Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision

In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate such concerns, but they suffer from poor performance due to modality forgetting, where the sequential objective overshadows modality representation. We propose a lightweight knowledge distillation solution that preserves both merits: retaining modality information and maintaining high efficiency. Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations. The supervision signals are distilled from the original modality representations, including both (1) holistic correlations, which quantify their overall associations, and (2) dissected correlation types, which refine their relationship facets (honing in on specific aspects like color or shape consistency). To further address the issue of modality forgetting, we propose an asynchronous learning step, allowing the original information to be retained longer for training the representation learning module. Our approach is compatible with various backbone architectures and outperforms the top baselines by 6.8% on average. We empirically demonstrate that preserving original feature associations from modality encoders significantly boosts task-specific recommendation adaptation. Additionally, we find that larger modality encoders (e.g., Large Language Models) contain richer feature sets which necessitate more fine-grained modeling to reach their full performance potential.Comment: Accepted by ECIR 202

RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.Comment: 8 pages, 4 figures. Conference paper accepted by AAAI 202

Research on the Structure of Peanut Allergen Protein Ara h1 Based on Aquaphotomics

Peanut allergy is becoming a life-threatening disease that could induce severe allergic reactions in modern society, especially for children. The most promising method applied for deallergization is heating pretreatment. However, the mechanism from the view of spectroscopy has not been illustrated. In this study, near-infrared spectroscopy (NIRS) combined with aquaphotomics was introduced to help us understand the detailed structural changes information during the heating process. First, near-infrared (NIR) spectra of Ara h1 were acquired from 25 to 80°C. Then, aquaphotomics processing tools including principal component analysis (PCA), continuous wavelet transform (CWT), and two-dimensional correlation spectroscopy (2D-COS) were utilized for better understanding the thermodynamic changes, secondary structure, and the hydrogen bond network of Ara h1. The results indicated that about 55°C could be a key temperature, which was the structural change point. During the heating process, the hydrogen bond network was destroyed, free water was increased, and the content of protein secondary structure was changed. Moreover, it could reveal the interaction between the water structure and Ara h1 from the perspective of water molecules, and explain the effect of temperature on the Ara h1 structure and hydrogen-bonding system. Thus, this study described a new way to explore the thermodynamic properties of Ara h1 from the perspective of spectroscopy and laid a theoretical foundation for the application of temperature-desensitized protein products

Few-shot image classification : current status and research trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation

Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensation for textual CBR in this paper. The goal of dataset condensation is to synthesize a small yet informative dataset, upon which models can achieve performance comparable to those trained on large datasets. While existing condensation approaches are tailored to classification tasks for continuous data like images or embeddings, direct application of them to CBR has limitations. To bridge this gap, we investigate efficient dataset condensation for content-based recommendation. Inspired by the remarkable abilities of large language models (LLMs) in text comprehension and generation, we leverage LLMs to empower the generation of textual content during condensation. To handle the interaction data involving both users and items, we devise a dual-level condensation method: content-level and user-level. At content-level, we utilize LLMs to condense all contents of an item into a new informative title. At user-level, we design a clustering-based synthesis module, where we first utilize LLMs to extract user interests. Then, the user interests and user embeddings are incorporated to condense users and generate interactions for condensed users. Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset. Extensive empirical findings from our study, conducted on three authentic datasets, substantiate the efficacy of the proposed method. Particularly, we are able to approximate up to 97% of the original performance while reducing the dataset size by 95% (i.e., on dataset MIND)

The feasibility of compensation for the azimuthal anisotropy of PS-converted waves in HTI media

This paper studies the influence of shear-wave splitting on the azimuthal behaviour of PS converted waves in HTI media. Theoretical analysis and synthetic study show that it is more accurate to separate the fast P-SV1 component from the slow P-SV2 component before compensating for azimuthal anisotropy, especially in water-saturated fractures. NMO corrections to the P-SV1 component in dry and water-saturated models can be improved by the application of the velocity ellipse

Evolutionary nonnegative matrix factorization with adaptive control of cluster quality

Nonnegative matrix factorization (NMF) approximates a given data matrix using linear combinations of a small number of nonnegative basis vectors, weighted by nonnegative encoding coefficients. This enables the exploration of the cluster structure of the data through the examination of the values of the encoding coefficients and therefore, NMF is often used as a popular tool for clustering analysis. However, its encoding coefficients do not always reveal a satisfactory cluster structure. To improve its effectiveness, a novel evolutionary strategy is proposed here to drive the iterative updating scheme of NMF and generate encoding coefficients of higher quality that are capable of offering more accurate and sharper cluster structures. The proposed hybridization procedure that relies on multiple initializations reinforces the robustness of the solution. Additionally, three evolving rules are designed to simultaneously boost the cluster quality and the reconstruction error during the iterative updates. Any clustering performance measure, such as either an internal one relying on the data itself or an external based on the availability of ground truth information, can be employed to drive the evolving procedure. The effectiveness of the proposed method is demonstrated via careful experimental designs and thorough comparative analyses using multiple benchmark datasets

A systematic review and meta-analysis of Danshen combined with mesalazine for the treatment of ulcerative colitis

Purpose: Current pharmacological treatments for Ulcerative Colitis (UC) have limitations. Therefore, it is important to elucidate any available alternative or complementary treatment, and Chinese herbal medicine shows the potential for such treatment. As a traditional Chinese herbal medicine, Danshen-related preparations have been reported to be beneficial for UC by improving coagulation function and inhibiting inflammatory responses. In spite of this, the credibility and safety of this practice are incomplete. Therefore, in order to investigate whether Danshen preparation (DSP) is effective and safe in the treatment of UC, we conducted a systematic review and meta-analysis.Methods: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database and CQVIP Database were searched for this review.The main observation indexes were the effect of DSP combined with mesalazine or DSP on the effective rate, platelet count (PLT), mean platelet volume (MPV) and C-reactive protein (CRP) of UC. The Cochrane risk of bias tool was used to assess the risk of bias. The selected studies were evaluated for quality and data processing using RevMan5.4 and Stata17.0 software.Results: A total of 37 studies were included. Among them, 26 clinical trials with 2426 patients were included and 11 animal experimental studies involving 208 animals were included. Meta-analysis results showed that compared with mesalazine alone, combined use of DSP can clearly improve the clinical effective rate (RR 0.86%, 95% CI:0.83–0.88, p < 0.00001) of UC. Furthermore it improved blood coagulation function by decreasing serum PLT and increasing MPV levels, and controlled inflammatory responses by reducing serum CRP, TNF-α, IL-6, and IL-8 levels in patients.Conclusion: Combining DSP with mesalazine for UC can enhance clinical efficacy. However, caution should be exercised in interpreting the results of this review due to its flaws, such as allocation concealment and uncertainty resulting from the blinding of the study.Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/myprospero.php, identifier PROSPERO: CRD4202229328

Pressure driven depolarization behavior of Bi 0.5 Na 0.5 TiO 3 based lead-free ceramics

Pressure driven depolarization behavior has been widely investigated for its scientific significance and practical applications. However, previous related studies were all based on lead-containing ferroelectric (FE) materials leading to detrimental environmental concerns. In the present work, we report the pressure driven depolarization behavior in Bi-based lead-free 0.97[(1-x)Bi0.5Na0.5TiO3-xBiAlO3)]-0.03K0.5Na0.5NbO3 (BNT-x) ceramics. Particularly, with increasing hydrostatic pressure from 0 MPa to 495 MPa, the remanent polarization of BNT-0.04 decreases from 30.7 µC/cm2 to 8.2 µC/cm2, reducing &$8764;73% of its initial value. The observed depolarization phenomenon is associated with the pressure induced polar FE-nonpolar relaxor phase transition. The results reveal BNT based ceramics as promising lead free candidates for mechanical-electrical energy conversion applications based on the pressure driven depolarization behavior.This work was supported by Chinese Academy of Sciences Research Equipment Development Project (No. YZ201332), National Program on Key Basic Research Project (973 Program) (No. 2012CB619406), Shanghai International Science and Technology Cooperation Project (No. 13520700700), and international partnership project of Chinese Academy of Science. Zhen Liu also acknowledges the support of Shanghai Sailing Program (No. 17YF1429700)