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We propose a deep learning based novel prediction framework for enhanced bandwidth reduction in motion transfer enabled video applications such as video conferencing, virtual reality gaming and privacy preservation for patient health monitoring. To model complex motion, we use the First Order Motion Model (FOMM) that represents dynamic objects using learned keypoints along with their local affine transformations. Keypoints are extracted by a self-supervised keypoint detector and organized in a time series corresponding to the video frames. Prediction of keypoints, to enable transmission using lower frames per second on the source device, is performed using a Variational Recurrent Neural Network (VRNN). The predicted keypoints are then synthesized to video frames using an optical flow estimator and a generator network. This efficacy of leveraging keypoint based representations in conjunction with VRNN based prediction for both video animation and reconstruction is demonstrated on three diverse datasets. For real-time applications, our results show the effectiveness of our proposed architecture by enabling up to 2x additional bandwidth reduction over existing keypoint based video motion transfer frameworks without significantly compromising video quality.

High-angular resolution imaging by ALMA has revealed the near-universality and diversity of substructures in protoplanetary disks. However, disks around M-type pre-main-sequence stars are still poorly sampled, despite the prevalence of M-dwarfs in the galaxy. Here we present high-resolution (~50 mas, 8 au) ALMA Band 6 observations of six disks around mid-M stars in Taurus. We detect dust continuum emission in all six disks, 12CO in five disks, and 13CO line in two disks. The size ratios between gas and dust disks range from 1.6 to 5.1. The ratio of about 5 for 2M0436 and 2M0450 indicates efficient dust radial drift. Four disks show rings and cavities and two disks are smooth. The cavity sizes occupy a wide range: 60 au for 2M0412, and ~10 au for 2M0434, 2M0436 and 2M0508. Detailed visibility modeling indicates that small cavities of 1.7 and 5.7 au may hide in the two smooth disks 2M0450 and CIDA 12. We perform radiative transfer fitting of the infrared SEDs to constrain the cavity sizes, finding that micron-sized dust grains may have smaller cavities than millimeter grains. Planet-disk interactions are the preferred explanation to produce the large 60 au cavity, while other physics could be responsible for the three ~10 au cavities under current observations and theories. Currently, disks around mid-to-late M stars in Taurus show a higher detection frequency of cavities than earlier type stars, although a more complete sample is needed to evaluate any dependence of substructure on stellar mass., Comment: 25 pages, 6 figures. Accepted for publication in ApJ

This study presents a mathematical model describing cloned hematopoiesis in chronic myeloid leukemia (CML) through a nonlinear system of differential equations. The primary objective is to understand the progression from normal hematopoiesis to the chronic and accelerated-acute phases in myeloid leukemia. The model incorporates intrinsic cellular division events in hematopoiesis and delineates the evolution of chronic myeloid leukemia into six compartments: cycling stem cells, quiescent stem cells, progenitor cells, differentiated cells, and terminally differentiated cells. Our analysis reveals the existence of three distinct steady states within the dynamical system, representing normal hematopoiesis, the chronic phase, and the accelerated-acute stage of the disease. We investigate the local and global stability of these steady states and provide a characterization of the hematopoietic states based on this analysis. Additionally, numerical simulations are included to illustrate the theoretical results.

Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonstrated the impressive performance of large generative language models on downstream tasks. However, existing generative language models generally neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. It can lead a language model to be dominated by common and easy-to-learn tokens, thereby overlooking the infrequent and difficult-to-learn ones. To alleviate that, we propose a MiLe Loss function for mitigating the bias of learning difficulties with tokens. During training, it can dynamically assess the learning difficulty of a to-be-learned token, according to the information entropy of the corresponding predicted probability distribution over the vocabulary. Then it scales the training loss adaptively, trying to lead the model to focus more on the difficult-to-learn tokens. On the Pile dataset, we train generative language models at different scales of 468M, 1.2B, and 6.7B parameters. Experiments reveal that models incorporating the proposed MiLe Loss can gain consistent performance improvement on downstream benchmarks., Comment: This paper has been accepted by NAACL 2024

The recent development of the accurate and efficient semilocal density functionals on the third rung of Jacob's ladder of density functional theory such as the revised regularized strongly constrained and appropriately normed (r2SCAN) density functional could enable the rapid and highly reliable prediction of the elasticity and temperature dependence of thermophysical parameters of refractory elements and their intermetallic compounds using quasi-harmonic approximation (QHA). Here, we present a comparative evaluation of the equilibrium cell volumes, cohesive energy, mechanical moduli, and thermophysical properties (Debye temperature and thermal expansion coefficient) for 22 transition metals using semilocal density functionals, including local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) and PBEsol generalized gradient approximations (GGA), and the r2SCAN meta-GGA. PBEsol and r2SCAN deliver the same level of accuracies for structural, mechanical and thermophysical properties. Otherwise, PBE and r2SCAN perform better than LDA and PBEsol for calculating cohesive energies of transition metals. Among the tested density functionals, r2SCAN provides an overall well-balanced performance for reliably computing the cell volumes, cohesive energies, mechanical properties, and thermophysical properties of various 3d, 4d, and 5d transition metals using QHA. Therefore, we recommend that r2SCAN could be employed as a workhorse method to evaluate the thermophysical properties of transition metal compounds and alloys in the high throughput workflows.

Tunable lasers, with the ability to continuously adjust their emission wavelengths, have found widespread applications across various fields such as biomedical imaging, coherent ranging, optical communications and spectroscopy. In these applications, a wide chirp range is advantageous for large spectral coverage and high frequency resolution. Besides, the frequency accuracy and precision also depend critically on the chirp linearity of the laser. While extensive efforts have been made on the development of many kinds of frequency-agile, widely tunable, narrow-linewidth lasers, wideband yet precise methods to characterize and to linearize laser chirp dynamics are also demanded. Here we present an approach to characterize laser chirp dynamics using an optical frequency comb. The instantaneous laser frequency is tracked over terahertz bandwidth with 1 MHz interval. Using this approach we calibrate the chirp performance of twelve tunable lasers from Toptica, Santec, New Focus, EXFO and NKT that are commonly used in fiber optics and integrated photonics. In addition, with acquired knowledge on laser chirp dynamics, we demonstrate a simple frequency-linearization scheme that enables coherent ranging without any optical or electronic linearization units. Our approach not only presents a novel wideband, high-resolution laser spectroscopy, but is also critical for sensing applications with ever-increasing requirements on performance.

Recent numerical simulations have revealed that dust clumping and planetesimal formation likely proceed in ring-like disc substructures, where dust gets trapped in weakly turbulent pressure maxima. The streaming instability has difficulty operating in such rings with external turbulence and no pressure gradient. To explore potential paths to planetesimal formation in this context, we analyse the stability of turbulent dust-trapping ring under the shearing sheet framework. We self-consistently establish the pressure maximum and the dust ring in equilibrium, the former via a balance of external forcing versus viscosity and the latter via dust drift versus turbulent diffusion. We find two types of $\gtrsim H$-scale instabilities ($H$ being the pressure scale height), which we term the dusty Rossby wave instability (DRWI). Type I is generalised from the standard RWI, which is stationary at the pressure maximum and dominates in relatively sharp pressure bumps. Type II is a newly identified travelling mode that requires the presence of dust. It can operate in relatively mild bumps, including many that are stable to the standard RWI, and its growth rate is largely determined by the equilibrium gas and dust density gradients. We further conduct two-fluid simulations that verify the two types of the DRWI. While Type I leads strong to dust concentration into a large gas vortex similar to the standard RWI, the dust ring is preserved in Type II, and meanwhile exhibiting additional clumping within the ring. The DRWI suggests a promising path towards formation of planetesimals/planetary embryos and azimuthally asymmetric dust structure from turbulent dust-trapping rings., Comment: 27 pages, 17 figures, accepted for publication in MNRAS

Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Some recent works explore driver attention prediction in critical or accident scenarios and find a positive role in helping accident prediction, while the promotion ability is constrained by the prediction accuracy of driver attention maps. In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP). Gate-DAP aims to learn the importance of different spatial, temporal, and modality information in driving scenarios with various road types, occasions, and light and weather conditions. The network connection gating in Gate-DAP consists of a spatial encoding network gating, long-short-term memory network gating, and information type gating modules. Each connection gating operation is plug-and-play and can be flexibly assembled, which makes the architecture of Gate-DAP transparent for evaluating different spatial, temporal, and information types for driver attention prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority of the proposed method with the comparison with state-of-the-art approaches. The code is available on https://github.com/JWFangit/Gate-DAP., Comment: Accepted by ITSC2023

Radiative turbulent mixing layers are expected to form pervasively at the phase boundaries in multiphase astrophysical systems. This inherently small scale structure is dynamically crucial because it directly regulates the mass, momentum and energy exchanges between adjacent phases. Previous studies on hydrodynamic turbulent mixing layers have revealed the interactions between cold and hot phases in the context of the circumgalactic medium, offering important insight into the fate of cold clouds traveling through hot galactic winds. However, the role of magnetic field has only been sparsely investigated. We perform a series of 3D magnetohydrodynamics (MHD) simulations of such mixing layers in the presence of weak to modest background magnetic field. We find that due to field amplification, even relatively weak background magnetic fields can significantly reduce the surface brightness and inflow velocity of the hot gas in the mixing layer. This reduction is attributed to a combination of magnetic pressure support and direct suppression of turbulent mixing, both of which alter the phase structures. Our results are largely independent of thermal conduction and converged with resolution, offering insights on the survival of cold gas in multiphase systems., Comment: 19 pages, 16 figures, accepted by MNRAS