Correspondence between neuroevolution and gradient descent

We show analytically that training a neural network by stochastic mutation or "neuroevolution" of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations. Our results provide a connection between two distinct types of neural-network training, and provide justification for the empirical success of neuroevolution

In-Vitro Application of Magnetic Hybrid Niosomes: Targeted siRNA-Delivery for Enhanced Breast Cancer Therapy

Even though the administration of chemotherapeutic agents such as erlotinib is clinically established for the treatment of breast cancer, its efficiency and the therapy outcome can be greatly improved using RNA interference (RNAi) mechanisms for a combinational therapy. However, the cellular uptake of bare small interfering RNA (siRNA) is insufficient and its fast degradation in the bloodstream leads to a lacking delivery and no suitable accumulation of siRNA inside the target tissues. To address these problems, non-ionic surfactant vesicles (niosomes) were used as a nanocarrier platform to encapsulate Lifeguard (LFG)-specific siRNA inside the hydrophilic core. A preceding entrapment of superparamagnetic iron-oxide nanoparticles (FexOy-NPs) inside the niosomal bilayer structure was achieved in order to enhance the cellular uptake via an external magnetic manipulation. After verifying a highly effective entrapment of the siRNA, the resulting hybrid niosomes were administered to BT-474 cells in a combinational therapy with either erlotinib or trastuzumab and monitored regarding the induced apoptosis. The obtained results demonstrated that the nanocarrier successfully caused a downregulation of the LFG gene in BT-474 cells, which led to an increased efficacy of the chemotherapeutics compared to plainly added siRNA. Especially the application of an external magnetic field enhanced the internalization of siRNA, therefore increasing the activation of apoptotic signaling pathways. Considering the improved therapy outcome as well as the high encapsulation efficiency, the formulated hybrid niosomes meet the requirements for a cost-effective commercialization and can be considered as a promising candidate for future siRNA delivery agents

Time-coded aperture for x-ray imaging

We describe a computational imaging system for x-ray tomography, where image capture and post-processing are co-designed to improve final image quality when relative motion of an experiment’s components during a single exposure causes motion blur. The idea is based on temporally encoding the motion during each exposure by fluttering the detector shutter open and closed with a known sequence for guaranteeing an invertible motion blur kernel. While generally applicable, we demonstrate our approach by simulating blurry data acquisition for transmission x-ray tomography and deblurring the reconstructed images. The results suggest that optimized pseudo-random binary time-coded apertures can yield successful reconstructions independent of the size of the blur kernel. This Letter is especially relevant to high-speed x-ray tomography applications where time-resolution is limited by the detector or available photon flux

Training neural networks using Metropolis Monte Carlo and an adaptive variant

Abstract We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis MC can train a neural net with an accuracy comparable to that of gradient descent (GD), if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network’s structure or neuron activations are strongly heterogenous, and we introduce an adaptive Monte Carlo algorithm (aMC) to overcome these limitations. The intrinsic stochasticity and numerical stability of the MC method allow aMC to train deep neural networks and recurrent neural networks in which the gradient is too small or too large to allow training by GD. MC methods offer a complement to gradient-based methods for training neural networks, allowing access to a distinct set of network architectures and principles.</jats:p

Joint ptycho-tomography reconstruction through alternating direction method of multipliers

We present the extension of ptychography for three-dimensional object reconstruction in a tomography setting. We describe the alternating direction method of multipliers (ADMM) as a generic reconstruction framework to efficiently solve the nonlinear optimization problem. In this framework, the ADMM breaks the joint reconstruction problem into two well-defined subproblems: ptychographic phase retrieval and tomographic reconstruction. In this paper, we use the gradient descent algorithm to solve both problems and demonstrate the efficiency of the proposed approach through numerical simulations. Further, we show that the proposed joint approach relaxes existing requirements for lateral probe overlap in conventional ptychography. Thus, it can allow more flexible data acquisition

Exploring the association between acute pancreatitis and biliary tract cancer : a large-scale population-based matched cohort study

Background: Biliary tract cancer (BTC) often goes undetected until its advanced stages, resulting in a poor prognosis. Given the anatomical closeness of the gallbladder and bile ducts to the pancreas, the inflammatory processes triggered by acute pancreatitis might increase the risk of BTC. Objective: To assess the association between acute pancreatitis and the risk of BTC. Methods: Using the Swedish Pancreatitis Cohort (SwePan), we compared the BTC risk in patients with a first-time episode of acute pancreatitis during 1990–2018 to a 1:10 matched pancreatitis-free control group. Multivariable Cox regression models, stratified by follow-up duration, were used to calculate hazard ratios (HRs), adjusting for socioeconomic factors, alcohol use, and comorbidities. Results: BTC developed in 0.94% of 85,027 acute pancreatitis patients and in 0.23% of 814,993 controls. The BTC risk notably increased within 3 months of hospital discharge (HR 82.63; 95% CI: 63.07–108.26) and remained elevated beyond 10 years of follow-up (HR 1.82; 95% CI: 1.35–2.47). However, the long-term risk of BTC subtypes did not increase with anatomical proximity to the pancreas, with a null association for gallbladder and extrahepatic tumors. Importantly, patients with acute pancreatitis had a higher occurrence of early-stage BTC within 2 years of hospital discharge than controls (13.0 vs. 3.6%; p-value &lt;0.01). Conclusion: Our nationwide study found an elevated BTC risk in acute pancreatitis patients; however, the risk estimates for BTC subtypes were inconsistent, thereby questioning the causality of the association. Importantly, the amplified detection of early-stage BTC within 2 years after a diagnosis of acute pancreatitis underscores the necessity for proactive BTC surveillance in these patients