pwc-archive/papers-with-abstracts huggingface dataset:
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
Authors: Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen
Date: 2024-11-07 00:00:00
Tasks: AutoML, Hyperparameter Optimization, Language Modeling, Language Modelling, Large Language Model, Model Selection

Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation into protein engineering remains challenging for biologists without specialized computational expertise. To address this gap, we propose AutoProteinEngine (AutoPE), an agent framework that leverages large language models (LLMs) for multimodal automated machine learning (AutoML) for protein engineering. AutoPE innovatively allows biologists without DL backgrounds to interact with DL models using natural language, lowering the entry barrier for protein engineering tasks. Our AutoPE uniquely integrates LLMs with AutoML to handle model selection for both protein sequence and graph modalities, automatic hyperparameter optimization, and automated data retrieval from protein databases. We evaluated AutoPE through two real-world protein engineering tasks, demonstrating substantial performance improvements compared to traditional zero-shot and manual fine-tuning approaches. By bridging the gap between DL and biologists' domain expertise, AutoPE empowers researchers to leverage DL without extensive programming knowledge. Our code is available at https://github.com/tsynbio/AutoPE.
Unveiling Placental Development in Circadian Rhythm-Disrupted Mice: A Photo-acoustic Imaging Study on Unstained Tissue
Authors: Melisa Natali Cizmeciyan, Nayce Ilayda Bektas, Narin Derin, Tugberk Denizaltı, Alireza Khoshzaban, Mehmet Burcin Unlu, Ciler Celik-Ozenci
Date: 2024-11-07 00:00:00
Tasks: Diagnostic, Rhythm

Introduction: Circadian rhythm disruption has garnered significant attention for its adverse effects on human health, particularly in reproductive medicine and fetal well-being. Assessing pregnancy health often relies on diagnostic markers such as the labyrinth zone (LZ) proportion within the placenta. This study aimed to investigate the impact of disrupted circadian rhythms on placental health and fetal development using animal models. Methods and Results: Employing unstained photo-acoustic microscopy (PAM) and hematoxylin and eosin (HE)-stained images, we found them mutually reinforcing. Our images revealed the role of MCRD on the LZ and fetus weight: a decrease in LZ area from 5.01-HE(4.25-PAM) mm2 to 3.58-HE (2.62-PAM) mm2 on day 16 and 6.48-HE(5.16-PAM) mm2 to 4.61-HE (3.03-PAM) mm2 on day 18, resulting in 0.71 times lower fetus weights. We have discriminated a decrease in the mean LZ to placenta area ratio from 64% to 47% on day 18 in mice with disrupted circadian rhythms with PAM. Discussion: The study highlights the negative influence of circadian rhythm disruption on placental development and fetal well-being. Reduced LZ area and fetal weights in the MCRD group suggest compromised placental function under disrupted circadian rhythms. PAM imaging proved to be an efficient technique for assessing placental development, offering advantages over traditional staining methods. These findings contribute to understanding the mechanisms underlying circadian disruption's effects on reproductive health and fetal development, emphasizing the importance of maintaining normal circadian rhythms for optimal pregnancy outcomes. Further research is needed to explore interventions to mitigate these effects and improve pregnancy outcomes.
Robust and Fast Bass local volatility
Authors: Hao Qin, Charlie Che, Ruozhong Yang, Liming Feng
Date: 2024-11-06 00:00:00
Tasks: Computational Efficiency

The Bass Local Volatility Model (Bass-LV), as studied in [Conze and Henry-Labordere, 2021], stands out for its ability to eliminate the need for interpolation between maturities. This offers a significant advantage over traditional LV models. However, its performance highly depends on accurate construction of state price densities and the corresponding marginal distributions and efficient numerical convolutions which are necessary when solving the associated fixed point problems. In this paper, we propose a new approach combining local quadratic estimation and lognormal mixture tails for the construction of state price densities. We investigate computational efficiency of trapezoidal rule based schemes for numerical convolutions and show that they outperform commonly used Gauss-Hermite quadrature. We demonstrate the performance of the proposed method, both in standard option pricing models, as well as through a detailed market case study.
Optimal Execution under Incomplete Information
Authors: Etienne Chevalier, Yadh Hafsi, Vathana Ly Vath
Date: 2024-11-07 00:00:00
Tasks: Point Processes

We study optimal liquidation strategies under partial information for a single asset within a finite time horizon. We propose a model tailored for high-frequency trading, capturing price formation driven solely by order flow through mutually stimulating marked Hawkes processes. The model assumes a limit order book framework, accounting for both permanent price impact and transient market impact. Importantly, we incorporate liquidity as a hidden Markov process, influencing the intensities of the point processes governing bid and ask prices. Within this setting, we formulate the optimal liquidation problem as an impulse control problem. We elucidate the dynamics of the hidden Markov chain's filter and determine the related normalized filtering equations. We then express the value function as the limit of a sequence of auxiliary continuous functions, defined recursively. This characterization enables the use of a dynamic programming principle for optimal stopping problems and the determination of an optimal strategy. It also facilitates the development of an implementable algorithm to approximate the original liquidation problem. We enrich our analysis with numerical results and visualizations of candidate optimal strategies.
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
Authors: Dante Everaert, Rohit Patki, Tianqi Zheng, Christopher Potts
Date: 2024-10-22 00:00:00
Tasks: Semantic Retrieval

Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered advancements in QAC system development. This paper addresses this gap by introducing AmazonQAC, a new QAC dataset sourced from Amazon Search logs, comprising 395M samples. The dataset includes actual sequences of user-typed prefixes leading to final search terms, as well as session IDs and timestamps that support modeling the context-dependent aspects of QAC. We assess Prefix Trees, semantic retrieval, and Large Language Models (LLMs) with and without finetuning. We find that finetuned LLMs perform best, particularly when incorporating contextual information. However, even our best system achieves only half of what we calculate is theoretically possible on our test data, which implies QAC is a challenging problem that is far from solved with existing systems. This contribution aims to stimulate further research on QAC systems to better serve user needs in diverse environments. We open-source this data on Hugging Face at https://huggingface.co/datasets/amazon/AmazonQAC.
On the analysis of saturated pressure to detect fatigue
Authors: Marcos Faundez-Zanuy, Josep Lopez-Xarbau, Moises Diaz, Manuel Garnacho-Castaño
Date: 2024-10-22 00:00:00

This paper examines the saturation of pressure signals during various handwriting tasks, including drawings, cursive text, capital words text, and signature, under different levels of fatigue. Experimental results demonstrate a significant rise in the proportion of saturated samples following strenuous exercise in tasks performed without resting wrist. The analysis of saturation highlights significant differences when comparing the results to the baseline situation and strenuous fatigue.
PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes
Authors: KeBin Peng, John Quarles, Kevin Desai
Date: 2024-11-04 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

In this paper, we propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, we use assume that points move along a straight line over short distances and then summarize it as a triangular constraint loss in two dimensional Euclidean space. To overcome the depth inconsistency problem around the edges, we propose a deformable support window module that learns features from different shapes of objects, making depth value more accurate around edge area. The proposed model is trained and tested on two outdoor datasets - KITTI and Make3D, as well as an indoor dataset - NYU Depth V2. The quantitative and qualitative results reported on these datasets demonstrate the success of our proposed model when compared against other approaches. Ablation study results on the KITTI dataset also validate the effectiveness of the proposed pixel movement prediction module as well as the deformable support window module.
We Urgently Need Intrinsically Kind Machines
Authors: Joshua T. S. Hewson
Date: 2024-10-21 00:00:00

Artificial Intelligence systems are rapidly evolving, integrating extrinsic and intrinsic motivations. While these frameworks offer benefits, they risk misalignment at the algorithmic level while appearing superficially aligned with human values. In this paper, we argue that an intrinsic motivation for kindness is crucial for making sure these models are intrinsically aligned with human values. We argue that kindness, defined as a form of altruism motivated to maximize the reward of others, can counteract any intrinsic motivations that might lead the model to prioritize itself over human well-being. Our approach introduces a framework and algorithm for embedding kindness into foundation models by simulating conversations. Limitations and future research directions for scalable implementation are discussed.
Combining Theory of Mind and Kindness for Self-Supervised Human-AI Alignment
Authors: Joshua T. S. Hewson
Date: 2024-10-21 00:00:00

As artificial intelligence (AI) becomes deeply integrated into critical infrastructures and everyday life, ensuring its safe deployment is one of humanity's most urgent challenges. Current AI models prioritize task optimization over safety, leading to risks of unintended harm. These risks are difficult to address due to the competing interests of governments, businesses, and advocacy groups, all of which have different priorities in the AI race. Current alignment methods, such as reinforcement learning from human feedback (RLHF), focus on extrinsic behaviors without instilling a genuine understanding of human values. These models are vulnerable to manipulation and lack the social intelligence necessary to infer the mental states and intentions of others, raising concerns about their ability to safely and responsibly make important decisions in complex and novel situations. Furthermore, the divergence between extrinsic and intrinsic motivations in AI introduces the risk of deceptive or harmful behaviors, particularly as systems become more autonomous and intelligent. We propose a novel human-inspired approach which aims to address these various concerns and help align competing objectives.
Enhancement of Approximation Spaces by the Use of Primals and Neighborhood
Authors: A. Çaksu Güler
Date: 2024-10-23 00:00:00

Rough set theory is one of the most widely used and significant approaches for handling incomplete information. It divides the universe in the beginning and uses equivalency relations to produce blocks. Numerous generalized rough set models have been put out and investigated in an effort to increase flexibility and extend the range of possible uses. We introduce four new generalized rough set models that draw inspiration from "neighborhoods and primals" in order to make a contribution to this topic. By minimizing the uncertainty regions, these models are intended to assist decision makers in more effectively analyzing and evaluating the provided data. We verify this goal by demonstrating that the existing models outperform certain current method approaches in terms of improving the approximation operators (upper and lower) and accuracy measurements. We claim that the current models can preserve nearly all significant aspects associated with the rough set model. Preserving the monotonic property, which enables us to assess data uncertainty and boost confidence in outcomes, is one of the intriguing characterizations derived from the existing models. With the aid of specific instances, we also compare the areas of the current approach. Finally, we demonstrate that the new strategy we define for our everyday health-related problem yields more accurate findings.
RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis
Authors: Amir Jabbary Lak, Reza Boostani, Farhan A. Alenizi, Amin Salih Mohammed, Seyed Mostafa Fakhrahmad
Date: 2024-07-25 00:00:00
Proceeding: Applied Soft Computing 2024 7
Tasks: Sentiment Analysis

Sentiment analysis of customer feedback is a pivotal component of Natural Language Processing (NLP), enabling businesses to gauge consumer emotions towards their products and services. The inherent variability of language introduces substantial challenges in the analysis of unstructured customer data. Sentiment analysis techniques generally fall into two categories: traditional methods, which involve the extraction of hand-crafted features and the application of machine learning algorithms, and end-to-end deep learning approaches that process raw data, transforming them layer by layer into high-level textual features. Each method presents its own balance between simplicity and speed versus analytical power and flexibility, and they all grapple with the need for extensive labeled data and computational resources. To address these challenges, this paper presents an innovative hybrid model that amalgamates RoBERTa, ResNeXt, BiLSTM, and self-attention mechanisms. This integration capitalizes on the collective strengths of these models to surmount the limitations inherent in each. The proposed model proves to be a powerful and efficient tool for sentiment analysis, demonstrating proficiency across various data types and tasks. We undertake a comprehensive evaluation of the proposed model’s accuracy and training efficiency using four benchmark datasets. Our investigation also explores the impact of different similarity measures and convolutional neural network architectures on the model’s performance. The results affirm that our model not only achieves high accuracy but also significantly reduces training time compared to RoBERTa’s fine-tuning. Furthermore, the model exhibits exceptional domain adaptability, particularly when fine-tuned on the IMDb dataset following initial training on the Yelp Review Full dataset. The practicality of the proposed model is underscored by its reduced computational complexity and its adeptness at navigating semantic and syntactic nuances.
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
Authors: Wentao Zhang, Jingyuan Wang, Yifan Yang, Leong Hou U
Date: 2024-10-31 00:00:00
Tasks: Representation Learning

Electronic maps consist of diverse entities, such as points of interest (POIs), road networks, and land parcels, playing a vital role in applications like ITS and LBS. Map entity representation learning (MapRL) generates versatile and reusable data representations, providing essential tools for efficiently managing and utilizing map entity data. Despite the progress in MapRL, two key challenges constrain further development. First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks. Second, the lack of unified benchmarks makes systematic evaluation and comparison of models difficult. To address these challenges, we propose a novel taxonomy for MapRL that organizes models based on functional module-such as encoders, pre-training tasks, and downstream tasks-rather than by entity type. Building on this taxonomy, we present a taxonomy-driven library, VecCity, which offers easy-to-use interfaces for encoding, pre-training, fine-tuning, and evaluation. The library integrates datasets from nine cities and reproduces 21 mainstream MapRL models, establishing the first standardized benchmarks for the field. VecCity also allows users to modify and extend models through modular components, facilitating seamless experimentation. Our comprehensive experiments cover multiple types of map entities and evaluate 21 VecCity pre-built models across various downstream tasks. Experimental results demonstrate the effectiveness of VecCity in streamlining model development and provide insights into the impact of various components on performance. By promoting modular design and reusability, VecCity offers a unified framework to advance research and innovation in MapRL. The code is available at https://github.com/Bigscity-VecCity/VecCity.
Agent-Guided Gaze Estimation Network by Two-Eye Asymmetry Exploration
Authors: Yichen Shi, Feifei Zhang, Wenming Yang, Guijin Wang, Nan Su
Date: 2024-10-30 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2024 10
Tasks: Gaze Estimation, regression

Gaze estimation is an important task in understanding human visual attention. Despite the performance gain brought by recent algorithm development, the task remains challenging due to two-eye appearance asymmetry resulting from head pose variation and nonuniform illumination. In this paper, we propose a novel architecture, Agent-guided Gaze Estimation Network (AGE-Net), to make full and efficient use of two-eye features. By exploring the appearance asymmetry and the consequent feature space asymmetry, we devise a main branch and two agent regression tasks. The main branch extracts related features of the left and right eyes from low-level semantics. Meanwhile, the agent regression tasks extract asymmetric features of the left and right eyes from high-level semantics, so as to guide the main branch to learn more about the eye feature space. Experiments show that our method achieves state-of-the-art gaze estimation task performance on both MPIIGaze and EyeDiap datasets.
PEP-GS: Perceptually-Enhanced Precise Structured 3D Gaussians for View-Adaptive Rendering
Authors: Junxi Jin, Xiulai Li, Haiping Huang, Lianjun Liu, Yujie Sun, Logan Liu
Date: 2024-11-08 00:00:00
Tasks: Kolmogorov-Arnold Networks

Recently, 3D Gaussian Splatting (3D-GS) has achieved significant success in real-time, high-quality 3D scene rendering. However, it faces several challenges, including Gaussian redundancy, limited ability to capture view-dependent effects, and difficulties in handling complex lighting and specular reflections. Additionally, methods that use spherical harmonics for color representation often struggle to effectively capture anisotropic components, especially when modeling view-dependent colors under complex lighting conditions, leading to insufficient contrast and unnatural color saturation. To address these limitations, we introduce PEP-GS, a perceptually-enhanced framework that dynamically predicts Gaussian attributes, including opacity, color, and covariance. We replace traditional spherical harmonics with a Hierarchical Granular-Structural Attention mechanism, which enables more accurate modeling of complex view-dependent color effects. By employing a stable and interpretable framework for opacity and covariance estimation, PEP-GS avoids the removal of essential Gaussians prematurely, ensuring a more accurate scene representation. Furthermore, perceptual optimization is applied to the final rendered images, enhancing perceptual consistency across different views and ensuring high-quality renderings with improved texture fidelity and fine-scale detail preservation. Experimental results demonstrate that PEP-GS outperforms state-of-the-art methods, particularly in challenging scenarios involving view-dependent effects and fine-scale details.
ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles
Authors: Kayo Yin, Chinmay Singh, Fyodor O. Minakov, Vanessa Milan, Hal Daumé III, Cyril Zhang, Alex X. Lu, Danielle Bragg
Date: 2024-11-08 00:00:00
Tasks: Articles

Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300 hours of American Sign Language (ASL). ASL STEM Wiki is the first continuous signing dataset focused on STEM, facilitating the development of AI resources for STEM education in ASL. We identify several use cases of ASL STEM Wiki with human-centered applications. For example, because this dataset highlights the frequent use of fingerspelling for technical concepts, which inhibits DHH students' ability to learn, we develop models to identify fingerspelled words -- which can later be used to query for appropriate ASL signs to suggest to interpreters.
Using Language Models to Disambiguate Lexical Choices in Translation
Authors: Josh Barua, Sanjay Subramanian, Kayo Yin, Alane Suhr
Date: 2024-11-08 00:00:00
Tasks: Machine Translation, Sentence, Translation

In translation, a concept represented by a single word in a source language can have multiple variations in a target language. The task of lexical selection requires using context to identify which variation is most appropriate for a source text. We work with native speakers of nine languages to create DTAiLS, a dataset of 1,377 sentence pairs that exhibit cross-lingual concept variation when translating from English. We evaluate recent LLMs and neural machine translation systems on DTAiLS, with the best-performing model, GPT-4, achieving from 67 to 85% accuracy across languages. Finally, we use language models to generate English rules describing target-language concept variations. Providing weaker models with high-quality lexical rules improves accuracy substantially, in some cases reaching or outperforming GPT-4.