Miracle-dz/UniFAR-FTUs huggingface dataset:
THERMAL STARLESS AMMONIA CORE SURROUNDED BY CCS IN THE ORION A CLOUD

We imaged two starless molecular cloud cores, TUKH083 and TUKH122, in the Orion A giant molecular cloud in the CCS and NH3 emission with the Very Large Array. TUKH122 contains one NH3 core “TUKH122-n,” which is elongated and has a smooth oval boundary. Where observed, the CCS emission surrounds the NH3 core. This configuration resembles that of the N2H+ and CCS distribution in the Taurus starless core L1544, a well-studied example of a dense prestellar core exhibiting infall motions. The linewidth of TUKH122-n is narrow (0.20 km s−1) in the NH3 emission line and therefore dominated by thermal motions. The smooth oval shape of the core boundary and narrow linewidth in N2H+ seem to imply that TUKH122-n is dynamically relaxed and quiescent. TUKH122-n is similar to L1544 in the kinetic temperature (10 K), linear size (0.03 pc), and virial mass (∼2 M ☉). Our results strongly suggest that TUKH122-n is on the verge of star formation. TUKH122-n is embedded in the 0.2 pc massive (virial mass ∼30 M ☉) turbulent parent core, while the L1544 NH3 core is embedded in the 0.2 pc less-massive (virial mass ∼10 M ☉) thermal parent core. TUKH083 shows complicated distribution in NH3, but was not detected in CCS. The CCS emission toward TUKH083 appears to be extended, and is resolved out in our interferometric observations.
Selection problems of Z^2-periodic entropy solutions and viscosity solutions

Z^2-periodic entropy solutions of hyperbolic scalar conservation laws and Z^2-periodic viscosity solutions of Hamilton-Jacobi equations are not unique in general. However, uniqueness holds for viscous scalar conservation laws and viscous Hamilton-Jacobi equations. Ugo Bessi ('03) investigated the convergence of approximate Z^2-periodic solutions to an exact one in the process of the vanishing viscosity method, and characterized this physically natural Z^2-periodic solution with the aid of Aubry-Mather theory. In this paper, a similar problem is considered in the process of the finite difference approximation under hyperbolic scaling. We present a selection criterion different from the one in the vanishing viscosity method, which exhibits difference in characteristics between the two approximation techniques.
Characterization and suppression of low-frequency noise in Si/SiGe quantum point contacts and quantum dots

We report on the effects of a global top gate on low-frequency noise in Schottky gate-defined quantum point contacts (QPCs) and quantum dots (QDs) in a modulation-doped Si/SiGe heterostructure. For a relatively large top gate voltage, the QPC current shows frequent switching with 1/f2 Lorentzian type charge noise. As the top gate voltage is decreased, the QPC pinch-off voltage becomes less negative, and the 1/f2 noise becomes rapidly suppressed in a homogeneous background 1/f noise. We apply this top-gating technique to double QDs to stabilize the charge state for the electron number down to zero.
GRAVITATIONAL WAVE SIGNATURES OF HYPERACCRETING COLLAPSAR DISKS

By performing two-dimensional special relativistic (SR) magnetohydrodynamic simulations, we study possible signatures of gravitational waves (GWs) in the context of the collapsar model for long-duration gamma-ray bursts. In our SR simulations, the central black hole is treated as an absorbing boundary. By doing so, we focus on the GWs generated by asphericities in neutrino emission and matter motions in the vicinity of the hyperaccreting disks. We compute nine models by adding initial angular momenta and magnetic fields parametrically to a precollapse core of a 35 M☉ progenitor star. As for the microphysics, a realistic equation of state is employed and the neutrino cooling is taken into account via a multi-flavor neutrino leakage scheme. To accurately estimate GWs produced by anisotropic neutrino emission, we perform a ray-tracing analysis in general relativity by a post-processing procedure. By employing a stress formula that includes contributions from both magnetic fields and SR corrections, we also study the effects of magnetic fields on the gravitational waveforms. We find that the GW amplitudes from anisotropic neutrino emission show a monotonic increase with time, whose amplitudes are much larger than those from matter motions of the accreting material. We show that the increasing trend of the neutrino GWs stems from the excess of neutrino emission in the direction near parallel to the spin axis illuminated from the hyperaccreting disks. We point out that a recently proposed future space-based interferometer like Fabry–Perot-type DECIGO would permit the detection of these GW signals within ≈100 Mpc.
Strain-dependent modulation of conductivity in single-layer transition-metal dichalcogenides

Quantum conductance calculations on the mechanically deformed monolayers of MoS$_2$ and WS$_2$ were performed using the non-equlibrium Green's functions method combined with the Landauer-B\"{u}ttiker approach for ballistic transport together with the density-functional based tight binding (DFTB) method. Tensile strain and compression causes significant changes in the electronic structure of TMD single layers and eventually the transition semiconductor-metal occurs for elongations as large as ~11% for the 2D-isotropic deformations in the hexagonal structure. This transition enhances the electron transport in otherwise semiconducting materials.
The a-cycle problem for transverse Ising ring

Traditionally, the transverse Ising model is mapped to the fermionic c-cycle problem, which neglects the boundary effect due to thermodynamic limit. If persisting on a perfect periodic boundary condition, we can get a so-called a-cycle problem that has not been treated seriously so far (Lieb et al 1961 Ann. Phys. 16 407). In this work, we show a little surprising but exact result in this respect. We find the odevity of the number of lattice sites, N, in the a-cycle problem plays an unexpected role even in the thermodynamic limit, N→∞, due to the boundary constraint. We pay special attention to the system with N(∈Odd)→∞, which is in contrast to the one with N(∈Even)→∞, because the former suffers a ring frustration. As a new effect, we find the ring frustration induces a low-energy gapless spectrum above the ground state. By proving a theorem for a new type of Toeplitz determinant, we demonstrate that the ground state in the gapless region exhibits a peculiar longitudinal spin–spin correlation. The entangled nature of the ground state is also disclosed by the evaluation of its entanglement entropy. At low temperature, new behavior of specific heat is predicted. We also propose an experimental protocol for observing the new phenomenon due to the ring frustration.
From Monolith to Microservices: A Semi-Automated Approach for Legacy to Modern Architecture Transition using Static Analysis

—Modern system architecture may increase the main- tainability of the system and promote the sustainability of the system. Nowadays, more and more organizations are looking towards microservice due to its positive impact on the business which can be translated into delivering quality products to the market faster than ever before. On top of that, native support of DevOps is also desirable. However, transforming legacy system architecture to modern architecture is challenging. As manual modernization is inefficient due to its time-intensive and the significant amount of effort required, the software architect is looking for an automated or semi-automated approach for easy and smooth transformation. Hence, this work proposed a semi-automated approach to transform legacy architecture to modern system architecture based on static analysis techniques. This bottom-up approach utilized legacy source code to adhere to the modern architecture framework. We studied the manual transformation pattern for architectural conversion and explore the possibility of providing transformation rules and guidelines. A task-based experiment was conducted to evaluate the correct- ness and efficiency of the approach. Two open-source projects were selected and several software architects participated in an architectural transformation task as well as in the survey. We found that the new approach promotes an efficient migration process and produces correct software artifacts with minimum errors rates.
Weak-lensing detection of intracluster filaments with ground-based data

According to the current standard model of Cosmology, matter in the Universe arranges itself along a network of filamentary structure. These filaments connect the main nodes of this so-called ’Cosmic Web’, which are clusters of galaxies. Although its large-scale distribution is clearly characterized by numerical simulations, constraining the dark matter content of the cosmic web in reality turns out to be di cult. The natural method of choice is gravitational lensing. However, the direct detection and mapping of the elusive filament signal is challenging and in this work we present two methods, specifically tailored to achieve this task. A linear matched filter aims at the detection of the smooth mass component of filaments and is optimized to perform a shear decomposition that follows the anisotropic component of the lensing signal. Filaments clearly inherit this property due to their morphology. At the same time, the contamination arising from the central massive cluster is controlled in a natural way. The filament 1 detection is of about 0:01 0:005 according to the filter’s template width and length, enabling the detection of structures out of reach with other approaches. The second, complementary method seeks to detect the clumpy component of filaments. The detection is determined by the number density of sub-clump identifications in an area enclosing the potential filament, as it was found within the observed field with the filter approach. We test both methods against Mock observations based on realistic N-Body simulations of filamentary structure and prove the feasibility of detecting filaments with ground-based data.
Cosmologically inspired Kastor-Traschen solution

We examine a hybrid of a Kastor–Traschen metric and a Friedmann–Robertson–Walker–Lemaitre solution. The problem is treated in a general number of dimensions D>=4, and we include the cosmological constant parameter into the Einstein–Maxwell equations. The matter source consists of two fluids—charged dust and neutral fluid with nonvanishing pressure. The equations of motion for the fluid and electromagnetic field are written down, and an exact solution generalizing the extremely charged Reissner–Nordstrom black hole to an arbitrary spatial curvature parameter is presented. We examine metric and singularities of curvature scalars, trapped horizons, and energy conditions.
Modeling scalar fields consistent with positive mass

This paper explores the conditions under which modified gravitational theories admit positive mass. Following Witten's spinor argument, it is argued that a single condition should be imposed upon a gauge connection in the supercovariant derivative. Under this condition, we present a simple formula for the divergence of the Nester tensor in Einstein's gravity with a general source. Applying this prescription to the Einstein-scalar system, we find that for a certain class of the gauge connection, a special kind of noncanonical scalar-field theory admits the positivity property in addition to the ordinary canonical scalar-field system. In both cases, the scalar potential can be written in terms of a superpotential. In the noncanonical case, we obtain the most general Bogomol'nyi-Prasad-Sommerfield solutions which preserve at least half of the supersymmetry.
Impedance evaluation of textile electrodes for EEG measurements

In this paper, we evaluated the impedance of textile electrodes for electroencephalography measurements. We compared the commercially available standard dry silver/silver chloride dry electrodes, copper-based, and silver-based textile electrodes by measuring impedance on the skin. First, on dry skin, the impedance range of the electrodes was 93–350 kΩ at 0 min; after 60 min, the range was 20–56 kΩ. The commercial electrode showed the lowest impedance of 20 kΩ, and the silver-based textile electrode showed the highest impedance of 56 kΩ in dry skin conditions after 60 min. When tap water was applied as an electrolyte, the impedance range was 13–15 kΩ at 0 min; after 60 min, the range was 17–22 kΩ for all three types of electrodes. In this case, the copper-based textile electrode showed the lowest impedance of 17 kΩ and the commercial electrode showed the highest impedance of 22 kΩ after 60 min. When saline water was applied as an electrolyte, at 0 min, the impedance range was 8–11 kΩ; after 60 min, the range became 11–12 kΩ for all three types of electrodes. Thus, all electrodes showed similar results. It was concluded that the copper-based textile electrodes were more suitable than the silver-based textile electrodes and comparable to commercial electrodes regarding impedance in all three skin conditions.
Inflation in Non-de Sitter Background with Coherent States

We use the excited coherent states built over the initial non-de Sitter modes, to study the modification of spectra of primordial scalar fluctuation. Non-de Sitter modes are actually the asymptotic solution of the inflaton field equation [J. High Energy Phys. 09 (2014) 020]. We build excited coherent states over the non-de Sitter modes and despite the lack of interactions in the Lagrangian, we find a non-zero one-point function. It is shown that the primordial non-Gaussianity resulting from excited-de Sitter modes depend both of time and background space-time. It is very tiny of order (≤ 10−24), at the Planck initial fixed time that confirmed by resent observations for single field inflation but it grows in the present epoch. Moreover, our results at the leading order are similar to what obtained with general initial states and in the dS limit leads to standard results [J. Cosmol. Astropart. Phys. 1202 (2012) 005]. We will show that the non-dS modes and its resulting spectrum are more usable for far past time limit.
L1-CAM in Mucinous Ovarian Carcinomas and Borderline Tumors

Mucinous ovarian carcinoma (MOC) is a rare histotype of primary ovarian carcinoma. Frequent pathogenic molecular alterations include mutations in KRAS, TP53, and overexpression of human epidermal growth factor receptor 2, but without having prognostic relevance. As L1-CAM (cell adhesion molecule) has previously shown prognostic relevance in other epithelial tumors of the female genital tract, we analyzed whether L1-CAM expression affected MOC prognosis. In addition, we investigated L1-CAM expression in mucinous borderline tumors (MBOTs) with and without adjacent MOC to identify its potential role in the pathogenesis of MOC. We examined a well-characterized collective of 39 MOCs by immunohistochemistry and compared their expression with clinicopathologic data. L1-CAM positivity was defined as any (even single-cell) positivity. Furthermore, we compared the L1-CAM expression in 20 MBOT regions adjacent to a MOC with that of 15 pure MBOTs. L1-CAM expression in MOC was significantly associated with recurrence, independent of tumor stage. Overall, 7/20 positive cases recurred versus 0/19 L1-CAM-negative cases (P=0.032), showing a significant difference in time to progression. Furthermore, the presence of at least 1 defined molecular alteration (L1-CAM, aberrant p53, or human epidermal growth factor receptor 2) was found more frequently in the MBOT regions adjacent to a MOC (14/20) than in pure MBOTs (3/15) (P=0.024). Expression of the tumor marker L1-CAM is frequent (51%) in MOC and is associated with tumor recurrence. The lack of L1-CAM may serve to characterize cases with a low risk of recurrence. Furthermore, the presence of specific molecular alterations in MBOTs is associated with adjacent carcinomas and may define potential pathways in tumor progression.
USING GAPS IN N-BODY TIDAL STREAMS TO PROBE MISSING SATELLITES

We use N-body simulations to model the tidal disruption of a star cluster in a Milky-Way-sized dark matter halo, which results in a narrow stream comparable to (but slightly wider than) Pal-5 or GD-1. The mean Galactic dark matter halo is modeled by a spherical Navarro–Frenk–White potential with subhalos predicted by the ΛCDM cosmological model. The distribution and mass function of the subhalos follow the results from the Aquarius simulation. We use a matched filter approach to look for “gaps” in tidal streams at 12 length scales from 0.1 kpc to 5 kpc, which appear as characteristic dips in the linear densities along the streams. We find that, in addition to the subhalos' perturbations, the epicyclic overdensities (EOs) due to the coherent epicyclic motions of particles in a stream also produce gap-like signals near the progenitor. We measure the gap spectra—the gap formation rates as functions of gap length—due to both subhalo perturbations and EOs, which have not been accounted for together by previous studies. Finally, we project the simulated streams onto the sky to investigate issues when interpreting gap spectra in observations. In particular, we find that gap spectra from low signal-to-noise observations can be biased by the orbital phase of the stream. This indicates that the study of stream gaps will benefit greatly from high-quality data from future missions.
Advanced Customer Activity Prediction Based on Deep Hierarchic Encoder-Decoders

Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional "offline" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach.
Evolving Hořava cosmological horizons

Several sets of radially propagating null congruence generators are exploited in order to form 3-dimensional marginally trapped surfaces, referred to as black hole and cosmological apparent horizons in a Hořava universe. Based on this method, we deal with the characteristics of the 2-dimensional space-like spheres of symmetry and the peculiarities of having trapping horizons. Moreover, we apply this method in standard expanding and contracting FLRW cosmological models of a Hořava universe to investigate the conditions under which the extra parameters of the theory may lead to trapped/anti-trapped surfaces both in the future and in the past. We also include the cases of negative time, referred to as the finite past, and discuss the formation of anti-trapped surfaces inside the cosmological apparent horizons.
3DCNN-Based Palpation Localization with Temporal Attention Module

Palpation is necessary in Traditional Chinese Medicine (TCM). In TCM, doctors need to touch patient’s wrist and exert pressure on it to obtain physiological signal. Locating the pulse position is an important step in palpation. Nowadays, researchers have proposed numerous methods to locate the pulse position accurately. In this paper, we propose an accurate and effective framework to locate the pulse position. Our framework is based on 3-dimensional convolutional neural network (3DCNN), and we propose a novel temporal attention module to further improve the performance. Our framework achieves superior results, the accuracy of locating the pulse position on a video with a resolution of 2048 × 1088 within 100 pixels is 97%. More and more cross-domain applications of computer science and medical science provides more ways for doctors to treat patients, such as telemedicine, computed tomography image analysis, and physiological signal analysis. Our research presents the great potential to be applied to palpation automation and intelligent medical treatment.
Eyebirds: Enabling the Public to Recognize Water Birds at Hand

Simple Summary Enabling the public to easily recognize water birds at hand has a positive effect on wetland bird conservation. An attention mechanism-based deep convolution neural network model (AM-CNN) is developed for water bird recognition. The model employs an effective strategy that enhances the perception of shallow image features in convolutional layers, and achieves up to 86.4% classification accuracy on our self-constructed image dataset of 548 global water bird species. The model is implemented as the mobile app of EyeBirds for smart phones. The app offers three main functions of bird image recognition, bird information and bird field survey to users. Overall, EyeBirds is useful to assist the public to easily recognize water birds and acquire bird knowledge. Abstract Enabling the public to easily recognize water birds has a positive effect on wetland bird conservation. However, classifying water birds requires advanced ornithological knowledge, which makes it very difficult for the public to recognize water bird species in daily life. To break the knowledge barrier of water bird recognition for the public, we construct a water bird recognition system (Eyebirds) by using deep learning, which is implemented as a smartphone app. Eyebirds consists of three main modules: (1) a water bird image dataset; (2) an attention mechanism-based deep convolution neural network for water bird recognition (AM-CNN); (3) an app for smartphone users. The waterbird image dataset currently covers 48 families, 203 genera and 548 species of water birds worldwide, which is used to train our water bird recognition model. The AM-CNN model employs attention mechanism to enhance the shallow features of bird images for boosting image classification performance. Experimental results on the North American bird dataset (CUB200-2011) show that the AM-CNN model achieves an average classification accuracy of 85%. On our self-built water bird image dataset, the AM-CNN model also works well with classification accuracies of 94.0%, 93.6% and 86.4% at three levels: family, genus and species, respectively. The user-side app is a WeChat applet deployed in smartphones. With the app, users can easily recognize water birds in expeditions, camping, sightseeing, or even daily life. In summary, our system can bring not only fun, but also water bird knowledge to the public, thus inspiring their interests and further promoting their participation in bird ecological conservation.