Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Prior-Guided Face Super-Resolution: A New Paradigm With Facial Component Prior

Recently, facial priors (e.g., facial parsing maps and facial landmarks) have been widely employed in prior-guided face super-resolution (FSR) because it provides the location of facial components and facial structure information, and helps predict the missing high-frequency (HF) information. However, most existing approaches suffer from two shortcomings: 1) the extracted facial priors are inaccurate since they are extracted from low-resolution (LR) or low-quality super-resolved (SR) face images and 2) they only consider embedding facial priors into the reconstruction process from LR to SR face images, thus failing to explore facial priors to generate LR face image. In this article, we propose a novel pre-prior guided approach that extracts facial prior information from original high-resolution (HR) face images and embeds them into LR ones to obtain HF information-rich LR face images, thereby improving the performance of face reconstruction. Specifically, a novel component hybrid method is proposed, which fuses HR facial components and LR facial background to generate new LR face images (namely, LRmix) via facial parsing maps extracted from HR face images. Furthermore, we design a component hybrid network (CHNet) that learns the LR to LRmix mapping function to ensure that the LRmix can be obtained from LR face images in testing and real-world datasets. Experimental results show that our proposed scheme significantly improves the reconstruction performance for FSR.
Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.
Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data

Abstract This paper presents the application of machine learning algorithms to identify pigs’ behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig’s back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for “eating,” 0.99 for “lying,” 0.93 for “walking,” and 0.91 for “standing” behaviors. The optimal WS was 7 s for “eating” and “lying,” and 3 s for “walking” and “standing. ” The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.
Frequency-Tuned Universal Adversarial Attacks on Texture Recognition

Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored. As part of our work, we find that limiting the perturbation’s $l_{p}$ norm in the spatial domain may not be a suitable way to restrict the perceptibility of universal adversarial perturbations for texture images. Based on the fact that human perception is affected by local visual frequency characteristics, we propose a frequency-tuned universal attack method to compute universal perturbations in the frequency domain. Our experiments indicate that our proposed method can produce less perceptible perturbations yet with a similar or higher white-box fooling rates on various DNN texture classifiers and texture datasets as compared to existing universal attack techniques. We also demonstrate that our approach can improve the attack robustness against defended models as well as the cross-dataset transferability for texture recognition problems.
Monadology, Information, and Physics Part 3 (revised): Inertia and Gravity

In Part 3, I will discuss the problems of inertia and gravity in Leibniz, and present three conjectures: (I) If Leibniz were really ready to insist on relativity, he would have to assert the relativity of inertial motion. (II) In Leibniz’s theories of dynamics and geometry, there was a struggle between his predilection for straight line and his adherence to an optimality principle. (III) Gravity, as well as inertia, can be considered as a universal feature of the world, so that the foundation of both may have a common root. Further, drawing on the results in Part 1 and Part 2, I will argue for the need of a unified interpretation of Leibniz’s metaphysics and dynamics. My three conjectures as regards Leibniz’s possible treatment of inertia and gravity are proposed along a unified interpretation, in terms of my informational reconstruction of Leibniz’s philosophy. Finally, the most important features of the informational interpretation are summarized. The synopsis of the whole paper (Part 1, 2, and 3) is added.
Unconventional Stereoerror Formation Mechanisms in Nonmetallocene Propene Polymerization Systems Revealed by DFT Calculations

An unconventional mechanism for the stereoerror formation in propene polymerization catalyzed by C1-symmetric salalen-M systems (M = Zr, Hf) is suggested by DFT calculations. While propagation happens with the ligand in its fac-mer conformation, a change of ligand wrapping mode from fac-mer to fac-fac is the main source of the lower stereoselectivities obtained with Zr and Hf. This is different for the Ti analogues, where the ligand fac-mer wrapping mode does not play a role. Activation strain analysis indicates that the preference for a chain stationary mechanism of the Zr/Hf species is due to the energy required to distort the reactants (ΔEStrain) rather than to their mutual interaction (ΔEInt).
Destigmatizing Borderline Personality Disorder: A Call to Action for Psychological Science

Despite recognition that borderline personality disorder (BPD) is one of the most stigmatized psychological disorders, destigmatization efforts have thus far focused on the views and actions of clinicians and the general public, neglecting the critical role that psychological science plays in perpetuating or mitigating stigma. This article was catalyzed by recent concerns about how research and editorial processes propagate stigma and thereby fail people with BPD and the scientists who study BPD. We provide a brief overview of the BPD diagnosis and its history. We then review how BPD has been stigmatized in psychological science, the gendered nature of BPD stigma, and the consequences of this stigmatization. Finally, we offer specific recommendations for researchers, reviewers, and editors who wish to use science to advance our understanding of BPD without perpetuating pejorative views of the disorder. These recommendations constitute a call to action to use psychological science in the service of the public good.
Cell-Mediated Immune Responses of Healthy Laboratory Volunteers to Sonicate Antigens Prepared from the Most Prevalent Strains of Mycobacterium tuberculosis from South India Harboring a Single Copy of IS 6110

Our restriction fragment length polymorphism (RFLP) studies have shown that the most prevalent (40%) strains of Mycobacterium tuberculosis from South India contain a single copy of the IS 6110 insertion sequence and are of importance in studying virulence and immunity. Sonicate antigens from seven such strains were used to study in vitro T-cell proliferation and gamma interferon (IFN- (cid:1) ) and interleukin-12 (IL-12) secretion as markers of protective immunity in 25 healthy subjects positive for puriﬁed protein derivative (PPD). The standard PPD and heat-killed H37Rv antigens induced the maximum levels of T-cell proliferation and IFN- (cid:1) secretion but low levels of IL-12. All sonicate antigens induced T-cell proliferation and IFN- (cid:1) secretion with strong positive correlation. Our results suggest that sonicate antigens from the most prevalent and recent strains of M . tuberculosis from clinical isolates have the potential to induce T-cell activation and may allow newer and speciﬁc antigens to be further characterized for diagnosis and vaccine development. Tuberculosis international problem likely become even more of the human immunodeﬁciency in regions
ALLSENSORS 2020 Proceedings

In this paper, a sputtering ceramic target based on SnO2 doped with 2 at. % Co was synthesized by solid-phase reaction method. A chemiresisive alcohol vapor sensor based on SnO2<Co> was manufactured by the high-frequency magnetron sputtering method. The alcohol sensing properties of the SnO2<Co> sensor under the ultraviolet (UV) illumination were examined at room temperature. The UVassisted alcohol sensor showed a sufficient response to low concentrations of alcohol vapor at room temperature. The Codoped SnO2 sensor has also demonstrated a good response to alcohol vapors at elevated operating temperature (200 °C).
A simple PCR-based method for the construction of RMCE donor vectors

Here we describe a simple method for generating donor vectors suitable for targeted transgenesis via recombinase mediated cassette exchange (RMCE) using the Φ C31 integrase. This PCR-based strategy employs small attB “tails” on the primers used to amplify a sequence of interest, permitting the rapid creation of transgenes for in vivo analysis. Several recent advances in transgenesis have made use of site-specific recombinases (SSRs) to achieve the integration of transgenes into predetermined locations in the genome (reviewed by B RANDA and D YMECKI 2004; V ENKEN and B ELLEN 2007). The capacity to target multiple insertions to a common location greatly facilitates comparisons of different transgenes by controlling for position effects. In one powerful approach called RMCE (reviewed by W IRTH et al. 2007), a selectable marker flanked by SSR recognition sequences is first integrated into the genome by other methods to create a genomic target “cassette”. Subsequently, a vector carrying a donor cassette consisting of a sequence of interest flanked by SSR recognition sequences that are compatible with those of the target cassette is introduced in the presence of the relevant SSR. clean exchange sequence events simply scoring the target
Reproduce experimental results from a paper Experiment Design for Data Science 2020W (188.992) - Exercise 2

In this document, the process of attempting to reproduce the setup, experiments, and results as explained in a certain scientific paper is documented. The selected paper [3] questions the bias of recommender systems towards rewarding algorithms that recommend popular items with the question: “Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems”. It deals with the task of exploring the question whether popularity could be a useful and reliable signal in recommendation or should be avoided due to biases. The authors address this question at a formal level. They illustrate and confirm their theoretical findings with empirical results. In [3] they identified and modeled conditions to determine answers, in terms of dependencies between key random variables. They built a crowdsourced dataset, with which they exemplify contradictions between the accuracy of biased and unbiased observations.
Face Recognition Technique for Processing Library Transactions

Biometrics is a term used to characterize a person's DNA, hand geometry, face, and so on or behavioral qualities, for example, hand signature, voice tone, keystrokes et cetera. Consequently, these natural attributes are novel for each person. As a rule, face recognition related advancements are ending up more prominent among biometric-based innovations that measure a person's common information. Hereditary biometrics has for the most part used to verify and recognize people by examining their physical attributes, for example, unique finger impression, eye iris, vein and so forth. The reason for this work is to show a Windows based continuous application framework utilizing face recognition calculations for performing Library Transaction. Rather than utilizing a Library card, a camera would catch pictures of faces of understudy, playing out the library transaction and contrast them and the photographs of the understudy in the database of Library to confirm the understudy's personality. Nearby Binary Patterns, face calculations will used to lessen the effect of light presentation that will influence the exactness of the framework.
ENDODONTIC MANAGEMENT OF A MANDIBULAR MOLAR WITH A SINGLE ROOT AND SINGLE CANAL DIAGNOSED WITH THE AID OF CONE-BEAM COMPUTED TOMOGRAPHY.

The aim of this article is to present endodontic management of mandibular first molar with a single root and single root canal with the aid of cone beam computed tomography. The accurate diagnosis of this unusual morphology was made with the aid of multiple angulated radiographs and cone beam computed tomography followed by a planned endodontic treatment of the tooth.
On the Mannheim Surface Offsets

In this paper, we study Mannheim surface offsets in dual space. By the aid of the E. Study Mapping, we consider ruled surfaces as dual unit spherical curves and define the Mannheim offsets of the ruled surfaces by means of dual geodesic trihedron (dual Darboux frame). We obtain the relationships between the invariants of Mannheim ruled surfaces. Furthermore, we give the conditions for these surface offset to be developable. Furthermore, we obtained that the dual spherical radius of curvature of offset surface is equal to dual offset angle.
Malware Classification by Ensemble Application of Convolutional and Recurrent Neural Networks

Malicious software written for malevolent purposes poses a serious threat to information security. With respect to information security for malware treatment, malicious codes must be correctly classified. In this paper, we propose an ensemble classification scheme for the convolutional neural network and recurrent neural network models. We then analyze the classification results of malicious software. These results are presented as a confusion matrix and cosine similarity. The performances of the classifiers are compared and visualized by using graphical representations. The performance of the proposed ensemble model was the highest at 96.50%, indicating its viability as an accurate classification model.
Perceptions, Practices and Challenges in Schools for Autistic Children: A Qualitative Exploration of Teachers and School Authorities’ Views in Bangladesh

Introduction: Globally around 1% people are suffering from autism disorder. Autism becomes a health crisis around the world. In Bangladesh, autism has already been identified as burden of diseases and it has been assumed that the magnitude is high and majority cases, is undetermined. Proper knowledge and education including schooling lead to significantly improve of overall situations. The study explored the perceptions, practices and challenges in the schools for autistic children among the teachers and authorities in urban city in Bangladesh. Methods: A qualitative study was conducted at the selected five schools for autism in urban Dhaka city. Face to face interviews were performed using in-depth interview of teachers and in-depth Key Informant Interview (KII) of school authorities, reviewing schools reports and documents, and classroom observations. Thematic analysis was performed for different thematic areas. Results: Teachers in the school dealing with autistic children don’t have any advance education on autism, all of them have learnt on autism after entered in the teaching profession. Teachers also mentioned on limited space at the schools which caused difficulties to manage large number of children within a small compartment. Conclusion: Teachers need comprehensive and advance training and education to deal with those special children for better quality life in Bangladesh. Schools environments also a key factor for better compliance for the children staying in the schools.
HYPERTENSION AND ITS ASSOCIATED MORBIDITY AMONG ELDERLY POPULATION IN NORTHERN SAUDI ARABIA

: Background: Certain unique health problems develop with increasing age like hypertension, diabetes mellitus, cardiovascular diseases, cerebrovascular accidents, obesity, dyslipidemia and various malignancies. Objectives: to estimate the prevalence of hypertension and its associated morbidity in elderly population of Arar city, Kingdom of Saudi Arabia. Methods: Data was collected from 252 elderly participant aged 60 years and above, attending 5 randomly selected primary healthcare centers in Arar city, during the period from 1 March to 30 June 2018. Participants were selected using a systemic random sampling procedure as we take every 2 nd patient. Collected data was analyzed using the SPSS program Version 22. P value of less than 0.05 was considered statistically significant. Results: Our study found that the prevalence of hypertension among elderly population was 45.6%, it was more prevalent among females by 54.8% than males 45.2% but there was no difference significant (p=0.246). As regards relations between hypertension and age group, 63.5% of hypertensive patients were 60-70 years old, 26.1% 71- 80 years old and only 10.4% more than 81 years old ,but there was non-significant correlations (p=0.492). There were 18.3% of hypertensive patients had DM (p=0.875). As regards the BMI, the majority (47%) of hypertensive patients were obese, 29.6% underweight, 20.9% over weight and only 2.6% were normal (p=0.082). Conclusion: In Arar city, KSA, about half (45.6% ) of the elderly population were hypertensive. Hypertension was more prevalent among females , in 60-70 years age group, about fifth of them were diabetics and about half of them were obese.
Incorporating shared savings programs into primary care

Background: In several countries, health care policies gear toward strengthening the position of primary care physicians. Primary care physicians are increasingly expected to take accountability for overall spending and quality. Yet traditional models of paying physicians do not provide adequate incentives for taking on this new role. Under a so-called shared savings program physicians are instead incentivized to take accountability for spending and quality, as the program lets them share in cost savings when quality targets are met. We provide a structured approach to designing a shared savings program for primary care, and apply this approach to the design of a shared savings program for a Dutch chain of primary care providers, which is currently being piloted. Methods: Based on the literature, we defined five building blocks of shared savings models that encompass the definition of the scope of the program, the calculation of health care expenditures, the construction of a savings benchmark, the assessment of savings and the rules and conditions under which savings are shared. We apply insights from a variety of literatures to assess the relative merits of alternative design choices within these building blocks. The shared savings program uses an econometric model of provider expenditures as an input to calculating a casemix-corrected benchmark. Results: The minimization of risk and uncertainty for both payer and provider is pertinent to the design of a shared savings program. In that respect, the primary care setting provides a number of unique opportunities for achieving cost and quality targets. Accountability can more readily be assumed due to the relatively long-lasting relationships between primary care physicians and patients. A stable population furthermore improves the confidence with which savings can be attributed to changes in population management. Challenges arise from the institutional context. The Dutch health care system has a fragmented structure and providers are typically small in size. Conclusion: Shared savings programs fit the concept of enhanced primary care. Incorporating a shared savings program into existing payment models could therefore contribute to the financial sustainability of this organizational form.
Modeling and Simulation of Metal AM Modeling and Simulation of Metal AM

Additive manufacturing with metal components is a complex, and currently cyclic, pro- cess due to the physical phenomena that are occurring. These phenomena can be mathematically modeled in order to predict the outcome of a specific aspect of the build. Coupling the mathematical models can then be used to develop a complete simulation, which can produce estimates for a range of characteristics for a part built using additive manufacturing techniques. This chapter will investigate the main models used in the simulation of metal AM. These models will include the modeling of thermal behavior, fluid dynamics, stress, and a selection of other auxiliary models, which are necessary to complete the simulations. For each of the models investigated, the various modeling techniques that have been developed will be presented along with their limitations, validation techniques, and parameters necessary to model the process correctly.