Miracle-dz/UniFAR-FTUs huggingface dataset:
How well do face masks protect the wearer compared to public perceptions?

Introduction There is a growing body of evidence to support the wearing of face masks to reduce spread of infectious respiratory pathogens, including SARS-CoV-2. However, the literature exploring the effectiveness of homemade fabric face masks is still in its infancy. Developing an evidence base is an important step to ensure that public policy is evidence based and truly effective. Methods Two methodologies were used in this study: quantitative fit testing of various face masks to indicate their effectiveness and a survey of 710 US residents about their perceptions of face mask effectiveness. N95, surgical and two fabric face masks were tested on an individual twenty five times each using a TSI 8038+ machine. Our survey was distributed by Qualtrics XM, asking participants to estimate the effectiveness of N95, surgical and fabric face masks. Results and Discussion Our results indicate that fabric face masks blocked between 62.6% and 87.1% of fine particles, whereas surgical masks protected against an average of 78.2% of fine particles. N95 masks blocked 99.6% of fine particles. Survey respondents tended to underestimate the effectiveness of masks, especially fabric masks. Together these results suggest that fabric masks may be a useful tool in the battle against the COVID-19 pandemic and that increasing public awareness of the effectiveness of fabric masks may help in this endeavour.
Newtonalized orthogonal matching pursuit for the linear frequency‐modulated pulse frequency agile radar

The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow. In this way, high range resolution profiles (HRRP) can be obtained without increasing the hardware system cost. Furthermore, the agility provides improved both robustness to jamming and spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP-FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, a postprocessing algorithm is then proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP-FAR.
A Pub-Sub Architecture to Promote Blockchain Interoperability

The maturing of blockchain technology leads to heterogeneity, where multiple solutions specialize in a particular use case. While the development of different blockchain networks shows great potential for blockchains, the isolated networks have led to data and asset silos, limiting the applications of this technology. Blockchain interoperability solutions are essential to enable distributed ledgers to reach their full potential. Such solutions allow blockchains to support asset and data transfer, resulting in the development of innovative applications. This paper proposes a novel blockchain interoperability solution for permissioned blockchains based on the publish/subscribe architecture. We implemented a prototype of this platform to show the feasibility of our design. We evaluate our solution by implementing examples of the different publisher and subscriber networks, such as Hyperledger Besu, which is an Ethereum client, and two different versions of Hyperledger Fabric. We present a performance analysis of the whole network that indicates its limits and bottlenecks. Finally, we discuss the extensibility and scalability of the platform in different scenarios. Our evaluation shows that our system can handle a throughput in the order of the hundreds of transactions per second.
First-principles study of the anomalous Hall effect based on exact muffin-tin orbitals

Based on the exact muffin-tin orbitals (EMTOs), we developed a first-principles method to calculate the current operators and investigated the anomalous Hall effect in bcc Fe as an example, with which we successfully separated the skew scattering contribution from the side jump and intrinsic contributions by fitting the scaling law with the introduction of sparse impurities. By investigating the temperature dependence of the anomalous Hall effect in bulk Fe, we predicted a fluctuated anomalous Hall angle as a function of temperature when considering only phonons, which, in the future, can be measured in experiments by suppressing magnon excitation, e.g., by applying a high external magnetic field.
The internal architecture of anaerobic sludge granules

The structural characteristics of well-formed anaerobic granular sludges were examined using scanning electron microscopy and light microscopy of thin sections. Transverse sections were found to consist of randomly orientated filaments of Methanothrix soehngenii. These were tightly packed with little space between the individual filament sheaths, revealing a ‘honeycomb’ appearance. Sections cut from wax-embedded granules were stained using a number of histochemical procedures. Detailed examination of the overall structure of the granules was possible, revealing the presence of filamentous bundles of Methanothrix surrounded by a matrix found to be carbohydrate in nature. Interpretation of serial sections through the depth of the granule allowed for the construction of a diagrammatic illustration of the internal structure. A relationship between the measured diameter of these microcolonies of Methanothrix and the distance from the centre of the granule was established. Together these results enabled a proposal to be made as to the possible mechanisms involved in the growth of granules. This supports previous suggestions relating to the involvement of Methanothrix and bacterial polymers.
Topological Interference Management With Adversarial Topology Perturbation: An Algorithmic Perspective

In this paper, we consider the topological interference management (TIM) problem in a dynamic setting, where an adversary perturbs network topology to prevent the exploitation of sophisticated coding opportunities (e.g., interference alignment). Focusing on a special class of network topology – chordal networks – we investigate algorithmic aspects of the TIM problem under adversarial topology perturbation. In particular, given the adversarial perturbation with respect to edge insertion/deletion, we propose a dynamic graph coloring algorithm that allows for a constant number of re-coloring updates against each inserted/deleted edge to achieve the information-theoretic optimality. This is a sharp reduction of the general graph re-coloring, whose optimal number of updates scales as the size of the network, thanks to the delicate exploitation of the structural properties of chordal graph classes.
Evidence of the Gaia–VLBI position differences being related to radio source structure

Context. We report the relationship between the Gaia–VLBI position differences and the magnitudes of source structure effects in VLBI observations. Aims. Because the Gaia–VLBI position differences are statistically significant for a considerable number of common sources, we discuss and attempt to explain these position differences based on VLBI observations and available source images at centimeter wavelengths. Methods. Based on the derived closure amplitude root mean square (CARMS), which quantifies the magnitudes of source structure effects in the VLBI observations used for building the third realization of the International Celestial Reference Frame, the arc lengths and normalized arc lengths of the position differences are examined in detail. The radio-jet directions and the directions of the Gaia–VLBI position differences are investigated for a small sample of sources. Results. Both the arc lengths and normalized arc lengths of the Gaia and VLBI positions are found to increase with the CARMS values. The majority of the sources with statistically significant position differences are associated with the sources having extended structure. Radio source structure is the one of the major factors of these position differences, and it can be the dominant factor for a number of sources. The vectors of the Gaia and VLBI position differences are parallel to the radio-jet directions, which is confirmed via stronger evidence.
An Information Bottleneck Problem with Rényi's Entropy

This paper considers an information bottleneck problem with the objective of obtaining a most informative representation of a hidden feature subject to a Rényi entropy complexity constraint. The optimal bottleneck trade-off between relevance (measured via Shannon's mutual information) and Rényi entropy cost is defined and an iterative algorithm for finding approximate solutions is provided. We also derive an operational characterization for the optimal trade-off by demonstrating that the optimal Rényi entropy-relevance trade-off is achievable by a simple time-sharing scalar coding scheme and that no coding scheme can provide better performance. Two examples where the optimal Shannon entropy-relevance tradeoff can be exactly determined are further given.
Regularizing double machine learning in partially linear endogenous models

We estimate the linear coefficient in a partially linear model with confounding variables. We rely on double machine learning (DML) and extend it with an additional regularization and selection scheme. We allow for more general dependence structures among the model variables than what has been investigated previously, and we prove that this DML estimator remains asymptotically Gaussian and converges at the parametric rate. The DML estimator has a two-stage least squares interpretation and may produce overly wide confidence intervals. To address this issue, we propose the regularization-selection regsDML method that leads to narrower confidence intervals. It is fully data driven and optimizes an estimated asymptotic mean squared error of the coefficient estimate. Empirical examples demonstrate our methodological and theoretical developments. Software code for our regsDML method will be made available in the R-package dmlalg.
Total Stability of SVMs and Localized SVMs

Regularized kernel-based methods such as support vector machines (SVMs) typically depend on the underlying probability measure $\mathrm{P}$ (respectively an empirical measure $\mathrm{D}_n$ in applications) as well as on the regularization parameter $\lambda$ and the kernel $k$. Whereas classical statistical robustness only considers the effect of small perturbations in $\mathrm{P}$, the present paper investigates the influence of simultaneous slight variations in the whole triple $(\mathrm{P},\lambda,k)$, respectively $(\mathrm{D}_n,\lambda_n,k)$, on the resulting predictor. Existing results from the literature are considerably generalized and improved. In order to also make them applicable to big data, where regular SVMs suffer from their super-linear computational requirements, we show how our results can be transferred to the context of localized learning. Here, the effect of slight variations in the applied regionalization, which might for example stem from changes in $\mathrm{P}$ respectively $\mathrm{D}_n$, is considered as well.
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. In this paper, we first test this hypothesis and reveal that a surprising degree of absolute position information is encoded in commonly used CNNs. We show that zero padding drives CNNs to encode position information in their internal representations, while a lack of padding precludes position encoding. This gives rise to deeper questions about the role of position information in CNNs: (i) What boundary heuristics enable optimal position encoding for downstream tasks? ; (ii) Does position encoding affect the learning of semantic representations? ; (iii) Does position encoding always improve performance? To provide answers, we perform the largest case study to date on the role that padding and border heuristics play in CNNs. We design novel tasks which allow us to quantify boundary effects as a function of the distance to the border. Numerous semantic objectives reveal the effect of the border on semantic representations. Finally, we demonstrate the implications of these findings on multiple real-world tasks to show that position information can both help or hurt performance.
Oriented Self-Assembly of Cyclic Peptide Nanotubes in Lipid Membranes

Polarized attenuated total reflectance (ATR), grazing angle reflection−absorption, and transmission Fourier transform infrared (FT-IR) spectroscopy methods have been used to investigate the orientation of peptide nanotubes in the functionally relevant environment of ordered phospholipid multibilayers. Eight-residue cyclic peptides of alternating d- and l-amino acids which self-assemble to form open-ended, tubular structures have been shown previously to function as transmembrane channels for ion transport. Although the tubular structure of the peptide assembly has been established in the pure solid state, this study presents the first detailed biophysical investigation of the peptide nanotubes within the context of the lipid film to afford a quantitative estimate of their angle of orientation relative to the lipid bilayer. We find by ATR IR that in accordance with the previous structure−function model hypothesis for a transport-competent channel, the central axis of nanotubes composed of cyclo[(l-Trp-d-Le.
A practical two-sample test for weighted random graphs

Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is to test the difference between two graph populations. Several statistical tests have been devised for this purpose in the context of binary graphs. However, many of the practical networks are weighted and existing procedures cannot be directly applied to weighted graphs. In this paper, we study the weighted graph two-sample hypothesis testing problem and propose a practical test statistic. We prove that the proposed test statistic converges in distribution to the standard normal distribution under the null hypothesis and analyze its power theoretically. The simulation study shows that the proposed test has satisfactory performance and it substantially outperforms the existing counterpart in the binary graph case. A real data application is provided to illustrate the method.
Blockchain-empowered Data-driven Networks

The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data. These paths are also hampered by significant challenges in terms of security, privacy, services provisioning, and network management. Blockchain, which is a technology for building distributed ledgers that provide an immutable log of transactions recorded in a distributed network, has become prominent recently as the underlying technology of cryptocurrencies and is revolutionizing data storage and processing in computer network systems. For future data-driven networks (DDNs), blockchain is considered as a promising solution to enable the secure storage, sharing, and analytics of data, privacy protection for users, robust, trustworthy network control, and decentralized routing and resource managements. However, many important challenges and open issues remain to be addressed before blockchain can be deployed widely to enable future DDNs. In this article, we present a survey on the existing research works on the application of blockchain technologies in computer networks and identify challenges and potential solutions in the applications of blockchains in future DDNs. We identify application scenarios in which future blockchain-empowered DDNs could improve the efficiency and security, and generally the effectiveness of network services.
Probing the Variation of the Intervalley Tunnel Coupling in a Silicon Triple Quantum Dot

Electrons confined in silicon quantum dots exhibit orbital, spin, and valley degrees of freedom. The valley degree of freedom originates from the bulk bandstructure of silicon, which has six degenerate electronic minima. The degeneracy can be lifted in silicon quantum wells due to strain and electronic confinement, but the “valley splitting” of the two lowest lying valleys is known to be sensitive to atomic-scale disorder. Large valley splittings are desirable to have a well-defined spin qubit. In addition, an understanding of the inter-valley tunnel coupling that couples different valleys in adjacent quantum dots is extremely important, as the resulting gaps in the energy level diagram may affect the fidelity of charge and spin transfer protocols in silicon quantum dot arrays. Here we use microwave spectroscopy to probe spatial variations in the valley splitting, and the intraand inter-valley tunnel couplings (tij and t ′ ij) that couple dots i and j in a triple quantum dot (TQD). We uncover large spatial variations in the ratio of inter-valley to intra-valley tunnel couplings t12/t12 = 0.90 and t ′ 23/t23 = 0.56. By tuning the interdot tunnel barrier we also show that t ′ ij scales linearly with tij , as expected from theory. The results indicate strong interactions between different valley states on neighboring dots, which we attribute to local inhomogeneities in the silicon quantum well.
Stoßaktivierungsmassenspektrometrie ‐ eine neue Sonde zur Strukturbestimmung von Ionen in der Gasphase

Treffen organische Ionen mit hoher Translationsenergie unelastisch auf neutrale Atome oder Molekule, so werden diese Ionen auf Kosten der Translationsenergie elektronisch angeregt. Die Anregungsenergie ermoglicht ein breites Spektrum von Zerfallsreaktionen, wobei das Intensitatsverhaltnis Auskunft uber die Struktur des betrachteten Ions gibt und auserdem Ruckschlusse auf den Entstehungsmechanismus gestattet.
Channel Measurement and Ray-Tracing-Statistical Hybrid Modeling for Low-Terahertz Indoor Communications

TeraHertz (THz) communications are envisioned as a promising technology, owing to its unprecedented multi-GHz bandwidth. One fundamental challenge when moving to new spectrum is to understand the science of radio propagation and develop an accurate channel model. In this paper, a wideband channel measurement campaign between 130 GHz and 143 GHz is investigated in a typical meeting room. Directional antennas are utilized and rotated for resolving the multi-path components (MPCs) in the angular domain. With careful system calibration that eliminates system errors and antenna effects, a realistic power delay profile is developed. Furthermore, a combined MPC clustering and matching procedure with ray-tracing techniques is proposed to investigate the cluster behavior and wave propagation of THz signals. In light of the measurement results, physical parameters and insights in the THz indoor channel are comprehensively analyzed, including the line-of-sight path loss, power distributions, temporal and spatial features, and correlations among THz multi-path characteristics. Finally, a hybrid channel model that combines ray-tracing and statistical methods is developed for THz indoor communications. Numerical results demonstrate that the proposed hybrid channel model shows good agreement with the measurement and outperforms the conventional statistical and geometric-based stochastic channel model in terms of the temporal-spatial characteristics.
Outflows, Shocks, and Coronal Line Emission in a Radio-selected AGN in a Dwarf Galaxy

Massive black holes (BHs) in dwarf galaxies can provide strong constraints on BH seeds; however, reliably detecting them is notoriously difficult. High-resolution radio observations were recently used to identify accreting massive BHs in nearby dwarf galaxies, with a significant fraction found to be non-nuclear. Here we present the first results of our optical follow-up of these radio-selected active galactic nuclei (AGNs) in dwarf galaxies using integral field unit (IFU) data from Gemini-North. We focus on the dwarf galaxy J1220+3020, which shows no clear optical AGN signatures in its nuclear Sloan Digital Sky Survey spectrum covering the radio source. With our new IFU data, we confirm the presence of an active BH via the AGN coronal line [Fe x] and enhanced [O i] emission coincident with the radio source. Furthermore, we detect broad Hα emission and estimate a BH mass of M BH = 104.9 M ⊙. We compare the narrow emission line ratios to standard BPT diagnostics and shock models. Spatially resolved BPT diagrams show some AGN signatures, particularly in [O i]/Hα, but overall do not unambiguously identify the AGN. A comparison of our data to shock models clearly indicates shocked emission surrounding the AGN. The physical model most consistent with the data is an active BH with a radiatively inefficient accretion flow that both photoionizes and shock-excites the surrounding gas. We conclude that feedback is important in radio-selected BHs in dwarf galaxies and that radio surveys may probe a population of low accretion rate BHs in dwarf galaxies that cannot be detected through optical surveys alone.
Simple prediction of immiscible metal alloying based on metastability analysis

It has been known that even though two elemental metals, $X$ and $Y$, are immiscible, they can form alloys on surfaces of other metal $Z$. In order to understand such surface alloying of immiscible metals, we study the energetic stability of binary alloys, $XZ$ and $YZ$, in several structures with various coordination numbers (CNs). By analyzing the formation energy modified to enhance the subtle energy difference between metastable structures, we find that $XZ$ and $YZ$ with B2-type structure (CN$=$8) become energetically stable when the $X$ and $Y$ metals form an alloy on the $Z$ metal surface. This is consistent with the experimental results for Pb-Sn alloys on metal surfaces such as Rh(111) and Ru(0001). Some suitable metal substrates are also predicted to form Pb-Sn alloys.
Lightning Generation in Moist Convective Clouds and Constraints on the Water Abundance in Jupiter

Recent Juno observations have greatly extended the temporal and spatial coverage of lightning detection on Jupiter. We use these data to constrain a model of moist convection and lightning generation in Jupiter's atmosphere, and derive a roughly solar abundance of water at the base of the water cloud. Shallow lightning, observed by Juno (Becker et al., 2020, https://doi.org/10.1038/s41586‐020‐2532‐1, Nature, 584, 55–58) and defined as flashes originating at altitudes corresponding to pressure less than 2 bars, is reproduced, as is lightning at a deeper range of pressures, including those below the water cloud base. It is found that the generation of lightning requires ammonia to stabilize liquid water at altitudes corresponding to sub‐freezing temperatures. We find a range of local water abundances in which lightning is possible, including subsolar values of water—consistent with other determinations of deep oxygen abundance.