Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital-order phase transition in 
Pr1−xCaxMnO3
 probed by photovoltaics

A new phase diagram of $Pr_{1-x}Ca_xMnO_3$ for x $\le$ 0.3 is derived that suggests a necessary revaluation of the phase diagram of other manganites in that doping region. Rather than an orbital ordered phase reaching up to high temperatures 850 K for x=0.1 and 950 K for x=0, we propose a loss of spontaneous order already near room temperature. Above this temperature, the phase is characterized by a finite orbital polarization and octahedral tilt pattern. The tilt pattern couples to the Jahn-Teller distortion and thus induces a remaining orbital order, which persists up to high temperatures, where the tilt order is lost as well. This explains the experimental observation of orbital order up to high temperatures. Anomalies at a temperature 220-260 K have been observed in epitaxial thin films of doping x=0.1 for photovoltaic effect, electric transport, magnetisation, optical and ultrafast transient pump probe studies. The onset of the polaron photovoltaic effect and the increase of the hot polaron relaxation time below $T_{OO}$ suggest a change in the orbital order. Finite-temperature simulations based on a tight-binding model with carefully adjusted parameters from first-principles calculations exhibit an orbital-order phase transition at $T_{OO} \approx$ 300 K for x=0.1. This is consistent with the experimental observation of a change in temperature dependent lattice parameter for bulk samples of the same doping at 300 K for x=0.1 and 350 K for x=0, typical for a second order phase transition.
A NOVEL FLUORESCENT HPLC-BASED METABOLIC LABELING TECHNIQUE REVEALS EFFECTS OF NUTRITIONAL CONTEXT ON DIETARY FATTY ACID PARTITIONING IN THE INTESTINAL ENTEROCYTES

The developing zebrafish is a well-established model system for studies of energy metabolism, and is amenable to genetic, physiological, and biochemical approaches. For the first five days of life, nutrients are absorbed from its endogenous maternally deposited yolk. At five days post-fertilization, the yolk is exhausted and the larva has a functional digestive system including intestine, liver, gallbladder, pancreas, and intestinal microbiota. The transparency of the larval zebrafish, and the genetic and physiological similarity of its digestive system to that of mammals make it a promising system in which to address questions of energy homeostasis relevant to human health. For example, apolipoprotein expression and function is similar in zebrafish and mammals, and transgenic animals may be used to examine both the transport of lipid from yolk to body in the embryo, and the trafficking of dietary lipids in the larva. Additionally, despite the identification of many fatty acid and lipid transport proteins expressed by vertebrates, the cell biological processes that mediate the transport of dietary lipids from the intestinal lumen to the interior of enterocytes remain to be elucidated. Genetic tractability and amenability to live imaging and a range of biochemical methods make the larval zebrafish an ideal model in which to address open questions in the field of lipid transport, energy homeostasis and nutrient metabolism.
Transforming Facial Weight of Real Images by Editing Latent Space of StyleGAN

We present an invert-and-edit framework to automatically transform facial weight of an input face image to look thinner or heavier by leveraging semantic facial attributes encoded in the latent space of Generative Adversarial Networks (GANs). Using a pre-trained StyleGAN as the underlying generator, we first employ an optimization-based embedding method to invert the input image into the StyleGAN latent space. Then, we identify the facial-weight attribute direction in the latent space via supervised learning and edit the inverted latent code by moving it positively or negatively along the extracted feature axis. Our framework is empirically shown to produce high-quality and realistic facial-weight transformations without requiring training GANs with a large amount of labeled face images from scratch. Ultimately, our framework can be utilized as part of an intervention to motivate individuals to make healthier food choices by visualizing the future impacts of their behavior on appearance.
The GALAH survey: Milky Way disc metallicity and alpha-abundance trends in combined APOGEE-GALAH catalogues

GALAH and APOGEE are two high resolution multi object spectroscopic surveys that provide fundamental stellar parameters and multiple elemental abundance estimates for $>$ 400,000 stars in the Milky Way. They are complimentary in both sky coverage and wavelength regime. Thus combining the two surveys will provide us a large sample to investigate the disc metallicity and alpha abundance trends. We use the Cannon data-driven approach selecting training sets from among $\sim$20,000 stars in common for the two surveys to predict the GALAH scaled stellar parameters from APOGEE spectra as well as APOGEE scaled stellar parameters from GALAH spectra. We provide two combined catalogues with GALAH scaled and APOGEE scaled stellar parameters each having $\sim$500,000 stars after quality cuts. With $\sim$470,000 stars that are common in both these catalogues, we compare the GALAH scaled and APOGEE scaled metallicity distribution functions (MDF), radial and vertical metallicity gradients as well as the variation of [$\alpha$/Fe] vs [Fe/H] trends along and away from the Galactic mid plane. We find mean metallicities of APOGEE scaled sample to be higher compared to that for the GALAH scaled sample. We find similar [$\alpha$/Fe] vs [Fe/H] trends using both samples consistent with previous observational as well as simulation based studies. Radial and vertical metallicity gradients derived using the two survey scaled samples are consistent except in the inner and outer Galactocentric radius bins. Our gradient estimates in the solar neighborhood are also consistent with previous studies and are backed by larger sample size compared to previous works.
Possibility of primordial black holes as the source of gravitational wave events in the advanced LIGO detector

The analysis of Gravitational Waves (GW) data from the Advanced LIGO provides the mass of each companion of binary black holes as the source of GWs. The observations reveal that the mass of events corresponding to the binary black holes are much larger than the mass of astrophysical black holes. In this work, we suggest the Primordial Black Holes (PBHs) as the source of LIGO events. Assuming that $100\%$ of the dark matter is made of PBHs, we estimate the rate at which these objects make binaries, merge and produce GWs as a function of redshift. The gravitational lensing of GWs by PBHs can also enhance the amplitude of the strain. We simulate GWs sourced by the binary PBHs, with the detection threshold of $S/N>10$ for both Livingston and Handford detectors. For the log-normal mass function of PBHs, we generate the expected distribution of events and compare our results with the observed events and find the best value of the mass function parameters (i.e $M_c =25 M_\odot$ and $\sigma=0.6$) in the lognormal mass function. Comparing the expected number of events with the number of observed ones reveals that even assuming all the dark matter is made of PBHs are not enough to produce the observed GW events and the astrophysical black holes should have the main contribution in the observed GW events.
Implications of inertial subrange scaling for stably stratified mixing

Abstract We investigate the effects of the turbulent dynamic range on active scalar mixing in stably stratified turbulence by adapting the theoretical passive scalar modelling arguments of Beguier, Dekeyser & Launder (1978) (Phys. Fluids, vol. 21 (3), pp. 307–310) and demonstrating their usefulness through consideration of the results of direct numerical simulations of statistically stationary homogeneous stratified and sheared turbulence. By analysis of inertial and inertial–convective subrange scalings, we show that the relationship between the active scalar and turbulence time scales is predicted by the ratio of the Kolmogorov and Obukhov–Corrsin constants, provided mean flow parameters permit the two subrange scalings to be appropriate approximations. We use the resulting relationship between time scales to parameterise an appropriate turbulent mixing coefficient $\varGamma \equiv \chi /\epsilon$, defined here as the ratio of available potential energy ($E_p$) and turbulent kinetic energy ($E_k$) dissipation rates. With the analysis presented here, we show that $\varGamma$ can be estimated by $E_p,E_k$ and a universal constant provided an appropriate Reynolds number is sufficiently high. This large Reynolds number regime appears here to occur at $ {{Re_b}} \equiv \epsilon / \nu N^{2} \gtrapprox 300$ where $\nu$ is the kinematic viscosity and $N$ is the characteristic buoyancy frequency. We propose a model framework for irreversible diapycnal mixing with robust theoretical parametrisation and asymptotic behaviour in this high-$ {{Re_b}}$ limit.
Experimental Realization of Gauge Transformation and Observation of Phase Transition in Spatial Optical Spin Systems

Statistical spin dynamics plays a key role to understand the working principle for novel optical Ising machines. Here we propose the gauge transformations for spatial optical spin systems, where a single spatial phase modulator simultaneously encodes spin configurations and programs interaction strengths. Thanks to gauge transformation, we experimentally evaluate the phase diagram of high-dimensional spin-glass equilibrium system with $100$ fully-connected spins. We identify that there is a spin-glass phase that the gauge-transformed spins become synchronized when disorder interactions are rather strong. Furthermore, we exploit the gauge transformation for high accuracy and good scalability in solving combinatorial optimization problems, for the spin number as large as $N=40000$. Our results show that the gauge transformation enables a promising approach for solving NP-hard problems with large-scale and ultra-fast optical computing.
Measurement of the mass anomalous dimension of near-conformal adjoint QCD with the gradient flow

The mass anomalous dimension is determined in SU(2) gauge theory coupled to $N_f$ fermions in the adjoint representation for $N_f = 2$, $3/2$, $1$ and $1/2$, where half-integer flavor numbers correspond to Majorana fermions. The numerical method is based on gradient flow. The results show near-conformal behavior for $N_f = 2$, $3/2$ and $1$. Particular emphasis is placed on $N_f = 2$, which is relevant for a strongly interacting extension of the Standard Model and has been studied in several previous investigations. We check whether the method is able to resolve discrepancies in earlier results for this theory. Overall, the method based on the gradient flow delivers reliable results in qualitative agreement with previously known numerical data.
Diversity-Enriched Option-Critic

Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting. However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a shrinking set of task relevant options. These occurrences not only void the need for temporal abstraction, they also affect performance. In this paper, we tackle these problems by learning a diverse set of options. We introduce an information-theoretic intrinsic reward, which augments the task reward, as well as a novel termination objective, in order to encourage behavioral diversity in the option set. We show empirically that our proposed method is capable of learning options end-to-end on several discrete and continuous control tasks, outperforms option-critic by a wide margin. Furthermore, we show that our approach sustainably generates robust, reusable, reliable and interpretable options, in contrast to option-critic.
The GALAH survey: Chemical homogeneity of the Orion complex

Due to its proximity, the Orion star forming region is often used as a proxy to study processes related to star formation and to observe young stars in the environment they were born in. With the release of Gaia DR2, the distance measurements to the Orion complex are now good enough that the 3D structure of the complex can be explored. Here we test the hypothesis that, due to non-trivial structure and dynamics, and age spread in the Orion complex, the chemical enrichment of youngest stars by early core-collapse supernovae can be observed. We obtained spectra of 794 stars of the Orion complex with the HERMES spectrograph at the Anglo Australian telescope as a part of the GALAH and GALAH-related surveys. We use the spectra of ∼300 stars to derive precise atmospheric parameters and chemical abundances of 25 elements for 15 stellar clusters in the Orion complex. We demonstrate that the Orion complex is chemically homogeneous and that there was no self-pollution of young clusters by core-collapse supernovae from older clusters; with a precision of 0.02 dex in relative alpha-elements abundance and 0.06 dex in oxygen abundance we would have been able to detect pollution from a single supernova, given a fortunate location of the SN and favourable conditions for ISM mixing. We estimate that the supernova rate in the Orion complex was very low, possibly producing no supernova by the time the youngest stars of the observed population formed (from around 21 to 8 Myr ago).
Bacterial Hsp90 mediates the degradation of aggregation-prone Hsp70-Hsp40 substrates preferentially by HslUV proteolysis

Whereas in eukaryotic cells, the Hsp90s are profusely-studied molecular chaperones controlling protein homeostasis together with Hsp70s, in bacteria, the function of Hsp90 (HtpG) and its collaboration with Hsp70 (DnaK) remains unknown. To uncover physiological processes depending on HtpG and DnaK, we performed comparative quantitative proteomic analyses of insoluble and total protein fractions from unstressed wild type E. coli, and from knockout mutants ΔdnaKdnaJ (ΔKJ), ΔhtpG (ΔG) and ΔdnaKdnaJΔhtpG (ΔKJG) and compared their growth rates under heat-stress also with ΔdnaKdnaJΔhslV. Whereas, expectedly, mutant ΔG showed no proteomic differences with wild-type, ΔKJ expressed more chaperones, proteases and ribosomes and dramatically less metabolic and respiratory enzymes. Unexpectedly, we found that ΔKJG showed higher levels of metabolic and respiratory enzymes and both ΔKJG and ΔdnaKdnaJΔhslV grew better at 37oC than ΔKJ. The results indicate that bacterial Hsp90 mediates the degradation of aggregation-prone Hsp70-Hsp40 substrates, preferably by the HslUV protease. Significance statement: The molecular chaperones Hsp70 and Hsp90 are among the most abundant and well-conserved proteins in all realms of life, forming together the core of the cellular proteostasis network. In eukaryotes, Hsp90 functions in collaboration with Hsp70; we studied this collaboration in E. coli, combining genetic studies with label-free quantitative proteomics in which both protein abundance and protein solubility were quantified. Bacteria lacking Hsp70 (DnaK) and its co-chaperone DnaJ (ΔdnaKdnaJ) grew slower and contained significantly less key metabolic and respiratory enzymes. Unexpectedly, an additional deletion of the Hsp90 (htpG) gene partially restored the WT phenotype. Deletion of the HslV protease in the ΔdnaKdnaJ background also improved growth, suggesting that bacterial Hsp90 mediates the degradation of Hsp70 substrates, preferentially through HslV. At 37oC ΔdnaKdnaJ E. coli mutants grow slower than wild type cells. Quantitative proteomics shows that compared to wild type cells, ΔdnaKdnaJ cells grown at 30oC contain significantly less key metabolic and respiratory enzymes. Unexpectedly, deletion of the HtpG gene in the Δ dnaKdnaJ background ameliorates growth at 37oC and partially restores the cellular levels of some metabolic and respiratory enzymes.
Generalized Negative Correlation Learning for Deep Ensembling

Ensemble algorithms offer state of the art performance in many machine learning applications. A common explanation for their excellent performance is due to the bias-variance decomposition of the mean squared error which shows that the algorithm's error can be decomposed into its bias and variance. Both quantities are often opposed to each other and ensembles offer an effective way to manage them as they reduce the variance through a diverse set of base learners while keeping the bias low at the same time. Even though there have been numerous works on decomposing other loss functions, the exact mathematical connection is rarely exploited explicitly for ensembling, but merely used as a guiding principle. In this paper, we formulate a generalized bias-variance decomposition for arbitrary twice differentiable loss functions and study it in the context of Deep Learning. We use this decomposition to derive a Generalized Negative Correlation Learning (GNCL) algorithm which offers explicit control over the ensemble's diversity and smoothly interpolates between the two extremes of independent training and the joint training of the ensemble. We show how GNCL encapsulates many previous works and discuss under which circumstances training of an ensemble of Neural Networks might fail and what ensembling method should be favored depending on the choice of the individual networks.
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.However, most of them are cumbersome and lack interpretability about their predictions.In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.
Level attraction and exceptional points in a resonant spin-orbit torque system

Level attraction can appear in driven systems where instead of repulsion two modes coalesce in a region separated by two exceptional points. This behavior was proposed for optomechanical and optomagnonic systems, and recently observed for dissipative cavity magnon-polaritons. We demonstrate that such a regime exists in a spin-orbit torque system where a magnetic oscillator is resonantly coupled to an electron reservoir. An instability mechanism necessary for mode attraction can be provided by applying an electric field. The field excites interband transitions between spin-orbit split bands leading to an instability of the magnetic oscillator. Two exceptional points then appear in the oscillator energy spectrum and the region of instability. We discuss conditions under which this can occur and estimate the electric field strength necessary for reaching the attraction region for a spin-orbit torque oscillator with Rashba coupling. A proposal for experimental detection is made using magnetic susceptibility measurements.
Knowledge-Base Practicality for Cybersecurity Research Ethics Evaluation

Research ethics in Information and Communications Technology has seen a resurgence in popularity in recent years. Although a number of general ethics standards have been issued, cyber security specifically has yet to see one. Furthermore, such standards are often abstract, lacking in guidance on specific practices. In this paper we compare peer-reviewed ethical analyses of condemned research papers to analyses derived from a knowledge base (KB) of concrete cyber security research ethics best practices. The KB we employ was compiled in prior work from a large random survey of research papers. We demonstrate preliminary evidence that such a KB can be used to yield comparable or more extensive ethical analyses of published cyber security research than expert application of standards like the Menlo Report. We extend the ethical analyses of the reviewed manuscripts, and calculate measures of the efficiency with which the expert versus KB methods yield ethical insights.
Compositional Scalable Object SLAM

We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large scale indoor reconstruction. To achieve this, we propose a novel semantically assisted data association strategy that obtains unambiguous persistent object landmarks, and a 2.5D compositional rendering method that enables reliable frame-to-model RGB-D tracking. Consequently, we deliver an optimized online implementation that can run at near frame rate with a single graphics card, and provide a comprehensive evaluation against state of the art baselines. An open source implementation will be provided at https://placeholder.
Running Hubble Tension and a H0 Diagnostic.

Hubble tension is routinely presented as a mismatch between the Hubble constant H_0 determined locally and a value inferred from the flat LCDM cosmology. In essence, the tension boils down to a disagreement between two numbers. Here, assuming the tension is cosmological in origin, we predict that within flat LCDM there should be other inferred values of H_0, and that a "running of H_0 with redshift" can be expected. These additional determinations of H_0 may be traced to a difference between the effective equation of state (EoS) of the Universe and the current standard model. We introduce a diagnostic that flags such a running of H_0.
Local vortex line topology and geometry in turbulence

Abstract The local streamline topology classification method of Chong et al. (Phys. Fluids A: Fluid Dyn., vol. 2, no. 5, 1990, pp. 765–777) is adapted and extended to describe the geometry of infinitesimal vortex lines. Direct numerical simulation (DNS) data of forced isotropic turbulence reveals that the joint probability density function (p.d.f.) of the second ($q_\omega$) and third ($r_\omega$) normalized invariants of the vorticity gradient tensor asymptotes to a self-similar bell shape for $Re_\lambda > 200$. The same p.d. f. shape is also seen at the late stages of breakdown of a Taylor–Green vortex suggesting the universality of the bell-shaped p.d. f. form in turbulent flows. Additionally, vortex reconnection from different initial configurations is examined. The local topology and geometry of the reconnection bridge is shown to be nearly identical in all cases considered in this work. Overall, topological characterization of the vorticity field provides a useful analytical basis for examining vorticity dynamics in turbulence and other fluid flows.