pwc-archive/papers-with-abstracts huggingface dataset:
Advantageous and disadvantageous inequality aversion can be taught through vicarious learning of others' preferences
Authors: Shen Zhang, Oriel FeldmanHall, Sébastien Hétu, A. Ross Otto
Date: 2024-05-10 00:00:00
Tasks: Fairness

While enforcing egalitarian social norms is critical for human society, punishing social norm violators often incurs a cost to the self. This cost looms even larger when one can benefit from an unequal distribution of resources (i.e. advantageous inequity), as in receiving a higher salary than a colleague with the identical role. In the Ultimatum Game, a classic test bed for fairness norm enforcement, individuals rarely reject (punish) such unequal proposed divisions of resources because doing so entails a sacrifice of one's own benefit. Recent work has demonstrated that observing another's punitive responses to unfairness can efficiently alter the punitive preferences of an observer. It remains an open question, however, whether such contagion is powerful enough to impart advantageous inequity aversion to individuals. Using a variant of the Ultimatum Game in which participants are tasked with responding to fairness violations on behalf of another 'Teacher' - whose aversion to advantageous (versus disadvantageous) inequity was systematically manipulated-we probe whether individuals subsequently increase their punishment unfair after experiencing fairness violations on their own behalf. In two experiments, we found individuals can acquire aversion to advantageous inequity 'vicariously' through observing (and implementing) the Teacher's preferences. Computationally, these learning effects were best characterized by a model which learns the latent structure of the Teacher's preferences, rather than a simple Reinforcement Learning account. In summary, our study is the first to demonstrate that people can swiftly and readily acquire another's preferences for advantageous inequity, suggesting in turn that behavioral contagion may be one promising mechanism through which social norm enforcement - which people rarely implement in the case of advantageous inequality - can be enhanced.
The Evolution of Language and Human Rationality
Authors: Robert Worden
Date: 2024-05-10 00:00:00

If language evolved by sexual selection to display superior intelligence, then we require conversational skills, to impress other people, gain high social status, and get a mate. Conversational skills include a Theory of Mind, a sense of self, self esteem and social emotions. To be impressive, we must converse fluently and fast. The syntax of an utterance is defined by fast unification of feature structures. The pragmatic skills of conversation are also learned and deployed as feature structures; we rehearse conversations as verbal thoughts. Many aspects of our mental lives (such as our Theory of Mind, and our social emotions) work by fast, pre conscious unification of learned feature structures, rather than rational deliberation. As we think, we use the Fast Theory of Mind to infer (unreliably) how a Shadow Audience will regard what we think, say, and do. These forces, which determine our motivations and actions, are less rational and deliberate than we like to suppose
Contribution of Coincidence Detection to Speech Segregation in Noisy Environments
Authors: Asaf Zorea, Miriam Furst
Date: 2024-05-09 00:00:00

This study introduces a biologically-inspired model designed to examine the role of coincidence detection cells in speech segregation tasks. The model consists of three stages: a time-domain cochlear model that generates instantaneous rates of auditory nerve fibers, coincidence detection cells that amplify neural activity synchronously with speech presence, and an optimal spectro-temporal speech presence estimator. A comparative analysis between speech estimation based on the firing rates of auditory nerve fibers and those of coincidence detection cells indicates that the neural representation of coincidence cells significantly reduces noise components, resulting in a more distinguishable representation of speech in noise. The proposed framework demonstrates the potential of brainstem nuclei processing in enhancing auditory skills. Moreover, this approach can be further tested in other sensory systems in general and within the auditory system in particular.
Characterization of mood and emotion regulation in females with PMS/PMDD using near-infrared spectroscopy to assess prefrontal cerebral blood flow and the mood questionnaire
Authors: Makiko Aoki, Masato Suzuki, Satoshi Suzuki, Kosuke Oiwa, Yoshitaka Maeda, Hisayo Okayama
Date: 2024-05-10 00:00:00

Many sexually mature women experience premenstrual syndrome (PMS) or premenstrual dysphoric mood disorder (PMDD). Current approaches for managing PMS and PMDD rely on daily mental condition recording, which many discontinue due to its impracticality. Hence, there's a critical need for a simple, objective method to monitor mental symptoms. One of the principal symptoms of PMDD is a dysfunction in emotional regulation, which has been demonstrated through brain-function imaging measurements to involve hyperactivity in the amygdala and a decrease in functionality in the prefrontal cortex (PFC). However, most research has been focused on PMDD, leaving a gap in understanding of PMS. Near-infrared spectroscopy (NIRS) measures brain activity by spectroscopically determining the amount of hemoglobin in the blood vessels. This study aimed to characterize the emotional regulation function in PMS. We measured brain activity in the PFC region using NIRS when participants were presented with emotion-inducing pictures. Furthermore, moods highly associated with emotions were assessed through questionnaires. Forty-six participants were categorized into non-PMS, PMS, and PMDD groups based on the gynecologist's diagnosis. POMS2 scores revealed higher negative mood and lower positive mood in the follicular phase for the PMS group, while the PMDD group exhibited heightened negative mood during the luteal phase. NIRS results showed reduced emotional expression in the PMS group during both phases, while no significant differences were observed in the PMDD group compared to non-PMS. It was found that there are differences in the distribution of mood during the luteal and follicular phase and in cerebral blood flow responses to emotional stimuli between PMS and PMDD. These findings suggest the potential for providing individuals with awareness of PMS or PMDD through scores on the POMS2 and NIRS measurements.
Automated Cell Structure Extraction for 3D Electron Microscopy by Deep Learning
Authors: Jin Kousaka, Atsuko H. Iwane, Yuichi Togashi
Date: 2024-05-10 00:00:00
Tasks: Segmentation

Modeling the 3D structures of cells and tissues is crucial in biology. Sequential cross-sectional images from electron microscopy provide high-resolution intracellular structure information. The segmentation of complex cell structures remains a laborious manual task for experts, demanding time and effort. This bottleneck in analyzing biological images requires efficient and automated solutions. In this study, the deep learning-based automated segmentation of biological images was explored to enable accurate reconstruction of the 3D structures of cells and organelles. An analysis system for the cell images of Cyanidioschyzon merolae, a primitive unicellular red algae, was constructed. This system utilizes sequential cross-sectional images captured by a focused ion beam scanning electron microscope (FIB-SEM). A U-Net was adopted and training was performed to identify and segment cell organelles from single-cell images. In addition, the segment anything model (SAM) and 3D watershed algorithm were employed to extract individual 3D images of each cell from large-scale microscope images containing numerous cells. Finally, the trained U-Net was applied to segment each structure within these 3D images. Through this procedure, the creation of 3D cell models could be fully automated. The adoption of other deep learning techniques and combinations of image processing methods will also be explored to enhance the segmentation accuracy further.
Champuru 2: Improved scoring of alignments and a user-friendly graphical interface
Authors: Yann Spöri, Jean-François Flot
Date: 2024-05-09 00:00:00
Tasks: EEG

Champuru is a web software tool that helps determine the two sequences present in mixed Sanger chromatograms obtained by sequencing simultaneously two DNA templates of unequal lengths. A previous version (Champuru 1.0) was published as a simple Perl CGI (Common Gateway Interface) application, but the server hosting it was discontinued, which prompted us to update it and develop it further. The new Champuru 2, implemented in Haxe and hosted at GitHub Pages, offers an improved graphical user interface as well as more sophisticated algorithms to compute alignment scores, making it more efficient at detecting the most likely alignment positions between forward and reverse traces. Champuru 2 now make it possible to analyze other offset pairs than the one detected as most likely by the selected algorithm. Champuru 2 is freely accessible at https://eeg-ebe.github.io/Champuru/, including both a graphical user interface (running a JavaScript version transpiled from the Haxe source code) and a compiled command-line version (obtained by transpiling the Haxe source code into C++).
The Impact of Financial Literacy, Social Capital, and Financial Technology on Financial Inclusion of Indonesian Students
Authors: Gen Norman Thomas, Siti Mutiara Ramadhanti Nur, Lely Indriaty
Date: 2024-05-10 00:00:00

This study aims to analyze the impact of financial literacy, social capital and financial technology on financial inclusion. The research method used a quantitative research method, in which questionnaires were distributed to 100 active students in the economics faculty at 7 private colleges in Tangerang, Indonesia. Based on the results of data processing using SPSS version 23, it results that financial literacy, social capital and financial technology partially have a positive and significant influence on financial inclusion. The results of this study provide input that financial literacy needs to be increased because it is not yet equivalent to financial inclusion, and reducing the gap between financial literacy and financial inclusion is only 2.74%. Another benefit of this research is to give an understanding to students that students should be independent actors or users of financial technology products and that students should become pioneers in delivering financial knowledge, financial behavior and financial attitudes to the wider community.
A Two-layer Stochastic Game Approach to Reinsurance Contracting and Competition
Authors: Zongxia Liang, Yi Xia, Bin Zou
Date: 2024-05-10 00:00:00

We propose a two-layer stochastic game model to study reinsurance contracting and competition in a market with one insurer and two competing reinsurers. The insurer negotiates with both reinsurers simultaneously for proportional reinsurance contracts that are priced using the variance premium principle. The reinsurance contracting between the insurer and each reinsurer is modeled as a Stackelberg game. The two reinsurers compete for business from the insurer and optimize the so-called relative performance, instead of their own surplus, and their competition is settled by a noncooperative Nash game. We obtain a sufficient and necessary condition, related to the competition degrees of the two reinsurers, for the existence of an equilibrium. We show that the equilibrium, if exists, is unique, and the equilibrium strategy of each player is constant, fully characterized in semiclosed form. Furthermore, we obtain interesting sensitivity results for the equilibrium strategies through both analytical and numerical studies.
eCAR: edge-assisted Collaborative Augmented Reality Framework
Authors: Jinwoo Jeon, Woontack Woo
Date: 2024-05-11 00:00:00
Tasks: Edge-computing, Object

We propose a novel edge-assisted multi-user collaborative augmented reality framework in a large indoor environment. In Collaborative Augmented Reality, data communication that synchronizes virtual objects has large network traffic and high network latency. Due to drift, CAR applications without continuous data communication for coordinate system alignment have virtual object inconsistency. In addition, synchronization messages for online virtual object updates have high latency as the number of collaborative devices increases. To solve this problem, we implement the CAR framework, called eCAR, which utilizes edge computing to continuously match the device's coordinate system with less network traffic. Furthermore, we extend the co-visibility graph of the edge server to maintain virtual object spatial-temporal consistency in neighboring devices by synchronizing a local graph. We evaluate the system quantitatively and qualitatively in the public dataset and a physical indoor environment. eCAR communicates data for coordinate system alignment between the edge server and devices with less network traffic and latency. In addition, collaborative augmented reality synchronization algorithms quickly and accurately host and resolve virtual objects. The proposed system continuously aligns coordinate systems to multiple devices in a large indoor environment and shares augmented reality content. Through our system, users interact with virtual objects and share augmented reality experiences with neighboring users.
Replication Study and Benchmarking of Real-Time Object Detection Models
Authors: Pierre-Luc Asselin, Vincent Coulombe, William Guimont-Martin, William Larrivée-Hardy
Date: 2024-05-11 00:00:00
Tasks: Benchmarking, object-detection, Object Detection, Real-Time Object Detection

This work examines the reproducibility and benchmarking of state-of-the-art real-time object detection models. As object detection models are often used in real-world contexts, such as robotics, where inference time is paramount, simply measuring models' accuracy is not enough to compare them. We thus compare a large variety of object detection models' accuracy and inference speed on multiple graphics cards. In addition to this large benchmarking attempt, we also reproduce the following models from scratch using PyTorch on the MS COCO 2017 dataset: DETR, RTMDet, ViTDet and YOLOv7. More importantly, we propose a unified training and evaluation pipeline, based on MMDetection's features, to better compare models. Our implementation of DETR and ViTDet could not achieve accuracy or speed performances comparable to what is declared in the original papers. On the other hand, reproduced RTMDet and YOLOv7 could match such performances. Studied papers are also found to be generally lacking for reproducibility purposes. As for MMDetection pretrained models, speed performances are severely reduced with limited computing resources (larger, more accurate models even more so). Moreover, results exhibit a strong trade-off between accuracy and speed, prevailed by anchor-free models - notably RTMDet or YOLOx models. The code used is this paper and all the experiments is available in the repository at https://github.com/Don767/segdet_mlcr2024.
Human-interpretable clustering of short-text using large language models
Authors: Justin K. Miller, Tristram J. Alexander
Date: 2024-05-12 00:00:00
Tasks: Clustering, Short Text Clustering, Text Clustering

Clustering short text is a difficult problem, due to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study clusters are found in the embedding space using Gaussian Mixture Modelling (GMM). The resulting clusters are found to be more distinctive and more human-interpretable than clusters produced using the popular methods of doc2vec and Latent Dirichlet Allocation (LDA). The success of the clustering approach is quantified using human reviewers and through the use of a generative LLM. The generative LLM shows good agreement with the human reviewers, and is suggested as a means to bridge the `validation gap' which often exists between cluster production and cluster interpretation. The comparison between LLM-coding and human-coding reveals intrinsic biases in each, challenging the conventional reliance on human coding as the definitive standard for cluster validation.
Using Model-Theoretic Approaches to Uncover Linguistic Organization
Authors: Olivia Griffin, Jerry Sun
Date: 2024-05-13 00:00:00

In this paper, we consider pluractional markers in Kaqchikel, Karuk, and Yurok. Like Balinese, each of these languages marks one type of pluractionality via reduplication, and a different type of pluractionality via non-reduplicative affixation. This paper serves as a proof-of-concept for applying model-theoretic approaches to language as a lens that can help us to recognize linguistic organization that is not apparent on the surface.
Music Emotion Prediction Using Recurrent Neural Networks
Authors: Xinyu Chang, Xiangyu Zhang, Haoruo Zhang, Yulu Ran
Date: 2024-05-10 00:00:00
Tasks: Music Recommendation, Prediction, Recommendation Systems

This study explores the application of recurrent neural networks to recognize emotions conveyed in music, aiming to enhance music recommendation systems and support therapeutic interventions by tailoring music to fit listeners' emotional states. We utilize Russell's Emotion Quadrant to categorize music into four distinct emotional regions and develop models capable of accurately predicting these categories. Our approach involves extracting a comprehensive set of audio features using Librosa and applying various recurrent neural network architectures, including standard RNNs, Bidirectional RNNs, and Long Short-Term Memory (LSTM) networks. Initial experiments are conducted using a dataset of 900 audio clips, labeled according to the emotional quadrants. We compare the performance of our neural network models against a set of baseline classifiers and analyze their effectiveness in capturing the temporal dynamics inherent in musical expression. The results indicate that simpler RNN architectures may perform comparably or even superiorly to more complex models, particularly in smaller datasets. We've also applied the following experiments on larger datasets: one is augmented based on our original dataset, and the other is from other sources. This research not only enhances our understanding of the emotional impact of music but also demonstrates the potential of neural networks in creating more personalized and emotionally resonant music recommendation and therapy systems.
Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature
Authors: Ruikai Yang, Fan He, Mingzhen He, Jie Yang, Xiaolin Huang
Date: 2024-05-13 00:00:00
Tasks: regression

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5\% across six real-world data sets.
Vector-borne threats: Sustainable approaches to their diagnosis and treatment
Authors: Areesha Naveed, Ayesha Haidar, Rameen Atique, Arshi Saeed, Bushra Anwar, Ambreen Talib, Uzma Bilal, Javeria Sharif, Ayesha Nadeem, Sania Tariq, Ayesha Muazzam, Abdul Samad
Date: 2024-05-10 00:00:00

Arbovirus is a vital, life-threatening disease worldwide and continues to be a significant problem while the world is dealing with the major coronavirus (COVID-19) pandemic. Vectors, mostly mosquitoes and ticks, transmit this disease. Dengue fever, chikungunya, and Zika viruses are the major threats because of their high incidence, public health burden, and clinically significant disease spectrum. These vector-borne disease causes one-fourth of annual deaths, leading to various infectious diseases. The arbovirus represents eight different families and 14 genera; most viruses belong to the family Bunyaviridae, and some also belong to Togaviridae, Reoviridae, and Flaviviridae. The arbovirus disease was isolated first in tropical and subtropical regions of South America and Africa and has high significance because of suitable environmental conditions for virus transmission and vector expansion. Its transmission cycle ranges from simple to highly complex. DENV is the most prevalent, results in febrile illness, and has transmission in 128 different countries. CHIKV causes infection in asymptomatic people, and the problems include nephritis, arthritis, myelitis, and acute encephalopathy. ZIKV-infected 80% of people are asymptomatic and may cause rashes, myalgia, fever, headache, and conjunctivitis. Vaccines for DENV are not clinically available; it is a primary arboviral infection in the world nowadays. The exposure of arbovirus diseases continues to be a global health problem regardless of continuing efforts. This review article will overview major arbovirus diseases and their diagnosis, treatment, and prevention strategies.
MambaOut: Do We Really Need Mamba for Vision?
Authors: Weihao Yu, Xinchao Wang
Date: 2024-05-13 00:00:00
Proceeding: CVPR 2025 1
Tasks: image-classification, Image Classification, Instance Segmentation, Mamba, Object Detection, Semantic Segmentation

Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks. Nevertheless, the performance of Mamba for vision is often underwhelming when compared with convolutional and attention-based models. In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics. For vision tasks, as image classification does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks. To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks. The code is available at https://github.com/yuweihao/MambaOut