nils-herrmann/clean_scirepeval huggingface dataset:
Attuning: A Communication Process between People with Severe and Profound Intellectual Disability and Their Interaction Partners.
Venue: Journal of applied research in intellectual disabilities JARID

People with severe and profound intellectual disability typically demonstrate a limited ability to communicate effectively. Most of their communications are non verbal, often idiosyncratic and ambiguous. This article aims to identify the process that regulates communications of this group of people with others and to describe the methodological approach that was used to achieve this.  In this qualitative study, two dyads consisting of a person with severe or profound intellectual and multiple disability and a teacher or carer were filmed as they engaged in school based activities. Two 1 hour videotapes were transcribed and analysed using grounded theory.  Attuning was identified within the theory proposed here as a central process that calibrates and regulates communication.  Attuning is conceptualized as a bidirectional, dyadic communication process. Understanding this process may support more effective communication between people with severe or profound intellectual and multiple disability and their interaction partners.
Semiotics of virtual reality as a communication process
Venue: Behav. Inf. Technol.

The experience of immersive virtual reality (VR) can be considered as a communication process between human beings, mediated by computer systems, which uses visualisation and other sensory stimulation. In this paper, we analyse how VR characteristics can be explored using semiotic theory and, with methods of generative semiotics, we explore aspects of narrative and interaction in VR. We propose a semiotic analysis of VR communication focusing on syntax, semantics and pragmatics and considering also some principles of generative semiotics. The syntactic level is analysed as determined by the characteristics of the visual communication adopted. The semantic of VR is related to the functional model chosen to realise the virtual system. The pragmatic of VR is based on the human computer interaction that changes the user's role. We explore how these aspects can be characterised in the context of VR communication design and what principles can be adopted for a VR application, and we present an analysis and a classification of the iconic signs that are being used in VR. Moreover, we present a framework that can be used to classify and describe different kinds of virtual reality systems and to better understand communication in VR, and we use it to classify eight popular systems for e learning and collaboration.
The communication process in Telenursing: integrative review.
Venue: Revista brasileira de enfermagem

to identify scientific evidence about the communication process in Telenursing and analyze them. METHOD integrative review performed in March 2014. The search strategy, structured with the descriptors "telenursing" and "communication" was implemented in the databases Medline, Bireme, Cinahl, Scopus, Web of Science, Scielo, and Cochrane. RESULTS ten studies were selected after inclusion and exclusion criteria. The main challenges were: the clinical condition of patients, the possibility for inadequate communication to cause misconduct, the absence of visual references in interactions without video, and difficulty understanding nonverbal communication.  distance imposes communicative barriers in all elements: sender, recipient and message; and in both ways of transmission, verbal and nonverbal. The main difficulty is to understand nonverbal communication. To properly behave in this context, nurses must receive specific training to develop abilities and communication skills.
Review of key telepsychiatry outcomes.
Venue: World journal of psychiatry

To conduct a review of the telepsychiatry literature.  We conducted a systematic search of the literature on telepsychiatry using the search terms, "telepsychiatry" "telemental health" "telecare" "telemedicine" "e health" and "videoconferencing" To meet criteria for inclusion, studies had to: (1) be published in a peer reviewed journal after the year 2000; (2) be written in English; (3) use videoconferencing technology for the provision of mental health assessment or treatment services; and (4) use an adequately powered randomized controlled trial design in the case of treatment outcome studies. Out of 1976 studies identified by searches in PubMed (Medline database) Ovid medline, PsychInfo, Embase, and EBSCO PSYCH, 452 met inclusion criteria. Studies that met all inclusion criteria were organized into one of six categories: (1) satisfaction; (2) reliability; (3) treatment outcomes; (4) implementation outcomes; (5) cost effectiveness; and (6) and legal issues. All disagreements were resolved by reassessing study characteristics and discussion.  Overall, patients and providers are generally satisfied with telepsychiatry services. Providers, however, tend to express more concerns about the potentially adverse of effects of telepsychiatry on therapeutic rapport. Patients are less likely to endorse such concerns about impaired rapport with their provider. Although few studies appropriately employ non inferiority designs, the evidence taken together suggests that telepsychiatry is comparable to face to face services in terms of reliability of clinical assessments and treatment outcomes. When non inferiority designs were appropriately used, telepsychiatry performed as well as, if not better than face to face delivery of mental health services. Studies using both rudimentary and more sophisticated methods for evaluating cost effectiveness indicate that telepsychiatry is not more expensive than face to face delivery of mental health services and that telepsychiatry is actually more cost effective in the majority of studies reviewed. Notwithstanding legal concerns about loss of confidentiality and limited capacity to respond to psychiatric emergencies, we uncovered no published reports of these adverse events in the use of telepsychiatry.  A large evidence base supports telepsychiatry as a delivery method for mental health services. Future studies will inform optimal approaches to implementing and sustaining telepsychiatry services.
Assessing the suitability of summary data for two sample Mendelian randomization analyses using MR Egger regression: the role of the I2 statistic
Venue: International journal of epidemiology

MR Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP) exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F statistic. The effect of NOME violation on MR Egger regression has yet to be studied.  An adaptation of the I2 statistic from the field of meta analysis is proposed to quantify the strength of NOME violation for MR Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR Egger causal estimate in the two sample MR context. We call it IGX2. The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example.  In simulated two sample MR analyses we show that, when a causal effect exists, the MR Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2) the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two sample summary data MR analysis to estimate the causal effect of low density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR Egger analyses for these data.  Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR Egger regression in the two sample summary data context. If IGX2 is sufficiently low (less than 90% inferences from the method should be interpreted with caution and adjustment methods considered.
Kinetics studies of the reversible partial decomposition reaction in Mg(BH4)2

Magnesium borohydride Mg(BH4)2 is a promising candidate for hydrogen storage due to its high hydrogen content and theoretically predicted low decomposition temperature. Hydrogenation of the completely decomposed Mg(BH4)2 requires high temperature, high H2 pressure and long reaction time. However, the partially decomposed compound can be rehydrogenated in much milder conditions. In this work, we study the reversible intermediate decomposition reaction in Mg(BH4)2. Gravimetric and volumetric measurements have shown that Mg(BH4)2 released up to 6.8 wt% H2 below 285 degC with the formation of amorphous MgBxHy intermediate(s) as found by infrared spectroscopic analysis. No crystalline decomposition reaction products were detected by powder X ray diffraction. Rehydrogenation at 260 280 degC yielded 2.5 wt% uptake and the formation of crystalline Mg(BH4)2. Kinetics modeling suggested that the decomposition is a complex process with possibly several reactions which are limited mostly by diffusion. The rehydrogenation reaction was governed by the Johns Mehl Avrami model with the nucleation at a constant rate and diffusion controlled growth mechanism.
Density Functional Theory Studies of the Methanol Decomposition Reaction on Graphene Supported Pt13 Nanoclusters

Defective graphene has been shown experimentally to be an excellent support for transition metal electrocatalysts in direct methanol fuel cells. Prior computational modeling has revealed that the improved catalytic activity of graphene supported metal clusters is in part due to increased resistance to catalyst sintering and to CO poisoning, but the increased reaction rate for the methanol decomposition reaction (MDR) is not yet fully explained. Using density functional theory, we investigate the adsorption and reaction thermodynamics of MDR intermediates on defective graphene supported Pt13 nanoclusters with realistic, low symmetry morphologies. We find that the support induced shifts in catalyst electronic structure correlate well with an overall change in adsorption behavior of MDR intermediates and that the reaction thermodynamics are modified in a way that suggests the potential of greater catalytic activity. We also show that adsorption energy predictors established for traditional heterogeneous cata.
In situ DRIFTS Studies on Cu, Ni and CuNi catalysts for Ethanol Decomposition Reaction
Venue: Catalysis Letters

Catalyst nanopowders containing copper and nickel were synthesized using solution combustion synthesis method for hydrogen production from ethanol decomposition. Detailed in situ DRIFTS studies were conducted on three catalysts (Cu, Ni and CuNi) between 50 and 400 degC to identify the reaction pathways leading to differences in product selectivity over these catalysts. The catalysts nanopowders were characterized before and after reaction using various techniques (XRD, SEM and TEM) to understand the effect of reaction on catalytically active nanopowders. DRIFTS studies indicate that ethanol decomposition on Cu surface proceeds via acetaldehyde formation at low temperature (200 300 degC) generates ethyl acetate and carbon dioxide at 400 degC. Ni was more selective for methane and carbon monoxide. CuNi catalysts follows a trend similar to Cu catalyst at low temperature producing relatively more stable acetaldehyde intermediate that Ni, however at temperature above 300 degC, it behaves more like Ni catalyst producing only methane and carbon monoxide.
Research on the Thermal Decomposition Reaction Kinetics and Mechanism of Pyridinol Blocked Isophorone Diisocyanate
Venue: Materials

A series of pyridinol blocked isophorone isocyanates, based on pyridinol including 2 hydroxypyridine, 3 hydroxypyridine, and 4 hydroxypyridine, was synthesized and characterized by 1H NMR, 13C NMR, and FTIR spectra. The deblocking temperature of blocked isocyanates was established by thermo gravimetric analysis (TGA) differential scanning calorimetry (DSC) and the CO2 evaluation method. The deblocking studies revealed that the deblocking temperature was increased with pyridinol nucleophilicity in this order: 3 hydroxypyridine 4 hydroxypyridine 2 hydroxypyridine. The thermal decomposition reaction of 4 hydroxypyridine blocked isophorone diisocyanate was studied by thermo gravimetric analysis. The Friedman Reich Levi (FRL) equation, Flynn Wall Ozawa (FWO) equation, and Crane equation were utilized to analyze the thermal decomposition reaction kinetics. The activation energy calculated by FRL method and FWO method was 134.6 kJ*mol 1 and 126.2 kJ*mol 1, respectively. The most probable mechanism function calculated by the FWO method was the Jander equation. The reaction order was not an integer because of the complicated reactions of isocyanate.
Hyper temporal SPOT NDVI dataset parameterization captures species distributions
Venue: Int. J. Geogr. Inf. Sci.

Hyper temporal SPOT NDVI images contain useful information about the environment in which a species occurs, including information such as the beginning, end, peak, and curvature of photosynthetically active vegetation (PAV) greenness signatures. This raises the question: can parameterization of hyper temporal SPOT NDVI images be useful to predict species distribution? A set of SPOT NDVI images for the whole of Ethiopia covering nine years was classified using the unsupervised ISODATA clustering algorithm to group similar NDVI pixel values. The HANTS (Harmonic ANalysis of Time Series) algorithm, that fits series of smoothing cosine waves, was then applied to the time series for each of the NDVI classes to generate seven output Fourier components. These components, together with the topographic parameters slope and elevation, were used as predictors in a species distribution model using MAXENT. Presence only data of one test species, Boswellia papyrifera, were modelled. This species is diminishing at an alarming rate and requires conservation. The performance of the model was evaluated by the area under curve (AUC) of the receiver operating characteristics value. The output distribution map was tested for its agreement with the NDVI clustering approach and conventional B. papyrifera distribution map using Kappa. The relative contributions of the first four predictors to the MAXENT in sequence were: 2nd harmonic phase, elevation, amplitude of the 1st harmonics, and amplitude of the 2nd harmonics. The average AUC test result for the 100 runs was 0.98 with a standard deviation of 0.002. The probability distribution map clearly shows high correlation with the B. papyrifera occurrence data. In addition, the distribution map was found to be in agreement with the NDVI clustered and conventional map with improved details. Classifying hyper temporal NDVI images and extracting their parameters through the use of the HANTS algorithm captures the PAV greenness behaviour (parameters) of the environment of the species studied. These parameters have proved successful in predicting the distribution of B. papyrifera.
Grad CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient based Localization

We propose a technique for making Convolutional Neural Network (CNN) based models more transparent by visualizing the regions of input that are "important" for predictions from these models or visual explanations. Our approach, called Gradient weighted Class Activation Mapping (Grad CAM) uses the class specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. Grad CAM is a strict generalization of the Class Activation Mapping. Unlike CAM, Grad CAM requires no re training and is broadly applicable to any CNN based architectures. We also show how Grad CAM may be combined with existing pixel space visualizations to create a high resolution class discriminative visualization (Guided Grad CAM) We generate Grad CAM and Guided Grad CAM visual explanations to better understand image classification, image captioning, and visual question answering (VQA) models. In the context of image classification models, our visualizations (a) lend insight into their failure modes showing that seemingly unreasonable predictions have reasonable explanations, and (b) outperform pixel space gradient visualizations (Guided Backpropagation and Deconvolution) on the ILSVRC 15 weakly supervised localization task. For image captioning and VQA, our visualizations expose the somewhat surprising insight that common CNN LSTM models can often be good at localizing discriminative input image regions despite not being trained on grounded image text pairs. Finally, we design and conduct human studies to measure if Guided Grad CAM explanations help users establish trust in the predictions made by deep networks. Interestingly, we show that Guided Grad CAM helps untrained users successfully discern a "stronger" deep network from a "weaker" one even when both networks make identical predictions.
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Venue: J. Mach. Learn. Res.

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: methods based on Additive Noise Models (ANMs) and Information Geometric Causal Inference (IGCI) We present the benchmark CAUSEEFFECTPAIRS that consists of data for 100 different causee ffect pairs selected from 37 data sets from various domains (e.g. meteorology, biology, medicine, engineering, economy, etc. and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real world benchmark data and in addition on artificially simulated data. Our empirical results on real world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the method based on Additive Noise Models that has originally been proposed by Hoyer et al. (2009) which obtains an accuracy of 63 10 and an AUC of 0.74 0.05 on the real world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.
Object recognition using local invariant features for robotic applications: A survey
Venue: Pattern Recognit.

The main goal of this survey is to present a complete analysis of object recognition methods based on local invariant features from a robotics perspective; a summary which can be used by developers of robot vision applications in the selection and development of object recognition systems. The survey includes a brief description of the main approaches reported in the literature, with more specific analyses of local interest point computation methods, local descriptor computation and matching methods, and geometric verification methods. Different methods are analyzed by considering the main requirements of robotics applications, such as real time operation with limited on board computational resources, and constrained observational conditions derived from the robot geometry (e.g. limited camera resolution) In addition, various object recognition systems are evaluated in a service robot domestic environment, where the final task to be performed by a service robot is the manipulation of objects. It can be concluded from the results reported that (i) the most suitable keypoint detectors are ORB, BRISK, Fast Hessian, and DoG, (ii) the most suitable descriptors are ORB, BRISK, SIFT, and SURF, (iii) the final performance of object recognition systems using local invariant features under real world conditions depends strongly on the geometric verification methods being used, and (iv) the best performing object recognition systems are built using ORB ORB and DoG SIFT keypoint descriptor combinations. ORB ORB based systems are faster, while DoG SIFT are more robust to real world conditions. A complete analysis of object recognition methods based on local invariant features from a robotics perspective is given.A brief description of the main approaches reported in the literature is included.Methods are analyzed by considering the main requirements of robotics applications.Best performing object recognition systems are built using ORB ORB and DoG SIFT keypoint descriptor combinations.
3D object recognition from large scale point clouds with global descriptor and sliding window
Venue: 2016 23rd International Conference on Pattern Recognition (ICPR)

We propose a novel method for the recognition of objects that match a given 3D model in large scale scene point clouds captured in indoor environments with a laser range finder. Since large scale indoor point clouds are greatly damaged by noise such as clutter, occlusion, hole, and measurement errors, it is difficult to exactly identify local correspondences between points in a target model point cloud and points in a scene point cloud, based on similarities between local descriptors computed at keypoints on both point clouds. To avoid such a problem, we suggest to utilize sliding window in order to match the input model and pieces of scene point clouds, both of which are represented with Bag of Features(BoF) A BoF representation of a window is efficiently calculated by using the integral image, which stores accumulated BoF vectors. Though BoF is robust to partial noises, it does not preserve any spatial information. Then, we propose a method to make a global descriptor of a window which is almost invariant to horizontal rotations of an object inside the divided window and roughly preserves spatial information by dividing sliding window into several parts. Experiments on real world data show that our approach offers better performance than a baseline method in terms of precision and recall.