Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing the superconducting gap structure in the noncentrosymmetric topological superconductor ZrRuAs

The superconducting gap structure of a topological crystalline insulator (TCI) candidate ZrRuAs ($T^{\rm on}_{\rm c}$ = 7.9(1) K) with a noncentrosymmetric crystal structure has been investigated using muon spin rotation/relaxation ($\mu$SR) measurements in transverse-field (TF) and zero-field (ZF) geometries. We also present the results of magnetization, electrical resistivity and heat capacity measurements on ZrRuAs, which reveal bulk superconductivity below 7.9~K. The temperature dependence of the effective penetration depth obtained from the analysis of the TF-$\mu$SR spectra below $T_{\rm c}$ is well described by an isotropic $s$-wave gap model as also inferred from an analysis of the heat capacity in the superconducting state. ZF $\mu$SR data do not show any significant change in the muon spin relaxation rate above and below the superconducting transition temperature indicating that time-reversal symmetry is preserved in the superconducting state of this material.
Zero-Cost Proxies for Lightweight NAS

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluating each model instead of full training. In this paper, we evaluate conventional reduced-training proxies and quantify how well they preserve ranking between multiple models during search when compared with the rankings produced by final trained accuracy. We propose a series of zero-cost proxies, based on recent pruning literature, that use just a single minibatch of training data to compute a model's score. Our zero-cost proxies use 3 orders of magnitude less computation but can match and even outperform conventional proxies. For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0.82, compared to 0.61 for EcoNAS (a recently proposed reduced-training proxy). Finally, we use these zero-cost proxies to enhance existing NAS search algorithms such as random search, reinforcement learning, evolutionary search and predictor-based search. For all search methodologies and across three different NAS datasets, we are able to significantly improve sample efficiency, and thereby decrease computation, by using our zero-cost proxies. For example on NAS-Bench-101, we achieved the same accuracy 4× quicker than the best previous result.
Mineralogical, Chemical, and Leaching Characteristics of Coal Combustion Bottom Ash from a Power Plant Located in Northern Poland

The present research is focused on chemical and mineralogical characterization of bottom ashes produced during coal combustion in a power plant operating in northern Poland. On the basis of these results we estimate a potential environmental hazard and try to find a possible application for studied materials. Bulk chemical composition of all studied bottom ash samples is homogenous and SiO2 and Al2O3 are the major components of samples and together with Fe2O3 represent more than 70 wt. %. All ashes were thus classified as type F ashes. CaO and SO3 concentrations are relatively low and reach up to 3.1 and 0.32 wt. %, respectively. The total concentrations of trace elements in the studied bottom ash samples are generally lower than those reported for the coal from different countries. The highest concentration of trace elements was noted for Co and Pb (up to 84 and 76 mg·kg -1 , respectively), and the lowest for Hg and Se (up to 0.2 and 2 mg·kg -1 ). Studied samples of bottom ash are mostly composed of mullite and quartz with some minor amounts of hematite and traces of calcite. Two types of leaching experiments (using distilled water and 0.05 mol/L of EDTA solution) show that the mobility of select inorganic pollutants (Co, Cu, As, Ni, Zn, and Pb) is relatively low and together with the results of total element concentration in bottom ash indicate that the potential risk associated with the release of trace elements from studied wastes is negligible. Therefore, studied wastes could be successfully used as a secondary raw material.
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery

Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of bits. Furthermore, PBFA can be conducted at run time on the weights stored in DRAM main memory. In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA. We organize weights that are interspersed in a layer into groups and employ a checksum-based algorithm on weights to derive a 2-bit signature for each group. At run time, the 2-bit signature is computed and compared with the securely stored golden signature to detect the bit-flip attacks in a group. After successful detection, we zero out all the weights in a group to mitigate the accuracy drop caused by malicious bit-flips. The proposed scheme is embedded in the inference computation stage. For the ResNet-18 ImageNet model, our method can detect 9.6 bit-flips out of 10 on average. For this model, the proposed accuracy recovery scheme can restore the accuracy from below 1% caused by 10 bit flips to above 69%. The proposed method has extremely low time and storage overhead. System-level simulation on gem5 shows that RADAR only adds < 1% to the inference time, making this scheme highly suitable for run-time attack detection and mitigation.
ALMA Observations of Massive Clouds in the Central Molecular Zone: Ubiquitous Protostellar Outflows

We observe 1.3 mm spectral lines at 2000 au resolution toward four massive molecular clouds in the Central Molecular Zone (CMZ) of the Galaxy to investigate their star formation activities. We focus on several potential shock tracers that are usually abundant in protostellar outflows, including SiO, SO, CH3OH, H2CO, HC3N, and HNCO. We identify 43 protostellar outflows, including 37 highly likely ones and 6 candidates. The outflows are found toward both known high-mass star-forming cores and less massive, seemingly quiescent cores, while 791 out of the 834 cores identified based on the continuum do not have detected outflows. The outflow masses range from less than 1 M ⊙ to a few tens of M ⊙, with typical uncertainties of a factor of 70. We do not find evidence of disagreement between relative molecular abundances in these outflows and in nearby analogs such as the well-studied L1157 and NGC 7538S outflows. The results suggest that (i) protostellar accretion disks driving outflows ubiquitously exist in the CMZ environment, (ii) the large fraction of candidate starless cores is expected if these clouds are at very early evolutionary phases, with a caveat on the potential incompleteness of the outflows, (iii) high-mass and low-mass star formation is ongoing simultaneously in these clouds, and (iv) current data do not show evidence of a difference between the shock chemistry in the outflows that determines the molecular abundances in the CMZ environment and in nearby clouds.
Directed Acyclic Graph Neural Networks

Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most common GNN architecture aggregates information from neighborhoods based on message passing. Its generality has made it broadly applicable. In this paper, we focus on a special, yet widely used, type of graphs -- DAGs -- and inject a stronger inductive bias -- partial ordering -- into the neural network design. We propose the \emph{directed acyclic graph neural network}, DAGNN, an architecture that processes information according to the flow defined by the partial order. DAGNN can be considered a framework that entails earlier works as special cases (e.g., models for trees and models updating node representations recurrently), but we identify several crucial components that prior architectures lack. We perform comprehensive experiments, including ablation studies, on representative DAG datasets (i.e., source code, neural architectures, and probabilistic graphical models) and demonstrate the superiority of DAGNN over simpler DAG architectures as well as general graph architectures.
The Gas Content and Stripping of Local Group Dwarf Galaxies

The gas content of the complete compilation of Local Group dwarf galaxies (119 within 2 Mpc) is presented using H i survey data. Within the virial radius of the Milky Way (224 kpc here), 53 of 55 dwarf galaxies are devoid of gas to limits of M H i < 104 M ⊙. Within the virial radius of M31 (266 kpc), 27 of 30 dwarf galaxies are devoid of gas (with limits typically <105 M ⊙). Beyond the virial radii of the Milky Way and M31, the majority of the dwarf galaxies have detected H i gas and H i masses higher than the limits. When the relationship between gas content and distance is investigated using a Local Group virial radius, more of the nondetected dwarf galaxies are within this radius (85 ± 1 of the 93 nondetected dwarf galaxies) than within the virial radii of the Milky Way and M31. Using the Gaia proper-motion measurements available for 38 dwarf galaxies, the minimum gas density required to completely strip them of gas is calculated. Halo densities between 10−5 and 5 × 10−4 cm−3 are typically required for instantaneous stripping at perigalacticon. When compared to halo density with radius expectations from simulations and observations, 80% of the dwarf galaxies with proper motions are consistent with being stripped by ram pressure at Milky Way pericenter. The results suggest that a diffuse gaseous galactic halo medium is important in quenching dwarf galaxies, and that a Local Group medium also potentially plays a role.
Magnetic properties of alternating Hubbard ladders

We investigate the Hubbard Hamiltonian on ladders where the number of sites per rung alternates between two and three. These geometries are bipartite with nonequal or equal number of sites on the two sublattices. Thus they share a key feature of the Hubbard model in a class of lattices which Lieb has shown analytically to exhibit long-range ferrimagnetic order while being amenable to powerful numeric approaches developed for quasi-one-dimensional geometries. The density matrix renormalization group (DMRG) method is used to obtain the groundstate properties, e.g., excitation gaps, charge and spin densities as well as their correlation functions at half filling. We show the existence of long-range ferrimagnetic order in the one-dimensional ladder geometries. Our work provides detailed quantitative results which complement the general theorem of Lieb for generalized bipartite lattices. It also addresses the issue of how the alternation between quasi-long-range order and spin liquid behavior for uniform ladders with odd and even numbers of legs might be affected by a regular alternation pattern.
CONCENTRATION INEQUALITIES FOR RANDOM WALKS ON PROPER HYPERBOLIC SPACES

We prove Hoeffding-type concentration inequalities around the drift for the displacement of non-elementary random walks on proper hyperbolic spaces. The bounds depend on the size of support of the measure as in the classical case of sums of independent random variables, but also on the norm of the driving probability measure in the left regular representation of the group of isometries. We deduce uniform bounds in the case of hyperbolic groups and effective bounds for simple linear groups of rank-one. These results follow from a more general, but less explicit, concentration statement that we prove for cocycles which satisfy a certain cohomological equation. Finally, we also deduce subgaussian concentration bounds around the top Lyapunov exponent of random matrix products in arbitrary dimension.
Few-shot Action Recognition with Prototype-centered Attentive Learning

Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode, the few samples in a meta-training task are split into support and query sets. The former is used to build a classifier, which is then evaluated on the latter using a query-centered loss for model updating. There are however two major limitations: lack of data efficiency due to the query-centered only loss design and inability to deal with the support set outlying samples and inter-class distribution overlapping problems. In this paper, we overcome both limitations by proposing a new Prototype-centered Attentive Learning (PAL) model composed of two novel components. First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective, in order to make full use of the limited training samples in each episode. Second, PAL further integrates a hybrid attentive learning mechanism that can minimize the negative impacts of outliers and promote class separation. Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
A Census of the Extended Neutral Hydrogen around 18 MHONGOOSE Galaxies

We present the analysis of the diffuse, low column density H i environment of 18 MHONGOOSE galaxies. We obtained deep observations with the Robert C. Byrd Green Bank Telescope and reached down to a 3σ column density detection limit of N HI = 6.3 × 1017 cm−2 over a 20 km s−1 line width. We analyze the environment around these galaxies, with a focus on H i gas that reaches column densities below N HI = 1019 cm−2. We calculate the total amount of H i gas in and around the galaxies, revealing that nearly all of these galaxies contained excess H i outside of their disks. We quantify the amount of diffuse gas in the maps of each galaxy, defined by H i gas with column densities below 1019 cm−2, and find a large spread in percentages of diffuse gas. However, by binning the percentage of diffuse H i into quarters, we find that the bin with the largest number of galaxies is the lowest quartile (0%–25% diffuse H i). We identified several galaxies that may be undergoing gas accretion onto the galaxy disk using multiple methods of analysis, including azimuthally averaging column densities beyond the disk, and identifying structure within our integrated intensity (moment 0) maps. We measured H i mass outside the disks of most of our galaxies, with rising cumulative flux even at large radii. We also find a strong correlation between the fraction of diffuse gas in a galaxy and its baryonic mass, and we test this correlation using both Spearman and Pearson correlation coefficients. We see evidence of a dark matter halo mass threshold of M halo ∼ 1011.1 M ☉ in which galaxies with high fractions of diffuse H i all reside below. It is in this regime that cold-mode accretion should dominate. Finally, we suggest a rotation velocity of v rot ∼ 80 km s−1 as an upper threshold to find diffuse-gas-dominated galaxies.
Semantic Disentangling Generalized Zero-Shot Learning

—Generalized Zero-Shot Learning (GZSL) aims to recognize images from both seen and unseen categories. Most GZSL methods typically learn to synthesize CNN visual features for the unseen classes by leveraging entire semantic information, e.g., tags and attributes, and the visual features of the seen classes. Within the visual features, we deﬁne two types of features that semantic-consistent and semantic-unrelated to represent the characteristics of images annotated in attributes and less informative features of images respectively. Ideally, the semantic-unrelated information is impossible to transfer by semantic-visual relationship from seen classes to unseen classes, as the corresponding characteristics are not annotated in the semantic information. Thus, the foundation of the visual feature synthesis is not always solid as the features of the seen classes may involve semantic-unrelated information that could interfere with the alignment between semantic and visual modalities. To address this issue, in this paper, we propose a novel feature disentangling approach based on an encoder-decoder architecture to factorize visual features of images into these two latent feature spaces to extract corresponding representations. Furthermore, a relation module is incorporated into this architecture to learn semantic-visual relationship, whilst a total correlation penalty is applied to encourage the disentanglement of two latent representations. The proposed model aims to distill quality semantic-consistent representations that capture intrinsic features of seen images, which are further taken as the generation target for unseen classes. Extensive experiments conducted on seven GZSL benchmark datasets have veriﬁed the state-of-the-art performance of the proposal.
The Peculiar Transient AT2018cow: A Possible Origin of a Type Ibn/IIn Supernova

We present our photometric and spectroscopic observations of the peculiar transient AT2018cow. The multiband photometry covers from peak to ∼70 days, and the spectroscopy ranges from 5 to ∼50 days. The rapid rise (t r ≲ 2.9 days), high luminosity (M V,peak ∼ −20.8 mag), and fast decline after peak make AT2018cow stand out from any other optical transients, whereas we find that its light curves show a high resemblance to those of Type Ibn supernovae. Moreover, the spectral energy distribution remains at a high temperature of ∼14,000 K at t > 15 days after discovery. The spectra are featureless in the first 10 days, while some broad emission lines due to H, He, C, and O emerge later, with velocity declining from ∼14,000 to ∼3000 km s−1 at the end of our observations. Narrow and weak He I emission lines emerge in the spectra at t > 20 days after discovery. These emission lines are reminiscent of the features seen in interacting supernovae like the Type Ibn and IIn subclasses. We fit the bolometric light curves with a model of circumstellar interaction and radioactive decay of 56Ni and find a good fit with ejecta mass M ej ∼ 3.16 M ⊙, circumstellar medium (CSM) mass M CSM ∼ 0.04 M ⊙, and ejected 56Ni mass M ⊙. The CSM shell might be formed in an eruptive mass ejection of the progenitor star. Furthermore, the host environment of AT2018cow implies a connection of AT2018cow with massive stars. Combining observational properties and the light-curve fitting results, we conclude that AT2018cow might be a peculiar interacting supernova that originated from a massive star.
Direct evidence of ferromagnetism in 
MnSb2Te4

The compound MnSb${}_{2}$Te${}_{4}$ is a close relative of the intrinsic antiferromagnetic topological insulator MnBi${}_{2}$Te${}_{4}$. Density functional theory predicts that a Weyl semimetal phase exists in ferromagnetic MnBi${}_{2}$Te${}_{4}$. Here, the authors fill in gaps in the magnetic imaging of MnSb${}_{2}$Te${}_{4}$ and confirm long-range ferromagnetic order in single crystals of ferromagnetic MnSb${}_{2}$Te${}_{4}$ using cryogenic magnetic force microscopy. The direct evidence of ferromagnetism in MnSb${}_{2}$Te${}_{4}$ paves the way for realizing the ferromagnetic Weyl semimetal phase in the family of Mn(Bi, Sb)${}_{2}$Te${}_{4}$.
Policy choices can help keep 4G and 5G universal broadband affordable

The United Nations Broadband Commission has committed the international community to accelerate universal broadband. However, the cost of meeting this objective, and the feasibility of doing so on a commercially viable basis, are not well understood. Using scenario analysis, this paper compares the global cost-effectiveness of different infrastructure strategies for the developing world to achieve universal 4G or 5G mobile broadband. Utilizing remote sensing and demand forecasting, least-cost network designs are developed for eight representative low and middle-income countries (Malawi, Uganda, Kenya, Senegal, Pakistan, Albania, Peru and Mexico), the results from which form the basis for aggregation to the global level. The cost of meeting a minimum 10 Mbps per user is estimated at USD 1.7 trillion using 5G Non-Standalone, approximately 0.6% of annual GDP for the developing world over the next decade. However, by creating a favorable regulatory environment, governments can bring down these costs by as much as three quarters, to USD 0.5 trillion (approximately 0.2% of annual GDP), and avoid the need for public subsidy. Providing governments make judicious choices, adopting fiscal and regulatory regimes conducive to lowering costs, universal broadband may be within reach of most developing countries over the next decade.
A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding

Intent classification, to identify the speaker’s intention, and slot filling, to label each token with a semantic type, are critical tasks in natural language understanding. Traditionally the two tasks have been addressed independently. More recently joint models that address the two tasks together have achieved state-of-the-art performance for each task and have shown there exists a strong relationship between the two. In this survey, we bring the coverage of methods up to 2021 including the many applications of deep learning in the field. As well as a technological survey, we look at issues addressed in the joint task and the approaches designed to address these issues. We cover datasets, evaluation metrics, and experiment design and supply a summary of reported performance on the standard datasets.
On the Parametrization and Statistics of Propagation Graphs

Propagation graphs (PGs) serve as a frequency-selective, spatially consistent channel model suitable for fast channel simulations in a scattering environment. So far, however, the parametrization of the model, and its consequences, have received little attention. In this contribution, we propose a new parametrization for PGs that adheres to the doubly exponentially decaying cluster structure of the Saleh-Valenzuela (SV) model. We show how to compute the newly proposed internal model parameters based on an approximation of the K-factor and the two decay rates from the SV model. Furthermore, via the singular values of multiple-input multiple-output (MIMO) channels, we compare the degrees of freedom (DoF) between our new and another frequently used parametrization. Specifically, we compare the DoF loss when the distance between antennas within the transmitter and receiver arrays or the average distance between scatterers decreases. Based on this comparison, it is shown that, in contrast to the typical parametrization, our newly proposed parametrization loses DoF in both scenarios, as one would expect from a spatially consistent channel model.