nils-herrmann/clean_scirepeval huggingface dataset:
Power electronics traction transformer

With the new generation of full low floor passenger trains, the constraints of weight and size on the traction transformer are becoming stronger. The ultimate target weight for the transformer is 1 kg/kVA. The reliability and the efficiency are also becoming more important. To address these issues, a multilevel topology using medium frequency transformers has been developed. It permits to reduce the weight and the size of the system and improves the global life cycle cost of the vehicle. The proposed multilevel converter consists of sixteen bidirectional direct current converters (cycloconverters) connected in series to the catenary 15 kV, 16.7 Hz through a choke inductor. The cycloconverters are connected to sixteen medium frequency transformers (400 Hz) that are fed by sixteen four-quadrant converters connected in parallel to a 1.8 kV DC link with a 2f filter. The control, the command and the hardware of the prototype are described in detail.
A Feature Study for Classification-Based Speech Separation at Low Signal-to-Noise Ratios

Speech separation can be formulated as a classification problem. In classification-based speech separation, supervised learning is employed to classify time-frequency units as either speech-dominant or noise-dominant. In very low signal-to-noise ratio (SNR) conditions, acoustic features extracted from a mixture are crucial for correct classification. In this study, we systematically evaluate a range of promising features for classification-based separation using six nonstationary noises at the low SNR level of -5 dB, which is chosen with the goal of improving human speech intelligibility in mind. In addition, we propose a new feature called multi-resolution cochleagram (MRCG). The new feature is constructed by combining four cochleagrams at different spectrotemporal resolutions in order to capture both the local and contextual information. Experimental results show that MRCG gives the best classification results among all evaluated features. In addition, our results indicate that auto-regressive moving average (ARMA) filtering, a post-processing technique for improving automatic speech recognition features, also improves many acoustic features for speech separation.
Microbial Safety of Pickled Fruits and Vegetables and Hurdle Technology

Escherichia coli O157:H7 is a member of the enterohemorrhagic group of pathogenic E. coli that has emerged as a foodborne pathogen of major public health concern. E. coli O157:H7 is highly tolerant of acidic pH and outbreaks attributed to this bacterium have been in many acidic foods which have pH level with similar to those of pickled products. Therefore, pickled vegetables, although acidic, may not safe. In pickling vegetables, the combination of preservation factors (heat, acetic acid, and salt) will contribute to increase the microbial safety. Recently, the concept of combining preservative factors for food preservation was developed, called ‘hurdle technology’. In hurdle technology, combination treatments are applied because it is expected that the use of combined preservative factors will have greater effectiveness at inactivating microorganisms than the use of any single factor. However, recent studies show that the combination of preservation factors can have unexpected antimicrobial activity. Therefore, this article includes an overall review of the microbial safety of fruits and vegetables, preservative method including major preservative factors used in pickling technology, concept and mechanism of hurdle technology and E. coli O157:H7.
Face Recognition Using Neural Network

3 Chapter 1  Identification is an action of recognizing or being recognized, in particular, identification of a thing or person from previous exposures or information. Identification these days is quite necessary as for security purposes. It can be done using biometric parameters such as finger prints, I.D scan, face recognition etc. Most probably the first well known example of a facial recognition system is because of Kohonen, who signified that an uncomplicated neural network could execute face recognition for aligned and normalized face images. The sort of network he recruited was by computing a face illustration by estimating the eigenvectors of the face image's autocorrelation pattern; these eigenvectors are currently called as`Eigen faces. But Kohonen's approach was not a real time triumph due to the need for accurate alignment and normalization. In successive years a great number of researchers attempted facial recognition systems based on edges, inter-feature spaces, and various neural network techniques. While many were victorious using small scale databases of aligned samples, but no one significantly directed the alternative practical problem of vast databases where the position and scale of the face was not known. An image is supposed to be outcome of two real variables, defined in the " real world " , for example, a(x, y) where 'a' is the amplitude in terms of brightness of the image at the real coordinate position (x, y). It is now practicable to operate multi-dimensional signals with systems that vary from simple digital circuits to complicated circuits, due to modern technology.  Image Analysis (input image->computation out)  Image Understanding (input image-> high-level interpretation out) 4 In this age of science and technology, images also attain wider opportunity due to the rapidly increasing significance of scientific visualization, for example microarray data in genetic research. To process the image firstly it is transformed into a digital form. Digitization comprises of sampling of image and quantization of sampled values. After transformed into a digital form, processing is performed. It introduces focal attention on image, or improvement of image features such as boundaries, or variation that make a graphic display more effective for representation & study. This technique does not enlarge the intrinsic information content in data. This technique is used to remove the unwanted observed image to reduce the effect of mortifications. Scope and precision of the knowledge of mortifications process and filter design are the basis of …
Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-theart learning-based [58, 59], and optimazation-based [23] approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details. The data and code are at https://github.com/yifita/3pu.
Creating Causal Embeddings for Question Answering with Minimal Supervision

A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using generalpurpose lexical models such as word embeddings. We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings. With causality as a use case, we implement this insight in three steps. First, we generate causal embeddings cost-effectively by bootstrapping cause-effect pairs extracted from free text using a small set of seed patterns. Second, we train dedicated embeddings over this data, by using task-specific contexts, i.e., the context of a cause is its effect. Finally, we extend a state-of-the-art reranking approach for QA to incorporate these causal embeddings. We evaluate the causal embedding models both directly with a casual implication task, and indirectly, in a downstream causal QA task using data from Yahoo! Answers. We show that explicitly modeling causality improves performance in both tasks. In the QA task our best model achieves 37.3% P@1, significantly outperforming a strong baseline by 7.7% (relative).
Flickr tag recommendation based on collective knowledge

Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging.
Probiotics Role in Control of Coccidiosis in Poultry Industry

Cereal products fermented by lactic acid bacteria are documented first in Egypt and Iraq during 2000-3000 B.C. These are one of the oldest fermented foods. In 1907, Elie Metcnikoff was the first scientist who not only observes but also put forward the scientific basics of fermentation. Then to explore gut bacteria intensive researches were made in late 1940s. In 2006 FAO and WHO give the complete definition of probiotics, living microbes beneficial for health provided in feed. For treatment of Coccidiosis probiotic combinations of different microbes such as Lactobacillus, Bifidibacterium and Streptococcus are used now days. Coccidiosis, a parasitic disease mainly of poultry sector, caused by Eimeria specie’s. Coccidiosis causes serious damage to the intestinal epithelium resulting in diarrhea. This problem can be effectively controlled by the use of feed probiotics.
Real-time simulation of traffic demand-supply interactions within DynaMIT

DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future tra c conditions, and provide consistent and unbiased information to travelers. To perform these tasks, e cient simulators have been designed to explicitly capture the interactions between transportation demand and supply. The demand re ects both the OD ow patterns and the combination of all the individual decisions of travelers while the supply re ects the transportation network in terms of infrastructure, tra c ow and tra c control. This paper describes the design and speci cation of these simulators, and discusses their interactions. Massachusetts Institute of Technology, Dpt of Civil and Environmental Engineering, Cambridge, Ma.  mba@mit.edu Ecole Polytechnique F ed erale de Lausanne, Dpt. of Mathematics, CH-1015 Lausanne, Switzerland.  Cambridge, Ma.  The Ohio State University, Columbus, Oh.
CrackDex: Universal and automatic DEX extraction method

With Android application packing technology evolving, there are more and more ways to harden APPs. Manually unpacking APPs becomes more difficult as the time needed for analyzing increase exponentially. At the beginning, the packing technology is designed to prevent APPs from being easily decompiled, tampered and re-packed. But unfortunately, many malicious APPs start to use packing service to protect themselves. At present, most of the antivirus software focus on APPs that are unpacked, which means if malicious APPs apply the packing service, they can easily escape from a lot of antivirus software. Therefore, we should not only emphasize the importance of packing, but also concentrate on the unpacking technology. Only by doing this can we protect the normal APPs, and not miss any harmful APPs at the same time. In this paper, we first systematically study a lot of DEX packing and unpacking technologies, then propose and develop a universal unpacking system, named CrackDex, which is capable of extracting the original DEX file from the packed APP. We propose three core technologies: simulation execution, DEX reassembling, and DEX restoration, to get the unpacked DEX file. CrackDex is a part of the Dalvik virtual machine, and it monitors the execution of functions to locate the unpacking point in the portable interpreter, then launches the simulation execution, collects the data of original DEX file through corresponding structure pointer, finally fulfills the unpacking process by reassembling the data collected. The results of our experiments show that CrackDex can be used to effectively unpack APPs that are packed by packing service in a universal approach without any other knowledge of packing service.
Integrated Design of Agile Missile Guidance and Autopilot Systems

Integrated Design of Agile Missile Guidance and Autopilot Systems By P. K. Menon and E. J. Ohlmeyer Abstract Recent threat assessments by the US Navy have indicated the need for improving the accuracy of defensive missiles. This objective can only be achieved by enhancing the performance of the missile subsystems and by finding methods to exploit the synergism existing between subsystems. As a first step towards the development of integrated design methodologies, this paper develops a technique for integrated design of missile guidance and autopilot systems. Traditional approach for the design of guidance and autopilot systems has been to design these subsystems separately and then to integrate them together before verifying their performance. Such an approach does not exploit any beneficial relationships between these and other subsystems. The application of the feedback linearization technique for integrated guidance-autopilot system design is discussed. Numerical results using a six degree-of-freedom missile simulation are given. Integrated guidance-autopilot systems are expected to result in significant improvements in missile performance, leading to lower weight and enhanced lethality. Both of these factors will lead to a more effective, lower-cost weapon system. Integrated system design methods developed under the present research effort also have extensive applications in high performance aircraft autopilot and guidance systems.
Gastrointestinal abnormalities in children with autistic disorder.

Our aim was to evaluate the structure and function of the upper gastrointestinal tract in a group of patients with autism who had gastrointestinal symptoms.



Thirty-six children (age: 5.7 +/- 2 years, mean +/- SD) with autistic disorder underwent upper gastrointestinal endoscopy with biopsies, intestinal and pancreatic enzyme analyses, and bacterial and fungal cultures. The most frequent gastrointestinal complaints were chronic diarrhea, gaseousness, and abdominal discomfort and distension.



Histologic examination in these 36 children revealed grade I or II reflux esophagitis in 25 (69.4%), chronic gastritis in 15, and chronic duodenitis in 24. The number of Paneth's cells in the duodenal crypts was significantly elevated in autistic children compared with non-autistic control subjects. Low intestinal carbohydrate digestive enzyme activity was reported in 21 children (58.3%), although there was no abnormality found in pancreatic function. Seventy-five percent of the autistic children (27/36) had an increased pancreatico-biliary fluid output after intravenous secretin administration. Nineteen of the 21 patients with diarrhea had significantly higher fluid output than those without diarrhea.



Unrecognized gastrointestinal disorders, especially reflux esophagitis and disaccharide malabsorption, may contribute to the behavioral problems of the non-verbal autistic patients. The observed increase in pancreatico-biliary secretion after secretin infusion suggests an upregulation of secretin receptors in the pancreas and liver. Further studies are required to determine the possible association between the brain and gastrointestinal dysfunctions in children with autistic disorder.
SQLScript: Efficiently Analyzing Big Enterprise Data in SAP HANA

Today, not only Internet companies such as Google, Facebook or Twitter do have Big Data but also Enterprise Information Systems store an ever growing amount of data (called Big Enterprise Data in this paper). In a classical SAP system landscape a central data warehouse (SAP BW) is used to integrate and analyze all enterprise data. In SAP BW most of the business logic required for complex analytical tasks (e.g., a complex currency conversion) is implemented in the application layer on top of a standard relational database. While being independent from the underlying database when using such an architecture, this architecture has two major drawbacks when analyzing Big Enterprise Data: (1) algorithms in ABAP do not scale with the amount of data and (2) data shipping is required. To this end, we present a novel programming language called SQLScript to efficiently support complex and scalable analytical tasks inside SAP’s new main-memory database HANA. SQLScript provides two major extensions to the SQL dialect of SAP HANA: A functional and a procedural extension. While the functional extension allows the definition of scalable analytical tasks on Big Enterprise Data, the procedural extension provides imperative constructs to orchestrate the analytical tasks. The major contributions of this paper are two novel functional extensions: First, an extended version of the MapReduce programming model for supporting parallelizable user-defined functions (UDFs). Second, compared to recursion in the SQL standard, a generalized version of recursion to support graph analytics as well as machine learning tasks.
Marginal Fit Comparison of CAD/CAM Crowns Milled from Two Different Materials.

To evaluate the marginal fit of CAD/CAM copings milled from hybrid ceramic (Vita Enamic) blocks and lithium disilicate (IPS e.max CAD) blocks, and to evaluate the effect of crystallization firing on the marginal fit of lithium disilicate copings.



A standardized metal die with a 1-mm-wide shoulder finish line was imaged using the CEREC AC Bluecam. The coping was designed using CEREC 3 software. The design was used to fabricate 15 lithium disilicate and 15 hybrid ceramic copings. Design and milling were accomplished by one operator. The copings were seated on the metal die using a pressure clamp with a uniform pressure of 5.5 lbs. A Macroview Microscope (14×) was used for direct viewing of the marginal gap. Four areas were imaged on each coping (buccal, distal, lingual, mesial). Image analysis software was used to measure the marginal gaps in μm at 15 randomly selected points on each of the four surfaces. A total of 60 measurements were made per specimen. For lithium disilicate copings the measurements for marginal gap were made before and after crystallization firing. Data were analyzed using paired t-test and Kruskal-Wallis test.



The overall mean difference in marginal gap between the hybrid ceramic and crystallized lithium disilicate copings was statistically significant (p < 0.01). Greater mean marginal gaps were measured for crystallized lithium disilicate copings. The overall mean difference in marginal gap before and after firing (precrystallized and crystallized lithium disilicate copings) showed an average of 62 μm increase in marginal gap after firing. This difference was also significant (p < 0.01).



A significant difference exists in the marginal gap discrepancy when comparing hybrid ceramic and lithium disilicate CAD/CAM crowns. Also crystallization firing can result in a significant increase in the marginal gap of lithium disilicate CAD/CAM crowns.
Modelling Wayfinding in Public Transport: Network Space and Scene Space

Wayfinding in the public transportation infrastructure takes place on traffic networks. These consist of lines that are interconnected at nodes. The network is the basis for routing decisions; it is usually presented in maps and through digital interfaces. But to the traveller, the stops and stations that make up the nodes are at least as important as the network, for it is there that the complexity of the system is experienced. These observations suggest that there are two cognitively different environments involved, which we will refer to as network space and scene space. Network space consists of the public transport network. Scene space consists of the environment at the nodes of the public transport system, through which travellers enter and leave the system and in which they change means of transport. We explore properties of the two types of spaces and how they interact to assist wayfinding. We also show how they can be modelled: for network space, graphs can be used; for scene space we propose a novel model based on cognitive schemata and
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders

We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders. They are applied to Monte Carlo-sampled configurations and have, a priori, no knowledge about the Hamiltonian or the order parameter. We find that the most promising algorithms are principal component analysis and variational autoencoders. Their predicted latent parameters correspond to the known order parameters. The latent representations of the models in question are clustered, which makes it possible to identify phases without prior knowledge of their existence. Furthermore, we find that the reconstruction loss function can be used as a universal identifier for phase transitions.
WHY DO MORE OPEN ECONOMIES HAVE BIGGER GOVERNMENTS ?

There exists a positive correlation between an economy's exposure to international trade and the size of its government. The correlation holds for most measures of government spending, in low-as well as high-income samples, and is robust to the inclusion of a wide range of controls. One explanation is that government spending plays a risk-reducing role in economies exposed to significant amount of external risk. The paper provides a range of evidence consistent with this hypothesis. In particular, the relationship between openness and government size is strongest when terms-of-trade risk is highest.
Screening for dyslexia, dyspraxia and Meares-Irlen syndrome in higher education.

This study reports a comparison of screening tests for dyslexia, dyspraxia and Meares-Irlen (M-I) syndrome in a Higher Education setting, the University of Worcester. Using a sample of 74 volunteer students, we compared the current tutor-delivered battery of 15 subtests with a computerized test, the Lucid Adult Dyslexia Screening test (LADS), and both of these with data on assessment outcomes. The sensitivity of this tutor battery was higher than LADS in predicting dyslexia, dyspraxia or M-I syndrome (91% compared with 66%) and its specificity was lower (79% compared with 90%). Stepwise logistic regression on these tests was used to identify a better performing subset of tests, when combined with a change in practice for M-I syndrome screening. This syndrome itself proved to be a powerful discriminator for dyslexia and/or dyspraxia, and we therefore recommend it as the first stage in a two-stage screening process. The specificity and sensitivity of the new battery, the second part of which comprises LADS plus four of the original tutor delivered subtests, provided the best overall performance: 94% sensitivity and 92% specificity. We anticipate that the new two-part screening process would not take longer to complete.