Numerical study of linear and circular model DNA chains confined in a slit: metric and topological properties

Advanced Monte Carlo simulations are used to study the effect of nano-slit confinement on metric and topological properties of model DNA chains. We consider both linear and circularised chains with contour lengths in the 1.2--4.8 $\mu$m range and slits widths spanning continuously the 50--1250nm range. The metric scaling predicted by de Gennes' blob model is shown to hold for both linear and circularised DNA up to the strongest levels of confinement. More notably, the topological properties of the circularised DNA molecules have two major differences compared to three-dimensional confinement. First, the overall knotting probability is non-monotonic for increasing confinement and can be largely enhanced or suppressed compared to the bulk case by simply varying the slit width. Secondly, the knot population consists of knots that are far simpler than for three-dimensional confinement. The results suggest that nano-slits could be used in nano-fluidic setups to produce DNA rings having simple topologies (including the unknot) or to separate heterogeneous ensembles of DNA rings by knot type.Comment: 12 pages, 10 figure

Generalizing DP-SGD with Shuffling and Batch Clipping

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the $f$-DP approach and introduces a new proof technique which allows us to derive simple closed form expressions and to also analyse group privacy. In particular, for $E$ epochs work and groups of size $g$, we show a $\sqrt{g E}$ DP dependency for batch clipping with shuffling.Comment: Update disclaimer

Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent

Each round in Differential Private Stochastic Gradient Descent (DPSGD) transmits a sum of clipped gradients obfuscated with Gaussian noise to a central server which uses this to update a global model which often represents a deep neural network. Since the clipped gradients are computed separately, which we call Individual Clipping (IC), deep neural networks like resnet-18 cannot use Batch Normalization Layers (BNL) which is a crucial component in deep neural networks for achieving a high accuracy. To utilize BNL, we introduce Batch Clipping (BC) where, instead of clipping single gradients as in the orginal DPSGD, we average and clip batches of gradients. Moreover, the model entries of different layers have different sensitivities to the added Gaussian noise. Therefore, Adaptive Layerwise Clipping methods (ALC), where each layer has its own adaptively finetuned clipping constant, have been introduced and studied, but so far without rigorous DP proofs. In this paper, we propose {\em a new ALC and provide rigorous DP proofs for both BC and ALC}. Experiments show that our modified DPSGD with BC and ALC for CIFAR-$10$ with resnet-$18$ converges while DPSGD with IC and ALC does not.Comment: 20 pages, 18 Figure

Efficient Palm-Line Segmentation with U-Net Context Fusion Module

Many cultures around the world believe that palm reading can be used to predict the future life of a person. Palmistry uses features of the hand such as palm lines, hand shape, or fingertip position. However, the research on palm-line detection is still scarce, many of them applied traditional image processing techniques. In most real-world scenarios, images usually are not in well-conditioned, causing these methods to severely under-perform. In this paper, we propose an algorithm to extract principle palm lines from an image of a person's hand. Our method applies deep learning networks (DNNs) to improve performance. Another challenge of this problem is the lack of training data. To deal with this issue, we handcrafted a dataset from scratch. From this dataset, we compare the performance of readily available methods with ours. Furthermore, based on the UNet segmentation neural network architecture and the knowledge of attention mechanism, we propose a highly efficient architecture to detect palm-lines. We proposed the Context Fusion Module to capture the most important context feature, which aims to improve segmentation accuracy. The experimental results show that it outperforms the other methods with the highest F1 Score about 99.42% and mIoU is 0.584 for the same dataset.Comment: Published in 2020 International Conference on Advanced Computing and Applications (ACOMP

Optimal detection in MIMO systems using spatial Sigma-Delta ADCs

Abstract The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.Abstract The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed

Antibiotic sales in rural and urban pharmacies in northern Vietnam: an observational study.

BACKGROUND: The irrational overuse of antibiotics should be minimized as it drives the development of antibiotic resistance, but changing these practices is challenging. A better understanding is needed of practices and economic incentives for antibiotic dispensing in order to design effective interventions to reduce inappropriate antibiotic use. Here we report on both quantitative and qualitative aspects of antibiotic sales in private pharmacies in northern Vietnam. METHOD: A cross-sectional study was conducted in which all drug sales were observed and recorded for three consecutive days at thirty private pharmacies, 15 urban and 15 rural, in the Hanoi region in 2010. The proportion of antibiotics to total drug sales was assessed and the revenue was calculated for rural and urban settings. Pharmacists and drug sellers were interviewed by a semi-structured questionnaire and in-depth interviews to understand the incentive structure of antibiotic dispensing. RESULTS: In total 2953 drug sale transactions (2083 urban and 870 rural) were observed. Antibiotics contributed 24% and 18% to the total revenue of pharmacies in urban and rural, respectively. Most antibiotics were sold without a prescription: 88% in urban and 91% in rural pharmacies. The most frequent reported reason for buying antibiotics was cough in the urban setting (32%) and fever in the rural area (22%). Consumers commonly requested antibiotics without having a prescription: 50% in urban and 28% in rural area. The qualitative data revealed that drug sellers and customer's knowledge of antibiotics and antibiotic resistance were low, particularly in rural area. CONCLUSION: Over the counter sales of antibiotic without a prescription remains a major problem in Vietnam. Suggested areas of improvement are enforcement of regulations and pricing policies and educational programs to increase the knowledge of drug sellers as well as to increase community awareness to reduce demand-side pressure for drug sellers to dispense antibiotics inappropriately

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations and update shared state that represents a jointly learned (global) model. We consider big data analysis where training data is distributed among local data sets in a heterogeneous way -- and we wish to move SGD computations to local compute nodes where local data resides. The results of these local SGD computations are aggregated by a central "aggregator" which mimics Hogwild!. We show how local compute nodes can start choosing small mini-batch sizes which increase to larger ones in order to reduce communication cost (round interaction with the aggregator). We improve state-of-the-art literature and show $O(\sqrt{K}$) communication rounds for heterogeneous data for strongly convex problems, where $K$ is the total number of gradient computations across all local compute nodes. For our scheme, we prove a \textit{tight} and novel non-trivial convergence analysis for strongly convex problems for {\em heterogeneous} data which does not use the bounded gradient assumption as seen in many existing publications. The tightness is a consequence of our proofs for lower and upper bounds of the convergence rate, which show a constant factor difference. We show experimental results for plain convex and non-convex problems for biased (i.e., heterogeneous) and unbiased local data sets.Comment: arXiv admin note: substantial text overlap with arXiv:2007.09208 AISTATS 202