Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial distribution of the aromatic and aliphatic carbonaceous nanograin features in the protoplanetary disk around HD 100546

Context. Carbonaceous nanograins are present at the surface of protoplanetary disks around Herbig Ae/Be stars, where most of the ultraviolet energy from the central star is dissipated. Efficiently coupled to the gas, they are unavoidable to understand the physics and chemistry of these disks. Furthermore, nanograins are able to trace the outer flaring parts of the disk and possibly the gaps from which the larger grains are missing. However, their evolution through the disks, from internal to external regions, is only poorly understood so far. Aims. Our aim is to examine the spatial distribution and evolution of the nanodust emission in the emblematic (pre-)transitional protoplanetary disk HD 100546. This disk shows many structures (annular gaps, rings, and spirals) and reveals very rich carbon nanodust spectroscopic signatures (aromatic, aliphatic) in a wide spatial range of the disk (~20−200 au). Methods. We analysed adaptive optics spectroscopic observations in the 3–4 μm range (angular resolution of ~0.1′′) and imaging and spectroscopic observations in the 8–12 μm range (angular resolution of ~0.3′′). The hyperspectral cube was decomposed into a sum of spatially coherent dust components using a Gaussian decomposition algorithm. We compared the data to model predictions using the heterogeneous dust evolution model for interstellar solids (THEMIS), which is integrated in the radiative transfer code POLARIS by calculating the thermal and stochastic heating of micro- and nanometre-sized dust grains for a given disk structure. Results. We find that the aromatic features at 3.3, 8.6, and 11.3 μm, and the aliphatic features between 3.4 and 3.5 μm are spatially extended; each band shows a specific morphology dependent on the local physical conditions. The aliphatic-to-aromatic band ratio, 3.4/3.3, increases with the distance from the star from ~0.2 (at 0.2′′ or 20 au) to ~0.45 (at 1′′ or 100 au), suggesting UV processing. In the 8–12 μm observed spectra, several features characteristic of aromatic particles and crystalline silicates are detected. Their relative contribution changes with the distance to the star. The model predicts that the features and adjacent continuum are due to different combinations of grain sub-populations, in most cases with a high dependence on the intensity of the UV field. The model reproduces the spatial emission profiles of the bands well, except for the inner 20-40 au, where the observed emission of the 3.3 and 3.4 μm bands is, unlike the predictions, flat and no longer increases with the UV field. Conclusions. With our approach that combines observational data in the near- to mid-IR and disk modelling, we deliver constraints on the spatial distribution of nano-dust particles as a function of the disk structure and radiation field.
Low-Complexity Design of Variable Bandedge Linear Phase FIR Filters With Sharp Transition Band

This paper presents a very low-complexity design of variable bandedge linear phase finite-impulse-response (FIR) filters with fixed sharp transition width. The idea is to first decompose the input signal into several channels in the frequency domain. The channel(s) involved with the transition band of the variable filter due to the variation of the bandedge is (are) shaped to produce the required transition band, and then summed up with the channels involved with the passband of the variable filter to produce the required frequency response. The proposed variable filter has extremely low complexity when the transition band is sharp, if compared with other techniques such as the Farrow structure. It is possible that the computational complexity of the variable filter is even lower than that of a corresponding fixed filter with the same transition width and ripple specifications implemented in its direct form.
UvA-DARE (Digital Academic Repository) Probabilistic Multileave Gradient Descent

Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm is able to effectively optimize linear ranking models solely from user interactions. We propose an extension of DBGD, called probabilistic multileave gradient descent (P-MGD) that builds on probabilistic multileave, a recently proposed highly sensitive and unbiased online evaluation method. We demonstrate that P-MGD signiﬁcantly outperforms state-of-the-art online learning to rank methods in terms of online performance, without sacriﬁcing ofﬂine performance and at greater learning speed.
Cyclic Composition operators on Segal-Bargmann space

Abstract We study the cyclic, supercyclic and hypercyclic properties of a composition operator Cϕ on the Segal-Bargmann space ℋ(ℰ), where ϕ(z) = Az + b, A is a bounded linear operator on ℰ, b ∈ ℰ with ||A|| ⩽ 1 and A*b belongs to the range of (I – A*A)½. Specifically, under some conditions on the symbol ϕ we show that if Cϕ is cyclic then A* is cyclic but the converse need not be true. We also show that if Cϕ* is cyclic then A is cyclic. Further we show that there is no supercyclic composition operator on the space ℋ(ℰ) for certain class of symbols ϕ.
ROS Based Visual Programming Tool for Mobile Robot Education and Applications

Visual programming languages (VPLs) provide coding without typing texts. VPL makes coding easy to programmers with automatically adding usually used some code structure. Beginners in coding have generally two main challenges; transforming ideas into logical expressions and syntax errors. Syntax errors are impossible with VPLs because of there is no forgotten parentheses and semicolons. VPLs provide to focus on algorithm for programmers. VPL is a new trend for educational robotic environments. In this study, Robot Operating System (ROS) compatible web based visual programming system has been developed for evarobot. ROS provides libraries and tools to help software developers create robot applications. It provides hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, and more. Blockly has been used as VPL for the study and to generate / use blocks (commucation, sensing etc.). Some applications were generated like teleoperation, SLAM and wander etc. In this system, communication between server and client is supported by rosbridge package. Web page connected to ROS which runs on server using roslibjs library. Rosbridge provides a JSON API to ROS functionality for non-ROS programs.
SUSFANS Deliverable document information

A lab experiment was conducted in France to evaluate the impact of different types of information on participants’ willingness-to-pay (WTP) and quantity choices for both beef burger meat and soy burger meat. Explanatory messages about the impact of beef and soy on health and environment were revealed to participants, before successive rounds of WTP determinations and quantity choices. Results show that the different WTP for beef are not statistically influenced by the successive rounds of messages. Conversely, messages significantly increase the WTP for soy meat, even if this effect is relatively weak. These explanatory messages lead to significant changes in the chosen quantities of beef and soy. A last round with the introduction of a high-quality beef leads to a statistically significant increase in the WTP for beef. Moreover, the selected quantities between beef and soy almost return to the initial quantities, namely the ones chosen before the revelation of messages. This reversal of chosen quantities towards more beef compared to soy underlines the participants’ sensitivity to beef quality, when meat substitutes are considered.
A ug 2 01 4 Local Privacy , Data Processing Inequalities , and Minimax Rates

Working under a model of privacy in which data remains private even from the statistician, we study the tradeoff between privacy guarantees and the utility of the resulting statistical estimators. We prove bounds on information-theoretic quantities, including mutual information and Kullback-Leibler divergence, that depend on the privacy guarantees. When combined with standard minimax techniques, including the Le Cam, Fano, and Assouad methods, these inequalities allow for a precise characterization of statistical rates under local privacy constraints. We provide a treatment of several canonical families of problems: mean estimation, parameter estimation in fixed-design regression, multinomial probability estimation, and nonparametric density estimation. For all of these families, we provide lower and upper bounds that match up to constant factors, and exhibit new (optimal) privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds.
Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint

Due to the boom in technical compute in the last few years, the world has seen massive advances in artificially intelligent systems solving diverse real-world problems. But a major roadblock in the ubiquitous acceptance of these models is their enormous computational complexity and memory footprint. Hence efficient architectures and training techniques are required for deployment on extremely low resource inference endpoints. This paper proposes an architecture for detection of alphabets in American Sign Language on an ARM Cortex-M7 microcontroller having just 496 KB of framebuffer RAM. Leveraging parameter quantization is a common technique that might cause varying drops in test accuracy. This paper proposes using interpolation as augmentation amongst other techniques as an efficient method of reducing this drop, which also helps the model generalize well to previously unseen noisy data. The proposed model is about 185 KB post-quantization and inference speed is 20 frames per second.
UvA-DARE ( Digital Academic Repository ) What Determines the Drop Size in Sprays ?

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Capacity Region of Two-users Weak Gaussian Interference Channel

Computing capacity of Gaussian Interference Channel (GIC) is complex since knowledge of input distributions is needed to find the mutual information terms in closed forms, which should be optimized over input distributions and associated resource allocation. The optimum solution may require dividing the available resources among several GIC (each called a "constituent region", hereafter) and apply time-sharing among them. The current article focuses on a single constituent region (meaning the constraints on resources are all satisfied with equality) for a 2-users weak GIC. It is shown that, by relying on a different, intuitively straightforward, interpretation of the underlying optimization problem, one can determine the encoding/decoding strategies in the process of computing the optimum solution. This is based on gradually moving along the boundary of the capacity region in infinitesimal steps, where the solution for the end point in each step is constructed and optimized relying on the solution at the step's starting point. This approach enables proving Gaussian distribution is optimum over the entire boundary, and also allows finding simple closed form solutions describing different parts of the capacity region. The solution for each constituent 2-users GIC coincides with the optimum solution to the Han Kobayashi (HK) system of constraints with i.i. d. (scalar) Gaussian inputs. Although the article is focused on 2-users weak GIC, the proof for optimality of Gaussian distribution is independent of the values of cross gains, and thereby is universally applicable to strong, mixed and Z interference channels, as well as to GIC with more than two users. In addition, the procedure for the construction of boundary is applicable for arbitrary cross gain values, by re-deriving various conditions that have been established assuming cross gains being less than one.
Building an Autonomous Lane Keeping Simulator Using Real-World Data and End-to-End Learning

Autonomous lane keeping is an important safety feature for intelligent vehicles. This paper presents a lane keeping simulator that is built with image projections of recorded data in conjunction with vehicle dynamics estimation. An end-to-end learning method using convolutional neural network (CNN) takes front-view camera data as input and produces the proper steering wheel angle to keep the vehicle in lane. A novel method of data augmentation is proposed using vehicle dynamic model and vehicle trajectory tracking, which can create additional training data as if a vehicle drives off-lane in any displacement and orientation. Experimental results demonstrate that the CNN model trained with the simulator can achieve higher accuracy for autonomous lane keeping and much lower failure rate. The simulator can serve as a platform for both training and evaluation of vision based autonomous driving algorithms. The experimental dataset is made available at http://computing.wpi. edu/dataset.html.
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness

Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed. While adversarial training (AT) is regarded as the most robust defense, it suffers from poor performance both on clean examples and under other types of attacks, e.g. attacks with larger perturbations. Meanwhile, regularizers that encourage uncertain outputs, such as entropy maximization (EntM) and label smoothing (LS) can maintain accuracy on clean examples and improve performance under weak attacks, yet their ability to defend against strong attacks is still in doubt. In this paper, we revisit uncertainty promotion regularizers, including EntM and LS, in the field of adversarial learning. We show that EntM and LS alone provide robustness only under small perturbations. Contrarily, we show that uncertainty promotion regularizers complement AT in a principled manner, consistently improving performance on both clean examples and under various attacks, especially attacks with large perturbations. We further analyze how uncertainty promotion regularizers enhance the performance of AT from the perspective of Jacobian matrices $\nabla_X f(X;\theta)$, and find out that EntM effectively shrinks the norm of Jacobian matrices and hence promotes robustness.
Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning

Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain gap between natural and medical images causes an inevitable performance bottleneck when applying to US image analysis. Our idea is to pretrain DNNs on US images directly to avoid this bottleneck. Due to the lack of annotated large-scale datasets of US images, we first construct a new large-scale US video-based image dataset named US-4, containing over 23,000 high-resolution images from four US video sub-datasets, where two sub-datasets are newly collected by our local experienced doctors. To make full use of this dataset, we then innovatively propose an US semi-supervised contrastive learning (USCL) method to effectively learn feature representations of US images, with a new sample pair generation (SPG) scheme to tackle the problem that US images extracted from videos have high similarities. Moreover, the USCL treats contrastive loss as a consistent regularization, which boosts the performance of pretrained backbones by combining the supervised loss in a mutually reinforcing way. Extensive experiments on down-stream tasks' fine-tuning show the superiority of our approach against ImageNet pretraining and pretraining using previous state-of-the-art semi-supervised learning approaches. In particular, our pretrained backbone gets fine-tuning accuracy of over 94%, which is 9% higher than 85% of the ImageNet pretrained model on the widely used POCUS dataset. The constructed US-4 dataset and source codes of this work will be made public.
Title Calderon preconditioner for the electric field integral equationwith layered medium Green ' s function

A Calderón preconditioner is developed for the analysis of electromagnetic scattering of perfect electrically conducting (PEC) objects embedded in a layered medium. The electric field integral equation (EFIE) is formulated with the kernel of layered medium Green’s function to account for the effects from the multilayered background. The Calderón projector is derived based on the general source-field relationship and the extinction theorem for inhomogeneous environment in electromagnetic theory. The Calderón identities can be naturally deduced based on this projector, which is then leveraged to precondition the EFIE with layered kernel. An alternative implementation is then proposed to make the implementation of the preconditioner as efficient as the one in free space. Different numerical examples are designed to show the performance of the preconditioner, where the objects are located in different positions with respect to the layered medium, or different types of excitation are adopted. It is shown that the proposed effective and robust preconditioner makes the EFIE system converge rapidly in all cases, independent of the discretization density.
Representation of 2D frame less visual space as a neural manifold and its information geometric interpretation

Representation of 2D frame less visual space as neural manifold and its modelling in the frame work of information geometry is presented. Origin of hyperbolic nature of the visual space is investigated using evidences from neuroscience. Based on the results we propose that the processing of spatial information, particularly estimation of distance, perceiving geometrical curves etc. in the human brain can be modeled in a parametric probability space endowed with Fisher-Rao metric. Compactness, convexity and differentiability of the space is analysed and found that they obey the axioms of G space, proposed by Busemann. Further it is shown that it can be considered as a homogeneous Riemannian space of constant negative curvature. It is therefore ensured that the space yields geodesics into it. Computer simulation of geodesics representing a number of visual phenomena and advocating the hyperbolic structure of visual space is carried out. Comparison of the simulated results with the published experimental data is presented.
Parameter: The Area of Microclimate Gradient Diurnal Dynamic for Characterization and Monitoring of Forest Ecosystem and Environment

Microclimate forests are usually described by the parameters: quantity of microclimate differences of interior-exterior, the depth of the effect of edge and gradient. These parameters can characterize ecosystem conditions but their quantities are often inconsistent and thus less valid for monitoring ecosystem and adjacent environmental changes.This paper introduces the concepts, methods, and the results of the application of the parameters: the area of microclimate gradiet diurnal dynamic which the advantage in: (1) characterize ecosystem conditions and their interactions with adjacent environments, (2) categorize transects (in forest ecosystems) based on ecosystem conditions and their interactions with adjacent environments, (3) monitoring the forest ecosystem changes (deforestration, natural damage etc), (4) determine the time of thermal equilibrium between forest and environment.
Total Knee Arthroplasty with a Lateral Centre of Rotation Design Retained Native Knee Joint Kinematics : A Cadaveric Study under Simulated Muscle Loads

Background: Since controversial results on the location of the centre of rotation in the native human knee joint exist, it is still unclear how the design of a total knee replacement should be in order to retain patients’ knee joint movement after Total knee Arthroplasty. Therefore, the aim of the present study was to determine individual native tibiofemoral joint kinematics as well as the centre of rotation and to investigate the influence of a total knee implant with a proposed lateral centre of rotation on the knee kinematics. Materials and methods: The rotational and translational tibiofemoral joint kinematics of 9 cadaveric knees were captured under simulated muscle loads between 10° and 60° of flexion in a closed kinematic chain experiment. The entire foot and ankle complex was kept intact in order to maintain more realistic experimental conditions. The individual kinematics of the native joint condition were studied and compared to those after Total Knee Arthoplatsy. Results: The rotational and translational kinematics showed quite a large variance, which can be explained with the individual anatomy. A medial centre of rotation was found in 8 specimens in the native joint, just one showed a lateral centre of rotation. After Total Knee Arthroplasty the centre of rotation changed to the lateral compartment in 5 specimens. Small differences were identified in the rotational kinematics between both experimental conditions, whereas the translation on both compartments and the centre of rotation showed greater changes. Conclusions: The investigated Total Knee Arthroplasty design had the ability to restore the individual joint kinematics, although the centre of rotation altered.
Clusterface: Joint Clustering and Classification for Set-Based Face Recognition

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios ‘on the wild’ or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.