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arXiv 2025年

作者： Fu, Honghao Zhang, Xingjian Concordia Institute for Information Systems Engineering Concordia University Montreal Canada QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Hong Kong

In the recent breakthrough result [MS24], Slofstra and Mastel show that there is a two-player one-round perfect zero-knowledge MIP∗ protocol for RE. We build on their result to show that there exists a succinct two-player one-round perfect zero-knowledge MIP∗ protocol for RE with polylog question size and O(1) answer size, or with O(1) question size and polylog answer size. To prove our result, we analyze the four central compression techniques underlying the MIP∗ = RE proof [JNV+20] — question reduction, oracularization, answer reduction, and parallel repetition — and show that they all preserve the perfect (as well as statistical and computational) zero-knowledge properties of the original protocol. Furthermore, we complete the study of the conversion between constraint-constraint and constraint-variable binary constraint system (BCS) nonlocal games, which provide a quantum information characterization of MIP∗ protocols. While Paddock [Pad22] established that any near-perfect strategy for a constraint-variable game can be mapped to a constraint-constraint version, we prove the converse, fully establishing their equivalence. © 2025, CC0.

关键词： Constraint theory

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings

Predicting Protein-Protein Interactions Using Self-Attention...

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International Conference on computing and Networking Technology (ICCNT)

作者： I. R. Oviya N Shanmukha Sravya Kalpana Raja Department of Computer Science Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model’s performance is assessed through $\mathbf{5 - f o l d}$ cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of $\mathbf{9 2. 2 8 \%}$ and $\mathbf{9 4. 0 8 \%}$, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets.

关键词： Proteins Accuracy Transfer learning Pathogenesis Artificial neural networks Organizations Predictive models Feature extraction Encoding Vectors

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A Two-Stage Emission Mismatch Penalty Game to Facilitate Carbon and Electricity Market Interaction 6

A Two-Stage Emission Mismatch Penalty Game to Facilitate Car...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Xu, Gaoyuan Shi, Jian Jiang, Wenqian Wu, Chenye Wang, Dan Han, Zhu The Chinese University of HongKong School of Data Science Shenzhen China Technology University of Houston Department of Engineering United States The Chinese University of HongKong School of Science and Engineering Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350347159

The electricity sector is undergoing a low carbon transition to combat the looming threats caused by climate change. Given the urgency of meeting the climate goals set by the Intergovernmental Panel on Climate Change (IPCC), the cap-and-trade carbon emission market has emerged as an instrumental tool for policy-makers to impose emission restrictions on generation companies (Gencos). In this paper, we seek to model the sophisticated interactions among the CMO-carbon market operator, ESO-electricity system operator, and Gencos in a coupled electricity and carbon market. We propose a two-stage structure in which CMO conducts a uniform price auction to allocate the gross emission allowance to each Genco in the form of hourly allowance allowances in the first stage. Then, in the next stage, ESO conducts an emission-aware economic dispatch whereas the Gencos may further adjust the hourly schedules based on the dispatch outcomes. The coupling of the carbon market and the electricity market is characterized by an emission mismatch penalty mechanism, and we develop an iterative solving algorithm to reach the equilibrium. Through numerical studies, we highlight that the proposed mechanism is effective in emission reduction and market management. © 2022 IEEE.

关键词： Power markets

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Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading

arXiv

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Brinton, Christopher G. College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States

Large Language Models (LLMs) can perform zero-shot learning on unseen tasks and few-shot learning on complex reasoning tasks. However, resource-limited mobile edge networks struggle to support long-context LLM serving for LLM agents during multi-round interactions with users. Unlike stateless computation offloading and static service offloading in edge computing, optimizing LLM serving at edge servers is challenging because LLMs continuously learn from context which raises accuracy, latency, and resource consumption dynamics. In this paper, we propose a joint model caching and inference offloading framework that utilizes test-time deep reinforcement learning (T2DRL) to optimize deployment and execution strategies for long-context LLM serving. In this framework, we analyze the performance convergence and design an optimization problem considering the utilization of context windows in LLMs. Furthermore, the T2DRL algorithm can learn in both the training phase and the testing phase to proactively manage cached models and service requests and adapt to context changes and usage patterns during execution. To further enhance resource allocation efficiency, we propose a double Dutch auction (DDA) mechanism, which dynamically matches supply and demand while maximizing social welfare. Finally, experimental results demonstrate that the T2DRL algorithm can reduce system costs by at least 30% compared to baselines while guaranteeing the performance of LLM agents in real-world perception and reasoning tasks. © 2025, CC BY.

关键词： Deep reinforcement learning

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Varshini Devi I I. R. Oviya Kalpana Raja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios.

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Expressive Completeness of Two-Variable First-Order Logic with Counting for First-Order Logic Queries on Rooted Unranked Trees

Expressive Completeness of Two-Variable First-Order Logic wi...

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Annual Symposium on Logic in Computer Science

作者： Jelle Hellings Marc Gyssens Jan Van Den Bussche Dirk Van Gucht Department of Computing and Software McMaster University Hamilton Canada Data Science Institute Hasselt University Diepenbeek Belgium Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

We consider the class of finite, rooted, unranked, unordered, node-labeled trees. Such trees are represented as structures with only the parent-child relation, in addition to any number of unary predicates for node labels. We prove that every unary first-order query over the considered class of trees is already expressible in two-variable first-order logic with counting. Somewhat to our surprise, we have not seen this result being conjectured in the extensive literature on logics for trees. Our proof is based on a global variant of local equivalence notions on nodes of trees. This variant applies to entire trees, and involves counting ancestors of locally equivalent nodes.

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： I. R. Oviya Shanmukha Sravya N Kalpana Raja Department of Computer Science Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models.

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RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States

Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. In this work, we propose a novel multiple RISs aided localization approach to address these challenges. The proposed method first estimates the angle-of-directions (AoDs) between the RISs and the user using the conditional sample mean approach, and then uses the estimated multiple AoD pairs to determine the user’s position. This approach only requires measuring the received signal strength at the BS for a set of randomly generated phase shifts across all RISs, thereby eliminating the need for real-time RIS phase shift design or user-to-BS pilot transmissions. Numerical results show that the proposed localization approach improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

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3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering 39

3DMambaIPF: A State Space Model for Iterative Point Cloud Fi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： State space methods

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MOS: UNLEASHING PARAMETER EFFICIENCY OF LOW-RANK ADAPTATION WITH MIXTURE OF SHARDS

arXiv

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arXiv 2024年

作者： Wang, Sheng Chen, Liheng Chen, Pengan Dong, Jingwei Xue, Boyang Jiang, Jiyue Kong, Lingpeng Wu, Chuan School of Computing and Data Science Hong Kong The University of Hong Kong Hong Kong School of Electrical Engineering China Xi’an Jiaotong University China Department of Systems Engineering and Engineering Management Hong Kong The Chinese University of Hong Kong Hong Kong

The rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served simultaneously. Targeting more parameter-efficient low-rank adaptation (LoRA), parameter sharing presents a promising solution. Empirically, our research into high-level sharing principles highlights the indispensable role of differentiation in reversing the detrimental effects of pure sharing. Guided by this finding, we propose Mixture of Shards (MOS), incorporating both inter-layer and intra-layer sharing schemes, and integrating four nearly cost-free differentiation strategies, namely subset selection, pair dissociation, vector sharding, and shard privatization. Briefly, it selects a designated number of shards from global pools with a Mixture-of-Experts (MoE)-like routing mechanism before sequentially concatenating them to low-rank matrices. Hence, it retains all the advantages of LoRA while offering enhanced parameter efficiency, and effectively circumvents the drawbacks of peer parameter-sharing methods. Our empirical experiments demonstrate approximately 8× parameter savings in a standard LoRA setting. The ablation study confirms the significance of each component. Our insights into parameter sharing and MOS method may illuminate future developments of more parameter-efficient finetuning methods. The code is officially available at https://***/Forence1999/MoS. Copyright © 2024, The Authors. All rights reserved.

关键词： Privatization

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