OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Unit Harmonization in Medical Informatics via Bayesian-Optimized Retrieval and Transformer-Based Re-ranking
Venue: arXiv cs.LG 2025
Year: 2025

Objective: To develop and evaluate a scalable methodology for harmonizing inconsistent units in large-scale clinical datasets, addressing a key barrier to data interoperability. Materials and Methods: We designed a novel unit harmonization system combining BM25, sentence embeddings, Bayesian optimization, and a bidirectional transformer based binary classifier for retrieving and matching laboratory test entries. The system was evaluated using the Optum Clinformatics Datamart dataset (7.5 billion entries). We implemented a multi-stage pipeline: filtering, identification, harmonization proposal generation, automated re-ranking, and manual validation. Performance was assessed using Mean Reciprocal Rank (MRR) and other standard information retrieval metrics. Results: Our hybrid retrieval approach combining BM25 and sentence embeddings (MRR: 0.8833) significantly outperformed both lexical-only (MRR: 0.7985) and embedding-only (MRR: 0.5277) approaches. The transformer-based reranker further improved performance (absolute MRR improvement: 0.10), bringing the final system MRR to 0.9833. The system achieved 83.39\% precision at rank 1 and 94.66\% recall at rank 5. Discussion: The hybrid architecture effectively leverages the complementary strengths of lexical and semantic approaches. The reranker addresses cases where initial retrieval components make errors due to complex semantic relationships in medical terminology. Conclusion: Our framework provides an efficient, scalable solution for unit harmonization in clinical datasets, reducing manual effort while improving accuracy. Once harmonized, data can be reused seamlessly in different analyses, ensuring consistency across healthcare systems and enabling more reliable multi-institutional studies and meta-analyses.
Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Automatic relationship extraction (RE) from biomedical literature is critical for managing the vast amount of scientific knowledge produced each year. In recent years, utilizing pre-trained language models (PLMs) has become the prevalent approach in RE. Several studies report improved performance when incorporating additional context information while fine-tuning PLMs for RE. However, variations in the PLMs applied, the databases used for augmentation, hyper-parameter optimization, and evaluation methods complicate direct comparisons between studies and raise questions about the generalizability of these findings. Our study addresses this research gap by evaluating PLMs enhanced with contextual information on five datasets spanning four relation scenarios within a consistent evaluation framework. We evaluate three baseline PLMs and first conduct extensive hyperparameter optimization. After selecting the top-performing model, we enhance it with additional data, including textual entity descriptions, relational information from knowledge graphs, and molecular structure encodings. Our findings illustrate the importance of i) the choice of the underlying language model and ii) a comprehensive hyperparameter optimization for achieving strong extraction performance. Although inclusion of context information yield only minor overall improvements, an ablation study reveals substantial benefits for smaller PLMs when such external data was included during fine-tuning.
Should AI Mimic People? Understanding AI-Supported Writing Technology Among Black Users
Venue: arXiv cs.HC 2025
Year: 2025

AI-supported writing technologies (AISWT) that provide grammatical suggestions, autocomplete sentences, or generate and rewrite text are now a regular feature integrated into many people's workflows. However, little is known about how people perceive the suggestions these tools provide. In this paper, we investigate how Black American users perceive AISWT, motivated by prior findings in natural language processing that highlight how the underlying large language models can contain racial biases. Using interviews and observational user studies with 13 Black American users of AISWT, we found a strong tradeoff between the perceived benefits of using AISWT to enhance their writing style and feeling like "it wasn't built for us". Specifically, participants reported AISWT's failure to recognize commonly used names and expressions in African American Vernacular English, experiencing its corrections as hurtful and alienating and fearing it might further minoritize their culture. We end with a reflection on the tension between AISWT that fail to include Black American culture and language, and AISWT that attempt to mimic it, with attention to accuracy, authenticity, and the production of social difference.
MIMIC-\RNum{4}-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp for Risk Prediction
Venue: arXiv cs.AI 2025
Year: 2025

A crucial component for clinical risk prediction is developing a reliable prediction model is collecting high-quality time series clinical events. In this work, we release such a dataset that consists of 22,588,586 Clinical Time Series events, which we term MIMIC-\RNum{4}-Ext-22MCTS. Our source data are discharge summaries selected from the well-known yet unstructured MIMIC-IV-Note \cite{Johnson2023-pg}. The general-purpose MIMIC-IV-Note pose specific challenges for our work: it turns out that the discharge summaries are too lengthy for typical natural language models to process, and the clinical events of interest often are not accompanied with explicit timestamps. Therefore, we propose a new framework that works as follows: 1) we break each discharge summary into manageably small text chunks; 2) we apply contextual BM25 and contextual semantic search to retrieve chunks that have a high potential of containing clinical events; and 3) we carefully design prompts to teach the recently released Llama-3.1-8B \cite{touvron2023llama} model to identify or infer temporal information of the chunks. The obtained dataset is informative and transparent that standard models fine-tuned on the dataset achieves significant improvements in healthcare applications. In particular, the BERT model fine-tuned based on our dataset achieves 10\% improvement in accuracy on medical question answering task, and 3\% improvement in clinical trial matching task compared with the classic BERT. The dataset is available at https://physionet.org/content/mimic-iv-ext-22mcts/1.0.0. The codebase is released at https://github.com/JingWang-RU/MIMIC-IV-Ext-22MCTS-Temporal-Clinical-Time-Series-Dataset.
Intersectional Divergence: Measuring Fairness in Regression
Venue: arXiv cs.LG 2025
Year: 2025

Fairness in machine learning research is commonly framed in the context of classification tasks, leaving critical gaps in regression. In this paper, we propose a novel approach to measure intersectional fairness in regression tasks, going beyond the focus on single protected attributes from existing work to consider combinations of all protected attributes. Furthermore, we contend that it is insufficient to measure the average error of groups without regard for imbalanced domain preferences. Accordingly, we propose Intersectional Divergence (ID) as the first fairness measure for regression tasks that 1) describes fair model behavior across multiple protected attributes and 2) differentiates the impact of predictions in target ranges most relevant to users. We extend our proposal demonstrating how ID can be adapted into a loss function, IDLoss, that satisfies convergence guarantees and has piecewise smooth properties that enable practical optimization. Through an extensive experimental evaluation, we demonstrate how ID allows unique insights into model behavior and fairness, and how incorporating IDLoss into optimization can considerably improve single-attribute and intersectional model fairness while maintaining a competitive balance in predictive performance.
SMSAT: A Multimodal Acoustic Dataset and Deep Contrastive Learning Framework for Affective and Physiological Modeling of Spiritual Meditation
Venue: arXiv cs.SD 2025
Year: 2025

Understanding how auditory stimuli influence emotional and physiological states is fundamental to advancing affective computing and mental health technologies. In this paper, we present a multimodal evaluation of the affective and physiological impacts of three auditory conditions, that is, spiritual meditation (SM), music (M), and natural silence (NS), using a comprehensive suite of biometric signal measures. To facilitate this analysis, we introduce the Spiritual, Music, Silence Acoustic Time Series (SMSAT) dataset, a novel benchmark comprising acoustic time series (ATS) signals recorded under controlled exposure protocols, with careful attention to demographic diversity and experimental consistency. To model the auditory induced states, we develop a contrastive learning based SMSAT audio encoder that extracts highly discriminative embeddings from ATS data, achieving 99.99% classification accuracy in interclass and intraclass evaluations. Furthermore, we propose the Calmness Analysis Model (CAM), a deep learning framework integrating 25 handcrafted and learned features for affective state classification across auditory conditions, attaining robust 99.99% classification accuracy. In contrast, pairwise t tests reveal significant deviations in cardiac response characteristics (CRC) between SM analysis via ANOVA inducing more significant physiological fluctuations. Compared to existing state of the art methods reporting accuracies up to 90%, the proposed model demonstrates substantial performance gains (up to 99%). This work contributes a validated multimodal dataset and a scalable deep learning framework for affective computing applications in stress monitoring, mental well-being, and therapeutic audio-based interventions.
Deep Autoencoder-Based Constellation Design in Multiple Access Channels
Venue: arXiv cs.IT 2025
Year: 2025

In multiple access channels (MAC), multiple users share a transmission medium to communicate with a common receiver. Traditional constellations like quadrature amplitude modulation are optimized for point-to-point systems and lack mechanisms to mitigate inter-user interference, leading to suboptimal performance in MAC environments. To address this, we propose a novel framework for constellation design in MAC that employs deep autoencoder (DAE)-based communication systems. This approach intelligently creates flexible constellations aware of inter-user interference, reducing symbol error rate and enhancing the constellation-constrained sum capacity of the channel. Comparisons against analytically derived constellations demonstrate that DAE-designed constellations consistently perform best or equal to the best across various system parameters. Furthermore, we apply the DAE to scenarios where no analytical solutions have been developed, such as with more than two users, demonstrating the adaptability of the model.
A Single-Bit Redundancy Framework for Multi-Dimensional Parametric Constraints
Venue: arXiv cs.IT 2025
Year: 2025

Constrained coding plays a key role in optimizing performance and mitigating errors in applications such as storage and communication, where specific constraints on codewords are required. While non-parametric constraints have been well-studied, parametric constraints, which depend on sequence length, have traditionally been tackled with ad hoc solutions. Recent advances have introduced unified methods for parametric constrained coding. This paper extends these approaches to multidimensional settings, generalizing an iterative framework to efficiently encode arrays subject to parametric constraints. We demonstrate the application of the method to existing and new constraints, highlighting its versatility and potential for advanced storage systems.
IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base
Venue: arXiv cs.RO 2025
Year: 2025

Robots are strongly expected as a means of replacing human tasks. If a robot has a human-like physicality, the possibility of replacing human tasks increases. In the case of household service robots, it is desirable for them to be on a human-like size so that they do not become excessively large in order to coexist with humans in their operating environment. However, robots with size limitations tend to have difficulty solving inverse kinematics (IK) due to mechanical limitations, such as joint angle limitations. Conversely, if the difficulty coming from this limitation could be mitigated, one can expect that the use of such robots becomes more valuable. In numerical IK solver, which is commonly used for robots with higher degrees-of-freedom (DOF), the solvability of IK depends on the initial guess given to the solver. Thus, this paper proposes a method for generating a good initial guess for a numerical IK solver given the target hand configuration. For the purpose, we define the goodness of an initial guess using the scaled Jacobian matrix, which can calculate the manipulability index considering the joint limits. These two factors are related to the difficulty of solving IK. We generate the initial guess by optimizing the goodness using the genetic algorithm (GA). To enumerate much possible IK solutions, we use the reachability map that represents the reachable area of the robot hand in the arm-base coordinate system. We conduct quantitative evaluation and prove that using an initial guess that is judged to be better using the goodness value increases the probability that IK is solved. Finally, as an application of the proposed method, we show that by generating good initial guesses for IK a robot actually achieves three typical scenarios.
On The Metric Nature of (Differential) Logical Relations
Venue: arXiv cs.LO 2025
Year: 2025

Differential logical relations are a method to measure distances between higher-order programs. They differ from standard methods based on program metrics in that differences between functional programs are themselves functions, relating errors in input with errors in output, this way providing a more fine grained, contextual, information. The aim of this paper is to clarify the metric nature of differential logical relations. While previous work has shown that these do not give rise, in general, to (quasi-)metric spaces nor to partial metric spaces, we show that the distance functions arising from such relations, that we call quasi-quasi-metrics, can be related to both quasi-metrics and partial metrics, the latter being also captured by suitable relational definitions. Moreover, we exploit such connections to deduce some new compositional reasoning principles for program differences.
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Time series anomaly detection is critical for system monitoring and risk identification, across various domains, such as finance and healthcare. However, for most reconstruction-based approaches, detecting anomalies remains a challenge due to the complexity of sequential patterns in time series data. On the one hand, reconstruction-based techniques are susceptible to computational deviation stemming from anomalies, which can lead to impure representations of normal sequence patterns. On the other hand, they often focus on the time-domain dependencies of time series, while ignoring the alignment of frequency information beyond the time domain. To address these challenges, we propose a novel Frequency-augmented Convolutional Transformer (FreCT). FreCT utilizes patch operations to generate contrastive views and employs an improved Transformer architecture integrated with a convolution module to capture long-term dependencies while preserving local topology information. The introduced frequency analysis based on Fourier transformation could enhance the model's ability to capture crucial characteristics beyond the time domain. To protect the training quality from anomalies and improve the robustness, FreCT deploys stop-gradient Kullback-Leibler (KL) divergence and absolute error to optimize consistency information in both time and frequency domains. Extensive experiments on four public datasets demonstrate that FreCT outperforms existing methods in identifying anomalies.
The Open-Source BlackParrot-BedRock Cache Coherence System
Venue: arXiv cs.AR 2025
Year: 2025

This dissertation revisits the topic of programmable cache coherence engines in the context of modern shared-memory multicore processors. First, the open-source BedRock cache coherence protocol is described. BedRock employs the canonical MOESIF coherence states and reduces implementation burden by eliminating transient coherence states from the protocol. The protocol's design complexity, concurrency, and verification effort are analyzed and compared to a canonical directory-based invalidate coherence protocol. Second, the architecture and microarchitecture of three separate cache coherence directories implementing the BedRock protocol within the BlackParrot 64-bit RISC-V multicore processor, collectively called BlackParrot-BedRock (BP-BedRock), are described. A fixed-function coherence directory engine implementation provides a baseline design for performance and area comparisons. A microcode-programmable coherence directory implementation demonstrates the feasibility of implementing a programmable coherence engine capable of maintaining sufficient protocol processing performance. A hybrid fixed-function and programmable coherence directory blends the protocol processing performance of the fixed-function design with the programmable flexibility of the microcode-programmable design. Collectively, the BedRock coherence protocol and its three BP-BedRock implementations demonstrate the feasibility and challenges of including programmable logic within the coherence system of modern shared-memory multicore processors, paving the way for future research into the application- and system-level benefits of programmable coherence engines.
LZD-style Compression Scheme with Truncation and Repetitions
Venue: arXiv cs.DS 2025
Year: 2025

Lempel-Ziv-Double (LZD) is a variation of the LZ78 compression scheme that achieves better compression on repetitive datasets. Nevertheless, prior research has identified computational inefficiencies and a weakness in its compressibility for certain datasets. In this paper, we introduce LZD+, an enhancement of LZD, which enables expected linear-time online compression by allowing truncated references. To avoid the compressibility weakness exhibited by a lower bound example, we propose LZDR (LZD-runlength compressed), a further enhancement on top of LZD+, which introduces a repetition-based factorization rule while maintaining linear expected time complexity. The both time bounds can be de-randomized by a lookup data structure like a balanced search tree with a logarithmic dependency on the alphabet size. Additionally, we present three flexible parsing variants of LZDR that yield fewer factors in practice. Comprehensive benchmarking on standard corpora reveals that LZD+, LZDR, and its flexible variants outperform existing LZ-based methods in the number of factors while keeping competitive runtime efficiency. However, we note that the difference in the number of factors becomes marginal for large datasets like those of the Pizza&Chili corpus.
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
Venue: arXiv cs.LG 2025
Year: 2025

We study a class of sequential decision-making problems with augmented predictions, potentially provided by a machine learning algorithm. In this setting, the decision-maker receives prediction intervals for unknown parameters that become progressively refined over time, and seeks decisions that are competitive with the hindsight optimal under all possible realizations of both parameters and predictions. We propose a minimax Markov Decision Process (minimax-MDP) framework, where the system state consists of an adversarially evolving environment state and an internal state controlled by the decision-maker. We introduce a set of future-imposed conditions that characterize the feasibility of minimax-MDPs and enable the design of efficient, often closed-form, robustly competitive policies. We illustrate the framework through three applications: multi-period inventory ordering with refining demand predictions, resource allocation with uncertain utility functions, and a multi-phase extension of the minimax-MDP applied to the inventory problem with time-varying ordering costs. Our results provide a tractable and versatile approach to robust online decision-making under predictive uncertainty.
Destructive Interference: Encoding Loss in the Overlap
Venue: arXiv cs.HC 2025
Year: 2025

Destructive Interference is a data visualization installation that representing the deaths and injuries caused by mass shootings in 2024 in the United States. I parametrically designed and fabricated an interlocking ring sculpture for each month of 2024; where the overall height corresponds to the level of violence in that month. Taller forms mark the deadliest months, while shorter ones reflect fewer casualties. Each inner ring encodes the number of people killed or injured, and each outer ring encodes the number of shootings and the number of days without them. The interlocking cylinders are powered via a motor to rotate, and lit from within. As the cylinders rotate, they cast overlapping shadows that represent those killed or injured by mass shootings. The goal of this work is to visualize otherwise overwhelming and disparate statistics in a way that is both physically present and emotionally resonant. By inviting viewers to step into and engage with these shadows, the piece creates space for reflection, conversation, and confrontation with the scale of this ongoing crisis.
Transferable Adversarial Attacks on Black-Box Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision Large Language Models (VLLMs) are increasingly deployed to offer advanced capabilities on inputs comprising both text and images. While prior research has shown that adversarial attacks can transfer from open-source to proprietary black-box models in text-only and vision-only contexts, the extent and effectiveness of such vulnerabilities remain underexplored for VLLMs. We present a comprehensive analysis demonstrating that targeted adversarial examples are highly transferable to widely-used proprietary VLLMs such as GPT-4o, Claude, and Gemini. We show that attackers can craft perturbations to induce specific attacker-chosen interpretations of visual information, such as misinterpreting hazardous content as safe, overlooking sensitive or restricted material, or generating detailed incorrect responses aligned with the attacker's intent. Furthermore, we discover that universal perturbations -- modifications applicable to a wide set of images -- can consistently induce these misinterpretations across multiple proprietary VLLMs. Our experimental results on object recognition, visual question answering, and image captioning show that this vulnerability is common across current state-of-the-art models, and underscore an urgent need for robust mitigations to ensure the safe and secure deployment of VLLMs.