pwc-archive/papers-with-abstracts huggingface dataset:
Cost-benefit analysis of ecosystem modelling to support fisheries management
Authors: Matthew H. Holden, Eva E. Plagányi, Elizabeth A. Fulton, Alexander B. Campbell, Rachel Janes, Robyn A. Lovett, Montana Wickens, Matthew P. Adams, Larissa Lubiana Botelho, Catherine M. Dichmont, Philip Erm, Kate J Helmstedt, Ryan F. Heneghan, Manuela Mendiolar, Anthony J. Richardson, Jacob G. D. Rogers, Kate Saunders, Liam Timms
Date: 2024-03-26 00:00:00
Tasks: Management

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits.
Response of Different Tomato Accessions to Biotic and Abiotic Stresses
Authors: Kamaran Salh Rasul, Nawroz Abdul-razzak Tahir
Date: 2024-03-24 00:00:00
Tasks: Diversity

Accessions are prospective sources of genetic variability, as well as valuable genetic resources to deal with present and future crop breeding difficulties. The assessment of population structure and genetic diversity of tomatoes (Solanum lycopersicum L.) that have been distributed in Iraqi Kurdistan region critical in breeding programs for the production of high-yielding cultivars as well as widening the genetic base of tomato. Using fruit quality indices and molecular markers, a panel of 64 tomato accessions taken from six provinces of Iraqi Kurdistan Region, were analyzed for genetic diversity and population structure. In the analysis of variance, the fruit phenotypic data revealed a high level of significant variability among tomato accessions. The most important characteristics for explaining fruit morphological variability, according to principal component analysis (PCA), were fruit weight, fruit size, fruit diameter, total soluble solids, and moisture content. Seven clades with different fruit characteristics were revealed in the cluster analysis. Genetic diversity and relationships among accessions were analyzed using thirteen inter simple sequence repeat (ISSR), twenty-six start codon-targeted (SCoT) polymorphisms, and fifteen conserved DNA-derived polymorphisms (CDDP). The ISSR, SCoT, and CDDP markers generated 121, 294, and 170 polymorphic bands, respectively, showing a high prevalence of polymorphism. The average polymorphism information content (PIC) values for ISSR, SCoT, and CDDP were 0.81, 0.84, and 0.84, respectively. The accessions were divided into two groups based on the cluster and STRUCTURE analysis results. The Mantel test revealed that three sets of markers had positive and significant relationships.
Revisiting Boehmer et al. (2021): Recent Period, Alternative Method, Different Conclusions
Authors: David Ardia, Clément Aymard, Tolga Cenesizoglu
Date: 2024-03-25 00:00:00

We reassess Boehmer et al. (2021, BJZZ)'s seminal work on the predictive power of retail order imbalance (ROI) for future stock returns. First, we replicate their 2010-2015 analysis in the more recent 2016-2021 period. We find that the ROI's predictive power weakens significantly. Specifically, past ROI can no longer predict weekly returns on large-cap stocks, and the long-short strategy based on past ROI is no longer profitable. Second, we analyze the effect of using the alternative quote midpoint (QMP) method to identify and sign retail trades on their main conclusions. While the results based on the QMP method align with BJZZ's findings in 2010-2015, the two methods provide different conclusions in 2016-2021. Our study shows that BJZZ's original findings are sensitive to the sample period and the approach to identify ROIs.
Alternatives to classical option pricing
Authors: W. Brent Lindquist, Svetlozar T. Rachev
Date: 2024-03-25 00:00:00

We develop two alternate approaches to arbitrage-free, market-complete, option pricing. The first approach requires no riskless asset. We develop the general framework for this approach and illustrate it with two specific examples. The second approach does use a riskless asset. However, by ensuring equality between real-world and risk-neutral price-change probabilities, the second approach enables the computation of risk-neutral option prices utilizing expectations under the natural world probability P. This produces the same option prices as the classical approach in which prices are computed under the risk neutral measure Q. The second approach and the two specific examples of the first approach require the introduction of new, marketable asset types, specifically perpetual derivatives of a stock, and a stock whose cumulative return (rather than price) is deflated.
SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations
Authors: Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang, Nicholas Altieri
Date: 2024-01-02 00:00:00

A clinical artificial intelligence (AI) system is often validated on a held-out set of data which it has not been exposed to before (e.g., data from a different hospital with a distinct electronic health record system). This evaluation process is meant to mimic the deployment of an AI system on data in the wild; those which are currently unseen by the system yet are expected to be encountered in a clinical setting. However, when data in the wild differ from the held-out set of data, a phenomenon referred to as distribution shift, and lack ground-truth annotations, it becomes unclear the extent to which AI-based findings can be trusted on data in the wild. Here, we introduce SUDO, a framework for evaluating AI systems without ground-truth annotations. SUDO assigns temporary labels to data points in the wild and directly uses them to train distinct models, with the highest performing model indicative of the most likely label. Through experiments with AI systems developed for dermatology images, histopathology patches, and clinical reports, we show that SUDO can be a reliable proxy for model performance and thus identify unreliable predictions. We also demonstrate that SUDO informs the selection of models and allows for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. The ability to triage unreliable predictions for further inspection and assess the algorithmic bias of AI systems can improve the integrity of research findings and contribute to the deployment of ethical AI systems in medicine.
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
Authors: Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul, Weixun Wang, Lewis Tunstall
Date: 2024-03-24 00:00:00
Tasks: reinforcement-learning

This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work. We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant gains in response quality that scale with model size, with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint. We publicly release the trained model checkpoints and code to facilitate further research and accelerate progress in the field (\url{https://github.com/vwxyzjn/summarize_from_feedback_details}).
Stochastic parameter reduced-order model based on hybrid machine learning approaches
Authors: Cheng Fang, Jinqiao Duan
Date: 2024-03-24 00:00:00
Tasks: Computational Efficiency, Hybrid Machine Learning, State Estimation

Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena makes it extremely challenging to develop full-order models (FOMs) and apply them to studying many quantities of interest. In contrast, appropriate reduced-order models (ROMs) are favored due to their high computational efficiency and ability to describe the key dynamics and statistical characteristics of natural phenomena. Taking the viscous Burgers equation as an example, this paper constructs a Convolutional Autoencoder-Reservoir Computing-Normalizing Flow algorithm framework, where the Convolutional Autoencoder is used to construct latent space representations, and the Reservoir Computing-Normalizing Flow framework is used to characterize the evolution of latent state variables. In this way, a data-driven stochastic parameter reduced-order model is constructed to describe the complex system and its dynamic behavior.
Temporal-Spatial Processing of Event Camera Data via Delay-Loop Reservoir Neural Network
Authors: Richard Lau, Anthony Tylan-Tyler, Lihan Yao, Rey de Castro Roberto, Robert Taylor, Isaiah Jones
Date: 2024-02-12 00:00:00

This paper describes a temporal-spatial model for video processing with special applications to processing event camera videos. We propose to study a conjecture motivated by our previous study of video processing with delay loop reservoir (DLR) neural network, which we call Temporal-Spatial Conjecture (TSC). The TSC postulates that there is significant information content carried in the temporal representation of a video signal and that machine learning algorithms would benefit from separate optimization of the spatial and temporal components for intelligent processing. To verify or refute the TSC, we propose a Visual Markov Model (VMM) which decompose the video into spatial and temporal components and estimate the mutual information (MI) of these components. Since computation of video mutual information is complex and time consuming, we use a Mutual Information Neural Network to estimate the bounds of the mutual information. Our result shows that the temporal component carries significant MI compared to that of the spatial component. This finding has often been overlooked in neural network literature. In this paper, we will exploit this new finding to guide our design of a delay-loop reservoir neural network for event camera classification, which results in a 18% improvement on classification accuracy.
Contrastive Learning for Regression on Hyperspectral Data
Authors: Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem
Date: 2024-02-12 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, regression, Representation Learning

Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.
Exploring and Applying Audio-Based Sentiment Analysis in Music
Authors: Etash Jhanji
Date: 2024-02-22 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The ability of a computational model to interpret musical emotions is largely unexplored and could have implications and uses in therapy and musical queuing. In this paper, two individual tasks are addressed. This study seeks to (1) predict the emotion of a musical clip over time and (2) determine the next emotion value after the music in a time series to ensure seamless transitions. Utilizing data from the Emotions in Music Database, which contains clips of songs selected from the Free Music Archive annotated with levels of valence and arousal as reported on Russel's circumplex model of affect by multiple volunteers, models are trained for both tasks. Overall, the performance of these models reflected that they were able to perform the tasks they were designed for effectively and accurately.
Boosting Few-Shot Learning via Attentive Feature Regularization
Authors: Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu, Xiangnan He
Date: 2024-03-23 00:00:00
Tasks: Few-Shot Learning

Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor. However, this mixing operation weakens the feature representation due to the linear interpolation and the overlooking of the importance of specific channels. To solve these issues, this paper proposes attentive feature regularization (AFR) which aims to improve the feature representativeness and discriminability. In our approach, we first calculate the relations between different categories of semantic labels to pick out the related features used for regularization. Then, we design two attention-based calculations at both the instance and channel levels. These calculations enable the regularization procedure to focus on two crucial aspects: the feature complementarity through adaptive interpolation in related categories and the emphasis on specific feature channels. Finally, we combine these regularization strategies to significantly improve the classifier performance. Empirical studies on several popular FSL benchmarks demonstrate the effectiveness of AFR, which improves the recognition accuracy of novel categories without the need to retrain any feature extractor, especially in the 1-shot setting. Furthermore, the proposed AFR can seamlessly integrate into other FSL methods to improve classification performance.
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization
Authors: Fanfei Meng, Chen-Ao Wang, LeLe Zhang
Date: 2024-02-11 00:00:00
Tasks: Computational Efficiency, Evolutionary Algorithms, Neural Architecture Search

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.
ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVD
Authors: Huaicheng Zhang, Wenhan Liu, Sheng Chang, Hao Wang, Jin He, Qijun Huang
Date: 2023-10-23 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2023 10

Recently, deep learning (DL) has enabled rapid advancements in electrocardiogram (ECG)-based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead systems based on the potential differences between electrodes placed on the limbs and the chest. When applying DL models, ECG signals are usually treated as synchronized signals arranged in Euclidean space, which is the abstraction and generalization of real space. However, conventional DL models typically merely focus on temporal features when analyzing Euclidean data. These approaches ignore the spatial relationships
of different leads, which are physiologically significant and useful for CVD diagnosis because different leads represent activities of specific heart regions. These relationships derived from spatial distributions of electrodes can be conveniently created in non-Euclidean data, making multi-lead ECGs better conform to their nature. Considering graph convolutional network (GCN) adept at analyzing non-Euclidean data, a novel spatial-temporal residual GCN for CVD diagnosis is proposed in this work. ECG signals are firstly divided into single-channel patches and transferred into nodes, which will be connected by spatiotemporal connections. The proposed model employs residual GCN blocks and feed-forward networks to alleviate oversmoothing and over-fitting. Moreover, residual connections and patch dividing enable the capture of global and detailed
spatial-temporal features. Experimental results reveal that the proposed model achieves at least a 5.85% and 6.80% increase in F1 over other state-of-the-art algorithms with similar parameters and computations in both PTB-XL and Chapman databases. It indicates that the proposed model provides a promising avenue for intelligent diagnosis with limited computing resources.
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
Authors: Vignesh Kothapalli, Tom Tirer, Joan Bruna
Date: 2023-07-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Community Detection, image-classification, Image Classification, Stochastic Block Model

Graph neural networks (GNNs) have become increasingly popular for classification tasks on graph-structured data. Yet, the interplay between graph topology and feature evolution in GNNs is not well understood. In this paper, we focus on node-wise classification, illustrated with community detection on stochastic block model graphs, and explore the feature evolution through the lens of the "Neural Collapse" (NC) phenomenon. When training instance-wise deep classifiers (e.g. for image classification) beyond the zero training error point, NC demonstrates a reduction in the deepest features' within-class variability and an increased alignment of their class means to certain symmetric structures. We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case. Then, we theoretically study this distinction. Specifically, we show that even an "optimistic" mathematical model requires that the graphs obey a strict structural condition in order to possess a minimizer with exact collapse. Interestingly, this condition is viable also for heterophilic graphs and relates to recent empirical studies on settings with improved GNNs' generalization. Furthermore, by studying the gradient dynamics of the theoretical model, we provide reasoning for the partial collapse observed empirically. Finally, we present a study on the evolution of within- and between-class feature variability across layers of a well-trained GNN and contrast the behavior with spectral methods.
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition
Authors: Zaijing Li1, Fengxiao Tang1*, Ming Zhao1*, Yusen Zhu2
Date: 2022-03-25 00:00:00
Proceeding: 星期五 08：42：57 UTC 2022 3
Tasks: Emotion Recognition, Emotion Recognition in Conversation, model, Sentence

Emotion recognition in conversation (ERC)
 aims to analyze the speaker’s state and iden
tify their emotion in the conversation. Re
cent works in ERC focus on context model
ing but ignore the representation of contextual
 emotional tendency. In order to extract multi
modal information and the emotional tendency
 of the utterance effectively, we propose a new
 structure named Emoformer to extract multi
modal emotion vectors from different modal
ities and fuse them with sentence vector to
 be an emotion capsule. Furthermore, we de
sign an end-to-end ERC model called Emo
Caps, which extracts emotion vectors through
 the Emoformer structure and obtain the emo
tion classification results from a context analy
sis model. Through the experiments with two
 benchmark datasets, our model shows better
 performance than the existing state-of-the-art
 models.
Declarative generation of RDF-star graphs from heterogeneous data
Authors: Julián Arenas-Guerrero, Ana Iglesias-Molina, David Chaves-Fraga, Daniel Garijo, Oscar Corcho, Anastasia Dimou
Date: 2024-03-20 00:00:00
Proceeding: Semantic Web 2024 3
Tasks: Data Integration, Knowledge Graphs, MORPH

RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.
Large Language Models Produce Responses Perceived to be Empathic
Authors: Yoon Kyung Lee, Jina Suh, Hongli Zhan, Junyi Jessy Li, Desmond C. Ong
Date: 2024-03-26 00:00:00

Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate empathic messages in response to posts describing common life experiences, such as workplace situations, parenting, relationships, and other anxiety- and anger-eliciting situations. Across two studies (N=192, 202), we showed human raters a variety of responses written by several models (GPT4 Turbo, Llama2, and Mistral), and had people rate these responses on how empathic they seemed to be. We found that LLM-generated responses were consistently rated as more empathic than human-written responses. Linguistic analyses also show that these models write in distinct, predictable ``styles", in terms of their use of punctuation, emojis, and certain words. These results highlight the potential of using LLMs to enhance human peer support in contexts where empathy is important.