Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies of fine pitch patterning by reel-to-reel processes for flexible electronic systems

Cost effective and high quality processes are needed for further development of flexible electronic systems. Applications of these structures are e. g. in the field of high density interconnection foils or polymer electronics. In this paper a lithography process for patterning copper on flexible foils using reel-to-reel methods is presented. The process allows the fabrication of well defined copper structures with typical feature sizes from 15 to 40 /spl mu/m using either etching or electroplating technology. Properties of the dry film photoresist used in the process as well as details of the technology are described. Influences of photolithography and etching on dimensional accuracy and resolution are given and some consequences on design rules are shown.
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector

We investigate the recently developed Bidi- rectional Encoder Representations from Transformers (BERT) model (Devlin et al. 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our best-performing model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.
Static Activation Function Normalization

Recent seminal work at the intersection of deep neural networks practice and random matrix theory has linked the convergence speed and robustness of these networks with the combination of random weight initialization and nonlinear activation function in use. Building on those principles, we introduce a process to transform an existing activation function into another one with better properties. We term such transform \emph{static activation normalization}. More specifically we focus on this normalization applied to the ReLU unit, and show empirically that it significantly promotes convergence robustness, maximum training depth, and anytime performance. We verify these claims by examining empirical eigenvalue distributions of networks trained with those activations. Our static activation normalization provides a first step towards giving benefits similar in spirit to schemes like batch normalization, but without computational cost.
A Framework of Distributed Pair Programming System

Pair programming is a programming practice in which two programmers use one computer to work together on the same analysis, design, and programming of the same software. Distributed pair programming supports two programmers to work on the same task from different locations. This paper first reviews the existing distributed pair programming tools, and then proposes some basic requirements of this kind of system. Finally, a framework of distributed pair programming system is presented. The framework allows not only to use the same editor between the pairs, but also to support different editors or developing environments between them.
Matrix Product State–Based Quantum Classifier

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ETo) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.
Senescence‐associated DNA methylation is stochastically acquired in subpopulations of mesenchymal stem cells

Replicative senescence has a major impact on function and integrity of cell preparations. This process is reflected by continuous DNA methylation (DNAm) changes at specific CpG dinucleotides in the course of in vitro culture, and such modifications can be used to estimate the state of cellular senescence for quality control of cell preparations. Still, it is unclear how senescence‐associated DNAm changes are regulated and whether they occur simultaneously across a cell population. In this study, we analyzed global DNAm profiles of human mesenchymal stem cells (MSCs) and human umbilical vein endothelial cells (HUVECs) to demonstrate that senescence‐associated DNAm changes are overall similar in these different cell types. Subsequently, an Epigenetic‐Senescence‐Signature, based on six CpGs, was either analyzed by pyrosequencing or by bar‐coded bisulfite amplicon sequencing. There was a good correlation between predicted and real passage numbers in bulk populations of MSCs (R2 = 0.67) and HUVECs (R2 = 0.97). However, when we analyzed the Epigenetic‐Senescence‐Signature in subclones of MSCs, the predictions revealed high variation and they were not related to the adipogenic or osteogenic differentiation potential of the subclones. Notably, in clonally derived subpopulations, the DNAm levels of neighboring CpGs differed extensively, indicating that these genomic regions are not synchronously modified during senescence. Taken together, senescence‐associated DNAm changes occur in a highly reproducible manner, but they are not synchronously co‐regulated. They rather appear to be acquired stochastically—potentially evoked by other epigenetic modifications.
A Ten-Qubit Solid-State Spin Register with Quantum Memory up to One Minute

Spins associated to single defects in solids provide promising qubits for quantum information processing and quantum networks. Recent experiments have demonstrated long coherence times, high-fidelity operations and long-range entanglement. However, control has so far been limited to a few qubits, with entangled states of three spins demonstrated. Realizing larger multi-qubit registers is challenging due to the need for quantum gates that avoid crosstalk and protect the coherence of the complete register. In this paper, we present novel decoherence-protected gates that combine dynamical decoupling of an electron spin with selective phase-controlled driving of nuclear spins. We use these gates to realize a 10-qubit quantum register consisting of the electron spin of a nitrogen-vacancy center and 9 nuclear spins in diamond. We show that the register is fully connected by generating entanglement between all 45 possible qubit pairs, and realize genuine multipartite entangled states with up to 7 qubits. Finally, we investigate the register as a multi-qubit memory. We show coherence times up to 63(2) seconds - the longest reported for a single solid-state qubit - and demonstrate that two-qubit entangled states can be stored for over 10 seconds. Our results enable the control of large quantum registers with long coherence times and therefore open the door to advanced quantum algorithms and quantum networks with solid-state spin qubits.
PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning

In this paper, we propose a phase shift deep neural network (PhaseDNN) which provides a wideband convergence in approximating a high dimensional function during its training of the network. The PhaseDNN utilizes the fact that many DNN achieves convergence in the low frequency range first, thus, a series of moderately-sized of DNNs are constructed and trained in parallel for ranges of higher frequencies. With the help of phase shifts in the frequency domain, implemented through a simple phase factor multiplication on the training data, each DNN in the series will be trained to approximate the target function's higher frequency content over a specific range. Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range. As a result, the proposed PhaseDNN system is able to convert wideband frequency learning to low frequency learning, thus allowing a uniform learning to wideband high dimensional functions with frequency adaptive training. Numerical results have demonstrated the capability of PhaseDNN in learning information of a target function from low to high frequency uniformly.
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications

The current research interest in autonomous driving is growing at a rapid pace, attracting great investments from both the academic and corporate sectors. In order for vehicles to be fully autonomous, it is imperative that the driver assistance system is adapt in road and lane keeping. In this paper, we present a methodological review of techniques with a focus on visual road detection and recognition. We adopt a pragmatic outlook in presenting this review, whereby the procedures of road recognition is emphasised with respect to its practical implementations. The contribution of this review hence covers the topic in two parts -- the first part describes the methodological approach to conventional road detection, which covers the algorithms and approaches involved to classify and segregate roads from non-road regions; and the other part focuses on recent state-of-the-art machine learning techniques that are applied to visual road recognition, with an emphasis on methods that incorporate convolutional neural networks and semantic segmentation. A subsequent overview of recent implementations in the commercial sector is also presented, along with some recent research works pertaining to road detections.
PB-FFD: A Point-Based Technique for Free-Form Deformation

This paper presents PB-FFD, a novel, interactive, point-based technique for performing free-form deformation of polygonal meshes. First, a volumetric deformation space is defined as the linear combination of overlapping, ellipsoidal radial basis functions (EBFs) of compact support. Mesh vertices are then parameterized with respect to local coordinate frames centered over the origins of the EBFs. As in traditional FFD, the mesh vertices are displaced in response to changes in the control-point positions. Hence, PB-FFD presents to the user an interface similar to that of traditional free-form deformation but does not require that a deformation lattice be explicitly constructed. PB-FFD also supports multiresolution control, direct manipulation of meshes, and automatic construction and refinement of the deformation space, among other benefits. This paper covers important implementation issues, discusses special cases that may arise during use of the technique, and offers advice to those who wish to implement PB-FFD.
Random Waterfilling in a Clustered Multiuser OFDM System

In this paper, a random waterfilling power allocation is proposed for performance enhancement in clustered multiuser orthogonal frequency division multiplexing (OFDM) systems in a low SNR regime. The proposed random waterfilling increases the multiuser diversity gain so that it achieves higher throughput than static equal power allocation. The relative gain over static equal power allocation increases as the received SNR decreases. Based on an upper bound on the normalized capacity of the proposed random waterfilling derived in this paper, it is shown that the capacity grows with number of users K like O(log2K-1/¿2K-1).
Near- to mid-infrared spectroscopy of the heavily obscured AGN LEDA 1712304 with AKARI/IRC

Context. Although heavily obscured active galactic nuclei (AGNs) have been found by many observational studies, the properties of the surrounding dust are poorly understood. Using AKARI/IRC spectroscopy, we discovered a new heavily obscured AGN in LEDA 1712304 which shows a deep spectral absorption feature due to silicate dust. Aims. We study the infrared (IR) spectral properties of circumnuclear silicate dust in LEDA 1712304. Methods. We performed IR spectral fitting, considering silicate dust properties such as composition, porosity, size, and crystallinity. Spectral energy distribution fitting was also performed on the flux densities in the UV to submillimeter range to investigate the global spectral properties. Results. The best-fit model indicates 0.1 μm-sized porous amorphous olivine (Mg2xFe2−2xSiO4; x = 0.4) with 4% crystalline pyroxene. The optical depth is τsil ∼ 2.3, while the total IR luminosity and stellar mass are estimated to be LIR = (5 ± 1)×1010 L⊙ and Mstar = (2.7 ± 0.8)×109 M⊙, respectively. In such low LIR and Mstar ranges, there are few galaxies that show such a large τsil. Conclusion. The silicate dust in the AGN torus of LEDA 1712304 has properties that are notably similar to those in other AGNs overall, but slightly different in the wing shape of the absorption profile. The porosity of the silicate dust suggests dust coagulation or processing in the circumnuclear environments, while the crystallinity suggests that the silicate dust is relatively fresh.
A Comparison of the Chemical Composition of Main-sequence and Giant Stars in the Open Cluster NCC 752

The chemical composition of stars in open clusters provides the best information on the chemical evolution of stars via comparison of main-sequence stars with evolved giants. This is a case study of the abundances in the dwarfs and giants in the old open cluster NGC 752. It is also a pilot program for automated abundance determinations, including equivalent-width measurements, stellar parameter determinations, and abundance analysis. We have found abundances of 31 element−ion combinations in 23 dwarfs and six giants. The mean cluster abundance of Fe is solar with [Fe/H] = −0.01 ± 0.06 with no significant difference between the dwarfs and giants. We find that the cluster abundances of other elements, including alpha-elements, to be at or slightly above solar levels. We find some evidence for CNO processing in the spectra of the giants. The enhancement of Na in giants indicates that the NeNa cycle has occurred. The abundances of Mg and Al are similar in the dwarfs and giants, indicating that the hotter MgAl cycle has not occurred. We find no evidence of s-process enhancements in the abundances of heavy elements in the giants.
Impact factors, Within-Journal Self-citations, and the Pinski-Narin Influence Weights: An analysis using both subfields and the complete Journal Citation Reports 2017

The Journal Impact Factor (JIF) is linearly sensitive to self-citations because each self-citation adds to the numerator, whereas the denominator is not affected. Pinski & Narin (1976) derived the Influence Weight (IW) as an alternative to Garfield's JIF. Whereas the JIF is based on raw citation counts normalized by the number of publications, IWs are based on the eigenvectors in the matrix of aggregated journal-journal citations without a reference to size: the cited and citing sides are combined by a matrix approach. IWs emerge as a vector after recursive iteration of the normalized matrix. Before recursion, IW is a (vector-based) non-network indicator of impact, but after recursion (i.e. repeated improvement by iteration), IWs can be considered a network measure of prestige among the journals in the (sub)graph as a representation of a field of science. As a consequence (not intended by Pinski & Narin in 1976), the self-citations are integrated at the field level and no longer disturb the analysis as outliers. In our opinion, this is a very desirable property of a measure of quality or impact. As illustrations, we use data of journal citation matrices already studied in the literature, and also the complete set of data in the Journal Citation Reports 2017 (n = 11,579 journals). The values of IWs are sometimes counter-intuitive and difficult to interpret. Furthermore, iterations do not always converge. Routines for the computation of IWs are made available at this http URL.
Minimalist large scale dynamo from shear-driven inhomogeneity

We show that non-axisymmetric, non-helical perturbations in an unstratified shear flow produce a shear-plane averaged electromotive force (EMF) proportional to a spatially dependent kinetic helicity. This new "shear-driven $\alpha$-effect" can amplify shear-plane averaged magnetic fields, but is undiscoverable when invoking the homogeneity assumption for dynamo coefficients. It is a candidate mechanism underlying large scale field generation in astrophysical, laboratory, and simulated shear flows, and highlights the essentiality of correctly averaging dynamo coefficients and fields.
Disease Identification From Unstructured User Input

A method to identify probable diseases from the unstructured textual input (eg, health forum posts) by incorporating a lexicographic and semantic feature based two-phase text classification module and a symptom-disease correlation-based similarity measurement module. One notable aspect of my approach was to develop a competent algorithm to extract all inherent features from the data source to make a better decision.
High temperature, high current, p-channel UMOS 4H-SiC IGBT

Commercial silicon Insulated Gate Bipolar Transistors (IGBTs) offer an excellent balance in terms of on-state voltage drop, speed, I-V safe operating area and MOS gate control. A p-channel UMOS IGBT is suitable for fabrication in 4H-SiC because: (a) extremely low resistivity p-type substrates are not considered feasible in SiC; (b) the inherent PMOS transistor offers superior high temperature reliability; (c) a UMOS structure offers a much higher channel packing density than other structures; and (d) lower micropipe density substrates are available in 4H-SiC as compared to 6H-SiC. This paper reports the design, fabrication and high temperature characterization of the first 4H-SiC IGBT with a high current rating (1.5 A) and excellent high temperature characteristics (operating up to 350/spl deg/C).
Multifunctional integrated photonics in the mid-infrared with suspended AlGaAs on silicon

The microscale integration of mid- and longwave-infrared photonics could enable the development of fieldable, robust chemical sensors, as well as highly efficient infrared frequency converters. However, such technology would be defined by the choice of material platform, which immediately determines the strength and types of optical nonlinearities available, the optical transparency window, modal confinement, and physical robustness. In this work, we demonstrate a new platform, suspended AlGaAs waveguides integrated on silicon, providing excellent performance in all of these metrics. We demonstrate low propagation losses within a span of nearly two octaves (1.26 to 4.6 $\mu$m) with exemplary performance of 0.45 dB/cm at $\lambda = 2.4$ $\mu$m. We exploit the high nonlinearity of this platform to demonstrate 1560 nm-pumped second-harmonic generation and octave-spanning supercontinuum reaching out to 2.3 $\mu$m with 3.4 pJ pump pulse energy. With mid-IR pumping, we generate supercontinuum spanning from 2.3 to 6.5 $\mu$m. Finally, we demonstrate the versatility of the platform with mid-infrared passive devices such as low-loss 10 $\mu$m-radius bends, compact power splitters with 96 $\pm$ 1% efficiency and edge couplers with 3.0 $\pm$ 0.1 dB loss. This platform has strong potential for multi-functional integrated photonic systems in the mid-IR.
Deep Discriminative Clustering Analysis

Traditional clustering methods often perform clustering with low-level indiscriminative representations and ignore relationships between patterns, resulting in slight achievements in the era of deep learning. To handle this problem, we develop Deep Discriminative Clustering (DDC) that models the clustering task by investigating relationships between patterns with a deep neural network. Technically, a global constraint is introduced to adaptively estimate the relationships, and a local constraint is developed to endow the network with the capability of learning high-level discriminative representations. By iteratively training the network and estimating the relationships in a mini-batch manner, DDC theoretically converges and the trained network enables to generate a group of discriminative representations that can be treated as clustering centers for straightway clustering. Extensive experiments strongly demonstrate that DDC outperforms current methods on eight image, text and audio datasets concurrently.
Spectral analysis of χ class data of GRS 1915+105 using TCAF solution

The class variable source GRS 1915+105 exhibits a wide range of time variabilities on timescales of a few seconds to a few days. Depending on the count rates in different energy bands and the nature of the conventional color-color diagram, the variabilities were classified into sixteen classes that were later sequenced in ascending order of Comptonization Efficiency (CE), which is the ratio of power-law and blackbody photons. However, CE estimation is based on an empirical model which does not provide us with a comprehensive picture regarding accretion flow dynamics around the central source. In reality, the accretion flow is comprised of two components: the high angular momentumKeplerian flow in the form of a radiatively efficient disk and a low angular momentumradiatively inefficient sub-Keplerian halo enveloping the disk. These two components contribute differently to the overall flux due to the differences in their radiative efficiencies. Therefore, it is necessary to analyze the spectral behaviors and time variabilities in terms of accretion rates. In χ class, X-ray flux is steady with no significant variation, however various χ subclasses are observed at different X-ray fluxes and variations of count rates across different χ subclasses must be linked to the variation of flow parameters such as the accretion rates, be it the Keplerian disk rate and/or the low angular momentum halo rate. This motivated us to analyze the spectra of the χ class data implementing the physical Two Component Advective Flow (TCAF) solution which directly extracts these two rates from spectral fits. We find that in the χ 2,4 classes, which are reportedly devoid of significant outflows, the spectra could be fitted well applying the TCAF solution alone. In the χ 1,3 classes, which are always linked with outflows, a cutoff power-law model is needed in addition to the TCAF solution. At the same time, the normalization required by this model along with the variation of photon index and exponential roll-off factor provides us with information on the relative dominance of the outflow in the latter two classes. TCAF fit also supplies us with the size and location of the Compton cloud along with its optical depth. Thus by fitting with TCAF, a physical understanding of the flow geometry in different χ classes of GRS 1915+105 has been obtained.