Chiral plasmon in gapped Dirac systems

We study the electromagnetic response and surface electromagnetic modes in a generic gapped Dirac material under pumping with circularly polarized light. The valley imbalance due to pumping leads to a net Berry curvature, giving rise to a finite transverse conductivity. We discuss the appearance of nonreciprocal chiral edge modes, their hybridization and waveguiding in a nanoribbon geometry, and giant polarization rotation in nanoribbon arrays

Controlled Heterogeneous Nucleation and Growth of Germanium Quantum Dots on Nanopatterned Silicon Dioxide and Silicon Nitride Substrates

Controlled heterogeneous nucleation and growth of Ge quantum dots (QDs) are demonstrated on SiO_2/Si_3N_4 substrates by means of a novel fabrication process of thermally oxidizing nanopatterned SiGe layers. The otherwise random self-assembly process for QDs is shown to be strongly influenced by the nanopatterning in determining both the location and size of the QDs. Ostwald ripening processes are observed under further annealing at the oxidation temperature. Both nanopattern oxidation and Ostwald ripening offer additional mechanisms for lithography for controlling the size and placement of the QDs

Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for brest cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier's performance

Exploiting A Priori Time Constant Ratio Information in Difference Equation Two-Thermocouple Sensor Characterization

The characterization of thermocouple sensors for temperature measurement in varying-flow enviroments is a challenging problem. Recently, the authors introduced novel different-equation-based algorithms that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. In particular, a linear least squares (LS) formulation of the characterization problem, which yields unbiased estimates when identified using generalized total LS, was introduced. These algorithms assume that time constants do not change during operation and are, therefore, appropriate for temperature measurement in homogenous constant-velocity liquid of gas flows. This paper develops an alternative B formulation of the characterization problem that has the major advantage of allowing exploitation of a priori knowledge of the ratio of the sensor time constants, thereby facilitating the implementation of computationally efficient algorithms that are less sensitive to measurement noise. A number of variants of the B formulation are developed, and appropriate unbiased estimators are identified. Monte Carlo simulation results are used to support the analysis

Non-farm employment, natural resource extraction, and poverty: evidence from household data for rural Vietnam

Natural resources are important in sustaining the livelihoods of rural households and the environment. However, over-exploitation is causing an alarming depletion of natural resources in many developing countries. At the same time, rapid economic growth has created non-farm employment opportunities for local people. In this context, examining the interrelationship between non-farm employment and natural resource extraction provides useful information for reducing resource extraction and improving rural households’ welfare. In this study, we use a dataset of 1780 identical households from three survey waves undertaken in 2010, 2013, and 2016 in Vietnam to (i) identify the determinants of rural households’ participation in non-farm activities, (ii) examine the interrelationship between non-farm employment and natural resource extraction, and (iii) investigate the impact of non-farm employment on rural households’ welfare. The findings from pooled sample estimations reveal that (i) cable internet at home and rural road quality positively affect households’ decisions to participate in non-farm employment; (ii) non-farm income and income from natural resource extraction have a negative association; and (iii) non-farm income significantly contributes to poverty reduction in both relative and absolute terms. Our findings suggest that improved provision of non-farm opportunities and increased investment in infrastructure and telecommunication are needed to improve rural households’ welfare and consequently reduce their natural resource exploitation. © 2022, The Author(s)

In Situ Two-Thermocouple Sensor Characterisation using Cross-Relation Blind Deconvolution with Signal Conditioning for Improved Robustness

Thermocouples are one of the most widely used temperature measurement devices due to their low cost, ease of manufacture and robustness. However, their robustness is obtained at the expense of limited sensor bandwidth. Consequently, in many applications signal compensation techniques are needed to recover the true temperature from the attenuated measurements. This, is turn, necessitates in situ thermocouple characterisation. Recently the authors proposed a novel characterisation technique based on the cross-relation method of blind deconvolution applied to the output of two thermocouples simultaneously measuring the same temperature. This offers a number of advantages over competing methods including low estimation variance and no need for a priori knowledge of the time constant ratio. A weakness of the proposed method is that it yields biased estimates in the presence of measurement noise. In this paper we propose the inclusion of a signal conditioning step in the characterisation algorithm to improve the robustness to noise. The enhanced performance of the resulting algorithm is demonstrated using both simulated and experimental data

A Comparative Study on Machine Learning Algorithms for Network Defense

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several selected benchmarks such as time to build models, kappa statistic, root mean squared error, accuracy by attack class, and percentage of correctly classified instances of the classifier algorithms

Predicting Virality on Networks Using Local Graphlet Frequency Distribution

The task of predicting virality has far-reaching consequences, from the world of advertising to more recent attempts to reduce the spread of fake news. Previous work has shown that graphlet distribution is an effective feature for predicting virality. Here, we investigate the use of aggregated edge-centric local graphlets around source nodes as features for virality prediction. These prediction features are used to predict expected virality for both a time-independent Hawkes model and an independent cascade model of virality. In the Hawkes model, we use linear regression to predict the number of Hawkes events and node ranking, while in the independent cascade model we use logistic regression to predict whether a k-size cascade will multiply by a factor X in size. Our study indicates that local graphlet frequency distribution can effectively capture the variances of the viral processes simulated by Hawkes process and independent-cascade process. Furthermore, we identify a group of local graphlets which might be significant in the viral processes. We compare the effectiveness of our methods with eigenvector centrality-based node choice