Miracle-dz/UniFAR-FTUs huggingface dataset:
How Subtle Changes in Navigation Links Affect Users' Search Strategies

While large scale changes to web pages have been shown to cause initial difficulties for users when they revisit sites, little work has been done to understand how small changes in web pages may affect user performance. Using eye movement data, this study investigated the impact of adding or removing a single shortcut link on the performance of users who visited a site twice. Participants visited a site and performed a search task, and then revisited the site either immediately or three weeks later and performed the same task. The short cut link was either consistently present, consistently absent, or was inconsistently present across the two sessions. Results indicate that in the consistent conditions, users exhibit roughly the same behaviors (page counts and eye scan patterns) in the second task as they did in the first, although they spent less time reading the body text. In the link-present-then-absent (YN) condition, users searching for the missing link looked at the navigation link area significantly more on their second visit than they did on their first visit and clicked to more pages. But they did not spend more time reading the content on the pages, indicating that they were expecting to recognize the target page when they found it. In the link-absent-then-present (NY) condition, almost half of the participants found the new link; there was an overall significant decrease in search times and a nonsignificant decrease in pages counts (due to those who didn't find the shortcut). Eye-tracking data showed a significant decrease in fixation counts in the content area but not the left navigation. As with the YN condition, this decrease in body text fixations suggests that participants spend less time reading the body text on a second visit due to memory of the target page. Overall these results suggest that users maintain a strong memory of link location and page structure, even after delays of up to three weeks after a single use.
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis

Among existing privacy attacks on the gradient of neural networks, \emph{data reconstruction attack}, which reverse engineers the training batch from the gradient, poses a severe threat on the private training data. Despite its empirical success on large architectures and small training batches, unstable reconstruction accuracy is also observed when a smaller architecture or a larger batch is under attack. Due to the weak interpretability of existing learning-based attacks, there is little known on why, when and how data reconstruction attack is feasible. In our work, we perform the first analytic study on the security boundary of data reconstruction from gradient via a microcosmic view on neural networks with rectified linear units (ReLUs), the most popular activation function in practice. For the first time, we characterize the insecure/secure boundary of data reconstruction attack in terms of the \emph{neuron exclusivity state} of a training batch, indexed by the number of \emph{\textbf{Ex}clusively \textbf{A}ctivated \textbf{N}eurons} (ExANs, i.e., a ReLU activated by only one sample in a batch). Intuitively, we show a training batch with more ExANs are more vulnerable to data reconstruction attack and vice versa. On the one hand, we construct a novel deterministic attack algorithm which substantially outperforms previous attacks for reconstructing training batches lying in the insecure boundary of a neural network. Meanwhile, for training batches lying in the secure boundary, we prove the impossibility of unique reconstruction, based on which an exclusivity reduction strategy is devised to enlarge the secure boundary for mitigation purposes.
Topological zeta functions of complex plane curve singularities

We study topological zeta functions of complex plane curve singularities using toric modifications and further developments. As applications of the research method, we prove that the topological zeta function is a topological invariant for complex plane curve singularities, we give a short and new proof of the monodromy conjecture for plane curves.
Real-Time System for Tracking and Classification of Pedestrians and Bicycles

Data on pedestrian and bicycle volumes are necessary for transportation planning, infrastructure design, and traffic management. Nevertheless, such data cannot be collected directly by the commonly used detectors (e.g., inductive loops, sonar, and microwaves). In this study, a pedestrian and bicycle tracking and classification system was developed to detect pedestrians and bicycles with a video camera. This system contained six modules: a video flow capture module, a movement detection module, a shadow removal module, a feature extraction module, a tracking module, and a classification module. The Gaussian mixture model was used to extract moving objects from an image sequence. In the tracking module, the most challenging part of this system, the trajectories were obtained by use of a Kalman filter. To identify pedestrians and bicycles, a backpropagation neural network was used in the classification module. Two other simple but effective algorithms were used to alleviate the negative impacts of shadows and occlusions. The system was tested at three sites under different traffic and environmental conditions. It has been confirmed that the accuracy for pedestrian detection was approximately 85% and the count error rate was less than 13% for bicycles at all test sites. The proposed system is a feasible alternative for the collection of data for nonmotorized travel modes.
Expanding the Colorectal Cancer Biomarkers Based on the Human Gut Phageome

Worldwide, by 2020, colorectal cancer has become the third most common cancer after lung and breast cancer. Phages are strictly host-specific, and this specificity makes them more accurate as biomarkers, but phage biomarkers for colorectal cancer have not been thoroughly explored. ABSTRACT With the increasing prevalence of colorectal cancer (CRC), extending the present biomarkers for the diagnosis of colorectal cancer is crucial. Previous studies have highlighted the importance of bacteriophages in gastrointestinal diseases, suggesting the potential value of gut phageome in early CRC diagnostic. Here, based on 317 metagenomic samples of three discovery cohorts collected from China (Hong Kong), Austria, and Japan, five intestinal bacteriophages, including Fusobacterium nucleatum, Peptacetobacter hiranonis, and Parvimonas micra phages were identified as potential CRC biomarkers. The five CRC enriched bacteriophagic markers classified patients from controls with an area under the receiver-operating characteristics curve (AUC) of 0.8616 across different populations. Subsequently, we used a total of 80 samples from China (Hainan) and Italy for validation. The AUC of the validation cohort is 0.8197. Moreover, to further explore the specificity of the five intestinal bacteriophage biomarkers in a broader background, we performed a confirmatory meta-analysis using two inflammatory bowel disease cohorts, ulcerative colitis (UC) and Crohn's disease (CD). Excitingly, we observed that the five CRC-enriched phage markers also exhibited high discrimination in UC (AUC = 78.02%). Unfortunately, the five CRC-rich phage markers did not show high resolution in CD (AUC = 48.00%). The present research expands the potential of microbial biomarkers in CRC diagnosis by building a more accurate classification model based on the human gut phageome, providing a new perspective for CRC gut phagotherapy. IMPORTANCE Worldwide, by 2020, colorectal cancer has become the third most common cancer after lung and breast cancer. Phages are strictly host-specific, and this specificity makes them more accurate as biomarkers, but phage biomarkers for colorectal cancer have not been thoroughly explored. Therefore, it is crucial to extend the existing phage biomarkers for the diagnosis of colorectal cancer. Here, we innovatively constructed a relatively accurate prediction model, including: three discovery cohorts, two additional validation cohorts and two cross-disease cohorts. A total of five possible biomarkers of intestinal bacteriophages were obtained. They are Peptacetobacter hiranonis Phage, Fusobacterium nucleatum animalis 7_1 Phage, Fusobacterium nucleatum polymorphum Phage, Fusobacterium nucleatum animalis 4_8 Phage, and Parvimonas micra Phage. This study aims at identifying fine-scale species-strain level phage biomarkers for colorectal cancer diseases, so as to expand the existing CRC biomarkers and provide a new perspective for intestinal phagocytosis therapy of colorectal cancer.
The QWERTY/EE concept, Quantifying Recyclability and Eco-Efficiency for End-of-Life Treatment of Consumer Electronic Products

The QWERTY/EE concept addresses recyclability and eco-efficiency of take-back and recycling of consumer electronic products, a topic currently receiving large international attention. Through the environmental part of the concept an alternative for usual weight based recycling percentages is presented. In addition, economic effects of take-back and recycling are included in a quantitative eco-efficiency approach for evaluating technological, design and policy strategies. The approach itself and the valuable insights in recycling of consumer electronic products are highly interesting for policy makers, legislators, product designers, manufacturers, recyclers, take-back system operators and scientists. The concept is applied on a large number of products and scenarios, based on extensive environmental and economic modeling of end-of-life processing. The results show how to set priorities to enhance end-of-life performance by properly aligning policy making, system operation, technology and Design for End-of-Life. From this perspective, the recently enacted European Directives on electronic waste, WEEE, and hazardous substances, RoHS, are reviewed, indicating substantial room for improvement and showing practical directions for future eco-efficient development of take-back and recycling of electronic products.
Commensals Serve as Natural Barriers to Mammalian Cells during Acanthamoeba castellanii Invasion

Acanthamoeba castellanii, an opportunistic protozoan widely present in the environment, can cause Acanthamoeba keratitis and encephalitis in humans. However, only a few reports describe how the ameba acts as an opportunistic pathogen. ABSTRACT Acanthamoeba castellanii is a free-living, pathogenic ameba found in the soil and water. It invades the body through ulcerated skin, the nasal passages, and eyes and can cause blinding keratitis and granulomatous encephalitis. However, the mechanisms underlying the opportunistic pathogenesis of A. castellanii remain unclear. In this study, we observed that commensal bacteria significantly reduced the cytotoxicity of the ameba on mammalian cells. This effect occurred in the presence of both Gram-positive and Gram-negative commensals. Additionally, commensals mitigated the disruption of cell junctions. Ex vivo experiments on mouse eyeballs further showed that the commensals protected the corneal epithelial layer. Together, these findings indicate that A. castellanii is pathogenic to individuals with a dysbiosis of the microbiota at infection sites, further highlighting the role of commensals as a natural barrier during parasite invasion. IMPORTANCE Acanthamoeba castellanii, an opportunistic protozoan widely present in the environment, can cause Acanthamoeba keratitis and encephalitis in humans. However, only a few reports describe how the ameba acts as an opportunistic pathogen. Our study showed that the normal microbiota interfered with the cytotoxicity of Acanthamoeba, persevered during Acanthamoeba invasion, and reduced corneal epithelium peeling in the mouse eyeball model. This suggests that commensals may act as a natural barrier against Acanthamoeba invasion. In future, individuals who suffer from Acanthamoeba keratitis should be examined for microbiota absence or dysbiosis to reduce the incidence of Acanthamoeba infection in clinical settings.
Engineering selectivity of Cutibacterium acnes phages by epigenetic imprinting

Cutibacterium acnes (C. acnes) is a gram-positive bacterium and a member of the human skin microbiome. Despite being the most abundant skin commensal, certain members have been associated with common inflammatory disorders such as acne vulgaris. The availability of the complete genome sequences from various C. acnes clades have enabled the identification of putative methyltransferases, some of them potentially belonging to restriction-modification (R-M) systems which protect the host of invading DNA. However, little is known on whether these systems are functional in the different C. acnes strains. To investigate the activity of these putative R-M and their relevance in host protective mechanisms, we analyzed the methylome of seven representative C. acnes strains by Oxford Nanopore Technologies (ONT) sequencing. We detected the presence of a 6-methyladenine modification at a defined DNA consensus sequence in strain KPA171202 and recombinant expression of this R-M system confirmed its methylation activity. Additionally, a R-M knockout mutant verified the loss of methylation properties of the strain. We studied the potential of one C. acnes bacteriophage (PAD20) in killing various C. acnes strains and linked an increase in its specificity to phage DNA methylation acquired upon infection of a methylation competent strain. We demonstrate a therapeutic application of this mechanism where phages propagated in R-M deficient strains selectively kill R-M deficient acne-prone clades while probiotic ones remain resistant to phage infection. Graphical abstract
Trajectory Planning for Hand-Over between Human and Robot

In the future, there will be many cases where robots should perform cooperative tasks with humans in daily life. In this paper, we focus on a hand-over motion as an example of cooperative work between a human and a robot, and propose an algorithm which enables a robot to perform a human-like motion. First we analyze trajectories and velocity patterns of a hand-over motion performed by two humans. From this experimental results we notice that a receiver’s motion during hand-over has some typical characteristics. We then confirm that a human-like motion can be generated using these characteristics. Finally, we plan the robot’s motion considering these results. Initially, two kinds of potential fields are used to generate a motion command which leads the robot along a trajectory similar to that followed by the human. In addition, more precise motion is considered at the end of the hand-over operation to guarantee accurate positioning and to soften the shock of contact. Experimental results using %link planar robot am show the validity of the proposed method.
Improving antimicrobial prescribing for upper respiratory infections in the emergency department: Implementation of peer comparison with behavioral feedback

Abstract Objective: To reduce inappropriate antibiotic prescribing for acute respiratory infections (ARIs) by employing peer comparison with behavioral feedback in the emergency department (ED). Design: A controlled before-and-after study. Setting: The study was conducted in 5 adult EDs at teaching and community hospitals in a health system. Patients: Adults presenting to the ED with a respiratory condition diagnosis code. Hospitalized patients and those with a diagnosis code for a non-respiratory condition for which antibiotics are or may be warranted were excluded. Interventions: After a baseline period from January 2016 to March 2018, 3 EDs implemented a feedback intervention with peer comparison between April 2018 and December 2019 for attending physicians. Also, 2 EDs in the health system served as controls. Using interrupted time series analysis, the inappropriate ARI prescribing rate was calculated as the proportion of antibiotic-inappropriate ARI encounters with a prescription. Prescribing rates were also evaluated for all ARIs. Attending physicians at intervention sites received biannual e-mails with their inappropriate prescribing rate and had access to a dashboard that was updated daily showing their performance relative to their peers. Results: Among 28,544 ARI encounters, the inappropriate prescribing rate remained stable at the control EDs between the 2 periods (23.0% and 23.8%). At the intervention sites, the inappropriate prescribing rate decreased significantly from 22.0% to 15.2%. Between periods, the overall ARI prescribing rate was 38.1% and 40.6% in the control group and 35.9% and 30.6% in the intervention group. Conclusions: Behavioral feedback with peer comparison can be implemented effectively in the ED to reduce inappropriate prescribing for ARIs.
NATO Code of Best Practice for C2 Assessment - Implications for SISO

In 1999, NATO published a first version of a Code of Best Practice (NCOBP) for Assessment of Command and Control (C2) that is focused on Article V operations, i.e., the analysis of ground forces at a tactical echelon in mid- to high-intensity conflict. It does not cover the extended mission spectrum of the alliance and the associated issues. Therefore, the NATO Research and Technology Organization (RTO) initiated a follow-on action tasking the expert group SAS-026 to develop a new version of the NCOBP that was to focus on operations other than war (OOTW) and the impact of significantly improved information technology and its implications for military organizations and operations. Although the new NCOBP has been developed to offer a broad guidance for the assessment of C2 for the purpose of supporting a variety of decision-makers and the conduct of C2 research, there are at least two of its eight sections dealing with topics and issues of high relevance to the domain of the Simulation Interoperability Standards Organization (SISO). In order to stimulate cooperation and discussion between the C2 analysis community and SISO, this paper provides an overview of the new NCOBP on the basis of the current state of its documentation emphasizing the chapters of special interest to SISO.
Kinematics analysis on a spherical robot

Spherical robot, rolling by altering its barycenter with the inside actuating device, has a spherical or spheroid housing, the motivity of which is supplied by the friction force between the housing and the ground while it rolling. Particular attention is paid to the research of spherical robot in recent years. This paper presents a new omnidirectional bi-driver spherical robot droved by two motors that directly drive the balancer to rotate about two orthogonal axes. The spherical robot is a nonholonomic system with 3 DOF while it rolls on the ground, so the spherical presented in this paper is a nonholonomic under-actuated system, featuring omnidirectional movement, simple configuration, and so on.
Application of Deep Learning Method in Emotional Brain computer Interface

Brain computer Interface (BCI) is attracting increasing attention in neural engineering, which can encode brain signals into control command. In recent years, with computer science growing, deep learning has been gradually applied to BCI system, which greatly increases the precision of target recognition in BCI. This paper first introduces several models commonly used in deep learning, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and Deep Belief Networks (DBN). Then, some research on BCI systems based on deep learning in various application scenarios is listed. In particular, emotional BCI has been widely studied among all kinds of BCI systems based on Electroencephalogram (EEG) signals. This paper also reviews research on the emotion BCI system based on deep learning. Finally, the current research status of deep learning in BCI systems and the factors affecting BCI recognition accuracy are summarized and the future research directions and the trend of emotional BCI system are discussed.
Intelligent Skin Cancer Detection System Based on Convolutional Neural Networks

Skin cancer is one of the most threatening types of cancer and has been rising over the past decade. Traditional skin detection methods can be time-consuming and inefficient. Convolutional neural network (CNN) is a powerful autonomous feature extraction method with high accuracy in diagnosing skin cancer. However, most existing skin cancer detection approaches based on CNN only consider a single model and lack intelligent communication. To address this problem, this paper proposes a skin cancer detection system using three CNNs to provide fast, accurate, intelligent, and diversified detection and provide feedback. The three CNNs used in this paper are VGG16, MobileNet and Inception_resnet_v2. Our System consists of three main components, Skin Image Analysis Module, Medical Chatbot Module, and UI Web Module. In Skin Image Analysis Module, we implement the skin cancer detection function of two types: classifying malign moles and benign moles and classifying 7 classes of Skin lesions. In Medical Chatbot Module, the function of online consultation between users and virtual doctors is realized. In UI Web Module, we provide functionalities including uploading their image of moles for detection. VGG16, MobileNet, and Inception_resnet_v2's accuracies and transfer learning techniques are used on ISIC, and HAM1000 datasets are 0.88, 0.90, and 0.93, respectively. Our system can allow users to upload an image of their mole, chat with a virtual assistant, and receive timely and reliable test results, thereby improving the survival rate of potential patients.
Identification of Small Molecule Inhibitors of the Pathogen Box against Vibrio cholerae

Cholera is a serious infectious disease in tropical regions causing millions of infections annually. Vibrio cholerae, the causative agent of cholera, has gained multi-antibiotic resistance over the years, posing greater threat to public health and current treatment strategies. Here we report two compounds that effectively target the growth of V. cholerae and have the potential to control cholera infection. ABSTRACT Antimicrobial resistance (AMR) has become a serious public and economic threat. The rate of bacteria acquiring AMR surpasses the rate of new antibiotics discovery, projecting more deadly AMR infections in the future. The Pathogen Box is an open-source library of drug-like compounds that can be screened for antibiotic activity. We have screened molecules of the Pathogen Box against Vibrio cholerae, the cholera-causing pathogen, and successfully identified two compounds, MMV687807 and MMV675968, that inhibit growth. RNA-seq analyses of V. cholerae after incubation with each compound revealed that both compounds affect cellular functions on multiple levels including carbon metabolism, iron homeostasis, and biofilm formation. In addition, whole-genome sequencing analysis of spontaneous resistance mutants identified an efflux system that confers resistance to MMV687807. We also identified that the dihydrofolate reductase is the likely target of MMV675968 suggesting it acts as an analog of trimethoprim but with a MIC 14-fold lower than trimethoprim in molar concentration. In summary, these two compounds that effectively inhibit V. cholerae and other bacteria may lead to the development of new antibiotics for better treatment of the cholera disease. IMPORTANCE Cholera is a serious infectious disease in tropical regions causing millions of infections annually. Vibrio cholerae, the causative agent of cholera, has gained multi-antibiotic resistance over the years, posing greater threat to public health and current treatment strategies. Here we report two compounds that effectively target the growth of V. cholerae and have the potential to control cholera infection.