nils-herrmann/clean_scirepeval huggingface dataset:
Depth-First Iterative-Deepening: An Optimal Admissible Tree Search

The complexities of various search algorithms are considered in terms of time, space, and cost of the solution paths. • Brute-force search . Breadth-first search (BFS) . Depth-first search (DFS) . Depth-first Iterative-deepening (DFID) . Bi-directional search • Heuristic search: best-first search .  IDA∗ The issue of storing information in DISK instead of main memory. Solving 15-puzzle.  20121120, Tsan-sheng Hsu c
A SPAD-Based QVGA Image Sensor for Single-Photon Counting and Quanta Imaging

A CMOS single-photon avalanche diode (SPAD)-based quarter video graphics array image sensor with 8-μm pixel pitch and 26.8% fill factor (FF) is presented. The combination of analog pixel electronics and scalable shared-well SPAD devices facilitates high-resolution, high-FF SPAD imaging arrays exhibiting photon shot-noise-limited statistics. The SPAD has 47 counts/s dark count rate at 1.5 V excess bias (EB), 39.5% photon detection probability (PDP) at 480 nm, and a minimum of 1.1 ns dead time at 1 V EB. Analog single-photon counting imaging is demonstrated with maximum 14.2-mV/SPAD event sensitivity and 0.06e- minimum equivalent read noise. Binary quanta image sensor (QIS) 16-kframes/s real-time oversampling is shown, verifying single-photon QIS theory with 4.6× overexposure latitude and 0.168e- read noise.
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder

Recent advances in neural autoregressive models have improve the performance of speech synthesis (SS). However, as they lack the ability to model global characteristics of speech (such as speaker individualities or speaking styles), particularly when these characteristics have not been labeled, making neural autoregressive SS systems more expressive is still an open issue. In this paper, we propose to combine VoiceLoop, an autoregressive SS model, with Variational Autoencoder (VAE). This approach, unlike traditional autoregressive SS systems, uses VAE to model the global characteristics explicitly, enabling the expressiveness of the synthesized speech to be controlled in an unsupervised manner. Experiments using the VCTK and Blizzard2012 datasets show the VAE helps VoiceLoop to generate higher quality speech and to control the expressions in its synthesized speech by incorporating global characteristics into the speech generating process.
Deep Reinforcement Learning of Abstract Reasoning from Demonstrations

Extracting a set of generalizable rules that govern the dynamics of complex, high-level interactions between humans based only on observations is a high-level cognitive ability. Mastery of this skill marks a significant milestone in the human developmental process. A key challenge in designing such an ability in autonomous robots is discovering the relationships among discriminatory features. Identifying features in natural scenes that are representative of a particular event or interaction (i.e. »discriminatory features») and then discovering the relationships (e.g., temporal/spatial/spatio-temporal/causal) among those features in the form of generalized rules are non-trivial problems. They often appear as a »chicken-and-egg» dilemma. This paper proposes an end-to-end learning framework to tackle these two problems in the context of learning generalized, high-level rules of human interactions from structured demonstrations. We employed our proposed deep reinforcement learning framework to learn a set of rules that govern a behavioral intervention session between two agents based on observations of several instances of the session. We also tested the accuracy of our framework with human subjects in diverse situations.
Large-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling

This chapter presents a stochastic unit commitment model for power systems and revisits parallel decomposition algorithms for these types of models. The model is a two-stage stochastic programming problem with first-stage binary variables and second-stage mixed-binary variables. The here-and-now decision is to find day-ahead schedules for slow thermal power generators. The wait-and-see decision consists of dispatching power and to schedule fast-start generators. We discuss advantages and limitations of different decomposition methods and provide an overview of available software packages. A large-scale numerical example is provided using a modified IEEE 118-bus system with uncertain wind power generation. 1 Stochastic Unit Commitment Model Unit commitment is a decision-making process that schedules power generation units and production levels over a planning horizon. This process is central to ensure efficiency and reliability of the power system operation. While fossil-fuel power plants produce 67% of the total electricity generation in US (according to 2014 data), renewable power continue to penetrate into the electricity market [16]. This trend has motivated the development of many unit commitment models that can mitigate uncertainty of renewable supply. In this chapter, we present a stochastic unit commitment model that determines on-off schedules of the generating units and Kibaek Kim Mathematics and Computer Science Division Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA e-mail: kimk@anl.gov Victor M. Zavala Mathematics and Computer Science Division Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA e-mail: vzavala@mcs.anl.gov
A Tutorial on Evolutionary Multiobjective Optimization

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. This paper gives an overview of evolutionary multiobjective optimization with the focus on methods and theory. On the one hand, basic principles of multiobjective optimization and evolutionary algorithms are presented, and various algorithmic concepts such as fitness assignment, diversity preservation, and elitism are discussed. On the other hand, the tutorial includes some recent theoretical results on the performance of multiobjective evolutionary algorithms and addresses the question of how to simplify the exchange of methods and applications by means of a standardized interface.
Fully automatic wrapper generation for search engines

When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements. In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines. Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling. The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page. Experimental results indicate that this technique can achieve very high extraction accuracy.
Subset of individuals with autism spectrum disorders and extreme macrocephaly associated with germline PTEN tumour suppressor gene mutations.

The genetic aetiology of autism remains elusive. Occasionally, individuals with Cowden syndrome (a cancer syndrome) and other related hamartoma disorders such as Bannayan-Riley-Ruvalcaba syndrome, Proteus syndrome, and Proteus-like conditions, are characterised by germline PTEN mutations, and may have neurobehavioural features resembling autism as well as overgrowth and macrocephaly. Therefore, we undertook PTEN gene mutation analysis in 18 subjects mainly prospectively ascertained with autism spectrum disorder and macrocephaly. Of these 18 autistic subjects (13 males and five females; ages 3.1-18.4 years) with a head circumference range from 2.5 to 8.0 standard deviations above the mean, three males (17%) carried germline PTEN mutations. These three probands had previously undescribed PTEN mutations: H93R (exon 4), D252G (exon 7), and F241S (exon 7). They had the larger head circumference measurements amongst all our study subjects. The three residues altered in our patients were highly evolutionarily conserved. We suggest that PTEN gene testing be considered for patients with autistic behaviour and extreme macrocephaly. The gene findings may impact on recurrence risks as well as medical management for the patient.
Challenges and Solutions in Fog Computing Orchestration

Fog computing, complementary to cloud computing, has recently emerged as a new paradigm that extends the computing infrastructure from the center to the edge of the network. This article explores the design of a fog computing orchestration framework to support IoT applications. In particular, we focus on how the widely adopted cloud computing orchestration framework can be customized to fog computing systems. We first identify the major challenges in this procedure that arise due to the distinct features of fog computing. Then we discuss the necessary adaptations of the orchestration framework to accommodate these challenges.
A mobile-health application to detect wandering patterns of elderly people in home environment

Wandering is a common and risky behavior in people with dementia (PWD). In this paper, we present a mobile healthcare application to detect wandering patterns in indoor settings. The application harnesses consumer electronics devices including WiFi access points and mobile phones and has been tested successfully in a home environment. Experimental results show that the mobile-health application is able to detect wandering patterns including lapping, pacing and random in real-time. Once wandering is detected, an alert message is sent using SMS (Short Message Service) to attending caregivers or physicians for further examination and timely interventions.
Design of Stepped-Impedance Combline Bandpass Filters With Symmetric Insertion-Loss Response and Wide Stopband Range

An enhanced stepped-impedance combline bandpass filter employs an array of stepped-impedance resonators with tapped-transformer coupling at input and output is presented in this study. This filter has enhanced performance, including symmetric insertion-loss response around the passband and wider stopband range. The structure is compact and suitable for multilayer realization because it is free of lumped capacitors and has fewer via-hole grounds. The circuit is investigated with the characteristic mode theory of coupled lines to prove the existence of multiple transmission zeros around the passband. Numerous diagrams are given for circuit design purposes. The second- and fourth-order bandpass filters at 2.45 GHz were designed, measured, and compared with the conventional combline structure to demonstrate their performance enhancement.
Parallel processing techniques for high performance image processing applications

The complexity of many image processing applications and their stringent performance requirements have come to a point where they can no longer meet the real time deadlines, if implemented on conventional architectures based on a single general-purpose processor. Acceleration of these algorithms can be done by parallel computing. Parallelism can be accomplished both at hardware and software levels by various tools and methodologies. The various methods hence discussed prove to be helpful and thus a combination of both the custom hardware and software tool helps in speeding up the image processing algorithm. Different methodologies that can be used for parallel computation are discussed.
Generating Smooth High-Quality Isosurfaces for Interactive Modeling and Visualization of Complex Terrains

In modeling and rendering of complex procedural terrains the extraction of isosurfaces is an important part. In this paper we introduce an approach to generate high-quality isosurfaces from regular grids at interactive frame rates. The surface extraction is a variation of Dual Marching Cubes and designed as a set of well-balanced parallel computation kernels. In contrast to a straightforward parallelization we generate a quadrilateral mesh with full connectivity information and 1-ring vertex neighborhood. We use this information to smooth the extracted mesh and to approximate the smooth subdivision surface for detail tessellation. Both improve the visual fidelity when modeling procedural terrains interactively. Moreover, our extraction approach is generally applicable, for example in the field of volume visualization.
The supervisory field and projective identification.

Taking as their starting point the Baranger and Baranger model of the "psychoanalytic field", the authors extend the notion of intersubjectivity in the analytic relationship to the supervision process. They use a practical example of a supervision to show the development of what they term the "supervisory field", formed from the superimposition of the two fields of analyst-patient and supervisor-supervisee. They emphasize the interplay of projective identifications with objects emanating from the inner world of the patient that are relived in the analytic relationship and transposed to the supervisory field. They believe that the concept of the "supervisory field" contributes to a deeper understanding of the unconscious processes occurring in the mind of analysand, supervisee and supervisor during supervision, particularly regarding the identification, comprehension and resolution of persistent disturbances in the supervisory process.
Intralesional injection of adipose-derived stem cells reduces hypertrophic scarring in a rabbit ear model

Redundant collagen deposition at sites of healing dermal wounds results in hypertrophic scars. Adipose-derived stem cells (ADSCs) exhibit promise in a variety of anti-fibrosis applications by attenuating collagen deposition. The objective of this study was to explore the influence of an intralesional injection of ADSCs on hypertrophic scar formation by using an established rabbit ear model. Twelve New Zealand albino rabbits were equally divided into three groups, and six identical punch defects were made on each ear. On postoperative day 14 when all wounds were completely re-epithelialized, the first group received an intralesional injection of ADSCs on their right ears and Dulbecco’s modified Eagle’s medium (DMEM) on their left ears as an internal control. Rabbits in the second group were injected with conditioned medium of the ADSCs (ADSCs-CM) on their right ears and DMEM on their left ears as an internal control. Right ears of the third group remained untreated, and left ears received DMEM. We quantified scar hypertrophy by measuring the scar elevation index (SEI) on postoperative days 14, 21, 28, and 35 with ultrasonography. Wounds were harvested 35 days later for histomorphometric and gene expression analysis. Intralesional injections of ADSCs or ADSCs-CM both led to scars with a far more normal appearance and significantly decreased SEI (44.04 % and 32.48 %, respectively, both P <0.01) in the rabbit ears compared with their internal controls. Furthermore, we confirmed that collagen was organized more regularly and that there was a decreased expression of alpha-smooth muscle actin (α-SMA) and collagen type Ι in the ADSC- and ADSCs-CM-injected scars according to histomorphometric and real-time quantitative polymerase chain reaction analysis. There was no difference between DMEM-injected and untreated scars. An intralesional injection of ADSCs reduces the formation of rabbit ear hypertrophic scars by decreasing the α-SMA and collagen type Ι gene expression and ameliorating collagen deposition and this may result in an effective and innovative anti-scarring therapy.
Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to OPEN ACCESS Remote Sens. 2014, 6 5020 the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.
Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose a new machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, a histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10 percent when icon clusters are used in the prediction model.
Breaking the Barriers to True Augmented Reality

Christian Sandor†, Martin Fuchs?, Alvaro Cassinelli†, Hao Li◦, Richard Newcombe×, Goshiro Yamamoto†, Steven Feiner∧ †Nara Institute of Science and Technology, Graduate School of Information Science, Japan ?Universität Stuttgart, Visualization Research Center, Germany ◦University of Southern California, Department of Computer Science, USA ×University of Washington, Department of Computer Science and Engineering, USA ∧Columbia University, Department of Computer Science, USA December 18, 2015
Polysaccharide extraction from Spirulina sp. and its antioxidant capacity.

To optimize polysaccharide extraction from Spirulina sp., the effect of solid-to-liquid ratio, extraction temperature and time were investigated using Box-Behnken experimental design and response surface methodology. The results showed that extraction temperature and solid-to-liquid ratio had a significant impact on the yield of polysaccharides. A polysaccharides yield of around 8.3% dry weight was obtained under the following optimized conditions: solid-to-liquid ratio of 1:45, temperature of 90°C, and time of 120 min. The polysaccharide extracts contained rhamnose, which accounted for 53% of the total sugars, with a phenolic content of 45 mg GAE/g sample.
Body integrity identity disorder and Gender Dysphoria : A pilot study to investigate similarities and differences

Body Integrity Identity Disorder (BIID) describes a phenomenon in which physically healthy people feel the constant desire for an impairment of their body. M. First  suggested to classify BIID as an identity disorder. The other main disorder in this respect is Gender Dysphoria. In this paper these phenomena are compared.  A questionnaire survey with transsexuals (number of subjects, N=19) and BIID sufferers (N=24) measuring similarities and differences. Age and educational level of the subjects are predominantly matched.  No differences were found between BIID and Gender Dysphoria with respect to body image and body perception (U-test: p-value=.757), age of onset (p=.841), the imitation of the desired identity (p=.699 and p=.938), the etiology (p=.299) and intensity of desire (p=.989 and p=.224) as well as in relation to a high level of suffering and impaired quality of life (p=.066).  There are many similarities between BIID and Gender Dysphoria, but the sample was too small to make general statements. The results, however, indicate that BIID can actually be classified as an identity disorder.
Interactivity, Game Creation, Design, Learning, and Innovation

This article presents the preliminary results of an exploratory experiment with BilliArT, an interactive installation for music-making. The aim is to extract useful information from the combination of different ways to approach to the art work, namely that of conservation, of the aesthetic experience, and of the artistic creativity. The long-term goal is to achieve a better understanding of how people engage with interactive installations, and ultimately derive an ontology for interactive art.
CL_ARRAY: A new generic library of multidimensional containers for c++ compilers with extension for OpenCL framework

This paper presents a new metaprogramming library, CL ARRAY, that offers multiplatform and generic multidimensional data containers for C++ specifically adapted for parallel programming. The CL ARRAY containers are built around a new formalism for representing the multidimensional nature of data as well as the semantics of multidimensional pointers and contiguous data structures. We also present OCL ARRAY VIEW, a concept based on metaprogrammed enveloped objects that supports multidimensional transformations and multidimensional iterators designed to simplify and formalize the interfacing process between OpenCL APIs, standard template library (STL) algorithms and CL ARRAY containers. Our results demonstrate improved performance and energy savings over the three most popular container libraries available to the developer community for use in the context of multi-linear algebraic applications.
Gendering Cybercrime

Very few cybercrimes are committed by females. Therefore, there has been a dearth of research on this topic. It is important that we understand the relationships between gender and cybercrime, to inform crime prevention strategies and understand the particular problems female offenders may face. This research draws from extensive data gathered in relation to cybercrime offenders, both male and female. The research explores the types of roles female computer crime offenders take on, and their social experiences, finding that, compared to males, they experience more adverse life events. Reasons for the lack of female involvement in cybercrime include the barriers female face when engaging with the predominantly masculine online communities that are important for learning and sharing information.