OpenRaiser/Intern-Atlas huggingface dataset:
Blockchain Amplification Attack
Venue: arXiv cs.CR 2025
Year: 2025

Strategies related to the blockchain concept of Extractable Value (MEV/BEV), such as arbitrage, front-, or back-running create strong economic incentives for network nodes to reduce latency. Modified nodes, that minimize transaction validation time and neglect to filter invalid transactions in the Ethereum peer-to-peer (P2P) network, introduce a novel attack vector -- a Blockchain Amplification Attack. An attacker can exploit those modified nodes to amplify invalid transactions thousands of times, posing a security threat to the entire network. To illustrate attack feasibility and practicality in the current Ethereum network ("mainnet"), we 1) identify thousands of similar attacks in the wild, 2) mathematically model the propagation mechanism, 3) empirically measure model parameters from our monitoring nodes, and 4) compare the performance with other existing Denial-of-Service attacks through local simulation. We show that an attacker can amplify network traffic at modified nodes by a factor of 3,600, and cause economic damages of approximately 13,800 times the amount needed to carry out the attack. Despite these risks, aggressive latency reduction may still be profitable enough for various providers to justify the existence of modified nodes. To assess this trade-off, we 1) simulate the transaction validation process in a local network and 2) empirically measure the latency reduction by deploying our modified node in the Ethereum test network ("testnet"). We conclude with a cost-benefit analysis of skipping validation and provide mitigation strategies against the blockchain amplification attack.
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
Venue: arXiv cs.LG 2025
Year: 2025

Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Previous studies employing machine learning faced limitations in feature selection and model interpretability, reducing their clinical applicability. This research aimed to develop an interpretable and accurate machine learning model to predict in-hospital sepsis mortality, addressing these gaps. Using ICU patient records from the MIMIC-III database, we extracted relevant data through a combination of literature review, clinical input refinement, and Random Forest-based feature selection, identifying the top 35 features. Data preprocessing included cleaning, imputation, standardization, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, resulting in a dataset of 4,683 patients with 17,429 admissions. Five models-Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor-were developed and evaluated. The Random Forest model demonstrated the best performance, achieving an accuracy of 0.90, AUROC of 0.97, precision of 0.93, recall of 0.91, and F1-score of 0.92. These findings underscore the potential of data-driven machine learning approaches to improve critical care, offering clinicians a powerful tool for predicting in-hospital sepsis mortality and enhancing patient outcomes.
Neural Network Emulator for Atmospheric Chemical ODE
Venue: arXiv cs.LG 2025
Year: 2025

Modeling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modeling. We consider atmospheric chemistry as a time-dependent Ordinary Differential Equation. To extract the hidden correlations between initial states and future time evolution, we propose ChemNNE, an Attention based Neural Network Emulator (NNE) that can model the atmospheric chemistry as a neural ODE process. To efficiently simulate the chemical changes, we propose the sinusoidal time embedding to estimate the oscillating tendency over time. More importantly, we use the Fourier neural operator to model the ODE process for efficient computation. We also propose three physical-informed losses to supervise the training optimization. To evaluate our model, we propose a large-scale chemical dataset that can be used for neural network training and evaluation. The extensive experiments show that our approach achieves state-of-the-art performance in modeling accuracy and computational speed.
Channel-Aware Distributed Transmission Control and Video Streaming in UAV Networks
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we study the problem of distributed transmission control and video streaming optimization for UAVs operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical layer, (ii) limited-size queues with delay-constrained packet arrival at the MAC layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the average throughput by optimizing the fading threshold. To this end, we jointly analyze the queue-related packet loss probabilities as well as the outage probability due to the low SINR. We introduce the DTC algorithm that maximizes the average throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed PSNR optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the JDVT-EC algorithm that enhances the average PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios. Notably, our approach improves the average throughput by 1.7% to 51.65% compared to various baselines. Additionally, we demonstrate an average PSNR increase of 0.24 dB and 1.7 dB compared to separately optimizing the fading threshold and video encoding rate, respectively.
Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective
Venue: arXiv cs.AR 2025
Year: 2025

Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed systems. Despite substantial work in this area, a comprehensive survey on characterizing and understanding GNNs from a computer architecture perspective is lacking. This work presents a comprehensive survey, proposing a triple-level classification method to categorize, summarize, and compare existing efforts, particularly focusing on their implications for parallel architectures and distributed systems. We identify promising future directions for GNN characterization that align with the challenges of optimizing hardware and software in parallel and distributed systems. Our survey aims to help scholars systematically understand GNN performance bottlenecks and execution patterns from a computer architecture perspective, thereby contributing to the development of more efficient GNN implementations across diverse parallel architectures and distributed systems.
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Conventional uncertainty-aware temporal difference (TD) learning often assumes a zero-mean Gaussian distribution for TD errors, leading to inaccurate error representations and compromised uncertainty estimation. We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning to enhance the flexibility of error distribution modeling by incorporating additional higher-order moment, particularly kurtosis, thereby improving the estimation and mitigation of data-dependent aleatoric uncertainty. We examine the influence of the shape parameter of the generalized Gaussian distribution (GGD) on aleatoric uncertainty and provide a closed-form expression that demonstrates an inverse relationship between uncertainty and the shape parameter. Additionally, we propose a theoretically grounded weighting scheme to address epistemic uncertainty by fully leveraging the GGD. We refine batch inverse variance weighting with bias reduction and kurtosis considerations, enhancing robustness. Experiments with policy gradient algorithms demonstrate significant performance gains.
AutoFL: A Tool for Automatic Multi-granular Labelling of Software Repositories
Venue: arXiv cs.SE 2025
Year: 2025

Software comprehension, especially of new code bases, is time consuming for developers, especially in large projects with multiple functionalities spanning various domains. One strategy to reduce this effort involves annotating files with meaningful labels that describe the functionalities contained. However, prior research has so far focused on classifying the whole project using README files as a proxy, resulting in little information gained for the developers. Our objective is to streamline the labelling of files with the correct application domains using source code as input. To achieve this, in prior work, we evaluated the ability to annotate files automatically using a weak labelling approach. This paper presents AutoFL, a tool for automatically labelling software repositories from source code. AutoFL allows multi-granular annotations including: \textit{file}, \textit{package}, and \textit{project} -level. We provide an overview of the tool's internals, present an example analysis for which AutoFL can be used, and discuss limitations and future work.
DanModCap: Designing a Danmaku Moderation Tool for Video-Sharing Platforms that Leverages Impact Captions with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Online video platforms have gained increased popularity due to their ability to support information consumption and sharing and the diverse social interactions they afford. Danmaku, a real-time commentary feature that overlays user comments on a video, has been found to improve user engagement, however, the use of Danmaku can lead to toxic behaviors and inappropriate comments. To address these issues, we propose a proactive moderation approach inspired by Impact Captions, a visual technique used in East Asian variety shows. Impact Captions combine textual content and visual elements to construct emotional and cognitive resonance. Within the context of this work, Impact Captions were used to guide viewers towards positive Danmaku-related activities and elicit more pro-social behaviors. Leveraging Impact Captions, we developed DanModCap, an moderation tool that collected and analyzed Danmaku and used it as input to large generative language models to produce Impact Captions. Our evaluation of DanModCap demonstrated that Impact Captions reduced negative antagonistic emotions, increased users' desire to share positive content, and elicited self-control in Danmaku social action to fostering proactive community maintenance behaviors. Our approach highlights the benefits of using LLM-supported content moderation methods for proactive moderation in a large-scale live content contexts.
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
Venue: arXiv cs.LG 2025
Year: 2025

Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper studies partial sensing forecast of long-term traffic, assuming sensors are available only at some locations. The problem is challenging due to the unknown data distribution at unsensed locations, the intricate spatio-temporal correlation in long-term forecasting, as well as noise to traffic patterns. We propose a Spatio-temporal Long-term Partial sensing Forecast model (SLPF) for traffic prediction, with several novel contributions, including a rank-based embedding technique to reduce the impact of noise in data, a spatial transfer matrix to overcome the spatial distribution shift from sensed locations to unsensed locations, and a multi-step training process that utilizes all available data to successively refine the model parameters for better accuracy. Extensive experiments on several real-world traffic datasets demonstrate its superior performance. Our source code is at https://github.com/zbliu98/SLPF
Abstract Interpretation of Temporal Safety Effects of Higher Order Programs
Venue: arXiv cs.PL 2025
Year: 2025

This paper describes a new abstract interpretation-based approach to verify temporal safety properties of recursive, higher-order programs. While prior works have provided theoretical impact and some automation, they have had limited scalability. We begin with a new automata-based "abstract effect domain" for summarizing context-sensitive dependent effects, capable of abstracting relations between the program environment and the automaton control state. Our analysis includes a new transformer for abstracting event prefixes to automatically computed context-sensitive effect summaries, and is instantiated in a type-and-effect system grounded in abstract interpretation. Since the analysis is parametric on the automaton, we next instantiate it to a broader class of history/register (or "accumulator") automata, beyond finite state automata to express some context-free properties, input-dependency, event summation, resource usage, cost, equal event magnitude, etc. We implemented a prototype evDrift that computes dependent effect summaries (and validates assertions) for OCaml-like recursive higher-order programs. As a basis of comparison, we describe reductions to assertion checking for higher-order but effect-free programs, and demonstrate that our approach outperforms prior tools Drift, RCaml/Spacer, MoCHi, and ReTHFL. Overall, across a set of 23 benchmarks, Drift verified 12 benchmarks, RCaml/Spacer verified 6, MoCHi verified 11, ReTHFL verified 18, and evDrift verified 21; evDrift also achieved a 6.3x, 5.3x, 16.8x, and 6.4x speedup over Drift, RCaml/Spacer, MoCHi, and ReTHFL, respectively, on those benchmarks that both tools could solve.
CodeFlow: Program Behavior Prediction with Dynamic Dependencies Learning
Venue: arXiv cs.SE 2025
Year: 2025

Predicting program behavior without execution is a critical task in software engineering. Existing models often fall short in capturing the dynamic dependencies among program elements. To address this, we present CodeFlow, a novel machine learning-based approach that predicts code coverage and detects runtime errors by learning both static and dynamic dependencies within the code. By using control flow graphs (CFGs), CodeFlow effectively represents all possible execution paths and the statistic relations between different statements, providing a more comprehensive understanding of program behaviors. CodeFlow constructs CFGs to represent possible execution paths and learns vector representations (embeddings) for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies by leveraging execution traces, which reflect the impacts among statements during execution. This combination enables CodeFlow to accurately predict code coverage and identify runtime errors. Our empirical evaluation demonstrates that CodeFlow significantly improves code coverage prediction accuracy and effectively localizes runtime errors, outperforming state-of-the-art models.
Boosting File Systems Elegantly: A Transparent NVM Write-ahead Log for Disk File Systems
Venue: arXiv cs.OS 2025
Year: 2025

We propose NVLog, an NVM-based write-ahead log for disk file systems, designed to transparently harness the high performance of NVM within the legacy storage stack. NVLog provides on-demand byte-granularity sync absorption, reserving the fast DRAM path for asynchronous operations, meanwhile occupying NVM space only temporarily. To accomplish this, we designed a highly efficient log structure, developed mechanisms to address heterogeneous crash consistency, optimized for small writes, and implemented robust crash recovery and garbage collection methods. Compared to previous solutions, NVLog is lighter, more stable, and delivers higher performance, all while leveraging the mature kernel software stack and avoiding data migration overhead. Experimental results demonstrate that NVLog can accelerate disk file systems by up to 15.09x and outperform NOVA and SPFS in various scenarios by up to 3.72x and 324.11x, respectively.
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Vision Transformers (ViTs) have gained significant attention, but their high computing cost limits the practical applications. While post-training quantization (PTQ) reduces model size and speeds up inference, it often degrades performance, especially in low-bit settings. We identify two key reasons for the performance degradation: 1) existing quantization methods fail to align with the power-law distribution of post-Softmax activations, and 2) reparameterizing post-LayerNorm activations leads to a performance drop due to the significant influence of outliers in the scaling factors. To address these challenges, we propose DopQ-ViT, a Distribution-friendly and Outlier-aware Post-training Quantization method for ViTs. First, DopQ-ViT introduces the Tan Quantizer (TanQ), which better preserves the power-law distribution of post-Softmax activations by focusing more on values near 1. Second, DopQ-ViT presents the MAD-guided Optimal Scaling Factor (MOSF), which selects the optimal scaling factor without introducing additional calculations. Extensive experiments across various ViT models and quantization settings demonstrate that DopQ-ViT, with the help of TanQ and MOSF, outperforms previous PTQ methods on both classification and detection tasks.
Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation
Venue: arXiv cs.CL 2025
Year: 2025

Multimodal large language models (MLLMs) have extended the success of large language models (LLMs) to multiple data types, such as image, text and audio, achieving significant performance in various domains, including multimodal translation, visual question answering and content generation. Nonetheless, existing systems are inefficient to train MLLMs due to substantial GPU bubbles caused by the heterogeneous modality models and complex data dependencies in 3D parallelism. This paper proposes Optimus, a distributed MLLM training system that reduces end-to-end MLLM training time. Optimus is based on our principled analysis that scheduling the encoder computation within the LLM bubbles can reduce bubbles in MLLM training. To make scheduling encoder computation possible for all GPUs, Optimus searches the separate parallel plans for encoder and LLM, and adopts a bubble scheduling algorithm to enable exploiting LLM bubbles without breaking the original data dependencies in the MLLM model architecture. We further decompose encoder layer computation into a series of kernels, and analyze the common bubble pattern of 3D parallelism to carefully optimize the sub-millisecond bubble scheduling, minimizing the overall training time. Our experiments in a production cluster show that Optimus accelerates MLLM training by 20.5%-21.3% with ViT-22B and GPT-175B model over 3072 GPUs compared to baselines.
AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling
Venue: arXiv cs.SE 2025
Year: 2025

Performance evaluation plays a crucial role in the development life cycle of large language models (LLMs). It estimates the model's capability, elucidates behavior characteristics, and facilitates the identification of potential issues and limitations, thereby guiding further improvement. Given that LLMs' diverse task-handling abilities stem from large volumes of training data, a comprehensive evaluation also necessitates abundant, well-annotated, and representative test data to assess LLM performance across various downstream tasks. However, the demand for high-quality test data often entails substantial time, computational resources, and manual efforts, sometimes causing the evaluation to be inefficient or impractical. To address these challenges, researchers propose active testing, which estimates the overall performance by selecting a subset of test data. Nevertheless, the existing active testing methods tend to be inefficient, even inapplicable, given the unique new challenges of LLMs (e.g., diverse task types, increased model complexity, and unavailability of training data). To mitigate such limitations and expedite the development cycle of LLMs, in this work, we introduce AcTracer, an active testing framework tailored for LLMs that strategically selects a small subset of test data to achieve a more accurate performance estimation for LLMs. AcTracer utilizes both internal and external information from LLMs to guide the test sampling process, reducing variance through a multi-stage pool-based active selection. Our experiment results demonstrate that AcTracer achieves state-of-the-art performance compared to existing methods across various tasks.
Longest Common Extensions with Wildcards: Trade-off and Applications
Venue: arXiv cs.DS 2025
Year: 2025

We study the Longest Common Extension (LCE) problem in a string containing wildcards. Wildcards (also called "don't cares" or "holes") are special characters that match any other character in the alphabet, similar to the character "?" in Unix commands or "." in regular expression engines. We consider the problem parametrized by $G$, the number of maximal contiguous groups of wildcards in the input string. Our main contribution is a simple data structure for this problem that can be built in $O(n (G/t) \log n)$ time, occupies $O(nG/t)$ space, and answers queries in $O(t)$ time, for any $t \in [1, G]$. Up to the $O(\log n)$ factor, this interpolates smoothly between the data structure of Crochemore et al. [JDA 2015], which has $O(nG)$ preprocessing time and space, and $O(1)$ query time, and a simple solution based on the "kangaroo jumping" technique [Landau and Vishkin, STOC 1986], which has $O(n)$ preprocessing time and space, and $O(G)$ query time. By establishing a connection between this problem and Boolean matrix multiplication, we show that our solution is optimal, up to subpolynomial factors, among combinatorial data structures when $G = Ω(n^ε)$ under a widely believed hypothesis. In addition, we develop a simple deterministic combinatorial algorithm for sparse Boolean matrix multiplication. We further establish a conditional lower bound for non-combinatorial data structures, stating that $O(nG/t^4)$ preprocessing time (resp. space) is optimal, up to subpolynomial factors, for any data structure with query time $t$ for a wide range of $t$ and $G$, assuming the well-established $\textsf{3SUM}$ (resp. $\textsf{Set-Disjointness}$) conjecture. Finally, we show that our data structure can be used to obtain efficient algorithms for approximate pattern matching and structural analysis of strings with wildcards.