Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Multi-region boundary element analysis and multi-layered Green's functions

This thesis aims to improve some aspects of the boundary element techniques in elastostatics and in particular its treatment of layered media. These include two areas of work: 1. The development of the partially discontinuous element method, that is, elements which are continuous on smooth boundaries but discontinuous at edges and comers, in order to address the well-known comer problem. This approach is relatively simple to implement whilst avoiding the computational disadvantages of discontinuous elements. We examine the effect of the offset distance between the free nodes and the element edges on accuracy and stability. This approach is implemented with automatic edge detection software, which incorporates partially discontinuous elements into BEM program without intervention by the user. This greatly reduces data preparation effort and makes the BEM an attractive option in practice. 2. In order to preserve the boundary-only discretization advantages of BEM, three-dimensional Green's functions in multi-layered systems are explored. These are computed using the cylindrical system of vector functions and the propagator matrix method. Numerical integration of these functions is problematic but a singularity extraction method is used to them accurately in the vicinity of the singularity. In this process, the Green's functions for the bi-material full space, are adopted instead of those for the homogeneous full space. The analytic work, which was necessary' to derive the necessary transformed functions in cylindrical vector space, is described in some detail. Numerical trials show that the current method is accurate and efficient, and superior to the previous approaches

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing

Language models (LMs) like BERT and GPT have revolutionized natural language processing (NLP). However, privacy-sensitive domains, particularly the medical field, face challenges to train LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring the preservation of data privacy. In this study, we systematically evaluate FL in medicine across $2$ biomedical NLP tasks using $6$ LMs encompassing $8$ corpora. Our results showed that: 1) FL models consistently outperform LMs trained on individual client's data and sometimes match the model trained with polled data; 2) With the fixed number of total data, LMs trained using FL with more clients exhibit inferior performance, but pre-trained transformer-based models exhibited greater resilience. 3) LMs trained using FL perform nearly on par with the model trained with pooled data when clients' data are IID distributed while exhibiting visible gaps with non-IID data. Our code is available at: https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin

Nitrogen, Cobalt Co-doped Fluorescent Magnetic Carbon Dots as Ratiometric Fluorescent Probes for Cholesterol and Uric Acid in Human Blood Serum

Detection of cholesterol and uric acid biomarkers is of great importance for clinical diagnosis of several serious diseases correlated with their variations in human blood serum. In this study, a new kind of well selective and highly sensitive ratiometric fluorescent probe for cholesterol and uric acid determination in human blood serum was innovatively developed on the basis of the inner filter effect (IFE) process of nitrogen, cobalt co-doped carbon dots (N,Co-CDs) with 2,3-diaminophenazine (DAP). DAP was the oxidative product during the oxidation reaction between ophenylenediamine and H2O2. Fluorescent magnetic N,Co-CDs possessing blue emission and magnetic property were prepared through a facile one-pot hydrothermal strategy by using citric acid, diethylenetriamine, and cobalt(II) chloride hexahydrate as precursors. N,Co-CDs exhibited good ferromagnetic property and excellent optical properties even in extremely harsh environmental conditions, implying the huge potential applications of such N,Co-CDs in biological areas. On the basis of the IFE process between N,Co-CDs and DAP, N,Co-CDs were applied to establish ratiometric fluorescent probes for the indirect detection of cholesterol and uric acid that participated in enzyme-catalyzed H2O2-generation reactions. The established IFEbased fluorescent probes exhibited relatively low detection limits of 3.6 nM for cholesterol and 3.4 nM for uric acid, respectively. The fluorescent probe was successfully utilized for the determination of cholesterol and uric acid in human blood serum with satisfying results, which provided an informed perspective on the applications of such doped CDs to explore the specific and sensitive nanoprobe in disease diagnoses and clinical therapy

Direct Determination of Electron-Phonon Coupling Matrix Element in a Correlated System

High-resolution electron energy loss spectroscopy measurements have been carried out on an optimally doped cuprate Bi2Sr2CaCu2O8+{\delta}. The momentum-dependent linewidth and the dispersion of an A1 optical phonon are obtained. Based on these data as well as the detailed knowledge of the electronic structure from angle-resolved photoemission spectroscopy, we develop a scheme to determine the full structure of electron-phonon coupling for a specific phonon mode, thus providing a general method for directly resolving the EPC matrix element in systems with anisotropic electronic structures