A5104636404- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum-Dot Cellular Automata
- Computational Drug Discovery Methods
- Advancements in Semiconductor Devices and Circuit Design
- Solar Radiation and Photovoltaics
- Protein Structure and Dynamics
- Reinforcement Learning in Robotics
- Data Visualization and Analytics
- Gaze Tracking and Assistive Technology
- Visual Attention and Saliency Detection
- Photovoltaic System Optimization Techniques
- Energy Load and Power Forecasting
- Constructed Wetlands for Wastewater Treatment
- Computational Geometry and Mesh Generation
- Receptor Mechanisms and Signaling
- Digital Image Processing Techniques
- Advanced Computing and Algorithms
- Neural Networks and Reservoir Computing
- Advanced Sensor and Control Systems
- Magnesium Oxide Properties and Applications
- Urban Stormwater Management Solutions
- Network Time Synchronization Technologies
- Model Reduction and Neural Networks
- Topic Modeling
Xi’an University
2025
Xi'an University of Technology
2025
Fudan University
2025
Space Engineering University
2024
Nanyang Technological University
2023-2024
Southeast University
2019-2024
University of California, San Francisco
2023
Sichuan Normal University
2023
Beijing University of Posts and Telecommunications
2022-2023
Technical University of Munich
2023
The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors Mac1 have great therapeutic potential, at outset COVID-19 pandemic, there were no well-validated this nor, indeed, family, making target pharmacological orphan. Here, we report structure-based discovery development several different chemical scaffolds exhibiting...
The research reported here is motivated by the now ubiquitous nature of web mapping services that provide remotely sensed imagery as a basemap option. Despite popularity basemaps, few strategies have been suggested to enhance their readability. Here, we describe controlled experiment leveraging eye tracking method explore potential enhancing when used for cartographic presentation. Specifically, 20 participants had movements recorded they visually searched areas interest in either an...
In this paper, we consider the optimized implementation of Multi Controlled Toffoli (MCT) using Clifford $+$ T gate sets. While there are several recent works in direction, here explicitly quantify trade-off (with concrete formulae) between depth (this means classical 2-controlled Toffoli) $n$-controlled (hereform will tell $n$-MCT) and number clean ancilla qubits. Additionally, achieve a reduced (and consequently, T-depth), which is an extension technique introduced by Khattar et al....
The rapid advancement of quantum computing has spurred widespread adoption, with cloud-based devices gaining traction in academia and industry. This shift raises critical concerns about the privacy security computations on shared, multi-tenant platforms accessed remotely. Recent studies have shown that crosstalk shared allows adversaries to interfere victim circuits within a neighborhood. While insightful, these works left unresolved questions regarding root cause crosstalk, effective...
Organophosphorus flame retardants (OPFRs) are important chemical additives that used in commercial products. However, owing to increasing health concerns, the discovery of new OPFRs has become imperative. Herein, we propose an explainable artificial intelligence-assisted product design (AIPD) methodological framework for screening novel, safe, and effective OPFRs. Using a deep neural network, established retardancy prediction model with accuracy 0.90. Employing SHapley Additive exPlanations...
Improving the performance of quantum adder is an important technical challenge with major impact on implementation efficient, large-scale computing. Continuing along this research direction, we propose a novel parallel-prefix based Ling expansion. We systematically explored classical structures for adders assessing their suitability to be realized in domain. Furthermore, enforces Logical OR and large fan-out, which require innovative solutions. addressed these challenges realize adder,...
Delineating the fingerprint or feature vector of a receptor/protein will facilitate structural and biological studies, as well rational design development drugs with high affinities selectivity. However, protein is complicated by its different functional regions that can bind to some partner(s), substrate(s), orthosteric ligand(s) allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate scoring-function-based computing protocol Molecular...
Task-oriented semantic communication has received growing interests, which can significantly reduce the amount of transmitted data without affecting task performance. In this paper, a novel system based on triplets (SCST) is proposed, in semantics represented via explainable triplets. Specifically, we propose extraction method to convert texts into triplets, be further compressed designed filtering method. The then will encoded and wireless channel complete intelligent tasks at receiver....
Abstract In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For addition of two n -bit numbers, our uses $$O(n)-O(\frac{n}{r})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>-</mml:mo> <mml:mfrac> <mml:mi>r</mml:mi> </mml:mfrac> </mml:math> qubits and $$O(n)+O(\frac{n}{r})$$ <mml:mo>+</mml:mo> T gates to get correct...
This work proposes a novel hardware Trojan detection method that leverages static structural features and behavioral characteristics in field programmable gate array (FPGA) netlists. Mapping of design sources to look-up-table (LUT) networks makes these explicit, allowing automated feature extraction further effective through machine learning. Four-dimensional are extracted for each signal random forest classifier is trained net classification. Experiments using Trust-Hub benchmarks show...
Large-scale quantum computers that can execute practical algorithms have the potential to solve complex problems are currently challenging for classical computers. This involves converting these into a form be processed by circuits, crucial process requires minimizing resources like qubit count, gate and circuit depth. Our work focuses on implementing optimizing foundational task of finance, known as option pricing, circuit. enables utilization computing benefits, within financial domain....
High penetration of Photovoltaic (PV) systems is variable resource as challenges to the stability and power quality electrical grids. Accurate prediction PV has been recognized a way solve this problem. An improved model for system based on Elman neural networks proposed in paper. Comparing with traditional BP network, context layer added Elman, which make it fewer iterations less computation. Except from irradiance temperature, four physical quantities, wind speed, direction wind, humidity,...
Real-time interaction has become increasingly important. At the same time, panoramic video gradually popular. In this paper, problem we study is predicting Field-of-View(FoV) at future moment when people are enjoying a dynamic immersive video. Existing methods either estimate viewing area based on previous trajectory, or predict FoV salient region in frames. Here, design new model to points moments. Firstly, point from viewer's trajectory using LSTM(Long Short-Term Memory) network. mean...
Photovoltaic power generation and grid-connected technologies are being used more widely. The accurate prediction of photovoltaic can be beneficial to the stable operation grid. An improved method for predicting output characteristics cells based on combination BP neural network Elman in different weather is proposed. Compared with traditional network, a context layer added Elman, thereby reducing number iterations amount calculation. According using artificial intelligence irradiance,...
Distributional reinforcement learning (DRL) enhances the understanding of effects randomness in environment by letting agents learn distribution a random return, rather than its expected value as standard learning. Meanwhile, challenge DRL is that policy evaluation typically relies on representation return distribution, which needs to be carefully designed. In this paper, we address for special class problems rely discounted linear quadratic regulator (LQR), call \emph{distributional LQR}....
Rapid progress in the design of scalable, robust quantum computing necessitates efficient circuit implementation for algorithms with practical relevance. For several algorithms, arithmetic kernels, particular, division plays an important role. In this manuscript, we focus on enhancing performance slow dividers by exploring choices its sub-blocks, such as, adders. Through comprehensive space exploration state-of-the-art addition building blocks, our work have resulted impressive achievement:...
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on development quantum circuits for integration into fault-tolerant computing architectures, with an emphasis minimizing $T$-depth. Specifically, we present novel implementations ReLU leaky functions, achieving constant $T$-depths 4 8, respectively. Leveraging lookup tables,...
Efficient quantum arithmetic circuits are commonly found in numerous algorithms of practical significance. Till date, the logarithmic-depth adders includes a constant coefficient k >= 2 while achieving Toffoli-Depth klog n + O(1). In this work, 160 alternative compositions carry-propagation structure comprehensively explored to determine optimal depth for adder. By extensively studying these structures, it is shown that an exact log O(1) achievable. This presents reduction by almost 50%...
Distributional reinforcement learning (DRL) enhances the understanding of effects randomness in environment by letting agents learn distribution a random return, rather than its expected value as standard RL. At same time, main challenge DRL is that policy evaluation typically relies on representation return distribution, which needs to be carefully designed. In this paper, we address for special class problems rely linear quadratic regulator (LQR) control, advocating new distributional...
Abstract In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For addition of two n-bit numbers, our uses O(n)-O(n/r) qubits and O(n)+O(n/r) T gates to get correct answer in T-depth O(r)+O(log(n/r)), where r is radix. Quantum has already attracted some attention because its low T-depth. Our work further reduces overall cost by introducing a layer. By analyzing performance T-depth, T-count, qubit count, it shown that proposed superior...