nils-herrmann/clean_scirepeval huggingface dataset:
Increasing FastSLAM accuracy for radar data by integrating the Doppler information

This paper presents a modified FastSLAM approach for the specific application of radar sensors using the Doppler information to increase the localization and map accuracy. The developed approach is based on the FastSLAM 2.0 algorithm. It is shown how the FastSLAM 2.0 approach can be significantly improved by taking the Doppler information into account. Therefore, the modelled, so-called expected Doppler, and the measured Doppler are compared for every detection. Both, simulations and experiments on real world data show the increase in accuracy of the modified FastSLAM approach by incorporating the Doppler measurements of automotive radar sensors. The proposed algorithm is compared to the state-of-the-art FastSLAM 2.0 algorithm and the vehicle odometry, whereas profiles of an Automotive Dynamic Motion Analyzer serve as the reference.
Queryable Semantics to Detect Cyber-Attacks: A Flow-Based Detection Approach

Cyber-attacks continue to increase worldwide, leading to significant loss or misuse of information assets. Most of the existing intrusion detection systems rely on per-packet inspection, a resource consuming task in today’s high speed networks. A recent trend is to analyze netflows (or simply flows) instead of packets, a technique performed at a relative low level leading to high false alarm rates. Since analyzing raw data extracted from flows lacks the semantic information needed to discover attacks, a novel approach is introduced, which uses contextual information to automatically identify and query possible semantic links between different types of suspicious activities extracted from flows. Time, location, and other contextual information mined from flows is applied to generate semantic links among alerts raised in response to suspicious flows. These semantic links are identified through an inference process on probabilistic semantic link networks (SLNs), which receive an initial prediction from a classifier that analyzes incoming flows. The SLNs are then queried at run-time to retrieve other relevant predictions. We show that our approach can be extended to detect unknown attacks in flows as variations of known attacks. An extensive validation of our approach has been performed with a prototype system on several benchmark datasets yielding very promising results in detecting both known and unknown attacks.
Chronic mitragynine (kratom) enhances punishment resistance in natural reward seeking and impairs place learning in mice.

Kratom (Mitragyna speciosa) is a widely abused herbal drug preparation in Southeast Asia. It is often consumed as a substitute for heroin, but imposing itself unknown harms and addictive burdens. Mitragynine is the major psychostimulant constituent of kratom that has recently been reported to induce morphine-like behavioural and cognitive effects in rodents. The effects of chronic consumption on non-drug related behaviours are still unclear. In the present study, we investigated the effects of chronic mitragynine treatment on spontaneous activity, reward-related behaviour and cognition in mice in an IntelliCage® system, and compared them with those of morphine and Δ-9-tetrahydrocannabinol (THC). We found that chronic mitragynine treatment significantly potentiated horizontal exploratory activity. It enhanced spontaneous sucrose preference and also its persistence when the preference had aversive consequences. Furthermore, mitragynine impaired place learning and its reversal. Thereby, mitragynine effects closely resembled that of morphine and THC sensitisation. These findings suggest that chronic mitragynine exposure enhances spontaneous locomotor activity and the preference for natural rewards, but impairs learning and memory. These findings confirm pleiotropic effects of mitragynine (kratom) on human lifestyle, but may also support the recognition of the drug's harm potential.
Do Physicians' Financial Incentives Affect Medical Treatment and Patient Health?

We investigate whether physicians' financial incentives influence health care supply, technology diffusion, and resulting patient outcomes. In 1997, Medicare consolidated the geographic regions across which it adjusts physician payments, generating area-specific price shocks. Areas with higher payment shocks experience significant increases in health care supply. On average, a 2 percent increase in payment rates leads to a 3 percent increase in care provision. Elective procedures such as cataract surgery respond much more strongly than less discretionary services. Non-radiologists expand their provision of MRIs, suggesting effects on technology adoption. We estimate economically small health impacts, albeit with limited precision.
Toward Intelligent Detection Modelling for Adversarial Samples in Convolutional Neural Networks

Deep Neural Networks (DNNs) are hierarchical nonlinear architectures that have been widely used in artificial intelligence applications. However, these models are vulnerable to adversarial perturbations which add changes slightly and are crafted explicitly to fool the model. Such attacks will cause the neural network to completely change its classification of data. Although various defense strategies have been proposed, existing defense methods have two limitations. First, the discovery success rate is not very high. Second, existing methods depend on the output of a particular layer in a specific learning structure. In this paper, we propose a powerful method for adversarial samples using Large Margin Cosine Estimate(LMCE). By iteratively calculating the large-margin cosine uncertainty estimates between the model predictions, the results can be regarded as a novel measurement of model uncertainty estimation and is available to detect adversarial samples by training using a simple machine learning algorithm. Comparing it with the way in which adversar- ial samples are generated, it is confirmed that this measurement can better distinguish hostile disturbances. We modeled deep neural network attacks and established defense mechanisms against various types of adversarial attacks. Classifier gets better performance than the baseline model. The approach is validated on a series of standard datasets including MNIST and CIFAR −10, outperforming previous ensemble method with strong statistical significance. Experiments indicate that our approach generalizes better across different architectures and attacks.
MetaSpace: Full-body Tracking for Immersive Multiperson Virtual Reality

Most current virtual reality (VR) interactions are mediated by hand-held input devices or hand gestures and they usually display only a partial representation of the user in the synthetic environment. We believe, representing the user as a full avatar that is controlled by natural movements of the person in the real world will lead to a greater sense of presence in VR. Possible applications exist in various domains such as entertainment, therapy, travel, real estate, education, social interaction and professional assistance. In this demo, we present MetaSpace, a virtual reality system that allows co-located users to explore a VR world together by walking around in physical space. Each user's body is represented by an avatar that is dynamically controlled by their body movements. We achieve this by tracking each user's body with a Kinect device such that their physical movements are mirrored in the virtual world. Users can see their own avatar and the other person's avatar allowing them to perceive and act intuitively in the virtual environment.
Measuring student engagement among elementary students: pilot of the Student Engagement Instrument--Elementary Version.

Early school withdrawal, commonly referred to as dropout, is associated with a plethora of negative outcomes for students, schools, and society. Student engagement, however, presents as a promising theoretical model and cornerstone of school completion interventions. The purpose of the present study was to validate the Student Engagement Instrument-Elementary Version (SEI-E). The psychometric properties of this measure were assessed based on the responses of an ethnically diverse sample of 1,943 students from an urban locale. Exploratory and confirmatory factor analyses indicated that the 4-factor model of student engagement provided the best fit for the current data, which is divergent from previous SEI studies suggesting 5- and 6-factor models. Discussion and implications of these findings are presented in the context of student engagement and dropout prevention.
Occurrence of penile intraepithelial neoplasia following adalimumab treatment for psoriatic arthritis.

with germline mutations of KRT1 included PPK. On the other hand, keratin genes were excluded as the susceptibility gene for a family with IHCM without PPK by linkage analysis with markers of chromosome 12q and 17q loci. Thus, the cases of IHCM without PPK might have mutations in another gene or be distinct disorder, although the characteristics of histologic and electron microscopic findings of the skin lesions are common. Further investigations are needed to clarify the susceptibility genes for IHCM.
Silicon nanowire based biosensing platform for electrochemical sensing of Mebendazole drug activity on breast cancer cells.

Electrochemical approaches have played crucial roles in bio sensing because of their Potential in achieving sensitive, specific and low-cost detection of biomolecules and other bio evidences. Engineering the electrochemical sensing interface with nanomaterials tends to new generations of label-free biosensors with improved performances in terms of sensitive area and response signals. Here we applied Silicon Nanowire (SiNW) array electrodes (in an integrated architecture of working, counter and reference electrodes) grown by low pressure chemical vapor deposition (LPCVD) system with VLS procedure to electrochemically diagnose the presence of breast cancer cells as well as their response to anticancer drugs. Mebendazole (MBZ), has been used as antitubulin drug. It perturbs the anodic/cathodic response of the cell covered biosensor by releasing Cytochrome C in cytoplasm. Reduction of cytochrome C would change the ionic state of the cells monitored by SiNW biosensor. By applying well direct bioelectrical contacts with cancer cells, SiNWs can detect minor signal transduction and bio recognition events, resulting in precise biosensing. Our device detected the trace of MBZ drugs (with the concentration of 2nM) on electrochemical activity MCF-7 cells. Also, experimented biological analysis such as confocal and Flowcytometry assays confirmed the electrochemical results.
A Robust Change-Point Detection Method by Eliminating Sparse Noises from Time Series

Singular Spectrum Transform (SST) is a fundamental subspace analysis technique which has been widely adopted for solving change-point detection (CPD) problems in information security applications. However, the performance of a SST based CPD algorithm is limited to the lack of robustness to corrupted observations with large noises in practice. Based on the observation that large noises in practical time series are generally sparse, in this paper, we study a combination of Robust Principal Component Analysis (RPCA) and SST to obtain a robust CPD algorithm dealing with sparse large noises. The sparse large noises are to be eliminated from observation trajectory matrices by performing a low-rank matrix recovery procedure of RPCA. The noise-eliminated matrices are then used to extract SST subspaces for CPD. The effectiveness of the proposed method is demonstrated through experiments based on both synthetic and real-world datasets. Experimental results show that the proposed method outperforms the competing state-of-the-arts in terms of detection accuracy for time series with sparse large noises.
Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches

In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The problem of modeling soccer data has become increasingly popular in the last few years, with the prediction of results being the most popular topic. In this paper, we propose a Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches. The model was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.
A wide band transition from waveguide to differential microstrip lines

A novel transition from rectangular waveguide to differential microstrip lines is illustrated in this paper. It transfers the dominant TE10 mode signal in a rectangular waveguide to a differential mode signal in the coupled microstrip lines. The common mode signal in the coupled microstrip lines is highly rejected. The transition was designed at 75 GHz, which is the center frequency of E band and simulated by a 3D EM simulator. It has a wide bandwidth of 19 GHz for -15 dB return loss of the waveguide port. Several prototypes of the transitions were fabricated and measured. The measurement results agree very well with the simulation. The compact size and the simple fabrication enable the transition to be employed in a number of millimeter-wave applications.
Investigating the relationship between library anxiety and emotional intelligence

This paper aims to investigate the relationship between library anxiety and emotional intelligence of Bushehr University of Medical Sciences’ students and Persian Gulf University’s students in Bushehr municipality. In this descriptive study which is of correlation type, 700 students of Bushehr University of Medical Sciences and the Persian Gulf University selected through stratified random sampling. Required data has been collected using normalized Siberia Shrink’s emotional intelligence questionnaire and localized Bostick’s library anxiety scale. The results show that the rate of library anxiety among students is less than average (91.73%) except “mechanical factors”. There is not a significant difference in all factors of library anxiety except “interaction with librarian” between male and female. The findings also indicate that there is a negative significant relationship between library anxiety and emotional intelligence (r= -0.41). According to the results, it seems that by improving the emotional intelligence we can decrease the rate of library anxiety among students during their search in a library. Emotional intelligence can optimize academic library’s productivity.
Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.
Analytical Modeling of Partially Shaded Photovoltaic Systems

As of today, the considerable influence of select environmental variables, especially irradiance intensity, must still be accounted for whenever discussing the performance of a solar system. Therefore, an extensive, dependable modeling method is required in investigating the most suitable Maximum Power Point Tracking (MPPT) method under different conditions. Following these requirements, MATLAB-programmed modeling and simulation of photovoltaic systems is presented here, by focusing on the effects of partial shading on the output of the photovoltaic (PV) systems. End results prove the reliability of the proposed model in replicating the aforementioned output characteristics in the prescribed setting. The proposed model is chosen because it can, conveniently, simulate the behavior of different ranges of PV systems from a single PV module through the multidimensional PV structure.
The public health impact of obesity.

The increase in obesity worldwide will have an important impact on the global incidence of cardiovascular disease, type 2 diabetes mellitus, cancer, osteoarthritis, work disability, and sleep apnea. Obesity has a more pronounced impact on morbidity than on mortality. Disability due to obesity-related cardiovascular diseases will increase particularly in industrialized countries, as patients survive cardiovascular diseases in these countries more often than in nonindustrialized countries. Disability due to obesity-related type 2 diabetes will increase particularly in industrializing countries, as insulin supply is usually insufficient in these countries. As a result, in these countries, an increase in disabling nephropathy, arteriosclerosis, neuropathy, and retinopathy is expected. Increases in the prevalence of obesity will potentially lead to an increase in the number of years that subjects suffer from obesity-related morbidity and disability. A 1% increase in the prevalence of obesity in such countries as India and China leads to 20 million additional cases of obesity. Prevention programs will stem the obesity epidemic more efficiently than weight-loss programs. However, only a few prevention programs have been developed or implemented, and the success rates reported to date have been low. Obesity prevention programs should be high on the scientific and political agenda in both industrialized and industrializing countries.
Wideband hybrid precoder for massive MIMO systems

This paper studies wideband hybrid precoder for downlink space-division multiple-access and orthogonal frequency-division multiple-access (SDMA-OFDMA) massive multi-input multi-output (MIMO) systems. We first derive an iterative algorithm to alternatingly optimize the phase-shifter based wideband analog precoder and low-dimensional digital precoders, then an efficient low-complexity non-iterative hybrid precoder proposes. Simulation results show that in wideband systems the performance of hybrid precoder is affected by the employed frequency-domain scheduling method and the number of available radio frequency (RF) chains, which can perform as well as narrowband hybrid precoder when greedy scheduling is employed and the number of RF chains is large.
Lasers in dermatology : Four decades of progress

Advances in laser technology have progressed so rapidly during the past decade that successful treatment of many cutaneous concerns and congenital defects, including vascular and pigmented lesions, tattoos, scars and unwanted haircan be achieved. The demand for laser surgery has increased as a result of the relative ease with low incidence of adverse postoperative sequelae. In this review, the currently available laser systems with cutaneous applications are outlined to identify the various types of dermatologic lasers available, to list their clinical indications and to understand the possible side effects.
Interoceptive robustness through environment-mediated morphological development

Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies.
TTP - A time-triggered protocol for fault-tolerant real-time systems

The Time-Triggered Protocol (TTP), which is intended for use in distributed real-time control applications that require a high dependability and guaranteed timeliness, is discussed. It integrates all services that are required in the design of a fault-tolerant real-time system, such as predictable message transmission, message acknowledgment in group communication, clock synchronization, membership, rapid mode changes, redundancy management, and temporary blackout handling. It supports fault-tolerant configurations with replicated nodes and replicated communication channels. TTP provides these services with a small overhead so it can be used efficiently on twisted pair channels as well as on fiber optic networks.
Estuarine Microbial Food Web Patterns in a Lake Erie Coastal Wetland

Composition and distribution of planktonic protists were examined relative to microbial food web dynamics (growth, grazing, and nitrogen cycling rates) at the Old Woman Creek (OWC) National Estuarine Research Reserve during an episodic storm event in July 2003. More than 150 protistan taxa were identified based on morphology. Species richness and microbial biomass measured via microscopy and flow cytometry increased along a stream–lake (Lake Erie) transect and peaked at the confluence. Water column ammonium (NH 4 + ) uptake (0.06 to 1.82 μM N h–1) and regeneration (0.04 to 0.55 μM N h–1) rates, measured using 15NH 4 + isotope dilution, followed the same pattern. Large light/dark NH 4 + uptake differences were observed in the hypereutrophic OWC interior, but not at the phosphorus-limited Lake Erie site, reflecting the microbial community structural shift from net autotrophic to net heterotrophic. Despite this shift, microbial grazers (mostly choreotrich ciliates, taxon-specific growth rates up to 2.9 d–1) controlled nanophytoplankton and bacteria at all sites by consuming 76 to 110% and 56 to 97% of their daily production, respectively, in dilution experiments. Overall, distribution patterns and dynamics of microbial communities in OWC resemble those in marine estuaries, where plankton productivity increases along the river–sea gradient and reaches its maximum at the confluence.