Miracle-dz/UniFAR-FTUs huggingface dataset:
An Inline Reticulorumen pH as Biomarker of Cow’s Reproduction and Health Status

study hypothesis is that interline registered pH of a cow’s reticulum can be used as an indicator or biomarker of its health and reproductive status. The main objective of the study was to examine the relationship of pH using the biomarkers of the automatic milking system (AMS) and some parameters of a cow's blood components. The following four main groups were used to classify the cow’s health status including; 15-30d. Postpartum, 1-34d. After insemination, 35 d. after insemination (not pregnant) and 35d. (Pregnant). Using the reticulum pH assay, the animals were categorized as pH <6.22 (5.3% of cows), pH 6.22-6.42 (42.1% of cows), pH 2.6-6.62 (21.1% of cows) and pH >6.62(10.5% of cows). Using milking robots, milk yield, its fat protein, lactose level, somatic cell count and the electron conductivity were registered. Other parameters assessed include the temperature and pH of the contents of reticulorumens. The assessment of the aforementioned parameters was done using specific smaX-tec boluses. Blood gas parameters were assessed using a blood gas analyzer (EPOC, Canada). The study findings indicated that pregnant cows have high pH during insemination than the non-pregnant cows. It was also noted that cows with low fat-protein ratio, lactose level, and high SCC had low reticulorumen pH. They also have the lowest blood PH. It was also noted that with the increase of reticulorumen pH, there is an increased level of blood potassium concentration, high hematocrit, low sodium, and carbon dioxide saturation.
Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.
Colloquium
: Heavy-electron quantum criticality and single-particle spectroscopy

Angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM) have become indispensable tools in the study of correlated quantum materials. Both probe complementary aspects of the single-particle excitation spectrum. Taken together, ARPES and STM have the potential to explore properties of the electronic Green's function, a central object of many-body theory. This review explicates this potential with a focus on heavy-electron quantum criticality, especially the role of Kondo destruction. A discussion on how to probe the Kondo destruction effect across the quantum-critical point using ARPES and STM measurements is presented. Particular emphasis is placed on the question of how to distinguish between the signatures of the initial onset of hybridization-gap formation, which is the "high-energy" physics to be expected in all heavy-electron systems, and those of Kondo destruction, which characterizes the low-energy physics and, hence, the nature of quantum criticality. Recent progress and possible challenges in the experimental investigations are surveyed, the STM and ARPES spectra for several quantum-critical heavy-electron compounds are compared, and the prospects for further advances are outlined.
Photomolecular High-Temperature Superconductivity

Superconductivity in organic conductors is often tuned by the application of chemical or external pressure. With this type of tuning, orbital overlaps and electronic bandwidths are manipulated, whilst the properties of the molecular building blocks remain virtually unperturbed.Here, we show that the excitation of local molecular vibrations in the charge-transfer salt $\kappa-(BEDT-TTF)_2Cu[N(CN)_2]Br$ induces a colossal increase in carrier mobility and the opening of a superconducting-like optical gap. Both features track the density of quasi-particles of the equilibrium metal, and can be achieved up to a characteristic coherence temperature $T^* \approxeq 50 K$, far higher than the equilibrium transition temperature $T_C = 12.5 K$. Notably, the large optical gap achieved by photo-excitation is not observed in the equilibrium superconductor, pointing to a light induced state that is different from that obtained by cooling. First-principle calculations and model Hamiltonian dynamics predict a transient state with long-range pairing correlations, providing a possible physical scenario for photo-molecular superconductivity.
The ALMaQUEST Survey – II. What drives central starbursts at z ∼ 0?

Starburst galaxies have elevated star formation rates (SFRs) for their stellar mass. In Ellison et al. (2018) we used integral field unit (IFU) maps of star formation rate surface density (Sigma_SFR) and stellar mass surface density (Sigma_*) to show that starburst galaxies in the local universe are driven by SFRs that are preferentially boosted in their central regions. Here, we present molecular gas maps obtained with the Atacama Large Millimeter Array (ALMA) observatory for 12 central starburst galaxies at z~0 drawn from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The ALMA and MaNGA data are well matched in spatial resolution, such that the ALMA maps of molecular gas surface density (Sigma_H2) can be directly compared with MaNGA maps at kpc-scale resolution. The combination of Sigma_H2, Sigma_* and Sigma_SFR at the same resolution allow us to investigate whether central starbursts are driven primarily by enhancements in star formation efficiency (SFE) or by increased gas fractions. By computing offsets from the resolved Kennicutt-Schmidt relation (Sigma_H2 vs. Sigma_SFR) and the molecular gas main sequence (Sigma_* vs. Sigma_H2), we conclude that the primary driver of the central starburst is an elevated SFE. We also show that the enhancement in Sigma_SFR is accompanied by a dilution in O/H, consistent with a triggering that is induced by metal poor gas inflow. These observational signatures are found in both undisturbed (9/12 galaxies in our sample) and recently merged galaxies, indicating that both interactions and secular mechanisms contribute to central starbursts.
Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Los Alamos is currently developing novel particle accelerator controls and diagnostics algorithms to enable higher quality beams with lower beam losses than is currently possible. The purpose of this workshop was to consider tuning and optimization challenges of a wide range of particle accelerators including linear proton accelerators such as the Los Alamos Neutron Science Center (LANSCE), rings such as the Advanced Photon Source (APS) synchrotron, free electron lasers (FEL) such as the Linac Coherent Light Source (LCLS) and LCLS-II, the European X-ray Free Electron Laser (EuXFEL), the Swiss FEL, and the planned MaRIE FEL, and plasma wake-field accelerators such as FACET, FACET-II, and AWAKE at CERN. One major challenge is an the ability to quickly create very high quality, extremely intense, custom current and energy profile beams while working with limited real time non-invasive diagnostics and utilizing time-varying uncertain initial beam distributions and accelerator components. Currently, a few individual accelerator labs have been developing and applying their own diagnostics tools and custom control and ML algorithms for automated machine tuning and optimization. The goal of this workshop was to bring together a group of accelerator physicists and accelerator related control and ML experts in order to define which controls and diagnostics would be most useful for existing and future accelerators and to create a plan for developing a new family of algorithms that can be shared and maintained by the community.
Nonsaturating magnetoresistance, anomalous Hall effect, and magnetic quantum oscillations in the ferromagnetic semimetal PrAlSi

We report a comprehensive investigation of the structural, magnetic, transport and thermodynamic properties of a single crystal PrAlSi, in comparison to its nonmagnetic analogue LaAlSi. PrAlSi exhibits a ferromagnetic transition at $T_C$ = 17.8 K which, however, is followed by two weak phase transitions at lower temperatures. Based on the combined dc and ac magnetic susceptibility measurements, we propose the two reentrant magnetic phases below $T_C$ to be spin glasses or ferromagnetic cluster glasses. When the magnetic glassy states are suppressed by small field, several remarkable features appear. These include a linear, nonsaturating magnetoresistance as a function of field that is reminiscent of a topological or charge-compensated semimetal, and a large anomalous Hall conductivity amounting to $\sim$2000 $\Omega ^{-1}$cm$^{-1}$. Specific-heat measurements indicate a non-Kramers doublet ground state and a relatively low crystal electric field splitting of the Pr$^{3+}$ multiplets of less than 100 K. Shubnikov-de Hass oscillations are absent in LaAlSi, whereas they are clearly observed below about 25 K in PrAlSi, with an unusual temperature dependence of the dominating oscillation frequency $F$. It increases from $F$ = 18 T at 25 K to $F$ = 33 T at 2 K, hinting at an emerging Fermi pocket upon cooling into the ordered phase. These results suggest that PrAlSi is a new system where a small Fermi pocket of likely relativistic fermions is strongly coupled to magnetism. Whether hybridization between $f$ and conduction band is also involved remains an intriguing open problem.
Quantum simulation of Unruh-DeWitt detectors with nonlinear optics

We propose a method for simulating an Unruh-DeWitt detector, coupled to a 1+1-dimensional massless scalar field, with a suitably engineered χ(2) nonlinear interaction. In this simulation, the parameter playing the role of the detector acceleration is played by the relative inverse-group-velocity gradient inside the nonlinear material. We identify experimental parameters that tune the detector energy gap, acceleration, and switching function. This system can simulate time-dependent acceleration, time-dependent detector energy gaps, and nonvacuum initial detector-field states. Furthermore, for very short materials, the system can simulate the weak anti-Unruh effect, in which the response of the detector decreases with acceleration.
Proposal for experimental observation of the twisted photons in transition and Vavilov-Cherenkov radiations

We propose to use Vavilov-Cherenkov (VC) and transition radiations as a source of twisted photons in a wide range of energies. The experimental setup to observe the orbital angular momentum of photons constituting those radiations is described. The radiation produced by relativistic electrons and ions in passing dielectric plates is considered. The experimental verification of the addition rule for the total angular momentum in the radiation of twisted photons by helically microbunched beams is proposed for VC and transition radiations. The parameters of the experiments are presented.
A redshift-dependent IRX–β dust attenuation relation for TNG50 galaxies

We study the relation between the UV-slope, $\beta$, and the ratio between the IR and UV luminosities of galaxies, IRX, using TNG50, the latest installment of the IllustrisTNG galaxy formation simulations. We select 7280 TNG50 star-forming galaxies with stellar mass above $10^9M_\odot$ at selected redshifts, $0 \leq z \leq 4$, and perform radiative transfer calculations with SKIRT to model the effects of ISM dust on the simulated stellar light. We adopt a Milky Way (MW) type dust and a fixed dust-to-metal ratio of 0.3 throughout and find that TNG50 star-forming main-sequence galaxies agree with the empirically-derived reference IRX-$\beta$ relations at $z \lesssim 1$. There appears to be a redshift-dependent systematic offset with respect to the reference relations, with the TNG50 IRX-$\beta$ median relation steepening towards higher redshifts. This is to first order driven by variations in intrinsic UV-slopes due to different star-formation histories of galaxies selected at different cosmic epochs. Once differences in the intrinsic UV-slope are corrected for, TNG50 galaxies exhibit a scatter of 0.3 dex in IRX at fixed $\beta$ at all studied redshifts. These galaxy-to-galaxy variations are correlated with intrinsic galaxy properties, such that at fixed UV-slope galaxies with higher star--formation rates, star--formation efficiencies, gas metallicities, and stellar masses exhibit larger IRX values. We additionally demonstrate a degeneracy between stellar age and dust type. The combination of young stellar population age with a SMC type of dust for high-redshift ($z=4$) TNG50 galaxies can result in an IRX-$\beta$ relation similar to what is found for low-redshift galaxies with MW dust. This hampers the use of the IRX-$\beta$ relation as a proxy for dust type. We provide a redshift-dependent fitting function for the IRX-$\beta$ relation with MW dust based on our numerical models.
Subhalo mass function and ultralight bosonic dark matter

Warm dark matter has recently become increasingly constrained by observational inferences about the low-mass end of the subhalo mass function, which would be suppressed by dark matter free streaming in the early Universe. In this work, we point out that a constraint can be placed on ultralight bosonic dark matter (often referred to as "fuzzy dark matter") based on similar considerations. Recent limits on warm dark matter from strong gravitational lensing of quasars and from fluctuations in stellar streams separately translate to a lower limit of $\sim 2.1 \times 10^{-21}$ eV on the mass of an ultralight boson comprising all dark matter. These limits are complementary to constraints on ultralight dark matter from the Lyman-$\alpha$ forest and are subject to a completely different set of assumptions and systematic uncertainties. Taken together, these probes strongly suggest that dark matter with a mass $\sim 10^{-22}$ eV is not a viable way to reconcile differences between cold dark matter simulations and observations of structure on small scales.
Efficient coding in the economics of human brain connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior—beyond the conventional network efficiency metric—for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity. Author Summary Macroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression.
On the origin of low escape fractions of ionizing radiation from massive star-forming galaxies at high redshift

The physical origin of low escape fractions of ionizing radiation derived from massive star-forming galaxies at z ∼ 3–4 is not well understood. We perform idealized disc galaxy simulations to understand how galactic properties such as metallicity and gas mass affect the escape of Lyman continuum (LyC) photons using radiation-hydrodynamic simulations with strong stellar feedback. We find that the luminosity-weighted escape fraction from a metal-poor (Z = 0.002) galaxy embedded in a halo of mass $M_{\rm h}\simeq 10^{11}\, \mathrm{M}_\odot$ is $\left\langle {f_{\rm esc}^{\rm 3D}}\right\rangle \simeq 10\, {{\ \rm per\ cent}}$. Roughly half of the LyC photons are absorbed within scales of 100 pc, and the other half is absorbed in the ISM ($\lesssim 2\, {\rm kpc}$). When the metallicity of the gas is increased to Z = 0.02, the escape fraction is significantly reduced to $\left\langle {f_{\rm esc}^{\rm 3D}}\right\rangle \simeq 1{{\ \rm per\ cent}}$ because young stars are enshrouded by their birth clouds for a longer time. In contrast, increasing the gas mass by a factor of 5 leads to $\left\langle {f_{\rm esc}^{\rm 3D}}\right\rangle \simeq 5\, {{\ \rm per\ cent}}$ because LyC photons are only moderately absorbed by the thicker disc. Our experiments suggest that high metallicity is likely more responsible for the low escape fractions observed in massive star-forming galaxies, supporting the scenario in which the escape fraction is decreasing with increasing halo mass. Finally, negligible correlation is observed between the escape fraction and surface density of star formation or galactic outflow rates.
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial

ABSTRACT In this manuscript, we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the Brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence.
Tailoring a web-based weight maintenance intervention for Northern Plains American Indian public university students

Objective: High levels of obesity-related health disparities are common among US American Indian (AI) populations. AI public university students often face unique challenges that may contribute to weight gain and related consequences. Few weight maintenance interventions have been developed that meet the needs of AI public university students. The goal of this study was to determine how to tailor Project Y.E. A.H. (Young Americans Eating and Active for Health), a web-delivered, weight maintenance tool developed for college students to make it culturally relevant for AI public university students. Methods: Three mini focus groups (N = 15) and five in-depth interviews (N = 11) were conducted with self-identified AI college students on the campus of South Dakota State University. Participants were recruited with assistance from the American Indian Education and Cultural Center and through campus publicity. Recorded sessions were transcribed and multistep coding was used to assess the aesthetic appeal of the website and determine variations of certain health-related factors. Results: AI public university students have specific and culturally influenced aesthetic, food and physical activity exposures and preferences. AI students may perceive unique stressors compared to non-AI students and practise cultural forms of stress relief. Conclusion: Specific and culturally relevant psychographic information regarding AI college student preferences is needed for the development of effective weight maintenance tools.
Effective Maxillary Protraction with Tandem Traction Bow Appliance

Tandem traction bow appliance (TTBA) promotes patient com pliance, because it is more esthetic and comfortable than extraoral appliances. TTBA should be used only in case where maxillary deficiency and normal mandible is present. Advantages of it includes good oral hygiene, early treatment of any Class III malocclusion, optimal retention, distribution of the forces for protraction to all maxillary teeth, free mandibular movement. It can be used in conjunction with fixed appliances if necessary. This paper includes two case reports. The treatment results in both the cases demonstrated significant skeletal and dental response to TTBA therapy. Skeletal change was primarily a result of anterior movement of the maxilla.