Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach

The problem of adaptive sampling for estimating probability mass functions (pmf) uniformly well is considered. Performance of the sampling strategy is measured in terms of the worst-case mean squared error. A Bayesian variant of the existing upper confidence bound (UCB) based approaches is proposed. It is shown analytically that the performance of this Bayesian variant is no worse than the existing approaches. The posterior distribution on the pmfs in the Bayesian setting allows for a tighter computation of upper confidence bounds which leads to significant performance gains in practice. Using this approach, adaptive sampling protocols are proposed for estimating SARS-CoV-2 seroprevalence in various groups such as location and ethnicity. The effectiveness of this strategy is discussed using data obtained from a seroprevalence survey in Los Angeles county.
Diffusion limited first contact of the ends of a polymer: Comparison of theory with simulation

The mean first passage time for the close approach of the ends of a polymer is investigated by computer simulation of the Brownian dynamics of a Rouse model. There are N bonds, of length b, and the contact distance is a. The results for N=50, 75, and 100 are compared with theoretical predictions by Wilemski and Fixman (WF), and by Szabo, Schulten, and Schulten (SSS). Theory differs from simulation by a factor of 1.3 to 3 when a is comparable to b but improves considerably when a is reduced. Closest agreement is obtained at a/b=0.1 for WF with a δ function sink, where theory is within the statistical error (≊5%) of the simulation for each case.
Determination of Inhalational Anesthetics in Field and Laboratory by SPME GC/MS

Exposure to inhalational anesthetics in health care workers could lead to several diseases and disorders. This study examined the applicability of solid phase microextraction for sampling and quantification of three inhalational anesthetics including; halothane, isoflurane, and sevoflurane in operating room air. Carboxen-Polydimethylsiloxane in retracted mode was selected and the effects of environmental parameters including temperature, humidity, and air velocity, were studied. There were no significant differences between sampling rates determined at different temperatures and air velocities. On the opposite, relative humidity has a significant effect on sampling rates. Comparison of the results between the developed SPME method and OSHA 103 method on standard test atmosphere and field showed satisfactory agreement.
Application of inductively coupled plasma mass spectrometry for total metal determination in silicon-containing solid samples using the microwave-assisted nitric acid–hydrofluoric acid–hydrogen peroxide–boric acid digestion system

The microwave-assisted conventional HNO3–HF–H2O2–H3BO3 digestion system has been explored for direct ICP-MS analysis of total metals in silicon-containing solid samples. In closed Teflon PFA vessels under microwave heating with temperature/pressure regulation, a 0.25 g portion of sample containing ⩽ 33% silicon was digested in 1.5 ml of HF (48%), 5 ml of concentrated HNO3 and 2 ml of H2O2(30%), followed by a second digestion stage with 12 ml of 5% m/v boric acid. The amount of HF used was approximately twice the stoichiometric requirement for 33% silicon contained in the samples, assuming all silicon exists as silica. The amount of boric acid used was the stoichiometric requirement for the HF added. With a sample dilution factor of 2000 (v/m), the end solution contained ⩽ 0.17% total dissolved solids for ICP-MS analysis. The ICP-MS system was calibrated by the method of external standards prepared in reagent blank solutions with in as the internal standard. The sensitivity of the In signal in a 5–8 h operation was only decreased by 5–10%. Background interferences from the digestion reagents were studied and corrected. The method developed is simple and rugged and suitable for routine analysis of at least 25 elements: Al, Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mg, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Th, Ti, Tl, U, V and Zn. The recoveries for most of these elements in standard reference materials including coal fly ash, urban particulate matter, sediment and soil were within 90–110%, and the relative standard deviations were within 5%. The method cannot determine B and is not suitable for Hg determination. The detection limits for Be and Se determination are not adequate.
KNN-enhanced Deep Learning Against Noisy Labels

Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing performance of DNN in the presence of noisy labels has become of paramount importance since collecting a large dataset will usually bring in noisy labels. Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for label cleanup. Our approach leverages DNNs for feature extraction and KNN for ground-truth label inference. We iteratively train the neural network and update labels to simultaneously proceed towards higher label recovery rate and better classification performance. Experiment results show that under the same setting, our approach outperforms existing label correction methods and achieves better accuracy on multiple datasets, e.g.,76.78% on Clothing1M dataset.
Frequency Subsampling of Ultrasound Nondestructive Measurements: Acquisition, Reconstruction, and Performance

In ultrasound nondestructive testing (NDT), a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the synthetic aperture focusing technique (SAFT). However, SAFT is suboptimal in terms of resolution and requires oversampling in the time domain to obtain a fine grid for the delay-and-sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and a large amount of measurement data in realistic 3-D scenarios when using oversampling. In the literature, the remedies to this are twofold. First, the amount of measurement data can be reduced using state-of-the-art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time-domain samples. Second, parametric reconstruction algorithms mostly rely on matrix–vector operations that can be implemented efficiently by exploiting the underlying structure of the model. In this article, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cramér–Rao bound (CRB) for the localizability of the defect coordinates. These subsampling strategies are then combined with an $\ell _{{1}}$ -minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model matrix. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying two-level Toeplitz structure. Finally, we show that high-resolution reconstructions from as low as a single Fourier coefficient per A-scan are possible based on simulated data and measurements from a steel specimen.
Two-Phase Learning for Overcoming Noisy Labels

To counter the challenge associated with noise labels, the learning strategy of deep neural networks must be differentiated over the learning period during the training process. Therefore, we propose a novel two-phase learning method, MORPH, which automatically transitions its learning phase at the point when the network begins to rapidly memorize false-labeled samples. In the first phase, MORPH starts to update the network for all the training samples before the transition point. Without any supervision, the learning phase is converted to the next phase on the basis of the estimated best transition point. Subsequently, MORPH resumes the training of the network only for a maximal safe set, which maintains the collection of almost certainly true-labeled samples at each epoch. Owing to its two-phase learning, MORPH realizes noise-free training for any type of label noise for practical use. Moreover, extensive experiments using six datasets verify that MORPH significantly outperforms five state-of-the art methods in terms of test error and training time.
The Pristine survey – XII. Gemini-GRACES chemo-dynamical study of newly discovered extremely metal-poor stars in the Galaxy

High-resolution optical spectra of 30 metal-poor stars selected from the \textit{Pristine} survey are presented, based on observations taken with the Gemini Observatory GRACES spectrograph. Stellar parameters T$_{\rm eff}$ and $\log g$ are determined using Gaia DR2 parallaxes and MIST/MESA metal-poor isochrones. GRACES spectra are used to determine chemical abundances (or upper-limits) for 20 elements (Li, O, Na, Mg, K, Ca, Ti, Sc, Cr, Mn, Fe, Ni, Cu, Zn, Y, Zr, Ba, La, Nd, Eu). These stars are confirmed to be metal-poor ([Fe/H]$<-2.5$), with higher precision than from earlier medium-resolution analyses. The chemistry for most targets is similar to that of other extremely metal-poor stars in the Galactic halo. Two stars near [Fe/H]$=-3.0$ have unusually low Ca and high Mg, suggestive of contributions from few SN~II, where alpha-element formation through hydrostatic nucleosynthesis was more efficient. Three new carbon-enhanced metal-poor stars are also identified (two CEMP-s and one potential CEMP-no star), when our chemical abundances are combined with carbon from previous medium-resolution analyses. The GRACES spectra also provide precision radial velocities ($\sigma_{\rm RV}\le0.2$ km s$^{-1}$) for dynamical orbit calculations with the Gaia DR2 proper motions. Most of our targets are dynamically associated with the Galactic halo; however, five stars with [Fe/H]$<-3$ have planar-like orbits, including one retrograde planar star. Another five stars are dynamically consistent with the Gaia-Sequoia accretion event; three have typical high [$\alpha$/Fe] ratios for their metallicities, whereas two are [Mg/Fe]-deficient, and one is a new CEMP-s candidate. These results are discussed in terms of the formation and early chemical evolution of the Galaxy.
SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation

This paper focuses on visual semantic navigation, the task of producing actions for an active agent to navigate to a specified target object category in an unknown environment. To complete this task, the algorithm should simultaneously locate and navigate to an instance of the category. In comparison to the traditional point goal navigation, this task requires the agent to have a stronger contextual prior to indoor environments. We introduce SSCNav, an algorithm that explicitly models scene priors using a confidence-aware semantic scene completion module to complete the scene and guide the agent's navigation planning. Given a partial observation of the environment, SSC-Nav first infers a complete scene representation with semantic labels for the unobserved scene together with a confidence map associated with its own prediction. Then, a policy network infers the action from the scene completion result and confidence map. Our experiments demonstrate that the proposed scene completion module improves the efficiency of the downstream navigation policies. Code and data: https://sscnav.cs. columbia.edu/
k-Factorization Subspace Clustering

Subspace clustering (SC) aims to cluster data lying in a union of low-dimensional subspaces. Usually, SC learns an affinity matrix and then performs spectral clustering. Both steps suffer from high time and space complexity, which leads to difficulty in clustering large datasets. This paper presents a method called k-Factorization Subspace Clustering (k-FSC) for large-scale subspace clustering. K-FSC directly factorizes the data into k groups via pursuing structured sparsity in the matrix factorization model. Thus, k-FSC avoids learning affinity matrix and performing eigenvalue decomposition, and hence has low time and space complexity on large datasets. An efficient algorithm is proposed to solve the optimization of k-FSC. In addition, k-FSC is able to handle noise, outliers, and missing data and applicable to arbitrarily large datasets and streaming data. Extensive experiments show that k-FSC outperforms state-of-the-art subspace clustering methods.
Six Years of Luminous X-Ray Emission from the Strongly Interacting Type-Ib SN2014C Captured by Chandra and NuSTAR

We present the first coordinated soft and hard 0.3–80 keV X-ray campaign of the extragalactic supernova SN2014C in the first ∼2307 days of its evolution. SN2014C initially appeared to be an ordinary type Ib explosion but evolved into a strongly-interacting hydrogen-rich SN IIn over ∼1 yr. We observed signatures of interaction with a dense medium across the X-ray spectrum, which revealed the presence of a ∼1−2M⊙ shell of material at ∼6 × 1016 cm from the progenitor. This finding challenges current understanding of hydrogen-poor core-collapse progenitor evolution. Potential scenarios to interpret these observations include (i) the ejection of the hydrogen envelope by the progenitor star in the centuries prior to the explosion; (ii) interaction of the fast Wolf-Rayet (WR) star wind with the slow, dense wind of the Red Super Giant phase, with an anomalously short WR phase.
Learning to Represent Programs with Heterogeneous Graphs

Code representation, which transforms programs into vectors with semantics, is essential for source code processing. We have witnessed the effectiveness of incorporating structural information (i.e., graph) into code representations in recent years. Specifically, the abstract syntax tree (AST) and the AST-augmented graph of the program contain much structural and semantic information, and most existing studies apply them for code representation. The graph adopted by existing approaches is homogeneous, i.e., it discards the type information of the edges and the nodes lying within AST. That may cause plausible obstruction to the representation model. In this paper, we propose to leverage the type information in the graph for code representation. To be specific, we propose the heterogeneous program graph (HPG), which provides the types of the nodes and the edges explicitly. Furthermore, we employ the heterogeneous graph transformer (HGT) architecture to generate representations based on HPG, considering the type of information during processing. With the additional types in HPG, our approach can capture complex structural information, produce accurate and delicate representations, and finally perform well on certain tasks. Our in-depth evaluations upon four classic datasets for two typical tasks (i.e., method name prediction and code classification) demonstrate that the heterogeneous types in HPG benefit the representation models. Our proposed $\text{HPG}+\text{HGT}$ also outperforms the SOTA baselines on the subject tasks and datasets.
Neutrino flavor mixing breaks isotropy in the early universe

The neutrino field is commonly assumed to be isotropic and homogeneous in the early universe. However, due to the large neutrino density, a small perturbation of the isotropy of the neutrino field could potentially be amplified by the non-linear flavor mixing caused by neutrino self-interactions. We carry out the first numerical simulations of the neutrino flavor evolution in a multi-angle anisotropic setting. Due to the computational challenges involved, we adopt a simplified framework consisting of a homogeneous universe with two angle bins — left and right moving modes — for neutrinos and antineutrinos, together with an approximate form for the collision term which goes beyond the commonly adopted damping approximation. By assuming a small initial left-right asymmetry of 𝒪(10-15), we convincingly demonstrate that flavor evolution can be affected in both mass orderings, with implications on the effective number of thermally excited neutrino species (N eff). Notably, the correction to N_ eff is comparable to higher order corrections from finite temperature QED effects in normal ordering. In addition, by assuming an initial lepton asymmetry in the neutrino sector of the same order as the baryon one [𝒪(10-9)], we find that the neutrino-antineutrino asymmetry grows by several orders of magnitude for isotropic as well as anisotropic initial conditions. This work clearly shows that it is imperative to critically revisit standard assumptions concerning neutrino flavor mixing in the early universe, especially in the light of possible implications on the cosmological observables.
Magnetic field resilient high kinetic inductance superconducting niobium nitride coplanar waveguide resonators

We characterize niobium nitride $\lambda/2$ coplanar waveguide resonators, which were fabricated from a 10nm thick film on silicon dioxide grown by sputter deposition. For films grown at 120°C we report a superconducting critical temperature of 7.4K associated with a normal square resistance of 1k$\Omega$ leading to a kinetic inductance of 192pH/$\Box$. We fabricated resonators with a characteristic impedance up to 4.1k$\Omega$ and internal quality factors $Q_\mathrm{i} > 10^4$ in the single photon regime at zero magnetic field. Moreover, in the many photons regime, the resonators present high magnetic field resilience with $Q_\mathrm{i} > 10^4$ in a 6T in-plane magnetic field as well as in a 300mT out-of-plane magnetic field. These findings make such resonators a compelling choice for cQED experiments involving quantum systems with small electric dipole moments operated in finite magnetic fields.
Influence of Structural Fluctuation on Enzyme Reaction Energy Barriers in Combined Quantum Mechanical/Molecular Mechanical Studies

To account for protein dynamics and to investigate the effect of different conformations on the enzyme reaction energy barrier, we have studied the initial step of the acylation reaction catalyzed by acetylcholinesterase (AChE) with a multiple QM/MM reaction path approach. The approach consists of two main components: generating enzyme−substrate conformations with classical molecular dynamics simulation and mapping out the minimum reaction energy path for each conformational snapshot with combined quantum mechanical/molecular mechanical (QM/MM) calculations. It is found that enzyme−substrate conformation fluctuations lead to significant differences in the calculated reaction energy barrier; however, the qualitative picture of the role of the catalytic triad and oxyanion hole in AChE catalysis is very consistent. Our results emphasize the importance of employing multiple starting structures in the QM/MM study of enzyme reactions and indicate that structural fluctuation is an integral part of the enzyme re.
Estimation of the mean function of functional data via deep neural networks

In this work, we propose a deep neural networks‐based method to perform non‐parametric regression for functional data. The proposed estimators are based on sparsely connected deep neural networks with rectifier linear unit (ReLU) activation function. We provide the convergence rate of the proposed deep neural networks estimator in terms of the empirical norm. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyse positron emission tomography images of patients with Alzheimer's disease obtained from the Alzheimer Disease Neuroimaging Initiative database.
Neutron diffraction study of Sr(OH)2 · H2O and β-Ba(OH)2 · H20

Structures cristallines des composes isostructuraux du titre. Groupe d'espace Pme2 1 . Les molecules d'eau sont reliees par des liaisons hydrogene tres fortes (distances intermoleculaires H-O tres courtes). Les ions hydroxydes ne sont pas relies par des liaisons H, a part un ion OH − du compose au Ba. Faible valeur des angles de liaison des molecules H 2 O
A Novel Transformation Approach of Shared-link Coded Caching Schemes for Multiaccess Networks

This paper studies the multiaccess caching systems formulated by Hachem et al., including a central server containing $N$ files connected to $K$ cache-less users through an error-free shared link, and $K$ cache-nodes, each equipped with a cache memory size of $M$ files. Each user has access to $L$ neighbouring cache-nodes with a cyclic wrap-around topology. The coded caching scheme proposed by Hachem et al. suffers from the case that $L$ does not divide $K$, where the needed number of transmissions (a.k.a. load) is at most four times the load expression for the case where $L$ divides $K$. Our main contribution is to propose a novel transformation approach to smartly extend the MN scheme to the multiaccess caching systems, such that the load expression of the scheme by Hachem et al for the case where $L$ divides $K$, remains achievable in full generality. The resulting scheme has the maximum local caching gain (i.e., the cached contents stored at any $L$ neighbouring cache-nodes are different such that each user can totally retrieve $L M$ files from the connected cache-nodes) and the same coded caching gain as the related MN scheme. Moreover, our transformation approach can also be used to extend other coded caching schemes (satisfying some conditions) for the original MN caching systems to the multiaccess systems, such that the resulting scheme achieves the maximum local caching gain and the same coded caching gain as the considered caching scheme.
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness

Consider a prediction setting where a few inputs (e.g., satellite images) are expensively annotated with the prediction targets (e.g., crop types), and many inputs are cheaply annotated with auxiliary information (e.g., climate information). How should we best leverage this auxiliary information for the prediction task? Empirically across three image and time-series datasets, and theoretically in a multi-task linear regression setting, we show that (i) using auxiliary information as input features improves in-distribution error but can hurt out-of-distribution (OOD) error; while (ii) using auxiliary information as outputs of auxiliary tasks to pre-train a model improves OOD error. To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and fine-tunes this model with the pseudolabels (self-training). We show both theoretically and empirically that In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error.