Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary status of Polaris

Hydrodynamic models of short--period Cepheids were computed to determine the pulsation period as a function of evolutionary time during the first and third crossings of the instability strip. The equations of radiation hydrodynamics and turbulent convection for radial stellar pulsations were solved with the initial conditions obtained from the evolutionary models of population I stars (X=0.7, Z=0.02) with masses from 5.2 to 6.5 Msol and the convective core overshooting parameter 0.1 <= aov <= 0.3. In Cepheids with period of 4 d the rate of pulsation period change during the first crossing of the instability strip is over fifty times larger than that during the third crossing. Polaris is shown to cross the instability strip for the first time and to be the fundamental mode pulsator. The best agreement between the predicted and observed rates of period change was obtained for the model with mass of 5.4 Msol and the overshooting parameter aov=0.25. The bolometric luminosity and radius are L = 1.26e3 Lsol and R = 37.5 Rsol, respectively. In the HR diagram Polaris is located at the red edge of the instability strip.
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The discriminator is trained on the crafted perturbations by the agent during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. ["relive a GAN"or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. Code will be available at https://github.com/rhr407/RELEVAGAN.
Audio-Visual Face Reenactment

This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to improve the structure of the reconstructed faces. Finally, we improve the visual quality of the generations by incorporating a carefully designed identity-aware generator module. The identity-aware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-of-the-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low bandwidth video calls. We release a demo video and additional information at http://cvit.iiit. ac.in/research/projects/cvit-projects/avfr.
Learning Autonomous Vehicle Safety Concepts from Demonstrations

Evaluating the safety of an autonomous vehicle (AV) depends on the behavior of surrounding agents which can be heavily influenced by factors such as environmental context and informally-defined driving etiquette. A key challenge is in determining a minimum set of assumptions on what constitutes reasonable foreseeable behaviors of other road users for the development of AV safety models and techniques. In this paper, we propose a data-driven AV safety design methodology that first learns "reasonable" behavioral assumptions from data, and then synthesizes an AV safety concept using these learned behavioral assumptions. We borrow techniques from control theory, namely high-order control barrier functions and Hamilton-Jacobi reachability, to provide inductive bias to aid interpretability, verifiability, and tractability of our approach. In our experiments, we learn an AV safety concept using demonstrations collected from a highway traffic-weaving scenario, compare our learned concept to existing baselines, and showcase its efficacy in evaluating real-world driving logs.
Practitioner Trajectories of Engagement with Ethics-Focused Method Creation

Design and technology practitioners are increasingly aware of the ethical impact of their work practices, desiring tools to support their ethical awareness across a range of contexts. In this paper, we report on findings from a series of co-design workshops with technology and design practitioners that supported their creation of a bespoke ethics-focused action plan. Using a qualitative content analysis and thematic analysis approach, we identified a range of roles and process moves that practitioners employed and illustrate the interplay of these elements of practitioners' instrumental judgment through a series of three cases, which includes evolution of the action plan itself, the ethical dilemmas or areas of support the action plan was intended to support, and how the action plan represents resonance for the practitioner that created it. We conclude with implications for supporting ethics in socio-technical practice and opportunities for the further development of ethics-focused methods that are resonant with the realities of practice.
Hunting for the signatures of molecular cloud formation

In order to understand how molecular clouds form in the Galactic interstellar medium, we would like to be able to map the structure and kinematics of the gas flows responsible for forming them. However, doing so is observationally challenging. CO, the workhorse molecule for studies of molecular clouds, traces only relatively dense gas and hence only allows us to study those portions of the clouds that have already assembled. Numerical simulations suggest that the inflowing gas that forms these clouds is largely composed of CO-dark H2. These same simulations allow us to explore the usefulness of different tracers of this CO-dark molecular material, and we use them here to show that the [CII] fine structure line is potentially a very powerful tracer of this gas and should be readily detectable using modern instrumentation.
Quantum spin Hall phase in GeSn heterostructures on silicon

Quantum phases of solid-state electron systems look poised to sustain exotic phenomena and a very rich spin physics. We propose a practical silicon-based architecture that spontaneously sustains topological properties, while being fully compatible with the high-volume manufacturing capabilities of modern microelectronic foundries. Here we show how Ge1-xSnx alloys, an emerging group IV semiconductor, can be engineered into junctions that demonstrate a broken gap alignment. We predict such basic building block undergo a quantum phase transition that can elegantly accommodate the existence of gate-controlled chiral edge states directly on Si. This will enable tantalizing prospects for designing integrated circuits hosting quantum spin hall insulators and advanced topological functionalities.
Optical Observations of the Rapidly Expanding Type Ia Supernova 2007gi

We present optical photometry and spectra for the Type Ia supernova (SN Ia) 2007gi in the nearby galaxy NGC 4036. SN 2007gi is characterized by extremely high-velocity (HV) features of the intermediate-mass elements (Si, Ca, and S), with expansion velocities (vexp) approaching ∼15,500 km s-1 near-maximum brightness (compared to ∼10,600 km s-1 for SNe Ia with normal vexp). SN 2007gi reached a B-band peak magnitude of 13.25 ± 0.04 mag with a decline rate of Δm15(B)(true) = 1.33 ± 0.09 mag. The B-band light curve of SN 2007gi demonstrated an interesting two-stage evolution during the nebular phase, with a decay rate of 1.16 ± 0.05 mag (100 days)-1 during t = 60–90 days and 1.61 ± 0.04 mag (100 days)-1 thereafter. Such behavior was also observed in the HV SN Ia 2006X, and might be caused by the interaction between supernova ejecta and circumstellar material (CSM) around HV SNe Ia. Based on a sample of a dozen well-observed R band (or unfiltered) light curves of SNe Ia, we confirm that the HV events may have a faster rise time to maximum than those with normal vexp.
MuS2: A Benchmark for Sentinel-2 Multi-Image Super-Resolution

—Insufﬁcient spatial resolution of satellite imagery, including Sentinel-2 data, is a serious limitation in many practical use cases. To mitigate this problem, super-resolution reconstruction is receiving considerable attention from the remote sensing community. When it is performed from multiple images captured at subsequent revisits, it may beneﬁt from information fusion, leading to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-life benchmark datasets—most of the research was performed for simulated data which do not fully reﬂect the operating conditions. In this letter, we introduce a new MuS2 benchmark for multi-image super-resolution reconstruction of Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the ﬁrst end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image
A Real2Sim2Real Method for Robust Object Grasping with Neural Surface Reconstruction

We explore an emerging technique, geometric Real2Sim2Real, in the context of object manipulation. We hypothesize that recent 3D modeling methods provides a path towards building digital replicas of real-world scenes that afford physical simulation and support robust manipulation algorithm learning. Since 6 DOF grasping is one the most important primitives for all manipulation tasks, we study whether geometric Real2Sim2Real can help us train a robust grasping network with high sample efficiency. We propose to reconstruct high-quality meshes from real-world point clouds using state-of-the-art neural surface reconstruction method (the Real2Sim step). Because most simulators take meshes for fast simulation, the reconstructed meshes enable grasp pose labels generation without human efforts. The generated labels can train grasp network that performs robustly in real evaluation scenes (the Sim2Real step). In synthetic and real experiments, we show that the Real2Sim2Real pipeline performs better than baseline grasp networks trained with a ${1}0^{4}\times$ larger dataset by mimicking geometric shapes of target objects in simulation. We also show that our method has better sample efficiency than training the grasping network with a retrieval-based scene reconstruction method. The benefit of the Real2Sim2Real pipeline comes from 1) decoupling scene modeling and grasp sampling into sub-problems, and 2) both sub-problems can be solved with sufficiently high quality using recent 3D learning algorithms and mesh-based physical simulation techniques. Video presentation available at this link.
Rapid reconstruction of compact binary sources using meshfree approximation

Bayesian inference of the properties of compact binary sources detected by gravitational wave detectors is a computationally challenging task. For the twin advanced LIGO detectors operating at design sensitivity, it is estimated to take several weeks to months of wall clock time to reconstruct a single binary neutron star source using current approaches. In this context, we present a new, computationally efficient way of rapidly reconstructing the source properties using a combination of numerical linear algebra and meshfree interpolation techniques. For a canonical binary neutron star system, we show that the method proposed in this Letter is ~ 4000 times faster than traditional algorithms, at a negligible loss of accuracy of O(10^{-5}) across the sample space. This implies that the properties of such sources can be accurately measured within a few minutes of their detection in upcoming science runs, which will have significant ramifications for their prompt electromagnetic follow-up. The blueprint of this idea can be applied to Bayesian inference in other domains
Joint Entropy Search for Multi-objective Bayesian Optimization

Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi-objective Bayesian optimization (BO) is a sample efficient strategy that can be deployed to solve these vector-valued optimization problems where access is limited to a number of noisy objective function evaluations. In this paper, we propose a novel information-theoretic acquisition function for BO called Joint Entropy Search (JES), which considers the joint information gain for the optimal set of inputs and outputs. We present several analytical approximations to the JES acquisition function and also introduce an extension to the batch setting. We showcase the effectiveness of this new approach on a range of synthetic and real-world problems in terms of the hypervolume and its weighted variants.
Synchrotron Cutoff in Ultraluminous X-Ray Sources

The origin of spectral curvature at energies E ≃ 10 keV in ultraluminous X-ray sources (ULXs) is not well understood. In this paper, we propose a novel mechanism based on synchrotron radiation to explain this cutoff. We show that relativistic plasma can give rise to the observed spectral curvature for neutron star magnetic fields due to the variation in the latitude of synchrotron radiation. We analyze the NuSTAR data of two bright pulsar ULXs, NGC 5907 ULX1 and NGC 7793 P13, and provide estimates of the physical parameters of these sources. We fit the data for synchrotron emission at various latitudes and show that the spectral cutoff in these cases can be explained for a large range of acceptable physical parameters, e.g., a semirelativistic plasma with γ ≃ 20 for high latitudes or a highly relativistic plasma (γ ≃ 105) for emission close to the electron’s orbital plane in a typical magnetic field of B ≃ 1012 G. We also discuss how such an emission mechanism can be distinguished from other proposed models. A corollary to our study is that most ULXs might be neutron stars as they display such a spectral cutoff.
Content-based Search for Deep Generative Models

The growing proliferation of customized and pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query. As each generative model produces a distribution of images, we formulate the search task as an optimization problem to select the model with the highest probability of generating similar content as the query. We introduce a formulation to approximate this probability given the query from different modalities, e.g., image, sketch, and text. Furthermore, we propose a contrastive learning framework for model retrieval, which learns to adapt features for various query modalities. We demonstrate that our method outperforms several baselines on Generative Model Zoo, a new benchmark we create for the model retrieval task.
Block-Structured Optimization for Subgraph Detection in Interdependent Networks

We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. Specifically, we design an effective, efficient, and parallelizable projection algorithm, namely Graph Block-structured Gradient Projection (GBGP), to optimize a general non-linear function subject to graph-structured constraints. We prove that our algorithm: 1) runs in nearly-linear time on the network size; 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications and conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.
Optima and Simplicity in Nature

Why are simple, regular, and symmetric shapes common in nature? Many natural shapes arise as solutions to energy minimisation or other optimisation problems, but is there a general relation between optima and simple, regular shapes and geometries? Here we argue from algorithmic information theory that for objective functions common in nature — based on physics and engineering laws — optimal geometries will be simple, regular, and symmetric. Further, we derive a null model prediction that if a given geometry is an optimal solution for one natural objective function, then it is a priori more likely to be optimal or close to optimal for another objective function.
Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems

Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this letter, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.
The Circumgalactic Medium of Extreme Emission Line Galaxies at z∼2: Resolved Spectroscopy and Radiative Transfer Modeling of Spatially Extended Lyα Emission in the KBSS-KCWI Survey

The resonantly scattered Lyα line illuminates the extended halos of neutral hydrogen in the circumgalactic medium of galaxies. We present integral field Keck Cosmic Web Imager observations of double-peaked, spatially extended Lyα emission in 12 relatively low-mass (M ⋆ ∼ 109 M⊙) z ∼ 2 galaxies characterized by extreme nebular emission lines. Using individual spaxels and small bins as well as radially binned profiles of larger regions, we find that for most objects in the sample the Lyα blue-to-red peak ratio increases, the peak separation decreases, and the fraction of flux emerging at line center increases with radius. We use new radiative transfer simulations to model each galaxy with a clumpy, multiphase outflow with radially varying outflow velocity, and self-consistently apply the same velocity model to the low-ionization interstellar absorption lines. These models reproduce the trends of peak ratio, peak separation, and trough depth with radius, and broadly reconcile outflow velocities inferred from Lyα and absorption lines. The galaxies in our sample are well-described by a model in which neutral, outflowing clumps are embedded in a hotter, more highly ionized inter-clump medium (ICM), whose residual neutral content produces absorption at the systemic redshift. The peak ratio, peak separation, and trough flux fraction are primarily governed by the line-of-sight component of the outflow velocity, the H i column density, and the residual neutral density in the ICM respectively. The azimuthal asymmetries in the line profile further suggest nonradial gas motions at large radii and variations in the H i column density in the outer halos.
Comet nuclei composition and evolution

Thanks to Rosetta orbiter's and Philae lander's data our knowledge of cometary nuclei composition has experienced a great advancement. The properties of 67P/CG nucleus are discussed and compared with other comets explored in the past by space missions. Cometary nuclei are made by a collection of ices, minerals, organic matter, and salts resulting in very dark and red-colored surfaces. When far from the Sun, exposed water and carbon dioxide ices are found only in few locations of 67P/CG where the exposure of pristine subsurface layers or the recondensation of volatile species driven by the solar heating and local terrain morphology can sustain their temporary presence on the surface. The nucleus surface appears covered by a dust layer of variable thickness. Dust grains appear mostly dehydrated and are made by an assemblage of minerals, organic matter, and salts. Spectral analysis shows that the mineral phase is dominated by silicates, fine-grained opaques and ammoniated salts. Aliphatic and aromatic groups, with the presence of the strong hydroxyl group, are identified within the organic matter. The surface composition of cometary nuclei evolves with heliocentric distance and seasonal cycling: approaching perihelion the increase of the solar flux boosts the activity through the sublimation of volatiles which in turn causes the erosion of surface layers, the exposure of ices, the activity in cliffs and pits, the collapse of overhangs and walls, and the mobilization and redistribution of dust. The evolution of color, composition, and texture changes occurring across different morphological regions of the nucleus are correlated with these processes. In this chapter we discuss 67P/CG nucleus composition and evolutionary processes as observed by Rosetta mission in the context of other comets previously explored by space missions or observed from Earth.
Water Simulation and Rendering from a Still Photograph

We propose an approach to simulate and render realistic water animation from a single still input photograph. We first segment the water surface, estimate rendering parameters, and compute water reflection textures with a combination of neural networks and traditional optimization techniques. Then we propose an image-based screen space local reflection model to render the water surface overlaid on the input image and generate real-time water animation. Our approach creates realistic results with no user intervention for a wide variety of natural scenes containing large bodies of water with different lighting and water surface conditions. Since our method provides a 3D representation of the water surface, it naturally enables direct editing of water parameters and also supports interactive applications like adding synthetic objects to the scene.