Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network

Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output. Moreover, most of them train a specific model for each up-scale factor. In this paper, we propose a multi-scale super resolution (MSSR) network. Our network consists of multi-scale paths to make the HR inference, which can learn to synthesize features from different scales. This property helps reconstruct various kinds of regions in HR images. In addition, only one single model is needed for multiple up-scale factors, which is more efficient without loss of restoration quality. Experiments on four public datasets demonstrate that the proposed method achieved state-of-the-art performance with fast speed

The 2021 X-ray outburst of magnetar SGR J1935+2154 -- I. Spectral properties

Over a period of multiple active episodes between January 2021 and January 2022, the magnetar SGR J1935+2154 emitted a total of 82 bursts observed by GECAM-B. Temporal and spectral analyses reveal that the bursts have an average duration of $\sim$145 ms and a fluence ranging from $1.2 \times 10^{-8} \ \mathrm{erg \cdot cm^{-2}}$to$3.7 \times 10^{-5} \ \mathrm{erg \cdot cm^{-2}}$(30 - 200 keV). The spectral properties of these bursts are similar to those of earlier active episodes. Specifically, we find that the emission area of the Double Black Body (BB2) model shows a Log-Linear correlation to its temperature, and there is a weak relation between fluence and$E_{\mathrm{peak}}$(or$\alpha$) in the Cut-Off Power Law (CPL) model. However, we note that the temperature distributions of BB2/BB models in GECAM-B samples are different from those in GBM-GECAM samples, due to differences in the energy range used for fitting. To understand this difference, we propose a Multi-Temperature Black Body (MBB) model, assuming that the BB temperatures follow a power law distribution. Our analysis shows that the minimum temperature$kT_{\mathrm{min}} \sim 5$ keV of the MBB model, which is consistent between GECAM-B and GBM-GECAM. This indicates that both samples originated from similar magnetar bursts. We also reveal the spectra of magnetar bursts tend to be soft. It indicates that magnetar bursts may be composed of multiple low BB temperatures and the majority of the BB temperatures are concentrated around the minimum temperature

Role of optical coherence tomography angiography in myopic choroidal neovascularization after intravitreal injections of Ranibizumab

AIM: To investigate the change of myopic choroidal neovascularization treated by ranibizumab and evaluate their value in monitoring the effect of anti- vascular endothelial growth factor(VEGF)therapy.METHODS: The study enrolled 30 patients(30 eyes)diagnosed with myopic choroidal neovascularization. All affected eyes were treated with intravitreal ranibizumab 0.05mL(10mg/mL). Best corrected visual acuity(BCVA), non-contact tonometer, ophthalmoscope, fundus fluorescein angiograph(FFA)and OCTA were evaluated monthly until 6mo. The changes of BCVA and central macular thickness(CMT)were compared at 1, 3 and 6mo after treatment.RESULTS: All patients received an average of 1.70±0.65 injections. BCVA was 0.96±0.17(LogMAR)before therapy, and BCVA 1, 3 and 6mo after treatment respectively improved by 0.23±0.09, 0.34±0.07, 0.38±0.11. The differences were significant(t=5.461, 8.191, 8.894; Pt=12.007, 13.360, 9.531; PCONCLUSION: Intravitreal ranibizumab for CNV secondary to pathologic myopia is effective and safe; OCTA is a noninvasive and time-saving new technology, and it also is a promising tool for clinicians to make preliminary diagnosis and assess treatment efficacy in the follow-up visits

Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

Screening Spin Lattice Interaction Using Deep Learning Approach

Atomic simulations hold significant value in clarifying crucial matters such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the magnetic moment. Nonetheless, precisely portraying the interaction energy and its impact on lattice and spin-driving forces, such as atomic force and magnetic torque, remains a formidable task in the computational domain. Consequently, there is no atomic-scale approach capable of elucidating the evolution of lattice and spin at the same time in magnetic materials. Addressing this knowledge deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with advanced deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.Comment: 8 pages, 4 figure

Magnetic-dielectric properties of NiFe2O4/PZT particulate composites

Particulate composites of lead–zirconate–titanate (PZT) and NiFe2O4 were prepared using conventional ceramic processing. The measured magnetoelectric (ME) response demonstrated strong dependence on the volume fraction of NiFe2O4 , the magnetic field, and the angle between the magnetic field and polarization in the ceramics. A large ME voltage coefficient of about 80 m V cm −1 Oe−1 was observed for 0.32NiFe2O4/0.68PZT composite ceramic. In particular, at low magnetic fields, the ceramics were found to have a large ME response, linearly varying with both dc and ac magnetic fields

Burst phase distribution of SGR J1935+2154 based on Insight-HXMT

On April 27, 2020, the soft gamma ray repeater SGR J1935+2154 entered its intense outburst episode again. Insight-HXMT carried out about one month observation of the source. A total number of 75 bursts were detected during this activity episode by Insight-HXMT, and persistent emission data were also accumulated. We report on the spin period search result and the phase distribution of burst start times and burst photon arrival times of the Insight-HXMT high energy detectors and Fermi Gamma-ray Burst Monitor (GBM). We find that the distribution of burst start times is uniform within its spin phase for both Insight-HXMT and Fermi-GBM observations, whereas the phase distribution of burst photons is related to the type of a burst's energy spectrum. The bursts with the same spectrum have different distribution characteristics in the initial and decay episodes for the activity of magnetar SGR J1935+2154.Comment: 12 pages, 9 figure

Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial

Background : By the early 1980s, tuberculosis treatment was shortened from 24 to 6 months, maintaining relapse rates of 1-2%. Subsequent trials attempting shorter durations have failed, with 4-month arms consistently having relapse rates of 15-20%. One trial shortened treatment only among those without baseline cavity on chest x-ray and whose month 2 sputum culture converted to negative. The 4-month arm relapse rate decreased to 7% but was still significantly worse than the 6-month arm (1.6%, P<0.01).  We hypothesize that PET/CT characteristics at baseline, PET/CT changes at one month, and markers of residual bacterial load will identify patients with tuberculosis who can be cured with 4 months (16 weeks) of standard treatment.Methods: This is a prospective, multicenter, randomized, phase 2b, noninferiority clinical trial of pulmonary tuberculosis participants. Those eligible start standard of care treatment. PET/CT scans are done at weeks 0, 4, and 16 or 24. Participants who do not meet early treatment completion criteria (baseline radiologic severity, radiologic response at one month, and GeneXpert-detectable bacilli at four months) are placed in Arm A (24 weeks of standard therapy). Those who meet the early treatment completion criteria are randomized at week 16 to continue treatment to week 24 (Arm B) or complete treatment at week 16 (Arm C). The primary endpoint compares the treatment success rate at 18 months between Arms B and C.Discussion: Multiple biomarkers have been assessed to predict TB treatment outcomes. This study uses PET/CT scans and GeneXpert (Xpert) cycle threshold to risk stratify participants. PET/CT scans are not applicable to global public health but could be used in clinical trials to stratify participants and possibly become a surrogate endpoint. If the Predict TB trial is successful, other immunological biomarkers or transcriptional signatures that correlate with treatment outcome may be identified. TRIAL REGISTRATION: NCT02821832