Miracle-dz/UniFAR-FTUs huggingface dataset:
Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture

With the rise of herbicide-resistant weeds, standard farming practices are no longer effective. Precision agriculture technologies can replace some of the standard practices but require information on the physical distribution of weed and crop species. In this work we outline a robotic weed management system and develop the image analysis portion of this system to test it with real aerial survey data. This portion of the system acts as an integral part of a robotic control loop, providing information on weed location and species to a robotic sprayer that will precisely apply the correct herbicide. The image analysis pipeline we develop proves the feasibility of obtaining this information from high altitude aerial surveys of agricultural land that already commonly take place. Our system performs well enough to reliably detect the presence of weed vegetation and to correctly classify it by species with an accuracy of 93.8 % between four classes of weed in images taken at an effective altitude of 80 feet (24.4 m).
Indoor localization method based on the sound signal - an Experimental Setup

Indoor localization is a not too easy task for autonomous mobile robots, especially if a good precision is required. This task has the purpose to find another robot or to accomplish together complex tasks with one or more robots within the team. This paper presents an indoor localization method of a leader robot by the other robots in the team based on the sound signal emitted by it.
Dynamic Traffic Feature Camouflaging via Generative Adversarial Networks

Traffic analysis attacks, including website fingerprinting and protocol fingerprinting, are widely adopted by Internet censorship to block a specific type of traffic. To mitigate these attacks, some advanced approaches such as traffic morphing and protocol tunneling techniques have been proposed. However, the existing traffic morphing/protocol tunneling techniques suffer from showing a strong traffic pattern or can be uncovered with a low false positive. Further, they mainly rely on learning the pattern for specific traffic, which makes it highly possible to be identified due to a lack of dynamics. In this paper, we propose a dynamic traffic camouflaging technique, coined FlowGAN, to dynamically morph traffic feature as another “normal” network flow to bypass Internet censorship. The core idea of FlowGAN is to automatically learn the features of the “normal” network flow, and dynamically morph the on-going traffic flows based on the learned features by the adoption of the recently proposed Generative Adversarial Networks (GAN) model. To measure the indistinguishability of the target traffic and the morphed traffic, we introduce a novel concept of ∊-indistinguishability. We evaluate the proposed method on a dataset involving 10,000 realworld flows, and experimental results show that the effectiveness and the efficiency of FlowGAN regarding ∊-indistinguishability, AUC, and latency. To the best of our knowledge, our work is the first one to adopt GAN for automatic traffic generation and censor circumvention.
Travel Your Desk? An Office Desk Substitution and its Effects on Cybersickness, Presence and Performance in an HMD-based Exploratory Analysis Task

In this work, we evaluate the feasibility of an office desk substitution in the context of a visual data analysis task involving travel. We measure the impact on cybersickness as well as the general task performance and presence. In the conducted user study $(n=52)$, surprisingly, and partially in contradiction to existing work, we found no significant differences for those core measures between the control condition without a virtual table and the condition containing a virtual table.
RefNet: A Reference-aware Network for Background Based Conversation

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversation (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, however, they either cannot generate natural responses or have difficulties in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address both issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly select a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.
Tuning the topological features of quantum-dot hydrogen and helium by a magnetic field

The topological charge of the spin texture in a quantum dot with spin-orbit couplings is shown analytically here to be stable against the ellipticity of the dot. It is directly tunable by a single magnetic field and is related to the \textit{sign} of the Lande $g$ factor. In a quantum-dot helium, the overall winding number could have different property from that of the single-electron case (quantum-dot hydrogen), since tuning the number of electron affects the winding number by the Coulomb interaction and the $z$ component angular momentum $\langle L^{}_z \rangle$. The density profile and the spin texture influence each other when the Coulomb interaction is present. When $\langle L^{}_z \rangle$ is biased away from an integer by the spin-orbit couplings, the rotational symmetry is broken which induces strong density deformation. The sign of the topological charge may also be reversed with increasing magnetic field. These findings are of major significance since the applied magnetic field alone now provides a direct route to control the topological properties of quantum dots.
Equation Systems of Generalized Hydrodynamics for Soft-Matter Quasicrystals

The equation systems of generalized hydrodynamics, or generalized dynamics for simplicity, for soft-matter quasicrystals were established. Considering the fluidity of the matter with high order we introduced a new elementary excitation---fluid phonon to the quasicrystals, and the equation of state is necessary and introduced too. By considering other two elementary excitations---phonons and phasons, a theory---generalized hydrodynamics for soft-matter quasicrystals is set up, in which the governing equations of the dynamics are reported in this letter.
Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification

Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state and task-related fMRI. Methods: Locally Linear Embedding of BOLD time-series (into each voxel’s respective tensor) was used to optimise feature selection. This uses Gauß’ Principle of Least Constraint to conserve quantities over both space and time. This conservation was assessed using LOOCV to greedily select time points in an incremental fashion on training data that was categorised in terms of psychiatric diagnoses. Findings: The embedded fMRI gave highly diagnostic performances (> 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets. Interpretation: Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts.
Energy-Efficient Proactive Caching for Fog Computing with Correlated Task Arrivals

Ahstract- With the proliferation of latency-critical applications, fog-radio network (FRAN) has been envisioned as a paradigm shift enabling distributed deployment of cloud-clone facilities at the network edge. In this paper, we consider proactive caching for a one-user one-access point (AP) fog computing system over a finite time horizon, in which consecutive tasks of the same type of application are temporarily correlated. Under the assumption of predicable length of the task-input bits, we formulate a long-term weighted-sum energy minimization problem with three-slot correlation to jointly optimize computation offloading policies and caching decisions subject to stringent per-slot deadline constraints. The formulated problem is hard to solve due to the mixed-integer non-convexity. To tackle this challenge, first, we assume that task-related information are perfectly known a priori, and provide offline solution leveraging the technique of semi-definite relaxation (SDR), thereby serving as theoretical upper bound. Next, based on the offline solution, we propose a sliding-window based online algorithm under arbitrarily distributed prediction error. Finally, the advantage of computation caching as well the proposed algorithm is verified by numerical examples by comparison with several benchmarks.
Computing Estimators of Dantzig Selector type via Column and Constraint Generation

We consider a class of linear-programming based estimators in reconstructing a sparse signal from linear measurements. Specific formulations of the reconstruction problem considered here include Dantzig selector, basis pursuit (for the case in which the measurements contain no errors), and the fused Dantzig selector (for the case in which the underlying signal is piecewise constant). In spite of being estimators central to sparse signal processing and machine learning, solving these linear programming problems for large scale instances remains a challenging task, thereby limiting their usage in practice. We show that classic constraint- and column-generation techniques from large scale linear programming, when used in conjunction with a commercial implementation of the simplex method, and initialized with the solution from a closely-related Lasso formulation, yields solutions with high efficiency in many settings.
Charged Taub-NUT solution in Lovelock gravity with generalized Wheeler polynomials

Wheeler's approach to finding exact solutions in Lovelock gravity has been predominantly applied to static spacetimes. This has led to a Birkhoff's theorem for arbitrary base manifolds in dimensions higher than four. In this work, we generalize the method and apply it to a stationary metric. Using this perspective, we present a Taub-NUT solution in eight-dimensional Lovelock gravity coupled to Maxwell fields. We use the first-order formalism to integrate the equations of motion in the torsion-free sector. The Maxwell field is presented explicitly with general integration constants, while the background metric is given implicitly in terms of a cubic algebraic equation for the metric function. We display precisely how the NUT parameter generalizes Wheeler polynomials in a highly nontrivial manner.
Debunking Seven Myths about 5G New Radio

New radio (NR) is a new wireless access technology developed as part of the fifth-generation (5G) of mobile communications to support a wide range of services, devices, and deployments. NR features spectrum flexibility, ultra-lean design, forward compatibility, low latency support, and advanced antenna technologies. There has been excitement about NR, sometimes clouded by confusion. This article is an attempt to summarize and overview the key features of NR by debunking seven of the more popular myths and revealing what NR really is. The seven topics include spectrum, flexible waveform and multiple access, LTE-NR interworking and coexistence, low latency support, massive machine type communications, non-terrestrial communications, and beyond radio.
Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples

Recent evidence suggests that deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to legitimate examples. However, most of the existing adversarial attacks generate adversarial examples with weak transferability, making it difficult to evaluate the robustness of DNNs under the challenging black-box setting. To address this issue, we propose two methods: Nesterov momentum iterative fast gradient sign method (N-MI-FGSM) and scale-invariant attack method (SIM), to improve the transferability of adversarial examples. N-MI-FGSM tries a better optimizer by applying the idea of Nesterov accelerated gradient to gradient-based attack method. SIM leverages the scale-invariant property of DNNs and optimizes the generated adversarial example by a set of scaled images as the inputs. Further, the two methods can be naturally combined to form a strong attack and enhance existing gradient attack methods. Empirical results on ImageNet and NIPS 2017 adversarial competition show that the proposed methods can generate adversarial examples with higher transferability than existing competing baselines.
Neutrino fluence from gamma-ray bursts: off-axis view of structured jets

We investigate the expected high-energy neutrino fluence from internal shocks produced in the relativistic outflow of gamma-ray bursts. Previous model predictions have primarily focused on on-axis observations of uniform jets. Here, we present a generalization to account for arbitrary viewing angles and jet structures. Based on this formalism, we provide an improved scaling relation that expresses off-axis neutrino fluences in terms of on-axis model predictions. We also find that the neutrino fluence from structured jets can exhibit a strong angular dependence relative to that of gamma-rays and can be far more extended. We examine this behaviour in detail for the recent short gamma-ray burst GRB 170817A observed in coincidence with the gravitational wave event GW170817.
Development of Atlas, a flexible data structure framework

This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather & Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. In this deliverable report, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. Atlas provides data structures for building various numerical strategies to solve equations on the sphere or limited area's on the sphere. These data structures may contain a distribution of points (grid) and, possibly, a composition of elements (mesh), required to implement the numerical operations required. Atlas can also represent a given field within a specific spatial projection. Atlas is capable of mapping fields between different grids as part of pre- and post-processing stages or as part of coupling processes whose respective fields are discretised on different grids or meshes.
Multifunctional composites for elastic and electromagnetic wave propagation

Significance We establish accurate microstructure-dependent cross-property relations for composite materials that link effective elastic and electromagnetic wave characteristics to one another, including effective wave speeds and attenuation coefficients. Our microstructure-dependent formulas enable us to explore the multifunctional wave characteristics of a broad class of disordered microstructures, including exotic disordered “hyperuniform” varieties, that can have advantages over crystalline ones, such as nearly optimal, direction-independent properties and robustness against defects. Applications include filters that transmit or absorb elastic or electromagnetic waves “isotropically” for a range of wavelengths. Our findings enable one to design multifunctional composites via inverse techniques, including the exterior components of spacecraft or building materials, heat sinks for CPUs, sound-absorbing housings for motors, and nondestructive evaluation of materials. Composites are ideally suited to achieve desirable multifunctional effective properties since the best properties of different materials can be judiciously combined with designed microstructures. Here, we establish cross-property relations for two-phase composite media that link effective elastic and electromagnetic wave characteristics to one another, including the respective effective wave speeds and attenuation coefficients, which facilitate multifunctional material design. This is achieved by deriving accurate formulas for the effective electromagnetic and elastodynamic properties that depend on the wavelengths of the incident waves and the microstructure via the spectral density. Our formulas enable us to explore the wave characteristics of a broad class of disordered microstructures because they apply, unlike conventional formulas, to a wide range of incident wavelengths (i.e., well beyond the long-wavelength regime). This capability enables us to study the dynamic properties of exotic disordered “hyperuniform” composites that can have advantages over crystalline ones, such as nearly optimal, direction-independent properties and robustness against defects. We specifically show that disordered “stealthy” hyperuniform microstructures exhibit novel wave characteristics (e.g., low-pass filters that transmit waves “isotropically” up to a finite wavenumber). Our cross-property relations for the effective wave characteristics can be applied to design multifunctional composites via inverse techniques. Design examples include structural components that require high stiffness and electromagnetic absorption; heat sinks for central processing units and sound-absorbing housings for motors that have to efficiently emit thermal radiation and suppress mechanical vibrations; and nondestructive evaluation of the elastic moduli of materials from the effective dielectric response.
Threading-Induced Dynamical Transition in Chimeric Polymers

The relationship between polymer topology and bulk rheology remains a key question in soft matter physics. Architecture-specific constraints (or threadings) are thought to control the dynamics of circular polymers in ring-linear blends where the dynamics are enslaved by the reptative motion of the linear fraction. Here we consider qualitatively different systems of linear and circular polymers, fused together in "chimeric" architectures. The simplest example of this family is a "tadpole"-shaped polymer -- a single ring fused to the end of a single linear chain. We show that polymers with this architecture display a threading-induced dynamical transition that substantially slows chain relaxation. Our findings shed light on how threadings control dynamics and may inform design principles for chimeric polymers with topologically-tunable bulk rheological properties.
Greedy Algorithms for Hybrid Compressed Sensing

Compressed sensing (CS) is a technique which uses fewer measurements than dictated by the Nyquist sampling theorem. The traditional CS with linear measurements achieves effective recovery, but it suffers from large bit consumption due to the precision required by those measurements. Then, the one-bit CS with binary measurements is proposed to save the bit budget, but it is infeasible when the energy information of signals is not available as a prior knowledge. Subsequently, the hybrid CS which combines traditional CS and one-bit CS appears, striking a balance between the pros and cons of both types of CS. Given that one-bit CS is optimal for the direction estimation of signals under noise with a fixed bit budget and that traditional CS is able to provide residue information and estimated signals, we focus on the design of greedy algorithms, which consist of the main steps of support detection and recovered signal updates, for hybrid CS in this paper. We propose two greedy algorithms for hybrid CS, with traditional CS offering signal estimates and updated residues, which help one-bit CS detect the support iteratively. Then, we provide a theoretical analysis of the error bound between the normalized original signal and the normalized estimated signal. Numerical results demonstrate the efficacy of the proposed greedy algorithms for hybrid CS in noisy environments.
The fractional porous medium equation on manifolds with conical singularities II

This is the second of a series of two papers that studies the fractional porous medium equation, ∂tu+(−Δ)σ(|u|m−1u)=0$\partial _t u +(-\Delta )^\sigma (|u|^{m-1}u )=0$ with m>0$m>0$ and σ∈(0,1]$\sigma \in (0,1]$ , posed on a Riemannian manifold with isolated conical singularities. The first aim of the article is to derive some useful properties for the Mellin–Sobolev spaces including the Rellich–Kondrachov theorem and Sobolev–Poincaré, Nash and Super Poincaré type inequalities. The second part of the article is devoted to the study the Markovian extensions of the conical Laplacian operator and its fractional powers. Then based on the obtained results, we establish existence and uniqueness of a global strong solution for L∞−$L_\infty -$ initial data and all m>0$m>0$ . We further investigate a number of properties of the solutions, including comparison principle, Lp−$L_p-$ contraction and conservation of mass. Our approach is quite general and thus is applicable to a variety of similar problems on manifolds with more general singularities.
Smaller stellar disc scale lengths in rich environments

We investigate the dependence of stellar disc scale lengths on environment for a sample of Sloan Digital Sky Survey Data Release 7 galaxies with published photometric bulge-disc decompositions. We compare disc scale lengths at fixed bulge mass for galaxies in an isolated field environment to galaxies in X-ray rich and X-ray poor groups. At low bulge mass, stellar disc scale lengths in X-ray rich groups are smaller compared to discs in both X-ray poor groups and in isolated field environments. This decrease in disc scale length is largely independent of halo mass, though shows some dependence on group-centric distance. We also find that stellar disc scale lengths are smaller in X-ray rich environments for a subset of star-forming galaxies and for galaxies of different morphological types. We note that disc scale lengths of low mass galaxies are known to have large systematic uncertainties, however we focus on differences between samples with the same measurement biases. Our results show that stellar disc scale lengths depend on X-ray brightness, a tracer of IGM density, suggesting a role for hydrodynamic processes such as ram-pressure stripping and/or starvation.