Probing gauge-phobic heavy Higgs bosons at high energy hadron colliders

We study the probe of the gauge-phobic (or nearly gauge-phobic) heavy Higgs bosons (GPHB) at high energy hadron colliders including the 14 TeV LHC and the 50 TeV Super Proton-Proton Collider (SppC). We take the process $pp\to t\bar t t\bar t$, and study it at the hadron level including simulating the jet formation and top quark tagging (with jet substructure). We show that, for a GPHB with $M^{}_H<800$ GeV, $M^{}_H$ can be determined by adjusting the value of $M^{}_H$ in the theoretical $p^{}_T(b_1)$ distribution to fit the observed $p^{}_T(b_1)$ distribution, and the resonance peak can be seen at the SppC for $M^{}_H$=800 GeV and 1 TeV.Comment: 6 pages, with 7 eps files for 7 figure

Anti-tumor effect of polysaccharides from rhizome of Curculigo orchioides Gaertn on cervical cancer

Purpose: To investigate the anti-tumor effects of polysaccharides from Curculigo orchioides (PDC) on cervical cancer and the possible mechanisms involved.Methods: A Box–Behnken design (BBD) was employed to optimize extraction conditions for PDC. The anti-tumor effect of PDC on cervical cancer was investigated in vivo in mice injected with Hela cells. The parameters measured were tumor volume and weight. In vitro anti-tumor effects of PDC were assessed by measuring expressions of caspase-3, caspase-9 and P53 proteins in Hela cells via ELISA assay. Thymus and spleen indices were calculated for assessment of PDC effect on immune function.Results: The optimum extraction conditions predicted by the response surface methodology (RSM) were: extraction time = 1.58 h, ratio-of-water-to-sample = 30.05 mL/g and extraction number = 1.95. PDC showed significant anti-tumor effect on cervical cancer in mice. It significantly increased thymus and spleen indices in mice; and significantly up-regulated expressions of caspase-3, caspase-9 and P53 proteins in HeLa cells.Conclusion: PDC has significant anti-tumor effect on cervical cancer in vivo and in vitro, most probably through mechanisms involving enhancement on immune function and induction of apoptosis.Keyword: Curculigo orchioides, Polysaccharides, Cervical cancer, HeLa cells, Apoptosi

HumanMAC: Masked Motion Completion for Human Motion Prediction

Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical performance based on an encoding-decoding style. The methods of this style work by first encoding previous motions to latent representations and then decoding the latent representations into predicted motions. However, in practice, they are still unsatisfactory due to several issues, including complicated loss constraints, cumbersome training processes, and scarce switch of different categories of motions in prediction. In this paper, to address the above issues, we jump out of the foregoing style and propose a novel framework from a new perspective. Specifically, our framework works in a masked completion fashion. In the training stage, we learn a motion diffusion model that generates motions from random noise. In the inference stage, with a denoising procedure, we make motion prediction conditioning on observed motions to output more continuous and controllable predictions. The proposed framework enjoys promising algorithmic properties, which only needs one loss in optimization and is trained in an end-to-end manner. Additionally, it accomplishes the switch of different categories of motions effectively, which is significant in realistic tasks, e.g., the animation task. Comprehensive experiments on benchmarks confirm the superiority of the proposed framework. The project page is available at https://lhchen.top/Human-MAC

IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models

In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs. To address this challenge, this paper introduces an influence-driven selective annotation method that aims to minimize annotation costs while improving the quality of in-context examples. The essence of our method is to select a pivotal subset from a large-scale unlabeled data pool to annotate for the subsequent sampling of prompts. Specifically, a directed graph is first constructed to represent unlabeled data. Afterward, the influence of candidate unlabeled subsets is quantified with a diffusion process. A simple yet effective greedy algorithm for unlabeled data selection is lastly introduced. It iteratively selects the data if it provides a maximum marginal gain with respect to quantified influence. Compared with previous efforts on selective annotations, our influence-driven method works in an end-to-end manner, avoids an intractable explicit balance between data diversity and representativeness, and enjoys theoretical support. Experiments confirm the superiority of the proposed method on various benchmarks, achieving better performance under lower time consumption during subset selection. The project page is available at https://skzhang1.github.io/IDEAL/.Comment: Accepted by ICLR 202

FABRICATION OF NANO-ELECTROMECHANICAL STRUCTURES DOWN TO 20 NM BY SPACER TECHNOLOGY

ABSTRACT Spacer technology has been developed to fabricate nanostructures for NEMS application. It provides a parallel nanofabrication method with double or quadplex device density at a certain lithography node. By controlling the deposited film thickness, the feature size of the SiO 2 spacer hard mask is reduced down to 35 nm. After the spacer pattern is transferred to Si, a precise thermal oxidation is performed to improve the profile and reduce the plasma damage. Finally, sublimation or HF vapor phase etching is introduced to release the nanostructures according to different structure dimensions. As a result, with better surface morphology, suspended Si nanobeams with a width of 20 nm are obtained. Actuated by mechanical vibration and electrostatic forces, vibrations of the obtained cantilever beams and fixed-fixed beams are observed in SEM. In addition, a metallic nano-nozzle with a diameter of 140 nm is established by electroless plating around the suspended Si nano-beam served as a mold. As a development of the spacer technology, nano-needle array is demonstrated at the cross points of crossed SiO 2 spacers by anisotropic etching. The diameters of the hybridized nano-needles are 300 nm so far and can be further reduced by smaller spacer dimension

Surface density-of-states on semi-infinite topological photonic and acoustic crystals

Iterative Green's function, based on cyclic reduction of block tridiagonal matrices, has been the ideal algorithm, through tight-binding models, to compute the surface density-of-states of semi-infinite topological electronic materials. In this paper, we apply this method to photonic and acoustic crystals, using finite-element discretizations and a generalized eigenvalue formulation, to calculate the local density-of-states on a single surface of semi-infinite lattices. The three-dimensional (3D) examples of gapless helicoidal surface states in Weyl and Dirac crystals are shown and the computational cost, convergence and accuracy are analyzed.Comment: 7 pages, 4 figure