Combining Professionalism, Nation Building and Public Service: The Professional Project of the Israeli Bar 1928-2002

Measuring tree morphology for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for example when measuring tree volume. Recent approaches on automating the measurement process rely on LIDAR measurements coupled with high-accuracy GPS. Usually each side of a row is reconstructed independently and then merged using GPS information. Such approaches have two disadvantages: (1) they rely on specialized and expensive equipment, and (2) since the reconstruction process does not simultaneously use information from both sides, side reconstructions may not be accurate. We also show that standard loop closure methods do not necessarily align tree trunks well. In this paper, we present a novel vision system that employs only an RGB-D camera to estimate morphological parameters. A semantics-based mapping algorithm merges the two-sides 3D models of tree rows, where integrated semantic information is obtained and refined by robust fitting algorithms. We focus on measuring tree height, canopy volume and trunk diameter from the optimized 3D model. Experiments conducted in real orchard

Exploring chromatin organization and transcription in S. pombe and hematopoietic development

DNA in the eukaryotic nucleus is organized into histone-DNA complex, so-called chromatin, through forming an array of nucleosomes. Each nucleosome consists of a 147bp DNA wrapped around a histone octamer harboring two of each H2A-H2B and H3-H4. Chromatin is orderly packed several times forming a chromosome structure. Active euchromatin and repressive heterochromatin are defined according to the degree of DNA compaction, of which euchromatin is open, and heterochromatin is condensed. Chromatin organization and its regulation always affect downstream gene transcriptions through different mechanisms, which consequently play crucial roles in many cellular and biological processes. In this thesis, we explored mechanisms of chromatin organization and its associated regulatory factors by using Schizosaccharomyces pombe. We identified an uncovered role of Abo1 in different heterochromatin locus. We demonstrated that Abo1 is involved in Clr4 mediated heterochromatin assembly through regulating H3K9me2 to H3K9me3 transition, related to distinct silencing machinery. We also performed multiple in vitro experiments to investigate the functional role of the chromatin remodeler Hrp3, which is the orthologue of human CHD1. We generated several mutant strains where the non-catalytic domains were individually deleted. Our result suggested non-catalytic domains could further affect ATP hydrolyzing activity, and may further affect the chromatin remodeling function. In this thesis, we also investigated the outcomes of epigenetic and transcriptional regulation in hematopoietic development. We performed analysis on CAGE libraries in various primary cell types from the Fantom 5 project to study the usage of alternative transcriptional start site (TSS). Through mapping the TSS to Refseq, we identified alternative TSS that can lead protein domain loss. The alternative TSSs were shown to be expressed at different levels in different cell types or developmental stages, particularly in blood cells. We further investigated the functional consequence of alternative TSSs usage for KDM2B in Jurkat T-cells. To identify critical novel epigenetic regulators for myeloid differentiation, we performed a CRISPR-cas9 screen. We identified the chromatin remodeler CHD2 as a crucial regulator for megakaryocyte differentiation in the PMA inducible K-562 cell model

Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes

Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning

Vision-language pre-training models (VLP) are vulnerable, especially to multimodal adversarial samples, which can be crafted by adding imperceptible perturbations on both original images and texts. However, under the black-box setting, there have been no works to explore the transferability of multimodal adversarial attacks against the VLP models. In this work, we take CLIP as the surrogate model and propose a gradient-based multimodal attack method to generate transferable adversarial samples against the VLP models. By applying the gradient to optimize the adversarial images and adversarial texts simultaneously, our method can better search for and attack the vulnerable images and text information pairs. To improve the transferability of the attack, we utilize contrastive learning including image-text contrastive learning and intra-modal contrastive learning to have a more generalized understanding of the underlying data distribution and mitigate the overfitting of the surrogate model so that the generated multimodal adversarial samples have a higher transferability for VLP models. Extensive experiments validate the effectiveness of the proposed method