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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Consensus Learning with Deep Sets for Essential Matrix Estimation 38

Consensus Learning with Deep Sets for Essential Matrix Estim...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Moran, Dror Margalit, Yuval Trostianetsky, Guy Khatib, Fadi Galun, Meirav Basri, Ronen Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel

Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning steps. Here, we propose a simpler network architecture based on Deep Sets. Given a collection of point matches extracted from two images, our method identifies outlier point matches and models the displacement noise in inlier matches. A weighted DLT module uses these predictions to regress the essential matrix. Our network achieves accurate recovery that is superior to existing networks with significantly more complex architectures. © 2024 Neural information processing systems foundation. All rights reserved.

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Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

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Computational Visual Media 2023年第3期9卷 513-529页

作者： Yang Yang Kecheng Lu Yu Wu Yunhai Wang Yi Cao Institute of Applied Physics and Computational Mathematics Beijing 100094China School of Computer Science and Technology Shandong UniversityQingdao 266237China

In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’*** effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational ***,this enables reconstruction of the original data more accurately than existing *** demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid *** experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.

关键词： correlation-awareness large-scale data multi-block methods probabilistic data summarization

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Dual Layer Cogni - Insight Deep-Mood Encoder: A Two- Tiered Approach for Depression Detection

Dual Layer Cogni - Insight Deep-Mood Encoder: A Two- Tiered ...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Firoz, Neda Berestneva, Olga Aksyonov, Sergey V. Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia

ISBN: (纸本)9798350395044

Depression (MDD) affects approximately 5% of adults globally, contributing to productivity loss and public health concerns. With 280 million people impacted, the risk of suicide and self-harm underscores its severity. Current treatments are inadequate, and the complex, heterogeneous nature of depression necessitates automated solutions for timely assessment and varied treatments. This study explores the transformative role of Artificial Intelligence in detecting depressive symptoms, leveraging NLP, and multimodal data analysis. The research introduces the 'Dual Layer Cogni-Insight Deep-Mood Encoder (DLCDME)' model, incorporating two-tiered feature encodings. Four pre-trained language models (BERT, MentalBERT, MentalRoBERTa, PsychBERT, and ClinicalBERT) are experimented and fused with linguistic features for comprehensive representation. A transformer encoder, multi-head attention, and LSTM fine-tuning enhances contextual text feature embeddings, demonstrating the efficacy of ClinicalBERT with transformer encoders for cost-effective large corpus handling. Contributions include a novel feature extraction approach, a thorough model analysis, and the superior performance of DLCDME over contemporary works, holding promise for clinical diagnosis. Our findings reveal that a) the application of ClinicalBERT over DLCDME significantly enhances the performance of depression detection, and b) employing dual feature encodings on text data with transformer attention for predicting depression yields an improved MAE of 3.40 and RMSE of 4.38. The model achieved a test accuracy of 95%, with F1 scores for precision, and recall recorded at 0.95, 0.96, 0.95 respectively. © 2024 IEEE.

关键词： Feature extraction

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Brief Announcement: Distributed Maximum Flow in Planar Graphs 38

Brief Announcement: Distributed Maximum Flow in Planar Graph...

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38th International Symposium on Distributed Computing, DISC 2024

作者： Abd-Elhaleem, Yaseen Dory, Michal Parter, Merav Weimann, Oren Department of Computer Science University of Haifa Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959773522

The dual of a planar graph G is a planar graph G∗ that has a vertex for each face of G and an edge for each pair of adjacent faces of G. The profound relationship between a planar graph and its dual has been the algorithmic basis for solving numerous (centralized) classical problems on planar graphs involving distances, flows, and cuts. In the distributed setting however, the only use of planar duality is for finding a recursive decomposition of G [DISC 2017, STOC 2019]. In this paper, we extend the distributed algorithmic toolkit (such as recursive decompositions and minor-aggregations) to work on the dual graph G∗. These tools can then facilitate various algorithms on G by solving a suitable dual problem on G∗. Given a directed planar graph G with hop-diameter D, our key result is an Õ(D2)-round algorithm1 for Single Source Shortest Paths on G∗, which then implies an Õ(D2)-round algorithm for Maximum st-Flow on G. Prior to our work, no Õ(Poly(D))-round algorithm was known for Maximum st-Flow. We further obtain a D·no(1)-rounds (1 + ϵ)-approximation algorithm for Maximum st-Flow on G when G is undirected and s and t lie on the same face. Finally, we give a near optimal Õ(D)-round algorithm for computing the weighted girth of G. The main challenges in our work are that G∗ is not the communication graph (e.g., a vertex of G is mapped to multiple vertices of G∗), and that the diameter of G∗ can be much larger than D (i.e., possibly by a linear factor). We overcome these challenges by carefully defining and maintaining subgraphs of the dual graph G∗ while applying the recursive decomposition on the primal graph G. The main technical difficulty, is that along the recursive decomposition, a face of G gets shattered into (disconnected) components yet we still need to treat it as a dual node. © Yaseen Abd-Elhaleem, Michal Dory, Merav Parter, and Oren Weimann.

关键词： Approximation algorithms

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From Donkeys to Kings in Tournaments 32

From Donkeys to Kings in Tournaments

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Abboud, Amir Grossman, Tomer Naor, Moni Solomon, Tomer Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Tel Aviv University Israel

ISBN: (纸本)9783959773386

A tournament is an orientation of a complete graph. A vertex that can reach every other vertex within two steps is called a king. We study the complexity of finding k kings in a tournament graph. We show that the randomized query complexity of finding k ≤ 3 kings is O(n), and for the deterministic case it takes the same amount of queries (up to a constant) as finding a single king (the best known deterministic algorithm makes O(n3/2) queries). On the other hand, we show that finding k ≥ 4 kings requires Ω(n2) queries, even in the randomized case. We consider the RAM model for k ≥ 4. We show an algorithm that finds k kings in time O(kn2), which is optimal for constant values of k. Alternatively, one can also find k ≥ 4 kings in time nω (the time for matrix multiplication). We provide evidence that this is optimal for large k by suggesting a fine-grained reduction from a variant of the triangle detection problem. © Amir Abboud, Tomer Grossman, Moni Naor, and Tomer Solomon;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph theory

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Nonlinearity in Large-Cycle Cellular Automata

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Complex Systems 2025年第1期34卷 129-160页

作者： Mukherjee, Sukanya Adak, Sumit Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata700091 India Department of Applied Mathematics and Computer Science DTU Compute Technical University of Denmark Kgs. Lyngby2800 Denmark

The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2n-1 where n ∈ , a cycle of length l can be generated by a cellular automaton (CA) of size ⌊log2 l⌋ + 1;this maximal-length CA is in fact the optimal (minimum-sized) CA to generate a cycle of length l. However, maximal-length CAs are, in general, linear and have some drawbacks. Moreover, it is nontrivial to address this problem for any l ∈ , that is, removing the constraint that l is of the form 2n-1. In this paper we therefore employ nonlinearity in the rules and aim to generate the near-optimal CA for any given cycle length l, meaning that the generated CAs will be of size n = (⌊log2 l⌋ + 1) + d, where the displacement d is as small as possible. We use a generic operator to concatenate a collection of component CAs of sizes n1, n2, …, nk by employing an evolutionary strategy. Here, n = n1 + n2 + ⋯ + nk and l ≥ lcm(1, 2, …, k ), where i (1 ≤ i ≤ k) is the maximum cycle length of a CA of size ni . An influencing factor in the design of component CAs is to achieve the near-optimal CA for a given cycle length. Here we consider nonlinear reversible large-cycle CAs as component CAs. We also measure the effectiveness of nonlinearity in large-cycle generation. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： CAs cellular automata evolution large-cycle CA neighborhood dependent nonlinearity reversible CAs

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Compound AI System for Personalized Learning: Integrating LLM Agents with Knowledge Graphs 6

Compound AI System for Personalized Learning: Integrating LL...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Antonov, Eduard Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Oblast Dolgoprudny141700 Russia

ISBN: (纸本)9798331541699

Personalized learning plays a critical role in enhancing educational outcomes by customizing experiences to meet individual learners' unique needs, preferences, and abilities. Although Large Language Models (LLMs) offer promising opportunities for personalization through natural language interactions, they face challenges related to maintaining long-term context and ensuring traceability of a learner's evolving knowledge. This paper introduces a composite AI framework that merges an LLM-powered conversational agent with Knowledge Graphs (KGs) structured according to Knowledge Space Theory (KST), enhanced by advanced retrieval tools and a robust long-term memory mechanism. This system autonomously orchestrates meaningful, context-aware dialogues while utilizing function calling to access various tools, effectively bridging the gap between dynamic interactions and structured knowledge representation. Our empirical evaluations indicate that the proposed system excels in maintaining coherent dialogues, accurately tracking learner progress, and providing reliable, personalized educational support. By addressing the limitations of standalone LLMs and traditional adaptive learning systems, this integrated approach signifies a notable advancement in intelligent educational technology. © 2024 IEEE.

关键词： Knowledge graph

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