OpenRaiser/Intern-Atlas huggingface dataset:
Determining distances and consensus between mutation trees
Venue: arXiv cs.CC 2025
Year: 2025

The mutational heterogeneity of tumours can be described with a tree representing the evolutionary history of the tumour. With noisy sequencing data there may be uncertainty in the inferred tree structure, while we may also wish to study patterns in the evolution of cancers in different patients. In such situations, understanding tree similarities is a key challenge, and therefore we present an approach to determine distances between trees. Considering the bounded height of trees, we determine the distances associated with the swap operations over strings. While in general, by solving the {\sc Maximum Common Almost $v$-tree} problem between two trees, we describe an efficient approach to determine the minimum number of operations to transform one tree into another. The inherent noise in current statistical methods for constructing mutation evolution trees of cancer cells presents a significant challenge: handling such collections of trees to determine a consensus tree that accurately represents the set and evaluating the extent of their variability or dispersion. Given a set of mutation trees and the notion of distance, there are at least two natural ways to define the ``target'' tree, such as a min-sum (\emph{median tree}) or a min-max (\emph{closest tree}) of a set of trees. Thus, considering a set of trees as input and dealing with the {\sc median} and {\sc closest} problems, we prove that both problems are \NP-complete, even with only three input trees. In addition, we develop algorithms to obtain upper bounds on the median and closest solutions, which are analysed by the experiments presented on generated and on real databases. We show a fast way to find consensus trees with better results than any tree in the input set while still preserving all internal structure.
Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU
Venue: arXiv cs.PL 2025
Year: 2025

Fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs) are emerging as solutions for data security in distributed environments. However, the widespread adoption of these encryption techniques is hindered by their significant computational overhead, primarily resulting from core cryptographic operations that involve large integer arithmetic. This paper presents a formalization of multi-word modular arithmetic (MoMA), which breaks down large bit-width integer arithmetic into operations on machine words. We further develop a rewrite system that implements MoMA through recursive rewriting of data types, designed for compatibility with compiler infrastructures and code generators. We evaluate MoMA by generating cryptographic kernels, including basic linear algebra subprogram (BLAS) operations and the number theoretic transform (NTT), targeting various GPUs. Our MoMA-based BLAS operations outperform state-of-the-art multi-precision libraries by orders of magnitude, and MoMA-based NTTs achieve near-ASIC performance on commodity GPUs.
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
Venue: arXiv cs.CV 2025
Year: 2025

Image matching, which aims to identify corresponding pixel locations between images, is crucial in a wide range of scientific disciplines, aiding in image registration, fusion, and analysis. In recent years, deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. However, when dealing with images captured under different imaging modalities that result in significant appearance changes, the performance of these algorithms often deteriorates due to the scarcity of annotated cross-modal training data. This limitation hinders applications in various fields that rely on multiple image modalities to obtain complementary information. To address this challenge, we propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals, incorporating diverse data from various sources, to train models to recognize and match fundamental structures across images. This capability is transferable to real-world, unseen cross-modality image matching tasks. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks using the same network weight, substantially outperforming existing methods, whether designed for generalization or tailored for specific tasks. This advancement significantly enhances the applicability of image matching technologies across various scientific disciplines and paves the way for new applications in multi-modality human and artificial intelligence analysis and beyond.
Design and Analysis of a Concatenated Code for Intersymbol Interference Wiretap Channels
Venue: arXiv cs.IT 2025
Year: 2025

We propose a two-stage concatenated coding scheme for reliable and information-theoretically secure communication over intersymbol interference wiretap channels. Motivated by the theoretical coding strategies that achieve the secrecy capacity, our scheme integrates low-density parity-check (LDPC) codes in the outer stage, forming a nested structure of wiretap codes, with trellis codes in the inner stage to improve achievable secure rates. The trellis code is specifically designed to transform the uniformly distributed codewords produced by the LDPC code stage into a Markov process, achieving tight lower bounds on the secrecy capacity. We further estimate the information leakage rate of the proposed coding scheme using an upper bound. To meet the weak secrecy criterion, we optimize degree distributions of the irregular LDPC codes at the outer stage, essentially driving the estimated upper bound on the information leakage rate to zero.
WebWalker: Benchmarking LLMs in Web Traversal
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-augmented generation (RAG) demonstrates remarkable performance across tasks in open-domain question-answering. However, traditional search engines may retrieve shallow content, limiting the ability of LLMs to handle complex, multi-layered information. To address it, we introduce WebWalkerQA, a benchmark designed to assess the ability of LLMs to perform web traversal. It evaluates the capacity of LLMs to traverse a website's subpages to extract high-quality data systematically. We propose WebWalker, which is a multi-agent framework that mimics human-like web navigation through an explore-critic paradigm. Extensive experimental results show that WebWalkerQA is challenging and demonstrates the effectiveness of RAG combined with WebWalker, through the horizontal and vertical integration in real-world scenarios.
GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs), such as GPT, are considered to learn the latent distributions within large-scale web-crawl datasets and accomplish natural language processing (NLP) tasks by predicting the next token. However, this mechanism of latent distribution modeling lacks quantitative understanding and analysis. In this paper, we propose a novel perspective that any language dataset can be represented by a Monte Carlo Language Tree (abbreviated as ``Data-Tree''), where each node denotes a token, each edge denotes a token transition probability, and each sequence has a unique path. Any GPT-like language model can also be flattened into another Monte Carlo Language Tree (abbreviated as ``GPT-Tree''). Our experiments show that different GPT models trained on the same dataset exhibit significant structural similarity in GPT-Tree visualization, and larger models converge more closely to the Data-Tree. More than 87\% GPT output tokens can be recalled by Data-Tree. These findings may confirm that the reasoning process of LLMs is more likely to be probabilistic pattern-matching rather than formal reasoning, as each model inference seems to find a context pattern with maximum probability from the Data-Tree. Furthermore, we provide deeper insights into issues such as hallucination, Chain-of-Thought (CoT) reasoning, and token bias in LLMs.
Solving Boolean satisfiability problems with resistive content addressable memories
Venue: arXiv cs.ET 2025
Year: 2025

Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers have been presented, but they were limited to academic problems in quadratic polynomial format. Here we present KLIMA, a k-Local In-Memory Accelerator with resistive Content Addressable Memories (CAMs) and Dot-Product Engines (DPEs) to accelerate the solution of high-order industry-relevant optimization problems, in particular Boolean Satisfiability. By co-designing the optimization heuristics and circuit architecture we improve the speed and energy to solution up to 182x compared to the digital state of the art.
Understanding the Practice, Perception, and Challenge of Blind or Low Vision Students Learning through Accessible Technologies in Non-Inclusive 'Blind Colleges'
Venue: arXiv cs.HC 2025
Year: 2025

In developing and underdeveloped regions, many 'Blind Colleges' exclusively enroll individuals with Blindness or Vision Impairment (BLV) for higher education. While advancements in accessible technologies have facilitated BLV student integration into 'Integrated Colleges,' their implementation in 'Blind Colleges' remains uneven due to complex economic, social, and policy challenges. This study investigates the practices, perceptions, and challenges of BLV students using accessible technologies in a Chinese 'Blind College' through a two-part empirical approach. Our findings demonstrate that tactile and digital technologies enhance access to education but face significant integration barriers. We emphasize the critical role of early education in addressing capability gaps, BLV students' aspirations for more inclusive educational environments, and the systemic obstacles within existing frameworks. We advocate for leveraging accessible technologies to transition 'Blind Colleges' into 'Integrated Colleges,' offering actionable insights for policymakers, designers, and educators. Finally, we outline future research directions on accessible technology innovation and its implications for BLV education in resource-constrained settings.
Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
Venue: arXiv cs.CV 2025
Year: 2025

Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the sclera contains distinct personal features. Deep learning-based sclera segmentation has achieved significant success compared to traditional methods that rely on hand-crafted features, primarily because it can autonomously extract critical output-related features without the need to consider potential physical constraints. However, achieving accurate sclera segmentation using these methods is challenging due to the scarcity of high-quality, fully labeled datasets, which depend on costly, labor-intensive medical acquisition and expertise. To address this challenge, this paper introduces a novel sclera segmentation framework that excels with limited labeled samples. Specifically, we employ a semi-supervised learning method that integrates domain-specific improvements and image-based spatial transformations to enhance segmentation performance. Additionally, we have developed a real-world eye diagnosis dataset to enrich the evaluation process. Extensive experiments on our dataset and two additional public datasets demonstrate the effectiveness and superiority of our proposed method, especially with significantly fewer labeled samples.
Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors
Venue: arXiv cs.LG 2025
Year: 2025

Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to poor accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success in natural language processing (NLP) and computer vision (CV) tasks, motivating their use in wireless applications. However, their use in indoor localization remains nascent, and directly applying Transformers for indoor localization can be both computationally intensive and exhibit limitations in accuracy. To address these challenges, in this work, we introduce a novel tokenization approach, referred to as Sensor Snapshot Tokenization (SST), which preserves variable-specific representations of power delay profile (PDP) and enhances attention mechanisms by effectively capturing multi-variate correlation. Complementing this, we propose a lightweight Swish-Gated Linear Unit-based Transformer (L-SwiGLU-T) model, designed to reduce computational complexity without compromising localization accuracy. Together, these contributions mitigate the computational burden and dependency on large datasets, making Transformer models more efficient and suitable for resource-constrained scenarios. Experimental results on simulated and real-world datasets demonstrate that SST and L-SwiGLU-T achieve substantial accuracy and efficiency gains, outperforming larger Transformer and CNN baselines by over 40% while using significantly fewer FLOPs and training samples.
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via
Venue: arXiv cs.LG 2025
Year: 2025

Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques
Venue: arXiv cs.CL 2025
Year: 2025

This study explores the fine-tuning (FT) of the Open Pre-trained Transformer (OPT-125M) for grammatical acceptability tasks using the CoLA dataset. By comparing Vanilla-Fine-Tuning (VFT), Pattern-Based-Fine-Tuning (PBFT), and Parameter-Efficient Fine-Tuning techniques (PEFT) like Low-Rank Adaptation (LoRA), we demonstrate significant improvements in computational efficiency while maintaining high accuracy. Our experiments reveal that while VFT achieves the highest accuracy (81.2%), LoRA enhancing FT by reducing memory usage and iteration time by more than 50%, and increases accuracy in PBFT case. Context Distillation (CD), though computationally efficient, underperformed with accuracy around 31%. Our findings contribute to democratizing access to large language models (LLM) by reducing computational barriers.
deepTerra -- AI Land Classification Made Easy
Venue: arXiv cs.CV 2025
Year: 2025

deepTerra is a comprehensive platform designed to facilitate the classification of land surface features using machine learning and satellite imagery. The platform includes modules for data collection, image augmentation, training, testing, and prediction, streamlining the entire workflow for image classification tasks. This paper presents a detailed overview of the capabilities of deepTerra, shows how it has been applied to various research areas, and discusses the future directions it might take.
Bridge-SR: Schrödinger Bridge for Efficient SR
Venue: arXiv cs.SD 2025
Year: 2025

Speech super-resolution (SR), which generates a waveform at a higher sampling rate from its low-resolution version, is a long-standing critical task in speech restoration. Previous works have explored speech SR in different data spaces, but these methods either require additional compression networks or exhibit limited synthesis quality and inference speed. Motivated by recent advances in probabilistic generative models, we present Bridge-SR, a novel and efficient any-to-48kHz SR system in the speech waveform domain. Using tractable Schrödinger Bridge models, we leverage the observed low-resolution waveform as a prior, which is intrinsically informative for the high-resolution target. By optimizing a lightweight network to learn the score functions from the prior to the target, we achieve efficient waveform SR through a data-to-data generation process that fully exploits the instructive content contained in the low-resolution observation. Furthermore, we identify the importance of the noise schedule, data scaling, and auxiliary loss functions, which further improve the SR quality of bridge-based systems. The experiments conducted on the benchmark dataset VCTK demonstrate the efficiency of our system: (1) in terms of sample quality, Bridge-SR outperforms several strong baseline methods under different SR settings, using a lightweight network backbone (1.7M); (2) in terms of inference speed, our 4-step synthesis achieves better performance than the 8-step conditional diffusion counterpart (LSD: 0.911 vs 0.927). Demo at https://bridge-sr.github.io.
Logarithmic Memory Networks (LMNs): Efficient Long-Range Sequence Modeling for Resource-Constrained Environments
Venue: arXiv cs.AI 2025
Year: 2025

Long-range sequence modeling is a crucial aspect of natural language processing and time series analysis. However, traditional models like Recurrent Neural Networks (RNNs) and Transformers suffer from computational and memory inefficiencies, especially when dealing with long sequences. This paper introduces Logarithmic Memory Networks (LMNs), a novel architecture that leverages a hierarchical logarithmic tree structure to efficiently store and retrieve past information. LMNs dynamically summarize historical context, significantly reducing the memory footprint and computational complexity of attention mechanisms from O(n2) to O(log(n)). The model employs a single-vector, targeted attention mechanism to access stored information, and the memory block construction worker (summarizer) layer operates in two modes: a parallel execution mode during training for efficient processing of hierarchical tree structures and a sequential execution mode during inference, which acts as a memory management system. It also implicitly encodes positional information, eliminating the need for explicit positional encodings. These features make LMNs a robust and scalable solution for processing long-range sequences in resource-constrained environments, offering practical improvements in efficiency and scalability. The code is publicly available under the MIT License on GitHub: https://github.com/AhmedBoin/LogarithmicMemory.
Finding $\forall\exists$ Hyperbugs using Symbolic Execution
Venue: arXiv cs.PL 2025
Year: 2025

Many important hyperproperties, such as refinement and generalized non-interference, fall into the class of $\forall\exists$ hyperproperties and require, for each execution trace of a system, the existence of another trace relating to the first one in a certain way. The alternation of quantifiers renders $\forall\exists$ hyperproperties extremely difficult to verify, or even just to test. Indeed, contrary to trace properties, where it suffices to find a single counterexample trace, refuting a $\forall\exists$ hyperproperty requires not only to find a trace, but also a proof that no second trace satisfies the specified relation with the first trace. As a consequence, automated testing of $\forall\exists$ hyperproperties falls out of the scope of existing automated testing tools. In this paper, we present a fully automated approach to detect violations of $\forall\exists$ hyperproperties in software systems. Our approach extends bug-finding techniques based on symbolic execution with support for trace quantification. We provide a prototype implementation of our approach, and demonstrate its effectiveness on a set of challenging examples.
Large Language Model Interface for Home Energy Management Systems
Venue: arXiv cs.AI 2025
Year: 2025

Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. This technology helps to reduce energy bills and offers greater demand-side flexibility that supports the power system stability. However, residents who lack a technical background may find it difficult to use HEMSs effectively, because HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs. Recently, Large-Language Models (LLMs) have demonstrated an outstanding ability in language understanding. Motivated by this, we propose an LLM-based interface that interacts with users to understand and parameterize their ``badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS. We further use Reason and Act method (ReAct) and few-shot prompting to enhance the LLM performance. Evaluating the interface performance requires multiple user--LLM interactions. To avoid the efforts in finding volunteer users and reduce the evaluation time, we additionally propose a method that uses another LLM to simulate users with varying expertise, ranging from knowledgeable to non-technical. By comprehensive evaluation, the proposed LLM-based HEMS interface achieves an average parameter retrieval accuracy of 88\%, outperforming benchmark models without ReAct and/or few-shot prompting.
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to traditional Artificial Neural Networks (ANNs). Two important questions when implementing SNNs are how to best encode existing data into spike trains and how to efficiently process these spike trains in hardware. This paper addresses both of these problems by incorporating the encoding into the learning process, thus allowing the network to learn the spike encoding alongside the weights. Furthermore, this paper proposes a hardware architecture based on a recently introduced differential-time representation for spike trains allowing decoupling of spike time and processing time. Together these contributions lead to a feedforward SNN using only Leaky-Integrate and Fire (LIF) neurons that surpasses 99% accuracy on the MNIST dataset while still being implementable on medium-sized FPGAs with inference times of less than 295us.