nils-herrmann/clean_scirepeval huggingface dataset:
Leadership and organizational change for implementation (LOCI): a randomized mixed method pilot study of a leadership and organization development intervention for evidence-based practice implementation

Leadership is important in the implementation of innovation in business, health, and allied health care settings. Yet there is a need for empirically validated organizational interventions for coordinated leadership and organizational development strategies to facilitate effective evidence-based practice (EBP) implementation. This paper describes the initial feasibility, acceptability, and perceived utility of the Leadership and Organizational Change for Implementation (LOCI) intervention. A transdisciplinary team of investigators and community stakeholders worked together to develop and test a leadership and organizational strategy to promote effective leadership for implementing EBPs.



Participants were 12 mental health service team leaders and their staff (n = 100) from three different agencies that provide mental health services to children and families in California, USA. Supervisors were randomly assigned to the 6-month LOCI intervention or to a two-session leadership webinar control condition provided by a well-known leadership training organization. We utilized mixed methods with quantitative surveys and qualitative data collected via surveys and a focus group with LOCI trainees.



Quantitative and qualitative analyses support the LOCI training and organizational strategy intervention in regard to feasibility, acceptability, and perceived utility, as well as impact on leader and supervisee-rated outcomes.



The LOCI leadership and organizational change for implementation intervention is a feasible and acceptable strategy that has utility to improve staff-rated leadership for EBP implementation. Further studies are needed to conduct rigorous tests of the proximal and distal impacts of LOCI on leader behaviors, implementation leadership, organizational context, and implementation outcomes. The results of this study suggest that LOCI may be a viable strategy to support organizations in preparing for the implementation and sustainment of EBP.
Trivializing The Energy Landscape Of Deep Networks

We study a theoretical model that connects deep learning to finding the ground state of the Hamiltonian of a spherical spin glass. Existing results motivated from statistical physics show that deep networks have a highly non-convex energy landscape with exponentially many local minima and energy barriers beyond which gradient descent algorithms cannot make progress. We leverage a technique known as topology trivialization where, upon perturbation by an external magnetic field, the energy landscape of the spin glass Hamiltonian changes dramatically from exponentially many local minima to “total trivialization”, i.e., a constant number of local minima. There also exists a transitional regime with polynomially many local minima which interpolates between these extremes. We show that a number of regularization schemes in deep learning can benefit from this phenomenon. As a consequence, our analysis provides order heuristics to choose regularization parameters and motivates annealing schemes for these perturbations.
Active Rectifier Using DQ Vector Control for Aircraft Power System

There is a general move in the aerospace industry to increase the amount of electrically powered equipment on future aircraft. This is generally referred to as the "more electric aircraft" and brings on a number of technical challenges that need to be addressed and overcome. Recent advancements in power electronics technology are enabling new systems to be developed and applied to aerospace applications. The growing trend is to connect the AC generator to the aircraft engine via a direct connection or a fixed ratio transmission thus, resulting in the generator providing a variable frequency supply. This move offers benefits to the aircraft such as reducing the weight and improving the reliability. Many aircraft power systems are now operating with a variable frequency over a typical range of 350 Hz to 800 Hz which varies with the engine speed[1,2]. This paper presents the results from a simple scheme for an adaptive control algorithm which could be suitable for use with an electric actuator (or other) aircraft load. The design of this system poses significant challenges due to the nature of the load range and supply frequency variation and requires many features such as: 1) Small input current harmonics to minimize losses., 2) Minimum size and weight to maximize portability and power density. Details will be given on the design methodology and simulation results obtained.
A case study of computer gaming for math: Engaged learning from gameplay?

Employing mixed-method approach, this case study examined the in situ use of educational computer games in a summer math program to facilitate 4th and 5th graders’ cognitive math achievement, metacognitive awareness, and positive attitudes toward math learning. The results indicated that students developed more positive attitudes toward math learning through five-week computer math gaming, but there was no significant effect of computer gaming on students’ cognitive test performance or metacognitive awareness development. The in-field observation and students’ think-aloud protocol informed that not every computer math drill game would engage children in committed learning. The study findings have highlighted the value of situating learning activities within the game story, making games pleasantly challenging, scaffolding reflections, and designing suitable off-computer activities.
A Collective Variational Autoencoder for Top-N Recommendation with Side Information

Recommender systems have been studied extensively due to their practical use in real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information has been widely utilized to address rating sparsity Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional, existing deep models tend to have large input dimensionality, which dominates their overall size. This makes them difficult to train, especially with insufficient inputs.
 Rather than learning item representations, in this paper, we propose to learn feature representations through deep learning from side information. Learning feature representations ensures a sufficient number of inputs to train a deep network. To achieve this, we propose to simultaneously recover user ratings and side information, by using a Variational Autoencoder (VAE). Specifically, user ratings and side information are encoded and decoded collectively through the same inference network and generation network. This is possible as both user ratings and side information are associated with items. To account for the heterogeneity of user ratings and side information, the final layer of the generation network follows different distributions. The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-N recommendation methods that use side information.
Cross Modal Distillation for Supervision Transfer

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers.
Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable

Sample size justification is an important consideration when planning a clinical trial, not only for the main trial but also for any preliminary pilot trial. When the outcome is a continuous variable, the sample size calculation requires an accurate estimate of the standard deviation of the outcome measure. A pilot trial can be used to get an estimate of the standard deviation, which could then be used to anticipate what may be observed in the main trial. However, an important consideration is that pilot trials often estimate the standard deviation parameter imprecisely. This paper looks at how we can choose an external pilot trial sample size in order to minimise the sample size of the overall clinical trial programme, that is, the pilot and the main trial together. We produce a method of calculating the optimal solution to the required pilot trial sample size when the standardised effect size for the main trial is known. However, as it may not be possible to know the standardised effect size to be used prior to the pilot trial, approximate rules are also presented. For a main trial designed with 90% power and two-sided 5% significance, we recommend pilot trial sample sizes per treatment arm of 75, 25, 15 and 10 for standardised effect sizes that are extra small (≤0.1), small (0.2), medium (0.5) or large (0.8), respectively.
THE HOW AND WHAT OF WHY : SOME DETERMINANTS AND CONSEQUENCES OF CAUSAL ATTRIBUTION 1

Kelley's attribution theory is investigated. Subjects filled out a questionnaire that reported 16 different responses ostensibly made by other people. These responses represented four verb categories—emotions, accomplishments, opinions, and actions—and, for experimental subjects, each was accompanied by high or low consensus information, high or low distinctiveness information, and high or low consistency information. Control subjects were not given any information regarding the response. All subjects were asked to attribute each response to characteristics of the person (i.e., the actor), the stimulus, the circumstances, or to some combination of these three factors. In addition, the subjects' expectancies for future response and stimulus generalization on the part of the actor were measured. The three information variables and verb category each had a significant effect on causal attribution and on expectancy for behavioral generalization.
Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.
Health policy – why research it and how: health political science

The establishment of policy is key to the implementation of actions for health. We review the nature of policy and the definition and directions of health policy. In doing so, we explicitly cast a health political science gaze on setting parameters for researching policy change for health. A brief overview of core theories of the policy process for health promotion is presented, and illustrated with empirical evidence. The key arguments are that (a) policy is not an intervention, but drives intervention development and implementation; (b) understanding policy processes and their pertinent theories is pivotal for the potential to influence policy change; (c) those theories and associated empirical work need to recognise the wicked, multi-level, and incremental nature of elements in the process; and, therefore, (d) the public health, health promotion, and education research toolbox should more explicitly embrace health political science insights. The rigorous application of insights from and theories of the policy process will enhance our understanding of not just how, but also why health policy is structured and implemented the way it is.
Efficacy, safety, quality control, marketing and regulatory guidelines for herbal medicines (phytotherapeutic agents).

This review highlights the current advances in knowledge about the safety, efficacy, quality control, marketing and regulatory aspects of botanical medicines. Phytotherapeutic agents are standardized herbal preparations consisting of complex mixtures of one or more plants which contain as active ingredients plant parts or plant material in the crude or processed state. A marked growth in the worldwide phytotherapeutic market has occurred over the last 15 years. For the European and USA markets alone, this will reach about $7 billion and $5 billion per annum, respectively, in 1999, and has thus attracted the interest of most large pharmaceutical companies. Insufficient data exist for most plants to guarantee their quality, efficacy and safety. The idea that herbal drugs are safe and free from side effects is false. Plants contain hundreds of constituents and some of them are very toxic, such as the most cytotoxic anti-cancer plant-derived drugs, digitalis and the pyrrolizidine alkaloids, etc. However, the adverse effects of phytotherapeutic agents are less frequent compared with synthetic drugs, but well-controlled clinical trials have now confirmed that such effects really exist. Several regulatory models for herbal medicines are currently available including prescription drugs, over-the-counter substances, traditional medicines and dietary supplements. Harmonization and improvement in the processes of regulation is needed, and the general tendency is to perpetuate the German Commission E experience, which combines scientific studies and traditional knowledge (monographs). Finally, the trend in the domestication, production and biotechnological studies and genetic improvement of medicinal plants, instead of the use of plants harvested in the wild, will offer great advantages, since it will be possible to obtain uniform and high quality raw materials which are fundamental to the efficacy and safety of herbal drugs.
Toric Topographically Customized Transepithelial, Pulsed, Very High-Fluence, Higher Energy and Higher Riboflavin Concentration Collagen Cross-Linking in Keratoconus

To report a novel application of toric topographically customized transepithelial collagen cross-linking (CXL) aiming to achieve refractive astigmatic changes in a keratoconic cornea.



Specially formulated riboflavin transepithelial administration and delivery of high-fluence UVA in a topographically customized pattern was applied in an eye with progressive keratoconus. Visual acuity, cornea clarity, keratometry, topography, and pachymetry with a multitude of modalities, as well as endothelial cell counts were evaluated for >6 months.



Uncorrected distance visual acuity changed from preoperative 20/40 to 20/25 at 6 months. A mean astigmatic reduction of 0.8 D, and significant cornea surface normalization was achieved 6 months postoperatively. There was some mild change in the epithelial distribution, with the treated area having a slight normalization in the average epithelial thickness.



We introduce herein the novel application of a topographically customizable transepithelial CXL in progressive keratoconus in order to achieve an astigmatic refractive effect and ectasia stabilization. This novel technique offers a nonablative and nonincisional approach to treat irregular astigmatism in ectatic cornea with rapid visual rehabilitation.
Biodegradable plastics from renewable sources

Plastic waste disposal is a huge ecotechnological problem and one of the approaches to solving this problem is the development of biodegradable plastics. This review summarizes data on their use, biodegradability, commercial reliability and production from renewable resources. Some commercially successful biodegradable plastics are based on chemical synthesis (i.e. polyglycolic acid, polylactic acid, polycaprolactone, and polyvinyl alcohol). Others are products of microbial fermentations (i.e. polyesters and neutral polysaccharides) or are prepared from chemically modified natural products (e.g., starch, cellulose, chitin or soy protein).
The relation between language, culture, and thought.

The relationship between culture, language, and thought has long been one of the most important topics for those who wish to understand the nature of human cognition. This issue has been investigated for decades across a broad range of research disciplines. However, there has been scant communication across these different disciplines, a situation largely arising through differences in research interests and discrepancies in the definitions of key terms such as 'culture,' 'language,' and 'thought.' This article reviews recent trends in research on the relation between language, culture and thought to capture how cognitive psychology and cultural psychology have defined 'language' and 'culture,' and how this issue was addressed within each research discipline. We then review recent research conducted in interdisciplinary perspectives, which directly compared the roles of culture and language. Finally, we highlight the importance of considering the complex interplay between culture and language to provide a comprehensive picture of how language and culture affect thought.
Triggering the sintering of silver nanoparticles at room temperature.

A new approach to achieve coalescence and sintering of metallic nanoparticles at room temperature is presented. It was discovered that silver nanoparticles behave as soft particles when they come into contact with oppositely charged polyelectrolytes and undergo a spontaneous coalescence process, even without heating. Utilizing this finding in printing conductive patterns, which are composed of silver nanoparticles, enables achieving high conductivities even at room temperature. Due to the sintering of nanoparticles at room temperature, the formation of conductive patterns on plastic substrates and even on paper is made possible. The resulting high conductivity, 20% of that for bulk silver, enabled fabrication of various devices as demonstrated by inkjet printing of a plastic electroluminescent device.
Diagnosing New York city's noises with ubiquitous data

Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the third largest category of complaints in the 311 data. As each complaint about noises is associated with a location, a time stamp, and a fine-grained noise category, such as "Loud Music" or "Construction", the data is actually a result of "human as a sensor" and "crowd sensing", containing rich human intelligence that can help diagnose urban noises. In this paper we infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs). We model the noise situation of NYC with a three dimension tensor, where the three dimensions stand for regions, noise categories, and time slots, respectively. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we recover the noise situation throughout NYC. The information can inform people and officials' decision making. We evaluate our method with four real datasets, verifying the advantages of our method beyond four baselines, such as the interpolation-based approach.
Does the chimpanzee have a theory of mind? 30 years later

On the 30th anniversary of Premack and Woodruff's seminal paper asking whether chimpanzees have a theory of mind, we review recent evidence that suggests in many respects they do, whereas in other respects they might not. Specifically, there is solid evidence from several different experimental paradigms that chimpanzees understand the goals and intentions of others, as well as the perception and knowledge of others. Nevertheless, despite several seemingly valid attempts, there is currently no evidence that chimpanzees understand false beliefs. Our conclusion for the moment is, thus, that chimpanzees understand others in terms of a perception-goal psychology, as opposed to a full-fledged, human-like belief-desire psychology.
Evaluation of Dedekind sums , Eisenstein cocycles , and special values of L-functions

We define certain higher-dimensional Dedekind sums that generalize the classical Dedekind-Rademacher sums, and show how to compute them effectively using a generalization of the continued-fraction algorithm. We present two applications. First, we show how to express special values of partial zeta functions associated to totally real number fields in terms of these sums via the Eisenstein cocycle introduced by the second author. Hence we obtain a polynomial-time algorithm for computing these special values. Second, we show how to use our techniques to compute certain special values of the Witten zetafunction, and compute some explicit examples.
Virtual assembly using virtual reality techniques

Virtual reality is a technology which is often regarded as a natural extension to 3D computer graphics with advanced input and output devices. This technology has only recently matured enough to warrant serious engineering applications. The integration of this new technology with software systems for engineering, design, and manufacturing will provide a new boost to the field of computer-aided engineering. One aspect of design and manufacturing which may be significantly affected by virtual reality is design for assembly. This paper presents a research effort aimed at creating a virtual assembly design environment.
TraCI: an interface for coupling road traffic and network simulators

Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to asses the usability of vehicular applications. In this article, we present <u>Tra</u>ffic <u>C</u>ontrol <u>I</u>nterface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns.
 In contrast to the existing approaches, i.e., generating mobility traces that are fed to a network simulator as static input files, the online coupling allows the adaptation of drivers' behavior during simulation runtime. This technique is not limited to a special traffic simulator or to a special network simulator. We introduce a general framework for controlling the mobility which is adaptable towards other research areas.
 We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.