Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based Global Localization of Unmanned Aerial Vehicles with Street View Images

In this paper, a vision-based method is proposed to globally localize an unmanned aerial vehicle (UAV) flying in the urban scenarios using Google Street View as prior information. Since the trajectory of the UAV with absolute scale cannot be recovered with the help of monocular visual odometry only, we leverage the known positional information of Google Street View to localize vehicles with respect to the surrounding buildings. First, a place recognition algorithm is presented to solve air-ground matching problem by searching aerial images taken from onboard camera within the geotagged image database. Then based on the matched images by place recognition and tracked features via visual odometry, a novel absolute scale estimation mechanism is proposed to estimate the pose of UAV in the real world coordinate system and to correct the scale drift of visual odometry. Furthermore, the pose graph with UAV's poses as vertexes and relative transformations as edges is optimized with SE(3) constraints to get trajectory of UAV in the real world coordinate system. Experiments are conducted on a 2km dataset recorded onboard by a camera-equipped UAV flying in the urban streets of Zurich, Switzerland, and the results show that our method is able to metrically localize the UAV without previously visiting or building 3D models in advance.
Deep Forest-Based Classification of Hyperspectral Images

The classification of hyperspectral images (HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network (CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN, nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis (PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-of-the-art methods.
A Road Condition Service Based on a Collaborative Mobile Sensing Approach

Road pavement conditions influence the daily lives of both drivers and passengers. Anomalies in road pavement can cause discomfort, increase stress, cause mechanical failures in vehicles and compromise safety of road users. Detecting and surveying road condition/anomalies requires expensive and specially designed equipment and vehicles, that cost considerable amounts of money, and require specialized workers to operate them. As an alternative, an emergent sensing paradigm is being discussed as a promising mechanism for collecting large-scale real-world data. In this paper we describe our experience on the design, implementation and deployment of a cloud based road anomaly information management service, that combines Collaborative Mobile Sensing and data-mining approaches, to provide a practical solution for detecting, identifying and managing road anomaly information. Additionally, we identify technical challenges and propose guidelines that may help to improve this type of services and applications.
Cooperative Co-evolution for Large Scale Optimization with Dynamic Variable Grouping via Marginal Product Modeling

Cooperative co-evolution (CC) algorithm is a promising method to deal with large scale optimization (LSGO) problem. One major challenge in CC is to design a good decomposition strategy to decompose original problem into subproblems. Linkage learning is a technique to search the linkage betweens decision variables. In this paper, we proposed a dynamic online grouping CC algorithm which employ the linkage learning method of extended compact genetic algorithm (ECGA). This linkage learning method build marginal product models of population and search for models according to minimum description length (MDL) criterion. A discretization technique and normalization method of MDL value is used to make the linkage learning more adaptive in large scale optimization. The decision variables will be regrouped according to the linkage learned when the optimization procedure is considered as stagnant. Experiments are conduct on CEC'10 LSGO benchmark function. The test results confirm the validity of this method.
Effect of Crystal Boundary Character and Crystal Orientation on Electromigration in Lead-free Solder Interconnects with Cyclic Twinning Structure

This study mainly examined the intermetallic compounds (IMC) migration path in flip-chip (FC) solder bump with cyclic twinning structure under current stressing. Long grain boundary was formed across the bump because of the twinning structure. If the orientation of the long crystal boundary was nearly aligned to the electron flow, IMCs migrated largely from the cathode to the anode along the long crystal boundary. Oppositely, if the orientation of the long crystal boundary was roughly normal to the electron flow orientation, it was the crystal orientation on each side of the long crystal boundary that determined the migration path of IMC.
Chemically Self-Propelled 3D-Printed Microbots

The self-propulsion ability and transfering continuous surrounding chemical fuels into mechanical movement, makes chemical microbots be a promising autonomous device in environmental and biomedical engineering. At present, chemical propulsion principle and motion model have been well studied. To realize more sophisticated function, more interest are being focused on complex 3D design in microbots. Here, we demonstrate a 3D chemically catalytic microbot with effective driving and reliable magnetic-response ability. The standing microstructure is fabricated by 3D micro-printing and covered by Ni and Pt layer in sputtering deposition process. The coating Pt layer provides the unidirectional catalytic propelled power. Under external magnetics field, the microbots' moving direction could be changed easily. The speed of the 3D microbots can reach 450J.1m/s in hydrogen peroxide solution of 30% concentration. The chemical microbots prepared by 3D micro-printing technique pave a way for the future complex 3D microbots.
A Triband Circularly Polarized Strip and SRR-Loaded Slot Antenna

In this communication, a simple and compact multiband circularly polarized (CP) slot antenna loaded with metallic strips and a split-ring resonator (SRR) is proposed. The multiband operation is obtained via exciting the SRR and copper strips by the microstrip-fed square slot antenna. The SRR resonates at 1.83 GHz, while the strips and the slot antenna provide resonances at 2.5 and 3.1 GHz. Resonant modes of loaded SRR and strips combined with the fundamental mode of the slot produce CP waves at the resonance frequencies of the proposed antenna. The proposed antenna allows for independent tuning of different bands via the use of the tilted strips and the SRR. Also, the sense of polarization at the resonance frequencies can be controlled by changing the orientation of the strips and the SRR with respect to the slot. The proposed antenna is fabricated on an FR-4 substrate of dimension $50 \times 50 \times 1.56$ mm3. Antenna performances in terms of 10 dB return loss bandwidth, 3 dB axial ratio bandwidth, gain, and efficiency are validated experimentally and are in a good agreement with the simulated results.
Generalized Bootstrap Supports for Phylogenetic Analyses of Protein Sequences Incorporating Alignment Uncertainty

Abstract. Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large‐scale progressive multiple alignment methods are numerically unstable when dealing with amino‐acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood (ML) trees estimated from these MSAs are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two‐thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared with the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.
Biological Event Trigger Identification with Noise Contrastive Estimation

Biological Event Extraction is an important task towards the goal of extracting biomedical knowledge from the scientific publications by capturing biomedical entities and their complex relations from the texts. As a crucial step in event extraction, event trigger identification, assigning words with suitable trigger category, has recently attracted substantial attention. As triggers are scattered in large corpus, traditional linguistic parsers are hard to generate syntactic features from them. Thereby, trigger sparsity problem restricts the model's learning process and becomes one of the main hinder in trigger identification. In this paper, we employ Noise Contrastive Estimation with Multi-Layer Perceptron model for solving triggers’ sparsity problem. Meanwhile, in the light of recent advance in word distributed representation, word-embedding feature generated by language model is utilized for semantic and syntactic information extraction. Finally, experimental study on commonly used MLEE dataset against baseline methods has demonstrated its promising result.
Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics - A Review

The contribution of Antarctica’s ice sheet to global sea-level rise depends on the very dynamic behavior of glaciers and ice shelves. One important parameter of ice-sheet dynamics is the location of glacier and ice-shelf fronts. Numerous remote sensing studies on Antarctic glacier and ice-shelf front positions exist, but no long-term record on circum-Antarctic front dynamics has been established so far. The article outlines the potential of remote sensing to map, extract, and measure calving front dynamics. Furthermore, this review provides an overview of the spatial and temporal availability of Antarctic calving front observations for the first time. Single measurements are compiled to a circum-Antarctic record of glacier and ice shelf retreat/advance. We find sufficient frontal records for the Antarctic Peninsula and Victoria Land, whereas on the West Antarctic Ice Sheet (WAIS), measurements only concentrate on specific glaciers and ice sheets. Frontal records for the East Antarctic Ice Sheet exist since the 1970s. Studies agree on the general retreat of calving fronts along the Antarctic Peninsula. East Antarctic calving fronts also showed retreating tendencies between 1970s until the early 1990s, but have advanced since the 2000s. Exceptions of this general trend are Victoria Land, Wilkes Land, and the northernmost Dronning Maud Land. For the WAIS, no clear trend in long-term front fluctuations could be identified, as observations of different studies vary in space and time, and fronts highly fluctuate. For further calving front analysis, regular mapping intervals as well as glacier morphology should be included. We propose to exploit current and future developments in Earth observations to create frequent standardized measurements for circum-Antarctic assessments of glacier and ice-shelf front dynamics in regard to ice-sheet mass balance and climate forcing.
Proton pump inhibitors: use and misuse in the clinical setting

ABSTRACT Introduction: The introduction of proton pump inhibitors (PPIs) into clinical practice has greatly improved our therapeutic approach to acid-related diseases for their efficacy and safety. Areas Covered: The following evidence-based indications for PPI use are acknowledged by many scientific societies: treatment of the various forms and complications of gastroesophageal reflux disease, eradication of H. pylori infection in combination with two or more antibiotics, short- and long-term therapy of H. pylori-negative peptic ulcers, healing, and prevention of NSAID/COXIB-associated gastric ulcers, co-therapy with endoscopic procedures to control upper digestive bleeding and medical treatment of Zollinger Ellison syndrome. Expert Commentary: Despite the above well-defined indications, however, the use of PPIs continues to grow every year in both western and eastern countries and the endless expansion of the PPI market has created important problems for many regulatory authorities for two relevant features: the progressive increase of the costs of therapy and the greater potential harms for the patients. The major reasons for the misuse of PPIs are the prevention of gastro-duodenal ulcers in patients without risk factors and the stress ulcer prophylaxis in non-intensive care units, steroid therapy alone, anti-platelet or anti-coagulant treatment in patients without risk of gastric injury and the overtreatment of functional dyspepsia.
Cryptanalysis of key exchange protocol based on tensor ergodic problem

Recently, Mao, Zhang, Wu et al. constructed two key exchange (KE) protocols based on tensor ergodic problem (TEP). Although they conjectured that these constructions can potentially resist quantum computing attack, they did not provide a rigorous security proof for their KE protocols. In this paper, applying the properties of ergodic matrix, we first present a polynomial time algorithm to solve the TEP problem using O(n6) arithmetic operations in the finite field, where n is the security parameter. Then, applying this polynomial time algorithm, we generate a common shared key for two TEP-based KE constructions, respectively. In addition, we also provide a polynomial time algorithm with O(n6) arithmetic operations that directly recovers the plaintext from a ciphertext for the KE-based encryption scheme. Thus, the TEP-based KE protocols and their corresponding encryption schemes are insecure.
A New Drone Accelerometer Calibration Method

Most small drone accelerometer calibration methods are relatively simple, but their accuracy is not high. In order to improve the accuracy of calibration without increasing the difficulty of operation, a calibration method based on smooth filtering and Levenberg-Marquardt algorithm is proposed. Firstly, the accelerometer error model is established. Then the smoothing filter is used to filter the accelerometer measurement data in different attitudes. Finally, the Levenberg-Marquardt algorithm is used to solve the zero offset of the accelerometer, the scale factor and the tri-axial non-orthogonal error. Experimental results show that the algorithm can quickly calibrate the accelerometer and improve the measurement accuracy of the accelerometer. It is suitable for application in the drone system.
Semantic Segmentation of RGB-NIR Images with Error-Correcting Output Codes

Scene understanding is strictly linked to image semantic segmentation, which is the process of associating each pixel of an image with a label, such as sky, clouds, road, building. This paper proposes a new semantic segmentation framework, in which Error-Correcting Output Codes (ECOC) are used to decompose the multiway classification problem into multiple binary classification subtasks. The binary output results are then converted into final class labels following a decoding table established at the beginning of the classification procedure. As part of the recognition framework, color descriptors and high-level visual features are extracted to represent the appearance of the patch surrounding each pixel of interest. The proposed method is validated on an image database containing RGB and Near-Infrared (NIR) imaaes.
Bayesian Calibration of AquaCrop Model

The AquaCrop simulation model, modelling the dynamic change of crop growth status, is an important crop management tool for quantifying crop yield response to water. To effectively simulate the soil water balance and the crop growth process, a number of system parameters and canopy state variables are inevitably adopted. As a result, certain key parameters need to be calibrated so that the AquaCrop model can achieve a better performance of prediction for various scales of regions. This paper aims to apply Bayesian technique to calibrate the AquaCrop model. In this approach, the prior information regarding the system parameters is expressed in the form of a uniform probability distribution. Then with the advent of output variable measurement (e.g. biomass) by remote sensing techniques, the parameter distributions are iteratively updated by using Bayesian Markov Chain Monte Carlo (MCMC) method. The calibrated system parameters are expressed by the posterior distributions and gained by distribution mean value. Finally, the Bayesian calibration is compared with the conventional optimisation based calibration in terms of biomass and canopy cover, where simulated annealing is chosen as the optimisation approach, indicating a better calibration performance can be achieved by using Bayesian methods. Consequently, it is recommended that Bayesian calibration is one promising approach to the problem of crop model calibration.
Wideband Long Range, Radiation Efficient Compact UHF RFID Tag

A long range compact, wideband UHF radio frequency identification (RFID) efficient tag is proposed. A novel technique of designing an RFID tag antenna with alphabetical pattern is explored. A double T-matching approach is used to enhance the impedance matching, radiation efficiency, and gain in the UHF band. The tag design is optimized for two different substrates (hard and flexible) and its performance is verified for Impinj Monja R6 and Higgs 4 ICs to make it competent for different practical applications in healthcare, military, supply chain, etc. The overall size of the tag is 89.5 mm × 25 mm. The prototypes of the proposed tag are fabricated for both the substrates and experimentally tested with the RFID Higgs 4 IC. The maximum measured range of the tag is 15.5 m for hard substrate and 18 m for flexible one.
Solving Non-smooth Constrained Programs with Lower Complexity than 𝒪(1/ε): A Primal-Dual Homotopy Smoothing Approach

We propose a new primal-dual homotopy smoothing algorithm for a linearly constrained convex program, where neither the primal nor the dual function has to be smooth or strongly convex. The best known iteration complexity solving such a non-smooth problem is O(e−1). In this paper, we show that by leveraging a local error bound condition on the dual function, the proposed algorithm can achieve a better primal convergence time of O\l(e−2/(2+β)log2(e−1)\r), where β∈(0,1] is a local error bound parameter. As an example application, we show that the distributed geometric median problem, which can be formulated as a constrained convex program, has its dual function non-smooth but satisfying the aforementioned local error bound condition with β=1/2, therefore enjoying a convergence time of O\l(e−4/5log2(e−1)\r). This result improves upon the O(e−1) convergence time bound achieved by existing distributed optimization algorithms. Simulation experiments also demonstrate the performance of our proposed algorithm.
Investigation of Corporate Entrepreneurship in South African Mining Companies

Corporate entrepreneurship (CE) implementation is seen as one of the solutions to ensure competitive advantage, especially in adverse economic times. The current economic state will require mining companies to be innovative. This study aimed to investigate the presence of CE in South African mining companies. The objective of the research was to investigate entrepreneurial behavior / activity and conduct a gap assessment for the successful implementation of CE. The results showed that mining companies do have entrepreneurial activity, administered at the corporate and business unit level, whose main reason is to remain competitive. Prominent entrepreneurial activity is through external and joint corporate venturing in order to consolidate markets and to diversify. The mining companies investigated do not have a dedicated CE framework. A specific CE framework should be decided on and embedded from top management down to the rest of the company. The mining companies currently operate on the CE opportunistic model; a shift to the CE producer model could be advantageous. Mining companies could place greater emphasis on remaining competitive through employing organizational rejuvenation, whereby they sustain and improve their competitive standing by modifying internal processes, structures, and capabilities.
Multistage Uncertain Random Bang-Bang Optimal Control

In this paper, a linear optimal control model is studied for a multistage uncertain random system. First, recurrence equations are presented for this model based on Bellman's Principle. Second, analytical expressions are derived for both the bang-bang controls and the optimal objective values of the model. Finally, an application is given to illustrate our results.
A Synthetic Method for Extremely Stable Thin Film of CsPbBr3 QDs and its Application on Light-emitting Diodes

The ability to be mixed with the polymers is very important for applications of perovskite quantum dots (QDs), such as in display, lighting and so on. Here, a method for fabricating a thin film composed of CsPbBr3 QDs and polydimethylsiloxane (PDMS) was reported. By using the ethyl acetate, a large number of ligands on the surface were removed and the combination with polymers can realize. Based on the thin film, an LED device was assembled and its luminous flux, luminous efficiency, color temperature and CIE color coordinates were measured. What’s more, the thin film revealed a splendid stability after being stored for 42 days without any protection, which show the broad prospects of it.
Mass Evidence Accumulation and Traveler Risk Scoring Engine in e-Border Infrastructure

This paper is concerned with mass evidence accumulation and risk assessment in a particular component of transportation systems and e-borders. We outline the challenges faced by contemporary border control technology and conduct a series of demonstrative experiments that cover critical scenarios, tasks, and states of both evidence accumulation and the traveler risk scoring engine. Using technology gap navigator methodology, this paper suggests an approach to traveler risk estimation based on a unified inference platform, such as a causal graphical model with various incorporated metrics of uncertainty.
Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification

Image classification is used for many tasks such as recognising handwritten digits, identifying the presence of pedestrians for self-driving cars, and even providing medical diagnosis from cell images. The current state-of-the-art solution for image classification, typically, uses convolutional neural networks (CNNs), however, there are limitations in this approach such as the need for manually crafted architectures and low interpretability. A genetic programming solution is proposed in this paper that aims to overcome these limitations, while also taking advantage of useful operators in CNNs such as convolutions and pooling. The new approach is tested on four widely used benchmark image datasets, and the experimental results show that the new method has achieved comparable performance to the state-of-the-art techniques. Furthermore, the automatically evolved programs are highly interpretable, and visualisations of those programs reveal interesting patterns.