A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Micro-blogging services can track users' geo-locations when users check-in their places or use geo-tagging which implicitly reveals locations. This "geo tracking" can help to find topics triggered by some events in certain regions. However, discovering such topics is very challenging because of the large amount of noisy messages (e.g. daily conversations). This paper proposes a method to model geographical topics, which can filter out irrelevant words by different weights in the local and global contexts. Our method is based on the Latent Dirichlet Allocation (LDA) model but each word is generated from either a local or a global topic distribution by its generation probabilities. We evaluated our model with data collected from Weibo, which is currently the most popular micro-blogging service for Chinese. The evaluation results demonstrate that our method outperforms other baseline methods in several metrics such as model perplexity, two kinds of entropies and KL-divergence of discovered topics

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.Comment: 15 pages, 8 figure

A Game-theoretic Framework for Privacy-preserving Federated Learning

In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \textit{semi-honest} adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical question: is it possible to prevent potential attacks in advance? To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks. We name this game the federated learning privacy game (FLPG), in which neither defenders nor attackers are aware of all participants' payoffs. To handle the \textit{incomplete information} inherent in this situation, we propose associating the FLPG with an \textit{oracle} that has two primary responsibilities. First, the oracle provides lower and upper bounds of the payoffs for the players. Second, the oracle acts as a correlation device, privately providing suggested actions to each player. With this novel framework, we analyze the optimal strategies of defenders and attackers. Furthermore, we derive and demonstrate conditions under which the attacker, as a rational decision-maker, should always follow the oracle's suggestion \textit{not to attack}

Role and therapeutic targets of P2X7 receptors in neurodegenerative diseases

The P2X7 receptor (P2X7R), a non-selective cation channel modulated by adenosine triphosphate (ATP), localizes to microglia, astrocytes, oligodendrocytes, and neurons in the central nervous system, with the most incredible abundance in microglia. P2X7R partake in various signaling pathways, engaging in the immune response, the release of neurotransmitters, oxidative stress, cell division, and programmed cell death. When neurodegenerative diseases result in neuronal apoptosis and necrosis, ATP activates the P2X7R. This activation induces the release of biologically active molecules such as pro-inflammatory cytokines, chemokines, proteases, reactive oxygen species, and excitotoxic glutamate/ATP. Subsequently, this leads to neuroinflammation, which exacerbates neuronal involvement. The P2X7R is essential in the development of neurodegenerative diseases. This implies that it has potential as a drug target and could be treated using P2X7R antagonists that are able to cross the blood-brain barrier. This review will comprehensively and objectively discuss recent research breakthroughs on P2X7R genes, their structural features, functional properties, signaling pathways, and their roles in neurodegenerative diseases and possible therapies

BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys

Integrated Pockels Laser

The development of integrated semiconductor lasers has miniaturized traditional bulky laser systems, enabling a wide range of photonic applications. A progression from pure III-V based lasers to III-V/external cavity structures has harnessed low-loss waveguides in different material systems, leading to significant improvements in laser coherence and stability. Despite these successes, however, key functions remain absent. In this work, we address a critical missing function by integrating the Pockels effect into a semiconductor laser. Using a hybrid integrated III-V/Lithium Niobate structure, we demonstrate several essential capabilities that have not existed in previous integrated lasers. These include a record-high frequency modulation speed of 2 exahertz/s (2.0$\times$10$^{18}$ Hz/s) and fast switching at 50 MHz, both of which are made possible by integration of the electro-optic effect. Moreover, the device co-lases at infrared and visible frequencies via the second-harmonic frequency conversion process, the first such integrated multi-color laser. Combined with its narrow linewidth and wide tunability, this new type of integrated laser holds promise for many applications including LiDAR, microwave photonics, atomic physics, and AR/VR

Semi-supervised federated heterogeneous transfer learning

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine learning models with distributed data stored in different silos without exposing sensitive information. Different from most existing FL approaches requiring data from different parties share either the same feature space or sample ID space, federated transfer learning (FTL), which is a recently proposed FL concept, is designed for situations where data from different parties differ not only in samples but also in feature space. However, like most traditional FL approaches, FTL methods also suffer from issues caused by insufficiency of overlapping data. In this paper, we propose a novel FTL framework referred to as Semi-Supervised Federated Heterogeneous Transfer Learning (SFHTL) to leverage on the unlabeled non-overlapping samples to reduce model overfitting as a result of insufficient overlapping training samples in FL scenarios. Unlike existing FTL approaches, SFHTL makes use of non-overlapping samples from all parties to expand the training set for each party to improve local model performance. Through extensive experimental evaluation based on real-world datasets, we demonstrate significant advantages of SFHTL over state-of-the-art approaches.Nanyang Technological UniversityNational Research Foundation (NRF)This research is supported, in part, by the National Natural Science Foundation of China under Grant NSFC 62106167; Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-019); the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore; Nanyang Assistant Professorship (NAP); the Joint NTUWeBank Research Centre on Fintech (Award No: NWJ-2020-008), Nanyang Technological University, Singapore; and Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) (NSC-2019- 011). Qiang Yang is supported in part by China National Key Research and Development Program of China under Grant No. 2018AAA0101100

Dissipation and Safety Analysis of Dimethomorph Application in Lychee by High-Performance Liquid Chromatography–Tandem Mass Spectrometry with QuEChERS

This study presents a method for analyzing dimethomorph residues in lychee using QuEChERS extraction and HPLC-MS/MS. The validation parameters for this method, which include accuracy, precision, linearity, and recovery, indicate that it meets standard validation requirements. Following first-order kinetics, the dissipation dynamic of dimethomorph in lychee was determined to range from 6.4 to 9.2 days. Analysis of terminal residues revealed that residues in whole lychee were substantially greater than those in the pulp, indicating that dimethomorph residues are predominantly concentrated in the peel. When applied twice and thrice at two dosage levels with pre-harvest intervals (PHIs) of 5, 7, and 10 days, the terminal residues in whole lychee ranged from 0.092 to 1.99 mg/kg. The terminal residues of the pulp ranged from 0.01 to 0.18 mg/kg, with the residue ratio of whole lychee to pulp consistently exceeding one. The risk quotient (RQ) for dimethomorph, even at the recommended dosage, was less than one, indicating that the potential for damage was negligible. This study contributes to the establishment of maximum residue limits (MRLs) in China by providing essential information on the safe application of dimethomorph in lychee orchards