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Jing Qiu

Publications and source records attributed to Jing Qiu.

At least 37 records · Page 2Linked to original sources

Applying Large Language Models to Power Systems: Potential Security Threats

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.

cs.AI↗

Quantum Fusion of Independent Networks Based on Multi-user Entanglement Swapping

With the advance development in quantum science, constructing a large-scale quantum network has become a hot area of future quantum information technology. Future quantum networks promise to enable many fantastic applications and will unlock fundamentally new technologies in information security and large-scale computation. The future quantum internet is required to connect quantum information processors to achieve unparalleled capabilities in secret communication and enable quantum communication between any two points on Earth. However, the existing quantum networks are basically constructed to realize the communication between the end users in their own networks. How to bridge different independent networks to form a fully-connected quantum internet becomes a pressing challenge for future networks. Here, we demonstrate the quantum fusion of two independent networks for the first time based on multiuser entanglement swapping, to merge two 10-user networks into a larger network with 18 users in quantum correlation layer. By performing the Bell state measurement between two nonneighboring nodes, the users from different networks can establish entanglement and ultimately every pair of the 18 users are able to communicate with each other using the swapped states. Our approach opens attractive opportunities for the establishment of quantum entanglement between remote nodes in different networks, which facilitates versatile quantum information interconnects and has great application in constructing large-scale intercity quantum communication networks.

quant-ph↗

New Lower Bounds for the Minimum Distance of Cyclic Codes and Applications to Locally Repairable Codes

Cyclic codes are an important class of linear codes. Bounding the minimum distance of cyclic codes is a long-standing research topic in coding theory, and several well-known and basic results have been developed on this topic. Recently, locally repairable codes (LRCs) have attracted much attention due to their repair efficiency in large-scale distributed storage systems. In this paper, by employing the singleton procedure technique, we first provide a sufficient condition for bounding the minimum distance of cyclic codes with typical defining sets. Secondly, by considering a specific case, we establish a connection between bounds for the minimum distance of cyclic codes and solutions to a system of inequalities. This connection leads to the derivation of new bounds, including some with general patterns. In particular, we provide three new bounds with general patterns, one of which serves as a generalization of the Betti-Sala bound. Finally, we present a generalized lower bound for a special case and construct several families of $(2, δ)$-LRCs with unbounded length and minimum distance $2δ$. It turns out that these LRCs are distance-optimal, and their parameters are new. To the best of our knowledge, this work represents the first construction of distance-optimal $(r, δ)$-LRCs with unbounded length and minimum distance exceeding $r+δ-1$.

cs.IT↗

Towards Carbon Transparency: A High-Resolution Carbon Emissions Database for China's Listed Companies

The dual-carbon goals of China necessitate precise accounting of company carbon emissions, vital for green development across all industries. Not only the company itself but also financial investors require accurate and comprehensive company-level emissions data for climate risk management. This paper introduces the structure and methodology of the High-resolution Database for Carbon Emissions of China-listed companies, integrating three primary data sources: self-disclosed environmental data from listed companies, long-accumulated national power emission data, and regional high-precision emission data derived from multi-source satellites. The database's innovation lies in the employment of artificial intelligence (AI) algorithms to aggregate multi-source satellite data. This approach enables the precise identification of carbon emission sources and the prediction of company-level carbon emissions. Consequently, this methodology robustly cross-validates self-reported direct emissions, enhancing the accuracy and granularity of company-level emission records. Central to the database's utility includes the provision of high-resolution company carbon emission data, which is not only highly accurate but also instrumental in carbon management and emission market transactions. By offering a more nuanced and verifiable picture of company emissions, the database supports China's broader efforts to meet its ambitious dual-carbon targets and transition towards a more sustainable and environmentally responsible economy.

physics.soc-ph↗

The 2023 Development of Room-Temperature Ambient-Pressure Superconductor: Vision and Future Trend of Power Systems

Room-Temperature Ambient-Pressure Superconductor (RTAPS) can achieve superconducting properties at room temperature and normal atmospheric pressure, eliminating the power system's transmission loss and enhancing power systems efficiency. This paper investigates the comprehensive implications and prospective applications of the recently discovered RTAPS, LK-99, in modern power systems. It explores the potential of RTAPS in reshaping modern power systems paradigms, providing the vision of future RTAPS-based power systems. Although debate surrounds its industrial implementations, RTAPS's benefits for electricity transmission, grid flexibility, improved energy storage, and renewable energy integration could be unprecedented. The paper delves into underlying opportunities and challenges, including RTAPS-based power flow methods evolution, security redefinition, computational efficiency, cost implications, and innovative market transaction forms to facilitate renewable energy competitiveness.

physics.soc-ph↗

Some new constructions of optimal linear codes and alphabet-optimal $(r,δ)$-locally repairable codes

In distributed storage systems, locally repairable codes (LRCs) are designed to reduce disk I/O and repair costs by enabling recovery of each code symbol from a small number of other symbols. To handle multiple node failures, $(r,δ)$-LRCs are introduced to enable local recovery in the event of up to $δ-1$ failed nodes. Constructing optimal $(r,δ)$-LRCs has been a significant research topic over the past decade. In \cite{Luo2022}, Luo \emph{et al.} proposed a construction of linear codes by using unions of some projective subspaces within a projective space. Several new classes of Griesmer codes and distance-optimal codes were constructed, and some of them were proved to be alphabet-optimal $2$-LRCs. In this paper, we first modify the method of constructing linear codes in \cite{Luo2022} by considering a more general situation of intersecting projective subspaces. This modification enables us to construct good codes with more flexible parameters. Additionally, we present the conditions for the constructed linear codes to qualify as Griesmer codes or achieve distance optimality. Next, we explore the locality of linear codes constructed by eliminating elements from a complete projective space. The novelty of our work lies in establishing the locality as $(2,p-2)$, $(2,p-1)$, or $(2,p)$-locality, in contrast to the previous literature that only considered $2$-locality. Moreover, by combining analysis of code parameters and the C-M like bound for $(r,δ)$-LRCs, we construct some alphabet-optimal $(2,δ)$-LRCs which may be either Griesmer codes or not Griesmer codes. Finally, we investigate the availability and alphabet-optimality of $(r,δ)$-LRCs constructed from our modified framework.

cs.IT↗

Quantum metrology in complex systems and experimental verification by quantum simulation

Quantum metrology based on quantum entanglement and quantum coherence improves the accuracy of measurement. In this paper, we briefly review the schemes of quantum metrology in various complex systems, including non-Markovian noise, correlated noise, quantum critical system. On the other hand, the booming development of quantum information allows us to utilize quantum simulation experiments to test the feasibility of various theoretical schemes and demonstrate the rich physical phenomena in complex systems, such as bound states in one-dimensional coupled cavity arrays, single-photon switches and routers.

quant-ph↗

STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification

The effectiveness of prompt learning has been demonstrated in different pre-trained language models. By formulating suitable template and choosing representative label mapping, prompt learning can be used as an efficient knowledge probe. However, finding suitable prompt in existing methods requires multiple experimental attempts or appropriate vector initialization on formulating suitable template and choosing representative label mapping, which it is more common in few-shot learning tasks. Motivating by PLM working process, we try to construct the prompt from task semantic perspective and thus propose the STPrompt -Semantic-guided and Task-driven Prompt model. Specifically, two novel prompts generated from the semantic dependency tree (Dep-prompt) and task-specific metadata description (Meta-prompt), are firstly constructed in a prompt augmented pool, and the proposed model would automatically select a suitable semantic prompt to motivating the prompt learning process. Our results show that the proposed model achieves the state-of-the-art performance in five different datasets of few-shot text classification tasks, which prove that more semantic and significant prompts could assume as a better knowledge proving tool.

cs.CL↗

High-performance green and blue quantum-dot light-emitting diodes with eliminated charge leakage

Quantum-dot light-emitting diodes (QD-LEDs) promise a new generation of efficient, low-cost, large-area, and flexible electroluminescent devices. However, the inferior performance of green and blue QD-LEDs is hindering the commercialization of QD-LEDs in display and solid-state lighting. Here, we demonstrate best-performing green and blue QD-LEDs with ~100% conversion of the injected charge carriers into emissive excitons. Key to this success is eliminating electron leakage at the organic/inorganic interface by using hole-transport polymers with low electron affinity and reduced energetic disorder. Our devices exhibit record-high peak external quantum efficiencies (28.7% for green, 21.9% for blue), exceptionally high efficiencies in wide ranges of luminance, and unprecedented stability (T95 lifetime: 580,000 h for green, 4,400 h for blue). The overall performance surpasses previously reported solution-processed green and blue LEDs.

physics.optics↗

Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph

Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system. Generating a complete hierarchical knowledge graph of the teaching domain by nodes of school, student, teacher, course, knowledge point and exercise type. The knowledge graph constructed in a crowdsourcing manner requires many users to participate collaboratively with fully consideration of teachers' guidance and users' mobilization issues. Based on the three subgraphs of knowledge graph, prominent teacher, student learning situation and suitable learning route could be visualized. Personalized exercises recommendation model is used to formulate the personalized exercise by algorithm based on the knowledge graph. Collaborative creation model is developed to realize the crowdsourcing construction mechanism. Though unfamiliarity with the learning mode of knowledge graph and learners' less attention to the knowledge structure, system based on Crowdsourcing Knowledge Graph can still get high acceptance around students and teachers

cs.DB↗

New Constructions of Optimal Cyclic (r,δ) Locally Repairable Codes from Their Zeros

An $(r, δ)$-locally repairable code ($(r, δ)$-LRC for short) was introduced by Prakash et al. \cite{Prakash2012} for tolerating multiple failed nodes in distributed storage systems, which was a generalization of the concept of $r$-LRCs produced by Gopalan et al. \cite{Gopalan2012}. An $(r, δ)$-LRC is said to be optimal if it achieves the Singleton-like bound. Recently, Chen et al. \cite{Chen2018} generalized the construction of cyclic $r$-LRCs proposed by Tamo et al. \cite{Tamo2015,Tamo2016} and constructed several classes of optimal $(r, δ)$-LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$, respectively in terms of a union of the set of zeros controlling the minimum distance and the set of zeros ensuring the locality. Following the work of \cite{Chen2018,Chen2019}, this paper first characterizes $(r, δ)$-locality of a cyclic code via its zeros. Then we construct several classes of optimal cyclic $(r, δ)$-LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$, respectively from the product of two sets of zeros. Our constructions include all optimal cyclic $(r,δ)$-LRCs proposed in \cite{Chen2018,Chen2019}, and our method seems more convenient to obtain optimal cyclic $(r, δ)$-LRCs with flexible parameters. Moreover, many optimal cyclic $(r,δ)$-LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$, respectively such that $(r+δ-1)\nmid n$ can be obtained from our method.

cs.IT↗

Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation

Cardiac coronary angiography is a major technology to assist doctors during cardiac interventional surgeries. Under the exposure of X-ray radiation, doctors inject contrast agents through catheters to determine the position and status of coronary vessels in real time. To get a coronary angiography video with a high frame rate, the doctor needs to increase the exposure frequency and intensity of the X-ray. This will inevitably increase the X-ray harm to both patients and surgeons. In this work, we innovatively utilize a deep-learning based video interpolation algorithm to interpolate coronary angiography videos. Moreover, we establish a new coronary angiography image dataset ,which contains 95,039 triplets images to retrain the video interpolation network model. Using the retrained network we synthesize high frame rate coronary angiography video from the low frame rate coronary angiography video. The average peak signal to noise ratio(PSNR) of those synthesized video frames reaches 34dB. Extensive experiment results demonstrate the feasibility of using the video frame interpolation algorithm to synthesize continuous and clear high frame rate coronary angiography video. With the help of this technology, doctors can significantly reduce exposure frequency and intensity of the X-ray during coronary angiography.

eess.IV↗

Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net

Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spatial--temporal information from a video sequence and a 2D CE--Net to accomplish the segmentation task of an image sequence. The input is a few continuous frames of angiographic video, and the output is a mask of segmentation result. From the results of segmentation and extraction, we can get good segmentation results despite the poor quality of coronary angiography video sequences.

eess.IV↗

Nonlinear interaction effects in a three-mode cavity optomechanical system

We investigate the resonant enhancement of nonlinear interactions in a three-mode cavity optomechanical system with two mechanical oscillators. By using the Keldysh Green's function technique we find that nonlinear effects on the cavity density of states can be greatly enhanced by the resonant scattering of two phononic polaritons, due to their small effective dissipation. In the large detuning limit and taking into account an upper bound on the achievable dressed coupling, the optimal point for probing the nonlinear effect is obtained, showing that such three-mode system can exhibit prominent nonlinear features also for relatively small values of $g/κ$.

quant-ph↗

Vcash: A Novel Reputation Framework for Identifying Denial of Traffic Service in Internet of Connected Vehicles

Trust management of Internet of connected vehicles has been a hot topic during the recent years with the rapid development of UGV technologies. However, existing resolutions based on trustworthiness verification among vehicles make the traffic event transmission quite inefficient. In this paper, we assume that the deployed RSUs can provide efficient communication between any pair of RSU and vehicle, and propose Vcash, a reputation framework for identifying denial of traffic service, to resolve the trustworthiness problem in the application level of the Internet of connected vehicles. In our reputation framework, every vehicle communicates with the RSU directly for traffic event verification, and spread verified traffic event notification. We borrow the idea of market trading, and set up trading rules to restrict the malicious vehicle's spread of false message, and to encourage vehicles to contribute to the traffic event monitoring and verification. To evaluate the effectiveness of our reputation framework, we conduct simulation experiment. Our experiment results indicate that our proposal manages to avoid bogus event spread, and a vehicle in our framework has to contribute to the traffic event detection to normally employ the traffic service.

cs.NI↗

Evaluating Reputation Management Schemes of Internet of Vehicles based on Evolutionary Game Theory

Conducting reputation management is very important for Internet of vehicles. However, most of the existing researches evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation which cannot represent the scenarios in reality. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users' attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.

cs.GT↗

Enhanced nonlinear interaction effects in a four-mode optomechanical ring

With a perturbative treatment based on the Keldysh Green's function technique, we study the resonant enhancement of nonlinear interaction effects in a four-mode optomechanical ring. In such a system, we identify five distinct types of resonant scattering between unperturbed polariton modes, induced by the nonlinear optomechanical interaction. By computing the cavity density of states and optomechanical induced transparency signal, we find that the largest nonlinear effects are induced by a decay process involving the two phonon-like polaritons. In contrast to the conventional two-mode optomechanical system, our proposed system can exhibit prominent nonlinear features even in the regime when the single-photon coupling is much smaller than the cavity damping.

quant-ph↗

Dark State Polarizing a Nuclear Spin in the Vicinity of a Nitrogen-Vacancy Center

The nuclear spin in the vicinity of a nitrogen-vacancy (NV) center possesses of long coherence time and convenient manipulation assisted by the strong hyperfine interaction with the NV center. It is suggested for the subsequent quantum information storage and processing after appropriate initialization. However, current experimental schemes are either sensitive to the inclination and magnitude of the magnetic field or require thousands of repetitions to achieve successful realization. Here, we propose polarizing a 13C nuclear spin in the vicinity of an NV center via a dark state. We demonstrate theoretically that it is robust to polarize various nuclear spins with different hyperfine couplings and noise strengths.

quant-ph↗