nils-herrmann/clean_scirepeval huggingface dataset:
Targeted Applications of Unmanned Aerial Vehicles (Drones) in Telemedicine.

Advances in technology have revolutionized the medical field and changed the way healthcare is delivered. Unmanned aerial vehicles (UAVs) are the next wave of technological advancements that have the potential to make a huge splash in clinical medicine. UAVs, originally developed for military use, are making their way into the public and private sector. Because they can be flown autonomously and can reach almost any geographical location, the significance of UAVs are becoming increasingly apparent in the medical field.



We conducted a comprehensive review of the English language literature via the PubMed and Google Scholar databases using search terms "unmanned aerial vehicles," "UAVs," and "drone." Preference was given to clinical trials and review articles that addressed the keywords and clinical medicine.



Potential applications of UAVs in medicine are broad. Based on articles identified, we grouped UAV application in medicine into three categories: (1) Prehospital Emergency Care; (2) Expediting Laboratory Diagnostic Testing; and (3) Surveillance. Currently, UAVs have been shown to deliver vaccines, automated external defibrillators, and hematological products. In addition, they are also being studied in the identification of mosquito habitats as well as drowning victims at beaches as a public health surveillance modality.



These preliminary studies shine light on the possibility that UAVs may help to increase access to healthcare for patients who may be otherwise restricted from proper care due to cost, distance, or infrastructure. As with any emerging technology and due to the highly regulated healthcare environment, the safety and effectiveness of this technology need to be thoroughly discussed. Despite the many questions that need to be answered, the application of drones in medicine appears to be promising and can both increase the quality and accessibility of healthcare.
Microenvironment and photosynthesis of zooxanthellae in scleractinian corals studied with microsensors for 02 , pH and light

During experimental light-dark cycles, O9 in the tissue of the colonial scleractinian corals Favia sp. and Acropora sp reached >250 % of air saturation after a few minutes in light. Immediately after darkenmg, 0; was depleted rapidly, and within 5 mm the 0; concentration at the tissue surface reached <2 % of air saturation. The pH of the tissue changed within 10 min from about 8.5 in the light to 7.3 m the dark. Oxygen and pH profiles revealed a diffusive boundary layer of flow-dependent thickness, which limited coral respiration in the dark. The light field at the tissue surface (measured as scalar irradiance, Eo) differed strongly with respect to light intensity and spectral composition from the incident collimated light (measured as downwelling irradiance, Ed) Scalar irradiance reached up to 180 % of Ed at the coral tissue surface for wavelengths subject to less absorption by the coral tissue (600 to 650 run and >680 nm). The scalar irradiance spectra exhibited bands of chlorophyll a (chl a ) (675 run), chl c (630 to 640 nm) and pendinin (540 nm) absorption and a broad absorption band due to chlorophyl l~ and carotenoids between 400 and 550 nm. The shape of both action spectra and photosynthesis vs irradiance (Pvs I) curves depended on the choice of the light intensity parameter. Calculations of miha1 slopes and onset of light saturation, Ik, showed that P vs Eo curves exhibit a lower initial slope and a higher 4 than corresponding Pvs Ed curves. Coral respiration in light was calculated as the difference between the measured gross and net photosynthesis, and was found to be >6 times higher at a saturating irradiance of 350 pEm m 2 s 1 than the dark respiration measured under identical hydrodynamic conditions (flow rate of 5 to 6 cm ssl).
A Sampling Theorem for Fractional Wavelet Transform With Error Estimates

As a generalization of the ordinary wavelet transform, the fractional wavelet transform (FRWT) is a very promising tool for signal analysis and processing. Many of its fundamental properties are already known; however, little attention has been paid to its sampling theory. In this paper, we first introduce the concept of multiresolution analysis associated with the FRWT, and then propose a sampling theorem for signals in FRWT-based multiresolution subspaces. The necessary and sufficient condition for the sampling theorem is derived. Moreover, sampling errors due to truncation and aliasing are discussed. The validity of the theoretical derivations is demonstrated via simulations.
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations

Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. Additionally, there exist input-agnostic perturbations, called universal adversarial perturbations, which can change the inference of target model on most of the data samples. However, existing methods to craft universal perturbations are (i) task specific, (ii) require samples from the training data distribution, and (iii) perform complex optimizations. Additionally, fooling ability of the crafted perturbations is proportional to the available training data. In this paper, we present a novel, generalizable and data-free approach for crafting universal adversarial perturbations. Independent of the underlying task, our objective achieves fooling via corrupting the extracted features at multiple layers. Therefore, the proposed objective is generalizable to craft image-agnostic perturbations across multiple vision tasks such as object recognition, semantic segmentation, and depth estimation. In the practical setting of black-box attack scenario, we show that our objective outperforms the data dependent objectives. Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations. Significant fooling rates achieved by our objective emphasize that the current deep learning models are now at an increased risk, since our objective generalizes across multiple tasks without the requirement of training data.
A Group Theoretic Perspective on Unsupervised Deep Learning

The modern incarnation of neural networks, now popularly known as Deep Learning (DL), accomplished record-breaking success in processing diverse kinds of signals vision, audio, and text. In parallel, strong interest has ensued towards constructing a theory of DL. This paper opens up a group theory based approach, towards a theoretical understanding of DL, in particular the unsupervised variant. First we establish how a single layer of unsupervised pre-training can be explained in the light of orbit-stabilizer principle, and then we sketch how the same principle can be extended for multiple layers.
Brain Tumor Segmentation: A Comparative Analysis

Five different threshold segmentation based approaches have been reviewed and compared over here to extract the tumor from set of brain images. This research focuses on the analysis of image segmentation methods, a comparison of five semi-automated methods have been undertaken for evaluating their relative performance in the segmentation of tumor. Consequently, results are compared on the basis of quantitative and qualitative analysis of respective methods. The purpose of this study was to analytically identify the methods, most suitable for application for a particular genre of problems. The results show that of the region growing segmentation performed better than rest in most cases.
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks

Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.
Digital beamforming in a large conformal phased array antenna for satellite TT&C — Demonstration

This paper presents the design, development, theoretical and measured results of Digital Beam Forming (DBF) hardware for use with a large, multi-beam conformal phased array antennas such as the Geodesic Dome Phased Array Antenna (GDPAA) shown in Figure 1[1]. The GDPAA system with DBF on receive can provide multiple simultaneous links for satellite tracking, telemetry, and command (TT&C) functions including adaptive pattern control for anti-jamming or interference suppression, and high resolution direction finding.
Toward the Discovery of Citation Cartels in Citation Networks

In this perspective, our goal is to present and elucidate a thus far largely overlooked problem that is arising in scientific publishing, namely the identification and discovery of citation cartels in citation networks. Taking from the well-known definition of a community in the realm of network science, namely that people within a community share significantly more links with each other as they do outside of this community, we propose that citation cartels are defined as groups of authors that cite each other disproportionately more than they do other groups of authors that work on the same subject. Evidently, the identification of citation cartels is somewhat different, although similar to the identification of communities in networks. We systematically expose the problem, provide theoretical examples, and outline an algorithmic guide on how to approach the subject.
Learning Taxonomic Relations from Heterogeneous Sources of Evidence

We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of vidence. In order to derive an optimal combination of these sources, we exploit a machine-learning approach, representing all the sources of evidence as first-or der features and training standard classifiers. We consider in particular different f a ures derived from WordNet, an approach matching Hearst-style patterns in a corpus and on the Web as well as further methods mentioned in the literature. In particul ar, we explore different classifiers as well as various strategies for dealing with un balanced datasets. We evaluate our approach by comparing the results with a refere nc taxonomy for the tourism domain.
Passive component integration in a ZVS flyback converter

A ZVS flyback converter is used as a case study to illustrate the use of PCB integration technology. Most of the power components with the partial exception of the input and output filter capacitors are integrated in the multilayer PCB structure. The functionality of the integration process is described with reference to the structure as well as the circuit functions. The integrated structure is evaluated with an experimental prototype operating at a power level of 65W. The problematic transmission of common mode EMI across the planar transformer is partly mitigated by a charge balancing shield on both primary and secondary windings
Coronary artery spasm--clinical features, diagnosis, pathogenesis, and treatment.

Coronary (artery) spasm plays an important role in the pathogenesis of ischemic heart disease, including stable angina, unstable angina, myocardial infarction, and sudden death. The prevalence of coronary spasm differs among populations, is higher in Japan and Korea than in the Western countries probably due to genetic as well as environmental factors. Coronary spasm occurs most often from midnight to early morning and is usually not induced by exercise in the daytime. The attacks of coronary spasm are associated with either ST segment elevation or depression, or negative U wave on ECG. Patients with multi-vessel coronary spasm may suffer from lethal arrhythmia, including advanced AV block, ventricular tachycardia or fibrillation, or even sudden death, and they are often resistant to conventional medical therapy including Ca-channel blockers (CCBs). Endothelial nitric oxide (NO) activity is reduced and markers of oxidative stress are elevated in patients with coronary spasm. Thrombogenesis is enhanced and plasma levels of hsCRP and P-selection are elevated in patients with coronary spasm. Thus, patients with coronary spasm have endothelial dysfunction and are suffering from a low-grade chronic inflammation. Polymorphisms of endothelial NO synthase, smoking, and low-grade inflammation are the most important risk factors for coronary spasm. Coronary spasm is a hyper-contraction of coronary smooth muscle triggered by an increase of intracellular Ca2+ in the presence of an increased Ca2+ sensitivity. It has been shown that RhoA/ROCK pathway is involved in Ca2+ sensitivity and that the reduced endothelial NO activity results in increased Ca2+ sensitivity through enhanced RhoA/ROCK pathway. Accordingly, it is possible that in addition to CCBs, RhoA/ROCK pathway blockers may prove to be useful for the treatment of coronary spasm.
Assessment of high-integrity embedded automotive control systems using hardware in the loop simulation

Sensor-based driver assistance systems often have a safety-related role in modern automotive designs. In this paper we argue that the current generation of “Hardware in the Loop” (HIL) simulators have limitations which restrict the extent to which testing of such systems can be carried out, with the consequence that it is more difficult to make informed decisions regarding the impact of new technologies and control methods on vehicle safety and performance prior to system deployment. In order to begin to address this problem, this paper presents a novel, low-cost and flexible HIL simulator. An overview of the simulator is provided, followed by detailed descriptions of the models that are employed. The effectiveness of the simulator is then illustrated using a case study, in which we examine the performance and safety integrity of eight different designs of a representative distributed embedded control system (a throttleand brake-by-wire system with adaptive cruise control capability). It is concluded that the proposed HIL simulator provides a highly effective and low-cost test environment for assessing and comparing new automotive control system implementations.
Biodiesel Production from Azolla filiculoides (Water Fern)

To assess the potential of Azolla filiculoides, total body collected from a rice farm in northern Iran as source for biodiesel production.  Solvent extraction using Soxhlet apparatus with chloroform-methanol (2:1 v/v) solvent blend was used to obtain crude oil from freeze-dried the Azolla plant. Acid-catalyzed transesterification was used to convert fatty acids (FA), monoglycerides (MG), diglycerides (DG) and triglycerides (TG) in the extracts to fatty acid methyl esters (FAMEs) by acid-catalyzed methylation. Gas chromatography–mass spectrometry (GC–MS) was employed to analyze the FAMEs in the macroalgae biodiesel.  The presence of myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), myristic acid (C14:0), stearic acid (C18:3), oleic acid (C18:1) and linoleic acid 9C18:2), eicosenoic acid (C20:1), eicosapentaenoic acid (C20:5), erucic acid (C22:1) and docosahexaenoic acid (C22:6) in the macroalgae biodiesel was confirmed.  The results indicate that biodiesel can be produced from macroalgae and that water fern is potentially an economical source of biodiesel due its ready availability and probable low cost.
A Safe Approximate Algorithm for Interprocedural Pointer Aliasing

During execution, when two or more names exist for the same location at some program point, we call them aliases. In a language which allows arbitrary pointers, the problem of determining aliases at a program point is &rgr;-space-hard [Lan92]. We present an algorithm for the Conditional May Alias problem, which can be used to safely approximate Interprocedural May Alias in the presence of pointers. This algorithm is as precise as possible in the worst case and has been implemented in a prototype analysis tool for C programs. Preliminary speed and precision results are presented.
Self-Stigma and Its Relationship with Victimization, Psychotic Symptoms and Self-Esteem among People with Schizophrenia Spectrum Disorders

Self-stigma is highly prevalent in schizophrenia and can be seen as an important factor leading to low self-esteem. It is however unclear how psychological factors and actual adverse events contribute to self-stigma. This study empirically examines how symptom severity and the experience of being victimized affect both self-stigma and self-esteem.



Persons with a schizophrenia spectrum disorder (N = 102) were assessed with a battery of self-rating questionnaires and interviews. Structural equation modelling (SEM) was subsequently applied to test the fit of three models: a model with symptoms and victimization as direct predictors of self-stigma and negative self-esteem, a model with an indirect effect for symptoms mediated by victimization and a third model with a direct effect for negative symptoms and an indirect effect for positive symptoms mediated by victimization.



Results showed good model fit for the direct effects of both symptoms and victimization: both lead to an increase of self-stigma and subsequent negative self-esteem. Negative symptoms had a direct association with self-stigma, while the relationship between positive symptoms and self-stigma was mediated by victimization.



Our findings suggest that symptoms and victimization may contribute to self-stigma, leading to negative self-esteem in individuals with a schizophrenia spectrum disorder. Especially for patients with positive symptoms victimization seems to be an important factor in developing self-stigma. Given the burden of self-stigma on patients and the constraining effects on societal participation and service use, interventions targeting victimization as well as self-stigma are needed.
PassFrame: Generating image-based passwords from egocentric videos

In this paper, we analyse first-person-view videos to develop a personalized user authentication mechanism. Our proposed algorithm generates provisional image-based passwords which benefit a variety of purposes such as unlocking a mobile device or fallback authentication. First, representative frames are extracted from the egocentric videos. Then, they are split into distinguishable segments before a clustering procedure is applied to discard repetitive scenes. The whole process aims to retain memorable images to form the authentication challenges. We integrate eye tracking data to select informative sequences of video frames and suggest another alternative method if an eye-facing camera is not available. To evaluate our system, we perform experiments in different settings including object-interaction activities and traveling contexts. Even though our mechanism produces variable graphical passwords, the log-in effort for the user is comparable with approaches based on static challenges. We verified the authentication scheme in the presence of an informed attacker and observed that the effort is significantly higher than that of the legitimate user.
A retrospective cephalometric investigation of two fixed functional orthodontic appliances in class II treatment: Functional Mandibular Advancer vs. Herbst appliance

The objective of the study is to compare skeletal and dental changes in class II patients treated with fixed functional appliances (FFA) that pursue different biomechanical concepts: (1) FMA (Functional Mandibular Advancer) from first maxillary molar to first mandibular molar through inclined planes and (2) Herbst appliance from first maxillary molar to lower first bicuspid through a rod-and-tube mechanism. Forty-two equally distributed patients were treated with FMA (21) and Herbst appliance (21), following a single-step advancement protocol. Lateral cephalograms were available before treatment and immediately after removal of the FFA. The lateral cephalograms were analyzed with customized linear measurements. The actual therapeutic effect was then calculated through comparison with data from a growth survey. Additionally, the ratio of skeletal and dental contributions to molar and overjet correction for both FFA was calculated. Data was analyzed by means of one-sample Student’s t tests and independent Student’s t tests. Statistical significance was set at p < 0.05. Although differences between FMA and Herbst appliance were found, intergroup comparisons showed no statistically significant differences. Almost all measurements resulted in comparable changes for both appliances. Statistically significant dental changes occurred with both appliances. Dentoalveolar contribution to the treatment effect was ≥70%, thus always resulting in ≤30% for skeletal alterations. FMA and Herbst appliance usage results in comparable skeletal and dental treatment effects despite different biomechanical approaches. Treatment leads to overjet and molar relationship correction that is mainly caused by significant dentoalveolar changes.
Automatic email response suggestion for support departments within a university

9 Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time.
Optical methods for sensing glucose.

This critical review covers the present state of the art in optical sensing of glucose. Following an introduction into the significance of (continuous) sensing of glucose and a brief look back, we discuss methods based on (a) monitoring the optical properties of intrinsically fluorescent or labeled enzymes, their co-enzymes and co-substrates; (b) the measurement of the products of enzymatic oxidation of glucose by glucose oxidase; (c) the use of synthetic boronic acids; (d) the use of Concanavalin A; and (e) the application of other glucose-binding proteins. We finally present an assessment in terms of the advantages and disadvantages of the various methods (237 references).