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Complex Cyber-Physical System (CPS) such as Unmanned Aerial System (UAS) got rapid development these years, but also became vulnerable to GPS spoofing, packets injection, buffer-overflow and other malicious attacks. Ensuring the behaviors of UAS always keeping secure no matter how the environment changes, would be a prospective direction for UAS security. This paper aims at introducing a pattern-based framework to describe the security properties of UAS, and presenting a reactive synthesis-based approach to implement the automatic generation of secure UAS controller. First, we study the operating mechanism of UAS and construct a high-level model consisting of actuator and monitor. Besides, we analyze the security threats of UAS from the perspective of hardware, software and cyber physics, and then summarize the corresponding specification patterns of security properties with LTL formulas. With the UAS model and security specification patterns, automatons for controller can be constructed by General Reactivity of Rank 1 (GR(1)) synthesis algorithm, which is a two-player game process between Unmanned Aerial Vehicle (UAV) and its environment. Finally, we expand the function of LTLMoP platform to implement the control simulation in multi-robot systems, providing secure behavior strategies under several attack scenarios.

It is well established that the entanglement entropy of a critical system generally scales logarithmically with system size. Yet, in this work, we report a new class of non-Hermitian critical transitions that exhibit dramatic divergent dips in their entanglement entropy scaling, strongly violating conventional logarithmic behavior. Dubbed scaling-induced exceptional criticality (SIEC), it transcends existing non-Hermitian mechanisms such as exceptional bound states and non-Hermitian skin effect (NHSE)-induced gap closures, which are nevertheless still governed by logarithmic entanglement scaling. Key to SIEC is its strongly scale-dependent spectrum, where eigenbands exhibit an exceptional crossing only at a particular system size. As such, the critical behavior is dominated by how the generalized Brillouin zone (GBZ) sweeps through the exceptional crossing with increasing system size, and not just by the gap closure per se. We provide a general approach for constructing SIEC systems based on the non-local competition between heterogeneous NHSE pumping directions, and show how a scale-dependent GBZ can be analytically derived to excellent accuracy. Beyond 1D free fermions, SIEC is expected to occur more prevalently in higher-dimensional or even interacting systems, where antagonistic NHSE channels generically proliferate. SIEC-induced entanglement dips generalize straightforwardly to kinks in other entanglement measures such as Renyi entropy, and serve as spectacular demonstrations of how algebraic and geometric singularities in complex band structures manifest in quantum information.

Investigating the implications of interlayer coupling on superconductivity is essential for comprehending the intrinsic mechanisms of high temperature superconductors. Van der Waals heterojunctions have attracted extensive research due to their exotic interlayer coupling. Here, we present a natural heterojunction superconductor of 6R-TaS2 that demonstrates a double-dome of superconductivity, in addition to, the reemergence of superconducting under high pressures. Our first principles calculation shows that the first dome of superconductivity in 6R-TaS2 can be attributed to changes in interlayer coupling and charge transfer. The second superconducting dome and the reemergence of superconductivity can be ascribed to changes in the density of states resulting from Fermi surface reconstruction, in which the DOS of T-layer and S p-orbitals play a crucial role. We have reported the first observation in TMDs that non-metallic atoms playing a dominant role in the reemergence of superconducting and the influence of two Lifshitz transitions on superconducting properties.

Dynamical decoupling represents an active approach towards the protection of quantum memories and quantum gates. Because dynamical decoupling operations can interfere with system's own time evolution, the protection of quantum gates is more challenging than that of quantum states. In this work, we put forward a simple but general approach towards the realization of higher-order protection of quantum gates and further execute the first cloud-based experimental demonstration of dynamical-decoupling-protected quantum gates at the first order and the second order. The central idea of our approach is to engineer (hence regain the control of) the gate Hamiltonian in coordination with higher-order dynamical decoupling sequences originally proposed for the protection of quantum memories. The physical demonstration on an IBM quantum processor indicates the effectiveness and potential of our approach on noisy intermediate scale quantum computers., Comment: 9 pages, 6 figures. Significantly revised, especially added the execution of the first cloud-based experimental demonstration of dynamical-decoupling-protected quantum gates at the first order and the second order

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design., Comment: version 2.0; 28 pages, 6 figures

Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the development of new models is restricted by the lack of dataset and benchmark training procedure. To the best of our knowledge, the existing open source datasets are far less to satisfy the needs of modern protein sequence-structure related research. To solve this problem, we present the first million-level protein structure prediction dataset with high coverage and diversity, named as PSP. This dataset consists of 570k true structure sequences (10TB) and 745k complementary distillation sequences (15TB). We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset. We validate the utility of this dataset for training by participating CAMEO contest in which our model won the first place. We hope our PSP dataset together with the training benchmark can enable a broader community of AI/biology researchers for AI-driven protein related research.