Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Planetary Mass with Deep Learning

While thousands of exoplanets have been confirmed, the known properties about individual discoveries remain sparse and depend on detection technique. To utilize more than a small section of the exoplanet data set, tools need to be developed to estimate missing values based on the known measurements. Here, we demonstrate the use of a neural network that models the density of planets in a space of six properties that is then used to impute a probability distribution for missing values. Our results focus on planetary mass, which neither the radial velocity nor transit techniques for planet identification can provide alone. The neural network can impute mass across the four orders of magnitude in the exoplanet archive, and return a distribution of masses for each planet that can inform us about trends in the underlying data set. The average error on this mass estimate from a radial velocity detection is a factor of 1.5 of the observed value, and 2.7 for a transit observation. The mass of Proxima Centauri b found by this method is M⊕, where the upper and lower bounds are derived from the root mean square deviation from the log mass probability distribution. The network can similarly impute the other potentially missing properties, and we use this to predict planet radius for radial velocity measurements, with an average error of a factor 1.4 of the observed value. The ability of neural networks to search for patterns in multidimensional data means that such techniques have the potential to greatly expand the use of the exoplanet catalog.
Greedy Algorithms for Fair Division of Mixed Manna

We consider a multi-agent model for fair division of mixed manna (i.e. items for which agents can have positive, zero or negative utilities), in which agents have additive utilities for bundles of items. For this model, we give several general impossibility results and special possibility results for three common fairness concepts (i.e. EF1, EFX, EFX3) and one popular efficiency concept (i.e. PO). We also study how these interact with common welfare objectives such as the Nash, disutility Nash and egalitarian welfares. For example, we show that maximizing the Nash welfare with mixed manna (or minimizing the disutility Nash welfare) does not ensure an EF1 allocation whereas with goods and the Nash welfare it does. We also prove that an EFX3 allocation may not exist even with identical utilities. By comparison, with tertiary utilities, EFX and PO allocations, or EFX3 and PO allocations always exist. Also, with identical utilities, EFX and PO allocations always exist. For these cases, we give polynomial-time algorithms, returning such allocations and approximating further the Nash, disutility Nash and egalitarian welfares in special cases.
Kondo scenario of the {\gamma}-{\alpha} phase transition in single crystalline Cerium thin films

The physical mechanism driving the $\gamma$-$\alpha$ phase transition of face-centre-cubic (fcc) cerium (Ce) remains controversial until now. In this work, high quality single crystalline fcc-Ce thin films were grown on Graphene/6$H$-SiC(0001) substrate, and explored by XRD and ARPES measurement. XRD spectra showed a clear $\gamma$-$\alpha$ phase transition at $T_{\gamma-\alpha}\approx$ 50 K, which is retarded by strain effect from substrate comparing with $T_{\gamma-\alpha}$ (about 140 K) of the bulk Ce metal. However, APRES spectra did not show any signature of $\alpha$-phase emerging in the surface-layer from 300 K to 17 K, which implied that $\alpha$-phase might form at the bulk-layer of our Ce thin films. Besides, an evident Kondo dip near Fermi energy was observed in the APRES spectrum at 80 K, indicting the formation of Kondo singlet states in $\gamma$-Ce. Furthermore, the DFT+DMFT calculations were performed to simulate the electronic structures and the theoretical spectral functions agreed well with the experimental ARPES spectra. In $\gamma$-Ce, the behavior of the self-energy's imaginary part at low frequency not only confirmed that the Kondo singlet states emerged at $T_{\rm KS} \geq 80$ K, but also implied that they became coherent states at a lower characteristic temperature ($T_{\rm coh}\sim 40$ K) due to the indirect RKKY interaction among $f$-$f$ electrons. Besides, $T_{\rm coh}$ from the theoretical simulation was close to $T_{\gamma-\alpha}$ from the XRD spectra. These issues suggested that the Kondo scenario might play an important role in the $\gamma$-$\alpha$ phase transition of cerium thin films.
Extracting galaxy merger timescales I: Tracking haloes with WhereWolf and spinning orbits with OrbWeaver

Hierarchical models of structure formation predict that dark matter halo assembly histories are characterised by episodic mergers and interactions with other haloes. An accurate description of this process will provide insights into the dynamical evolution of haloes and the galaxies that reside in them. Using large cosmological N-body simulations, we characterise halo orbits to study the interactions between substructure haloes and their hosts, and how different evolutionary histories map to different classes of orbits. We use two new software tools - WhereWolf, which uses halo group catalogues and merger trees to ensure that haloes are tracked accurately in dense environments, and OrbWeaver, which quantifies each halo's orbital parameters. We demonstrate how WhereWolf improves the accuracy of halo merger trees, and we use OrbWeaver to quantify orbits of haloes. We assess how well analytical prescriptions for the merger timescale from the literature compare to measured merger timescales from our simulations and find that existing prescriptions perform well, provided the ratio of substructure-to-host mass is not too small. In the limit of small substructure-to-host mass ratio, we find that the prescriptions can overestimate the merger timescales substantially, such that haloes are predicted to survive well beyond the end of the simulation. This work highlights the need for a revised analytical prescription for the merger timescale that more accurately accounts for processes such as catastrophic tidal disruption.
Data-Driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively considering the properties of the highly dynamic radio channel within the communication systems themselves. Since real world experiments are highly time-consuming and lack a controllable environment, performance evaluations and parameter studies for novel anticipatory vehicular communication systems are typically carried out based on network simulations. However, due to the required simplifications and the wide range of unknown parameters (e.g., Mobile Network Operator (MNO)-specific configurations of the network infrastructure), the achieved results often differ significantly from the behavior in real world evaluations. In this paper, we present Data-driven Network Simulation (DDNS) as a novel data-driven approach for analyzing and optimizing anticipatory vehicular communication systems. Different machine learning models are combined for achieving a close to reality representation of the analyzed system’s behavior. In a proof of concept evaluation focusing on opportunistic vehicular data transfer, the proposed method is validated against field measurements and system-level network simulation. In contrast to the latter, DDNS does not only provide massively faster result generation, it also achieves a significantly better representation of the real world behavior due to implicit consideration of cross-layer dependencies by the machine learning approach.
Pattern of Injuries in Homicidal Deaths in Bhopal Region

A serious danger posed in front of humanity is the danger from death by murder. In spite of all the measures, there has been increase in incidence of homicide all over the world including India. The present study was based on retrospective analysis of 218 homicidal deaths in three years period from January 2004 to December 2006 from autopsies done in the Department of Forensic Medicine and Toxicology, Gandhi Medical College, Bhopal and Medico legal Institute, Home (Police) department, Government of Madhya Pradesh. The present study is carried out to assess the most vulnerable age group, sex incidence, seasonal variation, hospitalization, survival time, fatal period, organs involved and weapons used in order to get better perspective of the situation. The incidence of homicide was found to be 3.82%, Male to female ratio was 2.9: 1. Third decade was most affected age group (39.44%). Homicide by physical assault was found in 84.4% cases while asphyxia in 35.6% cases. Stabbing was the most common type of injury (42.9% cases) and head & face was the most common site of body involved (33.5% cases). The data collected is compared with previously published literature.
Appearance Composing GAN: A General Method for Appearance-Controllable Human Video Motion Transfer

Due to the rapid development of GANs, there has been significant progress in the field of human video motion transfer which has a wide range of applications in computer vision and graphics. However, existing works only support motion-controllable video synthesis while appearances of different video components are bound together and uncontrollable, which means one person can only appear with the same clothing and background. Besides, most of these works are person-specific and require to train an individual model for each person, which is inflexible and inefficient. Therefore, we propose appearance composing GAN: a general method enabling control over not only human motions but also video appearances for arbitrary human subjects within only one model. The key idea is to exert layout-level appearance control on different video components and fuse them to compose the desired full video scene. Specifically, we achieve such appearance control by providing our model with optimal appearance conditioning inputs obtained separately for each component, allowing controllable component appearance synthesis for different people by changing the input appearance conditions accordingly. In terms of synthesis, a two-stage GAN framework is proposed to sequentially generate the desired body semantic layouts and component appearances, both are consistent with the input human motions and appearance conditions. Coupled with our ACGAN loss and background modulation block, the proposed method can achieve general and appearance-controllable human video motion transfer. Moreover, we build a dataset containing a large number of dance videos for training and evaluation. Experimental results show that, when applied to motion transfer tasks involving a variety of human subjects, our proposed method achieves appearance-controllable synthesis with higher video quality than state-of-arts based on only one-time training.
Gravitational waves from the quasicircular inspiral of compact binaries in Einstein-aether theory

We study gravitational waves emitted by a binary system of nonspinning bodies in a quasicircular inspiral within the framework of Einstein-aether theory. In particular, we compute explicitly and analytically the expressions for the time-domain and frequency-domain waveforms, gravitational wave polarizations, and response functions for both ground- and space-based detectors in the post-Newtonian approximation. We find that, when going beyond the leading order in the post-Newtonian approximation, the non-Einsteinian polarization modes contain terms that depend on both the first and the second harmonics of the orbital phase. We also calculate analytically the corresponding parametrized post-Einsteinian parameters, generalizing the existing framework to allow for different propagation speeds among the scalar, vector, and tensor modes, without assumptions about the magnitude of its coupling parameters, and meanwhile, allowing the binary system to have relative motions with respect to the aether field. Such results allow for the easy construction of Einstein-aether templates that could be used in Bayesian tests of general relativity in the future.
An Examination of the Career Paths and Professional Challenges of Women in Management Positions in Major University and College Transportation Departments

Women have been involved in the field of transportation since the 1800s and comprise almost half of today‟s workforce, yet the transportation industry continues to be male-dominated. The Transportation Research Board in its 2000 Task Force on Women‟s Issues in Transportation identified a need to learn more about women leaders in transportation. Such knowledge would help the transportation industry in the pursuit and hiring of women and also assist those wishing to pursue a career in transportation. This study provides information on the demographics, career paths, and professional challenges of women managers in major university transportation departments.
Accretion History of AGNs. II. Constraints on AGN Spectral Parameters Using the Cosmic X-Ray Background

We constrain X-ray spectral shapes for the ensemble of active galactic nuclei (AGNs) based on the shape of the cosmic X-ray background (CXB). Specifically, we rule out regions of X-ray spectral parameter space that do not reproduce the CXB in the energy range 1–100 keV. The key X-ray spectral parameters are the photon index, Γ; the cutoff energy, Ecutoff; and the reflection scaling factor, R. Assuming each parameter follows a Gaussian distribution, we first explore the parameter space using a Bayesian approach and a fixed X-ray luminosity function (XLF). For σE = 36 keV and σR = 0.14, fixed at the observed values from the Swift-BAT 70-month sample, we allow and to vary subject to reproducing the CXB. We report results for σΓ = 0.1–0.5. In an alternative approach, we define the parameter distributions, then forward model to fit the CXB by perturbing the XLF using a neural network. This approach allows us to rule out parameter combinations that cannot reproduce the CXB for any XLF. The marginalized conditional probabilities for the four free parameters are: and . We provide an interactive online tool for users to explore any combination of , σE, , σΓ, , and σR, including different distributions for each absorption bin, subject to the integral CXB constraint. The distributions observed in many AGN samples can be ruled out by our analysis, meaning these samples cannot be representative of the full AGN population. The few samples that fall within the acceptable parameter space are hard-X-ray-selected, commensurate with their having fewer selection biases.
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end. Towards this aim, we present BPnP, a novel network module that backpropagates gradients through a Perspective-n-Points (PnP) solver to guide parameter updates of a neural network. Based on implicit differentiation, we show that the gradients of a ``self-contained" PnP solver can be derived accurately and efficiently, as if the optimizer block were a differentiable function. We validate BPnP by incorporating it in a deep model that can learn camera intrinsics, camera extrinsics (poses) and 3D structure from training datasets. Further, we develop an end-to-end trainable pipeline for object pose estimation, which achieves greater accuracy by combining feature-based heatmap losses with 2D-3D reprojection errors. Since our approach can be extended to other optimization problems, our work helps to pave the way to perform learnable geometric vision in a principled manner. Our PyTorch implementation of BPnP is available on http://github.com/BoChenYS/BPnP.
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis

In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision. As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e,g. images from other farms) is usually dropped significantly. However, this problem has not been sufficiently explored, since many systems have shown excellent diagnostic performance due to the bias caused by the similarities in the dataset. In this study, we investigate this problem with experiments using more than 50,000 images of cucumber leaves, and propose an anti-overfitting pretreatment (AOP) for realizing practical image-based plant diagnosis systems. The AOP detects the area of interest (leaf, fruit etc.) and performs brightness calibration as a preprocessing step. The experimental results demonstrate that our AOP can improve the accuracy of diagnosis for unknown test images from different farms by 12.2% in a practical setting.
The effect of diffuse background on the spatially-resolved Schmidt relation in nearby spiral galaxies

Context. The global Schmidt law of star formation provides a power-law relation between the surface densities of star-formation rate (SFR) and gas, and successfully explains plausible scenarios of galaxy formation and evolution. However, star formation being a multi-scale process, requires spatially-resolved analysis for a better understanding of the physics of star formation. Aims. It has been shown that the removal of a diffuse background from SFR tracers, such as Hα, far-ultraviolet (FUV), infrared, leads to an increase in the slope of the sub-galactic Schmidt relation. We reinvestigate the local Schmidt relations in nine nearby spiral galaxies taking into account the effect of inclusion and removal of diffuse background in SFR tracers as well as in the atomic gas. Methods. We used multiwavelength data obtained as part of the Spitzer Infrared Nearby Galaxies Survey, Key Insights on Nearby Galaxies: a Far-Infrared Survey with Herschel, The H I Nearby Galaxy Survey, and HERA CO-Line Extragalactic Survey. Making use of a novel split of the overall light distribution as a function of spatial scale, we subtracted the diffuse background in the SFR tracers as well as the atomic gas. Using aperture photometry, we study the Schmidt relations on background subtracted and unsubtracted data at physical scales varying between 0.5–2 kpc. Results. The fraction of diffuse background varies from galaxy to galaxy and accounts to ∼34% in Hα, ∼43% in FUV, ∼37% in 24 μm, and ∼75% in H I on average. We find that the inclusion of diffuse background in SFR tracers leads to a linear molecular gas Schmidt relation and a bimodal total gas Schmidt relation. However, the removal of diffuse background in SFR tracers leads to a super-linear molecular gas Schmidt relation. A further removal of the diffuse background from atomic gas results in a slope ∼1.4 ± 0.1, which agrees with dynamical models of star formation accounting for flaring effects in the outer regions of galaxies.
ExoMol molecular line lists – XXXV. A rotation-vibration line list for hot ammonia

A new hot line list for 14NH3 is presented. The line list CoYuTe was constructed using an accurate, empirically refined potential energy surface and a CCSD(T)/aug-cc-pVQZ ab initio dipole moment surface of ammonia, previously reported. The line list is an improvement of the ammonia line list BYTe. The CoYuTe line list covers wavenumbers up to 20 000 cm−1, i.e. wavelengths beyond 0.5 μm for temperatures up to 1500 K. Comparisons with the high temperature experimental data from the literature show excellent agreement for wavenumbers below 6000 cm−1. The CoYuTe line list contains 16.9 billion transitions and is available from the ExoMol website (www.exomol.com) and the CDS data base.
Astrophysical and local constraints on string theory: Runaway dilaton models

One of the clear predictions of string theory is the presence of a dynamical scalar partner of the spin-2 graviton, known as the dilaton. This will violate the Einstein Equivalence Principle, leading to a plethora of possibly observable consequences which is a cosmological context include dynamical dark energy and spacetime variations of nature's fundamental constants. The runaway dilaton scenario of Damour, Piazza and Veneziano is a particularly interesting class of string theory inspired models which can in principle reconcile a massless dilaton with experimental data. Here we use the latest background cosmology observations, astrophysical and laboratory tests of the stability of the fine-structure constant and local tests of the Weak Equivalence Principle to provide updated constraints on this scenario, under various simplifying assumptions. Overall we find consistency with the standard $\Lambda$CDM paradigm, and we improve the existing constraints on the coupling of the dilaton to baryonic matter by a factor of six, and to the dark sector by a factor of two. At the one sigma level the current data already excludes dark sector couplings of order unity, which would be their natural value.
Gating Revisited: Deep Multi-Layer RNNs That can be Trained

We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM [16] and GRU [10] while being more robust against vanishing or exploding gradients. Stacking recurrent units into deep architectures suffers from two major limitations: (i) many recurrent cells (e.g., LSTMs) are costly in terms of parameters and computation resources; and (ii) deep RNNs are prone to vanishing or exploding gradients during training. We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network in the ”vertical” direction. We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified. Based on our analysis we design a new type of gated cell that better preserves gradient magnitude. We validate our design on a large number of sequence modelling tasks and demonstrate that the proposed STAR cell allows to build and train deeper recurrent architectures, ultimately leading to improved performance while being computationally more efficient.
Fano profile in a novel double cavity optomechanical system with harmonically bound mirrors

We investigate a double cavity optomechanical system generating single and double Fano resonance (multi-Fano). By altering a single parameter, the tunnelling rate g of the middle mirror, we are able to switch between single and double Fano line shapes. The first spectral line shape is stronger in the case of multi-Fano than in the case of single Fano. Also the behaviour of the steady state value of the displacement of the middle mirror, with respect to g, heavily influences the behaviour of double Fano lines in our scheme. This tunability along with using a single pump and signal/probe laser has an added advantage in situations where only low power consumption is available.