Correlation Aware Sparsified Mean Estimation Using Random Projection

We study the problem of communication-efficient distributed vector mean estimation, a commonly used subroutine in distributed optimization and Federated Learning (FL). Rand-$k$ sparsification is a commonly used technique to reduce communication cost, where each client sends $k < d$ of its coordinates to the server. However, Rand-$k$ is agnostic to any correlations, that might exist between clients in practical scenarios. The recently proposed Rand-$k$-Spatial estimator leverages the cross-client correlation information at the server to improve Rand-$k$'s performance. Yet, the performance of Rand-$k$-Spatial is suboptimal. We propose the Rand-Proj-Spatial estimator with a more flexible encoding-decoding procedure, which generalizes the encoding of Rand-$k$ by projecting the client vectors to a random $k$-dimensional subspace. We utilize Subsampled Randomized Hadamard Transform (SRHT) as the projection matrix and show that Rand-Proj-Spatial with SRHT outperforms Rand-$k$-Spatial, using the correlation information more efficiently. Furthermore, we propose an approach to incorporate varying degrees of correlation and suggest a practical variant of Rand-Proj-Spatial when the correlation information is not available to the server. Experiments on real-world distributed optimization tasks showcase the superior performance of Rand-Proj-Spatial compared to Rand-$k$-Spatial and other more sophisticated sparsification techniques.Comment: 32 pages, 13 figures. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, US

A hybrid performance evaluation approach for urban logistics using extended cross-efficiency with prospect theory and OWA operator

Urban logistics performance evaluation can provide reference for further improving its level. However, most performance evaluation for urban logistics premises that decision-makers (DMs) are completely rational, which may not conform to the actual situation. Therefore, this article aims to consider the DMs’ psychological factors in the performance evaluation of urban logistics. Specifically, the cross-efficiency evaluation (CEE) method with the DMs’ psychological factors is used to measure the urban logistics efficiency in the central area of Yangtze River Delta (YRD) urban agglomeration in China in 2019. The main contributions in this article are to propose a hybrid CEE method with prospect theory and ordered weighted average (OWA) operator for urban logistics industry and to expand the evaluation perspectives of urban logistics performance. The main conclusions are obtained: (1) The DMs’ optimism level can indeed affect the efficiency value and ranking of urban logistics. (2) The aggregation based on the OWA operator is fair and reasonable because it can make all self-evaluation efficiencies play the same role. (3) To make the efficiencies and rankings of urban logistics in the central area of the YRD have credibility and discrimination, the DMs’ optimism level range is best between 0.8 and 0.8177

Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks

Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious actors can covertly exploit these vulnerabilities in LLMs through poisoning attacks aimed at generating undesirable outputs. While poisoning attacks have received significant attention in the image domain (e.g., object detection), and classification tasks, their implications for generative models, particularly in the realm of natural language generation (NLG) tasks, remain poorly understood. To bridge this gap, we perform a comprehensive exploration of various poisoning techniques to assess their effectiveness across a range of generative tasks. Furthermore, we introduce a range of metrics designed to quantify the success and stealthiness of poisoning attacks specifically tailored to NLG tasks. Through extensive experiments on multiple NLG tasks, LLMs and datasets, we show that it is possible to successfully poison an LLM during the fine-tuning stage using as little as 1\% of the total tuning data samples. Our paper presents the first systematic approach to comprehend poisoning attacks targeting NLG tasks considering a wide range of triggers and attack settings. We hope our findings will assist the AI security community in devising appropriate defenses against such threats.Comment: 19 pages, 6 figures. Published at NeurIPS 2023 Workshop on Backdoors in Deep Learning: The Good, the Bad, and the Ugl

MeDReaders: a database for transcription factors that bind to methylated DNA

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/

Influence of environmental values on the typhoon risk perceptions of high school students: a case study in Ningbo, China

Typhoon is a severe natural disaster that would bring huge economic losses and casualties to society. High school students are more vulnerable compared with adults during typhoon. Improving risk perception of typhoon can assist high school students effectively respond to typhoon and reduce related losses. Environmental values play an important role in human’s perceptions and actions. Although typhoon is related with environment, few studies have investigated the influence of environmental values on typhoon risk perception of high school students. This study investigates typhoon risk perception of high school students in Ningbo, China, and further analyzes the influence of environmental values on the perception with structural equations model. Results reveal that environmental values have significantly positive impacts on the typhoon risk perception. The findings also demonstrate that disaster threats and the disaster management ability of the government have significant positive impacts on the typhoon risk perception. This study proposes suggestions and measures to improve the typhoon risk perception among high school students and provides a reference for typhoon prevention and reduction education in China

Structural optimization and biological evaluation of 1,5-disubstituted pyrazole-3-carboxamines as potent inhibitors of human 5-lipoxygenase

AbstractHuman 5-lipoxygenase (5-LOX) is a well-validated drug target and its inhibitors are potential drugs for treating leukotriene-related disorders. Our previous work on structural optimization of the hit compound 2 from our in-house collection identified two lead compounds, 3a and 3b, exhibiting a potent inhibitory profile against 5-LOX with IC50 values less than 1µmol/L in cell-based assays. Here, we further optimized these compounds to prepare a class of novel pyrazole derivatives by opening the fused-ring system. Several new compounds exhibited more potent inhibitory activity than the lead compounds against 5-LOX. In particular, compound 4e not only suppressed lipopolysaccharide-induced inflammation in brain inflammatory cells and protected neurons from oxidative toxicity, but also significantly decreased infarct damage in a mouse model of cerebral ischemia. Molecular docking analysis further confirmed the consistency of our theoretical results and experimental data. In conclusion, the excellent in vitro and in vivo inhibitory activities of these compounds against 5-LOX suggested that these novel chemical structures have a promising therapeutic potential to treat leukotriene-related disorders