Miracle-dz/UniFAR-FTUs huggingface dataset:
WNSA-Net: An Axial-Attention-Based Network for Schizophrenia Detection Using Wideband and Narrowband Spectrograms

Schizophrenia is a severe mental disease that affects patients' thoughts, feelings, and behaviors. Speech signal has proven to be a biomarker in the early diagnosis of schizophrenia. Previous studies on schizophrenic speech detection are mainly based on manual feature extraction engineering, which requires domain knowledge for researchers and has difficulties extracting effective features. This work proposes an end-to-end architecture, called Axial-attention-based Network using Wideband and Narrowband Spectrograms (WNSA-Net), to detect schizophrenia. Specifically, we adopt both wideband and narrowband spectrograms as inputs to represent speech signals using fine time and frequency structures. Then dilated convolution blocks are employed to capture detailed and long-range information in spectrograms. Axial-attention blocks are introduced to augment the information in feature maps along the time and frequency axes. In addition, we employ a gate mechanism to fuse the output feature maps from all channels. Experimental results on the Schizophrenia dataset and its subdatasets show that schizophrenic patients have difficulties in expressing emotions. To validate the performance of our WNSA-Net, experiments are conducted on Schizophrenia dataset and open-access TORGO database, achieving 97.37% and 98.16% accuracy in detecting schizophrenia and dysarthria, respectively. The results show promise for the proposed method in the diagnosis of disordered speech.
Effects of different regional cerebral blood flow on white matter hyperintensity in CADASIL patients

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an early-onset inherited small vessel disease. Decreased cerebral blood flow (CBF) may contribute to white matter hyperintensity (WMH) severity in CADASIL, but more evidence is needed to support this hypothesis. This study comprised six patients with CADASIL who harbored mutations in the coding sequence of NOTCH3 and twelve age-matched neurologically healthy controls. We collected clinical and imaging data from patients with CADASIL and divided the brain into four regions: WMH, normal-appearing white matter (NAWM), gray matter (GM), and global brain. We analyzed the relationship between CBF of each region and the WMH volume. Compared with the control group, CBF was significantly decreased in all four regions in the CADASIL group. Lower CBF in these regions was correlated with higher WMH volume in CADASIL. CBF in the NAWM, GM and global regions was positively correlated with that in WMH region. However, after correction tests, only CBF in the WMH region but not in NAWM, GM and global regions was associated with WMH volume. Our findings suggest that CBF in the WMH region is an influencing factor of the WMH severity in CADASIL.
Dissecting the Regulatory Network of Maize Phase Change in ZmEPC1 Mutant by Transcriptome Analysis

The developmental phase changes of maize are closely associated with the life span, environmental adaption, plant height, and disease resistance of the plant and eventually determines the grain yield and quality of maize. A natural mutant, Early Phase Change 1 (ZmEPC1), was selected from the inbred line KN5585. Compared with the wild type plant, the ZmEPC1 mutant exhibits deceased plant stature, accelerated developmental stages, and decreased leaf size. Through the transcriptome sequencing analysis of leaf samples at flowering stage, a total of 4583 differentially expressed genes (DEGs) were screened between the mutant and wild type, including 2914 down-regulated genes and 1669 up-regulated genes. The GO enrichment and KEGG enrichment analysis revealed that the DEGs were mainly involved in hormone response, hormone signal transduction, autophagy, JA response and signal response, photosynthesis, biotic/abiotic stress, and circadian rhythms. The RT-qPCR results revealed that the most tested DEGs display consistent expression alterations between V5 and FT stages. However, several genes showed opposite expression alterations. Strikingly, most of the JA biosynthesis and signaling pathway-related genes displayed diametrically expression alterations between V5 and FT stages. miR156, a key regulator of plant phase transition, exhibited significant down-regulated expression at V5 and FT stages. The expression of two miR156 target genes were both significantly different between mutants and wild type. In conclusion, ZmEPC1 was identified to be mainly involved in the regulation of JA-mediated signaling pathways and hormone response and signaling, which is possible to confer developmental phase change through miR156-SPLs pathway.
Constraining the redshifted 21-cm signal with the unresolved soft X-ray background

We use the observed unresolved cosmic X-ray background (CXRB) in the 0.5-2 keV band and existing upper limits on the 21-cm power spectrum to constrain the high-redshift population of X-ray sources, focusing on their effect on the thermal history of the Universe and the cosmic 21-cm signal. Because the properties of these sources are poorly constrained, we consider hot gas, X-ray binaries and mini-quasars (i.e., sources with soft or hard X-ray spectra) as possible candidates. We find that (1) the soft-band CXRB sets an upper limit on the X-ray efficiency of sources that existed before the end of reionization, which is one-to-two orders of magnitude higher than typically assumed efficiencies, (2) hard sources are more effective in generating the CXRB than the soft ones, (3) the commonly-assumed limit of saturated heating is not valid during the first half of reionization in the case of hard sources, with any allowed value of X-ray efficiency, (4) the maximal allowed X-ray efficiency sets a lower limit on the depth of the absorption trough in the global 21-cm signal and an upper limit on the height of the emission peak, while in the 21-cm power spectrum it sets a minimum amplitude and frequency for the high-redshift peaks, and (5) the existing upper limit on the 21-cm power spectrum sets a lower limit on the X-ray efficiency for each model. When combined with the 21-cm global signal, the CXRB will be useful for breaking degeneracies and helping constrain the nature of high-redshift heating sources.
Development and Evaluation of a CNN-LSTM Architecture based Neural Network for Time Optimization during EMI Measurements

In this paper, an approach is proposed to find the worst-case positions during the final measurement phase on critical frequencies in electromagnetic interference (EMI) measurements according to 47 CFR § 15.209 by using a developed measurement software and deep neural networks (DNN). Firstly, because of its advantage of incomplete connection, relatively simple model structure and strong data features extraction, a dimensional convolution neural network (1D CNN) was present to predict the positions that meet the maximum radiation emission level. Secondly, a hybrid deep learning neural network framework, that combines CNN with long short term memory(LSTM) was adopted to forecast the worst-case of the high variance emission levels. The DNNs were trained using real EMI measurements for different equipment under test (EUT) in a Semi Anechoic Chamber (SAC) by Cetecom GmbH in Essen, Germany. By predicting the position azimuth of the turntable and the height of the antenna, the required time to carry out the final measurement phase is effectively reduced.
The extent of and motivation for disaster information seeking behavior via social networking sites

Abstract This study adopts a research model founded on multiple theories to investigate potential influence of source credibility (SC), argument quality (AQ), self-efficacy (SEF), tolerance of ambiguity (TA), locus of control (LOC), and perceived usefulness of information (PUI) on individuals’ information-seeking behavior (ISB) of disaster information from social networking sites (SNSs). The structural equation modeling (SEM) analysis of 312 global responses reveals that, overall, individuals infrequently seek behavior about disaster information from SNSs. In addition, the source and content quality (SAQ) and SEF that emerge are significant antecedents of PUI, which in turn is a significant determinant of ISB. These findings and their implications are further discussed in the paper.
Embracing the Future: Digital School Culture in Tabanan High Schools

Digitalization has been an inevitable part of human lives, and education is not an exception. Digitalization is, however, only a part of the change. While digitalization is just using digital technologies and digitized data for an impact, digital transformation is broader adoption of digital technology and cultural change. It is more about the people, rather than the technology. One of the most prominent examples is the integration of the digital culture and the school culture, creating “digital school culture”. Concerns arise from the change, especially isolation from peers and decreased effectiveness on practical lessons . In this case, the creativity to integrate digital culture in online settings to accommodate these concerns is necessary. This research attempts to find the three high schools in Tabanan’s current state of digital school culture and plan of development for the future. The data is gained through semi-impromptu interviews with three high schools’ vice-principal of curriculum and vice-principal of student affairs in Tabanan. Their answers are then analyzed through digital culture and school culture theory. Their answers will also be compared with each other to figure out the similarities and differences in their implementation of digital school culture.Keywords: digital culture, school culture, digital school culture, digital transformation, digital native, digital immigrant, Tabanan high school
The In Vitro Inhibitory Effect of Selected Asteraceae Plants on Pancreatic Lipase Followed by Phenolic Content Identification through Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS)

Pancreatic lipase (PNLIP, EC 3.1.1.3) plays a pivotal role in the digestion of dietary lipids, a metabolic pathway directly related to obesity. One of the effective strategies in obesity treatment is the inhibition of PNLIP, which is possible to be achieved by specific phenolic compounds occurring in high abundance in some plants. In this study, a multidisciplinary approach is presented investigating the PNLIP inhibitory effect of 33 plants belonging in the Asteraceae botanical family. In the first stage of the study, a rapid and cost-efficient PNLIP assay in a 96-microwell plate format was developed and important parameters were optimized, e.g., the enzyme substrate. Upon PNLIP assay optimization, aqueous and dichloromethane Asteraceae plant extracts were tested and a cut-off inhibition level was set to further analyze only the samples with a significant inhibitory effect (inhibitory rate > 40%), using an ultra-high-performance liquid chromatography hybrid quadrupole time-of-flight mass spectrometry (UHPLC-q-TOF-MS) method. Specifically, a metabolomic suspect screening was performed and 69 phenolic compounds were tentatively identified, including phenolic acids, flavonoids, flavonoid-3-O-glycosides, and flavonoid-7-O-glycosides, amongst others. In the case of aqueous extracts, phytochemicals known for inducing PNLIP inhibitory effect, e.g., compounds containing galloyl molecules or caffeoylquinic acids, were monitored in Chrysanthemum morifolium, Grindella camporum and Hieracium pilosella extracts. All in all, the presented approach combines in vitro bioactivity measurements to high-end metabolomics to identify phenolic compounds with potential medicinal and/or dietary applications.
MULTIFRACTAL SOLAR EUV INTENSITY FLUCTUATIONS AND THEIR IMPLICATIONS FOR CORONAL HEATING MODELS

We investigate the scaling properties of the long-range temporal evolution and intermittency of Atmospheric Imaging Assembly/Solar Dynamics Observatory intensity observations in four solar environments: an active region core, a weak emission region, and two core loops. We use two approaches: the probability distribution function (PDF) of time series increments and multifractal detrended fluctuation analysis (MF-DFA). Noise taints the results, so we focus on the 171 Å waveband, which has the highest signal-to-noise ratio. The lags between pairs of wavebands distinguish between coronal versus transition region (TR) emission. In all physical regions studied, scaling in the range of 15–45 minutes is multifractal, and the time series are anti-persistent on average. The degree of anti-correlation in the TR time series is greater than that for coronal emission. The multifractality stems from long-term correlations in the data rather than the wide distribution of intensities. Observations in the 335 Å waveband can be described in terms of a multifractal with added noise. The multiscaling of the extreme-ultraviolet data agrees qualitatively with the radiance from a phenomenological model of impulsive bursts plus noise, and also from ohmic dissipation in a reduced magnetohydrodynamic model for coronal loop heating. The parameter space must be further explored to seek quantitative agreement. Thus, the observational “signatures” obtained by the combined tests of the PDF of increments and the MF-DFA offer strong constraints that can systematically discriminate among models for coronal heating.
The frequency dependence of scattering imprints on pulsar observations

Observations of pulsars across the radio spectrum are revealing a dependence of the characteristic scattering time ($\tau$) on frequency, which is more complex than the simple power law with a theoretically predicted power law index. In this paper we investigate these effects using simulated pulsar data at frequencies below 300 MHz. We investigate different scattering mechanisms, namely isotropic and anisotropic scattering, by thin screens along the line of sight, and the particular frequency dependent impact on pulsar profiles and scattering time scales of each. We also consider how the screen shape, location and offset along the line of sight lead to specific observable effects. We evaluate how well forward fitting techniques perform in determining $\tau$. We investigate the systematic errors in $\tau$ associated with the use of an incorrect fitting method and with the determination of an off-pulse baseline. Our simulations provide examples of average pulse profiles at various frequencies. Using these we compute spectra of $\tau$ and mean flux for different scattering setups. We identify setups that lead to deviations from the simple theoretical picture. This work provides a framework for interpretation of upcoming low frequency data, both in terms of modelling the interstellar medium and understanding intrinsic emission properties of pulsars.
Instability and vortex ring dynamics in a three-dimensional superfluid flow through a constriction

We study the instability of a superfluid flow through a constriction in three spatial dimensions. We consider a Bose–Einstein condensate at zero temperature in two different geometries: a straight waveguide and a torus. The constriction consists of a broad, repulsive penetrable barrier. In the hydrodynamic regime, we find that the flow becomes unstable as soon as the velocity at the classical (Thomas–Fermi) surface equals the sound speed inside the constriction. At this critical point, vortex rings enter the bulk region of the cloud. The nucleation and dynamics scenario is strongly affected by the presence of asymmetries in the velocity and density of the background condensate flow.
The Expression of Dispositional Behavior and Psychological Attitudes in the Context of Social Risk

The aim of the research is to reveal the distribution of psychological attitudes (preferences) of risk specialists according to the structural-operational components of the activity, to reveal the expression of social-psychological attitudes of self-confidence observed in specific situations with six-month changes, to review the connection among needs, situations and unconscious actions. Structural similarities of a person’s dispositional behavior and psychological attitudes are considered by us in terms of requirement, situation and unconscious actions. In a given social situation, the content of a person’s specific requirements can be significantly different depending on the specific role of the person. The psychological attitudes of professionals are mostly expressed in official activities, when a task arises to give preference to some structural-functional component (“motive-goal-action-result”). The analysis shows that changes in the social attitudes of professionals in the professional field related to the new situation and over time did not have a significant impact on the whole group. Meanwhile, the data of each participant has been changed to some extent.
Introduction to Radiological Physics and Radiation Dosimetry

Ionizing Radiation Quantities for Describing the Interaction of Ionizing Radiation with Matter Exponential Attenuation Charged-Particle and Radiation Equilibria Absorbed Dose in Radioactive Media Radioactive Decay Gamma- and X-Ray Interactions in Matter Charged-Particle Interactions in Matter X-Ray Production and Quality Cavity Theory Dosimetry Fundamentals Ionization Chambers Dosimetry and Calibration of Photon and Electron Beams with Cavity Ion Chambers Integrating Dosimeters Dosimetry by Pulse-Mode Detectors Neutron Interactions and Dosimetry References Appendixes Index.
Investigating the Use of BERT Anchors for Bilingual Lexicon Induction with Minimal Supervision

This paper investigates the use of static anchors 001 from transformer architectures for the task of 002 Bilingual Lexicon Induction. We revisit an ex-003 isting approach built around the ELMo archi-004 tecture and explore the use of the methodology 005 on the BERT family of language models. Ex-006 periments are performed and analysed for three 007 language pairs, combining English with three 008 target languages from very different language 009 families, Hindi, Dutch, and Russian. Although 010 the contextualised approach is not able to out-011 perform the SOTA VecMap method, we find 012 that it is easily adaptable to newer transformer 013 models and can compete with the MUSE ap-014 proach. An error analysis reveals interesting 015 trends accross languages and shows how the 016 method could be further improved by building 017 on the basic hypothesis that transformer embed-018 dings can indeed be decomposed into a static 019 anchor and a dynamic context component. We 020 make the code, the extracted anchors before 021 and after alignement and the modified train and 022 test sets available for use. 1 023
Learning About the Magnetar Swift J1834.9-0846 from its Wind Nebula

The first wind nebula around a magnetar was recently discovered in X-rays around Swift~J1834.9$-$0846. We study this magnetar's global energetics and the properties of its particle wind or outflows. At a distance of $\sim4\;$kpc, Swift~J1834.9$-$0846 is located at the center of the supernova remnant (SNR) W41 whose radius is $\sim 19\;$pc, an order of magnitude larger than that of the X-ray nebula ($\sim2\;$pc). The association with SNR W41 suggests a common age of $\sim5-100\;$kyr, while its spin-down age is $4.9$~kyr. A small natal kick velocity may partly explain why a wind nebula was detected around this magnetar but not around other magnetars, most of which appear to have larger kick velocities and may have exited their birth SNR. We find that the GeV and TeV source detected by Fermi/LAT and H.E. S.S. , respectively, of radius $\sim11\;$pc is most likely of hadronic origin. The dynamics and internal structure of the nebula are examined analytically to explain the nebula's current properties. Its size may naturally correspond to the diffusion-dominated cooling length of the X-ray emitting $e^+e^-$ pairs. This may also account for the spectral softening of the X-ray emission from the nebula's inner to outer parts. Analysis of the X-ray synchrotron nebula implies that (i) the nebular magnetic field is $\gtrsim 11\;\mu$G (and likely $\lesssim30\;\mu$G), and (ii) the nebula is not powered predominantly by the magnetar's quiescent spin-down-powered MHD wind, but by other outflows that contribute most of its energy. The latter are most likely associated with the magnetar's bursting activity, and possibly dominated by outflows associated with its past giant flares. The energy source for the required outflows cannot be the decay of the magnetar's dipole field alone, and is most likely the decay of its much stronger internal magnetic field.
Extract, Select and Rewrite: A New Modular Summarization Method

Prior works on supervised summarization are 001 mainly based on end-to-end models, leading 002 to low modularity, unfaithfulness and low in-003 terpretability. To address this, we propose a 004 new three-phase modular abstractive sentence 005 summarization method. We split up the sum-006 marization problem explicitly into three stages, 007 namely knowledge extraction, content selection 008 and rewriting. We utilize multiple knowledge 009 extractors to obtain relation triples from the 010 text, learn a fine-tuned classifier to select con-011 tent to be included in the summary and use 012 a fine-tuned BART rewriter to rewrite the se-013 lected triples into a natural language summary. 014 We find our model shows good modularity as 015 the modules can be trained separately and on 016 different datasets.The automatic and human 017 evaluations demonstrate that our new method is 018 competitive with state-of-the-art methods and 019 more faithful than end-to-end baseline models. 020 1 021
Eureka: A General Framework for Black-box Differential Privacy Estimators

Differential Privacy (DP) is a gold standard of privacy. Nonetheless, one challenge for non-privacy experts to utilize DP, is the difficulty in analyzing the privacy of their often quite complex tasks. Our work aims to address the above limitation. In a nutshell we devise a methodology for domain experts with limited knowledge of security to estimate the (differential) privacy of an arbitrary mechanism. Our Eureka moment is the utilization of a link—which we prove—between the problems of DP parameter-estimation and Bayes optimal classifiers in machine learning, which we believe can be of independent interest. Our estimator methodology uses this link to achieve two desirable properties: (1) it is black-box , i.e., does not require knowledge of the underlying mechanism, and (2) it has a theoretically-proven accuracy, which depends on the underlying classifier used. This allows domain experts to design mechanisms that they conjecture offer certain (differential) privacy guarantees—but maybe cannot prove it—and apply our method to confirm (or disprove) their conjecture. More concretely, we first prove a new impossibility result, stating that for the classical DP notion there is no black-box poly-time estimator of ( 𝜀,𝛿 ) -DP. This motivates a natural relaxation of DP, which we term relative DP . Relative DP preserves the desirable properties of DP—composition, robustness to post processing, and robustness to the disclosure of new data—and applies in most practical settings where privacy is desired. We then devise a black-box poly-time ( 𝜀,𝛿 ) -relative DP estimator—the first to support mechanisms with large output spaces while having tight accuracy bounds. As a result of independent interest, we apply this theory to develop the first approximate estimator for the standard, i.e., non-relative, definition of Distributional Differential Privacy (DDP) – aka noiseless privacy. To demonstrate both our theory and its practicality, we devise and benchmark a proof-of-concept implementation of our estimator. In reasonable execution time, our implementation reproduces tight, analytically computed 𝜀,𝛿 trade-off of Laplacian and Gaussian mechanisms—to our knowledge, the first black box estimator to do so, and for the Sparse Vector Technique, our outputs are comparable to that of a more specialized state-of-the-art ( 𝜀,𝛿 ) -DP estimator.