During EUROCRYPT 2019, Baetu et al. performed a classical key recovery attack utilizing plaintext checks (KR-PCA), and a quantum key recovery attack employing chosen ciphertext attacks (KR-CCA). NIST's weak version of nine submissions underwent a security analysis. This research delves into FrodoPKE, a system employing LWE techniques, and examines how its IND-CPA security is directly correlated to the hardness of LWE problem instances. We commence by evaluating the meta-cryptosystem and the quantum algorithm applied to resolving quantum LWE problems. We now examine the case of noise governed by a discrete Gaussian distribution, and recompute the probability of success for quantum LWE using Hoeffding's bound. To conclude, we offer a quantum key retrieval algorithm built on LWE under the premise of Chosen Ciphertext Attack, and we will examine Frodo's security. Our proposed method, in contrast to previous work by Baetu et al., shows a query reduction from 22 to 1, with no decrease in the probability of success.
Recent improvements in the design of deep learning generative adversarial networks have involved the Renyi cross-entropy and Natural Renyi cross-entropy, two generalizations of Shannon cross-entropy based on the Renyi type, as loss functions. We systematically determine the Renyi and Natural Renyi differential cross-entropy measures for a broad spectrum of standard continuous distributions from the exponential family, providing tabulated results for immediate and convenient referencing. We additionally provide a summary of the Renyi-type cross-entropy rates of stationary Gaussian processes and finite-alphabet time-invariant Markov sources.
Using the concept of minimum Fisher information, this paper explores the application of a quantum-like approach to market analysis. We are undertaking a comprehensive evaluation of the authenticity of squeezed coherent states as a means of formulating market strategies. Cariprazine This investigation centers on the representation of any squeezed coherent state relative to the basis of eigenvectors for market risk. A method for calculating the probability of a squeezed coherent state existing within one of these states is presented through a derived formula. The generalized Poisson distribution, a cornerstone of quantum risk, connects the description of squeezed coherent states in this field. We present a formula that calculates the total risk associated with a squeezed coherent strategy. Finally, we present a nuanced risk perspective, termed risk-of-risk, which corresponds to the second central moment within the generalized Poisson distribution. Albright’s hereditary osteodystrophy The numerical characterization of squeezed coherent strategies is presented here. Its interpretations are established by us, drawing upon the uncertainty principle that connects time and energy.
Our systematic examination focuses on the chaotic imprints within the quantum many-body system, specifically the extended Dicke model, which describes interacting two-level atoms coupled to a single bosonic mode. Atomic-scale interactions necessitate examining the impact they have on the model's chaotic properties. By exploring the energy spectral statistics and the structure of eigenstates, we expose the quantum signatures of chaos in this model, and also discuss the influence of atomic interactions. Atomic interaction's influence on the chaos boundary, as determined by eigenvalue- and eigenstate-based methods, is likewise investigated. We demonstrate that atomic interactions exert a more profound influence on spectral characteristics than on the intrinsic properties of eigenstates. The interatomic interaction's activation in the extended Dicke model leads to a qualitative enhancement of the integrability-to-chaos transition observed in the original Dicke model.
This paper details the multi-stage attentive network (MSAN), a convolutional neural network (CNN) architecture for motion deblurring, highlighting its strong generalization capabilities and efficiency. We utilize a multi-stage encoder-decoder network, incorporating self-attention, and train it using binary cross-entropy loss. Two key design approaches underpin the MSAN infrastructure. Leveraging the architecture of multi-stage networks, a novel end-to-end attention-based methodology is presented. This methodology integrates group convolution into the self-attention module, resulting in a decrease of computational burden and a concomitant enhancement of the model's ability to handle images with varied levels of blur. To combat the over-smoothing tendency of pixel loss, we propose the utilization of binary cross-entropy loss in the optimization of our model, ensuring superior deblurring results. To gauge the efficacy of our deblurring method, we performed exhaustive experiments involving multiple deblurring datasets. Our MSAN stands out in its performance, generalizing effectively while maintaining strong comparative results alongside current leading methods.
With reference to an alphabet's letters, entropy is the average number of binary digits required for the transmission of each individual character. When reviewing tables presenting statistical data, one notices that the frequency of digits 1 through 9 in the first numerical position varies significantly. The Shannon entropy H is determinable based on these probabilities. The Newcomb-Benford Law, while common, does not always uniformly apply; some distributions demonstrate a frequency of the leading digit '1' exceeding that of '9' by a factor exceeding 40. The occurrence probability of a specific first digit in this context can be derived using a power function with a negative exponent, p exceeding 1. Considering an NB distribution, the entropy of the first digits amounts to H = 288; however, different distributions—such as the diameters of craters on Venus or the mass of crushed mineral fragments—reveal entropy values of 276 and 204 bits per digit, respectively.
The qubit, the fundamental unit of quantum information, is characterized by two states, each presented by a 2×2 positive semi-definite Hermitian matrix having a trace of 1. Contributing to the program to axiomatize quantum mechanics, we characterize these states using an eight-point phase space, in the context of an entropic uncertainty principle. By employing Renyi entropy, a broader framework derived from Shannon entropy, we manage the signed phase-space probability distributions that arise in characterizing quantum states.
The concept of unitarity requires the black hole's final state, manifested as the remnants inside the event horizon after complete evaporation, to be uniquely determined. Assuming an ultraviolet theory possessing an infinite array of fields, we suggest that the final state's uniqueness is achievable via a mechanism analogous to the quantum mechanical portrayal of dissipation.
This study empirically examines long memory and reciprocal information flow between the estimated volatilities of five highly volatile cryptocurrency datasets. To quantify cryptocurrency volatility, we recommend the application of volatility estimators developed by Garman and Klass (GK), Parkinson, Rogers and Satchell (RS), Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC). This study quantifies the information flow between estimated volatilities using methodologies such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Renyi transfer entropy (RTE). Besides other analyses, Hurst exponent calculations explore the persistence of long memory in log returns and OHLC volatilities, based on methodologies involving simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. The sustained dependence and non-linear characteristics of cryptocurrency log returns and volatilities are substantiated by our results. In our analysis, all OHLC estimates exhibit statistically significant TE and ETE estimations. Bitcoin's volatility demonstrates the largest impact on Litecoin's, as reflected in the RS correlation. Likewise, BNB and XRP exhibit the most significant information exchange regarding volatility estimations derived from the GK, Parkinson's, and GK-YZ models. This research presents a viable method for incorporating OHLC volatility estimators in the quantification of information flow, offering a further selection for comparison alongside existing volatility estimators like stochastic volatility models.
Algorithms for clustering attribute graphs, which incorporate topological structure into node attributes to create robust representations, have demonstrated impressive effectiveness in diverse applications. While the topology showcases the local links between connected nodes, it omits the relationships between unconnected nodes, thus limiting the potential for further improvement in future clustering. The Auxiliary Graph for Attribute Graph Clustering (AGAGC) procedure is deployed to overcome this issue. We augment our current structure with a graph guided by node attributes, working as a supervisor. Laboratory Services An extra graph functions as an auxiliary supervisor, enhancing the existing one's capabilities. To build a trustworthy auxiliary graph, we propose a method for reducing noise. A more effective clustering model is constructed under the cooperative supervision of the pre-defined graph and an auxiliary graph. The embeddings from multiple layers are fused together to increase the ability of the representations to discriminate. For a clustering-aware learned representation, our self-supervisor offers a clustering module. Finally, the triplet loss method is used to train our model. Four benchmark datasets served as the foundation for the experimental investigation, the results of which signify that the proposed model's performance is superior or comparable to the current top-performing graph clustering models.
Recently, a semi-quantum bi-signature scheme (SQBS) was introduced by Zhao et al. This scheme employs W states, utilizing two quantum signers and a single classical verifier. This study's analysis uncovers three security issues impacting the SQBS scheme developed by Zhao et al. In Zhao et al.'s SQBS protocol, an insider attacker has the capability to perform an impersonation attack in the signature phase after previously executing an impersonation attack during the verification phase, thereby obtaining the private key.