On the Impossibility of General Parallel Fast-Forwarding of Hamiltonian Simulation

Hamiltonian simulation is one of the most important problems in the field of quantum computing. There have been extended efforts on designing algorithms for faster simulation, and the evolution time T for the simulation greatly affect algorithm runtime as expected. While there are some specific types of Hamiltonians that can be fast-forwarded, i.e., simulated within time o(T), for some large classes of Hamiltonians (e.g., all local/sparse Hamiltonians), existing simulation algorithms require running time at least linear in the evolution time T. On the other hand, while there exist lower bounds of ?(T) circuit size for some large classes of Hamiltonian, these lower bounds do not rule out the possibilities of Hamiltonian simulation with large but "low-depth" circuits by running things in parallel. As a result, physical systems with system size scaling with T can potentially do a fast-forwarding simulation. Therefore, it is intriguing whether we can achieve fast Hamiltonian simulation with the power of parallelism. In this work, we give a negative result for the above open problem in various settings. In the oracle model, we prove that there are time-independent sparse Hamiltonians that cannot be simulated via an oracle circuit of depth o(T). In the plain model, relying on the random oracle heuristic, we show that there exist time-independent local Hamiltonians and time-dependent geometrically local Hamiltonians on n qubits that cannot be simulated via an oracle circuit of depth o(T/n^c), where the Hamiltonians act on n qubits, and c is a constant. Lastly, we generalize the above results and show that any simulators that are geometrically local Hamiltonians cannot do the simulation much faster than parallel quantum algorithms

Administration of a Decoction of Sucrose- and Polysaccharide-Rich Radix Astragali (Huang Qi) Ameliorated Insulin Resistance and Fatty Liver but Affected Beta-Cell Function in Type 2 Diabetic Rats

The current investigation attempted to confirm the beneficial actions of a chemically characterized Radix Astragali decoction (AM-W) against type 2 diabetic (T2D) Sprague-Dawley (SD) rats. Using a case/control design, after 2 months of treatment with AM-W (500 mg/kg, daily i.p.) in T2D rats therapeutic outcomes were compared. Sucrose and Astragalus polysaccharides (ASPs) were shown to exist in nearly equal proportions in AM-W. Body weight loss, an improvement in insulin sensitivity, and an attenuation of fatty liver after AM-W administration in T2D rats were evident. Surprisingly, blood sugar, beta-cell function, and glucose tolerance in T2D rats did not improve with AM-W treatment. Further investigation indicated the deleterious effects of the addition of sucrose (100 and 500 μg/mL) and APSs (500 μg/mL) on glucose-stimulated insulin secretion and viability, respectively. In conclusion, a proper administration dosage and a reduction in the sucrose content are keys to maximizing the merits of this herb

Enhancing performance of ZnO dye-sensitized solar cells by incorporation of multiwalled carbon nanotubes

A low-temperature, direct blending procedure was used to prepare composite films consisting of zinc oxide [ZnO] nanoparticles and multiwalled carbon nanotubes [MWNTs]. The mesoporous ZnO/MWNT films were fabricated into the working electrodes of dye-sensitized solar cells [DSSCs]. The pristine MWNTs were modified by an air oxidation or a mixed acid oxidation treatment before use. The mixed acid treatment resulted in the disentanglement of MWNTs and facilitated the dispersion of MWNTs in the ZnO matrix. The effects of surface property and loading of MWNTs on DSSC performance were investigated. The performance of DSSCs was found to depend greatly on the type and the amount of MWNTs incorporated. At a loading of 0.01 wt%, the acid-treated MWNTs were able to increase the power conversion efficiency of fabricated cells from 2.11% (without MWNTs) to 2.70%

Counteracting UDP flooding attacks in SDN

Software-defined networking (SDN) is a new networking architecture with a centralized control mechanism. SDN has proven to be successful in improving not only the network performance, but also security. However, centralized control in the SDN architecture is associated with new security vulnerabilities. In particular, user-datagram-protocol (UDP) flooding attacks can be easily launched and cause serious packet-transmission delays, controller-performance loss, and even network shutdown. In response to applications in the Internet of Things (IoT) field, this study considers UDP flooding attacks in SDN and proposes two lightweight countermeasures. The first method sometimes sacrifices address-resolution-protocol (ARP) requests to achieve a high level of security. In the second method, although packets must sometimes be sacrificed when undergoing an attack before starting to defend, the detection of the network state can prevent normal packets from being sacrificed. When blocking a network attack, attacks from the affected port are directly blocked without affecting normal ports. The performance and security of the proposed methods were confirmed by means of extensive experiments. Compared with the situation where no defense is implemented, or similar defense methods are implemented, after simulating a UDP flooding attack, our proposed method performed better in terms of the available bandwidth, centralprocessing-unit (CPU) consumption, and network delay time

The Arabidopsis Malectin-Like/LRR-RLK IOS1 is Critical for BAK1-Dependent and BAK1-Independent Pattern-Triggered Immunity

Plasma membrane-localized pattern recognition receptors (PRRs) such as FLAGELLIN SENSING2 (FLS2), EF-TU RECEPTOR (EFR) and CHITIN ELICITOR RECEPTOR KINASE 1 (CERK1) recognize microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). A reverse genetics approach on genes responsive to the priming agent beta-aminobutyric acid (BABA) revealed IMPAIRED OOMYCETE SUSCEPTIBILITY1 (IOS1) as a critical PTI player. Arabidopsis thaliana ios1 mutants were hyper-susceptible to Pseudomonas syringae bacteria. Accordingly, ios1 mutants showed defective PTI responses, notably delayed up-regulation of the PTI-marker gene FLG22-INDUCED RECEPTOR-LIKE KINASE1 (FRK1), reduced callose deposition and mitogen-activated protein kinase activation upon MAMP treatment. Moreover, Arabidopsis lines over-expressing IOS1 were more resistant to bacteria and showed a primed PTI response. In vitro pull-down, bimolecular fluorescence complementation, co-immunoprecipitation, and mass spectrometry analyses supported the existence of complexes between the membrane-localized IOS1 and BRASSINOSTEROID INSENSITIVE1-ASSOCIATED KINASE1 (BAK1)-dependent PRRs FLS2 and EFR, as well as with the BAK1-independent PRR CERK1. IOS1 also associated with BAK1 in a ligand-independent manner, and positively regulated FLS2-BAK1 complex formation upon MAMP treatment. In addition, IOS1 was critical for chitin-mediated PTI. Finally, ios1 mutants were defective in BABA-induced resistance and priming. This work reveals IOS1 as a novel regulatory protein of FLS2-, EFR- and CERK1-mediated signaling pathways that primes PTI activation

Patient-oriented simulation based on Monte Carlo algorithm by using MRI data

<p>Abstract</p> <p>Background</p> <p>Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with <it>in vivo </it>MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.</p> <p>Methods</p> <p>In this study, an individualized brain is modeled based on <it>in vivo </it>MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.</p> <p>Results</p> <p>Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.</p> <p>Conclusions</p> <p>In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.</p

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art networks present extremely high computational complexity. This makes them difficult to be deployed on mobile devices with acceptable latency. Moreover, when deploying to different mobile devices, there is a large latency variation due to the difference and limitation of deep learning accelerators on mobile devices. In this paper, we conduct a search of portable network architectures for better quality-latency trade-off across mobile devices. We further present the effectiveness of widely used network optimizations for image deblurring task. This paper provides comprehensive experiments and comparisons to uncover the in-depth analysis for both latency and image quality. Through all the above works, we demonstrate the successful deployment of image deblurring application on mobile devices with the acceleration of deep learning accelerators. To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices. This paper provides practical deployment-guidelines, and is adopted by the championship-winning team in NTIRE 2020 Image Deblurring Challenge on Smartphone Track.Comment: CVPR 2020 Workshop on New Trends in Image Restoration and Enhancement (NTIRE

Surface Second Harmonic Generation from Topological Dirac Semimetal PdTe2_2

Recent experiments and calculations in topological semimetals have observed anomalously strong second-order optical nonlinearity, but yet whether the enhancement also occurs at surfaces of topological semimetals in general remains an open question. In this work, we tackle this problem by measuring polarization-dependent and rotational-anisotropy optical second harmonic generation (SHG) from centrosymmetric type-II Dirac semimetal PdTe$_2$. We found the SHG to follow C$_{3v}$ surface symmetry with a time-varying intensity dictated by the oxidation kinetics of the material after its surface cleavage, indicating the surface origin of SHG. Quantitative characterization of the surface nonlinear susceptibility indicates a large out-of-plane response of PdTe$_2$ with $|\chi_{ccc}^{(2)}|$ up to 25 $\times$ 10$^{-18}$ m$^2$/V. Our results support the topological surfaces/interfaces as a new route toward applications of nonlinear optical effects with released symmetry constraints, and demonstrate SHG as a viable means to in situ study of kinetics of topological surfaces