nils-herrmann/clean_scirepeval huggingface dataset:
Smoothly approximated support vector domain description
Venue: Pattern Recognit.

Support vector domain description (SVDD) is a well known tool for pattern analysis when only positive examples are reliable. The SVDD model is often fitted by solving a quadratic programming problem, which is time consuming. This paper attempts to fit SVDD in the primal form directly. However, the primal objective function of SVDD is not differentiable which prevents the well behaved gradient based optimization methods from being applicable. As such, we propose to approximate the primal objective function of SVDD by a differentiable function, and a conjugate gradient method is applied to minimize the smoothly approximated objective function. Extensive experiments on pattern classification were conducted, and compared to the quadratic programming based SVDD, the proposed approach is much more computationally efficient and yields similar classification performance on these problems. HighlightsSmoothly approximates the objective function of support vector domain description.Gradient based optimization method could be applied to the smoothed model.The proposed algorithms have asymptotic training complexity n 2 .The algorithm is easy to implement, without requiring any optimization package.
New MAX Phase Compound Mo 2 TiAlC 2 First principles Study

A theoretical study of the Mo 2 TiAlC 2 compound belonging to the MAX phases has been performed by using the first principles pseudopotential plane wave method within the generalized gradient approximation (GGA) We have calculated the structural, elastic, electronic and optical properties of Mo 2 TiAlC 2 To confirm mechanical stability, the elastic constants C ij are calculated. Other elastic parameters such as bulk modulus, shear modulus, compressibility, Young modulus, anisotropic factor, Pugh ratio, Poisson's ratio are also calculated. The energy band structure and density of states are calculated and analyzed. The results show that the electrical conductivity is metallic with a high density of states at the Fermi level in which Mo 4 d states dominate. Furthermore, the optical properties such as dielectric function, refractive index, photoconductivity, absorption coefficients, loss function and reflectivity are also calculated. Its reflectance spectrum shows that it has the potential to be used as a promising shielding material to avoid solar heating.
New ordered MAX phase Mo 2 TiAlC 2 Elastic and electronic properties from first principles

First principles computation on the basis of density functional theory (DFT) is executed with the CASTEP code to explore the structural, elastic, and electronic properties along with Debye temperature and theoretical Vickers' hardness of newly discovered ordered MAX phase carbide Mo2TiAlC2. The computed structural parameters are very reasonable compared with the experimental results. The mechanical stability is verified by using the computed elastic constants. The brittleness of the compound is indicated by both the Poisson's and Pugh's ratios. The new MAX phase is capable of resisting the pressure and tension and also has the clear directional bonding between atoms. The compound shows significant elastic anisotropy. The Debye temperature estimated from elastic moduli (B, G) is found to be 413.6 K. The electronic structure indicates that the bonding nature of Mo2TiAlC2 is a mixture of covalent and metallic with few ionic characters. The electron charge density map shows a strong directional Mo C Mo covalent bonding associated with a relatively weak Ti C bond. The calculated Fermi surface is due to the low dispersive Mo 4d like bands, which makes the compound a conductive one. The hardness of the compound is also evaluated and a high value of 9.01 GPa is an indication of its strong covalent bonding.
Growth mechanism of atomic layer deposited TiAlC metal gate based on TiCl 4 and TMA precursors

TiAlC metal gate for the metal oxide semiconductor field effect transistor (MOSFET) is grown by the atomic layer deposition method using TiCl4 and Al(CH3)3(TMA) as precursors. It is found that the major product of the TiCl4 and TMA reaction is TiAlC, and the components of C and Al are found to increase with higher growth temperature. The reaction mechanism is investigated by using x ray photoemission spectroscopy (XPS) Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) The reaction mechanism is as follows. Ti is generated through the reduction of TiCl4 by TMA. The reductive behavior of TMA involves the formation of ethane. The Ti from the reduction of TiCl4 by TMA reacts with ethane easily forming heterogenetic TiCH2, TiCH=CH2 and TiC fragments. In addition, TMA thermally decomposes, driving Al into the TiC film and leading to TiAlC formation. With the growth temperature increasing, TMA decomposes more severely, resulting in more C and Al in the TiAlC film. Thus, the film composition can be controlled by the growth temperature to a certain extent.
Optimization of process parameters to achieve spectrally selective TiAlC/TiAlCN/TiAlSiCN/TiAlSiCO/TiAlSiO high temperature solar absorber coating

TiAlC/TiAlCN/TiAlSiCN/TiAlSiCO/TiAlSiO tandem absorber was deposited on stainless steel substrate by using four cathode reactive direct current unbalanced magnetron sputtering system. The reactive gas flow rates (C2H2, N2 and O2) and thicknesses of each individual layers were varied to obtain the selective properties of the tandem absorber. The detailed effects of reactive gas flow rates and thicknesses of the individual layers on the optical properties were studied by using UV vis NIR spectrophotometer. Guiding factor in optimizing various process parameters was to achieve low reflectance in the solar spectrum region and high reflectance in the infrared region. The change in growth rate of the tandem absorber with reactive gas flow rate was studied using the thickness data, target voltage and target current. These results indicate a decrease in the growth rate of each individual layer of the tandem absorber with an increase in the flow rates of the reactive gases. The changes in bonding structure and chemical composition with reactive gas flow rates were studied by X ray photoelectron spectroscopy. The optimized tandem absorber deposited on stainless steel substrate shows absorptance of 0.960 and emittance of 0.15. The thicknesses of the optimized individual layers were ~62, 18, 20, 16, 27 nm, respectively.
Testing the New Historicism: 'Invisible Bullets" Reconsidered

A NY attempt to characterize New Historicism and evaluate its contribution to our understanding of Renaissance literature could legitimately confine itself to Stephen Greenblatt's "Invisible Bullets." He himself would probably regard this as his major Shakespearean project, having revised it three times and given it pride of place at the beginning of Shakespearean Negotiations: The Circulation of Social Energy in Renaissance England. The essay focuses on Henry WV and Henry V, but its argument is purportedly applicable to all the histories (23) and indeed to all Shakespeare's plays (40) It is arguably the most important study of Shakespeare and of Renaissance culture in the new historicist mode. Some have objected that it produces a view of Shakespeare which is politically unacceptable. My own concern, however, is less with Greenblatt's conclusions than with how he arrives at them: with the coherence of his arguments, his use of evidence, and the precise nature of his methodology. Given the cult status of this essay, and given too the institutional distinction of Greenblatt himself (winner, with Shakespearean Negotiations, of the MLA prize for An Outstanding Literary Study; member of MLA's Executive Council) an analysis of the kind I am offering here seems worthwhile. It constitutes in effect a localized inquiry into standards of excellence in the professional study of literature today, and has, I believe, some wide ranging implications.
Coherence loss of partially mode locked fibre laser
Venue: Scientific reports

Stochastically driven nonlinear processes limit the number of amplified modes in a natural system due to competitive mode interaction, which is accompanied by loss of coherence when increasing the complexity of the system. Specifically, we find that modulation instability, which exhibits great fluctuations when it spontaneously grows from noise in conservative systems, may possess a high degree of coherence in dissipative laser system with gain. Nonlinear mode interactions can be competitive or cooperative: adjusting the intracavity polarization state controls the process of loss of coherence. Single shot spectra reveal that, first, the fibre laser redistributes its energy from the center wavelength mode into sidebands through parametric instabilities. Subsequently, longitudinal modes are populated via cascaded four wave mixing. Parametric frequency conversion populates longitudinal modes with a random distribution of position, intensity and polarization, resulting in partially (rather than highly) coherent pulses. These dynamics unveil a new route towards complex pattern formation in nonlinear laser systems, and they may be also beneficial for the understanding of supercontinuum, Kerr combs phenomena, and optical rogue waves.
Deep Drone Object Detection and Tracking for Smart Drones on Embedded System

In recent years, drones have been widely adopted for aerial photography at much lower costs. However, capturing high quality pictures or videos using most advanced drones requires precise manual control and are very errorprone. We are proposing Deep Drone, an embedded system framework, to power drones with vision: letting the drone to do automatic detection and tracking. In this project, we implemented the vision component which is an integration of advanced detection and tracking algorithms. We implemented our system onto multiple hardware platforms, including both desktop GPU (NVIDIA GTX980) and embedded GPU (NVIDIA Tegra K1 and NVIDIA Tegra X1) and evaluated frame rate, power consumption and accuracy on several videos captured by the drone. Our system achieved real time performance at 71 frames per second(fps) for tracking and 1.6 fps for detection on NVIDIA TX1. The video demo of our detection and tracking algorithm has been uploaded to Youtube: https:/youtu. be/UTx2 5a488s.
Monolithically Integrated Wavelength Converter for Intra Data Center Routing Applications
Venue: IEEE Photonics Technology Letters

A wideband wavelength converter based on cross gain modulation has been demonstrated by using a monolithically integrated semiconductor optical amplifier and 12 channel distributed feedback lasers. Error free operation unicast wavelength conversion of 10 Gb/s non return to zero ON OFF keying data payload has been demonstrated with 3 dB power penalty and around 0.5 dB extra penalty for transmission over 2 km single mode fiber for intra data center application. Performance of the wavelength converter multicasting operation of two channels has also been assessed by controlling the distributed feedback lasers. Experimental results show 4.5 dB power penalty after multicast wavelength conversion. The wavelength conversion of uni /multi casting operations would provide a compact, low power, and low cost solution for wavelength routing based intra data center networking applications.
Deep Subspace Clustering with Sparsity Prior
Venue: IJCAI

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly seeking a subspace to fit each group. Most of existing methods are based on a shallow linear model, which may fail in handling data with nonlinear structure. In this paper, we propose a novel subspace clustering method deeP subspAce clusteRing with sparsiTY prior (PARTY) based on a new deep learning architecture. PARTY explicitly learns to progressively transform input data into nonlinear latent space and to be adaptive to the local and global subspace structure simultaneously. In particular, considering local structure, PARTY learns representation for the input data with minimal reconstruction error. Moreover, PARTY incorporates a prior sparsity information into the hidden representation learning to preserve the sparse reconstruction relation over the whole data set. To the best of our knowledge, PARTY is the first deep learning based subspace clustering method. Extensive experiments verify the effectiveness of our method.
Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi definite optimization problem subject to an additional rank constrain that involves only a very small number of variables. This is established by first reducing the problem to a quadratically constrained quadratic problem and then using its special structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact. When combined with the standard nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a priori information about the noise, co ocurrences, and percentage of outliers. These results are illustrated with several examples.
Overcoming Data Sparsity in Acoustic Modeling of Low Resource Language by Borrowing Data and Model Parameters from High Resource Languages
Venue: INTERSPEECH

In this paper, we propose two techniques to improve the acoustic model of a low resource language by: (i) Pooling data from closely related languages using a phoneme mapping algorithm to build acoustic models like subspace Gaussian mixture model (SGMM) phone cluster adaptive training (Phone CAT) deep neural network (DNN) and convolutional neural network (CNN) Using the low resource language data, we then adapt the afore mentioned models towards that language. (ii) Using models built from high resource languages, we first borrow subspace model parameters from SGMM/Phone CAT; or hidden layers from DNN/CNN. The language specific parameters are then estimated using the lowresource language data. The experiments were performed on four Indian languages namely Assamese, Bengali, Hindi and Tamil. Relative improvements of 10 to 30% were obtained over corresponding monolingual models in each case.
Method of Claim Based Technology Analysis for Strategic Innovation Management Using TPP Related Patents as Case Examples

Analysis of patents reveals trends in technology development in given domains, particularly for commercial adaptation concerning intellectual property (IP) protection. Such an approach allows enterprises to track IPs and evaluate their potential competitiveness vis a vis their competitors. Patent clustering is a core step of the entire patent analysis process for conducting technology analysis and can be utilized to group various patents into relatively consistent categories. Forecasting methods are used to develop optimal R&D strategies and anticipate potential outcomes. However, current clustering methods for such technology forecasting, based on general patent keywords or text mining have difficulty in carrying out categorization efficiently and precisely to provide decision makers with insights into technological trends. This research develops a new methodology of patent claim based technology clustering to predict IP protected technology frontiers as the critical references for the strategic innovation planning. An in depth patent analysis case study is conducted on two photon polymerization (TPP) technology to demonstrate the generalized methodology working in practice. With annotated elements in independent claims of patent documents, patentable features are identified for given patents. The patentable features to achieve the task of simplification have been highlighted. Afterward, patents are clustered in accordance to the identification of patentable features in the simplified sentences to provide the clusters with names and definitions. The proposed approach establishes a unique clustering principle so as to enhance the accuracy and credibility of patent analysis based on the legally protected patent claims. This approach provides insight into the landscape of future technological trends, particularly for TPP technologies.
Estudio comparativo del uso de farmacos antiepilepticos durante el embarazo en un periodo de 12 anos. Eficacia de los nuevos farmacos lamotrigina, levetiracetam y oxacarbamacepina

Resumen Introduccion El patron de uso de farmacos antiepilepticos (FAE) durante el embarazo difiere entre paises y esta cambiando. Se desconoce en que medida ello afecta a la poblacion espanola. La eficacia de los nuevos farmacos en el control de las crisis es motivo de preocupacion y puede haber cambiado a lo largo de los anos debido a un mejor conocimiento de su uso durante el embarazo. Con el objetivo de analizar estos 2 aspectos reportamos los resultados del registro EURAP Espana durante un periodo de 12 anos.  Tras el consentimiento informado, las pacientes son incluidas en el registro y evaluadas al inicio del embarazo, al final del segundo y tercer trimestres, despues del parto y al ano del nacimiento. Para los objetivos de este estudio hemos analizado: FAE, tipo de epilepsia, frecuencia de crisis por trimestres y a lo largo del embarazo, porcentaje de pacientes libres de crisis, y frecuencia de malformaciones congenitas mayores. Hemos comparado estas variables en 2 periodos (junio de 2001 octubre de 2007) y (enero de 2008 mayo de 2015) Resultados Un total de 304 monoterapias del periodo antiguo se comparan con 127 del periodo nuevo. Observamos un ascenso del uso de levetiracetam (LEV) y un descenso del uso de carbamacepina (CBZ) fenitoina y fenobarbital; un leve descenso del uso de valproato (VPA) y un leve aumento de lamotrigina (LTG) y oxacarbamacepina (OXC) El tipo de epilepsia se mantiene estable para CBZ y VPA, pero cambia para LTG, con menos epilepsias generalizadas tratadas con este farmaco en el periodo nuevo. Ello no se asocia con un cambio significativo de la frecuencia de crisis, pero si con un mejor control de las crisis de novo en el tercer trimestre. LEV se asocia a niveles de control de crisis similares a los de CBZ y VPA y mejor que con LTG. De las pacientes tratadas con LEV, un 64% tenian una epilepsia generalizada. Conclusiones El patron de uso de los diferentes FAE durante el embarazo esta cambiando en Espana, con menos uso de CBZ, fenitoina y fenobarbital y un aumento del uso de LEV. El tipo de epilepsia tambien cambia, con un porcentaje inferior de pacientes tratadas con LTG para epilepsias generalizadas. LEV controla las crisis de manera similar a los farmacos clasicos y mejor que la LTG.
Lapped steel effects on bridge pier behavior under moderate near fault motions

Extensive research for the Far Fault Ground Motion (FFGM) has been carried out in strong seismic regions, but limited research has been done for the Near Fault Ground Motion (NFGM) because of very few records. Shake table tests for NFGM were conducted to investigate the seismic behavior of Reinforced Concrete (RC) bridge columns with lap spliced longitudinal bars that are designed in low or moderate seismic region. Seven RC column specimens with a height of 1400 mm (55.2 in) and a diameter of 400 mm (15.8 in) were tested on a shaking table, and one alike specimen was quasi statically tested. The effect on lap spliced longitudinal reinforcing steel was investigated. Shaking test results are compared to those of the quasi static test. The displacement ductility was significantly decreased in RC column specimens with lap spliced longitudinal reinforcing bars. The effect of NFGM is compared to that of FFGM. The NFGM specimen showed somewhat less seismic capacity than the FFGM model from Fig. 4(d) Decrease of displacement ductility and energy dissipation was observed in the shake table test specimens in comparison with the quasi static model.
Zika Virus: A Global Threat to Humanity: A Comprehensive Review and Current Developments
Venue: North American journal of medical sciences

At present, one of greatest concerns of medical personnel is Zika virus (ZIKV) Though it has been reported for quite a long time, its rapid emergence, new modes of transmission, and more importantly, the congenital anomalies associated with it have made the situation worse. It was first detected in 1947. After that, this infection was found in the countries of Africa as well as Asia. At present, interestingly it has been reported from Brazil. Microcephaly and intracranial calcification have been postulated to be related to maternal infection with this virus. Though it is asymptomatic in maximum number of cases, the serious complications of the infection should be prevented at the earliest. No specific treatment and vaccine are available till now. But research continues and hopefully, success is not far off. The right information about this infection should reach patients as well as physicians. It will prevent unnecessary panic. In August, Brazil is going to organize the Olympic and Paralympic Games and all eyes are now focused on this. In this review article, the authors have tried to focus on the important points about this infection. The data were gathered after searching for relevant articles published in PubMed, the World Health Organization's (WHO) website, Centers for Disease Control and Prevention's (CDC) website, and some other related websites on the Internet.
2016 ESC/EAS Guidelines for the Management of Dyslipidaemias.
Venue: European heart journal

The Task Force for the Management of Dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS) Developed with the special contribution of the European Assocciation for Cardiovascular Prevention Rehabilitation (EACPR) ABI ankle brachial index
Transferability in Machine Learning: from Phenomena to Black Box Attacks using Adversarial Samples
Venue: ArXiv

Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial examples that affect one model often affect another model, even if the two models have different architectures or were trained on different training sets, so long as both models were trained to perform the same task. An attacker may therefore train their own substitute model, craft adversarial examples against the substitute, and transfer them to a victim model, with very little information about the victim. Recent work has further developed a technique that uses the victim model as an oracle to label a synthetic training set for the substitute, so the attacker need not even collect a training set to mount the attack. We extend these recent techniques using reservoir sampling to greatly enhance the efficiency of the training procedure for the substitute model. We introduce new transferability attacks between previously unexplored (substitute, victim) pairs of machine learning model classes, most notably SVMs and decision trees. We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96.19% misclassification rate) and Google (88.94% using only 800 queries of the victim model, thereby showing that existing machine learning approaches are in general vulnerable to systematic black box attacks regardless of their structure.