The Role of Tec Kinases in CD4\u3csup\u3e+\u3c/sup\u3e T Cell Activation: A Dissertation

The Tec family tyrosine kinases Itk, Tec and Rlk are expressed in T cells. Previous studies have established that these kinases are critical for TCR signaling, leading to the activation of PLCγ1. To further understand the functions of Tec kinases in T cell activation, we took three different approaches. First, we performed a thorough analysis of CD28-mediated signaling events and functional responses with purified naïve T cells from Itk-/- mice and a highly controlled stimulation system. Data from this set of studies definitively demonstrate that CD28 costimulation functions efficiently in naïve CD4+ T cells in the absence of Itk. Second, in order to further study the functions of Tec kinases in vivo, we generated transgenic mouse lines expressing a kinase-dead (KD) mutant of Tec on the Itk-/-Rlk-/- background, hoping to study mice that are functionally deficient for all three Tec kinases. The results hint the importance of the Tec kinases in T cell development and/or survival. Finally, in order to identify potential transcriptional targets of Itk, we used microarray technology to compare global gene expression profiles of naïve and stimulated Itk-/- versus Itk+/- CD4+ T cells. This analysis provided a short list of differentially expressed genes in Itk-/- versus Itk+/- CD4 T cells, providing a starting point for further studies of Itk in T cell activation. Collectively, these studies clarified the role of Itk in CD28 signaling, revealed some unexpected aspects of Tec family kinases in T cells, and indicated potential targets of Itk-dependent signaling pathways in T cells

Top Hypercharge

We propose a top hypercharge model with gauge symmetry SU(3)_C x SU(2)_L x U(1)_1 x U(1)_2 where the first two families of the Standard Model (SM) fermions are charged under U(1)_1 while the third family is charged under U(1)_2. The U(1)_1 x U(1)_2 gauge symmetry is broken down to the U(1)_Y gauge symmetry, when a SM singlet Higgs field acquires a vacuum expectation value. We consider the electroweak constraints, and compare the fit to experimental observables to that of the SM. We study the quark CKM mixing between the first two families and the third family, the neutrino masses and mixing, the flavour changing neutral current effects in meson mixing and decays, the Z' discovery potential at the Large Hadron Collider, the dark matter with a gauged Z_2 symmetry, and the Higgs boson masses.Comment: 17 pages and 4 figure

Direct CP violation in τ±→K±ρ0(ω)ντ→K±π+π−ντ\tau^\pm\rightarrow K^\pm \rho^0 (\omega)\nu_\tau \rightarrow K^\pm \pi^+\pi^-\nu_\tau

We study the direct CP violation in the $\tau^\pm\rightarrow K^\pm \rho^0 (\omega)\nu_\tau \rightarrow K^\pm \pi^+\pi^-\nu_\tau$ decay process in the Standard Model. An interesting mechanism involving the charge symmetry violating mixing between $\rho^0$ and $\omega$ is applied to enlarge the CP asymmetry. With this mechanism, the maximum differential and localized integrated CP asymmetries can reach $-(5.6^{+2.9}_{-1.7})\times10^{-12}$ and $6.3^{+2.4}_{-3.3}\times 10^{-11}$, respectively, which still leave plenty room for CP-violating New Physics to be discovered through this process

A CNN based system for predicting the implied volatility and option prices.

The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research of option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.12% performance enhancement in terms of RMSE

Fix-and-Optimize and Variable Neighborhood Search Approaches for Stochastic Multi-Item Capacitated Lot-Sizing Problems

We discuss stochastic multi-item capacitated lot-sizing problems with and without setup carryovers (also known as link lot size), S-MICLSP and S-MICLSP-L. The two models are motivated from a real-world steel enterprise. To overcome the nonlinearity of the models, a piecewise linear approximation method is proposed. We develop a new fix-and-optimize (FO) approach to solve the approximated models. Compared with the existing FO approach(es), our FO is based on the concept of “k-degree-connection” for decomposing the problems. Furthermore, we also propose an integrative approach combining our FO and variable neighborhood search (FO-VNS), which can improve the solution quality of our FO approach by diversifying the search space. Numerical experiments are performed on the instances following the nature of realistic steel products. Our approximation method is shown to be efficient. The results also show that the proposed FO and FO-VNS approaches significantly outperform the recent FO approaches, and the FO-VNS approaches can be more outstanding on the solution quality with moderate computational effort

Towards Stable Backdoor Purification through Feature Shift Tuning

It has been widely observed that deep neural networks (DNN) are vulnerable to backdoor attacks where attackers could manipulate the model behavior maliciously by tampering with a small set of training samples. Although a line of defense methods is proposed to mitigate this threat, they either require complicated modifications to the training process or heavily rely on the specific model architecture, which makes them hard to deploy into real-world applications. Therefore, in this paper, we instead start with fine-tuning, one of the most common and easy-to-deploy backdoor defenses, through comprehensive evaluations against diverse attack scenarios. Observations made through initial experiments show that in contrast to the promising defensive results on high poisoning rates, vanilla tuning methods completely fail at low poisoning rate scenarios. Our analysis shows that with the low poisoning rate, the entanglement between backdoor and clean features undermines the effect of tuning-based defenses. Therefore, it is necessary to disentangle the backdoor and clean features in order to improve backdoor purification. To address this, we introduce Feature Shift Tuning (FST), a method for tuning-based backdoor purification. Specifically, FST encourages feature shifts by actively deviating the classifier weights from the originally compromised weights. Extensive experiments demonstrate that our FST provides consistently stable performance under different attack settings. Without complex parameter adjustments, FST also achieves much lower tuning costs, only 10 epochs. Our codes are available at https://github.com/AISafety-HKUST/stable_backdoor_purification.Comment: NeurIPS 2023 paper. The first two authors contributed equall