Synthesis and Biological Study of Adenylyl Cyclase Inhibitors

Adenylyl cyclases (AC) is a critical family of enzymes which modulates the dynamic cellular level of cAMP, cyclic adenosine monophosphate. The study of cAMP showed that it is indispensable for the signal transduction cascades during many physiological processes, such as immune responses and metabolism which highly relate to cancers. Previous studies of AC inhibitors have been limited due to a lack of isoform-selective small molecule modulators. Selectivity of the molecules is imperative to the activation of only the desired AC inhibitor. The design of the described project was to test the structure activity relationship (SAR) by synthesizing a class of AC I inhibitors and then use the results to develop a small molecule with maximum selectivity for therapeutic targeting. Multi-step synthesis featured with epoxide ring-opening reaction followed by the Friedel–Crafts reaction. Compounds were differentiated by changing substituents on the nitrogen atom. The synthetic molecules have been tested via SAR of AC I inhibitor and IC50. Once synthesized, the compounds were tested for their inhibition rate and the results showed that the majority of scaffolds had great SAR rates at 40 µM and two also had impressive rates as low as 4 µM. Further investigation with IC50 studies is on-going. The results suggest that the current synthetic compounds are potentially great AC I inhibitors and further study will continue which will contribute to cancer research

Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers

Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead to significant information loss. Our quantitative experiments reveal that the impact of pruned tokens on performance should be noticeable. To address this issue, we propose a novel joint Token Pruning & Squeezing module (TPS) for compressing vision transformers with higher efficiency. Firstly, TPS adopts pruning to get the reserved and pruned subsets. Secondly, TPS squeezes the information of pruned tokens into partial reserved tokens via the unidirectional nearest-neighbor matching and similarity-based fusing steps. Compared to state-of-the-art methods, our approach outperforms them under all token pruning intensities. Especially while shrinking DeiT-tiny&small computational budgets to 35%, it improves the accuracy by 1%-6% compared with baselines on ImageNet classification. The proposed method can accelerate the throughput of DeiT-small beyond DeiT-tiny, while its accuracy surpasses DeiT-tiny by 4.78%. Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy. Code is available at https://github.com/megvii-research/TPS-CVPR2023.Comment: Accepted to CVPR202

Constrained Multiview Representation for Self-supervised Contrastive Learning

Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless, the inherent complexity of anatomical patterns and the random nature of lesion distribution in medical image segmentation pose significant challenges to the disentanglement of representations and the understanding of salient features. Methods guided by the maximization of mutual information, particularly within the framework of contrastive learning, have demonstrated remarkable success and superiority in decoupling densely intertwined representations. However, the effectiveness of contrastive learning highly depends on the quality of the positive and negative sample pairs, i.e. the unselected average mutual information among multi-views would obstruct the learning strategy so the selection of the views is vital. In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement. Additionally, we introduce an MI re-ranking strategy for representation selection, benefiting both the continuous MI estimating and representation significance distance measuring. Specifically, we harness multi-view representations extracted from the frequency domain, re-evaluating their significance based on mutual information across varying frequencies, thereby facilitating a multifaceted contrastive learning approach to bolster semantic comprehension. The statistical results under the five metrics demonstrate that our proposed framework proficiently constrains the MI maximization-driven representation selection and steers the multi-view contrastive learning process

Excitation of extraordinary modes inside the source of Saturn's kilometric radiation

The electron cyclotron maser instability (ECMI) of extraordinary mode waves was investigated with the parameters observed in Saturn's kilometric radiation (SKR) sources. Previous studies employed simplified dispersion relations, and did not consider the excitation of the relativistic (R) mode. This mode is introduced by considering the relativistic effect in plasmas consisting of both cold and hot electrons. Using particle-in-cell simulations, we investigated the excitation of R and X modes based on the measured data. Using the reported value of the density ratio of energetic to total electrons $n_e/n_0=24\%$, the most unstable mode is the R mode. The escaping X-mode emissions are amplified only if the energetic electrons are dominant with $n_e/n_0 \ge 90\%$. For these cases, only the X mode is excited and the R mode disappears due to its strong coupling. The results are well in line with the linear kinetic theory of ECMI. The properties of both the R and X modes are consistent with the observed SKR emissions. This raises questions about the nature of the measured electric field fluctuations within ``presumed'' SKR sources. The study provides new insights into the ECMI process relevant to SKR emission mechanisms

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.Comment: Accepted at NeurIPS 202

Towards Benchmarking GUI Compatibility Testing on Mobile Applications

GUI is a bridge connecting user and application. Existing GUI testing tasks can be categorized into two groups: functionality testing and compatibility testing. While the functionality testing focuses on detecting application runtime bugs, the compatibility testing aims at detecting bugs resulting from device or platform difference. To automate testing procedures and improve testing efficiency, previous works have proposed dozens of tools. To evaluate these tools, in functionality testing, researchers have published testing benchmarks. Comparatively, in compatibility testing, the question of ``Do existing methods indeed effectively assist test cases replay?'' is not well answered. To answer this question and advance the related research in GUI compatibility testing, we propose a benchmark of GUI compatibility testing. In our experiments, we compare the replay success rate of existing tools. Based on the experimental results, we summarize causes which may lead to ineffectiveness in test case replay and propose opportunities for improving the state-of-the-art

Coupled superconducting qudit-resonator system: Energy spectrum, state population, and state transition under microwave drive

Superconducting quantum multilevel systems coupled to resonators have recently been considered in some applications such as microwave lasing and high-fidelity quantum logical gates. In this work, using an rf-SQUID type phase qudit coupled to a microwave coplanar waveguide resonator, we study both theoretically and experimentally the energy spectrum of the system when the qudit level spacings are varied around the resonator frequency by changing the magnetic flux applied to the qudit loop. We show that the experimental result can be well described by a theoretical model that extends from the usual two-level Jaynes-Cummings system to the present four-level system. It is also shown that due to the small anharmonicity of the phase device a simplified model capturing the leading state interactions fits the experimental spectra very well. Furthermore we use the Lindblad master equation containing various relaxation and dephasing processes to calculate the level populations in the simpler qutrit-resonator system, which allows a clear understanding of the dynamics of the system under the microwave drive. Our results help to better understand and perform the experiments of coupled multilevel and resonator systems and can be applied in the case of transmon or Xmon qudits having similar anharmonicity to the present phase device.This work was supported by the Ministry of Science and Technology of China (Grants No. 2014CB921202, No. 2015CB921104, and No. 2016YFA0300601),the National Natural Science Foundation of China (Grants No. 91321208 and No. 11674380)the Key Research Program of the Chinese Academy of Sciences (Grant No. XDPB08-3)S.H. acknowledges support by the US NSF (PHY-1314861)