Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodic Leaky-Wave Antenna Based on Complementary Pair of Radiation Elements

In this paper, we propose and develop a leaky-wave antenna (LWA) by periodically loading shunt radiation element pairs along a host transmission line (TL). Each pair of radiation unit consists of one capacitive element and one inductive element, which are susceptance-complementary with each other. The complementary radiation element loading leads to an effective open-stopband (OSB) suppression, thereby facilitating a continuous leaky-wave beam scanning from backward, through broadside, to forward. Compared with the previously studied OSB-suppressed LWAs containing only one type of susceptive radiation elements, the proposed LWA features two unique properties: constant radiation efficiency over the operation band, and unit cell period only related to the host TL regardless of parameter values of the radiation elements. The aperture synthesis of the proposed LWA is hence straightforward as the radiation elements can be designed to be equally spaced. Experimental prototypes are designed and fabricated based on microstrip line structure as a proof of concept. Desired measured results agree well with the theoretical predictions.
Effectiveness of five personal shark-bite deterrents for surfers

The number of shark-human interactions and shark bites per capita has been increasing since the 1980s, leading to a rise in measures developed to mitigate the risk of shark bites. Yet many of the products commercially available for personal protection have not been scientifically tested, potentially providing an exaggerated sense of security to the people using them. We tested five personal shark deterrents developed for surfers (Shark Shield Pty Ltd [Ocean Guardian] Freedom+ Surf, Rpela, SharkBanz bracelet, SharkBanz surf leash, and Chillax Wax) by comparing the percentage of baits taken, distance to the bait, number of passes, and whether a shark reaction could be observed. We did a total of 297 successful trials at the Neptune Islands Group Marine Park in South Australia, during which 44 different white sharks (Carcharodon carcharias) interacted with the bait, making a total of 1413 passes. The effectiveness of the deterrents was variable, with the Freedom+ Surf affecting shark behaviour the most and reducing the percentage of bait taken from 96% (relative to the control board) to 40%. The mean distance of sharks to the board increased from 1.6 ± 0.1 m (control board) to 2.6 ± 0.1 m when the Freedom Surf+ was active. The other deterrents had limited or no measureable effect on white shark behavour. Based on our power analyses, the smallest effect size that could be reliably detected was ∼15%, which for the first time provides information about the effect size that a deterrent study like ours can reliably detect. Our study shows that deterrents based on similar principles—overwhelming a shark’s electroreceptors (the ampullae of Lorenzini) with electrical pulses—differ in their efficacy, reinforcing the need to test each product independently. Our results will allow private and government agencies and the public to make informed decisions about the use and suitability of these five products.
Distributed Observations in Meteorological Ensemble Data Assimilation and Forecasting

With the ever increasing amount of meteorological data available, from satellites in particular, it becomes more and more important to use as large fraction of this data as practically possible for operational weather forecasts. Two applications are shown where more data are used in ensemble data assimilation and forecasting by distributing satellite data among different ensemble members. For the Ensemble of Data Assimilations (EDA), a version of the perturbed observations ensemble Kalman filter, the ensemble mean error can in theory be reduced by replacing observation perturbations by distributing different subsets of observations to different members, but in practice this is complicated by observation error correlations and the need to maintain the same level of spread in the ensemble as before. For the ensemble forecasts, it is shown that the initial conditions can be improved by re-centring EDA perturbations on multiple equally good analyses that use different subsets of observations instead of re-centring all EDA perturbations on a single one of those analyses. It is shown how the ensemble mean error decreases for decreased error correlation between analyses. We estimate that in practice only 5–6 analysis are needed to obtain 80% of the root mean square error reduction that would be achieved with infinite number of analyses.
A clarion call for preventing taxonomical errors of dermatophytes using the example of the novel Trichophyton mentagrophytes genotype VIII uniformly isolated in the Indian epidemic of superficial dermatophytosis

An alarming pan Indian increase in the incidence of superficial dermatophytosis has been noticed over the past 5‐6 years. Recent studies have demonstrated emerging predominance of Trichophyton (T.) mentagrophytes as the causative organism in such cases. Interestingly, a distinct Indian genotype of T. mentagrophytes has been identified and recognised with the help of sequencing of the ITS region of the rDNA. That has, however, led to a basic confusion owing to the newly introduced taxonomy of dermatophytes in 2017. According to this most recently suggested classification and new taxonomy of dermatophytes, the former “T. mentagrophytes complex” is differentiated into T. mentagrophytes (zoophilic strains) and T. interdigitale (anthropophilic strains). We have noticed that in some recent studies the causative agent of the chronic, relapsing dermatophytosis outbreak in India has been described as T. interdigitale. In our opinion, it is very likely that these T. interdigitale strains isolated in Delhi and Chennai in India are indeed strains more closely related to the neotype of T. mentagrophytes and not strains of T. interdigitale. We therefore want to underscore the importance of a common nomenclature of species in accordance with the new taxonomy of dermatophytes. This would most likely facilitate better understanding of the issue amongst dermatologists and microbiologists in general. Mistaken identification of Trichophyton isolates not limited to India is very likely to occur due to the lack of appropriate molecular diagnosis which in turn is based on the already published data that presumably wrongly identify one species instead of the other.
A Low-Cost Hardware-in-the-Loop Agent-Based Simulation Testbed for Autonomous Vehicles

We have developed a flexible and low-cost hardware testbed for autonomous vehicle research and education. The testbed provides the ability to autonomously control multiple small vehicles within a 1200x900 mm environment and is entirely open source with regard to both hardware and software, making it easily reproducible. An agent-based approach is used for vehicle control, meaning that different agent models can be applied to each vehicle as desired for simulation. GPS-like position information is provided to the controller in real-time using a global computer vision system. Using our figure-of-eight test environment, we have demonstrated that our system can support six cars driving smoothly around the track whilst avoiding collisions with each other.
Profiling Network Traffic Behavior for the Purpose of Anomaly-Based Intrusion Detection

In this paper, we propose methods for profiling normal network traffic, methods that could be employed for the purpose of creating a baseline that would be used in the detection of threshold based anomalies in network traffic. This profiling is based on five proposed features of network traffic, and to illustrate, testing was done using recent and large data sets, and relying on various tools to statistically analyze network traffic. Although we have no pretensions of completeness, our results indicate that this is a promising approach to differentiate between normal and abnormal network traffic behavior, and therefore a promising contribution to anomaly based intrusion detection.
Chinese Image Caption Based on Deep Learning

Generating a sentence when given an image automatically, which is described as image caption, has attracted more and more attention from relative researchers in computer vision area. In this paper, we present a method to generate Chinese captions for images. We adopt a Chinese dataset called ICC dataset and propose an improved language generating model. In the experiment, we show that using ICC datasets and our model to generate Chinese captions is reasonable and efficient.
Effect of intraperitoneal or intracerebroventricular injection of streptozotocin on learning and memory in mice

Alteration of behavior and PSD proteins in cerebral cortex and hippocampal synaptosome in the Alzheimer's disease (AD) mouse model were determined. AD was established by intraperitoneal injection of streptozotocin (STZ) in neonatal mice (intraperitoneal AD group) or intracerebroventricular injection of STZ in adult mice (intracerebroventricular AD group). Body weight and blood sugar level were measured. Following Morris water maze (MWM) test and fear-conditioning test, cerebral cortex and hippocampus tissues were collected and the levels of PSD95 and shank3 proteins in these tissues were measured by western blot analysis. The body weight was reduced and the blood sugar concentration was increased in the intraperitoneal AD group compared with the control group. In contrast, the body weight was reduced, while the blood sugar concentration was not increased in the intracerebroventricular AD group compared with the control group. Escape latency in both AD groups was extended compared with the control group. The freezing time in the intraperitoneal AD group was increased, while in the intracerebroventricular AD group, the freezing time was reduced. PSD95 and shank3 proteins in the cerebral cortex in both AD groups were decreased compared with the control group. PSD95 in the hippocampus was reduced in both AD groups compared with the control group. Shank3 in the hippocampus in the intracerebroventricular AD group was significantly reduced compared with the control group. Intraperitoneal injection of STZ in neonatal mice led to elevated blood sugar, impaired spatial memory and enhanced emotional memory when they become adults. In contrast, intracerebroventricular injection of STZ in adults directly led to deteriorated spatial and emotional memory without alteration of blood sugar content, which could be associated with the changes of PSD95 and shank3 proteins in hippocampus.
Generalized Trajectory Control for Tree-Structured Aerial Manipulators

This paper develops and simulates a control scheme to allow for trajectory-based tracking of a multirotor-based aerial manipulator. The approach utilizes a low-level Inverse Dynamics controller that proves suitable for the general case of tree-structured aerial manipulators, and is capable of tracking any arbitrary point on the kinematic tree. Simulation results of common maneuvers for pick-and-place related tasks are presented to assess the performance of the system under varying configurations and trajectories.
An adaptive robust nonlinear observer for velocity and disturbance estimation for the translational motion of a quadrotor aircraft

An adaptive robust observer for UAVs position estimation problem is proposed in this paper. The observer design was obtained from a change of coordinates, where a nonlinear system was transformed to a canonical observable model. A practical stability was proved using the Lyapunov approach, and it is proved that the states converge inside a ball that depends on the high gain radius r, which is given in the proof. Simulation scenarios were carried out to validate the performance of the closed-loop system. These validation experiments were under ideal classical conditions without disturbance and then disturbance was added in the states to emulate a real system. Results showed the adaptive disturbance rejection of the observer. In addition, the observer was also validated online by measuring real data from outdoors flight test to estimate the velocity and disturbances of translational movement.
Configurable Event Correlation for Process Discovery from Object-Centric Event Data

Many modern enterprises are employing serviceoriented systems to execute their transactions. These systems generate an abundance of events, which can be analyzed to diagnose and improve business processes. However, the events are distributed over different data sources, as service-oriented systems are often implemented asWeb services involving different departments in an organization. They need to be consolidated in a process view since it makes no sense to analyze individual events. Existing approaches depend on an explicit case notion to correlate events. They work well on data from process-aware systems (e.g., BPM/WFM systems) which record events explicitly and each event is attached with a case id (a global identifier) to indicate its related process instance. However, most serviceoriented systems (e.g., ERP and CRM) produce object-centric data (e.g., database tables), which record events implicitly (e.g., through redo logs) and separately without a common case id. In this paper, we propose a novel approach to correlate events by the data perspective (e.g., by a so-called "object path"). Besides, we define the concepts of correlation patterns and evaluation metrics, which enable users to evaluate and select the best way to correlate events based on their needs.
Chinese Discourse Segmentation Using Bilingual Discourse Commonality

Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just through discriminating the functions of punctuations. In this paper, we argue that Chinese EDUs may not end at the punctuation positions and should follow the definition of EDU in RST-DT. With this definition, we conduct Chinese discourse segmentation with the help of English labeled data.Using discourse commonality between English and Chinese, we design an adversarial neural network framework to extract common language-independent features and language-specific features which are useful for discourse segmentation, when there is no or only a small scale of Chinese labeled data available. Experiments on discourse segmentation demonstrate that our models can leverage common features from bilingual data, and learn efficient Chinese-specific features from a small amount of Chinese labeled data, outperforming the baseline models.
Time-dependent QM/EM Simulation Method Applied to Carbon Nanotube

A hybrid method based on quantum mechanics and electromagnetics is used for simulation of the real time dynamics. The scalar and vector potentials are used to integrate Maxwell's equations in the time domain. The TDDFT-NEGF-EOM method is employed to simulate the electronic dynamics in the quantum mechanical region. When a classical electromagnetic wave penetrates into the quantum mechanical region, the entire system can be solved with Maxwell's equations.
An Enhanced Metric Learning for Person Reidentification

Metric learning is a key step in the process of person re-identification. Most metric learning methods are dedicated to proposing new sophisticated algorithms to improve matching accuracy currently. However, re-ranking methods have been ignored, which can be used to re-rank the result of metric learning and improve the matching accuracy of person re-identification effectively. In this paper, we propose a kind of re-ranking method which is based on max-min linear space transformation to deal with the outliers in final distance matrix of traditional metric learning and then we can re-rank the new distance to improve rei-dentification results. Generally, the distances is fixed between each image in gallery with every image in probe when the metric learning method is selected. But the range of the distances value is significantly different from image to image in gallery, we apply max-min criterion to linearly map all values to the specified subspace, which can greatly reduce the error between classes. The effectiveness of our re-ranking method to enhance XQDA algorithm is experimented on two challenging person reidentification databases, CUHK01 and VIPeR. The results show that the accuracy of state-of-the-art is improved.
Effects of transplanted adipose derived stem cells on the expressions of α-SMA and DCN in fibroblasts of hypertrophic scar tissues in rabbit ears

To study the effects of transplanted adipose derived stem cells (ADSCs) on the expressions of α-smooth muscle actin (α-SMA) and decorin (DCN) in fibroblasts of hypertrophic scar tissues in rabbit ears. Twelve New Zealand white rabbits were selected; the normal subcutaneous adipose tissues in inguinal region were removed, ADSCs were extracted via enzyme digestion, cultured in Dulbecco's modified Eagle's medium (DMEM) and inoculated into the culture dish (3–5×104 cells/ml). After the rabbit ear hypertrophic scar model was established successfully, the fibroblasts of hypertrophic scar tissues in rabbit ears were separated and cultured using the mechanical method combined with enzyme digestion, and the ADSCs and scar fibroblasts were cultured in non-contact Transwell co-culture system for 21 days (experimental group); the corresponding scar fibroblasts were cultured in an ordinary 6-well plate without any treatment for 21 days (control group). The content of collagen I in fibroblasts was detected using the enzyme-linked immunosorbent assay (ELISA) kit, the mRNA expressions of α-SMA and DCN were detected via reverse transcription-polymerase chain reaction (RT-PCR), the protein expressions of α-SMA and DCN were detected via western blot analysis, and the expressions and distribution of α-SMA and DCN were detected via immunofluorescence assay. The results of ELISA showed that the content of collagen I in experimental group was decreased significantly (p<0.01). The results of RT-PCR and western blot analysis revealed that the mRNA and protein expression levels of α-SMA were significantly decreased (P<0.01, but those of DCN were significantly increased (p<0.01). Moreover, the results of immunofluorescence assay showed that the expression of α-SMA in experimental group was significantly decreased, while the expression of DCN was significantly increased. ADSCs can inhibit the mRNA and protein expressions of α-SMA and promote the mRNA and protein expressions of DCN in in vitro culture system, and they are expected to be used in the prevention and treatment of pathological scars.
Synthesis of Large Sparse Arrays Using IDEA (Inflating-Deflating Exploration Algorithm)

We introduce an effective antenna array synthesis algorithm which is aimed to the generation of sparse arrays radiating patterns with arbitrary upper bounds. The approach based on a convex optimization exploits an iterative polygonal expansion and contraction of the sources, and is suitable for the synthesis of linear and planar arrays. Numerical simulations show that the proposed algorithm allows a degree of sparsification equal or better than the one achievable by concurrent algorithms, with a significantly lower computational burden with respect to the best ones, allowing the effective synthesis of sparse arrays of hundreds of wavelengths with simple office computers.
A Composite Malicious Peer Eviction Mechanism for Super-P2P Systems

Large-scale P2P applications (e.g., social networking, online gaming, video streaming) that host millions of users increasingly rely upon semi-structured super-P2P systems to provide efficient services in dynamic environments. Given the critical role of 'super peers' in such topologies, attackers target super peers due to the resultant high damage on P2P services. In this paper, we consider the prominent class of Outgoing Eclipse Attacks (OEA) where an attacker aims to block the communication by controlling all the outgoing connections of honest super peers. Our interest on OEA stems from the fact that our simulation studies reveal that OEAs can cause up to 90% of all service requests to fail. Our attack mitigation relies upon a novel (a) monitoring and (b) malicious peer eviction scheme based on a composite proactive and reactive mechanism. Our proactive mechanism enforces an upper bound on the number of connections an attacker can establish, whereas our reactive mechanism expels malicious peers from the overlay using a distributed consensus protocol. We show that our protection mechanism is highly effective and exhibits a low false-positive rate. Our extensive simulation study validates the analytical results over a large range of parameters with observed detection accuracies of 99% and throughput enhancements of up to 100% while entailing an overhead of less than 5%.
Optimized edge-based grasping method for a cluttered environment

This paper looks into the problem of grasping region localization along with suitable pose from a cluttered environment without any a priori knowledge of the object geometry. This end-to-end method detects the handles from a single frame of input sensor. The pipeline starts with the creation of multiple surface segments to detect the required gap in the first stage, and eventually helps in detecting boundary lines. Our novelty lies in the fact that we have merged color based edge and depth edge in order to get more reliable boundary points through which a pair of boundary line is fitted. Also this information is used to validate the handle by measuring the angle between the boundary lines and also by checking for amy potential occlusion. In addition, we also proposed an optimizing cost function based method to choose the best handle from a set of valid handles. The method proposed is tested on real-life datasets and is found to out form state of the art methods in terms of precision.
Fusing Spaceborne SAR Interferometry and Street View Images for 4D Urban Modeling

Obtaining city models in a large scale is usually achieved by means of remote sensing techniques, such as synthetic aperture radar (SAR) interferometry and optical image stereogrammetry. Despite the controlled quality of these products, such observation is restricted by the characteristics of their sensor platform, such as revisit time and spatial resolution. Over the last decade, the rapid development of online geographic information systems, such as Google map, has accumulated vast amount of online images. Despite their uncontrolled quality, these images constitute a set of redundant spatial-temporal observations of our dynamic 3D urban environment. These images contain useful information that can complement the remote sensing data, especially the SAR images. This paper presents a one of the first studies of fusing online street view images and spaceborne SAR images, for the reconstruction of spatial-temporal (hence 4D) city models. We describe a general approach to geometrically combine the information of these two types of images that are nearly impossible to even coregister without a precise 3D city model due to their distinct imaging geometry. It is demonstrated that, one can obtain a new kind of city model that includes high resolution optical texture for better scene understanding and the dynamics of individual buildings up to the precision of millimeter retrieved from SAR interferometry.