A SVD accelerated kernel-independent fast multipole method and its application to BEM

The kernel-independent fast multipole method (KIFMM) proposed in [1] is of almost linear complexity. In the original KIFMM the time-consuming M2L translations are accelerated by FFT. However, when more equivalent points are used to achieve higher accuracy, the efficiency of the FFT approach tends to be lower because more auxiliary volume grid points have to be added. In this paper, all the translations of the KIFMM are accelerated by using the singular value decomposition (SVD) based on the low-rank property of the translating matrices. The acceleration of M2L is realized by first transforming the associated translating matrices into more compact form, and then using low-rank approximations. By using the transform matrices for M2L, the orders of the translating matrices in upward and downward passes are also reduced. The improved KIFMM is then applied to accelerate BEM. The performance of the proposed algorithms are demonstrated by three examples. Numerical results show that, compared with the original KIFMM, the present method can reduce about 40% of the iterating time and 25% of the memory requirement.Comment: 19 pages, 4 figure

A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication

Feasibility Investigation for Online Elemental Monitoring of Iron and Steel Manufacturing Processes using Laser-Induced Breakdown Spectroscopy

The metallurgical industries are very important for social development. In order to improve the metallurgical techniques and quality of products, the real-time analysis and monitoring of iron and steel manufacturing processes are very significant. Laser-induced breakdown spectroscopy (LIBS) has been studied and applied for the contents measurement of iron and steel. In this paper, the remote open-path LIBS measurement was studied under different sample temperature, lens to target distance (LTD), sample angle conditions to clarify its online measurement features. The 3D profile measurement system of parallel laser beam fringes projection was also developed to measure the sample profile at different sample temperature. The measurement results demonstrated the robustness of remote open-path LIBS system and 3D profile measurement system. However, the correction is necessary to enhance the detection ability of LIBS online measurement. In order to improve the precision and accuracy of real-time elemental measurement, an innovative co-axial laser beam measurement system combining LIBS and 3D profile techniques is proposed to automatically adjust the focus unit and measure the sample components. The further study of this promising method will be developed for online application of iron and steel manufacturing processes

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such as item clicking in recommender systems, although implicitly exhibiting user preferences and could enhance the modeling of users, have not been deeply explored. The main reasons lie in the gap between language modeling and behavior modeling, as well as the incomprehension of LLMs about user-item relations. To address this issue, we propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimizes both kinds of agents together. Specifically, at each time step, we first prompt the user and item agents to interact autonomously. Then, based on the disparities between the agents' decisions and real-world interaction records, user and item agents are prompted to reflect on and adjust the misleading simulations collaboratively, thereby modeling their two-sided relations. The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions. The results show that these agents can demonstrate personalized behaviors akin to those of real-world individuals, sparking the development of next-generation user behavior simulation

Distributed Equivalent Substitution Training for Large-Scale Recommender Systems

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduces fully synchronous training to large-scale recommendation system for the first time by reducing communication, thus making the training of commercial recommender systems converge faster and reach better CTR. DES requires much less communication by substituting the weights-rich operators with the computationally equivalent sub-operators and aggregating partial results instead of transmitting the huge sparse weights directly through the network. Due to the use of synchronous training on large-scale Deep Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under ROC). We successfully apply DES training on multiple popular DLRMs of industrial scenarios. Experiments show that our implementation outperforms the state-of-the-art PS-based training framework, achieving up to 68.7% communication savings and higher throughput compared to other PS-based recommender systems.Comment: Accepted by SIGIR '2020. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 202

Recent Advances in RecBole: Extensions with more Practical Considerations

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole. In general, we focus on the flexibility and efficiency of RecBole in the past few months. More specifically, we have four development targets: (1) more flexible data processing, (2) more efficient model training, (3) more reproducible configurations, and (4) more comprehensive user documentation. Readers can download the above updates at: https://github.com/RUCAIBox/RecBole.Comment: 5 pages, 3 figures, 3 table

A SURF4-to-proteoglycan relay mechanism that mediates the sorting and secretion of a tagged variant of sonic hedgehog

SignificanceSonic Hedgehog (Shh) is a key signaling molecule that plays important roles in embryonic patterning, cell differentiation, and organ development. Although fundamentally important, the molecular mechanisms that regulate secretion of newly synthesized Shh are still unclear. Our study reveals a role for the cargo receptor, SURF4, in facilitating export of Shh from the endoplasmic reticulum (ER) via a ER export signal. In addition, our study provides evidence suggesting that proteoglycans promote the dissociation of SURF4 from Shh at the Golgi, suggesting a SURF4-to-proteoglycan relay mechanism. These analyses provide insight into an important question in cell biology: how do cargo receptors capture their clients in one compartment, then disengage at their destination?</p

Dl-3-n-Butylphthalide Reduces Cognitive Deficits and Alleviates Neuropathology in P301S Tau Transgenic Mice

Alzheimer’s disease (AD) is a destructive and burdensome neurodegenerative disease, one of the most common characteristics of which are neurofibrillary tangles (NFTs) that are composed of abnormal tau protein. Animal studies have suggested that dl-3-n-butylphthalide (dl-NBP) alleviates cognitive impairment in mouse models of APP/PS1 and SAMP8. However, the underlying mechanisms related to this remain unclear. In this study, we examined the effects of dl-NBP on learning and memory in P301S transgenic mice, which carry the human tau gene with the P301S mutation. We found that dl-NBP supplementation effectively improved behavioral deficits and rescued synaptic loss in P301S tau transgenic mice, compared with vehicle-treated P301S mice. Furthermore, we also found that it markedly inhibited the hyperphosphorylated tau at the Ser262 site and decreased the activity of MARK4, which was associated with tau at the Ser262 site. Finally, dl-NBP treatment exerted anti-inflammatory effects and reduced inflammatory responses in P301S mice. In conclusion, our results provide evidence that dl-NBP has a promising potential for the therapy of tauopathies, including AD