Miracle-dz/UniFAR-FTUs huggingface dataset:
Uniqueness Results of Algebraic Curves and Related Topics

It is well-known that if two complex polynomials P and Q share two values without counting multiplicities, then they are the same. Such problem is called the value sharing problem. In this dissertation, we study the value sharing problem for algebraic and holomorphic curves, as well as give its applications. We first improve the previous result of Ru-Xu [XR07] on value sharing for algebraic mappings from a compact Riemann surface into the n-dimensional projective space that agree on the pre-image for given hyperplanes located in general position, by using a new auxiliary function. Second, we study the value sharing problem for holomorphic mappings from punctured compact Riemann surfaces into the n-dimensional projective space. We also work on p-adic holomorphic curves which is similar to the algebraic curves. In the last chapter, we apply our results to the study of minimal surfaces, namely, the uniqueness theorem for Gauss maps of two minimal surfaces in the n-dimensional Euclidean space. The article which contains the majority results of this thesis has been accepted by the International Journal of Mathematics (see [RU17]).
Comparator-less ASK-PWM CDR Circuit for Forward Data Communication Over A Single Channel Wireless Power and Data Transfer System

In this work, a new clock and data recovery (CDR) scheme is proposed for amplitude shift keying (ASK) with pulse width modulation (PWM) encoding. Such a technique can fully recover the clock as well as the encoded data without the need for any comparator. The clock is recovered by passing the ASK-PWM encoded data to the frequency divider and frequency multiplier. Then, the data sampler can recover the data by sampling the ASK-PWM modulated data using a delayed version of the recovered clock with no additional processing. This technique is intended for a data transfer system over an inductively coupled wireless power transfer system (WPT) system for biomedical implants where the data will modulate the power carrier without the need for a dedicated data channel. A prototype example implementation of the CDR scheme on a 180 nm Complementary-Metal-Oxide-Semiconductor (CMOS) technology with a data rate of 2 Mbps is provided as a proof-of-concept. This CDR prototype has an area of 8000 μm2 and consumes a power of 129 microwatts from a 1.4 Voltage dc supply.
Electromagnetic Characterization and Mapping of Electrical Properties for Geo Materials at Radio Frequencies

This paper presents the various methods involved in extracting the electrical properties for Geo materials at radio frequencies from 10KHz – 500MHz. The radio frequency signal propagation gets affected by the characterization of the materials and their propagating medium. Extraction of frequency dependent complex relative permittivity and conductivity helps to provide complete database for numerical simulations of electromagnetic wave propagation. From the various methods investigated ASTM D4935 and probe method were selected for extracting electrical properties of the soil and parallel plate capacitor method for concrete. The measured data is compared with the reference data using Altair Feko.
Mapping Changes of Saltmarsh and Mangrove Vegetation Communities in Wagonga Inlet, NSW South Coast

Saltmarsh and mangroves play a significant role within estuarine systems, with the extent and distribution of these communities being used as one of the indicators of the ‘health’ of an estuary. In NSW, two comprehensive inventories of estuarine macrophytes exist for the entire NSW coastline (West and CCA). Based on a comparison of these inventories, Wagonga Inlet, on the NSW South was identified as one of the NSW estuaries that exhibited significant change. Using GIS aerial photograph interpretation, changes in the extent mangrove and saltmarsh were mapped between 1957 and 2010. The analysis found that there has been a significant increase in both the distribution and extent of mangroves around the Inlet growing from 11.37 ha in 1957 to 23.83 ha in 2010, with mangroves expanding up-slope into saltmarsh areas, down-slope onto both prograding fluvial deposits and a marine tidal delta, and laterally along the foreshore. A loss of saltmarsh communities was found with 17.66 ha being identified in 1957 and only 8.99 ha remaining within Wagonga Inlet in 2010 with this loss being the result of reclamation, drainage of wetland areas, lawn mowing along the interface between saltmarsh and terrestrial communities, down-slope expansion of Casuarina glauca and up-slope expansion of mangroves. The study also assessed the validity of comparing the two existing Estuarine Vegetation Inventories to monitor changes in macrophyte distribution within estuaries, as a tool for estuarine management. For Wagonga Inlet, the study found that the comparison had produced erroneous results for both saltmarsh and mangroves. It was found that, by using aerial photographs scanned at a high resolution, a high level of accuracy is achievable in identifying and mapping mangrove communities. Saltmarsh was poorly identified in both the West and CCA inventories and it was found that without extensive ground truthing, even modern GIS based API will significantly underestimate the extent of saltmarsh.
Ocean State Estimation for Global Ocean Monitoring: ENSO and Beyond ENSO

Ocean state estimation, often referred to as Ocean Reanalysis (ORA), is now routinely used for ocean initialization of seasonal forecast models and to monitor the thermal structure in the tropical Pacific. Advanced real-time ocean monitoring tools for ENSO (El Nino/Southern Oscillation) are now also being put in place. These diagnostics could potentially improve operational ENSO monitoring and forecasts by further enhancing our understanding of the physical processes in the evolution of ENSO events. Beyond ENSO, other climate modes of variability that are of societal interest include the Indian Ocean Dipole, Tropical Atlantic Variability, and the Pacific Decadal Oscillation (PDO). Preliminary monitoring tools have also been developed for these modes. The availability of multiple ORAs provides an opportunity to assess uncertainties in the products, to help identify gaps in observing systems as they impact forecasts, identify deficiencies in data assimilation schemes. They also provide the basis for future development of multi-model ensemble monitoring products.
Depth Dependence Removal in RGB-D Semantic Segmentation Model

RGB-D semantic segmentation is gaining increasing attention as it provides greater accuracy than traditional RGB semantic segmentation. The key idea of RGB-D semantic segmentation is to train a convolutional neural network (CNN) model using RGB-D images. Generally, the model can efficiently segment RGB images only when depth information is available. However, in reality, most cameras can only capture RGB channels, which presents a difficulty in accurately segmenting RGB images without depth. To solve the problem, a depth dependence removal method is proposed for RGB-D semantic segmentation model. The method adopts a mechanism of using simulated depth instead of real depth for semantic segmentation, which can not only make the model get rid of the dependence on real depth, but also maintain the accuracy advantage of the model. First, in the training phase, we utilize the depth relationship between different pixels in local area to build a depth similarity function, and use the function to boost convolution and pooling of CNN for achieving the accuracy improvement. Second, we construct an optimization function to seek simulated depth information from RGB images based on the depth similarity function. Finally, we employ the simulated depth to replace real depth for semantic segmentation, so as to remove the depth dependence of CNN. We apply the method to NYUv2 and SUN RGB-D datasets. The final results indicate that the proposed depth dependence removal method can achieve favorable segmentation for RGB images.
Position-Level Dynamic Coupling Analysis of Dual-Arm Underwater Vehicle Manipulator System

Dual-arm underwater vehicle manipulator system(UVMS) has great potential in underwater operation. However, due to the dynamic interaction, there are multiple dynamic coupling effects between two arms and between each arm and the base, which brings great challenges to the trajectory planning and coordinated control of UVMS. Firstly, we analyze the coupling effect of manipulator motion on base motion in position-level. A modeling method based on base centroid equivalent manipulator (BCEM) is proposed to analyze the coupling relationship between the movement of end effector and the position change of base centroid. Then, the dynamic coupling index is defined to evaluate the dynamic coupling degree, which can be used as the performance evaluation index of the interference of the robot motion planning on the base motion and the design criterion of the structure and mass distribution of the manipulator, as well as to assist in selecting the best installation position of the manipulator. Finally, some simulations were carried out to analyze the influencing factors of coupling effect of dual-arm UVMS. This work provides a theoretical basis for the design, performance evaluation, trajectory planning and control of dual-arm UVMS.
Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities

For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a $\mathrm{R}^{2}$ of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.
Understanding the Thermal Characteristics of SiC Power MOSFET Device for Power Modules Design

Due to the excellent characteristics of the SiC MOSFET, it is more and more widely used in power electronics applications. However, the device is confronted with severe thermal issues due to the smaller chip area. Accordingly, many pieces of research are aimed at better removing the heat energy of the device. However, while reducing thermal resistance, the number of layers inside the package tends to decrease, resulting in a decrease in its heat capacity. This is extremely detrimental to the reliability of the power module, therefore it is necessary to comprehensively consider thermal resistance and heat capacity. Besides, when designing and optimizing the power modules, the device is simply regarded as a uniform heat source, which will cause multiple key pieces of information to be ignored. Therefore, this paper analyzes the thermal characteristics of the SiC MOSFET under transient and steady-state conditions from the perspective of the device and packaging. Finally, guidelines for the design of power modules are provided based on the previous analysis.
DPL-SLAM: A SLAM System by Deep Learning Point Features and Line Features Integration

At present, the research of visual Simultaneous Localization and Mapping (SLAM) technology focuses on its robustness in complex environments. Since most of the manually extracted local features represented by ORBs do not perform satisfactorily in complex environments, researchers are focusing on the utilization of Convolutional Neural Networks (CNN) to extract the feature points. In other words, the end-to-end feature point extraction methods have become a hot research topic recently. In addition, one of the typical problems is the low-texture characteristic for the visual navigation in the complex environments. In this condition, the end-to-end feature point extraction methods are still insufficient in feature point extraction. Fortunately, the geometric elements, such as some line-based geometric primitives, are adopted to enhance the performance of feature extraction. This paper proposed the DPL-SLAM, a monocular vision SLAM system by integrating the CNN point features and line features. The experimental tests with TUM RGB-D dataset have shown that higher localization accuracy could be achieved in low-texture environments with geometrical features and high degree of robustness, especially when the proposed DPL-SLAM algorithm compared with the conventional ORB-SLAM2 algorithm.
Wildfire Classification Using Infrared Unmanned Aerial Vehicle Data with Convolutional Neural Networks

Forest fires can spread briskly to large-scale areas after they happen. Thus, early detection and intervention are of great importance. Unmanned aerial vehicles (UAVs) are beneficial technologies used for forest fire detection. Since flames emit very high heat and energy into their surroundings, they can be identify easily through the electro-optic infrared cameras mounted on UAVs as payloads. Detection of forest fires via UAVs has been performed by human observation in ground control stations. Convolutional Neural networks, which is effectual deep learning algorithms, are eligible for wildfire detection with UAV vision data. This paper presents a CNN based deep learning approach to the task of forest fire detection performed by human observation. We implemented state-of-art neural networks as feature extractors to the determined architecture to achieve adequate results. In the experiments, a UAV collected infrared forest fire images were used as the dataset. The experiment result clearly showed that our approach performed sufficiently on the dataset. The ResNet101-based architecture achieved the highest results in all evaluation metrics. It has confirmed itself to be the most efficient alternative with 99.20% test accuracy.
Fine-Tuning IndoBERT Model for Big Five Personality Prediction from Indonesian Social Media

The increasing amount of data generated from social media brings opportunities to produce various helpful knowledge and information, one of which is predicting a person’s personality. The advent of attention-based classification techniques and transformers brings promising results on multiple tasks in Natural Language Processing (NLP). In this study, we predicted the Big Five personalities from Indonesian-language social media by fine-tuning the IndoBERT model. The IndoBERT model is a Bidirectional Encoder Representation from Transformers (BERT)-based mono-language model trained in the Indonesian corpus. Based on the experimental results, the prediction model we proposed obtained an accuracy value of 72% and an F1-score of 71% using IndoBERT-base. Meanwhile, using IndoBERT-large can increase the accuracy value by 78% and the F1-score by 74%. The proposed model also outperforms previous models in predicting the Big Five personality.
Pharmacokinetics and Pharmacodynamic Target Attainment of Benzylpenicillin in an Adult Severely Ill Sub-Saharan African Patient Population

Background In intensive care (ICU) patients, systemic exposure of β-lactam antibiotics can be altered, and positive clinical outcome is associated with increasing fT > MIC ratios. In sub-Saharan African hospitals, benzylpenicillin (PEN) is frequently used for the empiric treatment of severe pneumococcal infections. Pharmacokinetic data for non-ICU hospitalized populations are lacking. Methods We performed a population pharmacokinetic (PPK) study in an adult Mozambican hospital population treated intravenously with PEN from October 2014 through November 2015. Four blood samples/patient were collected for total PEN (PENt) and unbound PEN (PENu) concentration measurement. We developed a PPK model through nonlinear mixed-effects analysis and performed simulations for different patient variable, dosing, and pharmacodynamic target scenarios. Results One hundred twelve participants yielded 387 PENt and 53 PENu concentrations. The median body mass index was 18.3 (range, 10.5-31.3) kg/m2 and the median albumin concentration and creatinine clearance (CrCl) were 29 (range, 12-44) g/L and 80 (range, 3-195) mL/minute, respectively. In a 1-compartment model, CrCl was positively correlated with PENt clearance. For infections with a microorganism with a minimum inhibitory concentration (MIC) of 1 mg/L, simulations demonstrated that with 3 million IU (1.8 g) every 6 hours, 74.1% would have a PENu concentration greater than the MIC during half of the dosing interval (fT > MIC = 50%), whereas this was 24.8% for the fT > MIC = 100% target. For pathogens with an MIC of 0.06 mg/L, these percentages were 98.2% and 72.3%, respectively. Conclusions Severely ill adult sub-Saharan African patients may be at high risk for underexposure to PENu during routine intermittent bolus dosing, especially when their renal function is intact and when infected with pathogens with intermediate susceptibility.
Linearization based robust reference tracking control of a compartmental epidemiological model

A robust reference tracking approach for epidemic models is proposed in this paper where the input is the disease transmission rate influenced by appropriate restrictions, and the controlled output is the number of infected people. The reference following is solved by input-output linearization-based asymptotic output tracking where the linearized model is equipped by an additional integrator. The control design uses a simple 4 compartment SEIR-type model, while the simulation of the closed loop is based on a more detailed model containing 8 compartments. It is shown using multiple simulations that the reference tracking fulfills the requirements even in the presence of significant parametric uncertainties. It is also illustrated that the proposed control design can also be used for the reconstruction of the disease transmission rate from past observations.
Fault Diagnosis and Isolation Prediction for Redundant Relays Based on Discrepancy Analysis

Protective relays are integral to the reliability of any electrical power system, and are fundamental to the decision-making of their protection systems. They support the detection and isolation of problems in the power system, so that the operation of unaffected parts can be maintained. ElectroMechanical Relays (EMRs) are still predominant around the globe in the high and extra high voltage transmission systems. Thus, ensuring the reliability and traceability of relays is of major importance. One way to achieve this is through parallel redundancy by implementing redundant sensor architectures, such as one-out-of-two (1oo2). In this paper, we propose a novel algorithm for the fault prognosis-i.e., detection and failure date prediction – and isolation prediction for redundant 1002 architectures. The algorithm predicts the failure ahead of time and provides an estimated date for the failure event. Our contribution in this work is on the fault isolation prediction, where we infer-ahead of time, before the occurrence of the fault-which relay of the pair will cause the failure. The fault isolation is achieved by means of Machine Learning (ML) based feature extraction and binary classification methods. We apply the algorithm on EMRs based solely on the discrepancy time signals of the opening and closing events of the relays. The algorithm has been tested on data from redundant EMRs from the publicly available SOReDD dataset. While relays are binary switches, our work could potentially not only be applied to other types of binary switches but also to binary sensors as they also produce a binary output signal.
Proceedings of the 23rd international conference on Machine learning

This volume, which is also available from http://www.machinelearning. org, the home page of the International Machine Learning Society, contains the technical papers accepted for presentation at ICML-2006, the 23rd International Conference on Machine Learning. ICML is an international forum for presentation and discussion of the latest results in the field of machine learning. This year, ICML was held at Carnegie Mellon University, in Pittsburgh, Pennsylvania, and was co-located with COLT-2006, the 19th Annual Conference on Computational Learning Theory.Coincidentally, Carnegie Mellon University was also the venue for the first ICML---the First Machine Learning Workshop, which was held in 1980. Instead of proceedings, a book was published (Machine Learning: an Artificial Intelligence Approach, ed. Michalski, Carbonell, and Mitchell, Morgan Kaufman, 1983) containing sixteen research papers, and also a "comprehensive bibliography" of the field of machine learning, as it stood in 1983. This bibliography contained 572 entries.In 2006, no less than 548 papers were submitted to ICML---nearly as many as were in the "comprehensive bibliography" published with the papers from the first ICML. These papers were subjected to a thorough review process. In the first round of reviewing, every paper received three reviews by program committee members. Authors were then given an opportunity to view the first-round reviews and respond to them. Led by a Senior Program Committee member, the reviewers then engaged in a discussion of the paper, leading finally to a decision by the Senior Program Committee member in charge of the paper. Papers could be accepted, rejected, or conditionally accepted; the 36 conditionally accepted papers were subject to an additional final round of review by the Senior Program Committee. Of the 548 submissions, 140 were accepted for publication, an acceptance rate of 25.5%. In addition to the technical talks, ICML-2006 also included seven tutorials and eleven workshops, which were held before and after the conference, respectively. Authors presented their papers both orally and in a poster session, allowing time for detailed discussions with any interested attendees of the conference. Each day of the main conference included an invited talk by a prominent researcher. We were very fortunate to be able to host David Haussler, of the University of California at Santa Cruz; Robert Schapire, of Princeton University; and Mandyam V. Srinivasan, of the Australian National University.