Miracle-dz/UniFAR-FTUs huggingface dataset:
Do authors of research funded by the Canadian Institutes of Health Research comply with its open access mandate?: A meta-epidemiologic study

Background Since 2008, the Canadian Institutes of Health Research (CIHR) has mandated that studies it funds either in whole or in part are required to publish their results as open access (OA) within 12 months of publication using either online repositories and/or OA journals. Yet, there is evidence that authors are poorly compliant with this mandate. Specifically, there has been an apparent decrease in OA publication after 2015, which coincides with a change in the OA policy during the same year. One particular policy change that may have contributed to this decline was lifting the requirement that authors deposit their article in an OA repository immediately upon publication. We investigated the proportion of OA compliance of CIHR-funded studies in the period before and after the policy change of 2015 with manual confirmation of both CIHR funding and OA status. Methods and findings We identified CIHR-funded studies published between the years 2014 to 2017 using a comprehensive search in the Web of Science (WoS). We took a stratified random sample from all four years (i.e. 2014 to 2017), with 250 studies from each year. Two authors independently reviewed the final full-text publications retrieved from the journal web page to determine to confirm CIHR funding, as indicated in the acknowledgements or elsewhere in the paper. For each study, we also collected bibliometric data that included citation count and Altmetric attention score Statistical analyses were conducted using two-tailed Fisher’s exact test with relative risk (RR). Among the 851 receiving CIHR funding published from 2014 to 2017, the percentage of CIHR-funded studies published as OA significantly decreased from 79.6% in 2014 to 70.3% in 2017 (RR = 0.88, 95% CI: 0.79–0.99, P = 0.028). When considering all four years, there was no significant difference in the percentage of CIHR-funded studies published as OA in both 2014 and 2015 compared to both 2016 and 2017 (RR = 0.97, 95% CI: 0.90–1.05, P = 0.493). Additionally, OA publications had significantly higher citation count (both in year of publication and in total) and higher attention scores (P<0.05). Conclusions Overall, we found that there was a significant decrease in the proportion of CIHR funded studies published as OA from 2014 compared to 2017, though this difference did not persist when comparing both 2014–2015 to 2016–2017. The primary limitation was the reliance of self-reported data from authors on CIHR funding status. We posit that this decrease may be attributable to CIHR’s OA policy change in 2015. Further exploration is warranted to both validate these studies using a larger dataset and, if valid, investigate the effects of potential interventions to improve the OA compliance, such as use of a CIHR publication database, and reinstatement of a policy for authors to immediately submit their findings to OA repositories upon publication.
Security Application of Encryption Technology in Network Communication

With the development and maturity of science and technology and the arrival of the Internet era, instant messaging software has become an indispensable communication method in people’s life due to its comprehensive functions, high performance and high stability. However, due to the open, interactive and global nature of the Internet, there are a series of security problems that cannot be ignored, such as information leakage and virus transmission. Information security is particularly important to us and directly affects whether our personal interests can be effectively protected. Therefore, strengthening information protection and avoiding information leakage are important issues that need to be solved urgently. This paper designs a system based on hybrid encryption by using symmetric encryption original AES and improved symmetric encryption AES to ensure the efficiency of people in the process of information transmission and communication.
Siderite Dissolution in Mars-analog Brines: Kinetics and Reaction Products

This study examines siderite (FeCO3) reactivity in MgCl2 and MgSO4 brines with varying salt concentrations (0.01M, 1M, and 3M) at both acidic (pH ∼ 2 and pH ≤ 2) and near-neutral (pH ∼ 7) conditions. We measured aqueous Fe concentrations through time to determine dissolution rates and characterized the solid reaction products with scanning electron microscopy, electron dispersive X-ray spectroscopy, and Raman spectroscopy. Iron-based siderite dissolution rates at pH 2 were equivalent in the 0.01M and 1M MgSO4 brines and slower in 3M MgSO4; rates in the MgCl2 brines slow systematically with increasing brine concentration for equivalent initial pH values. Fe-based dissolution rates could not be determined in the neutral pH experiments due to precipitation of iron (hydr)oxide phases. After 1 day in acidic brines, abundant etch pits were observed; however, in the neutral experiments, siderite was identified with Raman spectroscopy even after 1 yr of dissolution along with a range of iron (hydr)oxide phases. Scanning electron microscopy imaging of the neutral experiment products found Mg-sulfate brines produced a chaotic surface texture. Therefore, micron-scale textural observations could be used to discriminate between alteration in chloride and sulfate brines. Initial iron release rates were similar in dilute brines, but decreased by less than an order of magnitude in the two highest-concentration pH 2 brine experiments; therefore, siderite-bearing assemblages exposed to acidic fluids, regardless of salinity, would likely dissolve completely over geologically short periods of time, thus erasing siderite and likely other carbonate minerals from the geologic record.
Design and Implementation of a Wireless Iidentification Device

This system takes AT89 series single-chip microcomputer as the core, which constitutes the two ends of the wireless identification device: transponder and reader. The whole system is mainly composed of the smallest single-chip microcomputer system, binary code preset circuit, carrier generation circuit, analog switch control circuit, high-frequency amplifying filter circuit, display circuit and other modules. The transponder part uses the AT89S51 single-chip microcomputer to control the serial output of the preset binary code, uses the active crystal oscillator to generate the carrier wave, and outputs the modulated signal through the analog switch. The reader part amplifies the modulated signal with high frequency, filters the signal, and controls the light-emitting diode to turn on and off to display the identification signal and the four-bit binary code after the acquisition and processing by the single-chip microcomputer. This design realizes the functions of identification, binary code preset and display, single power supply, transmission distance greater than 5cm, time less than 5s, accuracy rate greater than 80%, power less than 2W, transponder storage and other functions.
A note on the pp-wave solution of minimal massive 3D gravity coupled with Maxwell–Chern–Simons theory

In this work, we examine a family of pp-wave solutions of minimal massive 3D gravity minimally coupled with the Maxwell–Chern–Simons theory. An elaborate investigation of the field equations shows that the theory admits pp-wave solutions provided that there exist an anti-self duality relation between the electric and the magnetic components of the Maxwell two-form field. By employing Noether–Wald formalism, we also construct Noether charges of the theory within exterior algebra formalism.
Identification and characterization of VapBC toxin–antitoxin system in Bosea sp. PAMC 26642 isolated from Arctic lichens

Toxin–antitoxin (TA) systems are genetic modules composed of a toxin interfering with cellular processes and its cognate antitoxin, which counteracts the activity of the toxin. TA modules are widespread in bacterial and archaeal genomes. It has been suggested that TA modules participate in the adaptation of prokaryotes to unfavorable conditions. The Bosea sp. PAMC 26642 used in this study was isolated from the Arctic lichen Stereocaulon sp. There are 12 putative type II TA loci in the genome of Bosea sp. PAMC 26642. Of these, nine functional TA systems have been shown to be toxic in Escherichia coli. The toxin inhibits growth, but this inhibition is reversed when the cognate antitoxin genes are coexpressed, indicating that these putative TA loci were bona fide TA modules. Only the BoVapC1 (AXW83_01405) toxin, a homolog of VapC, showed growth inhibition specific to low temperatures, which was recovered by the coexpression of BoVapB1 (AXW83_01400). Microscopic observation and growth monitoring revealed that the BoVapC1 toxin had bacteriostatic effects on the growth of E. coli and induced morphological changes. Quantitative real time polymerase chain reaction and northern blotting analyses showed that the BoVapC1 toxin had a ribonuclease activity on the initiator tRNAfMet, implying that degradation of tRNAfMet might trigger growth arrest in E. coli. Furthermore, the BoVapBC1 system was found to contribute to survival against prolonged exposure at 4°C. This is the first study to identify the function of TA systems in cold adaptation.
Detecting dark matter subhalos with the Nancy Grace Roman Space Telescope

The dark matter subhalo mass function is a promising way of distinguishing between dark matter models. While cold dark matter predicts halos down to Earth-sized masses, other dark matter models typically predict a cut-off in the subhalo mass function. Thus a definitive detection or limits on the existence of subhalos at small masses can give us insight into the nature of dark matter. If these subhalos exist in the Milky Way, they would produce weak lensing signatures, such as modified apparent positions, on background stars. These signatures would generate correlations in the apparent velocities and accelerations of these stars, which could be observable given sufficient astrometric resolution and cadence. The Nancy Grace Roman Space Telescope's Exoplanet Microlensing Survey will be perfectly suited to measuring the acceleration signatures of these halos. Here we forward model these acceleration signatures and explore the Roman Space Telescope's future constraints on lens profiles and populations. We find that the Roman Space Telescope could place competitive bounds on point source, Gaussian, and Navarro-Frenk-White (NFW) profile lenses that are complementary to other proposed methods. In particular, it could place 95% upper limits on the NFW concentration, $c_{200}<10^{2.5}$. We discuss possible systematic effects that could hinder these efforts, but argue they should not prevent the Roman Space Telescope from placing strong limits. We also discuss further analysis methods for improving these constraints.
AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer

Accompaniment arrangement is a difficult music generation task involving intertwined constraints of melody, harmony, texture, and music structure. Existing models are not yet able to capture all these constraints effectively, especially for long-term music generation. To address this problem, we propose AccoMontage, an accompaniment arrangement system for whole pieces of music through unifying phrase selection and neural style transfer. We focus on generating piano accompaniments for folk/pop songs based on a lead sheet (i.e., melody with chord progression). Specifically, AccoMontage first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure learning-based approaches, AccoMontage introduces a novel hybrid pathway, in which rule-based optimization and deep learning are both leveraged to complement each other for high-quality generation. Experiments show that our model generates well-structured accompaniment with delicate texture, significantly outperforming the baselines.
Molecular Line Observations in Two Dusty Star-forming Galaxies at z = 6.9

SPT0311-58 is the most massive infrared luminous system discovered so far during the Epoch of Reionization (EoR). In this paper, we present a detailed analysis of the molecular interstellar medium at z = 6.9, through high resolution observations of the CO(6–5), CO(7–6), CO(10–9), [C i](2–1), and p‐H2O(21,1 −20,2) lines and dust continuum emissions with the Atacama Large Millimeter/submillimeter Array. The system consists of a pair of intensely star-forming, gravitationally lensed galaxies (labeled West and East). The intrinsic far-infrared luminosity is (16 ± 4) × 1012 L ⊙ in West and (27 ± 4) × 1011 L ⊙ in East. We model the dust, CO, and [C i] using non-local thermodynamic equilibrium radiative transfer models and estimate the intrinsic gas mass to be (5.4 ± 3.4) × 1011 M ⊙ in West and (3.1 ± 2.7) × 1010 M ⊙ in East. We find that the CO spectral line energy distribution in West and East are typical of high-redshift submillimeter galaxies (SMGs). The CO-to-H2 conversion factor (α CO) and the gas depletion timescales estimated from the model are consistent with the high-redshift SMGs in the literature within the uncertainties. We find no evidence of evolution of depletion time with redshift in SMGs at z > 3. This is the most detailed study of molecular gas content of a galaxy in the EoR to date, with the most distant detection of H2O in a galaxy without any evidence for active galactic nuclei in the literature.
Optimizing the classification ability of CNN for SAR fully polarized radar data based on DCGAN

In recent years, the deep learning network is widely used. In the field of remote sensing images, due to the high cost of image acquisition, there are still too few training samples, which greatly limits the application of deep learning in SAR data classification. This paper proposes a method that is generating simulated SAR image by generative adversarial network, and uses the image as the training data of convolutional neural network. Aiming at the impact of the simulated images generated by DCGAN’s generator on the classification of convolutional neural networks. The results show that DCGAN can fully extract the main features of the image, and the convolution model based on DCGAN can make CNN have better classification ability and get rid of the dependence on the sample size. CNN can also make full use of simulation data. Whether it is test data set or random dataset, its F1 score can obviously surpass the classification ability without DCGAN’s simulated data. In experiments with different sample numbers, the highest F1 score is 93.6479 in the dataset with DCGAN’s simulated data. In another experiment, its F1 Score reached 87.32, higher than the dataset without DCGAN’s simulated data.
SwitchX: Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such a manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar-mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of-the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 and TinyImagenet) and use Fast Gradient Sign Method (ε = 0.05 to 0.3) and Projected Gradient Descent ( \(\epsilon =\frac{2}{255}\) to \(\frac{32}{255},~\alpha =\frac{2}{255}\) ) adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ∼35% improvements in clean accuracy and ∼6–16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, which is furthered with state-aware training. We obtain upto ∼21–22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16 × 16 and 32 × 32 crossbars using the CIFAR-10 and CIFAR-100 datasets.
Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs

Objective With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.
Computational Study of Crystallization Kinetics of Phase Change Materials

Thanks to their outstanding physical properties, phase-change materials (PCM) are considered as one of the most promising active switching materials for future, nonvolatile memory applications. As in modern Flash memories, a phase-change random access memory (PCRAM) is able to retain information without external power supply, but at the same time grants considerably faster read and write speeds than flash memories. In such a device, information is stored by utilizing the enormous contrast in the electrical resistance between the amorphous and the crystalline phase of PCM’s. Moreover fast and reversible transitions between these phases at elevated temperatures and very high stability at room temperature at the same time make them ideally suited for data storage applications in general, e.g. in optical data storage such as rewritable CD and DVD. The speed for the write operation is mainly controlled by the crystallization rate from the amorphous to the crystalline phase. The experimental investigation of this aspect is however extremely challenging because of the ultra short crystallization times of PCM’s at high temperatures. Computer simulations provide an alternative route to study the crystallization in these materials and become increasingly popular in recent years. In this thesis, both the crystallization kinetics as well as the structural properties of prototypical PCM’s are studied by a combination of ab initio molecular dynamics (AIMD), based on density functional theory (DFT) and metadynamics (MTD). MTD is a method to enhance the sampling of molecular dynamics (MD) simulations. After a brief review on PCM’s and providing the theoretical framework of this thesis as well as the computational details, the crystallization kinetics of Ag4In3Sb67Te26 (AIST) and Ge2Sb2Te5 (GST) is discussed. The former alloy is a growth-dominated PCM and was recently studied experimentally. Employing large models of AIST with planar interfaces, which correspond to the crystalline rim surrounding an amorphous bit, we show that our AIMD simulations of crystallization yield results, which agree very well with the experimental results, and reveal the corresponding microscopic growth kinetics. A quenching rate effect on the dynamic properties of our AIST models is found at low temperatures. The other compound (GST) is a nucleation-dominated
On the matrix version of new extended Gauss, Appell and Lauricella hypergeometric functions

Inspired by certain interesting recent extension of the gamma, beta and hypergeometric matrix functions, we introduce here new extension of the gamma and beta matrix function. We also introduce new extensions of the Gauss hypergeometric matrix function, confluent hypergeometric matrix function, Appell matrix function and Lauricella matrix function of three variables in terms of the new extended beta matrix function. Then we investigate certain properties of these extended matrix functions such as the integral representations, differential formulae and recurrence relations.
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised end-to-end network for learning shapes from point clouds and discovering the underlying constituent modeling primitives and operations as well. At the core is a three-level structure called CSG-Stump, consisting of a complement layer at the bottom, an intersection layer in the middle, and a union layer at the top. CSG-Stump is proven to be equivalent to CSG in terms of representation, therefore inheriting the interpretable, compact and editable nature of CSG while freeing from CSG’s complex tree structures. Particularly, the CSG-Stump has a simple and regular structure, allowing neural networks to give outputs of a constant dimensionality, which makes itself deep-learning friendly. Due to these characteristics of CSG-Stump, CSG-Stump Net achieves superior results compared to previous CSG-based methods and generates much more appealing shapes, as confirmed by extensive experiments.