nils-herrmann/clean_scirepeval huggingface dataset:
Artificial intelligence

S INCE ITS INAUGURATION in 1966, the ACM A.M. Tur-ing Award has recognized major contributions of lasting importance to computing. Through the years, it has become the most prestigious award in computing. To help celebrate 50 years of the ACM Turing Award and the visionaries who have received it, ACM has launched a campaign called " Panels in Print, " which takes the form of a collection of responses from Turing laureates, ACM award recipients and other ACM experts on a given topic or trend. ACM's celebration of 50 years of the ACM Turing Award will culminate with a conference June 23–24, 2017 at the Westin St. Francis in San Francis-co to highlight the significant impact of the contributions of ACM Turing laureates on computing and society, to look ahead to the future of technology and innovation, and to help inspire the next generation of computer scientists to invent and dream. Grace Murray Hopper recipient PEDRO FELZENSZWALB to respond to several questions about Artificial Intelligence. What have been the biggest breakthroughs in AI in recent years and what impact is it having in the real-world? RAJ REDDY: Ten years ago, I would have said it wouldn't be possible, in my lifetime, to recognize unre-hearsed spontaneous speech from an open population but that's exactly what Siri, Cortana and Alexa do. The same is happening with vision and robotics. We are by no means at the end of the activity in these areas, but we have enough working examples that society can benefit from these breakthroughs.  The biggest breakthrough in the last five or so years has been the use of deep learning, a particular kind of machine learning that uses neural networks. Stacking the network into many layers that learn increasingly abstract patterns as you go up the layers seems to be a fundamentally powerful idea, and it's been very successful in a surprisingly wide variety of applications—from speech recognition, to image recognition, to language understanding. What's interesting is we don't seem to be near the limit of what deep learning can do; we'll likely see many more powerful uses of it in the coming years.  Among the biggest technical advances I would include the development of scalable machine learning algorithms and the computational infrastructure to process and interact with huge data-sets. The latest example of these advances is deep learning. In computer vision deep learning has …
Effects of Dance Movement Therapy on Adult Patients with Autism Spectrum Disorder: A Randomized Controlled Trial

This study examines the effects of dance movement therapy (DMT) on empathy for adults with autism spectrum disorder (ASD). DMT based on the embodiment approach offers body-centered interventions, such as mirroring techniques, to address the needs of ASD patients. Accordingly, findings of a feasibility study suggest that DMT may be an effective approach for clients on the ASD spectrum. The present study is a randomized controlled trial that was conducted as a multicenter study within the framework of the EU-funded research project TESIS (Toward an Embodied Science of Intersubjectivity), and employed a two-factorial between-subject design. The treatment group (n = 35) participated in a 10-week manualized DMT intervention, whereas the control group (n = 22) received treatment only after a waiting period. Empathy, measured with the Cognitive and Emotional Empathy Questionnaire (CEEQ), was the main variable of interest, analyzed by a repeated measures analysis of variance. In order to also include incomplete data cases, we used the expectation-maximization algorithm for missing data estimation. Results suggest no significant changes in overall empathy between groups. We discuss the results and limitations, as well as future research options.
A new paradigm for synthesis of linear decompressors

For more than two decades, the key objective for synthesis of linear decompressors has been maximizing encoding efficiency. For combinational decompressors, encoding satisfiability is dynamically checked for each specified care bit. By contrast, for sequential linear decompressors (e.g. PRPGs), encoding is performed for each test cube; the resultant static encoding considers that a test cube is encodable only if all of its care bits are encodable. The paper introduces a new class of sequential linear decompressors that provides a trade-off between the computational complexity and the encoding efficiency of linear encoding. As a result, it becomes feasible to dynamically encode care bits before a test cube has been completed, and derive decompressor-implied scan cell values during test generation. The resultant dynamic encoding enables an identification of encoding conflicts during branch-and-bound search and a reduction of search space for dynamic compaction. Experimental results demonstrate that dynamic encoding consistently outperforms static encoding in a wide range of compression ratios.
Sharp feature detection in point clouds

This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.
Timeout Strategy-based Mobility Management for Software Defined Satellite Networks

Software Defined Satellite Network (SDSN) is a novel framework which brings Software Defined Network (SDN) technologies in the satellite networks. It has great potential to achieve effective and flexible management in the satellite networks. However, the frequent handovers will lead to an increase in the flow table size in SDSN. Due to the limited flow table space, a lot of flows will be dropped if the flow table is full during the handover. This is a burning issue to be solved for mobility management in SDSN. In this paper, we propose a heuristic Timeout Strategy-based Mobility Management algorithm for SDSN, named TSMM. TSMM aims to reduce the drop-flows during handover by considering two key points, the limited flow table space and satellite link handover. We implement TSMM mechanism and conduct contrast experiments. The experimental results verify the good performance in terms of transmission quality, an 8.2%-9.9% decrease in drop-flow rate, and a 6.9%–11.18% decrease in flow table size during the handover.
Miniaturized 3-bit Phase Shifter for 60 GHz Phased-Array in 65 nm CMOS Technology

The letter presents a compact 3-bit 90 ° phase shifter for phased-array applications at the 60 GHz ISM band (IEEE 802.11ad standard). The designed phase shifter is based on reflective-type topology using the proposed reflective loads with binary-weighted digitally-controlled varactor arrays and the transformer-type directional coupler. The measured eight output states of the implemented phase shifter in 65 nm CMOS technology, exhibit phase-resolution of 11.25 ° with an RMS phase error of 5.2 °. The insertion loss is 5.69 ± 1.22 dB at 60 GHz and the return loss is better than 12 dB over 54-66 GHz. The chip demonstrates a compact size of only 0.034 mm2.
Context based image analysis with application in dietary assessment and evaluation

Dietary assessment is essential for understanding the link between diet and health. We develop a context based image analysis system for dietary assessment to automatically segment, identify and quantify food items from images. In this paper, we describe image segmentation and object classification methods used in our system to detect and identify food items. We then use context information to refine the classification results. We define contextual dietary information as the data that is not directly produced by the visual appearance of an object in the image, but yields information about a user’s diet or can be used for diet planning. We integrate contextual dietary information that a user supplies to the system either explicitly or implicitly to correct potential misclassifications. We evaluate our models using food image datasets collected during dietary assessment studies from natural eating events.
An adaptive multigrid algorithm for simulating solid tumor growth using mixture models

In this paper we give the details of the numerical solution of a three-dimensional multispecies diffuse interface model of tumor growth, which was derived in (Wise et al.,  (2008)) and used to study the development of glioma in (Frieboes et al., NeuroImage 37 (2007) and tumor invasion in (Bearer et al., Cancer Research, 69 (2009)) and (Frieboes et al.,  (2010)). The model has a thermodynamic basis, is related to recently developed mixture models, and is capable of providing a detailed description of tumor progression. It utilizes a diffuse interface approach, whereby sharp tumor boundaries are replaced by narrow transition layers that arise due to differential adhesive forces among the cell-species. The model consists of fourth-order nonlinear advection-reaction-diffusion equations (of Cahn-Hilliard-type) for the cell-species coupled with reaction-diffusion equations for the substrate components. Numerical solution of the model is challenging because the equations are coupled, highly nonlinear, and numerically stiff. In this paper we describe a fully adaptive, nonlinear multigrid/finite difference method for efficiently solving the equations. We demonstrate the convergence of the algorithm and we present simulations of tumor growth in 2D and 3D that demonstrate the capabilities of the algorithm in accurately and efficiently simulating the progression of tumors with complex morphologies.
Analyzing software evolution through feature views

Features encapsulate the domain knowledge of a software system and thus are valuable sources of information for a reverse engineer. When analyzing the evolution of a system, we need to know how and which features were modified to recover both the change intention and extent, namely which source artifacts are affected. Typically, the implementation of a feature crosscuts a number of source artifacts. To obtain a mapping between features and the source artifacts, we exercise the features and capture their execution traces. However this results in large traces that are difficult to interpret. To tackle this issue we compact the traces into simple sets of source artifacts that participate in a feature’s runtime behavior. We refer to these compacted traces as feature views. Within a feature view, we partition the source artifacts into disjoint sets of characterized software entities. The characterization defines the level of participation of a source entity in the features. We then analyze the features over several versions of a system and we plot their evolution to reveal how and which features were affected by code changes. We show the usefulness of our approach by applying it to a case study where we address the problem of merging parallel development tracks of the same system.
Deciding parity games in quasipolynomial time

It is shown that the parity game can be solved in quasipolynomial time. The parameterised parity game â with <i>n</i> nodes and <i>m</i> distinct values (aka colours or priorities) â is proven to be in the class of fixed parameter tractable (<b>FPT</b>) problems when parameterised over <i>m</i>. Both results improve known bounds, from runtime <i>n</i><sup><i>O</i>(â<i>n</i>)</sup> to <i>O</i>(<i>n</i><sup>log(<i>m</i>)+6</sup>) and from an <b>XP</b>-algorithm with runtime <i>O</i>(<i>n</i><sup>Î(<i>m</i>)</sup>) for fixed parameter <i>m</i> to an <b>FPT</b>-algorithm with runtime <i>O</i>(<i>n</i><sup>5</sup>)+<i>g</i>(<i>m</i>), for some function <i>g</i> depending on <i>m</i> only. As an application it is proven that coloured Muller games with <i>n</i> nodes and <i>m</i> colours can be decided in time <i>O</i>((<i>m</i><sup><i>m</i></sup> Â· <i>n</i>)<sup>5</sup>); it is also shown that this bound cannot be improved to <i>O</i>((2<sup><i>m</i></sup> Â· <i>n</i>)<sup><i>c</i></sup>), for any <i>c</i>, unless <b>FPT</b> = <b>W[1]</b>.
Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient.

A new energy paradigm, consisting of greater reliance on renewable energy sources and increased concern for energy efficiency in the total energy lifecycle, has accelerated research into energy-related technologies. Due to their ubiquity, magnetic materials play an important role in improving the efficiency and performance of devices in electric power generation, conditioning, conversion, transportation, and other energy-use sectors of the economy. This review focuses on the state-of-the-art hard and soft magnets and magnetocaloric materials, with an emphasis on their optimization for energy applications. Specifically, the impact of hard magnets on electric motor and transportation technologies, of soft magnetic materials on electricity generation and conversion technologies, and of magnetocaloric materials for refrigeration technologies, are discussed. The synthesis, characterization, and property evaluation of the materials, with an emphasis on structure-property relationships, are discussed in the context of their respective markets, as well as their potential impact on energy efficiency. Finally, considering future bottlenecks in raw materials, options for the recycling of rare-earth intermetallics for hard magnets will be discussed.
Treatment of Darier's disease with photodynamic therapy.

Photodynamic therapy (PDT) using topical 5-aminolaevulinic acid (5-ALA) as a photosensitizer has been reported in the treatment of both neoplastic and benign cutaneous disorders.



To evaluate the efficacy of photodynamic therapy in selected patients with Darier's disease (keratosis follicularis).



Six patients with Darier's disease were assessed before and after treatment with PDT using 5-ALA and mean fluence rates of 110-150 mW cm-2.



Of the six patients, one was unable to tolerate the treatment. Of the remaining five, all experienced an initial inflammatory response that lasted two to three weeks. In four of the five patients, this was followed by sustained clearance or improvement over a followup period of six months to three years. Three of these four patients were on systemic retinoids and the fourth had discontinued acitretin prior to PDT. In the fifth patient partial improvement was followed by recurrence after etretinate therapy was discontinued. Biopsy specimens taken immediately after the procedure in two patients demonstrated a mild inflammatory cell infiltrate in the dermis. A biopsy obtained eighteen months after PDT from a successfully treated area showed no signs of Darier's disease and a subtle increase of collagen in the upper dermis.



Photodynamic therapy can be viewed as a potential adjunctive modality for Darier's disease but should not be considered as a substitute for retinoids in patients who require systemic treatment.
Development of the Standard CubeSat Deployer and a CubeSat Class PicoSatellite

Cal Poly students are participating in the development of a new class of picosatellite, the CubeSat. CubeSats are ideal as space development projects for universities around the world. In addition to their significant role in educating space scientists and engineers, CubeSats provide a low-cost platform for testing and space qualification of the next generation of small payloads in space. A key component of the project is the development of a standard CubeSat deployer. This deployer is capable of releasing a number of CubeSats as secondary payloads on a wide range of launchers. The standard deployer requires all CubeSats to conform to common physical requirements, and share a standard deployer interface. CubeSat development time and cost can be significantly reduced by the development of standards that are shared by a large number of spacecraft.
MetaPhlAn2 for enhanced metagenomic taxonomic profiling

1Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University Medical Center, Columbus, Ohio, USA. 2Department of Physics, Pohang University of Science and Technology, Pohang, Korea. 3School of Interdisciplinary Bioscience and Bioengineering, Pohang, Korea. 4Physics Department, The Ohio State University, Columbus, Ohio, USA. 5These authors contributed equally to this work.
The Turkish delight: a pliable graft for rhinoplasty.

In nose surgery, carved or crushed cartilage used as a graft has some disadvantages, chiefly that it may be perceptible through the nasal skin after tissue resolution is complete. To overcome these problems and to obtain a smoother surface, the authors initiated the use of Surgicel-wrapped diced cartilage. This innovative technique has been used by the authors on 2365 patients over the past 10 years: in 165 patients with traumatic nasal deformity, in 350 patients with postrhinoplasty deformity, and in 1850 patients during primary rhinoplasty. The highlights of the surgical procedure include harvested cartilage (septal, alar, conchal, and sometimes costal) cut in pieces of 0.5 to 1 mm using a no. 11 blade. The fine-textured cartilage mass is then wrapped in one layer of Surgicel and moistened with an antibiotic (rifamycin). The graft is then molded into a cylindrical form and inserted under the dorsal nasal skin. In the lateral wall and tip of the nose, some overcorrection is performed depending on the type of deformity. When the mucosal stitching is complete, this graft can be externally molded, like plasticine, under the dorsal skin. In cases of mild-to-moderate nasal depression, septal and conchal cartilages are used in the same manner to augment the nasal dorsum with consistently effective and durable results. In cases with more severe defects of the nose, costal cartilage is necessary to correct both the length of the nose and the projection of the columella. In patients with recurrent deviation of the nasal bridge, this technique provided a simple solution to the problem. After overexcision of the dorsal part of deviated septal cartilage and insertion of Surgicel-wrapped diced cartilage, a straight nose was obtained in all patients with no recurrence (follow-up of 1 to 10 years). The technique also proved to be highly effective in primary rhinoplasties to camouflage bone irregularities after hump removal in patients with thin nasal skin and/or in cases when excessive hump removal was performed. As a complication, in six patients early postoperative swelling was more than usual. In 16 patients, overcorrection was persistent owing to fibrosis, and in 11 patients resorption was excessive beyond the expected amount. A histologic evaluation was possible in 16 patients, 3, 6, and 12 months postoperatively, by removing thin slices of excess cartilage from the dorsum of the nose during touch-up surgery. This graft showed a mosaic-type alignment of graft cartilage with fibrous tissue connection among the fragments. In conclusion, this type of graft is very easy to apply, because a plasticine-like material is obtained that can be molded with the fingers, giving a smooth surface with desirable form and long-lasting results in all cases. The favorable results obtained by this technique have led the authors to use Surgicel-wrapped diced cartilage routinely in all types of rhinoplasty.
Characteristics of health IT outage and suggested risk management strategies: An analysis of historical incident reports in China

The healthcare industry has become increasingly dependent on using information technology (IT) to manage its daily operations. Unexpected downtime of health IT systems could therefore wreak havoc and result in catastrophic consequences. Little is known, however, regarding the nature of failures of health IT.



To analyze historical health IT outage incidents as a means to better understand health IT vulnerabilities and inform more effective prevention and emergency response strategies.



We studied news articles and incident reports publicly available on the internet describing health IT outage events that occurred in China. The data were qualitatively analyzed using a deductive grounded theory approach based on a synthesized IT risk model developed in the domain of information systems.



A total of 116 distinct health IT incidents were identified. A majority of them (69.8%) occurred in the morning; over 50% caused disruptions to the patient registration and payment collection functions of the affected healthcare facilities. The outpatient practices in tertiary hospitals seem to be particularly vulnerable to IT failures. Software defects and overcapacity issues, followed by malfunctioning hardware, were among the principal causes.



Unexpected health IT downtime occurs more and more often with the widespread adoption of electronic systems in healthcare. Risk identification and risk assessments are essential steps to developing preventive measures. Equally important is institutionalization of contingency plans as our data show that not all failures of health IT can be predicted and thus effectively prevented. The results of this study also suggest significant future work is needed to systematize the reporting of health IT outage incidents in order to promote transparency and accountability.
An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges

Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.