Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital AI-based Autonomous Refuelling Solution

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1% range-normalised and 1 deg, respectively. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure.
Investigation Plant Species Diversity and Physiographical Factors in Mountain Forest in North of Iran

Species diversity is one of the most important specifications of biological societies. Diversity of organisms, measurement of variety and examination of those hypotheses that are about reasons of diversity are such as affairs that have been desired by the ecologists for a long time. In this research, diversity of plant species in forest region, numbers of 60 sample plots in 256.00 square meters have been considered in random - systematic inventory was considered. In each sample plot, four micro-plots in 2.25 square meters in order to study on herbal cover, were executed that totally 240 micro-plots were considered. At each plot six diversity indices in relation to physiographic factors (slope, geographical aspect and altitude from the sea level) were studied. The results indicate that species diversity is more in the northern direction and also species diversity in slops less than 30% has the most amounts. Factor of altitude from the sea level did not have meaningful relation with species diversity. Through study on correlation of the numbers of species in sample plots with indices and also process and role of indices in different processors of analysis, Simpson's reciprocal index was suggested as suitable index in this type of studies.
A review of troubled cell indicators for discontinuous Galerkin method

In this paper, eight different troubled cell indicators (shock detectors) are reviewed for the solution of nonlinear hyperbolic conservation laws using discontinuous Galerkin (DG) method and a WENO limiter. Extensive simulations using one-dimensional and two-dimensional problems for various orders on the hyperbolic system of Euler equations are used to compare these troubled cell indicators. For one-dimensional problems, the performance of Fu and Shu indicator and the modified KXRCF indicator is better than other indicators. For two-dimensional problems, the performance of the artificial neural network (ANN) indicator of Ray and Hesthaven is quite good and the Fu and Shu and the modified KXRCF indicators are also good. These three indicators are suitable candidates for applications of DGM using WENO limiters though it should be noted that the ANN indicator is quite expensive and requires a lot of training.
SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset

We introduce SANPO, a large-scale egocentric video dataset focused on dense prediction in outdoor environments. It contains stereo video sessions collected across diverse outdoor environments, as well as rendered synthetic video sessions. (Synthetic data was provided by Parallel Domain.) All sessions have (dense) depth and odometry labels. All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels. To our knowledge, this is the first human egocentric video dataset with both large scale dense panoptic segmentation and depth annotations. In addition to the dataset we also provide zero-shot baselines and SANPO benchmarks for future research. We hope that the challenging nature of SANPO will help advance the state-of-the-art in video segmentation, depth estimation, multi-task visual modeling, and synthetic-to-real domain adaptation, while enabling human navigation systems. SANPO is available here: https://google-research-datasets.github. io/sanpo_dataset/
Uniqueness of an inverse electromagnetic coefficient problem with partial boundary data and its numerical resolution through an iterated sensitivity equation

In this paper we study an inverse boundary value problem for Maxwell's equations. The goal is to reconstruct perturbations in the refractive index of the medium inside an object from the knowledge of the tangential trace of an electric field on a part of the boundary of the domain. We first provide a uniqueness result for this inverse problem. Then, we propose a complete procedure to reconstruct numerically the perturbations, based on the minimization of a cost functional involving an iterated sensitivity equation.
A model ODE for the exponential asymptotics of nonlinear parasitic capillary ripples

In this work, we develop a linear model ODE to study the parasitic capillary ripples present on steep Stokes waves when a small amount of surface tension is included in the formulation. Our methodology builds upon the exponential asymptotic theory of Shelton&Trinh (J. Fluid Mech., vol. 939, 2022, A17), who demonstrated that these ripples occur beyond-all-orders of a small-surface-tension expansion. Our model equation, a linear ODE forced by solutions of the Stokes wave equation, forms a convenient tool to calculate numerical and asymptotic solutions. We show analytically that the parasitic capillary ripples that emerge in solutions to this linear model have the same asymptotic scaling and functional behaviour as those in the fully nonlinear problem. It is expected that this work will lead to the study of parasitic capillary ripples that occur in more general formulations involving viscosity or time-dependence.
Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets

Despite large advances in recent years, real-time capable motion planning for autonomous road vehicles remains a huge challenge. In this work, we present a decision module that is based on set-based reachability analysis: First, we identify all possible driving corridors by computing the reachable set for the longitudinal position of the vehicle along the lanelets of the road network, where lane changes are modeled as discrete events. Next, we select the best driving corridor based on a cost function that penalizes lane changes and deviations from a desired velocity profile. Finally, we generate a reference trajectory inside the selected driving corridor, which can be used to guide or warm start low-level trajectory planners. For the numerical evaluation we combine our decision module with a motion-primitive-based and an optimization-based planner and evaluate the performance on 2000 challenging CommonRoad traffic scenarios as well in the realistic CARLA simulator. The results demonstrate that our decision module is real-time capable and yields significant speed-ups compared to executing a motion planner standalone without a decision module.
Semi-Supervised Variational Inference over Nonlinear Channels

Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inference, for decoding unknown nonlinear channels. These methods, which include Monte Carlo expectation maximization and a variational autoencoder, make efficient use of few pilot symbols and the payload data. The best semi-supervised learning results are achieved with a variational autoencoder. For sufficiently many payload symbols, the variational autoencoder also has lower error rate compared to meta learning that uses the pilot data of the present as well as previous transmission blocks.
Carbon, oxygen and strontium isotopic systematics of Mediterranean white marbles used in the Antiquity

Many geological and petrographic systems have been utilized to determine the provenance of classical marble artefacts. Normally the first steps in sourcing a lithic artefact should be the macroscopic and thin section study with the petrographic microscope. Unfortunately, often to take sample, which is large enough to make thin section is not allowed from precious artefacts. A common procedure to analyse powder sample in various ways: The most widely used system today is that of stable δC and δO isotopic signatures. Previous studies showed that, unfortunately, many quarry fields overlap in values. Because of great advantage of isotopic ratio analysis, principally the need of only small samples and homogeneity over large areas, we decided to include the Sr/Sr isotopic ratios. Therefore we tested the δC, δO and Sr/Sr isotopic system, based on the data set entry in MissMarble measurement and information system of marble (Zoldfoldi et al. 2008), how fare the system characterises the provenance.
Achieving Autonomous Cloth Manipulation with Optimal Control via Differentiable Physics-Aware Regularization and Safety Constraints

Cloth manipulation is a category of deformable object manipulation of great interest to the robotics community, from applications of automated laundry-folding and home organizing and cleaning to textiles and flexible manufacturing. Despite the desire for automated cloth manipulation, the thin-shell dynamics and under-actuation nature of cloth present significant challenges for robots to effectively interact with them. Many recent works omit explicit modeling in favor of learning-based methods that may yield control policies directly. However, these methods require large training sets that must be collected and curated. In this regard, we create a framework for differentiable modeling of cloth dynamics leveraging an Extended Position-based Dynamics (XPBD) algorithm. Together with the desired control objective, physics-aware regularization terms are designed for better results, including trajectory smoothness and elastic potential energy. In addition, safety constraints, such as avoiding obstacles, can be specified using signed distance functions (SDFs). We formulate the cloth manipulation task with safety constraints as a constrained optimization problem, which can be effectively solved by mainstream gradient-based optimizers thanks to the end-to-end differentiability of our framework. Finally, we assess the proposed framework for manipulation tasks with various safety thresholds and demonstrate the feasibility of result trajectories on a surgical robot. The effects of the regularization terms are analyzed in an additional ablation study.
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors

Encoder-decoder networks become a popular choice for various medical image segmentation tasks. When they are trained with a standard loss function, these networks are not explicitly enforced to preserve the shape integrity of an object in an image. However, this ability of the network is important to obtain more accurate results, especially when there is a low-contrast difference between the object and its surroundings. In response to this issue, this work introduces a new shape-aware loss function, which we name FourierLoss. This loss function relies on quantifying the shape dissimilarity between the ground truth and the predicted segmentation maps through the Fourier descriptors calculated on their objects, and penalizing this dissimilarity in network training. Different than the previous studies, FourierLoss offers an adaptive loss function with trainable hyperparameters that control the importance of the level of the shape details that the network is enforced to learn in the training process. This control is achieved by the proposed adaptive loss update mechanism, which end-to-end learns the hyperparameters simultaneously with the network weights by backpropagation. As a result of using this mechanism, the network can dynamically change its attention from learning the general outline of an object to learning the details of its contour points, or vice versa, in different training epochs. Working on 2879 computed tomography images of 93 subjects, our experiments revealed that the proposed adaptive shape-aware loss function led to statistically significantly better results for liver segmentation, compared to its counterparts.
Offline and Online Use of Interval and Set-Based Approaches for Control and State Estimation: A Review of Methodological Approaches and Their Application

Control and state estimation procedures need to be robust against imprecisely known parameters, uncertainty in initial conditions, and external disturbances. Interval methods and other set-based techniques form the basis for the implementation of powerful approaches that can be used to identify parameters of dynamic system models in the presence of the aforementioned types of uncertainty. Moreover, they are applicable to a verified feasibility and stability analysis of controllers and state estimators. In addition to these offline approaches for analysis, interval and set-based methods have also been developed in recent years, which allow to solve the associated design tasks and to implement reliable techniques that are applicable online. The latter approaches include set-based model-predictive control, online parameter adaptation techniques for nonlinear variable-structure and backstepping controllers, interval observers, and fault diagnosis techniques. This paper provides an overview of the methodological background and reviews numerous practical applications for which interval and other set-valued approaches have been employed successfully.
Spacecraft Floating Potential Measurements for the Wind Spacecraft

Analysis of 8,804,545 electron velocity distribution functions, observed by the Wind spacecraft near 1 au between 2005 January 1 and 2022 January 1, was performed to determine the spacecraft floating potential, ϕ sc. Wind was designed to be electrostatically clean, which helps keep the magnitude of ϕ sc small (i.e., ∼5–9 eV for nearly all intervals) and the potential distribution more uniform. We observed spectral enhancements of ϕ sc at frequencies corresponding to the inverse synodic Carrington rotation period with at least three harmonics. The two-dimensional histogram of ϕ sc versus time also shows at least two strong peaks, with a potential third, much weaker peak. These peaks vary in time, with the intensity correlated with solar maximum. Thus, the spectral peaks and histogram peaks are likely due to macroscopic phenomena like coronal mass ejections (solar cycle dependence) and stream interaction regions (Carrington rotation dependence). The values of ϕ sc are summarized herein and the resulting data set is discussed.
Output-Feedback Nonlinear Model Predictive Control with Iterative State- and Control-Dependent Coefficients

By optimizing the predicted performance over a receding horizon, model predictive control (MPC) provides the ability to enforce state and control constraints. The present paper considers an extension of MPC for nonlinear systems that can be written in pseudo-linear form with state- and control-dependent coefficients. The main innovation is to apply quadratic programming iteratively over the horizon, where the predicted state trajectory is updated based on the updated control sequence. Output-feedback control is facilitated by using the block-observable canonical form for linear, time-varying dynamics. This control technique is illustrated on various numerical examples, including the Kapitza pendulum with slider-crank actuation, the nonholonomic integrator, the electromagnetically controlled oscillator, and the triple integrator with control-magnitude saturation.
Hierarchical reinforcement learning with natural language subgoals

Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions. However, it has been challenging to implement in realistic or open-ended environments. A main challenge has been to find the right space of sub-goals over which to instantiate a hierarchy. We present a novel approach where we use data from humans solving these tasks to softly supervise the goal space for a set of long range tasks in a 3D embodied environment. In particular, we use unconstrained natural language to parameterize this space. This has two advantages: first, it is easy to generate this data from naive human participants; second, it is flexible enough to represent a vast range of sub-goals in human-relevant tasks. Our approach outperforms agents that clone expert behavior on these tasks, as well as HRL from scratch without this supervised sub-goal space. Our work presents a novel approach to combining human expert supervision with the benefits and flexibility of reinforcement learning.
Algorithmic Pluralism A Structural Approach Towards Equal Opportunity

While the idea of equal opportunity enjoys a broad consensus, many disagree about what it means for opportunities to be equal. The algorithmic fairnesscommunity oftenrelieson formal approaches to quantitatively determineifopportunities are allocated equally. A more structural approach put forth by Joseph Fishkin focuses on the wider network of decisions that determine which opportunities are allocated to whom. In this so-called opportunity structure, decision points represent bottlenecks that are often chained together so that the output of one decision is an input to the next.By evaluating the severity and legitimacy of thesebottlenecks, Fishkin oﬀers a qualitative framework to assess whetherequal opportunity is infringed upon in a structural way. Weadopt thisstructural viewpoint anduse itto reframemany interdisciplinarydiscussions about equal opportunity in systemsof algorithmic decision-making. Drawing on examples from education, healthcare, and criminal justice, we recommend prioritizing regulatory and design-based interventions that alleviate severe bottlenecks in order to help expand access to opportunities in a pluralistic way.
Early diagnosis of chronic kidney disease in patients with diabetes in France: multidisciplinary expert opinion, prevention value and practical recommendations

ABSTRACT Diabetes is the leading cause of end-stage kidney disease (ESKD), accounting for approximately 50% of patients starting dialysis. However, the management of these patients at the stage of chronic kidney disease (CKD) remains poor, with fragmented care pathways among healthcare professionals (HCPs). Diagnosis of CKD and most of its complications is based on laboratory evidence. This article provides an overview of critical laboratory evidence of CKD and their limitations, such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), Kidney Failure Risk Equation (KFRE), and serum potassium. eGFR is estimated using the CKD-EPI 2009 formula, more relevant in Europe, from the calibrated dosage of plasma creatinine. The estimation formula and the diagnostic thresholds have been the subject of recent controversies. Recent guidelines emphasized the combined equation using both creatinine and cystatin for improved estimation of GFR. UACR on a spot urine sample is a simple method that replaces the collection of 24-hour urine. Albuminuria is the preferred test because of increased sensitivity but proteinuria may be appropriate in some settings as an alternative or in addition to albuminuria testing. KFRE is a new tool to estimate the risk of progression to ESKD. This score is now well validated and may improve the nephrology referral strategy. Plasma or serum potassium is an important parameter to monitor in patients with CKD, especially those on renin-angiotensin-aldosterone system (RAAS) inhibitors or diuretics. Pre-analytical conditions are essential to exclude factitious hyperkalemia. The current concept is to correct hyperkalemia using pharmacological approaches, resins or diuretics to be able to maintain RAAS blockers at the recommended dose and discontinue them at last resort. This paper also suggests expert recommendations to optimize the healthcare pathway and the roles and interactions of the HCPs involved in managing CKD in patients with diabetes.
Exercise-induced hypohydration impairs 3 km treadmill-running performance in temperate conditions

ABSTRACT Research assessing exercise-induced hypohydration on running performance in a temperate environment is scarce. Given the weight-bearing nature of running, the negative effects of hypohydration might be offset by the weight-loss associated with a negative fluid balance. Therefore, this study investigated the effect of exercise-induced hypohydration on running performance in temperate conditions. Seventeen intermittent games players (age 22 ± 1 y; VO2peak 52.5 ± 4.1 mL∙kg−1∙min−1) completed preliminary and familiarisation trials, and two experimental trials consisting of 12 blocks of 6 min of running (65% VO2peak; preload) with 1 min passive rest in-between, followed by a 3 km time trial (TT). During the preload, subjects consumed minimal fluid (60 mL) to induce hypohydration (HYP) or water to replace 95% sweat losses (1622 ± 343 mL; EUH). Body mass loss (EUH −0.5 ± 0.3%; HYP −2.2 ± 0.4%; P < 0.001), and other changes indicative of hypohydration, including increased serum osmolality, heart rate, thirst sensation, and decreased plasma volume (P ≤ 0.022), were apparent in HYP by the end of the preload. TT performance was ~6% slower in HYP (EUH 900 ± 87 s; HYP 955 ± 110 s; P < 0.001). Exercise-induced hypohydration of ~2% body mass impaired 3 km running TT performance in a temperate environment.
Weight-independent effects of Roux-en-Y gastric bypass surgery on remission of nonalcoholic fatty liver disease in mice

Obesity is a driver of non‐alcoholic fatty liver disease (NAFLD), and interventions that decrease body weight, such as bariatric surgery and/or calorie restriction (CR), may serve as effective therapies. This study compared the effects of Roux‐en‐Y gastric bypass surgery (RYGB) and CR on hepatic function in mice with obesity and NAFLD.