Familiarity-based Collaborative Team Recognition in Academic Social Networks

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose MOTO (faMiliarity-based cOllaborative Team recOgnition algorithm) to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale data set. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution

A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only consider the invisibility in the spatial domain without explicitly accounting for the generation of invisible triggers in the frequency domain, making the generated poisoned images be easily detected by recent defense methods. To address this issue, in this paper, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Discrete Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, the proposed DUBA adopts a novel attack strategy, in which the model is trained with weak triggers and attacked with strong triggers to further enhance the attack performance and stealthiness. We extensively evaluate DUBA against popular image classifiers on four datasets. The results demonstrate that it significantly outperforms the state-of-the-art backdoor attacks in terms of the attack success rate and stealthinessComment: 10 pages, 7 figures. Submit to ACM MM 202

Ketamine Inhibits Lung Fluid Clearance through Reducing Alveolar Sodium Transport

Ketamine is a broadly used anaesthetic for analgosedation. Accumulating clinical evidence shows that ketamine causes pulmonary edema with unknown mechanisms. We measured the effects of ketamine on alveolar fluid clearance in human lung lobes ex vivo. Our results showed that intratracheal instillation of ketamine markedly decreased the reabsorption of 5% bovine serum albumin instillate. In the presence of amiloride (a specific ENaC blocker), fluid resolution was not further decreased, suggesting that ketamine could decrease amiloride-sensitive fraction of AFC associated with ENaC. Moreover, we measured the regulation of amiloride-sensitive currents by ketamine in A549 cells using whole-cell patch clamp mode. Our results suggested that ketamine decreased amiloride-sensitive Na+ currents (ENaC activity) in a dose-dependent fashion. These data demonstrate that reduction in lung ENaC activity and lung fluid clearance following administration of ketamine may be the crucial step of the pathogenesis of resultant pulmonary edema

Learning World Models with Identifiable Factorization

Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines

Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propose an end-to-end beamforming network for 2D location guided speech separation merely given mixture signal. It first estimates discriminable direction and 2D location cues, which imply directions the sources come from in multi views of microphones and their 2D coordinates. These cues are then integrated into location-aware neural beamformer, thus allowing accurate reconstruction of two sources' speech signals. Experiments show that our proposed model not only achieves a comprehensive decent improvement compared to baseline systems, but avoids inferior performance on spatial overlapping cases.Comment: Accepted by Interspeech 2023. arXiv admin note: substantial text overlap with arXiv:2212.0340

Evaluation of the performance of a dengue outbreak detection tool for China

An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.This work was supported by the grants from Research and Promotion of Key Technology on Health Emergency Preparation and Dispositions (201202006), the National Key Science and Technology Project on Infectious Disease Surveillance Technique Platform of China (2012ZX10004-201) and Development of Early Warning Systems for Dengue Fever Based on Socio-ecological Factors (NHMRC APP1002608)

The epidemiology of Plasmodium vivax and Plasmodium falciparum malaria in China, 2004–2012: from intensified control to elimination

BACKGROUND In China, the national malaria elimination programme has been operating since 2010. This study aimed to explore the epidemiological changes in patterns of malaria in China from intensified control to elimination stages. METHODS Data on nationwide malaria cases from 2004 to 2012 were extracted from the Chinese national malaria surveillance system. The secular trend, gender and age features, seasonality, and spatial distribution by Plasmodium species were analysed. RESULTS In total, 238,443 malaria cases were reported, and the proportion of Plasmodium falciparum increased drastically from <10% before 2010 to 55.2% in 2012. From 2004 to 2006, malaria showed a significantly increasing trend and with the highest incidence peak in 2006 (4.6/100,000), while from 2007 onwards, malaria decreased sharply to only 0.18/100,000 in 2012. Males and young age groups became the predominantly affected population. The areas affected by Plasmodium vivax malaria shrunk, while areas affected by P. falciparum malaria expanded from 294 counties in 2004 to 600 counties in 2012. CONCLUSIONS This study demonstrated that malaria has decreased dramatically in the last five years, especially since the Chinese government launched a malaria elimination programme in 2010, and areas with reported falciparum malaria cases have expanded over recent years. These findings suggest that elimination efforts should be improved to meet these changes, so as to achieve the nationwide malaria elimination goal in China in 2020.This study was supported by grants from the Ministry of Science and Technology of China (2012ZX10004-201, 2012ZX10004-220) and the Ministry of Health of China (No. 201202006), and China UK Global Health Support Programme (grant no. GHSP-CS-OP1). S.I.H. is funded by a Senior Research Fellowship from the Wellcome Trust (#095066). S.I.H. also acknowledges funding support from the RAPIDD programme of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health

Digital photoprogramming of liquid-crystal superstructures featuring intrinsic chiral photoswitches

Dynamic patterning of soft materials in a fully reversible and programmable manner with light enables applications in anti-counterfeiting, displays and labelling technology. However, this is a formidable challenge due to the lack of suitable chiral molecular photoswitches. Here, we report the development of a unique intrinsic chiral photoswitch with broad chirality modulation to achieve digitally controllable, selectable and extractable multiple stable reflection states. An anti-counterfeiting technique, embedded with diverse microstructures, featuring colour-tunability, erasability, reversibility, multi-stability and viewing-angle dependency of pre-recorded patterns, is established with these photoresponsive superstructures. This strategy allows dynamic helical transformation from the molecular and supramolecular to the macroscopic level using light-activated intrinsic chirality, demonstrating the practicality of photoprogramming photonics