nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction

Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal CNN-MLP neural network architecture that utilizes both domain-specific feature engineering as well as learned representations from raw data. We illustrate the effectiveness of such network designs in the chemical sciences, for predicting biodegradability. DeepBioD, a multimodal CNN-MLP network is more accurate than either standalone network designs, and achieves an error classification rate of 0.125 that is 27% lower than the current state-of-theart. Thus, our work indicates that combining traditional feature engineering with representation learning can be effective, particularly in situations where labeled data is limited.
Analyzing Student Performance in Distance Learning with Genetic Algorithms and Decision Trees

Students who enrol in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulties in advancing beyond the introductory courses. We have embarked in an effort to analyse their academic performance throughout the academic year, as measured by the homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm based induction of decision trees and argue why our approach has a potential for developing into an alert tool.
WAKE-up: A wearable ankle knee exoskeleton

In this paper we present the alpha-prototype of the WAKE-up, a wearable robotic device for the rehabilitation of locomotion of pediatric subjects with neurological diseases such as Cerebral Palsy. The WAKE-up is an active knee-ankle orthosis. It is composed of two robotic modules for the rehabilitation of knee and ankle, respectively. Each module can be utilized either alone or together with the other one. The working principle is based on series elastic actuators (SEA), i.e., dc motors equipped with a torsional spring mounted in series to avoid the direct connection of the actuator with the patient's limb. A SEA permits the control of the force and the emulation of different orthoses with given value of stiffness. The torque transmission is achieved by a timing belt and it is mediated by a torsional spring. The experimental tests conducted on each modules confirmed a good precision of the spring deflection control (position error <; 2°) and good overall performances of the force control obtained with the spring stiffness chosen at the design phase.
Stiff man syndrome and related conditions.

The stiff man syndrome (SMS) and its variants, focal SMS, stiff limb (or leg) syndrome (SLS), jerking SMS, and progressive encephalomyelitis with rigidity and myoclonus (PERM), appear to occur more frequently than hitherto thought. A characteristic ensemble of symptoms and signs allows a tentative clinical diagnosis. Supportive ancillary findings include (1) the demonstration of continuous muscle activity in trunk and proximal limb muscles despite attempted relaxation, (2) enhanced exteroceptive reflexes, and (3) antibodies to glutamic acid decarboxylase (GAD) in both serum and spinal fluid. Antibodies to GAD are not diagnostic or specific for SMS and the role of these autoantibodies in the pathogenesis of SMS/SLS/PERM is the subject of debate and difficult to reconcile on the basis of our present knowledge. Nevertheless, evidence is emerging to suggest that SMS/SLS/PERM are manifestations of an immune-mediated chronic encephalomyelitis and immunomodulation is an effective therapeutic approach.
3.5-D Blocking Optimization for Stencil Computations on Modern CPUs and GPUs

Stencil computation sweeps over a spatial grid over multiple time steps to perform nearest-neighbor computations. The bandwidth-to-compute requirement for a large class of stencil kernels is very high, and their performance is bound by the available memory bandwidth. Since memory bandwidth grows slower than compute, the performance of stencil kernels will not scale with increasing compute density. We present a novel 3.5D-blocking algorithm that performs 2.5D-spatial and temporal blocking of the input grid into on-chip memory for both CPUs and GPUs. The resultant algorithm is amenable to both thread- level and data-level parallelism, and scales near-linearly with the SIMD width and multiple-cores. Our performance numbers are faster or comparable to state-of-the-art-stencil implementations on CPUs and GPUs. Our implementation of 7-point-stencil is 1.5X-faster on CPUs, and 1.8X faster on GPUs for single- precision floating point inputs than previously reported numbers. For Lattice Boltzmann methods, the corresponding speedup number on CPUs is 2.1X.
Cannabinoids and glial cells: possible mechanism to understand schizophrenia

Clinical and neurobiological findings have reported the involvement of endocannabinoid signaling in the pathophysiology of schizophrenia. This system modulates dopaminergic and glutamatergic neurotransmission that is associated with positive, negative, and cognitive symptoms of schizophrenia. Despite neurotransmitter impairments, increasing evidence points to a role of glial cells in schizophrenia pathobiology. Glial cells encompass three main groups: oligodendrocytes, microglia, and astrocytes. These cells promote several neurobiological functions, such as myelination of axons, metabolic and structural support, and immune response in the central nervous system. Impairments in glial cells lead to disruptions in communication and in the homeostasis of neurons that play role in pathobiology of disorders such as schizophrenia. Therefore, data suggest that glial cells may be a potential pharmacological tool to treat schizophrenia and other brain disorders. In this regard, glial cells express cannabinoid receptors and synthesize endocannabinoids, and cannabinoid drugs affect some functions of these cells that can be implicated in schizophrenia pathobiology. Thus, the aim of this review is to provide data about the glial changes observed in schizophrenia, and how cannabinoids could modulate these alterations.
Weather prediction based on fuzzy logic algorithm for supporting general farming automation system

Many automation and monitoring systems in agriculture do not have a calculation system for watering based on weather. Of these issues, will be discussed weather prediction system using fuzzy logic algorithm for supporting General Farming Automation. The weather calculation system works by taking a weather prediction data from the Weather Service Provider (WSP). Furthermore, it also retrieves soil moisture sensor value and rainfall sensor value. After that, the system will calculate using fuzzy logic algorithm whether the plant should be watered or not. The weather calculation system will help the performance of the General Farming Automation Control System in order to work automatically. So, the plants still obtain water and nutrients intake are not excessive.
Biodiesel Fuel Production from Algae as Renewable Energy

Biodiesel is biodegradable, less CO2 and NOx emissions. Continuous use of petroleum sourced fuels is now widely recognized as unsustainable because of depleting supplies and the contribution of these fuels to the accumulation of carbon dioxide in the environment. Renewable, carbon neutral, transport fuels are necessary for environmental and economic sustainability. Algae have emerged as one of the most promising sources for biodiesel production. It can be inferred that algae grown in CO2-enriched air can be converted to oily substances. Such an approach can contribute to solve major problems of air pollution resulting from CO2 evolution and future crisis due to a shortage of energy sources. This study was undertaken to know the proper transesterification, amount of biodiesel production (ester) and physical properties of biodiesel. In this study we used common species Oedogonium and Spirogyra to compare the amount of biodiesel production. Algal oil and biodiesel (ester) production was higher in Oedogonium than Spirogyra sp. However, biomass (after oil extraction) was higher in Spirogyra than Oedogonium sp. Sediments (glycerine, water and pigments) was higher in Spirogyra than Oedogonium sp. There was no difference of pH between Spirogyra and Oedogonium sp. These results indicate that biodiesel can be produced from both species and Oedogonium is better source than Spirogyra sp.
Hybrid analogies in conceptual innovation in science

Analogies are ubiquitous in science, both in theory and experiments. Based on an ethnographic study of a research lab in neural engineering, we focus on a case of conceptual innovation where the cross-breeding of two types of analogies led to a breakthrough. In vivo phenomena were recreated in two analogical forms: one, as an in vitro physical model, and the other, as a computational model of the first physical model. The computational model also embodied constraints drawn from the neuroscience and engineering literature. Cross connections and linkages were then made between these two analogical models, over time, to solve problems. We describe how the development of the intermediary, hybrid computational model led to a conceptual innovation, and subsequent engineering innovations. Using this case study, we highlight some of the peculiar features of such hybrid analogies that are now used widely in the sciences and engineering sciences, and the significant questions they raise for current theories of analogy.
A portable, extensible and fast stochastic volatility model calibration using multi and many-core processors

Financial markets change precipitously and on-demand pricing and risk models must be constantly recalibrated to reduce risk. However, certain classes of models are computationally intensive to robustly calibrate to intraday pricesstochastic volatility models being an archetypal example due to the nonconvexity of the objective function. In order to accelerate this procedure through parallel implementation, financial application developers are faced with an ever growing plethora of low-level high-performance computing frameworks such as OpenMP, OpenCL, CUDA, or SIMD intrinsics, and forced to make a tradeoff between performance versus the portability, flexibility and modularity of the code required to facilitate rapid in-house model development and productionization. This paper describes the acceleration of stochastic volatility model calibration on multi-core CPUs and GPUs using the Xcelerit platform. By adopting a simple programming model, the Xcelerit platform enables the application developer to write sequential, high-level C++ code, without concern for low-level highperformance computing frameworks. This platform provides the portability, flexibility and modularity required by application developers. Speedups of up to 30x and 293x are respectively achieved on an Intel Xeon CPU and NVIDIA Tesla K40 GPU, compared to a sequential CPU implementation. The Xcelerit platform implementation is further shown to be equivalent in performance to a low-level CUDA version. Overall, we are able to reduce the entire calibration process time of the sequential implementation from 6, 189 seconds to 183.8 and 17.8 seconds on the CPU and GPU respectively without requiring the developer to reimplement in low-level high performance computing frameworks.
MicroRNA conjugated gold nanoparticles and cell transfection.

While the importance of microRNAs (miRNAs) in cancer treatment or manipulation of genetic expression has been increasingly recognized for developing miRNA-based therapies, the controlled delivery of miRNAs into specific cells constitutes a challenging task. This report describes preliminary findings from an investigation of the conjugation of gold nanoparticles with miRNAs (miRNA-AuNPs) and their cell transfection. The immobilization of miRNAs on the AuNPs was detected, and the surface stability was substantiated by gel electrophoresis assessment of the highly charged characteristics of miRNA-AuNPs and their surface-exchange inactivity with a highly charged surfactant. The miRNA-AuNPs were tested in cell transfection using multiple myeloma cells, demonstrating efficient knockdown in the functional luciferase assay. The findings have important implications for understanding the mechanistic details of cell transfection involving miRNA-conjugated nanoparticles as biosensing or targeting probes.
Goal Reasoning and Trusted Autonomy

This chapter discusses the topic of Goal Reasoning and its relation to Trusted Autonomy. Goal Reasoning studies how autonomous agents can extend their reasoning capabilities beyond their plans and actions, to consider their goals. Such capability allows a Goal Reasoning system to more intelligently react to unexpected events or changes in the environment. We present two different models of Goal Reasoning: Goal-Driven Autonomy (GDA) and goal refinement. We then discuss several research topics related to each, and how they relate to the topic of Trusted Autonomy. Finally, we discuss several directions of ongoing work that are particularly interesting in the context of the chapter: using a model of inverse trust as a basis for adaptive autonomy, and studying how Goal Reasoning agents may choose to rebel (i.e., act contrary to a given command). Benjamin Johnson NRC Research Associate at the US Naval Research Laboratory; Washington, DC; USA e-mail: benjamin.johnson.ctr@nrl.navy.mil Michael W. Floyd Knexus Research Corporation; Springfield, VA; USA e-mail: michael.floyd@knexusresearch.com Alexandra Coman NRC Research Associate at the US Naval Research Laboratory; Washington, DC; USA e-mail: alexandra.coman.ctr.ro@nrl.navy.mil Mark A. Wilson Navy Center for Applied Research in AI, US Naval Research Laboratory; Washington, DC; USA e-mail: mark.wilson@nrl.navy.mil David W. Aha Navy Center for Applied Research in AI, US Naval Research Laboratory; Washington, DC; USA e-mail: david.aha@nrl.navy.mil
Functional thumb orthosis for type I and II boutonniere deformity on the dominant hand in patients with rheumatoid arthritis: a randomized controlled study.

To evaluate the effectiveness of a functional thumb orthosis on the dominant hand of patients with rheumatoid arthritis and boutonniere thumb.



Forty patients with rheumatoid arthritis and boutonniere deformity of the thumb were randomly distributed into two groups. The intervention group used the orthosis daily and the control group used the orthosis only during the evaluation. Participants were evaluated at baseline as well as after 45 and 90 days. Assessments were preformed using the O'Connor Dexterity Test, Jamar dynamometer, pinch gauge, goniometry and the Health Assessment Questionnaire. A visual analogue scale was used to assess thumb pain in the metacarpophalangeal joint.



Patients in the intervention group experienced a statistically significant reduction in pain. The thumb orthosis did not disrupt grip and pinch strength, function, Health Assessment Questionnaire score or dexterity in either group.



The use of thumb orthosis for type I and type II boutonniere deformities was effective in relieving pain.
Artistic Style Transfer for Videos and Spherical Images

Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by Gatys et al. based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong occlusion. Our second approach formulates video stylization as a learning problem. We propose a deep network architecture and training procedures that allow us to stylize arbitrary-length videos in a consistent and stable way, and nearly in real time. We show that the proposed methods clearly outperform simpler baselines both qualitatively and quantitatively. Finally, we propose a way to adapt these approaches also to 360$$^\circ $$ ∘ images and videos as they emerge with recent virtual reality hardware.
An introduction to I2C and SPI protocols

Today, at the low end of the communication protocols we find the inter-integrated circuit (I2C) and the serial peripheral interface (SPI) protocols. Both protocols are well suited for communications between integrated circuits for slow communication with on-board peripherals. The two protocols coexist in modern digital electronics systems, and they probably will continue to compete in the future, as both I2C and SPI are actually quite complementary for this kind of communication.
A combined approach for evaluating papers, authors and scientific journals

An integrated model for ranking scientific publications together with authors and journals recently presented in [Bini, Del Corso, Romani, ETNA 2008] is closely analyzed. The model, which relies on certain adjacency matrices H, K and F obtained from the relations of citation, authorship and publication, provides the ranking by means of the Perron vector of a stochastic matrix obtained by combining H, K and F . Some perturbation theorems concerning the Perron vector previously introduced by the authors are extended to more general cases and a counterexample to a property previously addressed by the authors is presented. The theoretical results confirm the consistency and effectiveness of our model. Some paradigmatic examples are reported together with some results obtained on a real set of data.
Air quality during the 2008 Beijing Olympic Games

China is taking major steps to improve Beijing’s air quality for the 2008 Olympic Games. However, concentrations of fine particulate matter and ozone in Beijing often exceed healthful levels in the summertime. Based on the US EPA’s Models-3/CMAQ model simulation over the Beijing region, we estimate that about 34% of PM2.5 on average and 35–60% of ozone during high ozone episodes at the Olympic Stadium site can be attributed to sources outside Beijing. Neighboring Hebei and Shandong Provinces and the Tianjin Municipality all exert significant influence on Beijing’s air quality. During sustained wind flow from the south, Hebei Province can contribute 50–70% of Beijing’s PM2.5 concentrations and 20–30% of ozone. Controlling only local sources in Beijing will not be sufficient to attain the air quality goal set for the Beijing Olympics. There is an urgent need for regional air quality management studies and new emission control strategies to ensure that the air quality goals for 2008 are met. r 2006
Distributed Diagnosis Using a Condensed Representation of Diagnoses With Application to an Automotive Vehicle

In fault detection and isolation, diagnostic test results are commonly used to compute a set of diagnoses, where each diagnosis points at a set of components which might behave abnormally. In distributed systems consisting of multiple control units, the test results in each unit can be used to compute local diagnoses while all test results in the complete system give the global diagnoses. It is an advantage for both repair and fault-tolerant control to have access to the global diagnoses in each unit since these diagnoses represent all test results in all units. However, when the diagnoses, for example, are to be used to repair a unit, only the components that are used by the unit are of interest. The reason for this is that it is only these components that could have caused the abnormal behavior. However, the global diagnoses might include components from the complete system and therefore often include components that are superfluous for the unit. Motivated by this observation, a new type of diagnosis is proposed, namely, the condensed diagnosis. Each unit has a unique set of condensed diagnoses which represents the global diagnoses. The benefit of the condensed diagnoses is that they only include components used by the unit while still representing the global diagnoses. The proposed method is applied to an automotive vehicle, and the results from the application study show the benefit of using condensed diagnoses compared to global diagnoses.
COBRASTS: A New Approach to Semi-supervised Clustering of Time Series

Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. This paper studies semi-supervised clustering in the context of time series. We show that COBRAS, a stateof-the-art semi-supervised clustering method, can be adapted to this setting. We refer to this approach as COBRAS. An extensive experimental evaluation supports the following claims: (1) COBRAS far outperforms the current state of the art in semi-supervised clustering for time series, and thus presents a new baseline for the field; (2) COBRAS can identify clusters with separated components; (3) COBRAS can identify clusters that are characterized by small local patterns; (4) a small amount of semi-supervision can greatly improve clustering quality for time series; (5) the choice of the clustering algorithm matters (contrary to earlier claims in the literature).
Global path planning method of mobile robot in uncertain environment

Path planning is one of the key technologies in the robot research. The aim of it is to find the shortest safe path in the objective environments. Firstly, the robot is transformed into particle by expanding obstacles method; the obstacle is transformed into particle by multi-round enveloping method. Secondly, we make the Voronoi graph of the particles of obstacle and find the skeleton topology about the feasible path. Following, a new arithmetic named heuristic bidirectional ant colony algorithm is proposed by joining the merit of ant colony algorithm, Dijkstra algorithm and heuristic algorithm, with which we can find the shortest path of the skeleton topology. After transforming the path planning into n-dimensions quadrate feasible region by coordinate transformation and solving it with particle swarm optimization, the optimization of the path planning is acquired.
The role of taurine in improving neural stem cells proliferation and differentiation.

Taurine is one of the most abundant amino acids in the central nervous system and has important functions in the promotion of brain development. This study aimed to determine the mechanistic role of taurine in improving neuronal proliferation, stem cell proliferation, and neural differentiation.



The data for this review were primarily retrieved from the PubMed database from 1985 to 2015 in English. The search string included the keywords taurine, brain development, neuronal, stem cell, proliferation, differentiation, and others. Relevant publications were identified, retrieved, and reviewed.



This review introduces the source, function, and mechanisms of taurine in brain development and provides additional detail regarding the mechanistic role of taurine in improving neuronal proliferation, stem cell proliferation, and neural differentiation. Many studies concerning these aspects are discussed.



Taurine plays an important role in brain development, including neuronal proliferation, stem cell proliferation, and differentiation, via several mechanisms. Taurine can be directly used in clinical applications to improve brain development because it has no toxic effects on humans.