Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Preprocessing on Convolutional Neural Networks for Medical Image Segmentation

In recent years, deep learning has become the leading method for medical image segmentation. While the majority of studies focus on developments of network architectures, several studies have shown that non-architectural factors also play a substantial role in performance improvement. An important factor is preprocessing. However, there is no agreement on which preprocessing steps work best for different applications. The aim of this study was to investigate the effect of preprocessing on model performance. To this end, we conducted a systematic evaluation of 24 preprocessing configurations on three clinical application datasets (brain, liver, and knee). Different configurations of normalization, region of interest selection, bias field correction, and resampling methods were applied before training one convolutional neural network. Performance varied up to 64 percentage points between configurations within one dataset. Across the three datasets, different configurations performed best. In conclusion, to improve model performance, preprocessing should be tuned to the specific segmentation application.
Physical Modeling of Asymmetric Spacers Resonant Tunneling Diodes (RTDs)

The Resonant Tunneling Diode (RTD) is one of the most promising candidates for room temperature generation of terahertz (THz) radiation. Therefore, many attempts have been reported to increase the oscillation frequency beyond 1 THz either by reducing the mesa area or by thickening the collector spacer layer. Reducing the mesa area would reduce the Negative Differential Conductance (NDC), while increasing the thickness of the collector spacer layer would lead to an increase in the peak voltage value and increasing the emitter spacer thickness would increase the oscillation frequency while maintaining low peak voltage value. This work presents the physical modelling of asymmetric spacer resonant tunneling diodes (RTDs) to increase the oscillation frequency while still maintaining a low peak voltage, high NDC, and high output power. Different thicknesses of emitter spacer layer (7.5 nm and 10 nm) are simulated with varying thicknesses of the quantum well (3.5 nm, 3nm, and 2.5 nm) to study their effects on the DC and RF characteristics of the RTDs. Increasing the Indium concentration in the quantum well region has improved the oscillation frequency while maintaining a low peak voltage.
Mga-Net: Multi-Scale Guided Attention Models for an Automated Diagnosis of Idiopathic Pulmonary Fibrosis (IPF)

We propose a Multi-scale, domain knowledge-Guided Attention model (MGA-Net) for a weakly supervised problem-disease diagnosis with only coarse scan-level labels. The use of guided attention models encourages the deep learning-based diagnosis model to focus on the area of interests (in our case, lung parenchyma), at different resolutions, in an end-to-end manner. The research interest is to diagnose subjects with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using an axial chest high resolution computed tomography (HRCT) scan. Our dataset contains 279 IPF patients and 423 non-IPF ILD patients. The network’s performance was evaluated by the area under the receiver operating characteristic curve (AUC) with standard errors (SE) using stratified five-fold cross validation. We observe that without attention modules, the IPF diagnosis model performs unsatisfactorily (AUC ± SE = 0.690 ± 0.194); by including unguided attention module, the IPF diagnosis model reaches satisfactory performance (AUC ± SE = 0.956 ± 0.040), but lack explainability; when including only guided high-or medium-resolution attention, the learned attention maps highlight the lung areas but the AUC decreases; when including both high- and medium-resolution attention, the model reaches the highest AUC among all experiments (AUC ± SE = 0. 971 ± 0.021) and the estimated attention maps concentrate on the regions of interests for this task. Our results suggest that, for a weakly supervised task, MGA-Net can utilize the population-level domain knowledge to guide the training of the network in an end-to-end manner, which increases both model accuracy and explainability.
The Effects of the US-China Trade Dispute on the Asymmetric Volatility in the Stock Market: An Investigation of EGARCH Model Analysis

This study empirically analyzes the influence of the trade disputes between China and the United States on the stock markets of the host countries. From 2000 to 2019, using 20 years of daily return series of S&P 500 and SSE composite stock price index data, after separating in the pre- and the post-trade dispute, we dynamically analyze the volatility clustering, asymmetric volatility, etc. by using time-variable volatility models such as EGARCH and TGARCH models. The main results are as follows. Firstly, in each of the U.S. and Chinese stock markets, volatility clustering was observed during the pre and the post-trade dispute, but its intensity increased in the U.S. market and conversely decreased in the Chinese market in the post-trade dispute, suggesting that the impact of the U.S. market to Chinese market is more sensitive. Secondly, after the trade dispute, the variability and persistence of the U.S market has decreased, while it has increased in the Chinese market. Thirdly, in the post-trade dispute, asymmetric volatility in the Chinese stock market is not noted, and the degree of asymmetric volatility that existed in the pre-trade dispute is weaker than the U.S. market. It confirms that in the Chinese market, investors are not sensitive to the increase in debt ratios and the increase in risk premium caused by the fall in stock prices during the crisis. Fourthly, during the trade dispute period, the possibility of adverse asymmetric volatility (ADV) in the Chinese stock market is still present, suggesting that Chinese investors respond more sensitively to good news than bad news. Thus, the speculative tendency, which is more sensitive to positive factors than negative ones, is found to likely increase in the Chinese market in the trade dispute period.To summarize the above results, in the case of market turbulence such as trade disputes, active intervention from government is able to effectively stabilize the market. Unlike developed stock markets, Chinese investors find the possibility that the contrarian strategies are more effective than “momentum” strategies.
A Stochastic Gradient Descent Approach for Hybrid mmWave Beamforming With Blockage and CSI-Error Robustness

In this study, we consider the downlink beamforming problem in millimeter wave (mmWave) systems subjected to both path blockages and imperfect channel state information (CSI), and propose a new robust hybrid sum-outage minimizing design as a solution. We first formulate the problem as an empirical risk minimization (ERM) stochastic learning problem, whose solution can be obtained by the alternate iteration of a baseband digital and a radio frequency (RF) analog Riemann manifold-constrained beamforming updates through a mini-batch stochastic gradient descent (MSGD) approach, with gradient minimizing update rules given in closed-form, and learning rates optimized based on the lower-bounds of the corresponding Lipschitz constants. Unlike existing solutions to the path blockage-robust mmWave beamforming problem, wherein out-of-band side information is required or perfect CSI is assumed, our method relies only on the estimates and statistical knowledge of the channel’s angles of departure (AoD) and complex gains, which are simultaneously captured in a Bernoulli-Gaussian model and used to generate the training data for the MSGD-based optimizer. Further, unlike preceding fully-digital or fully-connected hybrid contributions, the proposed scheme assumes a virtually-configured partially-connected setup; therefore, it is compatible with coordinated multipoint (CoMP) architectures, which are known to be crucial in terms of exploiting the full potential of mmWave systems. Simulation results confirm the effectiveness of our MSGD-based robust hybrid CoMP mmWave beamformer in mitigating the effects of path blockage and CSI error, demonstrating its superiority to state-of-the-art (SotA) alternatives.
Regulator peculiarities to the adaptation and evolution of local climate dynamics

The paper develops the regulator view on climate changes from the conceptual model of the energy converter under the hysteresis control with double synchronization (the so-called HDS-control) detailed by a reference adaptation. This model describes cause-and-effect relations between dynamic processes in weather, seasonal and climate terms and resolves the interannual variability problem. In this context, the principal reason to destroy rapidly the habitual local seasonal patterns seems to be connected with variation of parameters associated with the abilities of local ecosystems to influence on dynamics evolution and to "damp" transient overshoots generated by the HDS-control. Reconstructions made by processing the meteorological observations show that such destructions can be promoted by natural climate-related oscillations. The interpenetrating usage of both the bifurcation and control theories opens a way to understand why the recent local climate changes occur so stormy.
A New Few-Shot Learning Method of Digital PCR Image Detection

With the global pandemic of infectious diseases, the demand for accurate nucleic acid detection is daily increasing. The traditional threshold-based algorithms are adopted as the mainstream for processing the images of digital polymerase chain reaction (dPCR) now, but they are facing huge challenges on complex problems such as irregular noise, uneven illumination, and the lack of data. So, this paper proposed a novel few-shot learning method based on our improved YOLOv3 model with fast processing speed and high accuracy to deal with complicated situations. Besides, to reduce the requirement of the large training dataset and annotation time of deep neural networks, we proposed the Random Background Transfer Method (RBTM) and Source Traceability Annotation Method (STAM) as the data augmentation and annotation method separately, which exploit the prior knowledge of the data and successfully realized the few-shot learning. Bases on the domain knowledge of dPCR images, our method could effectively augment images and reduce the labeling time by 70% while retaining the visually prominent features and improves the detection accuracy from 63.96% of the traditional threshold-based algorithm to as high as 98.98%. With the optimal processing speed and accuracy, our method is the state-of-art strategy for the detection of dPCR images now.
Breast Mass Detection And Classification Algorithm Based On Temporal Subtraction Of Sequential Mammograms

Breast cancer screening with mammography is the most efficient way to reduce breast cancer mortality. However, the large population and the use of double reading creates a high workload that heavily burdens the efficiency of the radiologists. Hence, Computer-Aided Detection (CAD) systems are being developed to assist the radiologists. In this study, an algorithm for the automatic detection and classification of breast masses, based on temporal subtraction of sequential mammograms, image registration and machine learning, is presented. While, some previous studies in the literature proposed temporal analysis by creating a new feature vector, temporal subtraction takes into consideration the entire prior image. A new dataset, consisting of 40 cases (two time points and two views of each breast per patient, a total of 160 images), with precisely annotated mass locations was created. The accuracy of the classification of masses as benign or suspicious increased from 85.7% (using temporal analysis) to 92.9% (using temporal subtraction). The improvement was statistically significant with p < 0.05. These results demonstrate the effectiveness of temporal subtraction for the diagnosis of breast masses.
Automatic Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End-to-End Training

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer’s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. Fluorodeoxyglucose positron emission tomography (FDG-PET) is an effective biomarker of the symptom of AD’s, and has been used as medical imaging data for diagnosing AD’s. Mild cognitive impairment (MCI) is regarded as an early symptom of AD’s, and it has been shown that MCI also has a certain biomedical correlation with FDG-PET. In this paper, we explore how to use 3D FDG-PET images to realize the effective recognition of MCI’s, and thus achieve the early prediction of AD’s. This problem is then taken as the classification of three categories of FDG-PET images, including MCI, AD and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, each binary classification being realized with a CNN+SVM network. Then the outputs of the three CNN+SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN+SVM networks and a decision fusion strategy is exploited to realize the fusion of the outputs of three CNN+SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.
A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.
Unsupervised Adversarial Domain Adaptation for Multi-Label Classification of Chest X-Ray

In this paper we address the task of unsupervised domain adaptation for multi-label classification problems with convolutional neural networks. We particularly consider the domain shift in between X-ray data sets. Domain adaptation between different X-ray data sets is especially of practical and clinical importance to guarantee applicability across hospitals and clinics, which may use different machines for image acquisition. In contrast to the usual multi-class setting, in multi-label classification tasks multiple labels can be assigned to an input instance instead of just one label. While most related work focus on domain adaptation for multi-class tasks, we consider the more general case of multi-label classification across domains. We propose an adversarial domain adaptation approach, in which the discriminator is equipped with additional conditional information regarding the current classification output. Our experiments show promising and competitive results on publicly available data sets, compared to state of the art approaches.
Experimental Study of Synthesis Gas Combustion Chemistry and Ignition Behaviors.

x Chapter 1: Introduction 1 Chapter 2: Experimental and computational methods 20 Chapter 3: High-pressure, low-temperature ignition behavior of syngas mixtures 27 Chapter 4: Low-temperature ignition behavior of iso-octane 55 Chapter 5: The effect of impurities on syngas combustion 79 Chapter 6: Experimental study of OH time histories during syngas auto-ign. 109 Chapter 7: Summary, conclusions, and future work 127 Appendix A: Laser spectroscopy analysis code 133
Autonomous Configuration of Communication Systems for IoT Smart Nodes Supported by Machine Learning

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.
Stable Visual Summaries for Trajectory Collections

The availability of devices that track moving objects has led to an explosive growth in trajectory data. When exploring the resulting large trajectory collections, visual summaries are a useful tool to identify time intervals of interest. A typical approach is to represent the spatial positions of the tracked objects at each time step via a one-dimensional ordering; visualizations of such orderings can then be placed in temporal order along a time line. There are two main criteria to assess the quality of the resulting visual summary: spatial quality – how well does the ordering capture the structure of the data at each time step, and stability – how coherent are the orderings over consecutive time steps or temporal ranges? In this paper we introduce a new Stable Principal Component (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a trade-off between the spatial quality and stability. We conduct extensive computational experiments that quantitatively compare the orderings produced by ours and other stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability. We conclude that stable dimensionality reduction outperforms existing methods on stability, without sacrificing spatial quality or efficiency; in particular, our new SPC method does so at a fraction of the computational costs.
Automatic Aircraft Shadow Removal from Remote Sensing Images Using Mask-ShadowGAN

Objects with shadow may cause a problem for image classification. For example, it can separate one object into many objects. It can also alter the size or shape of the object resulting in misclassification. In this paper, we focus on removing aircraft shadow from remote sensing images where the shadows occur on wings, bodies, and tails. Since it is very difficult to get shadow-free aircraft images and a shadow aircraft image of the same type for the training part, we adopted Mask-ShadowGAN for solving this issue. The benefit of the Mask-ShadowGAN algorithm is that, in the training part, the technique does not require the same images that have both shadow and shadow-free. In the experiment, we evaluated our proposed technique using RMSE and Jaccard similarity index for measurement. The experimental result shows that our technique shows promising results. We present both best and worst result based on sorted similarity index.
Desbordante: a Framework for Exploring Limits of Dependency Discovery Algorithms

Automatic discovery of various types of database dependencies (functional, inclusion, matching, and others) is a topic that has received a great deal of attention in the recent years. The problem is formulated as following: having an unexplored dataset, find all dependencies that hold on this data. Such problem formulation arises in business and scientific applications and is aimed at the discovery of patterns in data. Metanome is a pioneering platform which was used to benchmark existing and develop new dependency discovery algorithms. It is notable since it was the first attempt to unify all existing discovery algorithms inside a single suite. However, it should be considered a research prototype rather than a system ready for industrial use. The core reason for this is the choice of the implementation platform (Java) and the absence of optimizations. In this paper we address the problem of high-performance dependency discovery. We present Desbordante - a platform that is intended to make the most of the available computational resources and thus to be more suitable for industrial use. Finally, we evaluate our system experimentally and pose a number of research questions related to the obtained performance and justify its necessity. More precisely we examine 1) whether the Java implementation is indeed worse than the C++ one, 2) is it possible to use simple tricks to improve Metanome's performance, 3) what are the exact reasons behind the performance gap, and 4) what are the user-facing benefits of switching the implementations.
Small Foreign Metal Objects Detection in X-Ray Images of Clothing Products Using Faster R-CNN and Feature Pyramid Network

Immediate and accurate detection of foreign metal objects (FMOs) in clothing products is important for guaranteeing human safety. This article proposes an online detection approach based on deep learning, which is suitable for detecting small FMOs from X-ray images of clothing packages. A conveyor belt X-ray scanning system is developed for image collection. The X-ray images are preprocessed by using the morphological erosion operation to improve the accuracy of FMOs detection. These images are then down-sampled to reduce the computation cost. Feature pyramid network (FPN) is adopted for aggregating feature maps with different resolutions, which proved to be effective for small FMOs detection. The stochastic gradient descent (SGD) is used to optimize a multitask loss. The trained model was tested offline on 200 X-ray images, which achieved precision = 0.999, recall = 0.988, F1-score = 0.993, and AP = 0.946. Compared to original Faster region-based convolutional neural networks (R-CNN), the proposed method significantly improved the performance for small FMOs detection in terms of precision and recall rate.