EUROCRYPT 2019 witnessed Baetu et al.'s demonstration of a classical key recovery method under plaintext checking attacks (KR-PCA), and a quantum key recovery method under chosen ciphertext attack conditions (KR-CCA). Nine submissions to NIST, which were found to have weaknesses, experienced a security analysis. FrodoPKE, an LWE-based public-key encryption scheme, is the subject of this study, and its IND-CPA security is demonstrably tied to the intractability of plain LWE problems. A foundational analysis of the meta-cryptosystem and quantum algorithm pertinent to quantum LWE challenges will be undertaken. Subsequently, we analyze the scenario where the noise adheres to a discrete Gaussian distribution, recalculating the quantum LWE success probability with the aid of Hoeffding's inequality. Lastly, we detail a quantum key retrieval algorithm leveraging LWE against a Chosen Ciphertext Attack, followed by an analysis of Frodo's security. Compared to the work of Baetu et al., our methodology demonstrates a reduction in queries from 22 to just 1, preserving the same success probability.

In recent advancements for the design of deep learning generative adversarial networks, two Renyi-type generalizations of the Shannon cross-entropy, the Renyi cross-entropy and the Natural Renyi cross-entropy, have been utilized as loss functions. This research details a closed-form derivation of Renyi and Natural Renyi differential cross-entropy measures for a diverse set of typical continuous distributions adherent to the exponential family, with tabulated results for accessibility. Moreover, we detail the Renyi-type cross-entropy rates between stationary Gaussian processes and between finite-alphabet time-invariant Markov sources.

A quantum-like depiction of the market, guided by the principle of minimum Fisher information, is the subject of this paper's investigation. We propose to examine the soundness of incorporating squeezed coherent states into market maneuvering tactics. accident & emergency medicine In this context, we focus on how any squeezed coherent state is represented within the eigenvectors' basis of the market risk observable. The probability of a system being in a squeezed coherent state, from among these states, is calculated via a derived formula. Squeezed coherent states, as they relate to risk in quantum mechanics, find their mathematical description within the generalized Poisson distribution framework. We articulate a formula that quantifies the overall risk for a compressed coherent strategy. A risk assessment paradigm, in the form of a risk-of-risk concept, is derived from the second central moment of the generalized Poisson distribution. (1S,3R)-RSL3 A significant numerical description of squeezed coherent strategies is this. The principle of time-energy uncertainty underpins our interpretations of it.

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. Due to the atom-atom interactions present, we must explore how atomic interaction influences the chaotic characteristics displayed by the model. Quantum signatures of chaos, as embedded within the model, are deduced by examining energy spectral statistics and the structure of eigenstates. We subsequently analyze the impact of atomic interactions. Furthermore, we explore the impact of atomic interaction on the chaos boundary, as ascertained by both eigenvalue- and eigenstate-based analyses. We ascertain that atomic interactions produce a more substantial effect on the statistical properties of the spectrum compared to their impact on the configuration of eigenstates. The Dicke model's integrability-to-chaos transition is qualitatively magnified when the interatomic interaction is introduced into the extended Dicke model.

In this paper, we introduce the multi-stage attentive network (MSAN), a convolutional neural network (CNN) architecture excelling in motion deblurring, due to its strong generalization and efficiency. A multi-stage encoder-decoder network, incorporating self-attention mechanisms, is constructed, and binary cross-entropy loss is employed for model training. Two core architectural designs characterize the MSAN. To enhance adaptability to diversely blurred images within a multi-stage network, we present a novel, end-to-end attention-based method. This method incorporates group convolution into the self-attention module, thereby mitigating computational expense. For improved model optimization, we advocate the substitution of pixel loss with binary cross-entropy loss. This change is intended to alleviate the detrimental over-smoothing effect of pixel loss, while maintaining satisfactory deblurring performance. Experiments on multiple deblurring datasets were conducted to evaluate the performance of our deblurring solution. Our MSAN demonstrates superior performance, generalizing effectively, and compares favorably to cutting-edge methodologies.

The average number of binary digits needed for communicating a single character from an alphabet's letters is equivalent to its entropy. An examination of statistical tables reveals varying frequencies of digits 1 through 9 in the leading numerical positions. From these probability values, the Shannon entropy H can be calculated. Though the Newcomb-Benford Law usually applies, instances exist where the leading digit '1' appears more than 40 times as often as the digit '9' in a distribution. 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. Whereas the entropy of the initial digits adhering to an NB distribution equals H = 288, different data distributions, such as the diameters of Venusian craters or the weights of fragmented minerals, have yielded entropy values of 276 and 204 bits per digit, respectively.

Each of the two states of the qubit, the elementary unit of quantum information, is represented by a 2×2 positive semi-definite Hermitian matrix with 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. We accomplish this task by employing Renyi entropy, a broadened interpretation of Shannon entropy, uniquely defined for the signed phase-space probability distributions that are inherent in quantum state representations.

Black hole evaporation, according to unitarity, must culminate in a single, definite final state, comprising the contents of the event horizon. Considering a UV theory with infinitely many fields, we posit that the uniqueness of the final state is attained through a mechanism analogous to quantum mechanical dissipation.

We empirically analyze the long memory properties and bidirectional information flow in the volatility estimates of five cryptocurrencies' highly volatile time series. Employing Garman and Klass (GK), Parkinson's, Rogers and Satchell (RS), Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators, we aim to quantify cryptocurrency volatilities. Methods like mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Renyi transfer entropy (RTE) are applied in this study to ascertain the information flow between the estimated volatilities. Computations of the Hurst exponent additionally assess the presence of long-range dependence within log returns and OHLC volatilities, applying simple R/S, corrected R/S, empirical, corrected empirical, and theoretical models. Our investigation reveals the long-term dependence and non-linear patterns in the log returns and volatilities of each cryptocurrency. The statistical significance of TE and ETE estimates is evident in our analysis of all OHLC estimates. Our findings indicate a maximal transmission of volatility from Bitcoin to Litecoin, as evidenced by the RS. By the same token, BNB and XRP show the most prominent exchange of volatility information based on the GK, Parkinson's, and GK-YZ calculations. This study highlights the practical application of OHLC volatility estimators to gauge information flow, presenting a supplemental strategy for comparing it to other volatility estimators, including stochastic volatility models.

Attribute graph clustering algorithms, leveraging topological structural information in node characteristics for constructing robust representations, have proven their efficacy in various application domains. In spite of highlighting local links between connected nodes, the presented topological structure misses the relationships between nodes not directly connected, thus limiting possible future clustering performance advancements. Our solution to this problem involves the Auxiliary Graph for Attribute Graph Clustering (AGAGC) technique. Utilizing node attributes, we develop an additional graph for supervision. Neurobiological alterations A supplemental graph aids the present supervisor as an auxiliary supervising element. Our approach involves noise filtering to produce a trustworthy auxiliary graph. Under the combined oversight of a pre-defined graph and an auxiliary graph, the effectiveness of the clustering model is enhanced. To improve the discriminating power of the representations, embeddings from multiple layers are integrated. For a clustering-aware learned representation, our self-supervisor offers a clustering module. Ultimately, our model is trained using the triplet loss metric. The findings from experiments conducted on four available benchmark datasets indicate that the proposed model achieves superior or equivalent performance compared to existing state-of-the-art graph clustering models.

Zhao et al. recently introduced a semi-quantum bi-signature scheme (SQBS), utilizing W states and featuring two quantum signers and a single classical verifier. This research paper identifies three security flaws in the SQBS scheme proposed 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.