nils-herrmann/clean_scirepeval huggingface dataset:
AN AT 89 C 51 MICROCONTROLLER BASED CONTROL CIRCUIT FOR DUAL THREE PHASE CONTROLLED RECTIFIER

A new concept of building the controller of a thyristor based three-phase dual converter is presented in this paper. The controller is implemented using mixed mode digital-analog circuitry to achieve optimized performance. The realtime six state pulse patterns needed for the converter are generated by a specially designed ROM based circuit synchronized to the power frequency by a phase-locked-loop. The phase angle and other necessary commands for the converter are managed by an AT89C51 microcontroller. The proposed architecture offers 128-steps in the phase angle control, a resolution sufficient for most converter applications. Because of the hybrid nature of the implementation, the controller can change phase angles online smoothly. The computation burden on the microcontroller is nominal and hence it can easily undertake the tasks of monitoring diagnostic data like overload, loss of excitation and phase sequence. Thus a full fledged system is realizable with only one microcontroller chip, making the control system economic, reliable and efficient.
Chapter 1: HPV in the etiology of human cancer.

The causal role of human papillomavirus (HPV) in all cancers of the uterine cervix has been firmly established biologically and epidemiologically. Most cancers of the vagina and anus are likewise caused by HPV, as are a fraction of cancers of the vulva, penis, and oropharynx. HPV-16 and -18 account for about 70% of cancers of the cervix, vagina, and anus and for about 30-40% of cancers of the vulva, penis, and oropharynx. Other cancers causally linked to HPV are non-melanoma skin cancer and cancer of the conjunctiva. Although HPV is a necessary cause of cervical cancer, it is not a sufficient cause. Thus, other cofactors are necessary for progression from cervical HPV infection to cancer. Long-term use of hormonal contraceptives, high parity, tobacco smoking, and co-infection with HIV have been identified as established cofactors; co-infection with Chlamydia trachomatis (CT) and herpes simplex virus type-2 (HSV-2), immunosuppression, and certain dietary deficiencies are other probable cofactors. Genetic and immunological host factors and viral factors other than type, such as variants of type, viral load and viral integration, are likely to be important but have not been clearly identified.
Enhanced fingerprint fuzzy vault based on distortion invariant minutiae structures

The fuzzy vault is a promising cryptosystem for the protection of fingerprint templates. However it requires the alignment of the query template to the enrolled one. Most existing implementations either rely on the extraction of the core which is not a guaranteed task, or use publicly available helper data which may leak information about the protected minutiae. We propose a new alignment approach for the fingerprint fuzzy vault based on distortion invariant minutiae features. Experimental results show improvements in genuine and false accept rates over previous implementations.
A Survey of the Recent Architectures of Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown state-of-the-art results on various competitive benchmarks. The powerful learning ability of deep CNN is largely achieved with the use of multiple non-linear feature extraction stages that can automatically learn hierarchical representation from the data. Availability of a large amount of data and improvements in the hardware processing units have accelerated the research in CNNs and recently very interesting deep CNN architectures are reported. The recent race in deep CNN architectures for achieving high performance on the challenging benchmarks has shown that the innovative architectural ideas, as well as parameter optimization, can improve the CNN performance on various vision-related tasks. In this regard, different ideas in the CNN design have been explored such as use of different activation and loss functions, parameter optimization, regularization, and restructuring of processing units. However, the major improvement in representational capacity is achieved by the restructuring of the processing units. Especially, the idea of using a block as a structural unit instead of a layer is gaining substantial appreciation. This survey thus focuses on the intrinsic taxonomy present in the recently reported CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multipath, width, feature map exploitation, channel boosting and attention. Additionally, it covers the elementary understanding of the CNN components and sheds light on the current challenges and applications of CNNs.
Macroeconomic Risk and Debt Overhang ∗

Since debt is typically riskier in recessions, transfers from equity holders to debt holders associated with each investment also tend to concentrate in recessions. Such systematic risk exposure of debt overhang has important implications for the investment and financing decisions of firms and on the ex ante costs of debt overhang. Using a calibrated dynamic capital structure/real option model, we show that the costs of debt overhang become significantly higher in the presence of macroeconomic risk. We also provide several new predictions that relate the cyclicality of a firm’s assets in place and growth options to its investment and capital structure decisions. We are grateful to Santiago Bazdresch, Bob Goldstein, David Mauer (WFA discussant), Erwan Morellec, Stew Myers, Chris Parsons, Michael Roberts, Antoinette Schoar, Neng Wang, Ivo Welch, and seminar participants at MIT, Federal Reserve Bank of Boston, Boston University, Dartmouth, University of Lausanne, University of Minnesota, the Third Risk Management Conference at Mont Tremblant, the Minnesota Corporate Finance Conference, and the WFA for their comments. MIT Sloan School of Management and NBER.
Global Explanations of Neural Networks: Mapping the Landscape of Predictions

A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduce neural network decisions to a single set of features. In response, we present an approach for generating global attributions called GAM, which explains the landscape of neural network predictions across subpopulations. GAM augments global explanations with the proportion of samples that each attribution best explains and specifies which samples are described by each attribution. Global explanations also have tunable granularity to detect more or fewer subpopulations. We demonstrate that GAM’s global explanations 1) yield the known feature importances of simulated data, 2) match feature weights of interpretable statistical models on real data, and 3) are intuitive to practitioners through user studies. With more transparent predictions, GAM can help ensure neural network decisions are generated for the right reasons.
Solution of a Large-Scale Traveling-Salesman Problem

It is shown that a certain tour of 49 cities, one in each of the 48 states and Washington, D C , has the shortest road distance T HE TRAVELING-SALESMAN PROBLEM might be described as follows: Find the shortest route (tour) for a salesman starting from a given city, visiting each of a specified group of cities, and then returning to the original point of departure. More generally, given an n by n symmetric matrix D={d,j), where du represents the 'distance' from / to J, arrange the points in a cyclic order in such a way that the sum of the du between consecutive points is minimal. Since there are only a finite number of possibilities (at most 3>' 2 (« —1)0 to consider, the problem is to devise a method of picking out the optimal arrangement which is reasonably efficient for fairly large values of n. Although algorithms have been devised for problems of similar nature, e.g., the optimal assignment problem,''** little is known about the traveling-salesman problem. We do not claim that this note alters the situation very much; what we shall do is outline a way of approaching the problem that sometimes, at least, enables one to find an optimal path and prove it so. In particular, it will be shown that a certain arrangement of 49 cities, one m each of the 48 states and Washington, D. C, is best, the du used representing road distances as taken from an atlas. * HISTORICAL NOTE-The origin of this problem is somewhat obscure. It appears to have been discussed informally among mathematicians at mathematics meetings for many years. Surprisingly little in the way of results has appeared in the mathematical literature.'" It may be that the minimal-distance tour problem was stimulated by the so-called Hamiltonian game' which is concerned with finding the number of different tours possible over a specified network The latter problem is cited by some as the origin of group theory and has some connections with the famou8 Four-Color Conjecture ' Merrill Flood (Columbia Universitj') should be credited with stimulating interest in the traveling-salesman problem in many quarters. As early as 1937, he tried to obtain near optimal solutions in reference to routing of school buses. Both Flood and A W. Tucker (Princeton University) recall that they heard about the problem first in a seminar talk by Hassler Whitney at Princeton in 1934 (although Whitney, …
Semi-Supervised Recursive Autoencoder

In this project, we implement the semi-supervised Recursive Autoencoders (RAE), and achieve the result comparable with result in  on the Movie Review Polarity dataset1. We achieve 76.08% accuracy, which is slightly lower than  ’s result 76.8%, with less vector length. Experiments show that the model can learn sentiment and build reasonable structure from sentence.We find longer word vector and adjustment of words’ meaning vector is beneficial, while normalization of transfer function brings some improvement. We also find normalization of the input word vector may be beneficial for training.
Structured Image Classification from Conditional Random Field with Deep Class Embedding

This paper presents a novel deep learning architecture to classify structured objects in datasets with a large number of visually similar categories. Our model extends the CRF objective function to a nonlinear form, by factorizing the pairwise potential matrix, to learn neighboring-class embedding. The embedding and the classifier are jointly trained to optimize this highly nonlinear CRF objective function. The non-linear model is trained on object-level samples, which is much faster and more accurate than the standard sequence-level training of the linear model. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, taken in varying settings and viewpoints, and shows significantly improved results compared to linear CRF modeling and sequence-level training.
A perivascular niche for brain tumor stem cells.

Cancers are believed to arise from cancer stem cells (CSCs), but it is not known if these cells remain dependent upon the niche microenvironments that regulate normal stem cells. We show that endothelial cells interact closely with self-renewing brain tumor cells and secrete factors that maintain these cells in a stem cell-like state. Increasing the number of endothelial cells or blood vessels in orthotopic brain tumor xenografts expanded the fraction of self-renewing cells and accelerated the initiation and growth of tumors. Conversely, depletion of blood vessels from xenografts ablated self-renewing cells from tumors and arrested tumor growth. We propose that brain CSCs are maintained within vascular niches that are important targets for therapeutic approaches.
Measurement and analysis of student engagement in university classes where varying levels of PBL methods of instruction are in use

Students participate more in a classroom and also report a better understanding of course concepts when steps are taken to actively engage them. The Student Engagement (SE) Survey was developed and used in this study for measuring student engagement at the class level and consisted of 14 questions adapted from the original National Survey of Student Engagement (NSSE) survey. The adapted survey examined levels of student engagement in 56 classes at an upper mid-western university in the USA. Campus-wide faculty members participated in a program for training them in innovative teaching methods including problem-based learning (PBL). Results of this study typically showed a higher engagement in higher-level classes and also those classes with fewer students. In addition, the level of engagement was typically higher in those classrooms with more PBL.
Rule Learning from Knowledge Graphs Guided by Embedding Models

Rules over a Knowledge Graph (KG) capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules is a major problem. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora. Experiments on real-world KGs demonstrate the effectiveness of our novel approach both with respect to the quality of the learned rules and fact predictions that they produce.
Nonlinear Modeling of a Guitar Loudspeaker Cabinet Digital Audio Effects That Emulate Analog Equipment Are Popular

Distortion is a desirable effect for sound coloration in ele ctric guitar amplifiers and effect processors. At high sound level s, particularly at low frequencies, the loudspeakers used in clas sic style cabinets are also a source of distortion. This paper present s a case study of measurements and digital modeling of a typical guit ar loudspeaker as a real-time audio effect. It demonstrates th e complexity of the driver behavior, which cannot be efficiently m odeled in true physical detail. A model with linear transfer fu nctions and static nonlinearity characteristics to approximate th e measured behavior is derived based upon physical arguments. An effici nt method to simulate radiation directivity is also proposed.
Collaborative data analytics for smart buildings: opportunities and models

Smart buildings equipped with state-of-the-art sensors and meters are becoming more common. Large quantities of data are being collected by these devices. For a single building to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build accurate models to help improve the smart buildings systems. Therefore, multiple buildings need to cooperate to amplify the benefits from the collected data and speed up the model building processes. Apparently, this is not so trivial and there are associated challenges. In this paper, we study the importance of collaborative data analytics for smart buildings, its benefits, as well as presently possible models of carrying it out. Furthermore, we present a framework for collaborative fault detection and diagnosis as a case of collaborative data analytics for smart buildings. We also provide a preliminary analysis of the energy efficiency benefit of such collaborative framework for smart buildings. The result shows that significant energy savings can be achieved for smart buildings using collaborative data analytics.
How to win the battle of ideas in corporate social responsibility : the International Pyramid Model of CSR

This paper reviews the definitions of Corporate Social Responsibility (CSR) as they have evolve over time. It traces the origins of the concept and creates a theoretical framework for international use, thus having the benefit of applicability in both developing as well as developed economies. The models of Carroll and Visser are integrated to produce The International Pyramid Model of CSR, which acknowledges the relative importance of economic, glocal, legal and ethical, and philanthropic aspects of the CSR concept. The primary innovation in the International Pyramid is the development of ‘glocal’ responsibilities, relating to the environment, socio-cultural matters, technology users, and political rights. Additionally, the International Pyramid condenses Carroll (Business Horizons 34(4):39–48, 1991) pyramid such that the separate legal and ethical responsibilities are merged into one ‘legal and ethical’ obligation. Furthermore, it offers flexibility by acknowledging that the various responsibilities it embodies can shift up or down the pyramid as priorities change, which is inevitable as businesses and economies differ cross-sectionally, and over time.
Fraud Detection on Financial Statements Using Data Mining Techniques

This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump, M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted.
Blockchain technology in the chemical industry : machine-to-machine electricity market

The purpose of this paper is to explore applications of blockchain technology related to the 4th Industrial Revolution (Industry 4.0) and to present an example where blockchain is employed to facilitate machine-to-machine (M2M) interactions and establish a M2M electricity market in the context of the chemical industry. The presented scenario includes two electricity producers and one electricity consumer trading with each other over a blockchain. The producers publish exchange offers of energy (in kWh) for currency (in USD) in a data stream. The consumer reads the offers, analyses them and attempts to satisfy its energy demand at a minimum cost. When an offer is accepted it is executed as an atomic exchange (multiple simultaneous transactions). Additionally, this paper describes and discusses the research and application landscape of blockchain technology in relation to the Industry 4.0. It concludes that this technology has significant under-researched potential to support and enhance the efficiency gains of the revolution and identifies areas for future research. Producer 2 • Issue energy • Post purchase offers (as atomic transactions) Consumer • Look through the posted offers • Choose cheapest and satisfy its own demand Blockchain Stream Published offers are visible here Offer sent
Question Generation for Question Answering

This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.
Simulating quantum computers with probabilistic methods

We investigate the boundary between classical and quantum computational power. This work consists of two parts. First we develop new classical simulation algorithms that are centered on sampling methods. Using these techniques we generate new classes of classically simulatable quantum circuits where standard techniques relying on the exact computation of measurement probabilities fail to provide efficient simulations. For example, we show how various concatenations of matchgate, Toffoli, Clifford, boundeddepth, Fourier transform and other circuits are classically simulatable. We also prove that sparse quantum circuits as well as circuits composed of CNOT and exp[iθX] gates can be simulated classically. In a second part, we apply our results to the simulation of quantum algorithms. It is shown that a recent quantum algorithm, concerned with the estimation of Potts model partition functions, can be simulated efficiently classically. Finally, we show that the exponential speed-ups of Simon’s and Shor’s algorithms crucially depend on the very last stage in these algorithms, dealing with the classical postprocessing of the measurement outcomes. Specifically, we prove that both algorithms would be classically simulatable if the function classically computed in this step had a sufficiently peaked Fourier spectrum.
Role of Polyphenols and Other Phytochemicals on Molecular Signaling

Optimized nutrition through supplementation of diet with plant derived phytochemicals has attracted significant attention to prevent the onset of many chronic diseases including cardiovascular impairments, cancer, and metabolic disorder. These phytonutrients alone or in combination with others are believed to impart beneficial effects and play pivotal role in metabolic abnormalities such as dyslipidemia, insulin resistance, hypertension, glucose intolerance, systemic inflammation, and oxidative stress. Epidemiological and preclinical studies demonstrated that fruits, vegetables, and beverages rich in carotenoids, isoflavones, phytoestrogens, and phytosterols delay the onset of atherosclerosis or act as a chemoprotective agent by interacting with the underlying pathomechanisms. Phytochemicals exert their beneficial effects either by reducing the circulating levels of cholesterol or by inhibiting lipid oxidation, while others exhibit anti-inflammatory and antiplatelet activities. Additionally, they reduce neointimal thickening by inhibiting proliferation of smooth muscle cells and also improve endothelium dependent vasorelaxation by modulating bioavailability of nitric-oxide and voltage-gated ion channels. However, detailed and profound knowledge on specific molecular targets of each phytochemical is very important to ensure safe use of these active compounds as a therapeutic agent. Thus, this paper reviews the active antioxidative, antiproliferative, anti-inflammatory, or antiangiogenesis role of various phytochemicals for prevention of chronic diseases.