Miracle-dz/UniFAR-FTUs huggingface dataset:
Noncircular iris segmentation based on weighted adaptive hough transform using smartphone database

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.
Supplementary Material for EDICT: Exact Diffusion Inversion via Coupled Transformations

We sample from 5 ImageNet classes (African Elephant, Ram, Egyptian Cat, Brown Bear, and Norfolk Terrier, validation set). Four experiments are performed, one swapping the pictured animal’s species to each of the other classes (20 species editing pairs in total), two contextual changes (A [animal] in the snow and A [animal] in a parking lot), and one stylistic (An impressionistic painting of a [animal]). The prompt for the species edit is simply A [animal]. Throughout, base prompts are of form A [animal]. Edits are performed with inversion strength s = 0.8 and steps S = 50. For computing CLIP score in the species example, the 5 text queries are of identical form A [animal]. CLIP text queries for other edits are as follows: A [animal] in the snow
Performance of Perioperative Tasks for Women Undergoing Anti-incontinence Surgery

Objectives Surgery for the correction of stress urinary incontinence is an elective procedure that can have a dramatic and positive impact on quality of life. Anti-incontinence procedures, like inguinal hernia repairs or cholecystectomies, can be classified as high-volume/low-morbidity procedures. The performance of a standard set of perioperative tasks has been suggested as one way to optimize quality of care in elective high-volume/low-morbidity procedures. Our primary objective was to evaluate the performance of 5 perioperative tasks—(1) offering nonsurgical treatment, (2) performance of a standard preoperative prolapse examination, (3) cough stress test, (4) postvoid residual test, and (5) intraoperative cystoscopy for women undergoing surgery for stress urinary incontinence—compared among surgeons with and without board certification in female pelvic medicine and reconstructive surgery (FPMRS). Study Design This study was a retrospective chart review of anti-incontinence surgical procedures performed between 2011 and 2013 at 9 health systems. Cases were reviewed for surgical volume, adverse outcomes, and the performance of 5 perioperative tasks and compared between surgeons with and without FPMRS certification. Results Non-FPMRS surgeons performed fewer anti-incontinence procedures than FPMRS-certified surgeons. Female pelvic medicine and reconstructive surgery surgeons were more likely to perform all 5 perioperative tasks compared with non-FPMRS surgeons. After propensity matching, FPMRS surgeons had fewer patients readmitted within 30 days of surgery compared with non-FPMRS surgeons. Conclusions Female pelvic medicine and reconstructive surgery surgeons performed higher volumes of anti-incontinence procedures, were more likely to document the performance of the 5 perioperative tasks, and were less likely to have their patients readmitted within 30 days.
NANOBIOTECHNOLOGY FOR IMPROVING TRANSDERMAL DRUG DELIVERY

: Skin delivery of certain medical products during treatment of local skin disease is one of the oldest ways to administer medicine, but skin delivery of medical products for transdermal carriers is relatively new and often used. The rapid development of transdermal carriers in the past several years is due to the advantages of transdermic administration over the classic oral administration. The difficulty in transdermic administration is represented by skin penetration of the products, because skin, through it’s nature, acts as a barrier in controlling the loss of water, electrolytes and other substances and also in preventing the penetration of medical substances from the outside environment. In order to enhance skin permeability for transdermal portage of medical substances, several methods, both passive and active, have been developed. This review aims to examine classic and novel modalities for skin delivery of drugs, including the development of dermal/transdermal nanocarriers.
Efficient Participating Media Rendering with Differentiable Regularization

Highly scattering media are common in the real world, e.g . , milk, skin, and cloud. Rendering participating media is challenging, especially for high-order scattering dominant media, since the light might have a large number of scattering events before leaving the surface. Monte Carlo-based methods usually require a long time to produce noise-free results. Based on the observation that lower-albedo media have less noise than higher-albedo media, we propose to reduce the variance of rendered results using differentiable regularization. We first render an image with the low-albedo participating media together with the gradient w.r. t. the albedo and then predict the final rendered image with a low-albedo image and a gradient image via a novel prediction function. To achieve higher quality, we also consider the gradient of the neighboring frames to provide a noise-free gradient image. Ultimately, our method can produce results with much less overall error than equal-time path tracing results.
A BERT-based Explainable System for COVID-19 Misinformation Identification

Misinformation related to COVID-19 can have serious consequences, such as undermining public health efforts to combat the pandemic. To address this problem, many COVID-19 misinformation detection models have been proposed. Most of them focused on classification accuracy, but none provides justifications or explanations for their classification output. In this paper, we present an explainable COVID-19 misin-formation detection system that classifies whether a claim (a sentence) related to COVID-19 is true, false, or partially true, using an existing BERT-based classification model. The sys-tem then provides rationales behind the predictions using the LIME XAI tool, which allow machine learning practitioners to understand in depth how the model works to debug, fine tune, and optimize the model. Furthermore, the system provides explanations for the prediction using relevant sentences extracted from news articles (which serve as the ground truth in the classification process). The sources of the news articles are listed along with a ranking of the credibility of the publisher of each article (e.g., high or medium). These pieces of information explain to end users how a classification decision is reached, what data sources were used to arrive at the decision and whether the data sources are trustworthy. Such information and explanations will instill trust in the end users of the system. To the best of our knowledge, our system is the first explainable fact checking system designed to combat COVID-19 misinformation. We present examples of explain-ability output provided by our system to demonstrate the effectiveness of the above explainability features. The proposed explainability framework can be readily applied to misinfor-mation identification models in other domains, e.g., politics, finance, sports, and entertainment.
Remote Monitoring for Heart Failure Management at Home

Early telemonitoring of weights and symptoms did not decrease heart failure hospitalizations but helped identify steps toward effective monitoring programs. A signal that is accurate and actionable with response kinetics for early re-assessment is required for the treatment of patients at high risk, while signal speci ﬁ cations differ for surveillance of low-risk patients. Tracking of congestion with cardiac ﬁ lling pressures or lung water content has shown most impact to decrease hospitalizations, while multiparameter scores from implanted rhythm devices have identi ﬁ ed patients at increased risk. Algorithms require better personalization of signal thresholds and interventions. The COVID-19 epidemic accelerated transition to remote care away from clinics, preparing for new digital health care platforms to accommodate multiple technologies and empower patients. Addressing inequities will require bridging the digital divide and the deep gap in access to HF care teams, who will not be replaced by technology but by care teams who can embrace it. (
Learning Layout Generation for Virtual Worlds

The emergence of the metaverse leads to rapidly rising demands for generating extensive 3D worlds. We consider that an engaging world is built upon a rational layout of multiple landuse areas (e.g., forest, meadow, farmland, etc). Towards this perspective, we propose a generative model of landuse distribution by learning geographic data. The model is based on transformer architecture that causally generates a 2D map of lan-duse layout, which can condition on spatial and semantic hints if one or both are provided. The model enables diverse layout generation with user controls and layout expansion by extending borders with partial inputs. To generate high-quality and satisfactory layouts, we devise a geometry objective function that supervises the model to perceive layout shapes and regularize the generations with geometric priors. We further devise a planning objective function that supervises the model to perceive progressive composition demands and suppress the generations deviating from controls. For evaluating the spatial distribution of the generations, we train an autoen-coder to embed landuse layouts into vectors so that real and generated data can be compared using the Wasser-stein metric inspired by Fr´echet inception distance.
Mean Field Analysis of Multi-Armed Bandit Games

Much of the classical work on algorithms for multi-armed bandits focuses on rewards that are stationary over time. By contrast, we study multi-armed bandit (MAB) games, where the rewards obtained by an agent also depend on how many other agents choose the same arm (as might be the case in many competitive or cooperative scenarios). Such systems are naturally nonstationary due to the interdependent evolution of agents, and in general MAB games can be intractable to analyze using typical equilibrium concepts (such as perfect Bayesian equilibrium). We introduce a general model of multi-armed bandit games, and study the dynamics of these games under a large system approximation. We investigate conditions under which the bandit dynamics have a steady state we refer to as a mean field steady state (MFSS). In an MFSS, the proportion of agents playing the various arms, called the population profile, is assumed stationary over time; the steady state definition then requires a consistency check that this stationary profile arises from the policies chosen by the agents.We establish the following results in the paper. First, we establish existence of an MFSS under broad conditions. Second, we show under a contraction condition that the MFSS is unique, and that the population profile converges to it from any initial state. Finally, we show that under the contraction condition, MFSS is a good approximation to the behavior of finite systems with many agents. The contraction condition requires that the agent population regenerates sufficiently often, and that the sensitivity of the reward function to the population profile is low enough. Through numerical experiments, we find that in settings with negative externalities among the agents, convergence obtains even when our condition is violated; while in settings with positive externalities among the agents, our condition is tighter.
An Investigation into whether psychopathic traits negatively contribute to moral judgements

High psychopathy and low morality have been associated as potential causes of anti-social and criminal behaviour. Literature suggests that high levels of psychopathic traits have a negative effect on an individual’s ability to make rational moral judgements. Much of this research focuses on offenders, creating a gap within research encompassing the typical population and whether the same affect is observed. It was hypothesised that high levels of psychopathic traits negatively affect moral judgement, however no predictions were made regarding specific traits. A sample of 50 students, ages ranging from 18 to 44 ( M = 22.18), were asked to complete two self-report scales The Triarchic Psychopathy Measure (TriPM) measured traits of meanness, disinhibition, and boldness , providing an overall score for psychopathy. The Moral Foundations Questionnaire (MFQ) measured the dimensions harm , fairness , authority , ingroup , and purity , providing an overall score for moral judgement. There was a negative correlation between psychopathy and moral values ( r = -.29, p = .04), evidence that higher levels of psychopathic traits can inhibit rational moral judgements. Meanness was negatively correlated with harm ( r = -.59, p = .0) and fairness ( r =-.28, p = .5), suggesting that care-based judgements are hindered when an individual displays a higher level of meanness. No other significant relationships were observed highlighting the role of meanness and the importance of care-relative moral judgements. Limitations of the study are discussed, providing suggestions for future research. Overall, the study supports that psychopathic traits can negatively contribute to moral judgement, extending this finding to the typical population also.
A Watertight Surface Reconstruction Method for CAD Models Based on Optimal Transport

Feature-preserving mesh reconstruction from point clouds is a challenging task. Implicit methods tend to fit smooth surfaces and cannot reconstruct sharp features. Explicit reconstruction methods are sensitive to noise and interpolate sharp features only when the points are distributed on the feature lines. We propose a water-tight surface reconstruction method based on optimal transport, which can faithfully reconstruct sharp features that are often present in CAD models. We formalize the surface reconstruction problem as minimizing the optimal transport cost between the point cloud and the reconstructed surface. The algorithm consists of initialization and refinement steps. In the initialization step, the convex hull of the point cloud is deformed under the guidance of the transport plan to obtain an initial approximate surface. Next, the mesh surface is optimized using operations including vertex relocation and edge collapses/flips to obtain feature-preserving re-sults. Experiments show that our method can preserve sharp features while being robust to noise and missing data.
A Compact 70–86 GHz Bandwidth Frequency Quadrupler with Transformer-Based Harmonic Reflectors in 28nm CMOS

A frequency quadrupler based on cascaded push-push frequency doublers is presented in this work. Push-push frequency doublers suffer from limited power efficiency and conversion gain, mainly due to second-harmonic feedback. Conventional harmonic reflectors minimize this undesired feedback introducing a common-mode second-harmonic resonance, at the price of increased area and reduced bandwidth. In this design the harmonic reflector is embedded into the input matching network, resulting in a more compact design. The coupling coefficient between the multiple windings of the transformer secondary is used to decouple the differential-mode inductance from the common-mode inductance, that acts as a wideband harmonic reflector. A common-gate transistor is stacked with the push-push pair to further boost the output power while reusing the same current. Two push-push frequency doublers are cascaded without additional power amplification stages. The quadrupler, implemented in 28nm CMOS, achieves a peak output power of 0 dBm and peak power efficiency of 50% at 77 GHz and the 3-dB bandwidth is from 70 to 86 GHz.
Effect of the Electric Field on the Biomineralization of Collagen

Collagen/hydroxyapatite hybrids are promising biomimetic materials that can replace or temporarily substitute bone tissues. The process of biomineralization was carried out through a double diffusion system. The methodological principle consisted in applying an electric field on the incubation medium to promote the opposite migration of ions into collagen membranes to form hydroxyapatite (HA) on the collagen membrane. Two physically separated solutions were used for the incubation medium, one rich in phosphate ions and the other in calcium ions, and their effects were evaluated against the traditional mineralization in Simulated Body Fluid (SBF). Pre-polarization of the organic membranes and the effect of incubation time on the biomineralization process were also assessed by FTIR and Raman spectroscopies.Our results demonstrated that the membrane pre-polarization significantly accelerated the mineralization process on collagen. On the other side, it was found that the application of the electric field influenced the collagen structure and its interactions with the mineral phase. The increment of the mineralization degree enhanced the photoluminescence properties of the collagen/HA materials, while the conductivity and the dielectric constant were reduced. These results might provide a useful approach for future applications in manufacturing biomimetic bone-like materials.
A meta-analysis of the moderating role of prior learning experience and mandatory participation on factors influencing MOOC learners’ continuance intention

Retaining learners has been an important issue for massive open online course (MOOC) platforms. Given the different, and even contradictory, conclusions in studies on the continuance intention of MOOC learners, this study selected 53 highly correlated empirical studies published from 2008 to 2022 and constructed a research model based on visual knowledge map analysis. Meta-analysis was applied to identify the key factors, and subgroup analysis was conducted to explore the moderating effect of mandatory participation and prior learning experience. The results show that attitude and satisfaction play the most significant role. Perceived usefulness, perceived ease of use, confirmation, social influence, perceived enjoyment, outcome expectation, self-efficacy and task-technology fit all play essential functions, while the direct impact of social presence requires further research. Prior learning experience and mandatory participation have moderating effects on perceived usefulness. MOOC developers should make more efforts and improvements in content quality, social quality and service quality. Implications for practice or policy: Learners’ continuance intention can be enhanced by improving individual perceived positive feelings related to MOOCs and individual satisfaction with MOOC platforms. Directors of mandatory courses in MOOCs should place greater emphasis on improving learners’ perceived ease of use of MOOC platforms. Superintendents of MOOC platforms need to be aware of the role of perceived usefulness of learners with less prior learning experience in their continuance intention.
Antibacterial Activity of Eight Medicinal Plants from the Traditional Pharmacopoeia of Niger

The emergence of multidrug bacterial resistance poses a great public health problem and requires a constant search for new antibacterial agents. However, Niger's flora possesses several medicinal plants used in traditional medicine to cure infectious diseases and can be used as sources of bioactive ingredients. This current study was designed to evaluate the antibacterial activity of eight plants used in the traditional pharmacopeia of Niger. The extracts were prepared by maceration using ethanol, methanol, and distilled water. The obtained extracts were screened against Salmonella spp. , Shigella spp. , and Escherichia coli using the microdilution method coupled with a resazurin-based assay. Phytochemical screening was performed using colorimetry, while the quantification of total polyphenols, total flavonoids, and total tannins was determined by spectrophotometry. Out of the eight plants obtained, five named Cassia italica, Limeum pterocarpum, Phyllanthus pentandrus, Strychnos innocua, and Ximenia americanum exhibited antibacterial activity with MICs ranging from 500 μg/mL to 2000 μg/mL. Phytochemical screening showed the presence of alkaloids, saponosides, tannins, flavonoids, terpenes/sterols, quinones, and polyphenols. The ethanolic and methanolic extracts of X. americana contained important quantities of total polyphenols, with 43.59 ± 0.15 and 41.97 ± 0.02 mg EAG/100 mg of extract, respectively. These extracts showed the highest contents of total tannins at 46.49 g/L and 45.52 g/L, respectively. For total flavonoids, the highest content was obtained with the methanolic extract of P. pentandrus, with 3.12 ± 0.01 mg QE/100 mg of extract. These findings justify the uses of these plants in traditional medicine for the treatment of infectious diseases such as diarrhea and can be used as starting points for the development of phytodrugs against infectious diarrhea.