nils-herrmann/clean_scirepeval huggingface dataset:
The NCEP Climate Forecast System

The Climate Forecast System (CFS), the fully coupled ocean-land-atmosphere dynamical seasonal prediction system that became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecast products produced by CPC. The atmospheric component of the CFS is a lower resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). The previous dynamical seasonal forecast system used at NCEP consisted of the 1998 GFS (a.k.a. MRF) and the GFDL MOM1 ocean model. In addition to the replacement of the oceanic and atmospheric components, there are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include: (i) The atmosphere-ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) The CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled 'tier-2' system, which 3 employed multiple bias and flux corrections); (iii) A set of fully coupled retrospective forecasts covering a 24 year period (1981-2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. In contrast " perfect " (i.e., observed) SST was prescribed in the production of the retrospective forecasts for the previously operational dynamical forecast system. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful à priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for …
Vasorelaxant effect of flavonoids through calmodulin inhibition: Ex vivo, in vitro, and in silico approaches.

In our search for potential antihypertensive agents, a series of structurally-related flavonoids was screened. Ex vivo and in vitro biological evaluations indicated that compounds 1-7 displayed an important vasorelaxant effect on the endothelium-intact (E(+)) and -denuded (E(-)) aortic rings test. Their in vitro anti-calmodulin (CaM) properties were determined by means of the inhibitory effect on the activation of the calmodulin-sensitive cAMP phosphodiesterase (PDE1) assay. Molecular modeling experiments were also performed in order to explore the probable binding site of 1-7 with CaM, and the results indicated that they could bind to the protein in the same pocket as trifluoperazine (TFP), a well-known CaM inhibitor.
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches
Inverse problem theory - and methods for model parameter estimation

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading inverse problem theory and methods for model parameter estimation is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
RLScore: Regularized Least-Squares Learners

RLScore is a Python open source module for kernel based machine learning. The library provides implementations of several regularized least-squares (RLS) type of learners. RLS methods for regression and classification, ranking, greedy feature selection, multi-task and zero-shot learning, and unsupervised classification are included. Matrix algebra based computational short-cuts are used to ensure efficiency of both training and cross-validation. A simple API and extensive tutorials allow for easy use of RLScore.
ARTHUR: A Collaborative Augmented Environment for Architectural Design and Urban Planning

Projects in the area of architectural design and urban planning typically engage several architects as well as experts from other professions. While the design and review meetings thus often involve a large number of cooperating participants, the actual design is still done by the individuals in the time between those meetings using desktop PCs and CAD applications. A real collaborative approach to architectural design and urban planning is often limited to early paper-based sketches. In order to overcome these limitations we designed and realized the Augmented Round Table, a new approach to support complex design and planning decisions for architects. While AR has been applied to this area earlier, our approach does not try to replace the use of CAD systems but rather integrates them seamlessly into the collaborative AR environment. The approach is enhanced by intuitive interaction mechanisms that can be easily configured for different application scenarios.
Group Nonnegative Matrix Factorization for EEG Classification

Given electroencephalogram (EEG) data measured from several subjects under the same conditions, our goal is to estimate common task-related bases in a linear model that capture intra-subject variations as well as inter-subject variations. Such bases capture the common phenomenon in group data, which is a core of group analysis. In this paper we present a method of nonnegative matrix factorization (NMF) that is well suited to analyzing EEG data of multiple subjects. The method is referred to as group nonnegative matrix factorization (GNMF) where we seek task-related common bases reflecting both intra-subject and inter-subject variations, as well as bases involving individual characteristics. We compare GNMF with NMF and some modified NMFs, in the task of learning spectral features from EEG data. Experiments on brain computer interface (BCI) competition data indicate that GNMF improves the EEG classification performance. In addition, we also show that GNMF is useful in the task of subject-tosubject transfer where the prediction for an unseen subject is performed based on a linear model learned from different subjects in the same group.
SoK : On the Soundness and Precision of Dynamic Taint Analysis

Taint analysis or dynamic information flow tracking is a key binary analysis technique for revealing data dependencies in programs. It has been used in many different applications, such as memory error detection, vulnerability analysis, malware analysis, and exploit diagnosis. While previous implementations are empirically effective for their chosen tasks, they use manually defined tainting rules which have not been proven to be sound (lack false negatives) or precise (lack false positives). Furthermore, even perfect tainting rules could be incorrectly implemented. We survey a number of existing systems, and find that all suffer from either unsoundness or imprecision, and many are quite imprecise. To improve the situation, we propose a set of formal methods to create tainting rules and verify their soundness and precision, and to verify the correctness of tainting implementations. To show the practicality of this approach, we build a new taint analysis system that is formally verified to be sound and in many cases precise at the instruction level. Our tests with real world workloads (tainted shell commands in both Windows and Linux, and keylogger detection) demonstrate observable advantages of having provable soundness and precision, as compared to existing taint analysis systems.
Hybrid Stabilization of Thoracic Spine Fractures with Sublaminar Bands and Transpedicular Screws: Description of a Surgical Alternative and Review of the Literature

Stabilization of unstable thoracic fractures with transpedicular screws is widely accepted. However, placement of transpedicular screws can cause complications, particularly in the thoracic spine with physiologically small pedicles. Hybrid stabilization, a combination of sublaminar bands and pedicle screws, might reduce the rate of misplaced screws and can be helpful in special anatomic circumstances, such as preexisting scoliosis and osteoporosis. We report about two patients suffering from unstable thoracic fractures, of T5 in one case and T3, T4, and T5 in the other case, with preexisting scoliosis and extremely small pedicles. Additionally, one patient had osteoporosis. Patients received hybrid stabilization with pedicle screws adjacent to the fractured vertebral bodies and sublaminar bands at the level above and below the pedicle screws. No complications occurred. Follow-up was 12 months with clinically uneventful postoperative courses. No signs of implant failure or loss of reduction could be detected. In patients with very small thoracic pedicles, scoliosis, and/or osteoporosis, hybrid stabilization with sublaminar bands and pedicle screws can be a viable alternative to long pedicle screw constructs.
Formulation of New Herbal Salvadora toothpaste with Anti-decay , Wound healing and caring tooth and Periodontal Diseases

Herbal toothpaste Salvadora with comprehensive effective materials for dental health ranging from antibacterial, detergent and whitening properties including benzyl isothiocyanate, alkaloids, and anions such as thiocyanate, sulfate, and nitrate with potential antibacterial feature against oral microbial flora, silica and chloride for oral disinfection and bleaching the tooth, fluoride to strengthen tooth enamel, and saponin with appropriate detergent, and resin which protects tooth enamel by placing on it and is aggregated in Salvadora has been formulated. The paste is also from other herbs extract including valerian and chamomile. Current toothpaste has antibacterial, anti-plaque, anti-tartar and whitening, and wood extract of the toothbrush strengthens the tooth and enamel, and prevents the cancellation of enamel.From the other side, resin present in toothbrush wood creates a proper covering on tooth enamel and protects it against decay and benzyl isothiocyanate and also alkaloids present in miswak wood gives Salvadora toothpaste considerable antibacterial and bactericidal effects. Anti-inflammatory effects of the toothpaste are for apigenin and alpha bisabolol available in chamomile extract and seskuiterpen components including valeric acid with sedating features give the paste sedating and calming effect to oral tissues.
HSV and RGB color histograms comparing for objects tracking among non overlapping FOVs, using CBTF

Object tracking over wide-areas, such as an airport, the downtown of a large city or any large public area, is done by multiple cameras. Especially in realistic application, those cameras have non overlapping Field of Views (FOVs). Multiple camera tracking is very important to establish correspondence among detected objects across different cameras. In this paper we investigate color histogram techniques to evaluate inter-camera tracking algorithm based on object appearances. We compute HSV and RGB color histograms in order to evaluate their performance in establishing correspondence between object appearances in different FOVs before and after Cumulative Brightness Transfer Function (CBTF).
Using Tweets to Predict the Stock Market

It is found that some “important” twitter users’ words can influence the stock prices of certain stocks. The stock price of Tesla – a famous electric automobile company – for example, recently seen a huge rise after Elon Musk, the CEO of Tesla, updated his twitter about the self-driving motors. Besides, the Dow Jones and S&P 500 indexes dropped by about one percent after the Twitter account of Associated Press falsely posted the message about an explosion in the White House.
Glaucoma detection from fundus images using MATLAB GUI

A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB GUI software.
Cannabis and its derivatives: review of medical use.

Use of cannabis is often an under-reported activity in our society. Despite legal restriction, cannabis is often used to relieve chronic and neuropathic pain, and it carries psychotropic and physical adverse effects with a propensity for addiction. This article aims to update the current knowledge and evidence of using cannabis and its derivatives with a view to the sociolegal context and perspectives for future research.



Cannabis use can be traced back to ancient cultures and still continues in our present society despite legal curtailment. The active ingredient, Δ9-tetrahydrocannabinol, accounts for both the physical and psychotropic effects of cannabis. Though clinical trials demonstrate benefits in alleviating chronic and neuropathic pain, there is also significant potential physical and psychotropic side-effects of cannabis. Recent laboratory data highlight synergistic interactions between cannabinoid and opioid receptors, with potential reduction of drug-seeking behavior and opiate sparing effects. Legal rulings also have changed in certain American states, which may lead to wider use of cannabis among eligible persons.



Family physicians need to be cognizant of such changing landscapes with a practical knowledge on the pros and cons of medical marijuana, the legal implications of its use, and possible developments in the future.
Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics

Cluster analysis has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data. Clustering has also been widely adopted by researchers within computer science and especially the database community. K-means is the most famous clustering algorithms. In this paper, the performance of basic k means algorithm is evaluated using various distance metrics for iris dataset, wine dataset, vowel dataset, ionosphere dataset and crude oil dataset by varying no of clusters. From the result analysis we can conclude that the performance of k means algorithm is based on the distance metrics for selected database. Thus, this work will help to select suitable distance metric for particular application.
Influencing choice without awareness

Forcing occurs when a magician influences the audience's decisions without their awareness. To investigate the mechanisms behind this effect, we examined several stimulus and personality predictors. In Study 1, a magician flipped through a deck of playing cards while participants were asked to choose one. Although the magician could influence the choice almost every time (98%), relatively few (9%) noticed this influence. In Study 2, participants observed rapid series of cards on a computer, with one target card shown longer than the rest. We expected people would tend to choose this card without noticing that it was shown longest. Both stimulus and personality factors predicted the choice of card, depending on whether the influence was noticed. These results show that combining real-world and laboratory research can be a powerful way to study magic and can provide new methods to study the feeling of free will.
Temporal logic control in dynamic environments with probabilistic satisfaction guarantees

Mobile robotic systems move in environments that are constantly changing due to the presence of dynamic obstacles. In this work we consider one such environment in which the dynamic nature comes from doors that can open or close during the robot's mission. We derive a solution to the automatic deployment of a robot from a temporal logic specification assuming three different levels of knowledge and sensing capabilities of the robot. Under each one of these settings, the motion of the robot is modeled either as a Markov decision process (MDP) or mixed observability MDP. The objective is to find a control strategy that maximizes the probability of satisfying a specification given in Probabilistic Computation Tree Logic (PCTL). We describe an optimal solution in one setting and sub-optimal, reactive solutions in the other two. We illustrate our methods with simulation results.
Quantifying the channel quality for interference-aware wireless sensor networks

Reliability of communications is key to expand application domains for sensor networks. Since Wireless Sensor Networks (WSN) operate in the license-free Industrial Scientific and Medical (ISM) bands and hence share the spectrum with other wireless technologies, addressing interference is an important challenge. In order to minimize its effect, nodes can dynamically adapt radio resources provided information about current spectrum usage is available.
 We present a new channel quality metric, based on availability of the channel over time, which meaningfully quantifies spectrum usage. We discuss the optimum scanning time for capturing the channel condition while maintaining energy-efficiency. Using data collected from a number of Wi-Fi networks operating in a library building, we show that our metric has strong correlation with the Packet Reception Rate (PRR). This suggests that quantifying interference in the channel can help in adapting resources for better reliability. We present a discussion of the usage of our metric for various resource allocation and adaptation strategies.
A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.
A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings

Cross-language learning allows one to use training data from one language to build models for another language. Many traditional approaches require word-level alignment sentences from parallel corpora, in this paper we define a general bilingual training objective function requiring sentence level parallel corpus only. We propose a variational autoencoding approach for training bilingual word embeddings. The variational model introduces a continuous latent variable to explicitly model the underlying semantics of the parallel sentence pairs and to guide the generation of the sentence pairs. Our model restricts the bilingual word embeddings to represent words in exactly the same continuous vector space. Empirical results on the task of cross lingual document classification has shown that our method is effective.
Application Of Wavelet Transform For Fault Diagnosisof Rolling Element Bearings

The rolling element bearingsare most critical components in a machine. Condition monitoring and fault diagnostics of these bearings are of great concern in industries as most rotating machine failures are often linked to bearing failures. This paper presents a methodology for fault diagnosis of rolling element bearings based on discrete wavelet transform (DWT) and wavelet packet transform (WPT). In order to obtain the useful information from raw data,db02 and db08 wavelets were adopted to decompose the vibration signal acquired from the bearing. Further Denoising technique based on wavelet analysis was applied. This de-noised signal was decomposed up to 7th level by wavelet packet transform (WPT) and 128 wavelet packet node energy coefficients were obtained and analyzed using db04 wavelet.The results show that wavelet packet node energy coefficients are sensitive to the faults in the bearing. The feasibility of the wavelet packet node energy coefficients for fault identification as an index representing the health condition of a bearing is established through this study.