nils-herrmann/clean_scirepeval huggingface dataset:
Expect Above Average Temperatures : Identifying the Economic Impacts of Climate Change

A rapidly growing empirical literature seeks to estimate the costs of future climate change from time series variation in weather. I formally analyze the consequences of a change in climate for economic outcomes. I show that those consequences are driven by changes in the distribution of realized weather and by expectations channels that capture how anticipated changes in the distribution of weather affect current and past investments. Studies that rely on time series variation in weather omit the expectations channels. Quantifying the expectations channels requires estimating how forecasts affect outcome variables and simulating how climate change would alter forecasts.
Path Planning through PSO Algorithm in Complex Environments

The path planning through Particle Swarm Optimizat ion (PSO) algorithm in complex environments is a novel approach for robotic path planning. The main challenges of robotics are its automation and detection capability and the preliminary step of achieving these is through optimized path establishment, format ion, flocking, and rendezvous, synchronizing and covering. Robotic Path Planning is one of the main problems that deals with computation of a collision-free path for the given robot, along with the given map on which it operates. Path establishment is possible only when the environment is known and the targets location is estimated. This work focuses on Path planning problem. We have considered two cases, first one is coordinated target tracking and reaching in the unknown environment through obstacles avoidance when the location of the target is known. The second case is co-ordinated target searching in an unknown and unexplored environment through obstacle avoidance. These results are showing for both target tracking and target searching. We are also showing the comparative results for this algorithm with o ther state of the art techniques. Parameters that asses these algorithms are no of node visit (move), exp lored area (coverage), Manhattan distance (energy) and time elapsed (time).
Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating System (ROS).
Prospective outcomes of young and middle-aged adults with medial compartment osteoarthritis treated with a proximal tibial opening wedge osteotomy.

The purpose of this study was to conduct a prospective outcome analysis of proximal tibial opening wedge osteotomies performed in young and middle-aged patients (aged <55 years) for the treatment of symptomatic medial compartment osteoarthritis of the knee.



A consecutive series of young and middle-aged adults who underwent proximal tibial opening wedge osteotomies for symptomatic medial compartment osteoarthritis and genu varus alignment were prospectively followed up. Patients were evaluated with preoperative and postoperative modified Cincinnati Knee Scores and International Knee Documentation Committee objective knee subscores for knee effusions and the single-leg hop. Calculations were made of the preoperative and postoperative long-leg radiographic mechanical weight-bearing axis, patellar height (Insall-Salvati index), and tibial slope. A separate cohort of asymptomatic patients was used to quantify tibial plateau anatomy to provide an objective description of the lower extremity mechanical axis.



There were 47 patients, with a mean age of 40.5 years, with a minimum of 2 years' follow-up, who formed this patient cohort. Modified Cincinnati Knee Scores improved significantly from 42.9 preoperatively to 65.1 at a mean of 3.6 years of follow-up. Radiographic analysis of a separate cohort showed the medial tibial eminence to be located at the 41% point along the tibial plateau from medial (0%) to lateral (100%). There was a significant improvement in malalignment: the mean mechanical axis passed through the tibial plateau at 23% of the distance along the proximal tibia preoperatively versus 54% postoperatively. The Insall-Salvati index decreased from 1.03 to 0.95 (P < .05), and posterior tibial slope increased from 9.4° to 11.7° (P < .05). Of the osteotomies, 3 (6%) were considered failures, defined by revision of the osteotomy or conversion to total knee arthroplasty.



Performing proximal tibial opening wedge osteotomies to treat symptomatic medial compartment osteoarthritis in carefully selected patients leads to a significant improvement in subjective and objective clinical outcome scores with correction of malalignment at a mean of 3.6 years postoperatively.
Design optimization of transformerless grid-connected PV inverters including reliability

This paper presents a new methodology for optimal design of transformerless Photovoltaic (PV) inverters targeting a cost-effective deployment of grid-connected PV systems. The optimal values and types of the PV inverter components are calculated such that the PV inverter Levelized Cost Of the Electricity (LCOE) generated during the PV system lifetime period is minimized. The LCOE is calculated also considering the failure rates of the components, which affect the reliability performance and lifetime maintenance cost of the PV inverter. A design example is presented, demonstrating that compared to the non-optimized PV inverter structures, the PV inverters designed using the proposed optimization methodology exhibit lower total manufacturing and lifetime maintenance cost and inject more energy into the electric-grid and by that minimizing LCOE.
LSTM recurrent networks learn simple context-free and context-sensitive languages

Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). We demonstrate LSTMs superior performance on context-free language benchmarks for RNNs, and show that it works even better than previous hardwired or highly specialized architectures. To the best of our knowledge, LSTM variants are also the first RNNs to learn a simple context-sensitive language, namely a(n)b(n)c(n).
Variational Bayesian Inference for Big Data Marketing Models 1

Hierarchical Bayesian approaches play a central role in empirical marketing as they yield individual-level parameter estimates that can be used for targeting decisions. MCMC methods have been the methods of choice for estimating hierarchical Bayesian models as they are capable of providing accurate individual-level estimates. However, MCMC methods are computationally prohibitive and do not scale well when applied to massive data sets that have become common in the current era of “Big Data”. We introduce to the marketing literature a new class of Bayesian estimation techniques known as variational Bayesian (VB) inference. These methods tackle the scalability challenge via a deterministic optimization approach to approximate the posterior distribution and yield accurate estimates at a fraction of the computational cost associated with simulation-based MCMC methods. We exploit and extend recent developments in variational Bayesian inference and highlight how two VB estimation approaches – Mean-field VB (that is analogous to Gibbs sampling) for conjugate models and Fixed-form VB (which is analogous to Metropolis-Hasting) for nonconjugate models – can be effectively combined for estimating complex marketing models. We also show how recent advances in parallel computing and in stochastic optimization can be used to further enhance the speed of these VB methods. Using simulated as well as real data sets, we apply the VB approaches to several commonly used marketing models (e.g. mixed linear, logit, selection, and hierarchical ordinal logit models), and demonstrate how the VB inference is widely applicable for marketing problems.
Private Information, Credit Risk and Graph Structure in P2P Lending Networks

This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using “private information” about communications and travels of borrowers. We found that P2P borrowers’ ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5 1⁄2% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error. 1. The Peer-to-Peer Lending Industry Peer-to-peer lending (P2P lending) involves the lending money to individuals or businesses through online services that match lenders with borrowers. The advantages of P2P lending arise from most business being conducted online; disadvantages arise from the perceived higher default risk than with loans made face-to-face, a perception that we investigated here. Many P2P loans are unsecured personal loans, though some of the largest amounts are lent to businesses; other forms of P2P lending include student loans, commercial and real estate loans, payday
Dual Business Models: Going beyond Spatial Separation

Despite substantial literature on managing two conflicting business models within one company, little has been done regarding qualitative studies investigating the organizational context and the installed integration and separation mechanisms between these two. Based on organizational context factors and business model elements, using the organizational ambidexterity as theoretical lens, this research paper presents a longitudinal case study conducted at a magazine publisher that intends to advance the comprehension of the interplay between traditional and online business models. Our analysis suggests that integration and separation mechanisms occur along all elements of a business model. The organizational context factors moderate the integration and separation mechanisms between the traditional and the online business model and uncover details regarding the conflict inherent in those dual business models.
Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics

Argumentative Zoning (AZ) is an analysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annotation experiments have however so far been restricted to one discipline, computational linguistics (CL). Here, we present a more informative AZ scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to CL. We use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. Our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.
Power maximization control of small wind system using permanent magnet synchronous generator

This article describes modeling and simulations to determine a method for the power performance evaluation of autonomous wind turbine system with this simple load scheme. The method applies to small systems equipped with a permanent magnet generator in the wind turbine a diode rectifier, boost converter and batteries. In this work, the aerodynamic characteristics of wind turbines and the power conversion system topology are explained. The maximum power tracking of the wind turbine generator system using the Matlab software is presented and its results show, at least in principle, that the maximum power tracking algorithm developed is suitable for wind turbine generation systems. Résumé Cet article décrit une méthode d’évaluation de la performance de la puissance d’une éolienne autonome avec son système de charge. La méthode s’applique à de petits systèmes équipés d’un générateur à aimant permanent couplé à une éolienne, suivi d’un redresseur et d’une batterie. Pour extraire la puissance maximale de cette chaîne de conversion, un algorithme est développé sous Matlab. Les résultats obtenus par simulation montrent que le rendement du système est très encourageant.
Greedy Attribute Selection

Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance. We present a caching scheme that makes attribute hillclimbing more practical computationally. We also compare the results of hillclimbing in attribute space with FOCUS and RELIEF on the two tasks.
Automatic Scoring of an Analytical Response-To-Text Assessment

In analytical writing in response to text, students read a complex text and adopt an analytic stance in their writing about it. To evaluate this type of writing at scale, an automated approach for Response to Text Assessment (RTA) is needed. With the long-term goal of producing informative feedback for students and teachers, we design a new set of interpretable features that operationalize the Evidence rubric of RTA. When evaluated on a corpus of essays written by students in grades 4-6, our results show that our features outperform baselines based on well-performing features from other types of essay assessments.
Design of SIW fed antipodal linearly tapered slot antenna array with hat-shaped dielectric loading for 60 GHz wireless communications

Substrate integrated waveguide (SIW) based antipodal linear tapered slot antenna (ALTSA) with dielectric loading at 60 GHz for wireless communication applications are presented. The hat shaped dielectric loaded ALTSA has gain of 17.29 dB with single element and 19.22 dB was achieved with two elements ALTSA. Both got good radiation efficiency of above 92%. The antennas are designed using Rogers RT/Duroid 6002 material with dielectric constant 2.94 and thickness of 0.254 mm. Ansys HFSS and CST MWS were used for the EM computations.
Sequential Attention

In this paper we propose a neural network model with a novel Sequential Attention layer that extends soft attention by assigning weights to words in an input sequence in a way that takes into account not just how well that word matches a query, but how well surrounding words match. We evaluate this approach on the task of reading comprehension (Who did What and CNN) and show that it dramatically improves a strong baseline like the Stanford Reader. The resulting model is competitive with the state of the art.
Microbiological, technological and therapeutic properties of kefir: a natural probiotic beverage

Kefir is a fermented milk beverage produced by the action of bacteria and yeasts that exist in symbiotic association in kefir grains. The artisanal production of the kefir is based on the tradition of the peoples of Caucasus, which has spread to other parts of the world, from the late 19(th) century, and nowadays integrates its nutritional and therapeutic indications to the everyday food choices of several populations. The large number of microorganisms present in kefir and their microbial interactions, the possible bioactive compounds resulting of microbial metabolism, and the benefits associated with the use this beverage confers kefir the status of a natural probiotic, designated as the 21(th) century yoghurt. Several studies have shown that kefir and its constituents have antimicrobial, antitumor, anticarcinogenic and immunomodulatory activity and also improve lactose digestion, among others. This review includes data on the technological aspects, the main beneficial effects on human health of kefir and its microbiological composition. Generally, kefir grains contain a relatively stable and specific microbiota enclosed in a matrix of polysaccharides and proteins. Microbial interactions in kefir are complex due to the composition of kefir grains, which seems to differ among different studies, although some predominant Lactobacillus species are always present. Besides, the specific populations of individual grains seem to contribute to the particular sensory characteristics present in fermented beverages. This review also includes new electron microscopy data on the distribution of microorganisms within different Brazilian kefir grains, which showed a relative change in its distribution according to grain origin.
An embedded, GSM based, multiparameter, realtime patient monitoring system and control — An implementation for ICU patients

Wireless, remote patient monitoring system and control using feedback and GSM technology is used to monitor the different parameters of an ICU patient remotely and also control over medicine dosage is provided. Measurement of vital parameters can be done remotely and under risk developing situation can be conveyed to the physician with alarm triggering systems in order to initiate the proper control actions. In the implemented system a reliable and efficient real time remote patient monitoring system that can play a vital role in providing better patient care is developed. This system enables expert doctors to monitor vital parameters viz body temperature, blood pressure and heart rate of patients in remote areas of hospital as well as he can monitor the patient when he is out of the premises. The system in addition also provides a feedback to control the dosage of medicine to the patient as guided by the doctor remotely, in response to the health condition message received by the doctor. Mobile phones transfer measured parameters via SMS to clinicians for further analysis or diagnosis. The timely manner of conveying the real time monitored parameter to the doctor and control action taken by him is given high priority which is very much needed and which is the uniqueness of the developed system. The system even facilitates the doctor to monitor the patient's previous history from the data in memory inbuilt in the monitoring device. Also data can be sent to several doctors incase a doctor fails to respond urgently.
Globally optimal toon tracking

The ability to identify objects or region correspondences between consecutive frames of a given hand-drawn animation sequence is an indispensable tool for automating animation modification tasks such as sequence-wide recoloring or shape-editing of a specific animated character. Existing correspondence identification methods heavily rely on appearance features, but these features alone are insufficient to reliably identify region correspondences when there exist occlusions or when two or more objects share similar appearances. To resolve the above problems, manual assistance is often required. In this paper, we propose a new correspondence identification method which considers both appearance features and motions of regions in a global manner. We formulate correspondence likelihoods between temporal region pairs as a network flow graph problem which can be solved by a well-established optimization algorithm. We have evaluated our method with various animation sequences and results show that our method consistently outperforms the state-of-the-art methods without any user guidance.
Ray: A Distributed Framework for Emerging AI Applications

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray—a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system’s control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.
Autonomous exploration of motor skills by skill babbling

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.
Witnessing spouse abuse and experiencing physical abuse: A “double whammy”?

Child witnesses to parental violence, both abused (N =40) and nonabused (N =44), who were temporarily residing in a battered women's shelter were compared with children from a similar economic background (N =66) on measures of anxiety, depression, and behavior problems. Mothers of the three groups of children (comparison, witness, abused/witness) across the age range from 4 to 12 years completed a behavior problem inventory; the youngsters responded to paper-and-pencil self-report measures. Results indicated that the abused/witness children were manifesting significantly more distress on the behavior problem measure than the comparison youngsters, with the witness children showing a moderate amount and the comparison children the least. However, these patterns were mediated by the age of the child. Implications of these differential findings are discussed.