An Analytical Approximation for the Excess Noise Factor of Avalanche Photodiodes with Dead Space

Approximate analytical expressions are derived for the mean gain and the excess noise factor of avalanche photodiodes including the effect of dead space. The analysis is based on undertaking a characteristic-equation approach to obtain an approximate analytical solution to the existing system of recurrence equations which characterize the statistics of the random multiplication gain. The analytical expressions for the excess noise factor and the mean gain are shown to be in good agreement with the exact results obtained from numerical solutions of the recurrence equations for values of the dead space reaching up to 20% of the width of the multiplication region

BOLD fMRI Simulation

Background: Brain functional magnetic resonance imaging (fMRI) is sensitive to changes in blood oxygenation level dependent (BOLD) brain magnetic states. The fMRI scanner produces a complex-valued image, but the calculation of the original BOLD magnetic source is not a mathematically tractable problem. We conduct numeric simulations to understand the BOLD fMRI model

Locating target at high speed using image decimation decomposition processing

We develop a decimation-decomposition processing technique that consists of judiciously selecting certain decimation-decomposed components of an image and then performing inter-component processing. For a (kx,ky)-decimation decomposition, there may be up to kxky decimation-decomposed components. The minimal surviving and maximal non-surviving lengths associated with inter-component processing algorithm allows for clutter suppression. By removing detection redundancies, one can locate the target at high speed. A “where-then-what” model is proposed for target tracking and recognition. It locates the target by-image decimation-decomposition processing first and then recognizes the target in question using a suitable image recognition technique

Fiat-Shamir for Bounded-Depth Adversaries

We study how to construct hash functions that can securely instantiate the Fiat-Shamir transformation against bounded-depth adversaries. The motivation is twofold. First, given the recent fruitful line of research of constructing cryptographic primitives against bounded-depth adversaries under worst-case complexity assumptions, and the rich applications of Fiat-Shamir, instantiating Fiat-Shamir hash functions against bounded-depth adversaries under worst-case complexity assumptions might lead to further applications (such as SNARG for P, showing the cryptographic hardness of PPAD, etc.) against bounded-depth adversaries. Second, we wonder whether it is possible to overcome the impossibility results of constructing Fiat-Shamir for arguments [Goldwasser, Kalai, FOCS ’03] in the setting where the depth of the adversary is bounded, given that the known impossibility results (against p.p.t. adversaries) are contrived. Our main results give new insights for Fiat-Shamir against bounded-depth adversaries in both the positive and negative directions. On the positive side, for Fiat-Shamir for proofs with certain properties, we show that weak worst-case assumptions are enough for constructing explicit hash functions that give $\mathsf{AC}^0[2]$-soundness. In particular, we construct an $\mathsf{AC}^0[2]$-computable correlation-intractable hash family for constant-degree polynomials against $\mathsf{AC}^0[2]$ adversaries, assuming $\oplus \mathsf{L}/\mathsf{poly} \not\subseteq \widetilde{\mathsf{Sum}}_{n^{-c}} \circ\mathsf{AC}^0[2]$ for some $c > 0$. This is incomparable to all currently-known constructions, which are typically useful for larger classes and against stronger adversaries, but based on arguably stronger assumptions. Our construction is inspired by the Fiat-Shamir hash function by Peikert and Shiehian [CRYPTO ’19] and the fully-homomorphic encryption scheme against bounded-depth adversaries by Wang and Pan [EUROCRYPT ’22]. On the negative side, we show Fiat-Shamir for arguments is still impossible to achieve against bounded-depth adversaries. In particular, • Assuming the existence of $\mathsf{AC}^0[2]$-computable CRHF against p.p.t. adversaries, for every poly-size hash function, there is a (p.p.t.-sound) interactive argument that is not $\mathsf{AC}^0[2]$-sound after applying Fiat-Shamir with this hash function. • Assuming the existence of $\mathsf{AC}^0[2]$-computable CRHF against $\mathsf{AC}^0[2]$ adversaries, there is an $\mathsf{AC}^0[2]$-sound interactive argument such that for every hash function computable by $\mathsf{AC}^0[2]$ circuits the argument does not preserve $\mathsf{AC}^0[2]$-soundness when applying Fiat-Shamir with this hash function. This is a low-depth variant of the result of Goldwasser and Kalai

Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI

Conventionally, brain function is inferred from the magnitude data of the complex-valued fMRI output. Since the fMRI phase image (unwrapped) provides a representation of brain internal magnetic fieldmap (by a constant scale difference), it can also be used to study brain function while providing a more direct representation of the brain's magnetic state. In this study, we collected a cohort of resting-state fMRI magnitude and phase data pairs from 600 subjects (age from 10 to 76, 346 males), decomposed the phase data by group independent component analysis (pICA), calculated the functional network connectivity (pFNC). In comparison with the magnitude-based brain function analysis (mICA and mFNC), we find that the pFNC matrix contains fewer significant functional connections (with p-value thresholding) than the mFNC matrix, which are sparsely distributed across the whole brain with near/far interconnections and positive/negative correlations in rough balance. We also find a few of brain rest sub-networks within the phase data, primarily in subcortical, cerebellar, and visual regions. Overall, our findings offer new insights into brain function connectivity in the context of a focus on the brain's internal magnetic state

An Analytical Approximation for the Excess Noise Factor of Avalanche Photodiodes with Dead Space

Abstract-Approximate analytical expressions are derived for the mean gain and the excess noise factor of avalanche photodiodes including the effect of dead space. The analysis is based on undertaking a characteristic-equation approach to obtain an approximate analytical solution to the existing system of recurrence equations which characterize the statistics of the random multiplication gain. The analytical expressions for the excess noise factor and the mean gain are shown to be in good agreement with the exact results obtained from numerical solutions of the recurrence equations for values of the dead space reaching up to 20% of the width of the multiplication region

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P \u3c 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer