pwc-archive/papers-with-abstracts huggingface dataset:
Neuron-Astrocyte Associative Memory
Authors: Leo Kozachkov, Jean-Jacques Slotine, Dmitry Krotov
Date: 2023-11-14 00:00:00

Astrocytes, the most abundant type of glial cell, play a fundamental role in memory. Despite most hippocampal synapses being contacted by an astrocyte, there are no current theories that explain how neurons, synapses, and astrocytes might collectively contribute to memory function. We demonstrate that fundamental aspects of astrocyte morphology and physiology naturally lead to a dynamic, high-capacity associative memory system. The neuron-astrocyte networks generated by our framework are closely related to popular machine learning architectures known as Dense Associative Memories or Modern Hopfield Networks. In their known biological implementations the ratio of stored memories to the number of neurons remains constant, despite the growth of the network size. Our work demonstrates that neuron-astrocyte networks follow superior, supralinear memory scaling laws, outperforming all known biological implementations of Dense Associative Memory. This theoretical link suggests the exciting and previously unnoticed possibility that memories could be stored, at least in part, within astrocytes rather than solely in the synaptic weights between neurons.
The brain uses renewal points to model random sequences of stimuli
Authors: Fernando A. Najman, Antonio Galves, Marcela Svarc, Claudia D. Vargas
Date: 2023-11-13 00:00:00
Tasks: Clustering, EEG

It has been classically conjectured that the brain assigns probabilistic models to sequences of stimuli. An important issue associated with this conjecture is the identification of the classes of models used by the brain to perform this task. We address this issue by using a new clustering procedure for sets of electroencephalographic (EEG) data recorded from participants exposed to a sequence of auditory stimuli generated by a stochastic chain. This clustering procedure indicates that the brain uses renewal points in the stochastic sequence of auditory stimuli in order to build a model.
Assessing the potential impact of environmental land management schemes on emergent infection disease risks
Authors: Christopher J. Banks, Katherine Simpson, Nicholas Hanley, Rowland R. Kao
Date: 2023-11-13 00:00:00
Tasks: Management

Financial incentives are provided by governments to encourage the plantation of new woodland to increase habitat, biodiversity, carbon sequestration, and other economic benefits for landowners. Whilst these are largely positive effects, it is worth considering that greater biodiversity and presence of wildlife species in proximity to agricultural holdings may pose a risk of disease transmission between wildlife and livestock. Wildlife transmission and the provision of a reservoir for infectious disease is particularly important in the transmission dynamics of bovine tuberculosis. In this paper we develop an economic model for changing land use due to forestry subsidies. We use this asses the impact on wild deer populations in the newly created woodland areas and the emergent infectious disease risk arising from the proximity of new and existing wild deer populations and existing cattle holdings. We consider an area in the South-West of Scotland, having existing woodland, deer populations, and extensive and diverse cattle farm holdings. In this area we find that, with a varying level of subsidy and plausible new woodland creation, the contact risk between areas of wild deer and cattle increases between 26% and 35% over the contact risk present with zero subsidy. This model provides a foundation for extending to larger regions and for examining potential risk mitigation strategies, for example the targeting of subsidy in low risk areas or provisioning for buffer zones between woodland and agricultural holdings.
Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns
Authors: Wensi Hu, Quan-Xing Liu, Bo wang, Nuo Xu, Lijuan Cui, Chi Xu
Date: 2023-11-13 00:00:00

In harsh environments, organisms may self-organize into spatially patterned systems in various ways. So far, studies of ecosystem spatial self-organization have primarily focused on apparent orders reflected by regular patterns. However, self-organized ecosystems may also have cryptic orders that can be unveiled only through certain quantitative analyses. Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes. By analyzing the high-resolution remotely sensed images across the American drylands, we demonstrate that it is not uncommon to find disordered hyperuniform vegetation states characterized by suppressed density fluctuations at long range. Such long-range hyperuniformity has been documented in a wide range of microscopic systems. Our finding contributes to expanding this domain to accommodate natural landscape ecological systems. We use theoretical modeling to propose that disordered hyperuniform vegetation patterning can arise from three generalized mechanisms prevalent in dryland ecosystems, including (1) critical absorbing states driven by an ecological legacy effect, (2) scale-dependent feedbacks driven by plant-plant facilitation and competition, and (3) density-dependent aggregation driven by plant-sediment feedbacks. Our modeling results also show that disordered hyperuniform patterns can help ecosystems cope with arid conditions with enhanced functioning of soil moisture acquisition. However, this advantage may come at the cost of slower recovery of ecosystem structure upon perturbations. Our work highlights that disordered hyperuniformity as a distinguishable but underexplored ecosystem self-organization state merits systematic studies to better understand its underlying mechanisms, functioning, and resilience.
Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry
Authors: Yuming Jiang, Devasahayam Arokia Balaya Rex, Dina Schuster, Benjamin A. Neely, Germán L. Rosano, Norbert Volkmar, Amanda Momenzadeh, Trenton M. Peters-Clarke, Susan B. Egbert, Simion Kreimer, Emma H. Doud, Oliver M. Crook, Amit Kumar Yadav, Muralidharan Vanuopadath, Martín L. Mayta, Anna G. Duboff, Nicholas M. Riley, Robert L. Moritz, Jesse G. Meyer
Date: 2023-11-13 00:00:00

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.
Effect of Wearing a New Prophylactic Orthosis on Postural Balance
Authors: Julien Romain, Ahlem Arfaoui, William Bertucci
Date: 2023-11-13 00:00:00

Purpose: The purpose of this study is to evaluate the effect of an innovative prophylactic knee orthosis on postural balance. This prophylactic knee orthosis is designed with a compression that is oriented in a chosen direction. The purpose of this compression is to improve stability in dynamic situations. Orthoses are used to provide functional improvements to knee problems. However, more scientific validation is needed for this type of product. Methods: 20 sportsmen in team sports performed a functional test: the Y-Balance Test. This reliable and reproducible test allows to evaluate the postural balance of the lower limb. The subjects were tested in 3 conditions: prophylactic orthosis with innovative compression, control orthosis (with no compression) and without orthosis. The average of the three trials were collected in each direction and condition. Results: The prophylactic orthosis had a better standardized score in the anterior direction (p<0.05) and a better composite score (p<0.05) than the control orthosis (no compression). However, there were no differences in the normalized score in the other directions. There were no significant differences between the prophylactic orthosis and without orthosis. Conclusion:Wearing the prophylactic orthosis improves postural balance compared to a orthosis with no compression. But there is no difference between the prophylactic orthosis and without orthosis on postural balance.
Revisiting Cont's Stylized Facts for Modern Stock Markets
Authors: Ethan Ratliff-Crain, Colin M. Van Oort, James Bagrow, Matthew T. K. Koehler, Brian F. Tivnan
Date: 2023-11-13 00:00:00
Tasks: Time Series

In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to synthesize empirical studies of financial price changes (returns), resulting in 11 statistical properties common to a large set of assets and markets. These properties are viewed as constraints a model should be able to reproduce in order to accurately represent returns in a market. It has not been established whether the characteristics Cont noted in 2001 still hold for modern markets following significant regulatory shifts and technological advances. It is also not clear whether a given time series of financial returns for an asset will express all 11 stylized facts. We test both of these propositions by attempting to replicate each of Cont's 11 stylized facts for intraday returns of the individual stocks in the Dow 30, using the same authoritative data as that used by the U.S. regulator from October 2018 - March 2019. We find conclusive evidence for eight of Cont's original facts and no support for the remaining three. Our study represents the first test of Cont's 11 stylized facts against a consistent set of stocks, therefore providing insight into how these stylized facts should be viewed in the context of modern stock markets.
Audit fees in auditor switching
Authors: Sarit Agami
Date: 2023-11-14 00:00:00

The auditor work is examining that a company's financial statements faithfully reflect its financial situation. His wage, the audit fees, are not fixed among all companies, but can be affected by the financial and structural characteristics of the company, as well as the characteristics of the firm he belongs to. Another factor that may affect his wage in an auditor switching, which can be resulted from changes in the company that may influence the fees. This paper examines the effect nature of the auditor switching on his wage, and the factors of the company characteristics and the economy data which determine the wage at switching. A product of the research are tools for predicting and evaluating the auditor wage at switching. These tools are important for the auditor himself, but also for the company manager to correctly determine the wage due to the possibility that the quality of the audit work depends on its fees. Two main results are obtained. First, the direction of the wage change in the switching year depends on the economic stability of the economy. Second, the switching effect on the direction and the change size in wage depends on the change size in the company characteristics before and after switching - a large change versus a stable one. We get that forecasting the change size in wage for companies with a larger change is their characteristics is paralleled to forecasting a wage increasing. And vice versa, forecasting the change size in wage for companies with a stable change in their characteristics is paralleled to forecasting a wage decreasing. But, whereas the former can be achieved based on the company characteristics and macroeconomics factors, the predictably of these characteristics and factors is negligible for the letter.
Relative intrinsic dimensionality is intrinsic to learning
Authors: Oliver J. Sutton, Qinghua Zhou, Alexander N. Gorban, Ivan Y. Tyukin
Date: 2023-10-10 00:00:00
Tasks: Binary Classification

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of thumb than a reliable property as high dimensionality alone is neither necessary nor sufficient for successful learning. Here, we introduce a new notion of the intrinsic dimension of a data distribution, which precisely captures the separability properties of the data. For this intrinsic dimension, the rule of thumb above becomes a law: high intrinsic dimension guarantees highly separable data. We extend this notion to that of the relative intrinsic dimension of two data distributions, which we show provides both upper and lower bounds on the probability of successfully learning and generalising in a binary classification problem
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation
Authors: Meghna Gummadi, Cassandra Kent, Karl Schmeckpeper, Eric Eaton
Date: 2023-10-04 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Scene Segmentation, Segmentation, Semantic Segmentation

Despite outstanding semantic scene segmentation in closed-worlds, deep neural networks segment novel instances poorly, which is required for autonomous agents acting in an open world. To improve out-of-distribution (OOD) detection for segmentation, we introduce a metacognitive approach in the form of a lightweight module that leverages entropy measures, segmentation predictions, and spatial context to characterize the segmentation model's uncertainty and detect pixel-wise OOD data in real-time. Additionally, our approach incorporates a novel method of generating synthetic OOD data in context with in-distribution data, which we use to fine-tune existing segmentation models with maximum entropy training. This further improves the metacognitive module's performance without requiring access to OOD data while enabling compatibility with established pre-trained models. Our resulting approach can reliably detect OOD instances in a scene, as shown by state-of-the-art performance on OOD detection for semantic segmentation benchmarks.
Algorithms for Object Detection in Substations
Authors: Bingying Jin, Yadong Liu, Qinlin Qian
Date: 2023-09-23 00:00:00
Tasks: Decoder, Object, object-detection, Object Detection, Object Recognition

Inspection of high-voltage power equipment is an effective way to ensure power supply reliability. Object recognition, one of the key technologies in automatic power equipment inspection, attracts attention of many researchers and engineers. Although quite a few existing models have some their own advantages, object relationship between equipment which is very important in this task is scarcely considered. This paper combining object relationship modeling and Transformer Model proposes a Relation Transformer Model. It has four parts -- backbone, encoder, decoder and prediction heads. With this structure, the proposed method shows in experiments a much better performance than other three commonly used models in object recognition in substation, largely promoting the development of automatic power equipment inspection.
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Authors: Xinyu Gong, Jason Holmes, Yiwei Li, Zhengliang Liu, Qi Gan, Zihao Wu, Jianli Zhang, Yusong Zou, Yuxi Teng, Tian Jiang, Hongtu Zhu, Wei Liu, Tianming Liu, Yajun Yan
Date: 2023-11-05 00:00:00
Tasks: Logical Reasoning, Multiple-choice

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized.
Cross-Dialect Sentence Transformation: A Comparative Analysis of Language Models for Adapting Sentences to British English
Authors: Shruti Dutta, Shashwat Mookherjee
Date: 2023-11-05 00:00:00
Tasks: Sentence, Translation

This study explores linguistic distinctions among American, Indian, and Irish English dialects and assesses various Language Models (LLMs) in their ability to generate British English translations from these dialects. Using cosine similarity analysis, the study measures the linguistic proximity between original British English translations and those produced by LLMs for each dialect. The findings reveal that Indian and Irish English translations maintain notably high similarity scores, suggesting strong linguistic alignment with British English. In contrast, American English exhibits slightly lower similarity, reflecting its distinct linguistic traits. Additionally, the choice of LLM significantly impacts translation quality, with Llama-2-70b consistently demonstrating superior performance. The study underscores the importance of selecting the right model for dialect translation, emphasizing the role of linguistic expertise and contextual understanding in achieving accurate translations.
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses
Authors: Youngeun Nam, Patara Trirat, Taeyoon Kim, Youngseop Lee, Jae-Gil Lee
Date: 2023-09-17 00:00:00
Proceeding: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 9
Tasks: Anomaly Detection, Time Series

Detecting anomalies in time series has become increasingly challenging as data collection technology develops, especially in realworld communication services, which require contextual information for precise prediction. To address this challenge, researchers usually use time-series decomposition to reveal underlying patterns, e.g., trends and seasonality. However, existing decomposition-based anomaly detectors do not explicitly consider such contextual information, limiting their ability to correctly detect contextual cases. This paper proposes Time-CAD, a new context-aware deep time-series decomposition framework to detect anomalies for a more practical scenario in real-world businesses. We verify the effectiveness of the novel design for integrating contextual information into deep time-series decomposition through extensive experiments on four real-world benchmarks, demonstrating improvements of up to 46% in time-series aware F1 score on average.
AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
Authors: Patara Trirat, Youngeun Nam, Taeyoon Kim, Jae-Gil Lee
Date: 2023-09-06 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2023 9
Tasks: Time Series

This paper presents AnoViz, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. AnoViz provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.
Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards
Authors: Jessica Yu, Kaitlin Castellani, Krista Forysinski, Paul Gustafson, James Lu, Emily Peterson, Martino Tran, Angela Yao, Jingxuan Zhao, and Michael Brauer
Date: 2021-03-21 00:00:00
Proceeding: Environmental Health 2021 3
Tasks: Sensitivity, Systematic Literature Review

Background: Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. Methods: A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. Results: Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region.