Miracle-dz/UniFAR-FTUs huggingface dataset:
Cellular Coverage-Aware Path Planning for UAVs

Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to mitigate interference and to lower the risk of dangerous connectivity outages. In this paper, several path planning strategies are proposed and evaluated to optimise wireless coverage for UAVs. A simulator using a real-life 3D map is used to evaluate the proposed algorithms for both 4G and 5G scenarios. We show that the proposed Coverage-Aware A* algorithm, which alters the UAV's flying altitude, is able to improve the mean SINR by 3-4dB and lower the cellular outage probability by a factor of 10. Furthermore, the outages that still occur have a 60% shorter length, hence posing a lower risk to induce harmful accidents.
Polarization in caustic-crossing binary microlensing events

Here, we revisit the polarization signal in caustic-crossing binary microlensing and introduce the maps of the polarization signals of the source star as a function of its position projected on the lens plane. The behavior of these maps depends on the source size in comparison with the caustic. If the source size is so smaller than the caustic curve, the maximum polarization signal occurs while the source edge is crossing the nearest folds to the position of the primary. When the source size is larger than the caustic, the polarization signal maximizes over a circular ring around the primary whose radius normalized to the source radius is ~0.96. In cusp caustic crossings, the polarization curves have three peaks, the largest and widest one happens when the source edge is on the corner of the cusp and the source center is inside the caustic curve. When the source is entirely inside the caustic, the polarization signal significantly decreases. Despite the low magnification factor between two parts of planetary caustics, its polarization signal is considerable and can reach to 0.2\% for early-type stars. While crossing the connection line between different parts of caustic, the polarimetry curve have three close peaks that the middle one appears when the source center is upon the connection line.
Indigenous invertebrate components in ecological restoration in agricultural landscapes

The challenge of community restoration is to understand and exploit the principles of ecological succession at all serai stages, by complementing and accelerating the processes of colonisation and regeneration. The main aim is to construct self-sustaining, appropriate communities, connected in the landscape, that meet conservation, landscape and crop production goals. Research, to date, has been biased towards the plant and soil components with little consideration for the animal element. We discuss the importance of this missing component, put forward our belief that it is essential for enhancing landscape biodiversity, given that invertebrates provide the bulk of the biodiversity in New Zealand, and ensure that many ecological functions are performed. We suggest that restoration is an ideal model system for testing succession theory.
Image-based reconstruction for strong-nonlinear transient problems by using an enhanced ReConNN

With the improvement of pattern recognition and feature extraction of the Deep Neural Networks (DNNs), more and more problems are attempted to solve from the view of images. Recently, the Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model, which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and impact. However, because the researched problems are most slightly changed process, the low-accuracy of the Convolutional Neural Network (CNN) and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation, and high-manpower. In this study, an improved ReConNN model is proposed to address the mentioned weaknesses. Through experiments, comparisons and analyses, the improved one is demonstrated to outperform in accuracy, efficiency and cost.
Quantum thermodynamics in a multipartite setting: A resource theory of local Gaussian work extraction for multimode bosonic systems

Quantum thermodynamics can be cast as a resource theory by considering free access to a heat bath, thereby viewing the Gibbs state at a fixed temperature as a free state and hence any other state as a resource. Here, we consider a multipartite scenario where several parties attempt at extracting work locally, each having access to a local heat bath (possibly with a different temperature), assisted with an energy-preserving global unitary. As a specific model, we analyze a collection of harmonic oscillators or a multimode bosonic system. Focusing on the Gaussian paradigm, we construct a reasonable resource theory of local activity for a multimode bosonic system, where we identify as free any state that is obtained from a product of thermal states (possibly at different temperatures) acted upon by any linear-optics (passive Gaussian) transformation. The associated free operations are then all linear-optics transformations supplemented with tensoring and partial tracing. We show that the local Gaussian extractable work (if each party applies a Gaussian unitary, assisted with linear optics) is zero if and only if the covariance matrix of the system is that of a free state. Further, we develop a resource theory of local Gaussian extractable work, defined as the difference between the trace and symplectic trace of the covariance matrix of the system. We prove that it is a resource monotone that cannot increase under free operations. We also provide examples illustrating the distillation of local activity and local Gaussian extractable work.
SAdam: A Variant of Adam for Strongly Convex Functions

The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependant $O(\sqrt{T})$ regret bound where $T$ is the time horizon. However, whether strong convexity can be utilized to further improve the performance remains an open problem. In this paper, we give an affirmative answer by developing a variant of Adam (referred to as SAdam) which achieves a data-dependant $O(\log T)$ regret bound for strongly convex functions. The essential idea is to maintain a faster decaying yet under controlled step size for exploiting strong convexity. In addition, under a special configuration of hyperparameters, our SAdam reduces to SC-RMSprop, a recently proposed variant of RMSprop for strongly convex functions, for which we provide the first data-dependent logarithmic regret bound. Empirical results on optimizing strongly convex functions and training deep networks demonstrate the effectiveness of our method.
Clinical correlates of submicroscopic deletions involving the ABL–BCR translocation region in chronic myeloid leukemia

Abstract: Recent reports indicate a prognostically detrimental effect of submicroscopic abl–bcr deletions associated with the break and fusion points of the derivative chromosome 9 [der(9)] in chronic myeloid leukemia (CML). In a retrospective cohort of 92 patients with CML, the incidence of an atypical D‐FISH pattern, that is consistent with a der(9) deletion was 20%. Complete clinical information was available in 82 patients and revealed no significant differences between 18 deleted and 64 non‐deleted cases in platelet count, circulating blast percentage, spleen size, or karyotype profile at presentation. However, der(9)‐deleted patients presented with significantly lower hemoglobin levels and higher leukocyte counts. At a median follow‐up of 31 months, the incidence of disease transformation, drug therapy response, and survival were similar between the two groups. These results are contrary to previous reports that suggested inferior survival as well as poor response to alpha interferon therapy in CML patients carrying der(9) deletions.
Authenticated teleportation with one-sided trust

We introduce a protocol for authenticated teleportation, which can be proven secure even when the receiver does not trust their measurement devices, and is experimentally accessible. We use the technique of self-testing from the device-independent approach to quantum information, where we can characterise quantum states and measurements from the exhibited classical correlations alone. First, we derive self-testing bounds for the Bell state and Pauli $\sigma_X, \sigma_Z$ measurements, that are robust enough to be implemented in the lab. Then, we use these to determine a lower bound on the fidelity of an untested entangled state to be used for teleportation. Finally, we apply our results to propose an experimentally feasible protocol for one-sided device-independent authenticated teleportation. This can be interpreted as a first practical authentication of a quantum channel, with additional one-sided device-independence.
Sound source separation algorithm using phase difference and angle distribution modeling near the target

In this paper we present a novel two-microphone sound source separation algorithm, which selects the signal from the target direction while suppressing signals from other directions. In this algorithm, which is referred to as Power Angle Information Near Target (PAINT), we first calculate phase difference for each time-frequency bin. From the phase difference, the angle of a sound source is estimated. For each frame, we represent the source angle distribution near the expected target location as a mixture of a Gaussian and a uniform distributions and obtain binary masks using hypothesis testing. Continuous masks are calculated from the binary masks using the Channel Weighting (CW) technique, and processed speech is synthesized using IFFT and the OverLap-Add (OLA) method. We demonstrate that the algorithm described in this paper shows better speech recognition accuracy compared to conventional approaches and our previous approaches.
Diurnal variation of dust and gas production in comet 67P/Churyumov-Gerasimenko at the inbound equinox as seen by OSIRIS and VIRTIS-M on board Rosetta

Context. On 27 April 2015, when comet 67P/Churyumov-Gerasimenko was at 1.76 au from the Sun and moving toward perihelion, the OSIRIS and VIRTIS-M instruments on board the Rosetta spacecraft simultaneously observed the evolving dust and gas coma during a complete rotation of the comet. Aims. We aim to characterize the spatial distribution of dust, H2O, and CO2 gas in the inner coma. To do this, we performed a quantitative analysis of the release of dust and gas and compared the observed H2O production rate with the rate we calculated using a thermophysical model. Methods. For this study we selected OSIRIS WAC images at 612 nm (dust) and VIRTIS-M image cubes at 612 nm, 2700 nm (H2O emission band), and 4200 nm (CO2 emission band). We measured the average signal in a circular annulus to study the spatial variation around the comet, and in a sector of the annulus to study temporal variation in the sunward direction with comet rotation, both at a fixed distance of 3.1 km from the comet center. Results. The spatial correlation between dust and water, both coming from the sunlit side of the comet, shows that water is the main driver of dust activity in this time period. The spatial distribution of CO2 is not correlated with water and dust. There is no strong temporal correlation between the dust brightness and water production rate as the comet rotates. The dust brightness shows a peak at 0° subsolar longitude, which is not pronounced in the water production. At the same epoch, there is also a maximum in CO2 production. An excess of measured water production with respect to the value calculated using a simple thermophysical model is observed when the head lobe and regions of the southern hemisphere with strong seasonal variations are illuminated (subsolar longitude 270°–50°). A drastic decrease in dust production when the water production (both measured and from the model) displays a maximum occurs when typical northern consolidated regions are illuminated and the southern hemisphere regions with strong seasonal variations are instead in shadow (subsolar longitude 50°–90°). Possible explanations of these observations are presented and discussed.
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up

We analyse the learning performance of Distributed Gradient Descent in the context of multi-agent decentralised non-parametric regression with the square loss function when i.i. d. samples are assigned to agents. We show that if agents hold sufficiently many samples with respect to the network size, then Distributed Gradient Descent achieves optimal statistical rates with a number of iterations that scales, up to a threshold, with the inverse of the spectral gap of the gossip matrix divided by the number of samples owned by each agent raised to a problem-dependent power. The presence of the threshold comes from statistics. It encodes the existence of a "big data" regime where the number of required iterations does not depend on the network topology. In this regime, Distributed Gradient Descent achieves optimal statistical rates with the same order of iterations as gradient descent run with all the samples in the network. Provided the communication delay is sufficiently small, the distributed protocol yields a linear speed-up in runtime compared to the single-machine protocol. This is in contrast to decentralised optimisation algorithms that do not exploit statistics and only yield a linear speed-up in graphs where the spectral gap is bounded away from zero. Our results exploit the statistical concentration of quantities held by agents and shed new light on the interplay between statistics and communication in decentralised methods. Bounds are given in the standard non-parametric setting with source/capacity assumptions.
LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

In this paper, we propose a simple yet effective framework, named LightTrack, for online human pose tracking. Existing methods usually perform human detection, pose estimation and tracking in sequential stages, where pose tracking is regarded as an offline bipartite matching problem. Our proposed framework is designed to be generic, efficient and truly online for top-down approaches. For efficiency, Single-Person Pose Tracking (SPT) and Visual Object Tracking (VOT) are incorporated as a unified on-line functioning entity, easily implemented by a replaceable single-person pose estimator. To mitigate offline optimization costs, the framework also unifies SPT with online identity association and sheds first light upon bridging multi-person keypoint tracking with Multi-Target Object Tracking (MOT). Specifically, we propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a Re-ID module. In contrary to other Re-ID modules, we use a graphical representation of human joints for matching. The skeleton-based representation effectively captures human pose similarity and is computationally inexpensive. It is robust to sudden camera shifts that introduce human drifting. The proposed framework is general enough to fit other pose estimators and candidate matching mechanisms. Extensive experiments show that our method outperforms other online methods and is very competitive with offline state-of-the-art methods while maintaining higher frame rates. Code and models are publicly available at https://github.com/Guanghan/lighttrack.
A modified indirect model predictive control for modular multilevel converters

Modular multilevel converter (MMC) is among the promising topologies used in high-voltage/power purposes, especially for high-voltage direct-current (HVDC) transmission systems. In this paper, a modified model predictive control (MPC) strategy for an MMC-HVDC system is presented. In the proposed MPC strategy, output current, circulating current and capacitors voltage are controlled separately. In order to reduce the computational burden of the controller, the output current is controlled directly without any optimization. For suppression of the circulating current, capacitors voltage variations are employed to settle the converter arms voltages to their reference values. In addition, the submodules (SMs) capacitor voltages are balanced by using a novel voltage sorting algorithm. In this method, the conventional voltage sorting algorithm is combined with the MPC-based strategy to determine which SMs should be turned on. SM capacitors are sorted based on an expression including their predicted capacitor voltage and the transition between the present and next states. Employing the SM transition states makes it possible to control the converter switching frequency. Performance of the proposed control strategy is verified using MATLAB/Simulink environment. The obtained results confirm the effectiveness of the proposed strategy in confining the converter switching frequency and reducing the circulating current and the controller computational burden.
Student Groups as Tutors in Information Systems Education - Students Perspectives on Collaboration and Outcomes

The study explores the potential of the research-teaching nexus in a peer-tutoring setting. During the Fall semester of 2016, students in an Information Systems course worked collaboratively on domain topics, assigned to them by the teacher and created educational material for their fellow students. Students’ tutoring role was concluding with a class presentation and a discussion session in each course lecture. The study focuses on students’ perspectives in the collaborating groups and the audience and analyzes how learning strategies in self-regulation, peer learning, and help seeking affect students’ experiences during group work. Analysis of student activity revealed four distinct patterns of collaboration. Findings suggest that students that rely more on group members for help were less satisfied by the communication among them. However, students were in general satisfied with their collaboration, being able to adapt the activity to their needs. Similarly, the teacher and the audience (students attending the student-tutoring sessions) evaluated positively students’ performance as teachers.
A hybrid recommendation algorithm based on weighted stochastic block model

Hybrid recommendation usually combines collaborative filtering with content-based filtering to exploit merits of both techniques. It is widely accepted that hybrid filtering outperforms the single algorithm, thus it has been the new trend in electronic commerce these years. In this paper, we propose a novel hybrid recommendation system based on weighted stochastic block model (WSBM). Our algorithm not only makes full use of content-based and collaborative filtering recommendation to solve the cold-start problem but also improves the accuracy of recommendation by selecting the nearest neighbor with WSBM. The experiment result shows that our proposed approach has better prediction and classification accuracy than traditional hybrid recommendation.
Effect of reduced nutritional supply on the metabolic activity and survival of cariogenic bacteria in vitro

ABSTRACT Sealed cariogenic bacteria are deprived from dietary carbohydrate, but could be provided with nutrients by pulpal fluids, with adaptive strain-specific activities being possible. We investigated survival and metabolic activity of the cariogenic bacteria Streptococcus sobrinus, Actinomyces naeslundii and Lactobacillus rhamnosus in different carbohydrate-limited media without carbon source (CLM), or containing glucose (CLM-G), albumin (CLM-A), or α1-acid glycoprotein (CLM-AGP) in vitro. Bacterial metabolite concentrations (lactate, pyruvate, oxaloacetate, citrate, acetate, formate, ethanol, acetoin) after 20 and 4 hours incubation, and bacterial numbers (CFU) after 24 hours incubation were analyzed using multivariate-analysis-of-variance (MANOVA). The medium (p = 0.02/MANOVA), strain and incubation-time (both p < 0.001) had significant impact on metabolite concentrations. Bacteria secreted mainly lactate (80.3 µg/106 bacteria S. sobrinus) and acetate (54.5 µg/106 bacteria A. naeslundii). Nearly all metabolites were produced in higher concentrations in S. sobrinus than in A. naeslundii or L. rhamnosus (p < 0.05/HSD). Metabolite concentration was significantly higher in CLM-G than in other media for most metabolites (p < 0.05). L. rhamnosus showed significantly lower survival than S. sobrinus and A. naeslundii (p < 0.05/HSD) regardless of the media, while S. sobrinus and A. naeslundii showed medium-specific survival. Survival of carbon starvation was strain- and medium-specific. Sustained organic acid production was found for all strains and media.
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos

The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the best strategy for combining variations in these models along with domain specific face preprocessing techniques through extensive experimentation to obtain state-of-the-art performance on publicly available video-based facial manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. Evaluation is performed on the recently introduced FaceForensics++ dataset, improving the previous state-of-the-art by up to 4.55% in accuracy.