Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study

Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel. In such setting, physical attacks that directly provide a malicious input through the input channel pose a bigger threat. We investigate the feasibility of conducting real-time physical attacks on face recognition systems using adversarial light projections. A setup comprising a commercially available web camera and a projector is used to conduct the attack. The adversary uses a transformation-invariant adversarial pattern generation method to generate a digital adversarial pattern using one or more images of the target available to the adversary. The digital adversarial pattern is then projected onto the adversary’s face in the physical domain to either impersonate a target (impersonation) or evade recognition (obfuscation). We conduct preliminary experiments using two open-source and one commercial face recognition system on a pool of 50 subjects. Our experimental results demonstrate the vulnerability of face recognition systems to light projection attacks in both white-box and black-box attack settings.
Magnetic braking at work in binaries

Context. In earlier papers, we aimed to reconstruct the progenitor systems of Algol-type semi-detached binaries. To this end, we developed a binary evolutionary code for the purpose of reproducing the orbital parameters, masses, and location in the HRD of well-observed Algol systems. In this code, the effects of mass and angular momentum losses and tidal coupling were included, but not magnetic braking at that point. In the present paper, we study the effects of magnetic braking on the rotation of the mass gainers in these systems. Aims. Equatorial velocities have been measured for a number of mass-gaining stars in interacting binaries. Tides tend to synchronize the rotation of the gainer, but many observed low equatorial velocities cannot be explained by tidal interactions alone. Methods. We added magnetic braking to our code to better reproduce the observed equatorial velocities. Results. Large equatorial velocities of mass-gaining stars are lowered by tidal interaction and magnetic braking. Tides are mainly at work at short orbital periods, leaving magnetic braking alone at work during longer orbital periods. Conclusions. Slow rotation of mass gainers in Algol-type binaries is mostly well reproduced by our code. However, (not observed) critical rotation of the gainer in some systems cannot be avoided by our calculations.
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm

Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random. We then did a "Region of Interest" (RoI) Pooling of the stock time-series graphs in order to predict future prices with existing ones. Generative Adversarial Network (GAN) is then used based on a competing pair of supervised learning algorithms, to regenerate future stock price projections on a real-time basis. The supervised learning algorithm used in this research, moreover, is original to this study and will have wider uses. With the ensemble of these algorithms, we are able to identify, to what extent, each specific macroeconomic factor influences the change of the Brownian/random market movement. In addition, our model will have a wider influence on the predictions of other Brownian movements.
A Recommender System of Extended Reality Experiences

Interest in Extended Reality (XR) technologies - such as virtual, augmented, and mixed reality - has increased, due to the opportunities offered by such technologies to provide users with live immersive digital experiences. In particular, these technologies for simulations have been widely used in recent years in entertainment and training industries, but have often been limited by the predictability of the simulation, and the lack of personalization in terms of simulation suggestions. This project proposes an Extended Reality simulator that uses Artificial Intelligence to suggest Extended Reality experiences (or scenarios) to users, through a collaborative filtering approach. This paper gives an overview of the extended reality simulators currently available, as well as the challenges involved, and describes how the proposed system resolves those challenges. It then illustrates the components of the developed software platform and investigates a collaborative filtering item-based recommendation system as a possible approach to propose Extended Reality experiences. The usage of this simulator for professional training can be highly valuable: the simulator will recommend personalized extended reality training experiences to the user, facilitating the learning of new skills.
Leaf Species Identification Using Multi Texton Histogram and Support Vector Machine

Studies in the identification and classification of plants are a very important task to be able to extract plant information. One of the important parts that include information of a plant is the leaf. A lot of research has been done to identify the species of plant based on the shape, texture, and other features of the leaf. In this paper, we discussed how to identify plant species based on leaves using the Multi Texton Histogram (MTH) method which combines two histogram values derived from color histograms and histogram orientation of the edge of a single into a single histogram. The classification model used is the Support Vector Machine (SVM) method. The classification result of 32 leaf species using SVM shows the accuracy of 91.10% in Multiclass One vs One and 89.90% in Multiclass One vs All which processed by SVM quadratic kernel. These results indicate that the use of MTH and SVM is effective in identifying leaf images from Flavia dataset.
LB+Trees

3DXPoint memory is the first commercially available NVM solution targeting mainstream computer systems. While 3DXPoint conforms to many assumptions about NVM in previous studies, we observe a number of distinctive features of 3DXPoint. For example, the number of modified words in a cache line does not affect the performance of 3DXPoint writes. This enables a new type of optimization: performing more NVM word writes per line in order to reduce the number of NVM line writes. We propose LB+-Tree, a persistent B+-Tree index optimized for 3DXPoint memory. LB+-Tree nodes are 256B or a multiple of 256B, as 256B is the internal data access size in 3DXPoint memory. We propose three techniques to improve LB+-Tree's insertion performance: (i) Entry moving, which reduces the number of NVM line writes for insertions by creating empty slots in the first line of a leaf node; (ii) Logless node split, which uses NAW (NVM Atomic Write) to reduce logging overhead; and (iii) Distributed headers, which makes (i) and (ii) effective for multi-256B nodes. Theoretical analysis shows that entry moving reduces the number of NVM line writes per insertion of the traditional design by at least 1.35x in a stable tree. Our micro-benchmark experiments on a real machine equipped with 3DXPoint memory shows that LB+-Tree achieves up to 1.12--2.92x speedups over state-of-the-art NVM optimized B+-Trees for insertions while obtaining similar search and deletion performance. Moreover, we study the benefits of LB+-Tree in two real-world systems: X-Engine, a commercial OLTP storage engine, and Memcached, an open source key-value store. X-Engine and Memcached results confirm our findings in the micro-benchmarks.
A Robust Method for Thai License Plate Recognition

License plate recognition (LPR) was proposed in various techniques for improving the accuracy rate. The LPR research is a challenging subject due to license plates generally being in distinctive colors, using distinctive text styles, formats, and language. Most methods acquire a high accuracy rate only when testing with a frontal view of a license plate image. In the real world of LPR applications, the image can be taken from any perspective not only that of the traditional method. Our approach has successfully handled the distortion of perspective images by combining the use of the EAST model and text recognition using Bi-LSTM with CTC loss through the rotation of the license plate. The experiments were carried out on an image that is taken from various angles of orientation which improve the accuracy from 28% to 90%.
Trust and Distrust as Artifacts of Language: A Latent Semantic Approach to Studying Their Linguistic Correlates

Trust and distrust are crucial aspects of human interaction that determine the nature of many organizational and business contexts. Because of socialization-borne familiarity that people feel about others, trust and distrust can influence people even when they do not know each other. Allowing that some aspects of the social knowledge that is acquired through socialization is also recorded in language through word associations, i.e., linguistic correlates, this study shows that known associations of trust and distrust can be extracted from an authoritative text. Moreover, the study shows that such an analysis can even allow a statistical differentiation between trust and distrust—something that survey research has found hard to do. Specifically, measurement items of trust and related constructs that were previously used in survey research along with items reflecting distrust were projected onto a semantic space created out of psychology textbooks. The resulting distance matrix of those items was analyzed by applying covariance-based structural equation modeling. The results confirmed known trust and distrust relationship patterns and allowed measurement of distrust as a distinct construct from trust. The potential of studying trust theory through text analysis is discussed.
Biological Stress Reactivity and Introspective Sensitivity: An Exploratory Study

Reaction to stressful events has an impact on several cognitive processes. High levels of stress can be detrimental to working memory, attention and decision-making. Here, we investigated whether individuals’ reactivity to stress is related to their introspective sensitivity (i.e., how well individuals monitor their own cognitive processes). To this aim, 27 participants (16 women, mean 20 years old) were exposed to a psychosocial stress protocol (trier social stress test, TSST), where individuals were asked to simulate a job interview and perform arithmetic calculations in front of a panel of experts. The salivary cortisol concentration, which is considered a hormonal index of stress reactivity, was collected during the TSST through the enzyme immunoassay DRG cortisol ELISA kit. Based on literature recommendations, we classified participants as responders and non-responders to the TSST. In a second session, through a visual search paradigm, we evaluated the introspective sensitivity of the participants. We evaluated how these individuals (i) monitor their own performance (through a confidence estimation), (ii) monitor their own attentional shifts (through a subjective number of scanned items estimation, SNSI), and (iii) monitor their own response times (through an introspective response time estimation, iRT). We found that individuals with lower biological reactivity to stress are more accurate in estimating their SNSI (p = 0.033) and iRT (p = 0.002), and in evaluating their own performance (p = 0.038) through their confidence. We argue that the effect of stress on introspection is not limited to a particular type of introspective evaluation, but rather consists of a general alteration of the introspective mechanism.
Twice-Daily Theta Burst Stimulation of the Dorsolateral Prefrontal Cortex Reduces Methamphetamine Craving: A Pilot Study

Objectives Transcranial magnetic stimulation (TMS) holds potential promise as a therapeutic modality for disorders of addiction. Our previous findings indicate that high-frequency repetitive transcranial magnetic stimulation (rTMS) over the left dorsal–lateral prefrontal cortex (DLPFC) and low-frequency rTMS over the right DLPFC can reduce drug craving for methamphetamine. One major issue with rTMS is the duration of treatment and hence potential dropout rate. Theta burst stimulation (TBS) has been recently shown to be non-inferior relative to repetitive transcranial magnetic stimulation for major depression. Here, we aim to compare the clinical efficacy and tolerability of intermittent and continuous theta burst stimulation protocols targeting left or right dorsolateral prefrontal cortex on methamphetamine craving in abstinent-dependent subjects. Methods In this randomized single-blind pilot study, 83 abstinent methamphetamine-dependent subjects from a long-term residential treatment program were randomly allocated into three groups: intermittent theta burst stimulation (iTBS) over the left DLPFC (active group), continuous theta burst stimulation (cTBS) over the left DLPFC (active control group), or cTBS over the right DLPFC (active group) was administered twice daily over 5 days for a total of 10 sessions. We measured the primary outcome of cue-induced craving and secondarily sleep quality, depression, anxiety, impulsivity scores, and adverse effects. Results We show a pre- vs. postintervention effect on craving, which, on paired t tests, showed that the effect was driven by iTBS of the left DLPFC and cTBS of the right DLPFC, reducing cue-induced craving but not cTBS of the left DLPFC. We did not show the critical group-by-time interaction. The secondary outcomes of depression, anxiety, and sleep were unrelated to the improvement in craving in the left iTBS and right cTBS group. In the first two sessions, self-reported adverse effects were higher with left iTBS when compared to right cTBS. The distribution of craving change suggested greater clinical response (50% improvement) with right cTBS and a bimodal pattern of effect with left iTBS, suggesting high interindividual variable response in the latter. Conclusion Accelerated twice-daily TBS appears feasible and tolerable at modulating craving and mood changes in abstinent methamphetamine dependence critically while reducing session length. We emphasize the need for a larger randomized controlled trial study with a sham control to confirm these findings and longer duration of clinically relevant follow-up. Clinical Trial Registration Chinese Clinical Trial Registry number, 17013610.
Regulation of Bottromycin Biosynthesis Involves an Internal Transcriptional Start Site and a Cluster-Situated Modulator

Bottromycin is a ribosomally synthesized and post-translationally modified peptide (RiPP) produced by several streptomycetes, including the plant pathogen Streptomyces scabies. There is significant interest in this molecule as it possesses strong antibacterial activity against clinically relevant multidrug resistant pathogens and is structurally distinct from all other antibiotics. However, studies into its efficacy are hampered by poor yields. An understanding of how bottromycin biosynthesis is regulated could aid the development of strategies to increase titres. Here, we use 5′-tag-RNA-seq to identify the transcriptional organization of the gene cluster, which includes an internal transcriptional start site that precedes btmD, the gene that encodes the bottromycin precursor peptide. We show that the gene cluster does not encode a master regulator that controls pathway expression and instead encodes a regulatory gene, btmL, which functions as a modulator that specifically affects the expression of btmD but not genes up- or downstream of btmD. In order to identify non-cluster associated proteins involved in regulation, proteins were identified that bind to the main promoter of the pathway, which precedes btmC. This study provides insights into how this deceptively complex pathway is regulated in the absence of a pathway specific master regulator, and how it might coordinate with the central metabolism of the cell.
Gene Regulatory Network Homoplasy Underlies Recurrent Sexually Dimorphic Fruit Fly Pigmentation

Traits that appear discontinuously along phylogenies may be explained by independent origins (homoplasy) or repeated loss (homology). While discriminating between these models is difficult, the dissection of gene regulatory networks (GRNs) which drive the development of such repeatedly occurring traits can offer a mechanistic window on this fundamental problem. The GRN responsible for the male-specific pattern of Drosophila (D.) melanogaster melanic tergite pigmentation has received considerable attention. In this system, a metabolic pathway of pigmentation enzyme genes is expressed in spatial and sex-specific (i.e., dimorphic) patterns. The dimorphic expression of several genes is regulated by the Bab transcription factors, which suppress pigmentation enzyme expression in females, by virtue of their high expression in this sex. Here, we analyzed the phylogenetic distribution of species with male-specific pigmentation and show that this dimorphism is phylogenetically widespread among fruit flies. The analysis of pigmentation enzyme gene expression in distantly related dimorphic and monomorphic species shows that dimorphism is driven by the similar deployment of a conserved metabolic pathway. However, sexually dimorphic Bab expression was found only in D. melanogaster and its close relatives. These results suggest that dimorphism evolved by parallel deployment of differentiation genes, but was derived through distinct architectures at the level of regulatory genes. This work demonstrates the interplay of constraint and flexibility within evolving GRNs, findings that may foretell the mechanisms of homoplasy more broadly.
Volatilomes of Bacterial Infections in Humans

Sense of smell in humans has the capacity to detect certain volatiles from bacterial infections. Our olfactory senses were used in ancient medicine to diagnose diseases in patients. As humans are considered holobionts, each person’s unique odor consists of volatile organic compounds (VOCs, volatilome) produced not only by the humans themselves but also by their beneficial and pathogenic micro-habitants. In the past decade it has been well documented that microorganisms (fungi and bacteria) are able to emit a broad range of olfactory active VOCs [summarized in the mVOC database (http://bioinformatics.charite.de/mvoc/)]. During microbial infection, the equilibrium between the human and its microbiome is altered, followed by a change in the volatilome. For several decades, physicians have been trying to utilize these changes in smell composition to develop fast and efficient diagnostic tools, particularly because volatiles detection is non-invasive and non-destructive, which would be a breakthrough in many therapies. Within this review, we discuss bacterial infections including gastrointestinal, respiratory or lung, and blood infections, focusing on the pathogens and their known corresponding volatile biomarkers. Furthermore, we cover the potential role of the human microbiota and their volatilome in certain diseases such as neurodegenerative diseases. We also report on discrete mVOCs that affect humans.
Short-Term Amoxicillin-Induced Perturbation of the Gut Microbiota Promotes Acute Intestinal Immune Regulation in Brown Norway Rats

The intestinal gut microbiota is essential for maintaining host health. Concerns have been raised about the possible connection between antibiotic use, causing microbiota disturbances, and the increase in allergic and autoimmune diseases observed during the last decades. To elucidate the putative connection between antibiotic use and immune regulation, we have assessed the effects of the antibiotic amoxicillin on immune regulation, protein uptake, and bacterial community structure in a Brown Norway rat model. Daily intra-gastric administration of amoxicillin resulted in an immediate and dramatic shift in fecal microbiota, characterized by a reduction of within sample (α) diversity, reduced variation between animals (β diversity), increased relative abundance of Bacteroidetes and Gammaproteobacteria, with concurrent reduction of Firmicutes, compared to a water control group. In the small intestine, amoxicillin also affected microbiota composition significantly, but in a different way than observed in feces. The small intestine of control animals was vastly dominated by Lactobacillus, but this genus was much less abundant in the amoxicillin group. Instead, multiple different genera expanded after amoxicillin administration, with high variation between individual animals, thus the small intestinal α and β diversity were higher in the amoxicillin group compared to controls. After 1 week of daily amoxicillin administration, total fecal IgA level, relative abundance of small intestinal regulatory T cells and goblet cell numbers were higher in the amoxicillin group compared to controls. Several bacterial genera, including Escherichia/Shigella, Klebsiella (Gammaproteobacteria), and Bifidobacterium, for which the relative abundance was higher in the small intestine in the amoxicillin group than in controls, were positively correlated with the fraction of small intestinal regulatory T cells. Despite of epidemiologic studies showing an association between early life antibiotic consumption and later prevalence of inflammatory bowel diseases and food allergies, our findings surprisingly indicated that amoxicillin-induced perturbation of the gut microbiota promotes acute immune regulation. We speculate that the observed increase in relative abundance of small intestinal regulatory T cells is partly mediated by immunomodulatory lipopolysaccharides derived from outgrowth of Gammaproteobacteria.