tree nodes by the geographic information they provide. For a more detailed analysis, we enable integration of the evolutionary relationships at a specific time in the tree. By sweeping through the phylogenetic tree in time, our method interpolates between finding bioregions based only on distributional data and finding spatially segregated clades, uncovering evolutionarily distinct bioregions at different time slices. We also introduce a way to segregate the connections between evolutionary branches at a selected time to enable exploration of overlapping evolutionarily distinct regions. We have implemented these methods in Infomap Bioregions, an interactive web application that makes it easy to explore the possibly hierarchical and fuzzy patterns of biodiversity on different scales in time and space.
An end-to-end framework for gene expression classification by integrating a background knowledge graph: application to cancer prognosis prediction
Authors: Kazuma Inoue, Ryosuke Kojima, Mayumi Kamada, Yasushi Okuno
Date: 2023-06-29 00:00:00
Tasks: Prognosis

Biological data may be separated into primary data, such as gene expression, and secondary data, such as pathways and protein-protein interactions. Methods using secondary data to enhance the analysis of primary data are promising, because secondary data have background information that is not included in primary data. In this study, we proposed an end-to-end framework to integrally handle secondary data to construct a classification model for primary data. We applied this framework to cancer prognosis prediction using gene expression data and a biological network. Cross-validation results indicated that our model achieved higher accuracy compared with a deep neural network model without background biological network information. Experiments conducted in patient groups by cancer type showed improvement in ROC-area under the curve for many groups. Visualizations of high accuracy cancer types identified contributing genes and pathways by enrichment analysis. Known biomarkers and novel biomarker candidates were identified through these experiments.
Blockchain scaling and liquidity concentration on decentralized exchanges
Authors: Basile Caparros, Amit Chaudhary, Olga Klein
Date: 2023-06-30 00:00:00

Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of blockchain scaling solutions, Arbitrum and Polygon, as our instruments. Lower gas fees on scaling solutions allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage.
Optimal Execution Using Reinforcement Learning
Authors: Cong Zheng, Jiafa He, Can Yang
Date: 2023-06-19 00:00:00
Tasks: Decision Making, Diversity, reinforcement-learning, Reinforcement Learning

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.
SAHAAYAK 2023 -- the Multi Domain Bilingual Parallel Corpus of Sanskrit to Hindi for Machine Translation
Authors: Vishvajitsinh Bakrola, Jitendra Nasariwala
Date: 2023-06-27 00:00:00
Tasks: Machine Translation, Sentence

The data article presents the large bilingual parallel corpus of low-resourced language pair Sanskrit-Hindi, named SAHAAYAK 2023. The corpus contains total of 1.5M sentence pairs between Sanskrit and Hindi. To make the universal usability of the corpus and to make it balanced, data from multiple domain has been incorporated into the corpus that includes, News, Daily conversations, Politics, History, Sport, and Ancient Indian Literature. The multifaceted approach has been adapted to make a sizable multi-domain corpus of low-resourced languages like Sanskrit. Our development approach is spanned from creating a small hand-crafted dataset to applying a wide range of mining, cleaning, and verification. We have used the three-fold process of mining: mining from machine-readable sources, mining from non-machine readable sources, and collation from existing corpora sources. Post mining, the dedicated pipeline for normalization, alignment, and corpus cleaning is developed and applied to the corpus to make it ready to use on machine translation algorithms.
Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domains
Authors: Zhi-Xuan Tai, Po-Chuan Chen
Date: 2023-06-17 00:00:00
Tasks: Articles, reinforcement-learning, Reinforcement Learning, Text Generation

Our research introduces an innovative Natural Language Generation (NLG) approach that aims to optimize user experience and alleviate the workload of human customer support agents. Our primary objective is to generate informal summaries for online articles and posts using an offline reinforcement learning technique. In our study, we compare our proposed method with existing approaches to text generation and provide a comprehensive overview of our architectural design, which incorporates crawling, reinforcement learning, and text generation modules. By presenting this original approach, our paper makes a valuable contribution to the field of NLG by offering a fresh perspective on generating natural language summaries for online content. Through the implementation of Empowering NLG, we are able to generate higher-quality replies in the online domain. The experimental results demonstrate a significant improvement in the average "like" score, increasing from 0.09954378 to 0.5000152. This advancement has the potential to enhance the efficiency and effectiveness of customer support services and elevate the overall user experience when consuming online content.
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
Authors: Ben Norman, Jeff Clune
Date: 2023-07-05 00:00:00
Tasks: Meta-Learning, Reinforcement Learning (RL)

Standard reinforcement learning (RL) agents never intelligently explore like a human (i.e. taking into account complex domain priors and adapting quickly based on previous exploration). Across episodes, RL agents struggle to perform even simple exploration strategies, for example systematic search that avoids exploring the same location multiple times. This poor exploration limits performance on challenging domains. Meta-RL is a potential solution, as unlike standard RL, meta-RL can learn to explore, and potentially learn highly complex strategies far beyond those of standard RL, strategies such as experimenting in early episodes to learn new skills, or conducting experiments to learn about the current environment. Traditional meta-RL focuses on the problem of learning to optimally balance exploration and exploitation to maximize the cumulative reward of the episode sequence (e.g., aiming to maximize the total wins in a tournament -- while also improving as a player). We identify a new challenge with state-of-the-art cumulative-reward meta-RL methods. When optimal behavior requires exploration that sacrifices immediate reward to enable higher subsequent reward, existing state-of-the-art cumulative-reward meta-RL methods become stuck on the local optimum of failing to explore. Our method, First-Explore, overcomes this limitation by learning two policies: one to solely explore, and one to solely exploit. When exploring requires forgoing early-episode reward, First-Explore significantly outperforms existing cumulative meta-RL methods. By identifying and solving the previously unrecognized problem of forgoing reward in early episodes, First-Explore represents a significant step towards developing meta-RL algorithms capable of human-like exploration on a broader range of domains.