Long time motion of NLS solitary waves in a confining potential

We study the motion of solitary-wave solutions of a family of focusing generalized nonlinear Schroedinger equations with a confining, slowly varying external potential, $V(x)$. A Lyapunov-Schmidt decomposition of the solution combined with energy estimates allows us to control the motion of the solitary wave over a long, but finite, time interval. We show that the center of mass of the solitary wave follows a trajectory close to that of a Newtonian point particle in the external potential $V(x)$ over a long time interval.Comment: 42 pages, 2 figure

THE PROMISED CONSTITUTION OF THE PROMISED LAND:THE ISRAELI CONSTITUTIONAL EXPERIENCE

THE PROMISED CONSTITUTION OF THE PROMISED LAND:THE ISRAELI CONSTITUTIONAL EXPERIENC

Collapse of an Instanton

We construct a two parameter family of collapsing solutions to the 4+1 Yang-Mills equations and derive the dynamical law of the collapse. Our arguments indicate that this family of solutions is stable. The latter fact is also supported by numerical simulations.Comment: 17 pages, 1 figur

Novel Combination Strategies to Enhance Immune Checkpoint Inhibition in Cancer Immunotherapy: A Narrative Review

Programmed cell death protein-1 (PD-1) is an immune checkpoint receptor that induces and maintains tolerance of T cells, invariant natural killer T (iNKT) cells, and natural killer (NK) cells, among other lymphocytes. Immune checkpoint inhibition by PD-1 blockade restores the lymphocytic immunostimulatory phenotype and has been successful in the treatment of various malignancies. However, while immune checkpoint blockade has been shown to provide robust antitumor treatment outcomes, its overall response rate remains low in a significant portion of cancer patients. An essential unmet need in cancer therapy is the development of novel pharmacologic strategies designed to lower rates of resistance associated with immune checkpoint blockade. Therefore, efforts that seek to enhance the efficacy of PD-1 inhibition possess profound immunotherapeutic potential. Here, three promising combination strategies that harness the antitumor effects of immune checkpoint inhibitors (ICIs) together with non-ICI antitumor therapeutic agents are reviewed. These agents include (1) ABX196, a potent inducer of iNKT cells, (2) chimeric antigen receptor (CAR)-T cell therapy, and (3) NK cell therapy. A comprehensive literature search was conducted using the PubMed and ClinicalTrials.gov databases for scientific articles and active trials, respectively, pertaining to immune checkpoint inhibition, iNKT cells, CAR-T cells, and NK cell immunotherapy. Preliminary clinical and preclinical data suggest that these combination treatment regimens greatly suppress tumor growth and may serve as innovative methods to enhance and optimize anticancer immunotherapy

Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately $12.2\%$ and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails