nils-herrmann/clean_scirepeval huggingface dataset:
Lithography and Other Patterning Techniques for Future Electronics

For all technologies, from flint arrowheads to DNA microarrays, patterning the functional material is crucial. For semiconductor integrated circuits (ICs), it is even more critical than for most technologies because enormous benefits accrue to going smaller, notably higher speed and much less energy consumed per computing function. The consensus is that ICs will continue to be manufactured until at least the ldquo22 nm noderdquo (the linewidth of an equal line-space pattern). Most patterning of ICs takes place on the wafer in two steps: (a) lithography, the patterning of a resist film on top of the functional material; and (b) transferring the resist pattern into the functional material, usually by etching. Here we concentrate on lithography. Optics has continued to be the chosen lithographic route despite its continually forecast demise. A combination of 193-nm radiation, immersion optics, and computer-intensive resolution enhancement technology will probably be used for the 45- and 32-nm nodes. Optical lithography usually requires that we first make a mask and then project the mask pattern onto a resist-coated wafer. Making a qualified mask, although originally dismissed as a ldquosupport technology,rdquo now represents a significant fraction of the total cost of patterning an IC largely because of the measures needed to push resolution so far beyond the normal limit of optical resolution. Thus, although optics has demonstrated features well below 22 nm, it is not clear that optics will be the most economical in this range; nanometer-scale mechanical printing is a strong contender, extreme ultraviolet is still the official front runner, and electron beam lithography, which has demonstrated minimum features less than 10 nm wide, continues to be developed both for mask making and for directly writing on the wafer (also known as ldquomaskless lithographyrdquo). Going from laboratory demonstration to manufacturing technology is enormously expensive ( $1 billion) and for good reason. Just in terms of data rate (mask pattern to resist pattern), today's exposure tools achieve about 10 Tb/s at an allowable error rate of about 1/h; this data rate will double with each generation. In addition, the edge placement precision required will soon be 30 parts per billion. There are so many opportunities for unacceptable performance that making the right decision goes far beyond understanding the underlying physical principles. But the benefits of continuing to be able to manufacture electronics at the 22-nm node and beyond appear to justify the investment, and there is no shortage of ideas on how to accomplish this.
End-to-End Representation Learning for Correlation Filter Based Tracking

The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.
Images Features Extraction of Tobacco Leaves

Images features extraction is very important for the grading process of flue-cured tobacco leaves. In this paper, a machine vision techniques base system is proposed for the automatic inspection of flue-cured tobacco leaves. Machine vision techniques are used in this system to solve problems of features extraction and analysis of tobacco leaves, which include features of color, size, shape andsurface texture. The experimental results show that this system is a viable way for the features extraction of tobacco leaves, and can be used for the automatic classification of tobacco leaves.
Fog computing: Security issues, solutions and robust practices

Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.
Compact Half Diamond Dual-Band Textile HMSIW On-Body Antenna

A novel wearable dual-band textile antenna, designed for optimal on-body performance in the 2.4 and 5.8 GHz Industrial, Scientific and Medical bands, is proposed. By using brass eye-lets and a combination of conducting and non-conductive textile materials, a half-mode substrate integrated waveguide cavity with ground plane is realized that is very compact and flexible, while still directing radiation away from the wearer. Additional miniaturization is achieved by adding a row of shorting vias and slots. Beside excellent free space performance in the 2.4 and 5.8 GHz bands, respectively, with measured impedance bandwidth of 4.9% and 5.1%, maximal measured free-space gain of 4.1 and 5.8 dBi, and efficiency of 72.8% and 85.6%, very stable on-body performance is obtained, with minimal frequency detuning when deploying the antenna on the human body and when bent around cylinders with radii of 75 and 40 mm. At 2.45 and 5.8 GHz, respectively, the measured on-body gain is 4.4 and 5.7 dBi, with sufficiently small calculated SAR values of 0.55 and 0.90 W/kg. These properties make the proposed antenna excellently suited for wearable on-body systems.
Measuring dimensions of intergenerational contact: factor analysis of the Queen's University Scale.

Intergenerational contact has been linked to a range of health outcomes, including greater engagement and lower depression. Measures of contact are limited. Informed by Allport's contact theory, the Queen's University Scale consists of items rating contact with elders. We administered the survey to a young adult sample (N = 606) to identify factors that may optimize intervention programming and enhance young persons' health as they age.



We conducted exploratory factor analysis (EFA) in the structural equation modeling framework and then confirmatory factor analysis with items pertaining to the general elder population.



EFAs did not yield an adequate factor structure. We tested two alternative confirmatory models based on findings from the EFA. Neither a second-order model nor a first-order model allowing double loadings and correlated errors proved adequate.



Difficulty finding an adequate factor solution reflects challenges to measuring intergenerational contact with this scale. Items reflect relevant topics but subscale models are limited in interpretability. Knox and colleagues' analyses led them to recommend a brief, global scale, but we did not find empirical support for such a measure. Next steps include development and testing of a reliable, valid scale measuring dimensions of contact as perceived by both youth and elders.
Sizing of Energy Storage for Microgrids

This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulated as a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper.
Fingerprint classification

A fingerprint classification algorithm is presented in this paper. Fingerprints are classified into five categories: arch, tented arch, left loop, right loop and whorl. The algorithm extracts singular points (cores and deltas) in a fingerprint image and performs classification based on the number and locations of the detected singular points. The classifier is invariant to rotation, translation and small amounts of scale changes. The classifier is rule-based, where the rules are generated independent of a given data set. The classifier was tested on 4000 images in the NIST-4 database and on 5400 images in the NIST-9 database. For he NIST-4 database, classification accuracies of 85.4% for the five-class problem and 91.1% for the four-class problem (with arch and tented arch placed in the same category) were achieved. Using a reject option, the four-class classification error can be reduced to less than 6% with 10% fingerprint images rejected. Similar classification performance was obtained on the NIST-9 database.
Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital

To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospitals information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights that facilitate the improvement of existing
MIPS : An Efficient Global Parametrization Method

The problem of parametrizing 3D data points is fundamental for many applications in computer-aided geometric design, e.g. surface fitting, texture mapping, and remeshing. We present a new method for constructing a global parametrization of a triangulated (topologically disklike) surface over a planar region with minimal distortion. In contrast to many existing approaches which need the boundary of the parametrization to be fixed in advance, the boundary develops naturally with this new algorithm.
Assessment of energy distribution losses for increasing penetration of distributed generation

High levels of penetration of distributed generation (DG) are a new challenge for traditional electric power systems. Power injections from DGs change network power flows modifying energy losses. Although it is considered that DG reduce losses, this paper shows that this is not always true. This paper presents an approach to compute annual energy losses variations when different penetration and concentration levels of DG are connected to a distribution network. In addition, the impact on losses of different DG technologies, such as combined heat and power, wind power, photovoltaic, and fuel-cells, is analyzed. Results show that energy losses variation, as a function of the DG penetration level, presents a characteristic U-shape trajectory. Moreover, when DG units are more dispersed along network feeders, higher losses reduction can be expected. Regarding DG technologies, it should be noted that wind power is the one that shows the worst behavior in losses reduction. Finally, DG units with reactive power control provide a better network voltage profile and lower losses.
ALOHA: an architectural-aware framework for deep learning at the edge

Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in multiple application domains. However, some steps further are needed towards the ubiquitous adoption of this kind of instrument. First, effort and skills required to develop new DL models, or to adapt existing ones to new use-cases, are hardly available for small- and medium-sized businesses. Second, DL inference must be brought at the edge, to overcome limitations posed by the classically-used cloud computing paradigm. This requires implementation on low-energy computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage. This work describes the ALOHA framework, that proposes a solution to these issue by means of an integrated tool flow that automates most phases of the development process. The framework introduces architecture-awareness, considering the target inference platform very early, already during algorithm selection, and driving the optimal porting of the resulting embedded application. Moreover it considers security, power efficiency and adaptiveness as main objectives during the whole development process.
SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats

Despite the popularity of MapReduce, there are several obstacles to applying it for developing scientific data analysis applications. Current MapReduce implementations require that data be loaded into specialized file systems, like the Hadoop Distributed File System (HDFS), whereas with rapidly growing size of scientific datasets, reloading data in another file system or format is not feasible. We present a framework that allows scientific data in different formats to be processed with a MapReduce like API. Our system is referred to as SciMATE, and is based on the MATE system developed at Ohio State. SciMATE is developed as a customizable system, which can be adapted to support processing on any of the scientific data formats. We have demonstrated the functionality of our system by creating instances that can be processing NetCDF and HDF5 formats as well as flat-files. We have also implemented three popular data mining applications and have evaluated their execution with each of the three instances of our system.
High Dynamic Range Imaging

While real scenes produce a wide range of brightness variations, current cameras use low dynamic range image detector that typically provide 256 levels of brightness data at each pixel. We propose methods to create High Dynamic Range images, the method to enhance the dynamic range of is based on capturing multiple exposure photographs of the scene. Even if there are few methods available for creating HDR images, HDR display technologies still lag behind. We present a tone mapping algorithm to display HDR images on Low Dynamic Range display devices. We also deal with histogram manipulation for painterly effect on output. Applications of HDR images are discussed in brief. An algorithm to create HDR images for still scenes in Color Filter Array (CFA) domain is proposed and successfully implemented.
On shared understanding in software engineering: an essay

Shared understanding is essential for efficient software engineering when the risk of unsatisfactory out-come and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area.
Effects of trust and Perceived value on customer loyalty by mediating role of customer satisfaction and mediating role of customer habit ( case study : Agricultural internet bank customers in Tehran )

According to the Importance of customer in competitive environment, especially in banking industry, regarding that the cost of catching (attracting) new customers is several times higher than the cost of keep customer loyal to organization, cause that examining ways of becoming loyal customer to product (goods/ services) has high preference in market research. The purpose of this study is to determine the Effects of trust and Perceived value on customer loyalty by mediating role of customer satisfaction and mediating role of customer habit . This research is in descriptive category. For measurement of information, questionnaires with five option Likert Spectrum have been used . In the present study, descriptive and inferential statistics were used to analyze the data using both SPSS and LISREL. Analysis of ANOVA was also used for adjustment test. In the descriptive level, using statistical parameters such as frequency and cumulative frequency, the charts were plotted and data were analysed, and al inferential level, Cronbach's alpha was used to test reliability of questionnaire, structural equation modeling (SEM) to confirm or reject hypotheses and confirmatory factor analysis was used to assess the ability of each variable to be explained by the questions in the questionnaire. Research findings revealed a significant positive relationship trust and customer loyalty. The findings of this research shows that satisfaction variable is mediator in trust and has a positive and significant influence on establishment of loyalty.
The Runtime System Problem Identification Method Based on Log Analysis

Currently system logs are an important source of information for system administrators to monitor system behaviors and to identify system problems. The manual examining is infeasible for the complex system and the existing automated methods for identifying system problems have different disadvantages such as the extreme dependency on the source code of the system, the low accuracy of predicting or identifying the system problems, or the requirement of the balanced and labeled training data set. This paper proposes a one- class Support Vector Machine (OCSVM) based method to identify the runtime system problems. Firstly, log sequences are generated for describing the running trajectories of the monitored system by parsing log messages; Secondly, variable length n-gram features are extracted, and moreover, the log sequences are represented as feature vectors based on these variable length n-gram features and Vector Space Model (VSM). Finally, all the feature vectors of the training log sequence set, which only includes the labeled normal log sequences, are input into OCSVM. Experimental results show that it performs better to use linear kernel to train OCSVM on our feature vectors than Gaussian kernel and the size of the sliding window hardly affects the performance of our method. Moreover, the proposed method achieves better performance on unbalanced training dataset than the method based on Random Indexing (RI) and weighted SVM.
Fatal acute intracranial injury, subdural hematoma, and retinal hemorrhages caused by stairway fall.

We describe an infant with an acute subdural hematoma, a fatal head injury, and severe hemorrhagic retinopathy caused by a stairway fall. His cerebral and ocular findings are considered diagnostic of abusive head trauma by many authors. Our literature search of serious injuries or fatalities from stairway or low-height falls involving young children yielded 19 articles of primary data. These articles are discrepant, making the classification of a young child's death following a reported short fall problematic. This case report contradicts the prevalent belief of many physicians dealing with suspected child abuse that low-height falls by young children are without exception benign occurrences and cannot cause fatal intracranial injuries and severe retinal hemorrhages. The irreparable harm to a caregiver facing an erroneous allegation of child abuse requires physicians to thoroughly investigate and correctly classify pediatric accidental head injuries.
Safety and Efficacy of Selective Neurectomy of the Gastrocnemius Muscle for Calf Reduction in 300 Cases

Liposuction alone is not always sufficient to correct the shape of the lower leg, and muscle reduction may be necessary. To assess the outcomes of a new technique of selective neurectomy of the gastrocnemius muscle to correct calf hypertrophy. Between October 2007 and May 2010, 300 patients underwent neurectomy of the medial and lateral heads of the gastrocnemius muscle at the Department of Cosmetic and Plastic Surgery, the Second People’s Hospital of Guangdong Province (Guangzhou, China) to correct the shape of their lower legs. Follow-up data from these 300 patients were analyzed retrospectively. Cosmetic results were evaluated independently by the surgeon, the patient, and a third party. Preoperative and postoperative calf circumferences were compared. The Fugl-Meyer motor function assessment was evaluated 3 months after surgery. The average reduction in calf circumference was 3.2 ± 1.2 cm. The Fugl-Meyer scores were normal in all patients both before and 3 months after surgery. A normal calf shape was achieved in all patients. Six patients complained of fatigue while walking and four of scar pigmentation, but in all cases, this resolved within 6 months. Calf asymmetry was observed in only two patients. The present series suggests that neurectomy of the medial and lateral heads of the gastrocnemius muscle may be safe and effective for correcting the shape of the calves. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Monocular 3D Scene Modeling and Inference: Understanding Multi-Object Traffic Scenes

Scene understanding has (again) become a focus of computer vision research, leveraging advances in detection, context modeling, and tracking. In this paper, we present a novel probabilistic 3D scene model that encompasses multi-class object detection, object tracking, scene labeling, and 3D geometric relations. This integrated 3D model is able to represent complex interactions like inter-object occlusion, physical exclusion between objects, and geometric context. Inference allows to recover 3D scene context and perform 3D multiobject tracking from a mobile observer, for objects of multiple categories, using only monocular video as input. In particular, we show that a joint scene tracklet model for the evidence collected over multiple frames substantially improves performance. The approach is evaluated for two different types of challenging onboard sequences. We first show a substantial improvement to the state-of-the-art in 3D multi-people tracking. Moreover, a similar performance gain is achieved for multi-class 3D tracking of cars and trucks on a new, challenging dataset.