nils-herrmann/clean_scirepeval huggingface dataset:
Perovskite solar cells: from materials to devices.

Perovskite solar cells based on organometal halide light absorbers have been considered a promising photovoltaic technology due to their superb power conversion efficiency (PCE) along with very low material costs. Since the first report on a long-term durable solid-state perovskite solar cell with a PCE of 9.7% in 2012, a PCE as high as 19.3% was demonstrated in 2014, and a certified PCE of 17.9% was shown in 2014. Such a high photovoltaic performance is attributed to optically high absorption characteristics and balanced charge transport properties with long diffusion lengths. Nevertheless, there are lots of puzzles to unravel the basis for such high photovoltaic performances. The working principle of perovskite solar cells has not been well established by far, which is the most important thing for understanding perovksite solar cells. In this review, basic fundamentals of perovskite materials including opto-electronic and dielectric properties are described to give a better understanding and insight into high-performing perovskite solar cells. In addition, various fabrication techniques and device structures are described toward the further improvement of perovskite solar cells.
Responses of midbrain dopamine neurons to behavioral trigger stimuli in the monkey.

Destruction of the midbrain dopamine (DA) system in Parkinsonian man and experimental animals leads to deficits in initiation of behavior, motor performance, and cognitive mechanisms. We have investigated the extracellular impulse activity of single midbrain DA neurons in unlesioned monkeys performing in a controlled behavioral task that was designed to paradigmatically test behavioral reactivity. Animals were trained to execute natural forelimb reaching movements for food reward in response to a trigger stimulus. Presumptive DA neurons were histologically located in the pars compacta of substantia nigra and in neighboring areas A8 and A10. They spontaneously discharged polyphasic impulses of relatively long duration (1.4-3.6 ms) and at low frequencies (0.5-8.5/s). Systemic injections of low doses of the DA autoreceptor agonist apomorphine (0.05-0.2 mg/kg) depressed the activity of virtually all thus tested DA neurons. In following established criteria, these characteristics strongly suggest the DAergic nature of the recorded neurons. The majority of midbrain DA neurons (70 of 128) responded to the behavioral trigger stimulus of the task with a short burst of impulses. Latencies ranged from 39 to 105 ms (median 65 ms) for onset and from 65 to 165 ms (median 95 ms) for peak of responses. Responses occurred before arm movement and at the time of or before onset of electromyographic (EMG) activity in prime mover muscles. Responses were time-locked to the stimulus and not to the onset of movement or EMG. Responses remained present in most neurons but were reduced when vision of the behavioral trigger stimulus was prevented while maintaining the associated acoustic signals. In another variation of the task, most neurons also responded to a stimulus that was physically identical to the behavioral trigger but to which the animal made no movement. The activity of a few DA neurons (11 of 128) was reduced following presentation of the behavioral trigger stimulus, with latencies comparable to those of activations. The activity of many DA neurons was increased (40 of 128) or reduced (22 of 128) during execution of the forelimb reaching movement. These changes were of a slow and moderate nature, and were minor compared with responses to the behavioral trigger stimulus. About half of movement-related neurons also responded to the behavioral trigger. The activity of a few DA neurons was increased (11 to 128) or reduced (1 to 128) when the food reward reached the mouth. These changes did not occur with spontaneous mouth movements. About half of these neurons also responded to the behavioral trigger.(ABSTRACT TRUNCATED AT 400
Cusps enable line attractors for neural computation.

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.
Prediction-Constrained Topic Models for Antidepressant Recommendation

Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervised latent Dirichlet allocation that balances two goals: faithful generative explanations of high-dimensional data and accurate prediction of associated class labels. Existing approaches fail to balance these goals by not properly handling a fundamental asymmetry: the intended task is always predicting labels from data, not data from labels. Our new prediction-constrained objective trains models that predict labels from heldout data well while also producing good generative likelihoods and interpretable topic-word parameters. In a case study on predicting depression medications from electronic health records, we demonstrate improved recommendations compared to previous supervised topic models and high-dimensional logistic regression from words alone.
Causation in Legal and Moral Reasoning

Causation looms large in legal and moral reasoning. People construct causal models of the social and physical world to understand what has happened, how and why, and to allocate responsibility and blame. This chapter explores people’s commonsense notion of causation, and shows how it underpins moral and legal judgments. As a guiding framework it uses the causal model framework (Pearl, 2000) rooted in structural models and counterfactuals, and shows how it can resolve many of the problems that beset standard butfor analyses. It argues that legal concepts of causation are closely related to everyday causal reasoning, and both are tailored to the practical concerns of responsibility attribution. Causal models are also critical when people evaluate evidence, both in terms of the stories they tell to make sense of evidence, and the methods they use to assess its credibility and reliability.
Identifying Cultural Changes Necessary in Traditional Plan-Driven Software Development Organizations when Preparing to Adopt Agile Principles

Organizational culture plays a critical role in the acceptance and adoption of agile principles by a traditional software development organization (Chan & Thong, 2008). Organizations must understand the differences that exist between traditional software development principles and agile principles. Based on an analysis of the literature published between 2003 and 2010, this study examines nine distinct organizational cultural factors that require change, including management style, communication, development team practices, knowledge management, and customer interactions.
Learning Hierarchical Decision Trees for Single-Image Super-Resolution

Sparse representation has been extensively studied for image super-resolution (SR), and it achieved great improvement. Deep-learning-based SR methods have also emerged in the literature to pursue better SR results. In this paper, we propose to use a set of decision tree strategies for fast and high-quality image SR. Our proposed SR using decision tree (SRDT) method takes the divide-and-conquer strategy, which performs a few simple binary tests to classify an input low-resolution (LR) patch into one of the leaf nodes and directly multiplies this LR patch with the regression model at that leaf node for regression. Both the classification process and the regression process take an extremely small amount of computation. To further boost the SR results, we introduce a SR using hierarchical decision trees (SRHDT) method, which cascades multiple layers of decision trees for SR and progressively refines the estimated high-resolution image. Inspired by the random forests approach, which combines regression models from an ensemble of decision trees, we propose to fuse regression models from relevant leaf nodes within the same decision tree to form a more robust approach. The SRHDT method with fused regression model (SRHDT_f) improves further the SRHDT method by 0.1-dB in PNSR. Our experimental results show that our initial approach, the SRDT method, achieves SR results comparable to those of the sparse-representation-based method and the deep-learning-based method, but our method is much faster. Furthermore, our enhanced version, the SRHDT_f method, achieves more than 0.3-dB higher PSNR than that of the A+ method, which is the state-of-the-art method in SR.
Quaternions and Rotations *

Up until now we have learned that a rotation in R3 about an axis through the origin can be represented by a 3×3 orthogonal matrix with determinant 1. However, the matrix representation seems redundant because only four of its nine elements are independent. Also the geometric interpretation of such a matrix is not clear until we carry out several steps of calculation to extract the rotation axis and angle. Furthermore, to compose two rotations, we need to compute the product of the two corresponding matrices, which requires twenty-seven multiplications and eighteen additions. Quaternions are very efficient for analyzing situations where rotations in R3 are involved. A quaternion is a 4-tuple, which is a more concise representation than a rotation matrix. Its geometric meaning is also more obvious as the rotation axis and angle can be trivially recovered. The quaternion algebra to be introduced will also allow us to easily compose rotations. This is because quaternion composition takes merely sixteen multiplications and twelve additions. The development of quaternions is attributed to W. R. Hamilton  in 1843. Legend has it that Hamilton was walking with his wife Helen at the Royal Irish Academy when he was suddenly struck by the idea of adding a fourth dimension in order to multiply triples. Excited by this breakthrough, as the couple passed the Broome Bridge of the Royal Canal, he carved the newfound quaternion equations
State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context.
A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.
Performance Analysis of Handover Measurements and Layer 3 Filtering for Utran LTE

Handover is one of the key functionalities which tries to keep a user equipment (UE) connected to the best base station (eNodeB). Handover is usually based on the downlink received signal strength (RSS) and carrier-to-interference ratio (CIR) measurements. Processing of the handover measurement is usually done in Layer 1 (LI) and Layer 3 (L3) by the UE, and handover is initiated by the serving eNodeB if certain event criteria are met. L3 filtering can be done in linear domain or decibel (dB) domain. A hard handover algorithm based on the downlink RSS and CIR measurements along with linear and dB domain L3 filtering has been studied by using a dynamic system level simulator for a 3GPP UTRAN LTE recommended scenario. The results suggest that RSS measurement with linear or dB domain L3 filtering is a better criterion for handover in terms of reduced number of handovers for a small penalty on the downlink CIR.
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data. As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation. These examples could either belong to already known or to yet undiscovered classes. Our algorithm is based on a new generalization of the Expected Model Output Change principle for deep architectures and is especially tailored to deep neural networks. Furthermore, we show easy-to-implement approximations that yield efficient techniques for active selection. Empirical experiments show that our method outperforms currently used heuristics.
Speed Control Of An Induction Motor By Using Indirect Vector Control Method

In this paper, an implementation of intelligent controller for speed control of an induction motor (IM) using indirect vector control method has been developed and analyzed in detail. The project is complete mathematical model of field orientation control (FOC) induction motor is described and simulated in MATLAB for studies a 50 HP(37KW), cage type induction motor has been considered . The comparative performance of Fuzzy Logic control technique has been presented and analyzed in this work. The present approach avoids the use of flux and speed sensor which increase the installation cost and mechanical robustness. The fuzzy logic controller is found to be a very useful techniques to obtain a high performance speed control. The indirect vector controlled induction motor drive involve decoupling of the stator current in to torque and flux producing components. KeywordsIM, FOC,PI, IVCIM.
Heat-Transfer and Solidification Model of Continuous Slab Casting : CON 1 D

A simple, but comprehensive model of heat transfer and solidification of the continuous casting of steel slabs is described, including phenomena in the mold and spray regions. The model includes a one-dimensional (1-D) transient finite-difference calculation of heat conduction within the solidifying steel shell coupled with two-dimensional (2-D) steady-state heat conduction within the mold wall. The model features a detailed treatment of the interfacial gap between the shell and mold, including mass and momentum balances on the solid and liquid interfacial slag layers, and the effect of oscillation marks. The model predicts the shell thickness, temperature distributions in the mold and shell, thickness of the resolidified and liquid powder layers, heat-flux profiles down the wide and narrow faces, mold water temperature rise, ideal taper of the mold walls, and other related phenomena. The important effect of the nonuniform distribution of superheat is incorporated using the results from previous threedimensional (3-D) turbulent fluid-flow calculations within the liquid pool. The FORTRAN program CONID has a user-friendly interface and executes in less than 1 minute on a personal computer. Calibration of the model with several different experimental measurements on operating slab casters is presented along with several example applications. In particular, the model demonstrates that the increase in heat flux throughout the mold at higher casting speeds is caused by two combined effects: a thinner interfacial gap near the top of the mold and a thinner shell toward the bottom. This modeling tool can be applied to a wide range of practical problems in continuous casters.
Deep Refinement Convolutional Networks for Human Pose Estimation

This work introduces a novel Convolutional Networkarchitecture (ConvNet) for the task of human poseestimation, that is the localization of body joints in a singlestatic image. The proposed coarse to fine architecture addressesshortcomings of the baseline architecture that stem from thefact that large inaccuracies of its coarse ConvNet cannot becorrected by the refinement ConvNet that refines the estimationwithin small windows of the coarse prediction. This is achievedby a) changes in architectural parameters that both increase theaccuracy of the coarse model and make the refinement modelmore capable of correcting the errors of the coarse model,b) the introduction of a Markov Random Field (MRF)-basedspatial model network between the coarse and the refinementmodel that introduces geometric constraints and c) a trainingscheme that adapts the data augmentation and the learning rateaccording to the difficulty of the data examples. The proposedarchitecture is trained in an end-to-end fashion. Experimentalresults show that the proposed method improves the baselinemodel and provides state of the art results on the FashionPose[8] and MPII benchmarks .
The use of uncertainty analysis as a food waste estimation tool.

Food waste going to landfill is a significant environmental issue with 33% of all food we buy simply being thrown away. Not only is this extremely wasteful but rotting food produces gases that are harmful to the environment and contributes to global warming. The UK government is committed to reducing the amount of household waste directly being disposed of at landfills by 10.1 million tonnes over 20 years from 2000 (Waste  Guidance for Local Authorities, Defra, 2004). As part of this the Waste Reduction Action Programme was set up to fund and facilitate innovative solutions to reduce waste to landfill. Part of that process was to assess bids by solution providers with regards the effectiveness of the technologies that they were offering. This was found to be a non-trivial task, with multiple input parameters and large variations in data. Establishing which parameters have the greatest effect on food waste estimation was therefore essential in any decision-making process. However, the large number of unknowns, assumptions and parameters makes this understanding both difficult and time consuming. A branch of mathematics known as uncertainty analysis can be used to analyse these types of situations quickly and effectively and is easily adapted to understanding of food waste estimation. This paper outlines the techniques used to develop an internet-based decision-making tool and demonstrates the methodology used with simple case studies.
Why do people buy virtual goods: A meta-analysis

During the last decade, virtual goods have become an important target of consumption online (especially in games, virtual worlds and social networking services) amongst physical and digital goods. In this study we investigate the question of why do people purchase virtual goods by conducting a meta-analysis (random effects model) of the existing quantitative body of literature (24 studies) on the topic. The meta-analysis revealed an important aspect of value of virtual goods: contrary to traditional goods, the reasons why people purchase virtual goods are tightly connected to the platform where they are sold in. These findings underline the significance of service design and its relationship to the formation of value of virtual goods: the value of virtual goods is context-bound, and therefore, bound to the environment where they are usable in. Most factors that were found to be significant predictors of purchase behavior (such as network effects, self-presentation, enjoyment, ease of use, flow and use of the platform) are directly related to the aspects and design of the platform beyond the general attitudes towards virtual goods themselves. Moreover, we found that enjoyment and prolonged use of the platform were more important predictors for purchases in virtual worlds than in games.
Control of twin rotor MIMO system using PID controller with derivative filter coefficient

This paper presents a proportional integral derivative (PID) controller with a derivative filter coefficient to control a twin rotor multiple input multiple output system (TRMS), which is a nonlinear system with two degrees of freedom and cross couplings. The mathematical modeling of TRMS is done using MATLAB/Simulink. The simulation results are compared with the results of conventional PID controller. The results of proposed PID controller with derivative filter shows better transient and steady state response as compared to conventional PID controller.