Is There Any Social Principle for LLM-Based Agents?

Focus on Large Language Model based agents should involve more than "human-centered" alignment or application. We argue that more attention should be paid to the agent itself and discuss the potential of social sciences for agents.Comment: 3 pages, 1 figur

TransNFV: Integrating Transactional Semantics for Efficient State Management in Virtual Network Functions

Managing shared mutable states in high concurrency state access operations is a persistent challenge in Network Functions Virtualization (NFV). This is particularly true when striving to meet chain output equivalence (COE) requirements. This paper presents TransNFV, an innovative NFV framework that incorporates transactional semantics to optimize NFV state management. The TransNFV integrates VNF state access operations as transactions, resolves transaction dependencies, schedules transactions dynamically, and executes transactions efficiently. Initial findings suggest that TransNFV maintains shared VNF state consistency, meets COE requirements, and skillfully handles complex cross-flow states in dynamic network conditions. TransNFV thus provides a promising solution to enhance state management and overall performance in future NFV platforms

Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data

In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model's performance. To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances. Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation. Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10% absolute increase in F1-score, and outperform the state-of-the-art models on ACE 2005 Chinese and English corpus. Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well

Interplay between multiple charge-density waves and the relationship with superconductivity in Pdx_xHoTe3_{3}

HoTe$_{3}$, a member of the rare-earth tritelluride ($R$Te$_{3}$) family, and its Pd-intercalated compounds, Pd$_x$HoTe$_{3}$, where superconductivity (SC) sets in as the charge-density wave (CDW) transition is suppressed by the intercalation of a small amount of Pd, are investigated using angle-resolved photoemission spectroscopy (ARPES) and electrical resistivity. Two incommensurate CDWs with perpendicular nesting vectors are observed in HoTe$_{3}$ at low temperatures. With a slight Pd intercalation ($x$ = 0.01), the large CDW gap decreases and the small one increases. The momentum dependence of the gaps along the inner Fermi surface (FS) evolves from orthorhombicity to near tetragonality, manifesting the competition between two CDW orders. At $x$ = 0.02, both CDW gaps decreases with the emergence of SC. Further increasing the content of Pd for $x$ = 0.04 will completely suppress the CDW instabilities and give rise to the maximal SC order. The evolution of the electronic structures and electron-phonon couplings (EPCs) of the multiple CDWs upon Pd intercalation are carefully scrutinized. We discuss the interplay between multiple CDW orders, and the competition between CDW and SC in detail.Comment: 6 pages, 5 figure

Single Image Super Resolution via Neighbor Reconstruction

Super Resolution (SR) is a complex, ill-posed problem where the aim is to construct the mapping between the low and high resolution manifolds of image patches. Anchored neighborhood regression for SR (namely A+  [27]) has shown promising results. In this paper we present a new regression-based SR algorithm that overcomes the limitations of A+ and benefits from an innovative and simple Neighbor Reconstruction Method (NRM). This is achieved by vector operations on an anchored point and its corresponding neighborhood. NRM reconstructs new patches which are closer to the anchor point in the manifold space. Our method is robust to NRM sparsely-sampled points: increasing PSNR by 0.5 dB compared to the next best method. We comprehensively validate our technique on standardised datasets and compare favourably with the state-of-the-art methods: we obtain PSNR improvement of up to 0.21 dB compared to previously-reported work

Structural network inference from time-series data using a generative model and transfer entropy

In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer’s disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small

Salmonella contamination and molecular typing in Huzhou from 2015 to 2021

ObjectiveTo understand the pollution status of foodborne pathogens in Huzhou， so as to provide basis for the prevention and control of foodborne diseases.MethodsAccording to GB 4789.4—2016， 1 463 samples in 5 food categories were collected from Huzhou City during 2015—2021 for Salmonella monitoring. Serotype， antibiotic sensitivity test and pulsed field gel electrophoresis （PFGE） were carried out to isolate Salmonella. The results were analyzed by Excel and SPSS 19.0 software.ResultsForty seven Salmonella strains were detected from 1 463 samples and the total detection rate was 3.21%. Among all kinds of food， the detection rate of Salmonella in livestock meat was the highest （6.61%，23/348）. A total of 19 serotypes of Salmonella were detected， of which the dominant serotype was Salmonella Typhimurium. Salmonella serotypes detected in various kinds foods were different. Twenty strains of Salmonella isolated from 2019 to 2021 were tested for drug sensitivity and PFGE. The results showed that the isolates had strong resistance to AMP and TET， with resistance rates of 70% （14/20） and 60% （12/20） respectively. Molecular typing showed that after Xba I enzyme digestion， 19 strains of Salmonella produced 11 PFGE bands with high polymorphism.ConclusionFrom 2015 to 2021， Salmonella was detected in five types of food sold in Huzhou City， including two types of ready to eat food （Chinese cold dishes and bulk cooked meat products）. The dominant serotype was Salmonella Typhimurium. The positive detection was mainly from farm market， which had potential risk of foodborne diseases. The corresponding monitoring and supervision should be paid attention to

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead