The global geometrical property of jet events in high-energy nuclear collisions

We present the first theoretical study of medium modifications of the global geometrical pattern, i.e., transverse sphericity ($S_{\perp}$) distribution of jet events with parton energy loss in relativistic heavy-ion collisions. In our investigation, POWHEG+PYTHIA is employed to make an accurate description of transverse sphericity in the p+p baseline, which combines the next-to-leading order (NLO) pQCD calculations with the matched parton shower (PS). The Linear Boltzmann Transport (LBT) model of the parton energy loss is implemented to simulate the in-medium evolution of jets. We calculate the event normalized transverse sphericity distribution in central Pb+Pb collisions at the LHC, and give its medium modifications. An enhancement of transverse sphericity distribution at small $S_{\perp}$ region but a suppression at large $S_{\perp}$ region are observed in A+A collisions as compared to their p+p references, which indicates that in overall the geometry of jet events in Pb+Pb becomes more pencil-like. We demonstrate that for events with 2 jets in the final-state of heavy-ion collisions, the jet quenching makes the geometry more sphere-like with medium-induced gluon radiation. However, for events with $\ge 3$~jets, parton energy loss in the QCD medium leads to the events more pencil-like due to jet number reduction, where less energetic jets may lose their energies and then fall off the jet selection kinematic cut. These two effects offset each other and in the end result in more jetty events in heavy-ion collisions relative to that in p+p.Comment: 9 pages, 9 figure

A Numerical Investigation of the Sparkjet Actuator in Multiple-shot Mode

AbstractComputational simulations were performed in multiple-shot mode to investigate the effects of the boundary conditions and the deposition energy on the performance of the Sparkjet actuator. The user define function (UDF) was applied in the source term of the energy equation to imitate the very arc current discharges which produce the synthetic flow. The method of numerical simulation is verified by the existing experimental and analytical data. Two parameters including the integration mass and momentum are defined to evaluate the performance of the Sparkjet actuator. The simulation results show that Sparkjet flow is more affected by the boundary conditions of the external walls of the cavity and its deposition energy. The performance of Sparkjet actuator drops with the increase of operation cycle when the wall of cavity is adiabatic. When the temperature of wall of cavity is constant, the integration mass and momentum during the exhaling stage decrease with the increase of temperature. The performance of actuator decreases with the increase of heat transfer coefficient when the wall of cavity is set to be coupled with effect of radiation and convection. The performance of actuator increases with increase of deposition energy

Matrix Formula of Differential Resultant for First Order Generic Ordinary Differential Polynomials

In this paper, a matrix representation for the differential resultant of two generic ordinary differential polynomials $f_1$ and $f_2$ in the differential indeterminate $y$ with order one and arbitrary degree is given. That is, a non-singular matrix is constructed such that its determinant contains the differential resultant as a factor. Furthermore, the algebraic sparse resultant of $f_1, f_2, \delta f_1, \delta f_2$ treated as polynomials in $y, y', y"$ is shown to be a non-zero multiple of the differential resultant of $f_1, f_2$. Although very special, this seems to be the first matrix representation for a class of nonlinear generic differential polynomials

Improved Test-Time Adaptation for Domain Generalization

The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time training (TTT), which adapts the learned model with test data, might be a promising solution to the problem. Generally, a TTT strategy hinges its performance on two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. Both previous arts and our experiments indicate that TTT may not improve but be detrimental to the learned model if those two factors are not properly considered. This work addresses those two factors by proposing an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically defining an auxiliary objective, we propose a learnable consistency loss for the TTT task, which contains learnable parameters that can be adjusted toward better alignment between our TTT task and the main prediction task. Second, we introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase. Through extensive experiments, we show that the proposed two strategies are beneficial for the learned model (see Figure 1), and ITTA could achieve superior performance to the current state-of-the-art methods on several DG benchmarks. Code is available at https://github.com/liangchen527/ITTA.Comment: Accepted by CVPR 202

SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly because of learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn the realistic motion coefficients, we explicitly model the connections between audio and different types of motion coefficients individually. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces. As for the head pose, we design PoseVAE via a conditional VAE to synthesize head motion in different styles. Finally, the generated 3D motion coefficients are mapped to the unsupervised 3D keypoints space of the proposed face render, and synthesize the final video. We conduct extensive experiments to show the superior of our method in terms of motion and video quality.Comment: Project page: https://sadtalker.github.i

(E)-N′-(2-Bromo­benzyl­idene)-2-fluoro­benzohydrazide

The title compound, C14H10BrFN2O, adopts an E geometry about the C=N bond. The dihedral angle between the mean planes of the two benzene rings is 81.5 (6)°. In the crystal, mol­ecules are linked through inter­molecular N—H⋯O hydrogen bonds, forming chains running along the b axis