Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbifold Tutte embeddings

Injective parameterizations of surface meshes are vital for many applications in Computer Graphics, Geometry Processing and related fields. Tutte's embedding, and its generalization to convex combination maps, are among the most popular approaches for computing parameterizations of surface meshes into the plane, as they guarantee injectivity, and their computation only requires solving a sparse linear system. However, they are only applicable to disk-type and toric surface meshes. In this paper we suggest a generalization of Tutte's embedding to other surface topologies, and in particular the common, yet untreated case, of sphere-type surfaces. The basic idea is to enforce certain boundary conditions on the parameterization so as to achieve a Euclidean orbifold structure. The orbifold-Tutte embedding is a seamless, globally bijective parameterization that, similarly to the classic Tutte embedding, only requires solving a sparse linear system for its computation. In case the cotangent weights are used, the orbifold-Tutte embedding globally minimizes the Dirichlet energy and is shown to approximate conformal and four-point quasiconformal mappings. As far as we are aware, this is the first fully-linear method that produces bijective approximations to conformal mappings. Aside from parameterizations, the orbifold-Tutte embedding can be used to generate bijective inter-surface mappings with three or four landmarks and symmetric patterns on sphere-type surfaces.
Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

Background Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. Objective This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. Methods Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. Results A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students’ learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. Conclusions The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.
Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
Improved Calculation of aS Resilience against IP Prefix Hijacking

Network prefix hijacking is still a serious threat in the Internet. Confirmed incidents in the recent past have shown that even small autonomous systems (ASs) are able to manipulate routing information with huge global impact. Even though countermeasures exist, they are not established at large scale yet. Monitoring of the actual routing state is the only mean to provide at least information about prefix hijacking events for single ASs. Given topology information the resilience of an AS against prefix hijacking attacks can be determined. This paper proposes an improved formula to calculate the resilience of an AS against prefix hijacking. Additionally, the role of internet exchange points (IXPs) and the peering opportunities they provide are evaluated. Such opportunities allow for establishing links between ASs at very low cost. Current peering opportunities are derived from a collection of member data gathered from European IXPs. Furthermore, the effect of additional links on an AS's resilience is investigated by combining actual peering and individual peering opportunities.
Effects of postharvest treatment and heat stress on availability of wheat antioxidants.

This research evaluated the effects of postharvest treatment and heat stress on the availability of wheat antioxidants using Ankor and Trego wheat varieties. The grain, bran, and 40-mesh bran samples of both Ankor and Trego wheat were kept at 25, 60, and 100 degrees C for 9 days. Samples taken at day 0, 1, 2, 3, 5, and 9 were extracted with pure ethanol and examined for antioxidant properties including the scavenging activity against peroxyl (ORAC), cation ABTS, and 2,2-diphenyl-1-picryhydrazyl (DPPH.) radicals, as well as total phenolic content (TPC) and phenolic acid composition. Both heat stress and postharvest treatment significantly altered the antioxidant properties of wheat grain fractions. The ORAC values of Ankor bran and corresponding 40-mesh bran samples kept at 100 degrees C for 9 days reduced to 61 and 40% of that at day 0 on a per dry weight basis, respectively, while the ORAC values of the grain samples showed no significant change. The overall loss of DPPH. scavenging capacity was 38 and 100% for the bran and 40-mesh Ankor bran samples, respectively, and was 47 and 60% in the bran and 40-mesh Trego bran samples, respectively, whereas no reduction was detected in the grain samples under the same heat stress. Heat stress and postharvest treatment had similar effects on ABTS. + scavenging capacities and TPC values of grain and fractions of both varieties. These data suggest that whole grain as opposed to its fractions is a preferred form of long-term storage for better preserving natural antioxidants and that the reduction of the particle size may accelerate the loss of natural antioxidants in wheat bran during storage and thermal processing but may enhance the releasable amount of wheat antioxidants from bran.
Depth Map Restoration From Undersampled Data

Depth map sensed by low-cost active sensor is often limited in resolution, whereas depth information achieved from structure from motion or sparse depth scanning techniques may result in a sparse point cloud. Achieving a high-resolution (HR) depth map from a low resolution (LR) depth map or densely reconstructing a sparse non-uniformly sampled depth map are fundamentally similar problems with different types of upsampling requirements. The first problem involves upsampling in a uniform grid, whereas the second type of problem requires an upsampling in a non-uniform grid. In this paper, we propose a new approach to address such issues in a unified framework, based on sparse representation. Unlike, most of the approaches of depth map restoration, our approach does not require an HR intensity image. Based on example depth maps, sub-dictionaries of exemplars are constructed, and are used to restore HR/dense depth map. In the case of uniform upsampling of LR depth map, an edge preserving constraint is used for preserving the discontinuity present in the depth map, and a pyramidal reconstruction strategy is applied in order to deal with higher upsampling factors. For upsampling of non-uniformly sampled sparse depth map, we compute the missing information in local patches from that from similar exemplars. Furthermore, we also suggest an alternative method of reconstructing dense depth map from very sparse non-uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques. We provide a variety of qualitative and quantitative results to demonstrate the efficacy of our approach for depth map restoration.
CoCl2-simulated hypoxia in skeletal muscle cell lines: Role of free radicals in gene up-regulation and induction of apoptosis

Since it was suggested that cobalt chloride (CoCl2) could mimic the O2 sensing role of mitochondria by increasing reactive oxygen species (ROS) generation during normoxia, we studied the correlation between CoCl2-generation of free radicals and the induction of a hypoxic cellular response in myogenic cell lines. In both L6C5 and C2C12 cell lines, exposure to CoCl2 induced an increase of intracellular oxidants, the accumulation of HIF-1α protein, and the expression of vascular endothelial growth factor (VEGF) and/or iNOS genes. On the other hand, only ascorbic acid, but not trolox, was effective in lowering the CoCl2 gene up-regulation. Neither the cytotoxicity nor the apoptosis induced by CoCl2 in skeletal muscle cells were modified by culture supplementation with either ascorbic acid or trolox. Thus, CoCl2 treatment of myogenic cell lines may represent a useful and convenient in vitro model to study gene modulation induced by hypoxia in skeletal muscle, although cellular loss induced by this metal may involve mechanisms other than HIF-1α stabilization. It is unlikely, however, that ROS would represent the main mediators of CoCl2 effects on muscle cells.
Joint code acquisition and Doppler shift estimation method for DSSS-MSK signal

Acquisition of Direct Sequence Spread Spectrum-Minimum Shift Keying (DSSS-MSK) signal in low signal to noise (SNR) and high dynamic environment will impact the overall performance of the receiving system seriously. The proposed all-digital IF receiver has a serial structure, transforming the DSSS-MSK signal into approximating DSSSBPSK signal using the matched filter. The matched filter is designed according to the known frequency response based on convex optimization. Then, the signals are regrouped by spreading code period. Finally, combining Doppler frequency shift compensation with the parallel code acquisition algorithm based on FFT, the PN code phase difference and Doppler frequency shift are captured simultaneously. Simulation results show that the proposed algorithm has 7dB and 8dB SNR improvement than delay correlation method and ML-FFT method respectively. Furthermore, the proposed algorithm has quick acquisition rate, wide acquisition range, high acquisition accuracy, low complexity and is suitable for low SNR environment.
Extracting Linguistic Features From Opinion Data Streams For Multi-Domain Sentiment Analysis

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences containing in a training set. Then, such relationships are aggregated in a semantic structured together with either polarity and domain information. Such information is exploited in order to have a more fine-grained representation of the learned sentiment information. When the polarity of a new text has to be computed, such a text is converted in the same semantic representation that is used (i) for detecting the domain to which the text belongs to, and then (ii), once the domain is assigned to the text, the polarity is extracted from the index-like structure. First experiments performed by using the Blitzer dataset for training the system demonstrated the feasibility of the proposed approach.
Statistical Adjustments and Uncontrolled Studies

Many evaluations of social interventions are based on uncontrolled assignments of individuals to treatment groups. Statistical adjustments are often used to compensate for naturally occurring differences between groups. There is much confusion and controversy about the adequacy of these statistical methods. A variety of interrelated problems have been identified, including measurement error, unequal growth rates across groups, and regression artifacts. In this article it is shown that these problems can all be subsumed under a general conceptual framework, as particular examples of model misspecincation. This perspective is helpful in revealing clearly the nature of the problems posed by lack of experimental control. The important case of linear adjustment (analysis of covariance) is given special attention. An expression is derived for the proportion of bias remaining after adjustment, in terms of easily interpretable parameters. Implications of these results for research and evaluation design are considered.
A Robust Quasi-Dense Wide Baseline Matching Method

The paper proposes a robust three-view matching method to produce quasi-dense 3D points in multi-view stereo. In order to overcome the limitations of traditional wide baseline match propagation methods, the proposed method simultaneously models illumination variations and perspective distortions in images, and performs an effective affine parameter error detection and rectification mechanism in the process of match propagation. Experiments show the proposed method can effectively enhance the stability and robustness of match propagation, and achieve better performance in both accuracy and quantity of the quasi-dense 3D points.
Histone deacetylase 6 interference protects mice against experimental stroke-induced brain injury via activating Nrf2/HO-1 pathway

ABSTRACT Cerebral stroke is a fatal disease with increasing incidence. The study was to investigate the role and mechanism of Histone deacetylase 6 (HDAC6) on experimental stroke-induced brain injury. The recombinant shRNA-HDAC6 or scramble shRNA lentivirus was transfected to ICR mice. Then, the ischemia/reperfusion injury (I/RI) mice were constructed using middle cerebral artery occlusion (MCAO) method. Brain TTC staining was used to determine infarct areas. Serum levels of oxidative stress-related factors were detected by enzyme linked immunosorbnent assay (ELISA). Realtime-qPCR (RT-qPCR) and Western blot were used to respectively detect mRNA and protein levels. HDAC6 was up-regulated in brain I/RI mice (MCAO group), and down-regulated again in MCAO mice transfected with shRNA-HDAC6 (MCAO + shRNA group). The infarct area of the MCAO mice was increased, neurological scores were higher, and serum protein levels of 3-NT, 4-HNE and 8-OHdG were higher. HDAC6 interference attenuated above effects. Though protein levels of Nrf2 and HO-1 in cytoplasm increased slightly in MCAO group, they increased significantly by HDAC6 interference. The protein levels of Nrf2 in cytoblast decreased significantly in MCAO group, and increased markedly by HDAC6 interference. HDAC6 interference protected mice against experimental stroke-induced brain injury via Nrf2/HO-1 pathway.
Untold Stories of ERP Systems Implementation: Role of Ownership and Governance, Scope Management, and Employee Empowerment

Much prior research on enterprise resource planning (ERP) systems implementation has been conducted in western countries that have different social and organizational cultures from countries in the other parts of the world, such as the Arab World. In this paper, we examine ERP systems implementations in Saudi Arabia which is an important economic frontier in the world. Our key focus was to understand the role of three important aspects of ERP systems implementations—i.e., ownership and governance, scope management, and employee empowerment. We conducted six case studies and found that the nature of ownership and governance played a significant hindering role during implementation. We further found that Saudi organizations faced major challenges during implementation with respect to managing the scope of implementation. Finally, we found that owners and top management were deeply concerned about losing their control over employees following ERP systems implementations. We offer theoretical and practical implications.
Development of a Non-Contact ECG Application Unobtrusively Embedded into a Bed

In this paper, the authors publish a novel designed capacitive ECG system, which can be unobtrusively implemented into a bed. To avoid user rejection, the capacitive electrodes were designed flexibly and with a large surface. In addition, the electrode surface is interfaced by a wire to the circuit boards, which increases the noise sensitivity. Therefore, the proposed solution focuses on noise filtering and shielding for the novel designed electrodes. Already considered in the design of these electrodes is a special guard and ground design, which reduces the impact of noise. Furthermore, different settings of analog filters improve the signal quality, which is proved by test measurements. These test measurements were compared to each other regarding the different filter settings in order to identify the most suitable filter for future developments. Through using analog filters, an almost real-time processing of data is made possible. By the proposed application, the home security for heart diseases and for the elderly can be improved, as well as offers different opportunities for sensor fusion in future.
Real Time Android Ransomware Detection by Analyzed Android Applications

Recently, damage caused by ransomware has been increasing in PC and Android environments. There are many studies into real-time ransomware detection because the most important time to prevent encryption is before ransomware is able to execute its malicious process. Traditional analyses determine an application is ransomware or not by static/dynamic methods. Those analyses can serve as components of a method to detect ransomware in real time. However, problems can occur such as the inability to detect new/variant/unknown ransomware. These types require signed patches from a trusted party that can only be created after attacks occur. In a previous study into realtime new/variant/unknown ransomware detection in a PC environment, important files are monitored and only programs that have been previously analyzed and evaluated as nonmalicious are allowed. As such, programs that have not been analyzed are restricted from accessing important files. In an Android environment, this method can be applied using Android applications to prevent emerging threats and verify consistency with user intent. Thus, this paper proposes a method of detecting new/variant/unknown ransomware in real time in an Android environment.
The relative impact of socioeconomic status and childhood trauma on Black-White differences in paranoid personality disorder symptoms.

The current study examines mechanisms of racial differences in symptoms of paranoid personality disorder (PPD) in a sample of adults ages 55-64 from the St. Louis, MO area. Socioeconomic status (SES) and childhood trauma were tested as intervening variables in the association between race and PPD symptoms using structural equation modeling. PPD symptoms were modeled as a latent variable composed of items from the PPD scales of the Multi-Source Assessment of Personality Pathology self and informant reports and the Structured Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) Personality. Childhood trauma was measured using the Traumatic Life Events Questionnaire, and SES was a composite of parent education, participant education, and annual household income. Blacks exhibited higher levels of PPD symptoms across the 3 personality measures, reported significantly lower SES, and reported greater childhood trauma. The proposed model was a good fit to the data, and the effect of race on PPD symptoms operated mainly through SES. The indirect effect through SES was stronger for males. Findings suggest that racial differences in PPD symptoms are partly explained by problems more commonly experienced by Black individuals.
A 3-Layer Structural Analysis Framework for Chinese Text Understanding

How to make the machine better understand human language has always been a hot topic. It greatly influences the efficiency and accuracy of knowledge extraction, intelligent question answering and other nature language processing applications. For this reason, researchers put forward multilingual analysis and language modeling methods, for example, the distributed feature representation fitting for the neural network models and so on. This paper argues that language analysis and modeling should start with the characteristics of the language itself. In seeing this, we have thoroughly studied a series of basic NLP analysis techniques and present our own three-layer language processing and analysis model. This model takes into account the characteristics of language at all levels, and tries to preserve most of the attributes of the sentence. Such design deals with the analysis and representation of language from a brand new perspective, which may inspire many other applications in the field of NLP.
Model of Functioning of Information System for Institute of Distance Education of Specialists of Onboard Communications

Modern education is the integration of content and learning technologies. Distance learning is an opportunity to study individually, regardless of the place and time, the opportunity to learn all my life. In all countries of the world there is an increase in the number of students remotely, the number of universities using them in the educational process is growing; a large number of international educational structures are being created, etc. currently, fundamentally new priorities are being identified in the educational sphere, the most important of which are the informatization of the management activities of educational organizations.The relevance of the topic is due to the fact that the problem of development and implementation of distance learning (DL) in higher education is particularly acute for the domestic education system due to the increasing requirements for the quality, volume and pace of the educational process and dissatisfaction with the existing system of higher education. The choice of this topic is primarily due to the interest in modern educational technologies and new systems approaches in this area, the priority of which is currently DL