Asymmetric Protocols for Scalable High-Rate Measurement-Device-Independent Quantum Key Distribution Networks

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate detector side channels and prevent all attacks on detectors. The future of MDI-QKD is a quantum network that provides service to many users over untrusted relay nodes. In a real quantum network, the losses of various channels are different and users are added and deleted over time. To adapt to these features, we propose a type of protocols that allow users to independently choose their optimal intensity settings to compensate for different channel losses. Such protocol enables a scalable high-rate MDI-QKD network that can easily be applied for channels of different losses and allows users to be dynamically added/deleted at any time without affecting the performance of existing users.Comment: Changed the title to better represent the generality of our method, and added more discussions on its application to alternative protocols (in Sec. II, the new Table II, and Appendix E with new Fig. 9). Added more conceptual explanations in Sec. II on the difference between X and Z bases in MDI-QKD. Added additional discussions on security of the scheme in Sec. II and Appendix

Pre-fixed Threshold Real Time Selection Method in Free-space Quantum Key Distribution

Free-space Quantum key distribution (QKD) allows two parties to share a random key with unconditional security, between ground stations, between mobile platforms, and even in satellite-ground quantum communications. Atmospheric turbulence causes fluctuations in transmittance, which further affect the quantum bit error rate (QBER) and the secure key rate. Previous post-selection methods to combat atmospheric turbulence require a threshold value determined after all quantum transmission. In contrast, here we propose a new method where we pre-determine the optimal threshold value even before quantum transmission. Therefore, the receiver can discard useless data immediately, thus greatly reducing data storage requirement and computing resource. Furthermore, our method can be applied to a variety of protocols, including, for example, not only single-photon BB84, but also asymptotic and finite-size decoy-state BB84, which can greatly increase its practicality

Behavioral analysis of anisotropic diffusion in image processing

©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.541424In this paper, we analyze the behavior of the anisotropic diffusion model of Perona and Malik (1990). The main idea is to express the anisotropic diffusion equation as coming from a certain optimization problem, so its behavior can be analyzed based on the shape of the corresponding energy surface. We show that anisotropic diffusion is the steepest descent method for solving an energy minimization problem. It is demonstrated that an anisotropic diffusion is well posed when there exists a unique global minimum for the energy functional and that the ill posedness of a certain anisotropic diffusion is caused by the fact that its energy functional has an infinite number of global minima that are dense in the image space. We give a sufficient condition for an anisotropic diffusion to be well posed and a sufficient and necessary condition for it to be ill posed due to the dense global minima. The mechanism of smoothing and edge enhancement of anisotropic diffusion is illustrated through a particular orthogonal decomposition of the diffusion operator into two parts: one that diffuses tangentially to the edges and therefore acts as an anisotropic smoothing operator, and the other that flows normally to the edges and thus acts as an enhancement operator

The Effect of Environmental Regulation on Technological Advancement : Based on Empirical Analysis of Chinese Provincial Panel Data

This paper is an empirical study of the effect of environmental regulation upon technological advancement based on the panel data collected from 30 provinces, municipalities and autonomous regions in mainland China from 2000 to 2010. The results show that on a national basis environmental regulation has a positive effect upon technological advancement, but there lies regional disparity between the east, middle and west of China: The effect is positive in the east while negative in the middle and west. This paper further proves the effect of environmental regulation upon technological progress follows an inverted "N"-shaped curve and then puts forward some policy suggestions

Reverse gyrase functions in genome integrity maintenance by protecting DNA breaks in vivo

Reverse gyrase introduces positive supercoils to circular DNA and is implicated in genome stability maintenance in thermophiles. The extremely thermophilic crenarchaeon Sulfolobus encodes two reverse gyrase proteins, TopR1 (topoisomerase reverse gyrase 1) and TopR2, whose functions in thermophilic life remain to be demonstrated. Here, we investigated the roles of TopR1 in genome stability maintenance in S. islandicus in response to the treatment of methyl methanesulfonate (MMS), a DNA alkylation agent. Lethal MMS treatment induced two successive events: massive chromosomal DNA backbone breakage and subsequent DNA degradation. The former occurred immediately after drug treatment, leading to chromosomal DNA degradation that concurred with TopR1 degradation, followed by chromatin protein degradation and DNA-less cell formation. To gain a further insight into TopR1 function, the expression of the enzyme was reduced in S. islandicus cells using a CRISPR-mediated mRNA interference approach (CRISPRi) in which topR1 mRNAs were targeted for degradation by endogenous III-B CRISPR-Cas systems. We found that the TopR1 level was reduced in the S. islandicus CRISPRi cells and that the cells underwent accelerated genomic DNA degradation during MMS treatment, accompanied by a higher rate of cell death. Taken together, these results indicate that TopR1 probably facilitates genome integrity maintenance by protecting DNA breaks from thermo-degradation in vivo

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation. To address this limitation, we introduce Co-Prediction Prompt Tuning for noise correction in FET, which leverages multiple prediction results to identify and correct noisy labels. Specifically, we integrate prediction results to recall labeled labels and utilize a differentiated margin to identify inaccurate labels. Moreover, we design an optimization objective concerning divergent co-predictions during fine-tuning, ensuring that the model captures sufficient information and maintains robustness in noise identification. Experimental results on three widely-used FET datasets demonstrate that our noise correction approach significantly enhances the quality of various types of training samples, including those annotated using distant supervision, ChatGPT, and crowdsourcing.Comment: Accepted by Findings of EMNLP 2023, 11 page

A Boundary Offset Prediction Network for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER typically assign entity types to text spans, resulting in an imbalanced sample space and neglecting the connections between non-entity and entity spans. To address these issues, we propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection. Furthermore, our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets. We conduct experiments on eight widely-used NER datasets, and the results demonstrate that our proposed BOPN outperforms previous state-of-the-art methods.Comment: Accepted by Findings of EMNLP 2023, 13 page