nils-herrmann/clean_scirepeval huggingface dataset:
Multiple-shot human re-identification by Mean Riemannian Covariance Grid

Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model combining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets.
Joint Channel Training and Feedback for FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel-state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we first exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.
The effect of balance training on postural control in people with multiple sclerosis using the CAREN virtual reality system: a pilot randomized controlled trial

Multiple sclerosis (MS) is a multi-focal progressive disorder of the central nervous system often resulting in diverse clinical manifestations. Imbalance appears in most people with multiple sclerosis (PwMS). A popular balance training tool is virtual reality (VR) with several advantages including increased compliance and user satisfaction. Therefore, the aim of this pilot RCT (Trial registration number, date: ISRCTN14425615, 21/01/2016) was to examine the efficacy of a 6-week VR balance training program using the computer assisted rehabilitation environment (CAREN) system (Motek Medical BV, Amsterdam, Netherlands) on balance measures in PwMS. Results were compared with those of a conventional balance exercise group. Secondary aims included the impact of this program on the fear of falling. Thirty-two PwMS were equally randomized into the VR intervention group or the control group. Each group received balance training sessions for 6 consecutive weeks, two sessions per week, 30 min sessions. Clinical balance tests and instrumented posturography outcome measures were collected upon initiation of the intervention programs and at termination. Final analysis included 30 patients (19 females, 11 males; mean age, (S.D.) = 45.2 (11.6) years; mean EDSS (S.D.) = 4.1 (1.3), mean disease duration (S.D.) = 11.0 (8.9) years). Both groups showed a main effect of time on the center of pressure (CoP) path length with eyes open (F = 5.278, P = .024), sway rate with eyes open (F = 5.852, P = .035), Functional Reach Test (F = 20.841, P = .001), Four Square Step Test (F = 9.011, P = .031) and the Fear of Falls self-reported questionnaire (F = 17.815, P = .023). In addition, significant differences in favor of the VR program were observed for the group x time interactions of the Functional Reach Test (F = 10.173, P = .009) and fear of falling (F = 6.710, P = .021). We demonstrated that balance training based on the CAREN device is an effective method of balance training for PwMS.
Anastomosing Hemangioma of the Ovary: A Clinicopathological Study of Six Cases with Stromal Luteinization

We report six cases of anastomosing hemangioma of the ovary. All lesions were unilateral and arose in 43 to 81 year old females. In all but one patient, the tumor was asymptomatic and represented incidental finding. The exception was a tumor associated with massive ascites and elevated CA 125. The tumors were, on cut section, spongy and dark violet in color. The size of tumors ranged from 0.5 to 3.5 cm. All lesions showed the same histological features and consisted of capillary sized anastomosing vessels with sinusoid-like pattern intermingled with sporadic medium sized vessels. Interestingly, in all cases there were areas of luteinized cells at the tumor periphery, which ranged from rare small nests to multiple and commonly confluent areas. In one tumor, components of mature adipose tissue were present. Immunohistochemically, all tumors were CD31 and CD34 positive. Other markers examined were negative, including; estrogen receptor, progesterone receptor, androgen receptor, and D2–40. Proliferative activity (Ki-67 index) was very low in all cases. Anastomosing hemangioma is a rare entity, only 8 lesions occurring in ovary has been described from its initial description in 2009. We report six additional cases with their clinicopathological correlation.
CGMOS: Certainty Guided Minority OverSampling

Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using minority oversampling, whereas the SMOTE algorithm is a successful oversampling algorithm with numerous extensions. SMOTE extensions do not have a theoretical guarantee during training to work better than SMOTE and in many instances their performance is data dependent. In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance. The proposed approach considers the classification performance of both the majority and minority classes. In the proposed approach CGMOS (Certainty Guided Minority OverSampling) new data points are added by considering certainty changes in the dataset. The paper provides a proof that the proposed algorithm is guaranteed to work better than SMOTE for training data. Further, experimental results on 30 real-world datasets show that CGMOS works better than existing algorithms when using 6 different classifiers.
The relationship between nature connectedness and happiness: a meta-analysis

Research suggests that contact with nature can be beneficial, for example leading to improvements in mood, cognition, and health. A distinct but related idea is the personality construct of subjective nature connectedness, a stable individual difference in cognitive, affective, and experiential connection with the natural environment. Subjective nature connectedness is a strong predictor of pro-environmental attitudes and behaviors that may also be positively associated with subjective well-being. This meta-analysis was conducted to examine the relationship between nature connectedness and happiness. Based on 30 samples (n = 8523), a fixed-effect meta-analysis found a small but significant effect size (r = 0.19). Those who are more connected to nature tended to experience more positive affect, vitality, and life satisfaction compared to those less connected to nature. Publication status, year, average age, and percentage of females in the sample were not significant moderators. Vitality had the strongest relationship with nature connectedness (r = 0.24), followed by positive affect (r = 0.22) and life satisfaction (r = 0.17). In terms of specific nature connectedness measures, associations were the strongest between happiness and inclusion of nature in self (r = 0.27), compared to nature relatedness (r = 0.18) and connectedness to nature (r = 0.18). This research highlights the importance of considering personality when examining the psychological benefits of nature. The results suggest that closer human-nature relationships do not have to come at the expense of happiness. Rather, this meta-analysis shows that being connected to nature and feeling happy are, in fact, connected.
Low power sensor design for IoT and mobile healthcare applications

This paper reviews recent advances in radar sensor design for low-power healthcare, indoor real-time positioning and other applications of IoT. Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy, detection range and power consumption. While many of the reported designs were prototypes for concept verification, several integrated radar systems have been demonstrated with reliable measured results with demo systems. A performance comparison of latest radar chip designs has been provided to show their features of different architectures. With great development of IoT, short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.
Agricultural Crop Yield Prediction Using Artificial Neural Network Approach Miss .

By considering various situations of climatologically phenomena affecting local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield. Various researches have been done exploring the connections between large-scale climatologically phenomena and crop yield. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, to increase their effectiveness. Crop prediction methodology is used to predict the suitable crop by sensing various parameter of soil and also parameter related to atmosphere. Parameters like type of soil, PH, nitrogen, phosphate, potassium, organic carbon, calcium, magnesium, sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity. For that purpose we are used artificial neural network (ANN).
The Computation of All 4R Serial Spherical Wrists With an Isotropic Architecture

A spherical wrist of the serial type is said to be isotropic if it can attain a posture whereby the singular values of its Jacobian matrix are all identical and nonzero. What isotropy brings about is robustness to manufacturing, assembly, and measurement errors, thereby guaranteeing a maximum orientation accuracy. In this paper we investigate the existence of redundant isotropic architectures, which should add to the dexterity of the wrist under design by virtue of its extra degree of freedom. The problem formulation leads to a system of eight quadratic equations with eight unknowns. The Bezout number of this system is thus 2 = 256, its BKK bound being 192. However, the actual number of solutions is shown to be 32. We list all solutions of the foregoing algebraic problem. All these solutions are real, but distinct solutions do not necessarily lead to distinct manipulators. Upon discarding those algebraic solutions that yield no new wrists, we end up with exactly eight distinct architectures, the eight corresponding manipulators being displayed at their isotropic posture.
Haplogroup Prediction from Y-STR Values Using a Bayesian-Allele-Frequency Approach

A new Bayesian allele-frequency approach to predicting the Y-chromosome haplogroup from a set of Y-STR marker values is presented and compared to other approaches. The method has been implemented in an Excel-based program, where an arbitrary number of STR markers may be input and a “goodness of fit” score for 15 haplogroups (C, E3a, E3b, G, H, I1a, I1b1, I1b2, J, L, N, O, Q, R1a, and R1b) and the Bayesian probability for each haplogroup is returned. This method has been applied to 100 R1b haplotypes, 50 I1a haplotypes (all having 37 STR markers available), and 54 of the YCC sample set (having 20 STR markers available).
An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data.



Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data.



We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94.



The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy.



The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation

Different evaluation measures assess different character istics of machine learning algorithms. The empirical evaluation of alg orithms and classifiers is a matter of on-going debate among researchers. Most measu res in use today focus on a classifier’s ability to identify classes correctl y. We note other useful properties, such as failure avoidance or class discrimi nation, and we suggest measures to evaluate such properties. These measures – Youd en’s index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the fie ld of electronic negotiations. We also list other learning problems which ma y benefit from the application of these measures.
Analytical methods for estimating equivalent thermal conductivity in impregnated electrical windings formed using Litz wire

This paper discusses two analytical methods used in estimating the equivalent thermal conductivity of impregnated electrical windings constructed with Litz wire. Both methods are based on a double-homogenisation approach consecutively employing the individual winding conductors and wire bundles. The first method is suitable for Litz wire with round-profiled enamel-coated conductors and round-shaped bundles; whereas the second method is tailored for compacted Litz wires with conductors and/or bundles having square or rectangular profiles. The work conducted herein expands upon established methods for cylindrical conductor forms , and develops an equivalent lumped-parameter thermal network for rectangular forms. This network derives analytical formulae which represents the winding's equivalent thermal conductivity and directly accounts for any thermal anisotropy. The estimates of equivalent thermal conductivity from theoretical, analytical and finite element (FE) methods have been supplemented with experimental data using impregnated winding samples and are shown to have good correlation.
Development of a Single-Phase PWM AC Controller

An AC chopper controller with symmetrical Pulse-Width Modulation (PWM) is proposed to achieve better performance for a single-phase induction motor compared to phase-angle control line-commutated voltage controllers and integral-cycle control of thyristors. Forced commutated device IGBT controlled by a microcontroller was used in the AC chopper which has the advantages of simplicity, ability to control large amounts of power and low waveform distortion. In this paper the simulation and hardware models of a simple single phase IGBT An AC controller has been developed which showed good results.
Kernel Null Space Methods for Novelty Detection

Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and Image Net. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.
Is trust robust?: an analysis of trust-based recommendation

Systems that adapt to input from users are susceptible to attacks from those same users. Recommender systems are common targets for such attacks since there are financial, political and many other motivations for influencing the promotion or demotion of recommendable items [2].Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy and robustness of recommendations. In this paper we examine the effect of using five different trust models in the recommendation process on the robustness of collaborative filtering in an attack situation. In our analysis we also consider the quality and accuracy of recommendations. Our results caution that including trust models in recommendation can either reduce or increase prediction shift for an attacked item depending on the model-building process used, while highlighting approaches that appear to be more robust.
Cellular mechanisms of tissue fibrosis. 5. Novel insights into liver fibrosis.

Liver fibrosis is the common scarring reaction associated with chronic liver injury that results from prolonged parenchymal cell injury and/or inflammation. The fibrogenic response is characterized by progressive accumulation of extracellular matrix components enriched in fibrillar collagens and a failure of matrix turnover. This process is driven by a heterogeneous population of hepatic myofibroblasts, which mainly derive from hepatic stellate cells and portal fibroblasts. Regression of fibrosis can be achieved by the successful control of chronic liver injury, owing to termination of the fibrogenic reaction following clearance of hepatic myofibroblasts and restoration of fibrolytic pathways. Understanding of the complex network underlying liver fibrogenesis has allowed the identification of a large number of antifibrotic targets, but no antifibrotic drug has as yet been approved. This review will highlight recent advances regarding the mechanisms that regulate liver fibrogenesis and fibrosis regression, with special focus on novel signaling pathways and the role of inflammatory cells. Translation of these findings to therapies will require continued efforts to develop multitarget therapeutic approaches that will improve the grim prognosis of liver cirrhosis.
Therapeutic potential of culinary-medicinal mushrooms for the management of neurodegenerative diseases: diversity, metabolite, and mechanism.

Mushrooms have long been used not only as food but also for the treatment of various ailments. Although at its infancy, accumulated evidence suggested that culinary-medicinal mushrooms may play an important role in the prevention of many age-associated neurological dysfunctions, including Alzheimer's and Parkinson's diseases. Therefore, efforts have been devoted to a search for more mushroom species that may improve memory and cognition functions. Such mushrooms include Hericium erinaceus, Ganoderma lucidum, Sarcodon spp., Antrodia camphorata, Pleurotus giganteus, Lignosus rhinocerotis, Grifola frondosa, and many more. Here, we review over 20 different brain-improving culinary-medicinal mushrooms and at least 80 different bioactive secondary metabolites isolated from them. The mushrooms (either extracts from basidiocarps/mycelia or isolated compounds) reduced beta amyloid-induced neurotoxicity and had anti-acetylcholinesterase, neurite outgrowth stimulation, nerve growth factor (NGF) synthesis, neuroprotective, antioxidant, and anti-(neuro)inflammatory effects. The in vitro and in vivo studies on the molecular mechanisms responsible for the bioactive effects of mushrooms are also discussed. Mushrooms can be considered as useful therapeutic agents in the management and/or treatment of neurodegeneration diseases. However, this review focuses on in vitro evidence and clinical trials with humans are needed.
‘ It ' s just drama ’ : teen perspectives on conflict and aggression in a networked era

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Web resources for language modeling in conversational speech recognition

This article describes a methodology for collecting text from the Web to match a target sublanguage both in style (register) and topic. Unlike other work that estimates n-gram statistics from page counts, the approach here is to select and filter documents, which provides more control over the type of material contributing to the n-gram counts. The data can be used in a variety of ways; here, the different sources are combined in two types of mixture models. Focusing on conversational speech where data collection can be quite costly, experiments demonstrate the positive impact of Web collections on several tasks with varying amounts of data, including Mandarin and English telephone conversations and English meetings and lectures.