Miracle-dz/UniFAR-FTUs huggingface dataset:
The Haugh Unit Correction for Egg Weight is not Adequate for Comparing Eggs from Chickens of Different Lines and Ages

Abstract The Haugh unit is the accepted commercial and research standard for measuring albumen quality of chicken eggs. This unit has been criticized because it assumes a fixed relation between egg weight and albumen height. However, the amount of albumen in an egg differs between genetic strains and changes with the age of the hen. Data are presented to show that no fixed regression is adequate for comparing eggs from hens of different strains of chickens. With the increasing age of the hen, the weight of the egg increases in a quadratic fashion while the albumen height decreases linearly. The Haugh unit also declines more rapidly than does the actual albumen height because of the overcompensation for egg weight in the Haugh unit calculation. Correlation coefficients between Haugh unit, albumen height, and the log of the albumen height were very high. When comparing eggs from diverse groups of hens, the Haugh unit correction for egg weight will likely introduce more error than it will eliminate.
Compactly supported Wannier functions and strictly local projectors

Wannier functions that are maximally localized help in understanding many properties of crystalline materials. In the absence of topological obstructions, they are at least exponentially localized. In some cases such as flat-band Hamiltonians, it is possible to construct Wannier functions that are even more localized, so that they are compactly supported thus having zero support outside their corresponding locations. Under what general conditions is it possible to construct compactly supported Wannier functions? We answer this question in this paper. Specifically, we show that in 1d non-interacting tight-binding models, strict locality of the projection operator is a necessary and sufficient condition for a subspace to be spanned by a compactly supported orthogonal basis, independent of lattice translation symmetry. For any strictly local projector, we provide a procedure for obtaining such a basis. For higher dimensional systems, we discuss some additional conditions under which an occupied subspace is spanned by a compactly supported orthogonal basis, and show that the corresponding projectors are topologically trivial in many cases. We also show that a projector in arbitrary dimensions is strictly local if and only if for any chosen axis, its image is spanned by hybrid Wannier functions that are compactly supported along that axis.
The kinematics and dark matter fractions of TNG50 galaxies at z = 2 from an observational perspective

We contrast the gas kinematics and dark matter contents of $z=2$ star-forming galaxies (SFGs) from state-of-the-art cosmological simulations within the $\Lambda$CDM framework to observations. To this end, we create realistic mock observations of massive SFGs ($M_*>4\times10^{10} M_{\odot}$, SFR $>50~M_{\odot}$ yr$^{-1}$) from the TNG50 simulation of the IllustrisTNG suite, resembling near-infrared, adaptive-optics assisted integral-field observations from the ground. Using observational line fitting and modeling techniques, we analyse in detail the kinematics of seven TNG50 galaxies from five different projections per galaxy, and compare them to observations of twelve massive SFGs by Genzel et al. (2020). The simulated galaxies show clear signs of disc rotation but mostly exhibit more asymmetric rotation curves, partly due to large intrinsic radial and vertical velocity components. At identical inclination angle, their one-dimensional velocity profiles can vary along different lines of sight by up to $\Delta v=200$ km s$^{-1}$. From dynamical modelling we infer rotation speeds and velocity dispersions that are broadly consistent with observational results. We find low central dark matter fractions compatible with observations ($f_{\rm DM}^v(<R_e)=v_{\rm DM}^2(R_e)/v_{\rm circ}^2(R_e)\sim0.32\pm0.10$), however for disc effective radii $R_e$ that are mostly too small: at fixed $R_e$ the TNG50 dark matter fractions are too high by a factor of $\sim2$. We speculate that the differences in gas kinematics and dark matter content compared to the observations may be due to physical processes that are not resolved in sufficient detail with the numerical resolution available in current cosmological simulations.
Strategies for Efficient Executions of Irregular Message-Driven Parallel Applications on GPU Systems

Message-driven executions with over-decomposition of tasks constitute an important model for parallel programming and have been demonstrated for irregular applications. Supporting efficient execution of such message-driven irregular applications on GPU systems presents a number of challenges related to irregular data accesses and computations. In this work, we have developed strategies including coalescing irregular data accesses and combining with data reuse, and adaptive methods for hybrid executions to minimize idling. We have integrated these runtime strategies into our {\em G-Charm} framework for efficient execution of message-driven parallel applications on hybrid GPU systems. We demonstrate our strategies for irregular applications with an N-Body simulations and a molecular dynamics application and show that our dynamic strategies result in 8-38\% reduction in execution times for these irregular applications over the corresponding static strategies that are amenable for regular applications.
Vibrational signatures for the identification of single-photon emitters in hexagonal boron nitride

Color centers in h-BN are among the brightest emission centers known yet the origins of these emission centers are not well understood. Here, using first-principles calculations in combination with the generating function method, we systematically elucidate the coupling of specific defects to the vibrational degrees of freedom. We show that the lineshape of many defects exhibits strong coupling to high frequency phonon modes and that C$_{\text{N}}$, C$_{\text{B}}$, C$_{\text{B}}$-C$_{\text{N}}$ dimer and V$_{\text{B}}$ can be associated with experimental lineshapes. Our detailed theoretical study serves as a guide to identify optically active defects in h-BN that can suit specific applications in photonic-based quantum technologies, such as single photon emitters, hybrid spin-photon interfaces, or spin-mechanics interfaces.
NOMA for Multiple Access Channel in Indoor Visible Light Communications

In this letter, we consider a scenario of indoor visible light communications (VLC) where multiple transmitters (Txs) are communicating to a single receiver and each Tx uses a single light emitting diode (LED). For the considered multiple access channel scenario, we propose a generalized scheme based on non-orthogonal multiple access (NOMA) that can be applied for any number of Txs and any desired spectral efficiency for each of the Txs. We evaluate the performance of the proposed scheme using successive interference cancellation (SIC) based decoding, joint maximum likelihood (JML) decoding, and a combination of SIC and JML decoding. The numerical results indicate superior performance of the proposed NOMA-VLC scheme as compared to the state-of-the-art orthogonal multiple access schemes for VLC.
Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations

While high-resolution pathology images lend themselves well to ‘data hungry’ deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this article, we propose a self-supervised convolutional neural network (CNN) framework to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our proposed framework, termed as Self-Path, employs multi-task learning where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input images. We introduce novel pathology-specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the pathology-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for histopathology image classification when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available.
Piercing through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields. II. Are Highly Obscured AGNs the Missing Link in the Merger-triggered AGN–Galaxy Coevolution Models?

By using a large, highly obscured ( ) active galactic nucleus (AGN) sample (294 sources at z ∼ 0–5) selected from detailed X-ray spectral analyses in the deepest Chandra surveys, we explore distributions of these X-ray sources in various optical/infrared/X-ray color–color diagrams and their host-galaxy properties, aiming at characterizing the nuclear obscuration environment and the triggering mechanism of highly obscured AGNs. We find that the refined Infrared Array Camera (IRAC) color–color diagram fails to identify the majority of X-ray-selected, highly obscured AGNs, even for the most luminous sources with . Over 80% of our sources will not be selected as heavily obscured candidates using the flux ratio of and R − K > 4.5 criteria, implying complex origins and conditions for the obscuring materials that are responsible for the heavy X-ray obscuration. The average star formation rate (SFR) of highly obscured AGNs is similar to that of stellar mass- (M*-) and z-controlled normal galaxies, while a lack of quiescent hosts is observed for the former. Partial correlation analyses imply that highly obscured AGN activity (traced by ) appears to be more fundamentally related to M*, and no dependence of on either M* or SFR is detected. Morphology analyses reveal that 61% of our sources have a significant disk component, while only ∼27% of them exhibit irregular morphological signatures. These findings together point toward a scenario where secular processes (e.g., galactic-disk instabilities), instead of mergers, are most probable to be the leading mechanism that triggers accretion activities of X-ray-selected, highly obscured AGNs.
The Palomar Transient Factory Core-collapse Supernova Host-galaxy Sample. I. Host-galaxy Distribution Functions and Environment Dependence of Core-collapse Supernovae

Several thousand core-collapse supernovae (CCSNe) of different flavors have been discovered so far. However, identifying their progenitors has remained an outstanding open question in astrophysics. Studies of SN host galaxies have proven to be powerful in providing constraints on the progenitor populations. In this paper, we present all CCSNe detected between 2009 and 2017 by the Palomar Transient Factory. This sample includes 888 SNe of 12 distinct classes out to redshift z ≈ 1. We present the photometric properties of their host galaxies from the far-ultraviolet to the mid-infrared and model the host-galaxy spectral energy distributions to derive physical properties. The galaxy mass function of Type Ic, Ib, IIb, II, and IIn SNe ranges from 105 to 1011.5 M ⊙, probing the entire mass range of star-forming galaxies down to the least-massive star-forming galaxies known. Moreover, the galaxy mass distributions are consistent with models of star-formation-weighted mass functions. Regular CCSNe are hence direct tracers of star formation. Small but notable differences exist between some of the SN classes. Type Ib/c SNe prefer galaxies with slightly higher masses (i.e., higher metallicities) and star formation rates than Type IIb and II SNe. These differences are less pronounced than previously thought. H-poor superluminous supernovae (SLSNe) and SNe Ic-BL are scarce in galaxies above 1010 M ⊙. Their progenitors require environments with metallicities of < 0.4 and < 1 solar, respectively. In addition, the hosts of H-poor SLSNe are dominated by a younger stellar population than all other classes of CCSNe. Our findings corroborate the notion that low metallicity and young age play an important role in the formation of SLSN progenitors.
On the Importance of Local Information in Transformer Based Models

The self-attention module is a key component of Transformer-based models, wherein each token pays attention to every other token. Recent studies have shown that these heads exhibit syntactic, semantic, or local behaviour. Some studies have also identified promise in restricting this attention to be local, i.e., a token attending to other tokens only in a small neighbourhood around it. However, no conclusive evidence exists that such local attention alone is sufficient to achieve high accuracy on multiple NLP tasks. In this work, we systematically analyse the role of locality information in learnt models and contrast it with the role of syntactic information. More specifically, we first do a sensitivity analysis and show that, at every layer, the representation of a token is much more sensitive to tokens in a small neighborhood around it than to tokens which are syntactically related to it. We then define an attention bias metric to determine whether a head pays more attention to local tokens or to syntactically related tokens. We show that a larger fraction of heads have a locality bias as compared to a syntactic bias. Having established the importance of local attention heads, we train and evaluate models where varying fractions of the attention heads are constrained to be local. Such models would be more efficient as they would have fewer computations in the attention layer. We evaluate these models on 4 GLUE datasets (QQP, SST-2, MRPC, QNLI) and 2 MT datasets (En-De, En-Ru) and clearly demonstrate that such constrained models have comparable performance to the unconstrained models. Through this systematic evaluation we establish that attention in Transformer-based models can be constrained to be local without affecting performance.
Model-Based Deep Reinforcement Learning for High-Dimensional Problems, a Survey

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample complexity of most deep reinforcement learning methods is high, precluding their use in some important applications. Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. Current deep learning methods use high-capacity networks to solve high-dimensional problems. Unfortunately, high-capacity models typically require many samples, negating the potential benefit of lower sample complexity in model-based methods. A challenge for deep model-based methods is therefore to achieve high predictive power while maintaining low sample complexity. In recent years, many model-based methods have been introduced to address this challenge. In this paper, we survey the contemporary model-based landscape. First we discuss definitions and relations to other fields. We propose a taxonomy based on three approaches: using explicit planning on given transitions, using explicit planning on learned transitions, and end-to-end learning of both planning and transitions. We use these approaches to organize a comprehensive overview of important recent developments such as latent models. We describe methods and benchmarks, and we suggest directions for future work for each of the approaches. Among promising research directions are curriculum learning, uncertainty modeling, and use of latent models for transfer learning.
Multi‐mask self‐supervised learning for physics‐guided neural networks in highly accelerated magnetic resonance imaging

Self‐supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self‐supervised learning methods for physics‐guided reconstruction networks split acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network, while the other is used to define the training loss. In this study, we propose an improved self‐supervised learning strategy that more efficiently uses the acquired data to train a physics‐guided reconstruction network without a database of fully sampled data. The proposed multi‐mask self‐supervised learning via data undersampling (SSDU) applies a holdout masking operation on the acquired measurements to split them into multiple pairs of disjoint sets for each training sample, while using one of these pairs for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi‐mask SSDU is applied on fully sampled 3D knee and prospectively undersampled 3D brain MRI datasets, for various acceleration rates and patterns, and compared with the parallel imaging method, CG‐SENSE, and single‐mask SSDU DL‐MRI, as well as supervised DL‐MRI when fully sampled data are available. The results on knee MRI show that the proposed multi‐mask SSDU outperforms SSDU and performs as well as supervised DL‐MRI. A clinical reader study further ranks the multi‐mask SSDU higher than supervised DL‐MRI in terms of signal‐to‐noise ratio and aliasing artifacts. Results on brain MRI show that multi‐mask SSDU achieves better reconstruction quality compared with SSDU. The reader study demonstrates that multi‐mask SSDU at R = 8 significantly improves reconstruction compared with single‐mask SSDU at R = 8, as well as CG‐SENSE at R = 2.
Estimating the Magnitude and Phase of Automotive Radar Signals Under Multiple Interference Sources With Fully Convolutional Networks

Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles, generating corrupted range profiles and range-Doppler maps. In order to extract distance and velocity of multiple targets from range-Doppler maps, the interference affecting each range profile needs to be mitigated. In this paper, we propose a fully convolutional neural network for automotive radar interference mitigation. In order to train our network in a real-world scenario, we introduce a new data set of realistic automotive radar signals with multiple targets and multiple interferers. To our knowledge, we are the first to apply weight pruning in the automotive radar domain, obtaining superior results compared to the widely-used dropout. While most previous works successfully estimated the magnitude of automotive radar signals, we propose a deep learning model that can accurately estimate the phase. For instance, our novel approach reduces the phase estimation error with respect to the commonly-adopted zeroing technique by half, from 12.55 degrees to 6.58 degrees. Considering the lack of databases for automotive radar interference mitigation, we release as open source our large-scale data set that closely replicates the real-world automotive scenario for multiple interference cases, allowing others to objectively compare their future work in this domain. Our data set is available for download at: http://github.com/ristea/arim-v2.
A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
RESIK and RHESSI observations of the 20 September 2002 flare

Context. Soft X-ray spectra (3.33 Å–6.15 Å) from the RESIK instrument on CORONAS-F constitute a unique database for the study of the physical conditions of solar flare plasmas, enabling the calculation of differential emission measures. The two RESIK channels for the shortest wavelengths overlap with the lower end of the Ramaty High Energy Solar Spectroscopic Imager (RHESSI) spectral energy range, which is located around 3 keV, making it possible to compare both data sets. Aims. We aim to compare observations from RESIK and RHESSI spectrometers and cross-correlate these instruments. Observations are compared with synthetic spectra calculated based on the results of one-dimensional hydrodynamical (1D-HD) modelling. The analysis was performed for the flare on 20 September 2002 (SOL2002-09-20T09:28). Methods. We estimated the geometry of the flaring loop, necessary for 1D-HD modelling, based on images from RHESSI and the Extreme-Ultraviolet Imaging Telescope aboard the Solar and Heliospheric Observatory. The distribution of non-thermal electrons (NTEs) was determined from RHESSI spectra. The 1D-HD model assumes that non-thermal electrons with a power-law spectrum were injected at the apex of the flaring loop. The NTEs then heat and evaporate the chromosphere, filling the loop with hot and dense plasma radiating in soft X-rays. The total energy of electrons was constrained by comparing observed and calculated fluxes from Geostationary Operational Environmental Satellite 1–8 Å data. We determined the temperature and density at every point of the flaring loop throughout the evolution of the flare, calculating the resulting X-ray spectra. Results. The synthetic spectra calculated based on the results of hydrodynamic modelling for the 20 September 2002 flare are consistent within a factor of two with the observed RESIK spectra during most of the duration of the flare. This discrepancy factor is probably related to the uncertainty on the cross-calibration between RESIK and RHESSI instruments.
Performance Characterization and Bottleneck Analysis of Hyperledger Fabric

Hyperledger Fabric is a popular open-source project for deploying permissioned blockchains. Many performance characteristics of the latest Hyperledger Fabric (e.g., performance characteristics of each phase, the impacts of ordering services, bottleneck and scalability) are still not well understood due to the performance complexity of distributed systems. We conducted a thorough performance evaluation on the first long term support release of Hyperledger Fabric. We studied the performance characteristics of each phase, including execute, order, and the validate phase, according to Hyperledger Fabric’s new execute-order-validate architecture. We also studied the ordering services, including Solo, Kafka, and Raft. Our experimental results showed some findings as follows. 1) The execution phase exhibited a good scalability under the OR endorsement policy but not with the AND endorsement policy. 2) We were not able to find a significant performance difference between the three ordering services. 3) The validate phase was likely to be the system bottleneck due to the low validation speed of chaincode. Overall, our work helps to understand and improve Hyperledger Fabric.