AI Bias in the Insurance Industry

A discussion of the rapid integration of AI in the US insurance industry, the legal and ethical challenges arising from AI ...
IEEE

Quantized Distributed Nonconvex Optimization Algorithms With Linear Convergence Under the Polyak–Łojasiewicz Condition

Abstract: This article considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To ...
PNAS

Correction for Anand et al., Convolutional neural networks outperform other presence-only species distribution modeling algorithms

The authors note that, due to a production error, Figures 1–4 appeared in the incorrect order. Figure 1 should appear as Figure 2, Figure 2 should appear as Figure 3, Figure 3 should appear as Figure ...
Frontiers

Distributed hierarchical control of islanded active distribution networks with dynamic consensus algorithm and adaptive virtual impedance

In the event of an upstream grid fault, the Active Distribution Network (ADN) can switch to autonomous operation, coordinating controllable Distributed Generators (DGs) and Energy Storage Systems (ESS ...
Forbes

AMI 1.0 First, Algorithms Next: How AI Keeps Distribution Affordable—Now

Electricity affordability isn't a slogan; it's the scorecard. As distribution companies absorb electric vehicle (EV) load growth, aging assets, extreme weather and a decarbonizing grid, every ...
The National Law Review

California Passes Broad Limits on “Common Pricing Algorithms”

We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
financefeeds

Good and Bad Crypto Algorithms Explained Simply

Cryptography secures communication in banking, messaging, and blockchain. Good algorithms (AES, RSA, ECC, SHA-2/3, ChaCha20) are secure, efficient, and widely trusted. Bad algorithms (DES, MD5, SHA-1, ...
Scientific Research Publishing

Kundu, R. (2023). YOLO Algorithm for Object Detection Explained [+Examples].

ABSTRACT: The study adapts several machine-learning and deep-learning architectures to recognize 63 traditional instruments in weakly labelled, polyphonic audio synthesized from the proprietary Sound ...
GitHub

PyCUDA primal-dual algorithms for TV/TGV-constrained imaging problems

Algorithms, examples and tests for denoising, deblurring, zooming, dequantization and compressive imaging with total variation (TV) and second-order total generalized variation (TGV) regularization.
Frontiers

Distributed quantile regression over sensor networks via the primal–dual hybrid gradient algorithm

As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...
IEEE

Distributed Proximal Algorithms for Nonsmooth Optimization: Unified Convergence Analysis

Abstract: This paper studies two classes of nonsmooth distributed optimization problems with coupled constraints, in which the local cost function of each agent consists of a Lipschitz differentable ...