pwc-archive/papers-with-abstracts huggingface dataset:
High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior
Authors: The Tien Mai
Date: 2025-05-13 00:00:00
Tasks: Data Augmentation, regression

Censored response variables--where outcomes are only partially observed due to known bounds--arise in numerous scientific domains and present serious challenges for regression analysis. The Tobit model, a classical solution for handling left-censoring, has been widely used in economics and beyond. However, with the increasing prevalence of high-dimensional data, where the number of covariates exceeds the sample size, traditional Tobit methods become inadequate. While frequentist approaches for high-dimensional Tobit regression have recently been developed, notably through Lasso-based estimators, the Bayesian literature remains sparse and lacks theoretical guarantees. In this work, we propose a novel Bayesian framework for high-dimensional Tobit regression that addresses both censoring and sparsity. Our method leverages the Horseshoe prior to induce shrinkage and employs a data augmentation strategy to facilitate efficient posterior computation via Gibbs sampling. We establish posterior consistency and derive concentration rates under sparsity, providing the first theoretical results for Bayesian Tobit models in high dimensions. Numerical experiments show that our approach outperforms favorably with the recent Lasso-Tobit method. Our method is implemented in the R package tobitbayes, which can be found on Github.
Doubly Robust Fusion of Many Treatments for Policy Learning
Authors: Ke Zhu, Jianing Chu, Ilya Lipkovich, Wenyu Ye, Shu Yang
Date: 2025-05-12 00:00:00
Tasks: Fairness

Individualized treatment rules/recommendations (ITRs) aim to improve patient outcomes by tailoring treatments to the characteristics of each individual. However, when there are many treatment groups, existing methods face significant challenges due to data sparsity within treatment groups and highly unbalanced covariate distributions across groups. To address these challenges, we propose a novel calibration-weighted treatment fusion procedure that robustly balances covariates across treatment groups and fuses similar treatments using a penalized working model. The fusion procedure ensures the recovery of latent treatment group structures when either the calibration model or the outcome model is correctly specified. In the fused treatment space, practitioners can seamlessly apply state-of-the-art ITR learning methods with the flexibility to utilize a subset of covariates, thereby achieving robustness while addressing practical concerns such as fairness. We establish theoretical guarantees, including consistency, the oracle property of treatment fusion, and regret bounds when integrated with multi-armed ITR learning methods such as policy trees. Simulation studies show superior group recovery and policy value compared to existing approaches. We illustrate the practical utility of our method using a nationwide electronic health record-derived de-identified database containing data from patients with Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma.
Interest Changes: Considering User Interest Life Cycle in Recommendation System
Authors: Yinjiang Cai, Jiangpan Hou, Yangping Zhu, Yuan Nie
Date: 2025-05-13 00:00:00
Tasks: Recommendation Systems

In recommendation systems, user interests are always in a state of constant flux. Typically, a user interest experiences a emergent phase, a stable phase, and a declining phase, which are referred to as the "user interest life-cycle". Recent papers on user interest modeling have primarily focused on how to compute the correlation between the target item and user's historical behaviors, without thoroughly considering the life-cycle features of user interest. In this paper, we propose an effective method called Deep Interest Life-cycle Network (DILN), which not only captures the interest life-cycle features efficiently, but can also be easily integrated to existing ranking models. DILN contains two key components: Interest Life-cycle Encoder Module constructs historical activity histograms of the user interest and then encodes them into dense representation. Interest Life-cycle Fusion Module injects the encoded dense representation into multiple expert networks, with the aim of enabling the specific phase of interest life-cycle to activate distinct experts. Online A/B testing reveals that DILN achieves significant improvements of +0.38% in CTR, +1.04% in CVR and +0.25% in duration per user, which demonstrates its effectiveness. In addition, DILN inherently increase the exposure of users' emergent and stable interests while decreasing the exposure of declining interests. DILN has been deployed on the Lofter App.
Lost in Transliteration: Bridging the Script Gap in Neural IR
Authors: Andreas Chari, Iadh Ounis, Sean MacAvaney
Date: 2025-05-13 00:00:00
Tasks: Information Retrieval, Retrieval, Transliteration

Most human languages use scripts other than the Latin alphabet. Search users in these languages often formulate their information needs in a transliterated -- usually Latinized -- form for ease of typing. For example, Greek speakers might use Greeklish, and Arabic speakers might use Arabizi. This paper shows that current search systems, including those that use multilingual dense embeddings such as BGE-M3, do not generalise to this setting, and their performance rapidly deteriorates when exposed to transliterated queries. This creates a ``script gap" between the performance of the same queries when written in their native or transliterated form. We explore whether adapting the popular ``translate-train" paradigm to transliterations can enhance the robustness of multilingual Information Retrieval (IR) methods and bridge the gap between native and transliterated scripts. By exploring various combinations of non-Latin and Latinized query text for training, we investigate whether we can enhance the capacity of existing neural retrieval techniques and enable them to apply to this important setting. We show that by further fine-tuning IR models on an even mixture of native and Latinized text, they can perform this cross-script matching at nearly the same performance as when the query was formulated in the native script. Out-of-domain evaluation and further qualitative analysis show that transliterations can also cause queries to lose some of their nuances, motivating further research in this direction.
TikTok Search Recommendations: Governance and Research Challenges
Authors: Taylor Annabell, Robert Gorwa, Rebecca Scharlach, Jacob van de Kerkhof, Thales Bertaglia
Date: 2025-05-13 00:00:00

Like other social media, TikTok is embracing its use as a search engine, developing search products to steer users to produce searchable content and engage in content discovery. Their recently developed product search recommendations are preformulated search queries recommended to users on videos. However, TikTok provides limited transparency about how search recommendations are generated and moderated, despite requirements under regulatory frameworks like the European Union's Digital Services Act. By suggesting that the platform simply aggregates comments and common searches linked to videos, it sidesteps responsibility and issues that arise from contextually problematic recommendations, reigniting long-standing concerns about platform liability and moderation. This position paper addresses the novelty of search recommendations on TikTok by highlighting the challenges that this feature poses for platform governance and offering a computational research agenda, drawing on preliminary qualitative analysis. It sets out the need for transparency in platform documentation, data access and research to study search recommendations.
SweRank: Software Issue Localization with Code Ranking
Authors: Revanth Gangi Reddy, Tarun Suresh, JaeHyeok Doo, Ye Liu, Xuan Phi Nguyen, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Heng Ji, Shafiq Joty
Date: 2025-05-07 00:00:00
Tasks: Descriptive

Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language issue description (e.g., bug report, feature request), is a critical yet time-consuming aspect of software development. While recent LLM-based agentic approaches demonstrate promise, they often incur significant latency and cost due to complex multi-step reasoning and relying on closed-source LLMs. Alternatively, traditional code ranking models, typically optimized for query-to-code or code-to-code retrieval, struggle with the verbose and failure-descriptive nature of issue localization queries. To bridge this gap, we introduce SweRank, an efficient and effective retrieve-and-rerank framework for software issue localization. To facilitate training, we construct SweLoc, a large-scale dataset curated from public GitHub repositories, featuring real-world issue descriptions paired with corresponding code modifications. Empirical results on SWE-Bench-Lite and LocBench show that SweRank achieves state-of-the-art performance, outperforming both prior ranking models and costly agent-based systems using closed-source LLMs like Claude-3.5. Further, we demonstrate SweLoc's utility in enhancing various existing retriever and reranker models for issue localization, establishing the dataset as a valuable resource for the community.
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Authors: Jakub Macina, Nico Daheim, Ido Hakimi, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
Date: 2025-02-26 00:00:00
Tasks: Benchmarking

Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run evaluation that reflects the pedagogical abilities of models. To fill this gap, we present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation. MathTutorBench contains a collection of datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching. To score the pedagogical quality of open-ended teacher responses, we train a reward model and show it can discriminate expert from novice teacher responses with high accuracy. We evaluate a wide set of closed- and open-weight models on MathTutorBench and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching. Rather, pedagogy and subject expertise appear to form a trade-off that is navigated by the degree of tutoring specialization of the model. Furthermore, tutoring appears to become more challenging in longer dialogs, where simpler questioning strategies begin to fail. We release the benchmark, code, and leaderboard openly to enable rapid benchmarking of future models.
Cultural Value Differences of LLMs: Prompt, Language, and Model Size
Authors: Qishuai Zhong, Yike Yun, Aixin Sun
Date: 2024-06-17 00:00:00

Our study aims to identify behavior patterns in cultural values exhibited by large language models (LLMs). The studied variants include question ordering, prompting language, and model size. Our experiments reveal that each tested LLM can efficiently behave with different cultural values. More interestingly: (i) LLMs exhibit relatively consistent cultural values when presented with prompts in a single language. (ii) The prompting language e.g., Chinese or English, can influence the expression of cultural values. The same question can elicit divergent cultural values when the same LLM is queried in a different language. (iii) Differences in sizes of the same model (e.g., Llama2-7B vs 13B vs 70B) have a more significant impact on their demonstrated cultural values than model differences (e.g., Llama2 vs Mixtral). Our experiments reveal that query language and model size of LLM are the main factors resulting in cultural value differences.
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
Authors: Paul Röttger, Musashi Hinck, Valentin Hofmann, Kobi Hackenburg, Valentina Pyatkin, Faeze Brahman, Dirk Hovy
Date: 2025-02-12 00:00:00

Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns about issue bias, where an LLM tends to present just one perspective on a given issue, which in turn may influence how users think about this issue. So far, it has not been possible to measure which issue biases LLMs actually manifest in real user interactions, making it difficult to address the risks from biased LLMs. Therefore, we create IssueBench: a set of 2.49m realistic prompts for measuring issue bias in LLM writing assistance, which we construct based on 3.9k templates (e.g. "write a blog about") and 212 political issues (e.g. "AI regulation") from real user interactions. Using IssueBench, we show that issue biases are common and persistent in state-of-the-art LLMs. We also show that biases are remarkably similar across models, and that all models align more with US Democrat than Republican voter opinion on a subset of issues. IssueBench can easily be adapted to include other issues, templates, or tasks. By enabling robust and realistic measurement, we hope that IssueBench can bring a new quality of evidence to ongoing discussions about LLM biases and how to address them.
Information Content Weighting for Perceptual Image Quality Assessment
Authors: Zhou Wang, Qiang Li
Date: 2011-05-01 00:00:00
Proceeding: IEEE Transactions on Image Processing 2011 5
Tasks: Full reference image quality assessment, Image Quality Assessment

Many state-of-the-art perceptual image quality as-
sessment (IQA) algorithms share a common two-stage structure:
local quality/distortion measurement followed by pooling. While
significant progress has been made in measuring local image
quality/distortion, the pooling stage is often done in ad-hoc ways,
lacking theoretical principles and reliable computational models.
This paper aims to test the hypothesis that when viewing natural
images, the optimal perceptual weights for pooling should be
proportional to local information content, which can be estimated
in units of bit using advanced statistical models of natural images.
Our extensive studies based upon six publicly-available sub-
ject-rated image databases concluded with three useful findings.
First, information content weighting leads to consistent improve-
ment in the performance of IQA algorithms. Second, surprisingly,
with information content weighting, even the widely criticized
peak signal-to-noise-ratio can be converted to a competitive
perceptual quality measure when compared with state-of-the-art
algorithms. Third, the best overall performance is achieved by
combining information content weighting with multiscale struc-
tural similarity measures.
Sparse Feature Fidelity for Perceptual Image Quality Assessment
Authors: Hua-Wen Chang, Hua Yang, Yong Gan, Ming-Hui Wang
Date: 2013-10-01 00:00:00
Proceeding: IEEE Transactions on Image Processing 2013 10
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.
A Survey on Bridging VLMs and Synthetic Data
Authors: Mohammad Ghiasvand Mohammadkhani, Saeedeh Momtazi, Hamid Beigy
Date: 2025-05-09 00:00:00
Proceeding: OpenReview 2025 5
Tasks: Survey

Vision-language models (VLMs) have significantly advanced multimodal AI by learning joint representations of visual and textual data. However, their progress is hindered by challenges in acquiring high-quality, aligned datasets, including issues of cost, privacy, and scarcity. On the other hand, synthetic data, created through the use of generative AI—which can even include VLMs—offers a scalable and cost-effective solution to these challenges. This paper presents the first comprehensive survey on bridging VLMs and synthetic data, exploring both the role of synthetic data in VLMs and the role of VLMs in synthetic data. First, we provide a preliminary overview by briefly explaining the architecture of two basic VLMs and, after studying a large number of previous works, offer an extensive survey of the previously proposed methodologies and potential future directions in this area.
Cross-Link Interference Mitigation With Over-the-Air Pilot Forwarding for Dynamic TDD
Authors: Jia-Hui Bi, Shaoshi Yang, Xiao-Yang Wang, Yu-Song Luo, Sheng Chen
Date: 2025-05-11 00:00:00

Dynamic time-division duplex (D-TDD) aided mobile communication systems bear the potential to achieve significantly higher spectral efficiency than traditional static TDD based systems. However, strong cross-link interference (CLI) may be caused by different transmission directions between adjacent cells in D-TDD systems, thus degrading the performance. Most existing CLI mitigation schemes require sharing certain information among base stations (BSs) via backhaul links. This strategy is usually expensive and suffers high latency. Alternatively, we propose a pilot information sharing scheme based on over-the-air forwarding of the downlink pilot of the interfering BS to the interfered BS via a wireless terminal, along with a dedicated CLI channel estimation method. Simulation results demonstrate that thanks to the proposed pilot information sharing scheme the classic interference rejection combining (IRC) receiver achieves a signal detection performance highly comparable to that of the IRC detector with perfect pilot information, necessitating no information sharing among BSs via backhaul links. Furthermore, the proposed CLI channel estimation scheme reduces the impact of errors introduced by pilot forwarding, thereby improving the performance of both CLI channel estimation and signal detection.
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Authors: Giovanni Catalani, Michael Bauerheim, Frédéric Tost, Xavier Bertrand, Joseph Morlier
Date: 2025-04-24 00:00:00
Tasks: Operator learning, Super-Resolution

Recent advances in Neural Fields have enabled powerful, discretization-invariant methods for learning neural operators that approximate solutions of Partial Differential Equations (PDEs) on general geometries. Building on these developments, we introduce enf2enf, an encoder--decoder methodology for predicting steady-state Partial Differential Equations with non-parameterized geometric variability, based on recently proposed Equivariant Neural Field architectures. In enf2enf, input geometries are encoded into latent point cloud embeddings that inherently preserve geometric grounding and capture local phenomena. The resulting representations are then combined with global parameters and directly decoded into continuous output fields, thus efficiently modeling the coupling between geometry and physics. By leveraging the inductive biases of locality and translation invariance, our approach is able to capture fine-scale physical features as well as complex shape variations, thereby enhancing generalization and physical compliance. Extensive experiments on a high-fidelity aerodynamic dataset, a hyper-elastic material benchmark, and multi-element airfoil geometries, demonstrate that the proposed model achieves superior or competitive performance compared to state-of-the-art graph based, operator learning, and neural field methods. Notably, our method supports real time inference and zero-shot super-resolution, enabling efficient training on low-resolution meshes while maintaining high accuracy on full-scale discretizations.
Multi-scale convolutional transformer network for motor imagery brain-computer interface
Authors: Wei Zhao, Baocan Zhang, Haifeng Zhou, Dezhi Wei, Chenxi Huang & Quan Lan
Date: 2025-04-15 00:00:00
Proceeding: Scientific Reports 2025 4
Tasks: 4-task Classification, Brain Computer Interface, EEG, Motor Imagery

Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model’s generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer’s robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer.
Muhammmed Fawas
Authors: Muhammmed Fawas
Date: 2025-05-14 00:00:00
Proceeding: 14/5 2025 5
Tasks: Marketing

Abdul rasaq muhammed toheer who is born on / 4/ 2006/ august in ilorin, His parent are Mr. and Mrs. Abdul rasaq who are native of kwara state, but the live in ilorin kwara state, Nigeria. he went to His Zumuratu Nursery and secondary school in ilorin, when he is in pry 4 he left His Zumuratu Nursery and secondary school and went to Centra igboro LGEA school, ilorin and went to ILORIN GRAMMER SCHOOL IGS .After he finished his school he was introduced to a great business know as Network Marketing ([Neolife][1]) as he joined the business he has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but he sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. He has been doing this for the past 2 years.