OpenRaiser/Intern-Atlas huggingface dataset:
Quadratic Kernel for Cliques or Trees Vertex Deletion
Venue: arXiv cs.DS 2025
Year: 2025

We consider \textsc{Cliques or Trees Vertex Deletion}, which is a hybrid of two fundamental parameterized problems: \textsc{Cluster Vertex Deletion} and \textsc{Feedback Vertex Set}. In this problem, we are given an undirected graph $G$ and an integer $k$, and asked to find a vertex subset $X$ of size at most $k$ such that each connected component of $G-X$ is either a clique or a tree. Jacob et al. (ISAAC, 2024) provided a kernel of $O(k^5)$ vertices for this problem, which was recently improved to $O(k^4)$ by Tsur (IPL, 2025). Our main result is a kernel of $O(k^2)$ vertices. This result closes the gap between the kernelization result for \textsc{Feedback Vertex Set}, which corresponds to the case where each connected component of $G-X$ must be a tree. Although both \emph{cluster vertex deletion number} and \emph{feedback vertex set number} are well-studied structural parameters, little attention has been given to parameters that generalize both of them. In fact, the lowest common well-known generalization of them is clique-width, which is a highly general parameter. To fill the gap here, we initiate the study of the \emph{cliques or trees vertex deletion number} as a structural parameter. We prove that \textsc{Longest Cycle}, which is a fundamental problem that does not admit $o(n^k)$-time algorithm unless ETH fails when $k$ is the clique-width, becomes fixed-parameter tractable when parameterized by the cliques or trees vertex deletion number.
Computation of Graph Polynomials via Tree Decomposition: Theory, Algorithms, and Python Implementation
Venue: arXiv cs.DM 2025
Year: 2025

Graph polynomials encode fundamental combinatorial invariants of graphs. Their computation is investigated using tree and path decomposition frameworks, with formal definitions of treewidth, k-trees, and pathwidth establishing the structural basis for algorithmic efficiency. Explicit algorithms are constructed for each polynomial, leveraging decomposition order and state transformation mappings to enable tractable computation on graphs of bounded treewidth. Python implementations validate the methods, and computational complexity is analyzed with respect to sparse and k-degenerate graph classes. These results advance decomposition-based approaches for polynomial computation in algebraic graph theory.
The Principles of Human-like Conscious Machine
Venue: arXiv cs.AI 2025
Year: 2025

Determining whether another system, biological or artificial, possesses phenomenal consciousness has long been a central challenge in consciousness studies. This attribution problem has become especially pressing with the rise of large language models and other advanced AI systems, where debates about "AI consciousness" implicitly rely on some criterion for deciding whether a given system is conscious. In this paper, we propose a substrate-independent, logically rigorous, and counterfeit-resistant sufficiency criterion for phenomenal consciousness. We argue that any machine satisfying this criterion should be regarded as conscious with at least the same level of confidence with which we attribute consciousness to other humans. Building on this criterion, we develop a formal framework and specify a set of operational principles that guide the design of systems capable of meeting the sufficiency condition. We further argue that machines engineered according to this framework can, in principle, realize phenomenal consciousness. As an initial validation, we show that humans themselves can be viewed as machines that satisfy this framework and its principles. If correct, this proposal carries significant implications for philosophy, cognitive science, and artificial intelligence. It offers an explanation for why certain qualia, such as the experience of red, are in principle irreducible to physical description, while simultaneously providing a general reinterpretation of human information processing. Moreover, it suggests a path toward a new paradigm of AI beyond current statistics-based approaches, potentially guiding the construction of genuinely human-like AI.
Drum-to-Vocal Percussion Sound Conversion and Its Evaluation Methodology
Venue: arXiv cs.SD 2025
Year: 2025

This paper defines the novel task of drum-to-vocal percussion (VP) sound conversion. VP imitates percussion instruments through human vocalization and is frequently employed in contemporary a cappella music. It exhibits acoustic properties distinct from speech and singing (e.g., aperiodicity, noisy transients, and the absence of linguistic structure), making conventional speech or singing synthesis methods unsuitable. We thus formulate VP synthesis as a timbre transfer problem from drum sounds, leveraging their rhythmic and timbral correspondence. To support this formulation, we define three requirements for successful conversion: rhythmic fidelity, timbral consistency, and naturalness as VP. We also propose corresponding subjective evaluation criteria. We implement two baseline conversion methods using a neural audio synthesizer, the real-time audio variational autoencoder (RAVE), with and without vector quantization (VQ). Subjective experiments show that both methods produce plausible VP outputs, with the VQ-based RAVE model yielding more consistent conversion.
Learning from Gene Names, Expression Values and Images: Contrastive Masked Text-Image Pretraining for Spatial Transcriptomics Representation Learning
Venue: arXiv cs.CV 2025
Year: 2025

Spatial transcriptomics aims to connect high-resolution histology images with spatially resolved gene expression. To achieve better performance on downstream tasks such as gene expression prediction, large-scale pre-training is required to obtain generalisable representations that can bridge histology and transcriptomics across tissues, protocols, and laboratories. Existing cross-modal pre-training approaches for spatial transcriptomics rely on either gene names or expression values in isolation, which strips the gene branch of essential semantics and breaks the association between each gene and its quantitative magnitude. In addition, by restricting supervision to image-text alignment, these methods ignore intrinsic visual cues that are critical for learning robust image features. We present CoMTIP, the first Contrastive Masked Text-Image Pretraining framework that jointly learns from images, gene names, and expression values while capturing fine-grained visual context for spatial transcriptomics. The vision branch uses Masked Feature Modeling to reconstruct occluded patches and learn context-aware image embeddings. The text branch applies a scalable Gene-Text Encoder that processes all gene sentences in parallel, enriches each gene and its numerical value with dedicated embeddings, and employs Pair-aware Adversarial Training (PAAT) to preserve correct gene-value associations. Image and text representations are aligned in a shared InfoNCE-optimised space. Experiments on public spatial transcriptomics datasets show that CoMTIP not only surpasses previous methods on diverse downstream tasks but also achieves zero-shot gene expression prediction, a capability that existing approaches do not provide.
Are You Really Empathic? Evidence from Trait, State and Speaker-Perceived Empathy, and Physiological Signals
Venue: arXiv cs.HC 2025
Year: 2025

When someone claims to be empathic, it does not necessarily mean they are perceived as empathic by the person receiving it. Empathy promotes supportive communication, yet the relationship between listeners' trait and state empathy and speakers' perceptions remains unclear. We conducted an experiment in which speakers described a personal incident and one or more listeners responded naturally, as in everyday conversation. Afterwards, speakers reported perceived empathy, and listeners reported their trait and state empathy. Reliability of the scales was high (Cronbach's $α= 0.805$--$0.888$). Nonparametric Kruskal-Wallis tests showed that speakers paired with higher trait-empathy listeners reported greater perceived empathy, with large effect sizes. In contrast, state empathy did not reliably differentiate speaker outcomes. To complement self-reports, we collected electrodermal activity and heart rate from listeners during the conversations, which shows that high trait empathy listeners exhibited higher physiological variability.
Understanding Digital Gifting Through Messengers Across Cultures: A Comparative Study of University Students in South Korea, China, and Japan
Venue: arXiv cs.HC 2025
Year: 2025

Digital gift-giving has become a key means of maintaining social relationships, but most existing research has focused on gifting within global e-commerce or social media platforms. The emergence of messenger-based gifting in East Asia, where Korea, Japan, and China each have distinct and deeply rooted gifting traditions, remains underexplored. This study examines how in-app gifting services on the most widely used messaging platforms in South Korea (KakaoTalk), Japan (LINE), and China (WeChat) reflect and reshape culturally embedded gifting practices. Through semi-structured interviews with 26 university students, we found that KakaoTalk facilitates frequent, informal exchanges aligned with Korea's emphasis on broad social ties; LINE supports selective and carefully presented gifts, reflecting Japanese norms of formality and sincerity; and WeChat's Hongbao feature enables playful, communal monetary exchanges largely detached from traditional, obligation-driven gifting. Drawing on these findings, we propose the Channel-Oriented Gifting Cycle model, which extends classical gift-exchange theory by showing that the choice of gifting platform is not merely logistical but a culturally meaningful part of the gifting process. We conclude with design implications for culturally sensitive digital gifting services.
Prototype-Based Pseudo-Label Denoising for Source-Free Domain Adaptation in Remote Sensing Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Source-Free Domain Adaptation (SFDA) enables domain adaptation for semantic segmentation of Remote Sensing Images (RSIs) using only a well-trained source model and unlabeled target domain data. However, the lack of ground-truth labels in the target domain often leads to the generation of noisy pseudo-labels. Such noise impedes the effective mitigation of domain shift (DS). To address this challenge, we propose ProSFDA, a prototype-guided SFDA framework. It employs prototype-weighted pseudo-labels to facilitate reliable self-training (ST) under pseudo-labels noise. We, in addition, introduce a prototype-contrast strategy that encourages the aggregation of features belonging to the same class, enabling the model to learn discriminative target domain representations without relying on ground-truth supervision. Extensive experiments show that our approach substantially outperforms existing methods.
VidCLearn: A Continual Learning Approach for Text-to-Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-video generation is an emerging field in generative AI, enabling the creation of realistic, semantically accurate videos from text prompts. While current models achieve impressive visual quality and alignment with input text, they typically rely on static knowledge, making it difficult to incorporate new data without retraining from scratch. To address this limitation, we propose VidCLearn, a continual learning framework for diffusion-based text-to-video generation. VidCLearn features a student-teacher architecture where the student model is incrementally updated with new text-video pairs, and the teacher model helps preserve previously learned knowledge through generative replay. Additionally, we introduce a novel temporal consistency loss to enhance motion smoothness and a video retrieval module to provide structural guidance at inference. Our architecture is also designed to be more computationally efficient than existing models while retaining satisfactory generation performance. Experimental results show VidCLearn's superiority over baseline methods in terms of visual quality, semantic alignment, and temporal coherence.
A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination
Venue: arXiv cs.RO 2025
Year: 2025

Humans and animals can make real-time adjustments to movements by imagining their action outcomes to prevent unanticipated or even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception-action loops to handle physical interaction-induced uncertainties in perception and system modeling within complex systems. Inspired by the action-awareness capability of animal intelligence, this study proposes an imagination-inspired motion planner (I-MP) framework that specifically enhances robots' action reliability by imagining plausible spatial states for approaching. After topologizing the workspace, I-MP build perception-action loop enabling robots autonomously build contact models. Leveraging fixed-point theory and Hausdorff distance, the planner computes convergent spatial states under interaction characteristics and mission constraints. By homogenously representing multi-dimensional environmental characteristics through work, the robot can approach the imagined spatial states via real-time computation of energy gradients. Consequently, experimental results demonstrate the practicality and robustness of I-MP in complex cluttered environments.
Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime
Venue: arXiv cs.CV 2025
Year: 2025

Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR require the use of extensive annotated sets for training, making them impractical for low-resource domains like historical archives or limited-size modern collections. This paper introduces a novel framework that, unlike the standard HTR model paradigm, can leverage mild prior knowledge of lexical characteristics; this is ideal for scenarios where labeled data are scarce. We propose an iterative bootstrapping approach that aligns visual features extracted from unlabeled images with semantic word representations using Optimal Transport (OT). Starting with a minimal set of labeled examples, the framework iteratively matches word images to text labels, generates pseudo-labels for high-confidence alignments, and retrains the recognizer on the growing dataset. Numerical experiments demonstrate that our iterative visual-semantic alignment scheme significantly improves recognition accuracy on low-resource HTR benchmarks.
VCE: Safe Autoregressive Image Generation via Visual Contrast Exploitation
Venue: arXiv cs.CV 2025
Year: 2025

Recently, autoregressive image generation models have wowed audiences with their remarkable capability in creating surprisingly realistic images. Models such as GPT-4o and LlamaGen can not only produce images that faithfully mimic renowned artistic styles like Ghibli, Van Gogh, or Picasso, but also potentially generate Not-Safe-For-Work (NSFW) content, raising significant concerns regarding copyright infringement and ethical use. Despite these concerns, methods to safeguard autoregressive text-to-image models remain underexplored. Previous concept erasure methods, primarily designed for diffusion models that operate in denoising latent space, are not directly applicable to autoregressive models that generate images token by token. To address this critical gap, we propose Visual Contrast Exploitation (VCE), a novel framework comprising: (1) an innovative contrastive image pair construction paradigm that precisely decouples unsafe concepts from their associated content semantics, and (2) a sophisticated DPO-based training approach that enhances the model's ability to identify and leverage visual contrastive features from image pairs, enabling precise concept erasure. Our comprehensive experiments across three challenging tasks-artist style erasure, explicit content erasure, and object removal-demonstrate that our method effectively secures the model, achieving state-of-the-art results while erasing unsafe concepts and maintaining the integrity of unrelated safe concepts. The code and models are available at https://github.com/Maplebb/VCE.
MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Venue: arXiv cs.DC 2025
Year: 2025

Multimodal large language models (MLLMs) enable powerful cross-modal inference but impose significant computational and latency burdens, posing severe challenges for deployment in resource-constrained environments. In this paper, we propose MoA-Off, an adaptive heterogeneous modality-aware offloading framework with edge-cloud collaboration for efficient MLLM inference. MoA-Off introduces a lightweight heterogeneous modality-aware module that estimates the complexity of heterogeneous inputs through multi-dimensional feature analysis. Then, an adaptive edge-cloud collaborative offloading strategy is proposed that dynamically schedules workloads between edge and cloud based on modality-aware complexity scores and real-time system states. The experimental results demonstrate that MoA-Off can achieve over 30% reduction in latency and 30%-65% decrease in resource overhead while maintaining competitive accuracy compared to traditional approaches.
Bridging the gap between training and inference in LM-based TTS models
Venue: arXiv cs.SD 2025
Year: 2025

Recent advancements in text-to-speech (TTS) have shown that language model (LM) based systems offer competitive performance compared to traditional approaches. However, in training, TTS models use ground-truth (GT) tokens as prefixes to predict the next token, while in inference these tokens are not available, a gap between training and inference that is often neglected. In this study, we propose a prompt-guided hybrid training scheme to mitigate exposure bias in popular LM-based TTS systems. Our core idea is to adopt a hybrid training paradigm that combines teacher forcing with free running, thereby introducing self-generated tokens into the training process. This makes the training mode more consistent with inference, reducing the training-inference gap. In addition, we incorporate an EOS prediction mechanism during training to detect incorrect sequence termination and adaptively control the free running process. Experimental results provide a comprehensive evaluation of the impact of exposure bias on LM-based TTS, and demonstrate that our method effectively narrows the training-inference gap, thereby improving the quality of synthesized long-form speech.
Towards Generalized Synapse Detection Across Invertebrate Species
Venue: arXiv cs.CV 2025
Year: 2025

Behavioural differences across organisms, whether healthy or pathological, are closely tied to the structure of their neural circuits. Yet, the fine-scale synaptic changes that give rise to these variations remain poorly understood, in part due to persistent challenges in detecting synapses reliably and at scale. Volume electron microscopy (EM) offers the resolution required to capture synaptic architecture, but automated detection remains difficult due to sparse annotations, morphological variability, and cross-dataset domain shifts. To address this, we make three key contributions. First, we curate a diverse EM benchmark spanning four datasets across two invertebrate species: adult and larval Drosophila melanogaster, and Megaphragma viggianii (micro-WASP). Second, we propose SimpSyn, a single-stage Residual U-Net trained to predict dual-channel spherical masks around pre- and post-synaptic sites, designed to prioritize training and inference speeds and annotation efficiency over architectural complexity. Third, we benchmark SimpSyn against Buhmann et al.'s Synful [1], a state-of-the-art multi-task model that jointly infers synaptic pairs. Despite its simplicity, SimpSyn consistently outperforms Synful in F1-score across all volumes for synaptic site detection. While generalization across datasets remains limited, SimpSyn achieves competitive performance when trained on the combined cohort. Finally, ablations reveal that simple post-processing strategies - such as local peak detection and distance-based filtering - yield strong performance without complex test-time heuristics. Taken together, our results suggest that lightweight models, when aligned with task structure, offer a practical and scalable solution for synapse detection in large-scale connectomic pipelines.
FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks
Venue: arXiv cs.RO 2025
Year: 2025

Contact-rich manipulation is crucial for robots to perform tasks requiring precise force control, such as insertion, assembly, and in-hand manipulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms is often costly and requires additional hardware design. To overcome these issues, we propose FILIC, a Force-guided Imitation Learning framework with impedance torque control. FILIC integrates a Transformer-based IL policy with an impedance controller in a dual-loop structure, enabling compliant force-informed, force-executed manipulation. For robots without force/torque sensors, we introduce a cost-effective end-effector force estimator using joint torque measurements through analytical Jacobian-based inversion while compensating with model-predicted torques from a digital twin. We also design complementary force feedback frameworks via handheld haptics and VR visualization to improve demonstration quality. Experiments show that FILIC significantly outperforms vision-only and joint-torque-based methods, achieving safer, more compliant, and adaptable contact-rich manipulation. Our code can be found in https://github.com/TATP-233/FILIC.
Ultra-short-term solar power forecasting by deep learning and data reconstruction
Venue: arXiv cs.LG 2025
Year: 2025

The integration of solar power has been increasing as the green energy transition rolls out. The penetration of solar power challenges the grid stability and energy scheduling, due to its intermittent energy generation. Accurate and near real-time solar power prediction is of critical importance to tolerant and support the permeation of distributed and volatile solar power production in the energy system. In this paper, we propose a deep-learning based ultra-short-term solar power prediction with data reconstruction. We decompose the data for the prediction to facilitate extensive exploration of the spatial and temporal dependencies within the data. Particularly, we reconstruct the data into low- and high-frequency components, using ensemble empirical model decomposition with adaptive noise (CEEMDAN). We integrate meteorological data with those two components, and employ deep-learning models to capture long- and short-term dependencies towards the target prediction period. In this way, we excessively exploit the features in historical data in predicting a ultra-short-term solar power production. Furthermore, as ultra-short-term prediction is vulnerable to local optima, we modify the optimization in our deep-learning training by penalizing long prediction intervals. Numerical experiments with diverse settings demonstrate that, compared to baseline models, the proposed method achieves improved generalization in data reconstruction and higher prediction accuracy for ultra-short-term solar power production.