pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain Adaptation
Authors: Lei Chu, Abdullah Alghafis, Andreas F. Molisch
Date: 2023-10-11 00:00:00
Tasks: Domain Adaptation, Representation Learning, Transfer Learning

Localization in GPS-denied outdoor locations, such as street canyons in an urban or metropolitan environment, has many applications. Machine Learning (ML) is widely used to tackle this critical problem. One challenge lies in the mixture of line-of-sight (LOS), obstructed LOS (OLOS), and non-LOS (NLOS) conditions. In this paper, we consider a semantic localization that treats these three propagation conditions as the ''semantic objects", and aims to determine them together with the actual localization, and show that this increases accuracy and robustness. Furthermore, the propagation conditions are highly dynamic, since obstruction by cars or trucks can change the channel state information (CSI) at a fixed location over time. We therefore consider the blockage by such dynamic objects as another semantic state. Based on these considerations, we formulate the semantic localization with a joint task (coordinates regression and semantics classification) learning problem. Another problem created by the dynamics is the fact that each location may be characterized by a number of different CSIs. To avoid the need for excessive amount of labeled training data, we propose a multi-task deep domain adaptation (DA) based localization technique, training neural networks with a limited number of labeled samples and numerous unlabeled ones. Besides, we introduce novel scenario adaptive learning strategies to ensure efficient representation learning and successful knowledge transfer. Finally, we use Bayesian theory for uncertainty modeling of the importance weights in each task, reducing the need for time-consuming parameter finetuning; furthermore, with some mild assumptions, we derive the related log-likelihood for the joint task and present the deep homoscedastic DA based localization method.
Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model
Authors: Edson Pindza, Jules Clement Mba, Sutene Mwambi, Nneka Umeorah
Date: 2023-10-14 00:00:00

Cryptocurrencies and Bitcoin, in particular, are prone to wild swings resulting in frequent jumps in prices, making them historically popular for traders to speculate. A better understanding of these fluctuations can greatly benefit crypto investors by allowing them to make informed decisions. It is claimed in recent literature that Bitcoin price is influenced by sentiment about the Bitcoin system. Transaction, as well as the popularity, have shown positive evidence as potential drivers of Bitcoin price. This study considers a bivariate jump-diffusion model to describe Bitcoin price dynamics and the number of Google searches affecting the price, representing a sentiment indicator. We obtain a closed formula for the Bitcoin price and derive the Black-Scholes equation for Bitcoin options. We first solve the corresponding Bitcoin option partial differential equation for the pricing process by introducing artificial neural networks and incorporating multi-layer perceptron techniques. The prediction performance and the model validation using various high-volatile stocks were assessed.
Geometric slow-fast analysis of a hybrid pituitary cell model with stochastic ion channel dynamics
Authors: Francesco Montefusco, Morten Gram Pedersen
Date: 2023-10-18 00:00:00

To obtain explicit understanding of the behavior of dynamical systems, geometrical methods and slow-fast analysis have proved to be highly useful. Such methods are standard for smooth dynamical systems, and increasingly used for continuous, non-smooth dynamical systems. However, they are much less used for random dynamical systems, in particular for hybrid models with discrete, random dynamics. Indeed, the analysis of such systems has typically been done by studying the corresponding deterministic system and considering how noise perturbs the deterministic geometrical structures. Here we propose a geometrical method that works directly with the hybrid system. We illustrate our approach through an application to a hybrid pituitary cell model in which the stochastic dynamics of very few active large-conductance potassium (BK) channels is coupled to a deterministic model of the other ion channels and calcium dynamics. To employ our geometric approach, we exploit the slow-fast structure of the model. The random fast subsystem is analyzed by considering discrete phase planes, corresponding to the discrete number of open BK channels, and stochastic events correspond to jumps between these planes. The evolution within each plane can be understood from nullclines and limit cycles, and the overall dynamics, e.g., whether the model produces a spike or a burst, is determined by the location at which the system jumps from one plane to another. Our approach is generally applicable to other scenarios to study discrete random dynamical systems defined by hybrid stochastic-deterministic models.
Robust Trading in a Generalized Lattice Market
Authors: Chung-Han Hsieh, Xin-Yu Wang
Date: 2023-10-17 00:00:00

This paper introduces a novel robust trading paradigm, called \textit{multi-double linear policies}, situated within a \textit{generalized} lattice market. Distinctively, our framework departs from most existing robust trading strategies, which are predominantly limited to single or paired assets and typically embed asset correlation within the trading strategy itself, rather than as an inherent characteristic of the market. Our generalized lattice market model incorporates both serially correlated returns and asset correlation through a conditional probabilistic model. In the nominal case, where the parameters of the model are known, we demonstrate that the proposed policies ensure survivability and probabilistic positivity. We then derive an analytic expression for the worst-case expected gain-loss and prove sufficient conditions that the proposed policies can maintain a \textit{positive expected profits}, even within a seemingly nonprofitable symmetric lattice market. When the parameters are unknown and require estimation, we show that the parameter space of the lattice model forms a convex polyhedron, and we present an efficient estimation method using a constrained least-squares method. These theoretical findings are strengthened by extensive empirical studies using data from the top 30 companies within the S\&P 500 index, substantiating the efficacy of the generalized model and the robustness of the proposed policies in sustaining the positive expected profit and providing downside risk protection.
1,1-Diphenyl-2-picrylhydrazyl and superoxide anion radical scavenging 1 activities of heterocyclic 2-oxo-1,2,3,4-tetrahydropyrimidines
Authors: Shahida Perveen, Qurat-ul-Ain, Sarosh Iqbal, Sheeba Wajid, Khalid Muhammad khan, Muhammad Iqbal Choudhary
Date: 2023-10-16 00:00:00

To investigate1,1-Diphenyl-2-picrylhydrazyl (DPPH) and superoxide radical (SOR) 17 scavenging activities of 2-oxo-1,2,3,4-tetrahydropyrimidines derivatives. Free radicals are 18 highly unstable and reactive molecules/atoms. In the body, free radicals form during 19 normal and abnormal metabolism in the body and cause serious damage to other 20 biomolecules through generating oxidative stress (OS). If free radicals induced OS does 21 not neutralized properly, host multiple pathologies including several types of cancers.
Decision Tree for Protein Biomarker Selection for Clinical Applications
Authors: Katharina Waury
Date: 2023-10-12 00:00:00

Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure as biomarker candidates cannot be validated or translated to immunoassays. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, I present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development while minimal time and effort is required.
Complexity reduction by symmetry: uncovering the minimal regulatory network for logical computation in bacteria
Authors: Luis A. Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A. Makse
Date: 2023-10-17 00:00:00
Tasks: Decision Making

Symmetry principles play an important role in geometry, and physics, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. Biological systems are often highly complex and may consist of a large number of interacting parts. Using symmetry fibrations, the relevant symmetries for biological "message-passing" networks, we introduce a scheme, called Complexity Reduction by Symmetry or ComSym, to reduce the gene regulatory networks of Escherichia coli and Bacillus subtilis bacteria to core networks in a way that preserves the dynamics and uncovers the computational capabilities of the network. Gene nodes in the original network that share isomorphic input trees are collapsed by the fibration into equivalence classes called fibers, whereby nodes that receive signals with the same "history" belong to one fiber and synchronize. Then we reduce the networks to its minimal computational core via k-core decomposition. This computational core consists of a few strongly connected components or "signal vortices", in which signals can cycle through. While between them, these "signal vortices" transmit signals in a feedforward manner. These connected components perform signal processing and decision making in the bacterial cell by employing a series of genetic toggle-switch circuits that store memory, plus oscillator circuits. These circuits act as the central computation device of the network, whose output signals then spread to the rest of the network. Our reduction method opens the door to narrow the vast complexity of biological systems to their minimal parts in a systematic way by using fundamental theoretical principles of symmetry.
The era of the ARG: an empiricist's guide to ancestral recombination graphs
Authors: Alexander L. Lewanski, Michael C. Grundler, Gideon S. Bradburd
Date: 2023-10-18 00:00:00

In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in empirical evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.
Design Patterns of Biological Cells
Authors: Steven S. Andrews, H. Steven Wiley, Herbert M. Sauro
Date: 2023-10-11 00:00:00

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology in order to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
Aperiodic MEG abnormality in patients with focal to bilateral tonic-clonic seizures
Authors: Kirandeep Kaur, Jonathan J Horsley, Csaba Kozma, Gerard R Hall, Thomas W Owen, Yujiang Wang, Guarav Singh, Sarat P Chandra, Manjari Tripathi, Peter N Taylor
Date: 2023-10-18 00:00:00

Aperiodic activity is a physiologically distinct component of the electrophysiological power spectrum. It is suggested to reflect the balance of excitation and inhibition in the brain, within selected frequency bands. However, the impact of recurrent seizures on aperiodic activity remains unknown, particularly in patients with severe bilateral seizures. Here, we hypothesised greater aperiodic abnormality in the epileptogenic zone, in patients with focal to bilateral tonic clonic (FBTC) seizures, and earlier age of seizure onset. Pre-operative magnetoencephalography (MEG) recordings were acquired from 36 patients who achieved complete seizure freedom (Engel I outcome) post-surgical resection. A normative whole brain map of the aperiodic exponent was computed by averaging across subjects for each region in the hemisphere contralateral to the side of resection. Selected regions of interest were then tested for abnormality using deviations from the normative map in terms of z-scores. Resection masks drawn from postoperative structural imaging were used as an approximation of the epileptogenic zone. Patients with FBTC seizures had greater abnormality compared to patients with focal onset seizures alone in the resection volume (p=0.003, area under the ROC curve = 0.78 ). Earlier age of seizure onset was correlated with greater abnormality of the aperiodic exponent in the resection volume (correlation coefficient = -0.3, p= 0.04)) as well as the whole cortex (rho = -0.33, p=0.03). The abnormality of the aperiodic exponent did not significantly differ between the resected and non-resected regions of the brain. Abnormalities in aperiodic components relate to important clinical characteristics such as severity and age of seizure onset. This suggests the potential use of the aperiodic band power component as a marker for severity of epilepsy.
STW-MD: A Novel Spatio-Temporal Weighting and Multi-Step Decision Tree Method for Considering Spatial Heterogeneity in Brain Gene Expression Data
Authors: Shanjun Mao, Xiao Huang, Runjiu Chen, Chenyang Zhang, Yizhu Diao, Zongjin Li, Qingzhe Wang, Shan Tang, Shuixia Guo
Date: 2023-10-18 00:00:00

Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of the mechanisms underlying brain development or disorders associated with abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. Results: In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle, and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development. Availability: The code of STW-MD is available at https://github.com/tsnm1/STW-MD.
Mismatch Negativity: time for deconstruction
Authors: Francoise Lecaignard, Jeremie Mattout
Date: 2023-10-17 00:00:00

Error signals are the cornerstone of predictive coding and are widely considered essential to sensory perception and beyond. The mismatch negativity (MMN) is arguably the most emblematic and most studied brain error signal. It is affected in many brain disorders. However, its precise algorithmic function and the underlying physiology remain mysterious. Over the past decade, theoretical and computational explanations have been put forward. They highlight a paradox: the MMN is considered a signature of context-dependent perceptual learning, although it is defined as an evoked response averaged across trials, thus neglecting the information carried by error signal fluctuations over time. We propose to deconstruct the MMN, by virtue of hypothesis driven computational approaches whose aim it to account for these fluctuations.
Mental causation in a physical world: A self-causation model of downward causation
Authors: Yoshiyuki Ohmura, Yasuo Kuniyoshi
Date: 2023-10-16 00:00:00

Downward causation is self-causation, the causel effect from the whole to its parts, and is considered a promising theory for the problem of mental causation. However, it remains to be clarified how an irreducible but supervenient downward causal power can arise. Here, we argue that a feedback control of lower micro-level synaptic weights using higher macro-level algebraic structural feedback errors is a model of downward causation. The feedback control consists of two mechanisms: observation of the feedback error and control of the feedback error. The two mechanisms can be implemented at two different levels of the hierarchy. Macro-level feedback error is described by algebra of supervenient macro-level functions, independent of the external cause. The supervenient macro-level feedback error constraining of the micro synaptic weights through feedback control is downward causation.
Neural activity in quarks language: Lattice Field Theory for a network of real neurons
Authors: Giampiero Bardella, Simone Franchini, Liming Pan, Riccardo Balzan, Surabhi Ramawat, Emiliano Brunamonti, Pierpaolo Pani, Stefano Ferraina
Date: 2023-10-13 00:00:00

Brain-computer interfaces surged extraordinary developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro- and meso-scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neurons activity, and generalize the maximum entropy model for neural networks so that also the time evolution of the system is taken into account. This is obtained by bridging particle physics and neuroscience, paving the way to particle physics-inspired models of neocortex.
Shedding light on social learning
Authors: Kingsley J. A. Cox, Paul R. Adams
Date: 2023-10-13 00:00:00

Culture involves the origination and transmission of ideas, but the conditions in which culture can emerge and evolve are unclear. We constructed and studied a highly simplified neural-network model of these processes. In this model ideas originate by individual learning from the environment and are transmitted by communication between individuals. Individuals (or "agents") comprise a single neuron which receives structured data from the environment via plastic synaptic connections. The data are generated in the simplest possible way: linear mixing of independently fluctuating sources and the goal of learning is to unmix the data. To make this problem tractable we assume that at least one of the sources fluctuates in a nonGaussian manner. Linear mixing creates structure in the data, and agents attempt to learn (from the data and possibly from other individuals) synaptic weights that will unmix, i.e., to "understand" the agent's world. For a variety of reasons even this goal can be difficult for a single agent to achieve; we studied one particular type of difficulty (created by imperfection in synaptic plasticity), though our conclusions should carry over to many other types of difficulty. We previously studied whether a small population of communicating agents, learning from each other, could more easily learn unmixing coefficients than isolated individuals, learning only from their environment. We found, unsurprisingly, that if agents learn indiscriminately from any other agent (whether or not they have learned good solutions), communication does not enhance understanding. Here we extend the model slightly, by allowing successful learners to be more effective teachers, and find that now a population of agents can learn more effectively than isolated individuals. We suggest that a key factor in the onset of culture might be the development of selective learning.
Neural Dysfunction Underlying Working Memory Processing at Different Stages of the Illness Course in Schizophrenia:A Comparative Meta-analysis
Authors: Yuhao Yao, Shufang Zhang, Boyao Wang, Gaofeng Zhao, Hong Deng, Ying Chen
Date: 2023-10-13 00:00:00

Schizophrenia (SCZ), as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk (CHR), first-episode psychosis (FEP), and long-term SCZ, using meta-analyses of functional magnetic resonance imaging (fMRI) studies. Following a systematic literature search, fifty-six whole-brain task-based fMRI studies (15 for CHR, 16 for FEP, 25 for long-term SCZ) were included. The separate and pooled neurofunctional mechanisms among CHR, FEP and long-term SCZ were generated by Seed-based d Mapping toolbox. The CHR and FEP groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of SCZ. Individuals with FEP showed lower activation in left inferior parietal lobule than those with long-term SCZ, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.