On the efficiency of Hamiltonian-based quantum computation for low-rank matrices

We present an extension of Adiabatic Quantum Computing (AQC) algorithm for the unstructured search to the case when the number of marked items is unknown. The algorithm maintains the optimal Grover speedup and includes a small counting subroutine. Our other results include a lower bound on the amount of time needed to perform a general Hamiltonian-based quantum search, a lower bound on the evolution time needed to perform a search that is valid in the presence of control error and a generic upper bound on the minimum eigenvalue gap for evolutions. In particular, we demonstrate that quantum speedup for the unstructured search using AQC type algorithms may only be achieved under very rigid control precision requirements.Comment: 17 pages, no figures, to appear in JM

Power Saving Experiments for Large Scale Global Optimization

Green computing, an emerging field of research that seeks to reduce excess power consumption in high performance computing (HPC), is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power aware components suitable for large scale green computing research. DIRECT is a deterministic global optimization algorithm, implemented in the mathematical software package VTDIRECT95. This paper explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management

The weak localization for the alloy-type Anderson model on a cubic lattice

We consider alloy type random Schr\"odinger operators on a cubic lattice whose randomness is generated by the sign-indefinite single-site potential. We derive Anderson localization for this class of models in the Lifshitz tails regime, i.e. when the coupling parameter $\lambda$ is small, for the energies $E \le -C \lambda^2$.Comment: 45 pages, 2 figures. To appear in J. Stat. Phy

Predictive extended state observer-based repetitive controller for uncertain systems with input delay

This article presents a predictive extended state observer-based repetitive controller (PESO-RC) to simultaneously track and reject periodic signals on systems with long input delay and parameter uncertainties. First, a novel extended state observer (ESO) is proposed to tackle periodic signals on processes with input delay. Then a simple low pass filter is incorporated and tuned to improve robustness against modelling errors. Moreover, the modified repetitive controller (MRC) is integrated to enhance the performance when compensating periodic signals without affecting the overall system’s stability. Stability criteria and robust stability analysis under modelling errors are studied to develop tuning guidelines. Furthermore, validation of the proposed controller and comparison studies are simulated in MATLAB and tested on a brushless DC servo motor which highlight the superior performance of PESO-RC

A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the "Decompose-and-Query" framework (D&Q). This framework guides the model to think and utilize external knowledge similar to ReAct, while also restricting its thinking to reliable information, effectively mitigating the risk of hallucinations. Experiments confirm the effectiveness of D&Q: On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases; on the HotPotQA question-only setting, D&Q achieved an F1 score of 59.6%. Our code is available at https://github.com/alkaidpku/DQ-ToolQA

PWDFT-SW: Extending the Limit of Plane-Wave DFT Calculations to 16K Atoms on the New Sunway Supercomputer

First-principles density functional theory (DFT) with plane wave (PW) basis set is the most widely used method in quantum mechanical material simulations due to its advantages in accuracy and universality. However, a perceived drawback of PW-based DFT calculations is their substantial computational cost and memory usage, which currently limits their ability to simulate large-scale complex systems containing thousands of atoms. This situation is exacerbated in the new Sunway supercomputer, where each process is limited to a mere 16 GB of memory. Herein, we present a novel parallel implementation of plane wave density functional theory on the new Sunway supercomputer (PWDFT-SW). PWDFT-SW fully extracts the benefits of Sunway supercomputer by extensively refactoring and calibrating our algorithms to align with the system characteristics of the Sunway system. Through extensive numerical experiments, we demonstrate that our methods can substantially decrease both computational costs and memory usage. Our optimizations translate to a speedup of 64.8x for a physical system containing 4,096 silicon atoms, enabling us to push the limit of PW-based DFT calculations to large-scale systems containing 16,384 carbon atoms

Electrical characteristics and metallogenic prediction of Baishawo rare metal deposit in northeast Hunan Province

Objective  The aim of this study is to characterize the deep electrical structure of the Baishawo pegmatite-type deposits, explore the spatial distribution of rare metal mineralization models and ore-conferring carriers, and elucidate the relationship between the location of ore veins to provide a basis for prediction in rare metal mineral exploration.   Methods  Through the controlled-source audio-geomagnetic method (CSAMT) and the conjugate gradient method of inverse measurement of three exploration profiles, deep hidden rock (ore) bodies were detected. Then, by combining these results with the information from drill holes and elemental geochemical anomalies, a comprehensive analysis was carried out to reveal the distribution of the hidden dikes and the mineralization characteristics.   Results  The study showed that the concealed pegmatite veins are mainly located in the Lengjiaxi Group, where they are concentrated on the top of the granite body and around tectonic fissures and have burial depths ranging from 50 to 300 m. The geophysical signature of pegmatite vein mineralization is a high-resistance vein-like geologic body intruded from a deep high-resistance body into a shallow low-resistance body.   Conclusion  The determined distribution and burial depth of concealed pegmatite veins, identified potential concealed veins in the study area, and geophysical characteristics of the pegmatite vein mineralization provide a basis for understanding the formation mechanism of fracture zone-constrained pegmatite veins. Significance The research results provide a scientific basis for follow-up exploration of the Baishawo deposit, reveal the formation mechanism of the pegmatite veins, and provide reliable physical exploration and prediction technology and experience useful for rare metal exploration in northeast Hunan and south China

Experimental study on slope consolidation sand tanks based on the artificial ground freezing method

Slope stability is an important factor affecting safe production in large surface coal mines. Most traditional slope fixation methods adopt grouting and anchor reinforcement. However, cementing rock and soil using conventional methods is challenging in areas with high water content. This study innovatively proposes the application of artificial ground freezing technology to reinforce the slopes of large-scale open-pit coal mines and verifies the technical feasibility of this technique. Five conditions are investigated via sandbox tests and the development characteristics of the freezing temperature field are analyzed. The results show that different water contents, lithologies, and the presence or absence of seepage affect freezing. Specifically, when the water content is 7.3%, the freezing radius is 220 mm; when the water content is increased to 27.4%, the freezing radius extends to 300 mm. Sand has the fastest freezing rate at 0.072°C/h, while lignite has the slowest freezing rate at 0.061°C/h. The temperature drop rates are 0.068°C and 0.058°C/h for the impermeable and seepage conditions, respectively. Moreover, the fitting function y = A1e-x/A2+A3 adequately describes the temperature profile decrease. The results of the study provide a basis for the precise design of freezing programs to further realize green and low-carbon, large-scale slope stabilization

Inflammasomes: novel therapeutic targets for metabolic syndrome?

Chronic inflammation is a hallmark for Metabolic Syndrome (MetS). It is also one of the most important risk factors for insulin resistance and metabolic disorders. Inflammasomes, which are intracellular multiprotein complexes within the innate immune system, regulate the production and maturation of pro-inflammatory cytokines including interleukin-1β (IL-1β) and IL-18 upon sensing pathogens or danger signals in the cytosol. A growing body of evidence indicates that inflammasomes play a pivotal role in the pathophysiology and progression of metabolic diseases, as deficiency in the key component of inflammasomes protects mice from high fat diet induced obesity and insulin resistance. Thus, in this review, we will summarize the role of inflammasomes in MetS and how to treat MetS by targeting inflammasomes. This may provide novel insights and therapeutic targets for treating metabolic disorders

Open-CD: A Comprehensive Toolbox for Change Detection

We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a unified platform that covers many popular change detection methods and contemporary modules. It not only includes training and inference codes, but also provides some useful scripts for data analysis. We believe this toolbox is by far the most complete change detection toolbox. In this report, we introduce the various features, supported methods and applications of Open-CD. In addition, we also conduct a benchmarking study on different methods and components. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new change detectors. Code and models are available at \url{https://github.com/likyoo/open-cd}. Pioneeringly, this report also includes brief descriptions of the algorithms supported in Open-CD, mainly contributed by their authors. We sincerely encourage researchers in this field to participate in this project and work together to create a more open community. This toolkit and report will be kept updated.Comment: 9 page