pwc-archive/papers-with-abstracts huggingface dataset:
Demand-driven train timetabling for air and intercity high-speed rail synchronization service
Authors: Yangsheng Jiang, Shuiwang Chen, Wenyao an, Lu Hu, Yan Li, Jun Liu
Date: 2022-03-31 00:00:00
Proceeding: Transportation Letters 2022 3

Synchronizing air and intercity high-speed railway (A-IHSR) services has recently been developing vigorously. Train timetables are critical for improving A-IHSR synchronization. This paper proposes a method to 
optimize intercity high-speed railway (IHSR) timetables based on the dynamic passenger demand of the AIHSR. An integer non-linear programming (INLP) model is formulated to minimize the passenger waiting 
time, in which rigorous train capacity, oversaturation situation, and skip-stop are also taken into consideration. We also extend the train timetabling model by considering the synchronization events of flight-train 
and the train circulation plan. Then, this model is reformulated as a relaxed non-linear programming (NLP) 
model. . A multimodal nomad algorithm (MNA) is proposed to solve it. The train circulation plan solves by 
the Gurobi solver. A numerical experiment based on data of the operation of the Jinnan–Binzhou HSR is 
implemented to demonstrate the efficiency and performance of our proposed model and algorithm.
Porosity and topological properties of triply periodic minimal surfaces
Authors: Sergei Ermolenko, Pavel Snopov
Date: 2024-06-23 00:00:00

Triple periodic minimal surfaces (TPMS) have garnered significant interest due to their structural efficiency and controllable geometry, making them suitable for a wide range of applications. This paper investigates the relationships between porosity and persistence entropy with the shape factor of TPMS. We propose conjectures suggesting that these relationships are polynomial in nature, derived through the application of machine learning techniques. This study exemplifies the integration of machine learning methodologies in pure mathematical research. Besides the conjectures, we provide the mathematical models that might have the potential implications for the design and modeling of TPMS structures in various practical applications.
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
Authors: Areg Karapetyan, Aaron Chung Hin Chow, Samer Madanat
Date: 2024-06-06 00:00:00
Tasks: Depth Estimation, Flood Inundation Mapping, Image-to-Image Regression, Image-to-Image Translation, Prediction, Surrogate Hydrodynamic Modeling

In light of growing threats posed by climate change in general and sea level rise (SLR) in particular, the necessity for computationally efficient means to estimate and analyze potential coastal flood hazards has become increasingly pressing. Data-driven supervised learning methods serve as promising candidates that can dramatically expedite the process, thereby eliminating the computational bottleneck associated with traditional physics-based hydrodynamic simulators. Yet, the development of accurate and reliable coastal flood prediction models, especially those based on Deep Learning (DL) techniques, has been plagued with two major issues: (1) the scarcity of training data and (2) the high-dimensional output required for detailed inundation mapping. To remove this barrier, we present a systematic framework for training high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. We test the proposed workflow on different existing vision models, including a fully transformer-based architecture and a Convolutional Neural Network (CNN) with additive attention gates. Additionally, we introduce a deep CNN architecture tailored specifically to the coastal flood prediction problem at hand. The model was designed with a particular focus on its compactness so as to cater to resource-constrained scenarios and accessibility aspects. The performance of the developed DL models is validated against commonly adopted geostatistical regression methods and traditional Machine Learning (ML) approaches, demonstrating substantial improvement in prediction quality. Lastly, we round up the contributions by providing a meticulously curated dataset of synthetic flood inundation maps of Abu Dhabi's coast produced with a physics-based hydrodynamic simulator, which can serve as a benchmark for evaluating future coastal flood prediction models.
Large-Scale Contextual Market Equilibrium Computation through Deep Learning
Authors: Yunxuan Ma, Yide Bian, Hao Xu, Weitao Yang, Jingshu Zhao, Zhijian Duan, Feng Wang, Xiaotie Deng
Date: 2024-06-11 00:00:00
Tasks: Deep Learning

Market equilibrium is one of the most fundamental solution concepts in economics and social optimization analysis. Existing works on market equilibrium computation primarily focus on settings with relatively few buyers. Motivated by this, our paper investigates the computation of market equilibrium in scenarios with a large-scale buyer population, where buyers and goods are represented by their contexts. Building on this realistic and generalized contextual market model, we introduce MarketFCNet, a deep learning-based method for approximating market equilibrium. We start by parameterizing the allocation of each good to each buyer using a neural network, which depends solely on the context of the buyer and the good. Next, we propose an efficient method to unbiasedly estimate the loss function of the training algorithm, enabling us to optimize the network parameters through gradient. To evaluate the approximated solution, we propose a metric called Nash Gap, which quantifies the deviation of the given allocation and price pair from the market equilibrium. Experimental results indicate that MarketFCNet delivers competitive performance and significantly lower running times compared to existing methods as the market scale expands, demonstrating the potential of deep learning-based methods to accelerate the approximation of large-scale contextual market equilibrium.
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
Authors: Hyun Myung Kim, Kangwook Jang, Hoirin Kim
Date: 2024-06-24 00:00:00
Tasks: Audio Deepfake Detection, DeepFake Detection, Face Swapping, Speech Synthesis

As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown. In this paper, we propose a novel adaptive centroid shift (ACS) method that updates the centroid representation by continually shifting as the weighted average of bonafide representations. Our approach uses only bonafide samples to define their centroid, which can yield a specialized centroid for one-class learning. Integrating our ACS with one-class learning gathers bonafide representations into a single cluster, forming well-separated embeddings robust to unseen spoofing attacks. Our proposed method achieves an equal error rate (EER) of 2.19% on the ASVspoof 2021 deepfake dataset, outperforming all existing systems. Furthermore, the t-SNE visualization illustrates that our method effectively maps the bonafide embeddings into a single cluster and successfully disentangles the bonafide and spoof classes.
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
Authors: Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adriano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin Jr., Maria Perez-Ortiz
Date: 2024-06-17 00:00:00
Tasks: Benchmarking, counterfactual, Resume Scoring

The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) for resume scoring, revealing significant issues of reverse gender hiring bias and overdebiasing. Our contributions are fourfold: Firstly, we introduce a new construct grounded in labour economics, legal principles, and critiques of current bias benchmarks: hiring bias can be categorized into two types: Level bias (difference in the average outcomes between demographic counterfactual groups) and Spread bias (difference in the variance of outcomes between demographic counterfactual groups); Level bias can be further subdivided into statistical bias (i.e. changing with non-demographic content) and taste-based bias (i.e. consistent regardless of non-demographic content). Secondly, the framework includes rigorous statistical and computational hiring bias metrics, such as Rank After Scoring (RAS), Rank-based Impact Ratio, Permutation Test, and Fixed Effects Model. Thirdly, we analyze gender hiring biases in ten state-of-the-art LLMs. Seven out of ten LLMs show significant biases against males in at least one industry. An industry-effect regression reveals that the healthcare industry is the most biased against males. Moreover, we found that the bias performance remains invariant with resume content for eight out of ten LLMs. This indicates that the bias performance measured in this paper might apply to other resume datasets with different resume qualities. Fourthly, we provide a user-friendly demo and resume dataset to support the adoption and practical use of the framework, which can be generalized to other social traits and tasks.
Duplicate Detection with GenAI
Authors: Ian Ormesher
Date: 2024-06-17 00:00:00
Tasks: Management

Customer data is often stored as records in Customer Relations Management systems (CRMs). Data which is manually entered into such systems by one of more users over time leads to data replication, partial duplication or fuzzy duplication. This in turn means that there no longer a single source of truth for customers, contacts, accounts, etc. Downstream business processes become increasing complex and contrived without a unique mapping between a record in a CRM and the target customer. Current methods to detect and de-duplicate records use traditional Natural Language Processing techniques known as Entity Matching. In this paper we show how using the latest advancements in Large Language Models and Generative AI can vastly improve the identification and repair of duplicated records. On common benchmark datasets we find an improvement in the accuracy of data de-duplication rates from 30 percent using NLP techniques to almost 60 percent using our proposed method.
Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Range
Authors: Dogan Kutay Pekcan, Hongyi Liao, Ender Ayanoglu
Date: 2024-06-23 00:00:00
Tasks: 2k, Quantization

To maximize the received power at a user equipment, the problem of optimizing a reconfigurable intelligent surface (RIS) with a limited phase range R < 2{\pi} and nonuniform discrete phase shifts with adjustable gains is addressed. Necessary and sufficient conditions to achieve this maximization are given. These conditions are employed in two algorithms to achieve the global optimum in linear time for R {\ge} {\pi} and R < {\pi}, where R is the limited RIS phase range. With a total number of N(2K + 1) complex vector additions, it is shown for R {\ge} {\pi} and R < {\pi} that the global optimality is achieved in NK or fewer and N(K + 1) or fewer steps, respectively, where N is the number of RIS elements and K is the number of discrete phase shifts which may be placed nonuniformly over the limited phase range R. In addition, we define two quantization algorithms that we call nonuniform polar quantization (NPQ) algorithm and extended nonuniform polar quantization (ENPQ) algorithm, where the latter is a novel quantization algorithm for RISs with a significant phase range restriction, i.e., R < {\pi}. With NPQ, we provide a closed-form solution for the approximation ratio with which an arbitrary set of nonuniform discrete phase shifts can approximate the continuous solution. We also show that with a phase range limitation, equal separation among the nonuniform discrete phase shifts maximizes the normalized performance. Furthermore, we show that the gain of using K {\ge} 3 with R < {\pi}/2 and K {\ge} 4 with R < {\pi} is only marginal. Finally, we prove that when R < 2{\pi}/3, ON/OFF selection for the RIS elements brings significant performance compared to the case when the RIS elements are strictly ON.
Switching Controller Synthesis for Hybrid Systems Against STL Formulas
Authors: Han Su, Shenghua Feng, Sinong Zhan, Naijun Zhan
Date: 2024-06-24 00:00:00

Switching controllers play a pivotal role in directing hybrid systems (HSs) towards the desired objective, embodying a ``correct-by-construction'' approach to HS design. Identifying these objectives is thus crucial for the synthesis of effective switching controllers. While most of existing works focus on safety and liveness, few of them consider timing constraints. In this paper, we delves into the synthesis of switching controllers for HSs that meet system objectives given by a fragment of STL, which essentially corresponds to a reach-avoid problem with timing constraints. Our approach involves iteratively computing the state sets that can be driven to satisfy the reach-avoid specification with timing constraints. This technique supports to create switching controllers for both constant and non-constant HSs. We validate our method's soundness, and confirm its relative completeness for a certain subclass of HSs. Experiment results affirms the efficacy of our approach.
WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist
Authors: Chenyu Ren, Yazhou Zhang, Daihai He, Jing Qin
Date: 2024-06-16 00:00:00
Tasks: Language Modelling, Large Language Model

Large language models (LLMs) are raging over the medical domain, and their momentum has carried over into the mental health domain, leading to the emergence of few mental health LLMs. Although such mental health LLMs could provide reasonable suggestions for psychological counseling, how to develop an authentic and effective doctor-patient relationship (DPR) through LLMs is still an important problem. To fill this gap, we dissect DPR into two key attributes, i.e., the psychologist's empathy and proactive guidance. We thus present WundtGPT, an empathetic and proactive mental health large language model that is acquired by fine-tuning it with instruction and real conversation between psychologists and patients. It is designed to assist psychologists in diagnosis and help patients who are reluctant to communicate face-to-face understand their psychological conditions. Its uniqueness lies in that it could not only pose purposeful questions to guide patients in detailing their symptoms but also offer warm emotional reassurance. In particular, WundtGPT incorporates Collection of Questions, Chain of Psychodiagnosis, and Empathy Constraints into a comprehensive prompt for eliciting LLMs' questions and diagnoses. Additionally, WundtGPT proposes a reward model to promote alignment with empathetic mental health professionals, which encompasses two key factors: cognitive empathy and emotional empathy. We offer a comprehensive evaluation of our proposed model. Based on these outcomes, we further conduct the manual evaluation based on proactivity, effectiveness, professionalism and coherence. We notice that WundtGPT can offer professional and effective consultation. The model is available at huggingface.
Full-Space Wireless Sensing Enabled by Multi-Sector Intelligent Surfaces
Authors: Yumeng Zhang, Xiaodan Shao, Hongyu Li, Bruno Clerckx, Rui Zhang
Date: 2024-06-22 00:00:00
Tasks: Sensitivity

The multi-sector intelligent surface (IS), benefiting from a smarter wave manipulation capability, has been shown to enhance channel gain and offer full-space coverage in communications. However, the benefits of multi-sector IS in wireless sensing remain unexplored. This paper introduces the application of multi-sector IS for wireless sensing/localization. Specifically, we propose a new self-sensing system, where an active source controller uses the multi-sector IS geometry to reflect/scatter the emitted signals towards the entire space, thereby achieving full-space coverage for wireless sensing. Additionally, dedicated sensors are installed aligned with the IS elements at each sector, which collect echo signals from the target and cooperate to sense the target angle. In this context, we develop a maximum likelihood estimator of the target angle for the proposed multi-sector IS self-sensing system, along with the corresponding theoretical limits defined by the Cram\'er-Rao Bound. The analysis reveals that the advantages of the multi-sector IS self-sensing system stem from two aspects: enhancing the probing power on targets (thereby improving power efficiency) and increasing the rate of target angle (thereby enhancing the transceiver's sensitivity to target angles). Finally, our analysis and simulations confirm that the multi-sector IS self-sensing system, particularly the 4-sector architecture, achieves full-space sensing capability beyond the single-sector IS configuration. Furthermore, similarly to communications, employing directive antenna patterns on each sector's IS elements and sensors significantly enhances sensing capabilities. This enhancement originates from both aspects of improved power efficiency and target angle sensitivity, with the former also being observed in communications while the latter being unique in sensing.
On the Principles behind Opinion Dynamics in Multi-Agent Systems of Large Language Models
Authors: Pedro Cisneros-Velarde
Date: 2024-06-18 00:00:00
Tasks: Multiple-choice

We study the evolution of opinions inside a population of interacting large language models (LLMs). Every LLM needs to decide how much funding to allocate to an item with three initial possibilities: full, partial, or no funding. We identify biases that drive the exchange of opinions based on the LLM's tendency to find consensus with the other LLM's opinion, display caution when specifying funding, and consider ethical concerns in its opinion. We find these biases are affected by the perceived absence of compelling reasons for opinion change, the perceived willingness to engage in discussion, and the distribution of allocation values. Moreover, tensions among biases can lead to the survival of funding for items with negative connotations. We also find that the final distribution of full, partial, and no funding opinions is more diverse when an LLM freely forms its opinion after an interaction than when its opinion is a multiple-choice selection among the three allocation options. In the latter case, consensus is mostly attained. When agents are aware of past opinions, they seek to maintain consistency with them, changing the opinion dynamics. Our study is performed using Llama 3 and Mistral LLMs.
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili
Authors: Jesse Atuhurra, Hiroyuki Shindo, Hidetaka Kamigaito, Taro Watanabe
Date: 2024-03-26 00:00:00
Tasks: Multilingual NLP, Text Generation, Word Embeddings

Many attempts have been made in multilingual NLP to ensure that pre-trained language models, such as mBERT or GPT2 get better and become applicable to low-resource languages. To achieve multilingualism for pre-trained language models (PLMs), we need techniques to create word embeddings that capture the linguistic characteristics of any language. Tokenization is one such technique because it allows for the words to be split based on characters or subwords, creating word embeddings that best represent the structure of the language. Creating such word embeddings is essential to applying PLMs to other languages where the model was not trained, enabling multilingual NLP. However, most PLMs use generic tokenization methods like BPE, wordpiece, or unigram which may not suit specific languages. We hypothesize that tokenization based on syllables within the input text, which we call syllable tokenization, should facilitate the development of syllable-aware language models. The syllable-aware language models make it possible to apply PLMs to languages that are rich in syllables, for instance, Swahili. Previous works introduced subword tokenization. Our work extends such efforts. Notably, we propose a syllable tokenizer and adopt an experiment-centric approach to validate the proposed tokenizer based on the Swahili language. We conducted text-generation experiments with GPT2 to evaluate the effectiveness of the syllable tokenizer. Our results show that the proposed syllable tokenizer generates syllable embeddings that effectively represent the Swahili language.
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
Authors: Jesse Atuhurra, Iqra Ali, Tatsuya Hiraoka, Hidetaka Kamigaito, Tomoya Iwakura, Taro Watanabe
Date: 2024-03-29 00:00:00
Tasks: Image-text matching, Object Recognition, Text Matching

Large language models (LLMs) have increased interest in vision language models (VLMs), which process image-text pairs as input. Studies investigating the visual understanding ability of VLMs have been proposed, but such studies are still preliminary because existing datasets do not permit a comprehensive evaluation of the fine-grained visual linguistic abilities of VLMs across multiple languages. To further explore the strengths of VLMs, such as GPT-4V \cite{openai2023GPT4}, we developed new datasets for the systematic and qualitative analysis of VLMs. Our contribution is four-fold: 1) we introduced nine vision-and-language (VL) tasks (including object recognition, image-text matching, and more) and constructed multilingual visual-text datasets in four languages: English, Japanese, Swahili, and Urdu through utilizing templates containing \textit{questions} and prompting GPT4-V to generate the \textit{answers} and the \textit{rationales}, 2) introduced a new VL task named \textit{unrelatedness}, 3) introduced rationales to enable human understanding of the VLM reasoning process, and 4) employed human evaluation to measure the suitability of proposed datasets for VL tasks. We show that VLMs can be fine-tuned on our datasets. Our work is the first to conduct such analyses in Swahili and Urdu. Also, it introduces \textit{rationales} in VL analysis, which played a vital role in the evaluation.
Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter
Authors: Nuredin Ali, Charles Chuankai Zhang, Ned Mayo, Stevie Chancellor
Date: 2024-04-01 00:00:00
Tasks: Depression Detection

Social media data has been used for detecting users with mental disorders, such as depression. Despite the global significance of cross-cultural representation and its potential impact on model performance, publicly available datasets often lack crucial metadata related to this aspect. In this work, we evaluate the generalization of benchmark datasets to build AI models on cross-cultural Twitter data. We gather a custom geo-located Twitter dataset of depressed users from seven countries as a test dataset. Our results show that depression detection models do not generalize globally. The models perform worse on Global South users compared to Global North. Pre-trained language models achieve the best generalization compared to Logistic Regression, though still show significant gaps in performance on depressed and non-Western users. We quantify our findings and provide several actionable suggestions to mitigate this issue.