Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Super-Resolution

This study presents a chronological overview of the single image super-resolution problem. We first define the problem thoroughly and mention some of the serious challenges. Then the problem formulation and the performance metrics are defined. We give an overview of the previous methods relying on reconstruction based solutions and then continue with the deep learning approaches. We pick 3 landmark architectures and present their results quantitatively. We see that the latest proposed network gives favorable output compared to the previous methods.
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection

In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common but challenging task in many industries, which plays an important role in network monitoring, facility maintenance, information security, and so on. However, it is very difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world, and complicated abnormal patterns. From recent studies, we are inspired by Nouveau VAE (NVAE) and propose our anomaly detection model: Time series to Image VAE (T2IVAE), an unsupervised model based on NVAE for univariate series, transforming 1D time series to 2D image as input, and adopting the reconstruction error to detect anomalies. Besides, we also apply the Generative Adversarial Networks based techniques to T2IVAE training strategy, aiming to reduce the overfitting. We evaluate our model performance on three datasets, and compare it with other several popular models using F1 score. T2IVAE achieves 0.639 on Numenta Anomaly Benchmark, 0.651 on public dataset from NASA, and 0.504 on our dataset collected from real-world scenario, outperforms other comparison models.
Characterizing the line emission from molecular clouds

Context. The traditional approach to characterize the structure of molecular clouds is to map their line emission. Aims. We aim to test and apply a stratified random sampling technique that can characterize the line emission from molecular clouds more efficiently than mapping. Methods. We sampled the molecular emission from the Perseus cloud using the H2 column density as a proxy. We divided the cloud into ten logarithmically spaced column density bins, and we randomly selected ten positions from each bin. The resulting 100 cloud positions were observed with the IRAM 30 m telescope, covering the 3 mm-wavelength band and parts of the 2 and 1 mm bands. Results. We focus our analysis on the 11 molecular species (plus isotopologs) detected toward most column density bins. In all cases, the line intensity is tightly correlated with the H2 column density. For the CO isotopologs, the trend is relatively flat, while for high-dipole moment species such as HCN, CS, HCO+, and HNC, the trend is approximately linear. To reproduce this behavior, we developed a cloud model in which the gas density increases with column density, and where most species have abundance profiles characterized by an outer photodissociation edge and an inner freeze-out drop. With this model, we determine that the intensity behavior of the high-dipole moment species arises from a combination of excitation effects and molecular freeze out, with some modulation from optical depth. This quasi-linear dependence with the H2 column density makes the gas at low column densities dominate the cloud-integrated emission. It also makes the emission from most high-dipole moment species proportional to the cloud mass inside the photodissociation edge. Conclusions. Stratified random sampling is an efficient technique for characterizing the emission from whole molecular clouds. When applied to Perseus, it shows that despite the complex appearance of the cloud, the molecular emission follows a relatively simple pattern. A comparison with available studies of whole clouds suggests that this emission pattern may be common.
Ultra Fast Astronomy: Optimized Detection of Multimessenger Transients

Ultra Fast Astronomy is a new frontier becoming enabled by improved detector technology allowing discovery of optical transients on millisecond to nanosecond time scales. These may reveal counterparts of energetic processes such as fast radio bursts, gamma ray bursts, gravitational wave events, or play a role in the optical search for extraterrestrial intelligence (oSETI). We explore some example science cases and their optimization under constrained resources, basically how to distribute observations along the spectrum of short duration searches of many targets or long searches over fewer targets. As a demonstration of the method we present some analytic and some numerical optimizations, of both raw detections and science characterization such as an information matrix analysis of constraining a burst delay – flash duration relation.
"Let's Eat Grandma": When Punctuation Matters in Sentence Representation for Sentiment Analysis

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insigniﬁcant information; consequently, they are routinely eliminated in the pre-processing phase as they are shown to improve task performance. In this paper, we hypothesize that punctuation could play a signiﬁcant role in sentiment analysis and propose a novel representation model to improve syntactic and contextual performance. We corroborate our ﬁndings by conducting experiments on publicly available datasets and verify that our model can identify the sentiments more accurately over other state-of-the-art base-line methods.
Modeling Particle Transport in Astrophysical Outflows and Simulations of Associated Emissions from Hadronic Microquasar Jets

In this work, after improving the formulation of the model on particle transport within astrophysical plasma outflows and constructing the appropriate algorithms, we test the reliability and effectiveness of our method through numerical simulations on well-studied galactic microquasars as the SS 433 and the Cyg X-1 systems. Then, we concentrate on predictions of the associated emissions, focusing on detectable high-energy neutrinos and γ -rays originated from the extragalactic M33 X-7 system, which is an X-ray binary discovered in 2006, located in the neighboring galaxy Messier 33, and has not yet been modeled in detail. The particle and radiation energy distributions, produced from magnetized hadronic astrophysical jets in the context of our method, are assumed to originate from decay and scattering processes taking place among the secondary particles created when hot (relativistic) protons of the jet scatter on thermal (cold) ones (p-p interaction mechanism inside the jet). These distributions are computed by solving the system of coupled integrodifferential transport equations of multiparticle processes (reactions chain) following the inelastic proton-proton (p-p) collisions. For the detection of such high-energy neutrinos as well as multiwavelength (radio, X-ray, and gamma-ray) emissions, extremely sensitive space telescopes and other γ -ray and neutrino detection instruments are in operation or have been designed like the CTA, IceCube, ANTARES, KM3NeT, and IceCube-Gen-2.
Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search

Text-based person search aims at retrieving target person in an image gallery using a descriptive sentence of that person. It is very challenging since modal gap makes effectively extracting discriminative features more difficult. Moreover, the inter-class variance of both pedestrian images and descriptions is small. So comprehensive information is needed to align visual and textual clues across all scales. Most existing methods merely consider the local alignment between images and texts within a single scale (e.g. only global scale or only partial scale) then simply construct alignment at each scale separately. To address this problem, we propose a method that is able to adaptively align image and textual features across all scales, called NAFS (i.e.Non-local Alignment over Full-Scale representations). Firstly, a novel staircase network structure is proposed to extract full-scale image features with better locality. Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales. Then, instead of separately aligning features at each scale, a novel contextual non-local attention mechanism is applied to simultaneously discover latent alignments across all scales. The experimental results show that our method outperforms the state-of-the-art methods by 5.53% in terms of top-1 and 5.35% in terms of top-5 on text-based person search dataset. The code is available at https://github.com/TencentYoutuResearch/PersonReID-NAFS
Mean exit time for diffusion on irregular domains

Many problems in physics, biology, and economics depend upon the duration of time required for a diffusing particle to cross a boundary. As such, calculations of the distribution of first passage time, and in particular the mean first passage time, is an active area of research relevant to many disciplines. Exact results for the mean first passage time for diffusion on simple geometries, such as lines, discs and spheres, are well-known. In contrast, computational methods are often used to study the first passage time for diffusion on more realistic geometries where closed-form solutions of the governing elliptic boundary value problem are not available. Here, we develop a perturbation solution to calculate the mean first passage time on irregular domains formed by perturbing the boundary of a disc or an ellipse. Classical perturbation expansion solutions are then constructed using the exact solutions available on a disc and an ellipse. We apply the perturbation solutions to compute the mean first exit time on two naturally-occurring irregular domains: a map of Tasmania, an island state of Australia, and a map of Taiwan. Comparing the perturbation solutions with numerical solutions of the elliptic boundary value problem on these irregular domains confirms that we obtain a very accurate solution with a few terms in the series only. MATLAB software to implement all calculations is available at https://github.com/ProfMJSimpson/Exit_time.
LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning

Named entity detection and Parts-of-speech tagging are the key tasks for many NLP applications. Although the current state of the art methods achieved near perfection for long, formal, structured text there are hindrances in deploying these models on memory-constrained devices such as mobile phones. Furthermore, the performance of these models is degraded when they encounter short, informal, and casual conversations. To overcome these difficulties, we present LiteMuL – a lightweight on-device sequence tagger that can efficiently process the user conversations using a Multi-Task Learning (MTL) approach. To the best of our knowledge, the proposed model is the first on-device MTL neural model for sequence tagging. Our LiteMuL model is about 2.39 MB in size and achieved an accuracy of 0.9433 (for NER), 0.9090 (for POS) on the CoNLL 2003 dataset. The proposed LiteMuL not only outperforms the current state of the art results but also surpasses the results of our proposed on-device task-specific models, with accuracy gains of up to 11% and model-size reduction by 50%-56%. Our model is competitive with other MTL approaches for NER and POS tasks while outshines them with a low memory footprint. We also evaluated our model on custom-curated user conversations and observed impressive results.
Ferrofluidic Manipulator: Automatic Manipulation of Nonmagnetic Microparticles at the Air–Ferrofluid Interface

Manipulation of small-scale matter is a fundamental topic in micro and nanorobotics. Numerous magnetic robotic systems have been developed for the manipulation of microparticles in an ambient environment, liquid, as well as on the air–liquid interface. These systems move intrinsically magnetic or magnetically tagged objects by inducing a magnetic torque or force. However, most of the materials found in nature are nonmagnetic. Here, we report a ferrofluidic manipulator for automatic 2-D manipulation of nonmagnetic objects floating on top of a ferrofluid. The manipulation system employs 8-cm-scale solenoids, which can move nonmagnetic particles by deforming the air–ferrofluid interface. Using linear programming, we can control the motion of the nonmagnetic particles with a predefined trajectory of a line, square, and circle with a precision of 25.1 <inline-formula><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 19.5, 34.4 <inline-formula><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 28.4, and 33.4 <inline-formula><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 26.6 μm, respectively. The ferrofluidic manipulator is versatile with the materials and the shapes of the objects under manipulation. We have successfully manipulated particles made of polyethylene, polystyrene, a silicon chip, and poppy and sesame seeds. This article shows a promising venue for the manipulation of living and nonliving matter at the air–liquid interface.
Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development

—Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases the selection burden of software developers in developing service-based systems (such as mashups). How to recommend suitable follow-up component services to develop new mashups has become a fundamental problem in service-oriented software engineering. Most of the existing service recommendation approaches are designed for mashup development in the single-round recommendation scenario. It is hard for them to update recommendation results in time according to developers’ requirements and behaviors (e.g., instant service selection). To address this issue, we propose a deep-learning-based interactive service recommendation framework named DLISR, which aims to capture the interactions among the target mashup, selected services, and the next service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending the next service. We also design two separate models for learning interactions from the perspectives of content information and historical invocation information, respectively, as well as a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR outperforms several state-of-the-art service recommendation methods in the online interactive scenario for developing new mashups iteratively.
Critical points in the CP N−1 model

We use scale invariant scattering theory to obtain the exact equations determining the renormalization group fixed points of the two-dimensional CP N−1 model, for N real. Also due to special degeneracies at N = 2 and 3, the space of solutions for N ⩾ 2 reduces to that of the O(N 2 − 1) model, and accounts for a zero temperature critical point. For N < 2 the space of solutions becomes larger than that of the O(N 2 − 1) model, with the appearance of new branches of fixed points relevant for criticality in gases of intersecting loops.
Polarizability of molecular clusters as calculated by a dipole interaction model

We have developed and investigated a dipole interaction model for calculating the polarizability of molecular clusters. The model has been parametrized from the frequency-dependent molecular polarizability as obtained from quantum chemical calculations for a series of 184 aliphatic, aromatic, and heterocyclic compounds. A damping of the interatomic interaction at short distances is introduced in such a way as to retain a traceless interaction tensor and a good description of the damping over a wide range of interatomic distances. By adopting atomic polarizabilities in addition to atom-type parameters describing the damping and the frequency dependence, respectively, the model is found to reproduce the molecular frequency-dependent polarizability tensor calculated with ab initio methods. A study of the polarizability of four dimers has been carried out: the hydrogen fluoride, methane, benzene, and urea dimers. We find in general good agreement between the model and the quantum chemical results over a wide .
Effects of application of nitrogen fertilizer on concentrations of P, K, S, Ca, Mg, Na, Cl, Mn, Fe, Cu and Zn in perennial ryegrass/white clover pastures in south‐western Victoria, Australia

Effects of timing and rate of N fertilizer application on concentrations of P, K, S, Ca, Mg, Na, Cl, Mn, Fe, Cu and Zn in herbage from perennial ryegrass/white clover pastures were studied at two sites in south-western Victoria, Australia. Nitrogen fertilizer (0, 15, 25, 30, 45 and 60 kg ha–1) was applied as urea in mid-April, early May, mid-May, early June and mid-June 1996 to pastures grazed by dairy cows. At Site 1, N fertilizer resulted in a linear increase in P, K, S, Mg and Cl concentrations in herbage and a linear decrease in Ca concentration. For all times of application, concentrations of P, K, Ca, Mg and Cl in herbage increased by 0·0048, 0·08, −0·010, 0·0013 and 0·053 g kg–1 dry matter (DM) per kg N applied respectively. For S concentration, maximum responses occurred in mid-May (0·012 g kg–1 DM per kg N applied). At Site 2, N fertilizer resulted in a linear increase in P, S and Na concentrations in herbage, a linear decrease in Ca concentration and a curvilinear increase in K and Cl concentration. The maximum responses for P, S and K concentrations in herbage occurred for the N application in mid-June and were 0·015, 0·008 and 0·47 g kg–1 DM per kg N applied respectively. For Cl concentration, the maximum response occurred for the N application in early June and was 0·225 g kg–1 DM per kg N applied. Overall, applications of N fertilizer up to 60 kg ha–1 did not alter herbage mineral concentration to levels that might affect pasture growth or animal health.
Structures of campanin and rubrocampanin, two novel acylated anthocyanins with p-hydroxybenzoic acid from the flowers of bellflower, Campanula medium L.

The structures of campanin 1 and rubrocampanin 2, major antocyanins from flowers of the purple and pink cultivars of Campanula medium L. , have been identified as 3-O-[(6-O-α-L-rhamnosyl)-β-D-glucopyranosyl]-7-O-[6-O-(4-{6-O-[4-(6-O-p-hydroxybenzoyl-β-D- glucopyranosyloxy)benzoyl]-β-D-glucopyranosyloxy}benzoyl)-β-D-glucopyranosides] of delphinidin, and pelargonidin, respectively, by means of chromatography, FAB-MS, and NMR including negative NOE difference (DIFNOE) spectroscopy. The colour stabilities of both the p-hydroxybenzoylanthocyanins 1 and 2 were examined and found to be relatively unstable compared with that of platyconin, 3-O-[6-O-(α-L-rhamnopyranosyl)-β-D-glucopyranosyl]-7-O-{6-O-[(E)-4-O-{6-O-[(E)-4-O-(β-D-glucopyranosyl)-caffeoyl]-β-D-glucopyranosyl}caffeoyl]-β-D-glucopyranosyl}delphinidin, in neutral aqueous solution.
Oxidation and Volatilization of Silica-Formers in Water Vapor

At high temperatures, SiC and Si3N4 react with water vapor to form a SiO2 scale. SiO2 scales also react with water vapor to form a volatile Si(OH)4 species. These simultaneous reactions, one forming SiO2 and the other removing SiO2, are described by paralinear kinetics. A steady state, in which these reactions occur at the same rate, is eventually achieved. After steady state is achieved, the oxide found on the surface is a constant thickness, and recession of the underlying material occurs at a linear rate. The steady-state oxide thickness, the time to achieve steady state, and the steady-state recession rate can be described in terms of the rate constants for the oxidation and volatilization reactions. In addition, the oxide thickness, the time to achieve steady state, and the recession rate also can be determined from parameters that describe a water-vapor-containing environment. Accordingly, maps have been developed to show these steady-state conditions as a function of reaction rate constants, pressure, and gas velocity. These maps can be used to predict the behavior of SiO2 formers in water-vapor-containing environments, such as combustion environments. Finally, these maps are used to explore the limits of the paralinear oxidation model for SiC and Si3N4.
Transpiration from a kiwifruit vine as estimated by the heat pulse technique and the Penman-Monteith equation

Abstract Heat pulse velocities were measured at 4 depths in the stem of a kiwifruit plant (Actinidia chinensis Planchon) throughout one lateautumn day, and sap velocities and total stem sap flux were calculated from physical principles without resorting to empirical calibration. Concurrent measurements of net radiation, air temperature, vapour pressure deficit, wind speed, and stomatal conductance were utilised in a second, independent calculation of transpiration based on the Penman-Monteith combination equation. Results indicated that most flow occurred in the outer ring or rings of the stem xylem tissue, and there was a close relationship between fluxes estimated by the 2 methods. We conclude that heat pulse velocity measurement is potentially a practicable method for measuring transpiration rates in kiwifruit.
Characterisation of Prostate Lesions Using Transrectal Shear Wave Elastography (SWE) Ultrasound Imaging: A Systematic Review

Simple Summary Shear wave elastography is an ultrasound-based imaging modality that can delineate prostate lesions that are stiffer, possibly due to cancerous changes. Using evidence from 16 published studies, including more than 2200 patients with suspected or biopsy-proven prostate cancer, we showed that shear wave elastography has good sensitivity and specificity for the detection of prostate cancer. Abstract Background: ultrasound-based shear wave elastography (SWE) can non-invasively assess prostate tissue stiffness. This systematic review aims to evaluate SWE for the detection of prostate cancer (PCa) and compare diagnostic estimates between studies reporting the detection of all PCa and clinically significant PCa (csPCa). Methods: a literature search was performed using the MEDLINE, EMBASE, Cochrane Library, ClinicalTrials.gov, and CINAHL databases. Studies evaluating SWE for the detection of PCa using histopathology as reference standard were included. Results: 16 studies including 2277 patients were included for review. Nine studies evaluated SWE for the detection of PCa using systematic biopsy as a reference standard at the per-sample level, with a pooled sensitivity and specificity of 0.85 (95% CI = 0.74–0.92) and 0.85 (95% CI = 0.75–0.91), respectively. Five studies evaluated SWE for the detection of PCa using histopathology of radical prostatectomy (RP) specimens as the reference standard, with a pooled sensitivity and specificity of 0.71 (95% CI = 0.55–0.83) and 0.74 (95% CI = 0.42–0.92), respectively. Sub-group analysis revealed a higher pooled sensitivity (0.77 vs. 0.62) and specificity (0.84 vs. 0.53) for detection of csPCa compared to all PCa among studies using RP specimens as the reference standard. Conclusion: SWE is an attractive imaging modality for the detection of PCa.