Amplitude Constrained Vector Gaussian Wiretap Channel: Properties of the Secrecy-Capacity-Achieving Input Distribution.

This paper studies the secrecy capacity of an -dimensional Gaussian wiretap channel under a peak power constraint. This work determines the largest peak power constraint R¯n, such that an input distribution uniformly distributed on a single sphere is optimal; this regime is termed the low-amplitude regime. The asymptotic value of R¯n as goes to infinity is completely characterized as a function of noise variance at both receivers.

The Perfect Match: Assessment of Sample Collection Efficiency for Immunological and Molecular Findings in Different Types of Fabrics.

Body fluid identification at crime scenes can be crucial in retrieving the appropriate evidence that leads to the perpetrator and, in some cases, the victim. For this purpose, immunochromatographic tests are simple, fast and suitable for crime scenes. The potential sample is retrieved with a swab, normally a cotton swab, moistened in a specific buffer.

Improved Information-Theoretic Generalization Bounds for Distributed, Federated, and Iterative Learning.

We consider information-theoretic bounds on the expected generalization error for statistical learning problems in a network setting. In this setting, there are nodes, each with its own independent dataset, and the models from the nodes have to be aggregated into a final centralized model. We consider both simple averaging of the models as well as more complicated multi-round algorithms.

Finite-Sample Bounds on the Accuracy of Plug-in Estimators of Fisher Information.

Finite-sample bounds on the accuracy of Bhattacharya's plug-in estimator for Fisher information are derived. These bounds are further improved by introducing a clipping step that allows for better control over the score function. This leads to superior upper bounds on the rates of convergence, albeit under slightly different regularity conditions.

Amplitude Constrained MIMO Channels: Properties of Optimal Input Distributions and Bounds on the Capacity.

In this work, the capacity of multiple-input multiple-output channels that are subject to constraints on the support of the input is studied. The paper consists of two parts. The first part focuses on the general structure of capacity-achieving input distributions.