Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network

Device-free localization (DFL) based on the received signal strength (RSS) measurements of radio frequency (RF)links is the method using RSS variation due to the presence of the target to localize the target without attaching any device. The majority of DFL methods utilize the fact the link will experience great attenuation when obstructed. Thus that localization accuracy depends on the model which describes the relationship between RSS loss caused by obstruction and the position of the target. The existing models is too rough to explain some phenomenon observed in the experiment measurements. In this paper, we propose a new model based on diffraction theory in which the target is modeled as a cylinder instead of a point mass. The proposed model can will greatly fits the experiment measurements and well explain the cases like link crossing and walking along the link line. Because the measurement model is nonlinear, particle filtering tracing is used to recursively give the approximate Bayesian estimation of the position. The posterior Cramer-Rao lower bound (PCRLB) of proposed tracking method is also derived. The results of field experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that the tracking error of proposed model is improved by at least 36 percent in the single target case and 25 percent in the two targets case compared to other models.Comment: This paper has been withdrawn by the author due to some mistake

EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation

Predicting user preferences and sequential dependencies based on historical behavior is the core goal of sequential recommendation. Although attention-based models have shown effectiveness in this field, they often struggle with inference inefficiency due to the quadratic computational complexity inherent in attention mechanisms, especially with long-range behavior sequences. Drawing inspiration from the recent advancements of state space models (SSMs) in control theory, which provide a robust framework for modeling and controlling dynamic systems, we introduce EchoMamba4Rec. Control theory emphasizes the use of SSMs for managing long-range dependencies and maintaining inferential efficiency through structured state matrices. EchoMamba4Rec leverages these control relationships in sequential recommendation and integrates bi-directional processing with frequency-domain filtering to capture complex patterns and dependencies in user interaction data more effectively. Our model benefits from the ability of state space models (SSMs) to learn and perform parallel computations, significantly enhancing computational efficiency and scalability. It features a bi-directional Mamba module that incorporates both forward and reverse Mamba components, leveraging information from both past and future interactions. Additionally, a filter layer operates in the frequency domain using learnable Fast Fourier Transform (FFT) and learnable filters, followed by an inverse FFT to refine item embeddings and reduce noise. We also integrate Gate Linear Units (GLU) to dynamically control information flow, enhancing the model's expressiveness and training stability. Experimental results demonstrate that EchoMamba significantly outperforms existing models, providing more accurate and personalized recommendations.Comment: arXiv admin note: text overlap with arXiv:2403.03900 by other author

Maximin Fairness with Mixed Divisible and Indivisible Goods

We study fair resource allocation when the resources contain a mixture of divisible and indivisible goods, focusing on the well-studied fairness notion of maximin share fairness (MMS). With only indivisible goods, a full MMS allocation may not exist, but a constant multiplicative approximate allocation always does. We analyze how the MMS approximation guarantee would be affected when the resources to be allocated also contain divisible goods. In particular, we show that the worst-case MMS approximation guarantee with mixed goods is no worse than that with only indivisible goods. However, there exist problem instances to which adding some divisible resources would strictly decrease the MMS approximation ratio of the instance. On the algorithmic front, we propose a constructive algorithm that will always produce an $\alpha$-MMS allocation for any number of agents, where $\alpha$ takes values between $1/2$ and $1$ and is a monotone increasing function determined by how agents value the divisible goods relative to their MMS values.Comment: Appears in the 35th AAAI Conference on Artificial Intelligence (AAAI), 202

Constructing Media-based Enterprise Networks for Stock Market Risk Analysis

Stock comovement analysis is essential to understand the mechanism of stock markets. Previous studies focus on the comovement from the perspectives of fundamentals or preferences of investors. In this article, we propose a framework to explore the comovements of stocks in terms of their relationships in Web media. This is achieved by constructing media-based enterprise networks in terms of the co-exposure in news reports of stocks and mutual attentions among them. Our experiments based on CSI 300 listed firms show the significant comovements of stocks brought out by their behaviors in Web media. Furthermore, utilizing media based enterprise networks can help us identify the most influential firms which can stir up the stock markets

An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs

Cyber-physical systems (CPSs) integrate cyber and physical components and enable them to interact with each other to meet user needs. The needs for CPSs span rich application domains such as healthcare and medicine, smart home, smart building, etc. This indicates that CPSs are all about solving real-world problems. With the increasing abundance of sensing devices and effectors, the problems wanted to solve with CPSs are becoming more and more complex. It is also becoming increasingly difficult to extract and express CPS requirements accurately. Problem frame approach aims to shape real-world problems by capturing the characteristics and interconnections of components, where the problem diagram is central to expressing the requirements. CPSs requirements are generally presented in domain-specific documents that are normally expressed in natural language. There is currently no effective way to extract problem diagrams from natural language documents. CPSs requirements extraction and modeling are generally done manually, which is time-consuming, labor-intensive, and error-prone. Large language models (LLMs) have shown excellent performance in natural language understanding. It can be interesting to explore the abilities of LLMs to understand domain-specific documents and identify modeling elements, which this paper is working on. To achieve this goal, we first formulate two tasks (i.e., entity recognition and interaction extraction) and propose a benchmark called CPSBench. Based on this benchmark, extensive experiments are conducted to evaluate the abilities and limitations of seven advanced LLMs. We find some interesting insights. Finally, we establish a taxonomy of LLMs hallucinations in CPSs requirements modeling using problem diagrams. These results will inspire research on the use of LLMs for automated CPSs requirements modeling.Comment: 12 pages, 8 figure

Highly Selective Transformation of Biomass Derivatives to Valuable Chemicals by Single-Atom Photocatalyst Ni/TiO2

Selective C-C cleavage of the biomass derivative glycerol under mild conditions has been recognised as a promising yet challenging synthesis route to produce value-added chemicals. Here, a highly selective catalyst is presented for the transformation of glycerol to the high-value product glycolaldehyde, which is composed of nickel single atoms confined to the titanium dioxide surface. Driven by light, the catalyst operates under ambient conditions using air as a green oxidant. The optimised catalyst shows a selectivity of over 60% to glycolaldehyde, resulting in 1058 μmol·gCat -1 ·h-1 production rate, and nearly 3 times higher turnover number than NiOx nanoparticle-decorated TiO2 photocatalyst. Diverse operando and in-situ spectroscopies (including operando XANES, in-situ XPS, O2 -TPD, EXAFS, etc.) unveil the unique function of the Ni single atom, which can significantly promote oxygen adsorption, work as an electron sink and accelerate the production of superoxide radicals, thereby improving the selectivity towards glycolaldehyde over other by-products. This article is protected by copyright. All rights reserved

Discovery of entomopathogenic fungi across geographical regions in southern China on pine sawyer beetle Monochamus alternatus and implication for multi-pathogen vectoring potential of this beetle

Entomopathogen-based biocontrol is crucial for blocking the transmission of vector-borne diseases; however, few cross-latitudinal investigations of entomopathogens have been reported for vectors transmitting woody plant diseases in forest ecosystems. The pine sawyer beetle Monochamus alternatus is an important wood borer and a major vector transmitting pine wilt disease, facilitating invasion of the pinewood nematode Bursaphelenchus xylophilus (PWN) in China. Due to the limited geographical breadth of sampling regions, species diversity of fungal associates (especially entomopathogenic fungi) on M. alternatus adults and their potential ecological functions have been markedly underestimated. In this study, through traditional fungal isolation with morphological and molecular identification, 640 fungal strains (affiliated with 15 genera and 39 species) were isolated from 81 beetle cadavers covered by mycelia or those symptomatically alive across five regional populations of this pest in southern China. Multivariate analyses revealed significant differences in the fungal community composition among geographical populations of M. alternatus, presenting regionalized characteristics, whereas no significant differences were found in fungal composition between beetle genders or among body positions. Four region-representative fungi, namely, Lecanicillium attenuatum (Zhejiang), Aspergillus austwickii (Sichuan), Scopulariopsis alboflavescens (Fujian), and A. ruber (Guangxi), as well as the three fungal species Beauveria bassiana, Penicillium citrinum, and Trichoderma dorotheae, showed significantly stronger entomopathogenic activities than other fungi. Additionally, insect-parasitic entomopathogenic fungi (A. austwickii, B. bassiana, L. attenuatum, and S. alboflavescens) exhibited less to no obvious phytopathogenic activities on the host pine Pinus massoniana, whereas P. citrinum, Purpureocillium lilacinum, and certain species of Fusarium spp.—isolated from M. alternatus body surfaces—exhibited remarkably higher phytopathogenicity. Our results provide a broader view of the entomopathogenic fungal community on the vector beetle M. alternatus, some of which are reported for the first time on Monochamus spp. in China. Moreover, this beetle might be more highly-risk in pine forests than previously considered, as a potential multi-pathogen vector of both PWN and phytopathogenic fungi

Fracture network complexity of tight sandstone and its influencing factors

Objective  Fracture network analysis plays an important role in oil and gas exploration and development. However, complexity analysis of tight sandstone fracture networks and their control factors is relatively lagging. Based on an experimental study of the dynamic evolution of the complex fracture network in tight sandstone, the fractal and multifractal spectral characteristics of the fracture network were defined, and the complexity and main controlling factors of the fracture network were analyzed. Fracture network complexity analysis of tight sandstone plays an important role in hydraulic fracturing optimization, fracture network prediction, and fracture modeling.  Methods  Rock mechanics and X-ray computed tomography scan experiments determined the characteristics of rock mechanics and fracture networks . The microstructure and fracture network fractal characteristics of tight sandstone were quantitatively characterized by SEM and fracture network fractal analysis.  Results  The results showed that the quartz content of tight sandstone ranges from 28.08 to 52.88%, clay content ranges from 11.54 to 25.45%, particle size ranges from 61.18 to 184.55 μm, and porosity ranges from 8.125 to 10.296%. Uniaxial compressive strength ranges from 69.09 to 188.33 MPa, and the elastic modulus ranges from 31.69 to 92.76 GPa. The fractal dimension (DB) ranges from 1.28 to 2.35 and average spectral width (Δα) ranges from 1.0851 to 1.3638.  Conclusion  The initiation and propagation of fractures extend through the entire stress–strain process. The complexity of the fracture network of tight sandstone is mainly controlled by microscopic fabric characteristics, and has obvious confining pressure as well as scale effects. The DB of the three-dimensional fracture network and average Δα of the multifractal spectrum represents the complexity and heterogeneity of the fracture spatial distribution, respectively, and are relatively independent. As the content of quartz, feldspar, and other brittle minerals in sandstone increases, the porosity of the reservoir increases, particle size of the sandstone decreases, DB of the fracture network increases, and average Δα decreases. In the absence of confining pressure, the complexity of the sample fracture network is mainly controlled by the microscopic fabric characteristics, and the complexity increases with increase of axial pressure. When present confining pressure plays a leading role; the higher it is, the lower the DB value, and the higher the mean Δα value. Clay minerals are unconducive to complex fractures formation. The mean values of DB and Δα of small-scale samples are greater than those of large-scale samples. The elastic modulus and compressive strength of sandstone are positively correlated with DB and mean Δα