Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking

Deep neural networks have significantly advanced person re-identification (ReID) applications in the realm of the industrial internet, yet they remain vulnerable. Thus, it is crucial to study the robustness of ReID systems, as there are risks of adversaries using these vulnerabilities to compromise industrial surveillance systems. Current adversarial methods focus on generating attack samples using misclassification feedback from victim models (VMs), neglecting VM's cognitive processes. We seek to address this by producing authentic ReID attack instances through VM cognition decryption. This approach boasts advantages like better transferability to open-set ReID tests, easier VM misdirection, and enhanced creation of realistic and undetectable assault images. However, the task of deciphering the cognitive mechanism in VM is widely considered to be a formidable challenge. In this paper, we propose a novel inconspicuous and controllable ReID attack baseline, LCYE (Look Closer to Your Enemy), to generate adversarial query images. Specifically, LCYE first distills VM's knowledge via teacher-student memory mimicking the proxy task. This knowledge prior serves as an unambiguous cryptographic token, encapsulating elements deemed indispensable and plausible by the VM, with the intent of facilitating precise adversarial misdirection. Further, benefiting from the multiple opposing task framework of LCYE, we investigate the interpretability and generalization of ReID models from the view of the adversarial attack, including cross-domain adaption, cross-model consensus, and online learning process. Extensive experiments on four ReID benchmarks show that our method outperforms other state-of-the-art attackers with a large margin in white-box, black-box, and target attacks. The source code can be found at https://github.com/MingjieWang0606/LCYE-attack_reid

Managing tourist congestion: Insights from Chinese package tours to the UK and Ireland

The UNWTO notes that the successful management of tourist congestion is highly dependent on controlling travel demand. It is surprising, therefore, that demand management has been largely overlooked in the tourism literature, as have the roles of both tour operators and package tours in contributing to congestion or overtourism. Tour operators wield considerable power in ‘channelling’ customers to certain destinations and consequently play a major role in contributing to unsustainable mass tourist congestion. This research visualizes the spatial patterns of People’s Republic of China package tour itineraries at peak season to the UK, which is then confirmed by statistical tests. The study confirms the important role of tour operators and package tours in distributing tourists in the UK and in confirming and accentuating its ‘hotspots’. It highlights the power relationships and the spatial dynamism in the formation of overtourism. The study makes recommendations for managing tourist congestion in the post-pandemic world in the UK and elsewhere, largely related to encouraging tour operators and travel agencies to diversify their tourist product offerings

Human nasal wash RNA-Seq reveals distinct cell-specific innate immune responses in influenza versus SARS-CoV-2

BACKGROUND Influenza A virus (IAV) and SARS-CoV-2 are pandemic viruses causing millions of deaths, yet their clinical manifestations are distinctly different. METHODS With the hypothesis that upper airway immune and epithelial cell responses are also distinct, we performed single-cell RNA sequencing (scRNA-Seq) on nasal wash cells freshly collected from adults with either acute COVID-19 or influenza or from healthy controls. We focused on major cell types and subtypes in a subset of donor samples. Results Nasal wash cells were enriched for macrophages and neutrophils for both individuals with influenza and those with COVID-19 compared with healthy controls. Hillock-like epithelial cells, M2-like macrophages, and age-dependent B cells were enriched in COVID-19 samples. A global decrease in IFN-associated transcripts in neutrophils, macrophages, and epithelial cells was apparent in COVID-19 samples compared with influenza samples. The innate immune response to SARS-CoV-2 appears to be maintained in macrophages, despite evidence for limited epithelial cell immune sensing. Cell-to-cell interaction analyses revealed a decrease in epithelial cell interactions in COVID-19 and highlighted differences in macrophage-macrophage interactions for COVID-19 and influenza. Conclusions Our study demonstrates that scRNA-Seq can define host and viral transcriptional activity at the site of infection and reveal distinct local epithelial and immune cell responses for COVID-19 and influenza that may contribute to their divergent disease courses. Funding Massachusetts Consortium on Pathogen Readiness, the Mathers Foundation, and the Department of Defense (W81XWH2110029) COVID-19 Expansion for AIRe Program

High‐Performance Doped Silver Films: Overcoming Fundamental Material Limits for Nanophotonic Applications

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137336/1/adma201605177-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137336/2/adma201605177_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137336/3/adma201605177.pd

Predicting physiological responses of dairy cows using comprehensive variables

peer reviewedHeat stress is increasingly affecting the production, health, and reproduction of dairy cows. Previous studies used limited variables as predictors of physiological responses, and the developed models poorly predict animal responses in evaporatively cooled environments. The aim of this study was to build machine learning models using comprehensive variables to predict physiological responses of dairy cows raised on an actual dairy farm equipped with sprinklers. Four algorithms including random forests, gradient boosting machines, artificial neural networks (ANN), and regularized linear regression were used to predict respiration rate (RR), vaginal temperature (VT), and eye temperature (ET) with 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The classification performance of the predicted values in recognizing individual heat stress states was compared with commonly used thermal indices. The performance on the testing sets shows that the ANN models yielded the lowest root mean squared error for predicting RR (13.24 breaths/min), VT (0.30 °C), and ET (0.29 °C). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high ambient temperature, and low wind speed contributed most to high ET predictions. When determining the ground-truth heat stress state by the actual RR, the best classification performance was yielded by the predicted RR with an accuracy of 77.7%; when determining the ground-truth heat stress state by the actual VT, the best classification performance was yielded by the predicted VT with an accuracy of 75.3%. This study demonstrates the ability of ANN in predicting physiological responses of dairy cows raised on actual farms with access to sprinklers. Adding more predictors other than meteorological parameters into training could increase predictive performance. Recognizing the heat stress state of individual animals, especially those at the highest risk, based on the predicted physiological responses and their interpretations can inform better heat abatement decisions

Visual analysis of lung neuroendocrine tumors based on CiteSpace knowledge graph

ObjectiveThe relevant literatures in the field of pulmonary neuroendocrine tumor were analyzed to understand the lineage, hot spots and development trends of research in this tumor.MethodThe Web of Science core collection was searched for English-language literature about neuroendocrine tumors of the lung published between 2000 and 2022. CiteSpace software was imported for visualization analysis of countries, institutions, co-cited authors and co-cited journals and sorting of high-frequency keywords, as well as co-cited references and keyword co-occurrence, clustering and bursting display.ResultsA total of 594 publications on neuroendocrine tumours of the lung were available, from 2000 to 2022, with an overall upward trend of annual publications in the literature. Authors or institutions from the United States, Italy, Japan and China were more active in this field, but there was little cooperation among the major countries. Co-cited references and keyword co-occurrence and cluster analysis showed that research on diagnostic instruments, pathogenesis, ectopic ACTH signs, staging and prognosis and treatment was a current research hotspot. The keyword bursts suggested that therapeutic approaches might be a key focus of future research into the field for pulmonary neuroendocrine tumors.ConclusionOver these 20 years, research related to neuroendocrine tumors of the lung has increased in fervour, with research on diagnostic instruments, pathogenesis, ectopic ACTH signs, staging and prognosis, and treatment being the main focus of research. Therapeutic treatments may be the future research trend in this field

Evaluation of environmental and physiological indicators in lactating dairy cows exposed to heat stress.

peer reviewedThis study aimed to better understand environmental heat stress and physiological heat strain indicators in lactating dairy cows. Sixteen heat stress indicators were derived using microenvironmental parameters that were measured at the surrounding of cows and at usual fixed locations in the barn by using handheld and fixed subarea sensors, respectively. Twenty high-producing Holstein-Friesian dairy cows (> 30.0 kg/day) from an intensive dairy farm were chosen to measure respiration rate (RR), vaginal temperature (VT), and body surface temperature of forehead (FT), eye (ET), and muzzle (MT). Our results show that microenvironments measured by the handheld sensor were slightly warmer and drier than those measured by the fixed subarea sensor; however, their derived heat stress indicators correlated equally well with physiological indicators. Interestingly, ambient temperature (Ta) had the highest correlations with physiological indicators and the best classification performance in recognizing actual heat strain state. Using segmented mixed models, the determined Ta thresholds for maximum FT, mean FT, RR, maximum ET, mean ET, VT, mean MT, and maximum MT were 24.1 °C, 24.2 °C, 24.4 °C, 24.6 °C, 24.6 °C, 25.3 °C, 25.4 °C, and 25.4 °C, respectively. Thus, we concluded that the fixed subarea sensor is a reliable tool for measuring cows' microenvironments; Ta is an appropriate heat stress indicator; FT, RR, and ET are good early heat strain indicators. The results of this study could be helpful for dairy practitioners in a similar intensive setting to detect and respond to heat strain with more appropriate indicators