Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Southern Denmark Preoperative Hypoalgesia After Cold Pressor Test and Aerobic Exercise is Associated With Pain Relief 6 Months After Total Knee Replacement Vaegter,

Objectives: Chronic pain after total knee replacement (TKR) is not uncommon. Preoperative impaired conditioning pain modulation (CPM) has been used to predict chronic postoperative pain. Interestingly, exercises reduce pain sensitivity in patients with knee osteoarthritis. This pilot study investigated the association between exercise-induced hypoalgesia (EIH) and CPM on post-TKR pain relief. Methods: Before and six months post-TKR, 14 patients with chronic knee osteoarthritis performed the cold pressor test on the non-affected leg and two exercise conditions (bicycling and isometric knee extension), randomized and counterbalanced. Before and during the cold pressor test and after exercises test-stimuli were applied to extract the pain Copyright © 2016 Wolters Kluwer Health, Inc. Unauthorized reproduction of the article is prohibited. sensitivity difference: Computer-controlled cuff inflation on the affected lower leg until the subjects detected the cuff pain threshold (cPPT) and subsequently the pain tolerance threshold (cPTT). 2) Manual pressure pain thresholds (PPTs) at the legs, arm and shoulder. Clinical pain intensity (numerical rating scale) and psychological distress (questionnaires) were assessed. Results: Clinical pain intensity, psychological distress, cPPT and PPT at the affected leg improved post-TKR compared with pre-TKR (P<0.05). Preoperatively, the CPM and bicycling EIH assessed by the increase in cPTT correlated with reduction in NRS pain scores post-TKR (P<0.05). Improved CPM and EIH responses after TKR were significantly correlated with reduction in NRS pain scores post-TKR (P<0.05). Discussion: In knee osteoarthritis patients, hypoalgesia after cold pressor stimulation and aerobic exercise assessed preoperatively by cuff algometry was associated with pain relief six months after TKR. EIH as a novel preoperative screening tool should be further investigated in larger studies.
Do Random and Chaotic Sequences Really Cause Different PSO Performance? Further Results

Empirical results show that PSO performance may be different if using either chaotic or random sequences to drive the algorithm's search dynamics. We analyze the phenomenon by evaluating the performance based on a benchmark of test functions and comparing random and chaotic sequences according to equality or difference in underlying distribution or density. Our results show that the underlying distribution is the main influential factor in performance and thus the assumption of general and systematic performance differences between chaos and random appears not plausible.
Object-oriented mapping in dynamic environments

Grid maps, especially occupancy grid maps, are ubiquitous in many mobile robot applications. To simplify the process of learning the map, grid maps subdivide the world into a grid of cells, whose occupancies are independently estimated using only measurements in the perceptual field of the particular cell. However, the world consists of objects that span multiple cells, which means that measurements falling onto a cell provide evidence on the occupancy of other cells belonging to the same object. This correlation is not captured by current models. In this work, we present a way to generalize the update of grid maps relaxing the assumption of independence by modeling the relationship between the measurements and the occupancy of each cell as a set of latent variables, and jointly estimating those variables and the posterior of the map. Additionally, we propose a method to estimate the latent variables by clustering based on semantic labels and an extension to the Normal Distributions Transfer Occupancy Map (NDT-OM) to facilitate the proposed map update method. We perform comprehensive experiments of map creation and localization with real world data sets, and show that the proposed method creates better maps in highly dynamic environments compared to state-of-the-art methods. Finally, we demonstrate the ability of the proposed method to remove occluded objects from the map in a lifelong map update scenario.
Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet

Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discrete actions while neglecting the dynamic constraints of the robots and unknown behaviors of the targets. This work proposes an online task and motion coordination algorithm that ensures an explicitly-bounded estimation uncertainty for the target states, while minimizing the average number of active robots. The robots have a limited-range perception to actively track a limited number of targets simultaneously, of which their future control decisions are all unknown. It includes: (i) the assignment of monitoring tasks, modeled as a flexible size multiple vehicle routing problem with time windows (m-MVRPTW), given the predicted target trajectories with uncertainty measure in the road-networks; (ii) the nonlinear model predictive control (NMPC) for optimizing the robot trajectories under uncertainty and safety constraints. It is shown that the robots can switch between active and inactive roles dynamically online as required by the unknown monitoring task. The proposed methods are validated via large-scale simulations of up to $100$ robots and targets.
The Indefinite Proximal Gradient Method

We introduce a variant of the proximal gradient method in which the quadratic term is diagonal but may be indefinite, and is safeguarded by a trust region. Our method is a special case of the proximal quasi-Newton trust-region method of arXiv:2103.15993v3. We provide closed-form solution of the step computation in certain cases where the nonsmooth term is separable and the trust region is defined in infinity norm, so that no iterative subproblem solver is required. Our analysis expands upon that of arXiv:2103.15993v3 by generalizing the trust-region approach to problems with bound constraints. We provide an efficient open-source implementation of our method, named TRDH, in the Julia language in which Hessians approximations are given by diagonal quasi-Newton updates. TRDH evaluates one standard proximal operator and one indefinite proximal operator per iteration. We also analyze and implement a variant named iTRDH that performs a single indefinite proximal operator evaluation per iteration. We establish that iTRDH enjoys the same asymptotic worst-case iteration complexity as TRDH. We report numerical experience on unconstrained and bound-constrained problems, where TRDH and iTRDH are used both as standalone and subproblem solvers. Our results illustrate that, as standalone solvers, TRDH and iTRDH improve upon the quadratic regularization method R2 of arXiv:2103.15993v3 but also sometimes upon their quasi-Newton trust-region method, referred to here as TR-R2, in terms of smooth objective value and gradient evaluations. On challenging nonnegative matrix factorization, binary classification and data fitting problems, TRDH and iTRDH used as subproblem solvers inside TR improve upon TR-R2 for at least one choice of diagonal approximation.
Norm Growth, Non-uniqueness, and Anomalous Dissipation in Passive Scalars

We construct a divergence-free velocity field $u:[0,T] \times \mathbb{T}^2 \to \mathbb{R}^2$ satisfying $$u \in C^\infty([0,T];C^\alpha(\mathbb{T}^2)) \quad \forall \alpha \in [0,1)$$ such that the corresponding drift-diffusion equation exhibits anomalous dissipation for every smooth initial data. We also show that, given any $\alpha_0<1$, the flow can be modified such that it is uniformly bounded only in $C^{\alpha_0}(\mathbb{T}^2)$ and the regularity of solutions satisfy sharp (time-integrated) bounds predicted by the Obukhov-Corrsin theory. The proof is based on a general principle implying $H^1$ growth for all solutions to the transport equation, which may be of independent interest.
A Microdata Approach for Germany

This paper investigates empirically the consumer demand of environmentally relevant goods for Germany, as well as their relationship to the demand for leisure. Higher prices for energy goods like gas, electricity or fuel oil due to higher indirect taxation amongst others may have serious welfare and distributional effects for households. Also, there is very little evidence of the labor market implications of environmental taxation, as there is e.g. no quantification of labor supply effects, respectively leisure demand effects for Germany. Using a demand system to estimate the price, cross-price and income effects of the goods mobility, electricity, heating and leisure from microdata, there will also be accounted for the extensive demand for leisure, which is the not negligible labor market participation. Additionally, the extensive and intensive leisure demand is combined to total leisure demand elasticities, which can then be used for welfare and behavior analyses.
Quadcopter Trajectory Time Minimization and Robust Collision Avoidance via Optimal Time Allocation

Autonomous navigation requires robots to generate trajectories for collision avoidance efficiently. Although plenty of previous works have proven successful in generating smooth and spatially collision-free trajectories, their solutions often suffer from suboptimal time efficiency and potential unsafety, particularly when accounting for uncertainties in robot perception and control. To address this issue, this paper presents the Robust Optimal Time Allocation (ROTA) framework. This framework is designed to optimize the time progress of the trajectories temporally, serving as a post-processing tool to enhance trajectory time efficiency and safety under uncertainties. In this study, we begin by formulating a non-convex optimization problem aimed at minimizing trajectory execution time while incorporating constraints on collision probability as the robot approaches obstacles. Subsequently, we introduce the concept of the trajectory braking zone and adopt the chance-constrained formulation for robust collision avoidance in the braking zones. Finally, the non-convex optimization problem is reformulated into a second-order cone programming problem to achieve real-time performance. Through simulations and physical flight experiments, we demonstrate that the proposed approach effectively reduces trajectory execution time while enabling robust collision avoidance in complex environments.
Breaking down the relationship between academic impact and scientific disruption

We examine the tension between academic impact - the volume of citations received by publications - and scientific disruption. Intuitively, one would expect disruptive scientific work to be rewarded by high volumes of citations and, symmetrically, impactful work to also be disruptive. A number of recent studies have instead shown that such intuition is often at odds with reality. In this paper, we break down the relationship between impact and disruption with a detailed correlation analysis in two large data sets of publications in Computer Science and Physics. We find that highly disruptive papers tend to be cited at higher rates than average. Contrastingly, the opposite is not true, as we do not find highly impactful papers to be particularly disruptive. Notably, these results qualitatively hold even within individual scientific careers, as we find that - on average - an author's most disruptive work tends to be well cited, whereas their most cited work does not tend to be disruptive. We discuss the implications of our findings in the context of academic evaluation systems, and show how they can contribute to reconcile seemingly contradictory results in the literature.
Detection of Weak Organic Anion – Transporting Polypeptide 1B Inhibition by Probenecid with Plasma-Based Coproporphyrin in Humans s

Probenecid (PROB) is a clinical probe inhibitor of renal organic anion transporter (OAT) 1 and OAT3 that inhibits in vitro activity of hepatic drug transporters OATP1B1 and OATP1B3. It was hypothesized that PROB could potentially affect the disposition of OATP1B drug substrates. The plasma levels of the OATP1B endogenous biomarker candidates,including coproporphyrin I (CPI), CPIII, hexadecanedioate (HDA)
Towards Last-layer Retraining for Group Robustness with Fewer Annotations

Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations and poor generalization on minority groups. The recent deep feature reweighting (DFR) technique achieves state-of-the-art group robustness via simple last-layer retraining, but it requires held-out group and class annotations to construct a group-balanced reweighting dataset. In this work, we examine this impractical requirement and find that last-layer retraining can be surprisingly effective with no group annotations (other than for model selection) and only a handful of class annotations. We first show that last-layer retraining can greatly improve worst-group accuracy even when the reweighting dataset has only a small proportion of worst-group data. This implies a"free lunch"where holding out a subset of training data to retrain the last layer can substantially outperform ERM on the entire dataset with no additional data or annotations. To further improve group robustness, we introduce a lightweight method called selective last-layer finetuning (SELF), which constructs the reweighting dataset using misclassifications or disagreements. Our empirical and theoretical results present the first evidence that model disagreement upsamples worst-group data, enabling SELF to nearly match DFR on four well-established benchmarks across vision and language tasks with no group annotations and less than 3% of the held-out class annotations. Our code is available at https://github.com/tmlabonte/last-layer-retraining.
Neural Machine Translation Models Can Learn to be Few-shot Learners

The emergent ability of Large Language Models to use a small number of examples to learn to perform in novel domains and tasks, also called in-context learning (ICL). In this work, we show that a much smaller model can be trained to perform ICL by fine-tuning towards a specialized training objective, exemplified on the task of domain adaptation for neural machine translation. With this capacity for ICL, the model can take advantage of relevant few-shot examples to adapt its output towards the domain. We compare the quality of this domain adaptation to traditional supervised techniques and ICL with a 40B-parameter Large Language Model. Our approach allows efficient batch inference on a mix of domains and outperforms state-of-the-art baselines in terms of both translation quality and immediate adaptation rate, i.e. the ability to reproduce a specific term after being shown a single example.
PRIEST: Projection Guided Sampling-Based Optimization for Autonomous Navigation

Efficient navigation in unknown and dynamic environments is crucial for expanding the application domain of mobile robots. The core challenge stems from the non-availability of a feasible global path for guiding optimization-based local planners. As a result, existing local planners often get trapped in poor local minima. In this letter, we present a novel optimizer that can explore multiple homotopies to plan high-quality trajectories over long horizons while still being fast enough for real-time applications. We build on the gradient-free paradigm by augmenting the trajectory sampling strategy with a projection optimization that guides the samples toward a feasible region. As a result, our method can recover from the frequently encountered pathological cases wherein all the sampled trajectories lie in the high-cost region. We push the state-of-the-art (SOTA) in the following respects. Over the navigation stack of the Robot Operating System (ROS), we show an improvement of 7–13% in success rate and up to two times in total travel time metric. On the same benchmarks and metrics, our approach achieves up to 44% improvement over model predictive path integral (MPPI) and its recent variants. On simple point-to-point navigation tasks, our optimizer is up to two times more reliable than SOTA gradient-based solvers, as well as sampling-based approaches such as the Cross-Entropy Method (CEM) and VPSTO.
Soil moisture modelling using TWI and satellite imagery in the Stockholm region

Soil moisture is an important element in hydrological land-surface processes as well as land atmosphere interactions and has proven useful in numerous agronomical, climatological and meteorological studies. Since hydrological soil moisture estimates are usually point-based measurements at a specific site and time, spatial and temporal dynamics of soil moisture are difficult to capture. Soil moisture retrieval techniques in remote sensing present possibilities to overcome the abovementioned limitations by continuously providing distributed soil moisture data atdifferent scales and varying temporal resolutions. The main purpose of this study is to derive soil moisture estimates for the Stockholm region by means of two different approaches from a hydrological and a remote sensing point of view and the comparison of both methods. Soil moisture is both modelled with the Topographic Wetness Index (TWI) based on digital elevation data and with the Temperature‐Vegetation Dryness Index (TVDI) as a representation of land surface temperature and Normalized Difference Vegetation Index (NDVI) ratio. Correlations of both index distributions are investigated. Possible index dependencies onvegetation cover and underlying soil types are explored. Field measurements of soil moistureare related to the derived indices. The results indicate that according to a very low Pearson correlation coefficient of 0.023, nolinear dependency between the two indices existed. Index classification in low, medium and high value categories did not result in higher correlations. Neither index distribution is found to berelated to soil types and only the TVDI correlates alongside changes in vegetation cover distribution. In situ measured values correlate better with TVDIs, although neither index is considered to give superior results in the area due to low correlation coefficients. The decision which index to apply is dependent on available data, intent of usage and scale. The TWI surface is considered to be a more suitable soil moisture representation for analyses on smaller scaleswhereas the TVDI should prove more valuable on a larger, regional scale. The lack of correlation between the indices is attributed to the fact that they differ greatly in their underlying theories. However, the synthesis of hydrologic modelling and remote sensing is a promising field of research. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological predictions.
Fluorodeoxyglucose Positron-Emission Tomography-CT Imaging of Carotidynia

R.R. Amaravadi S.C. Behr P.D. Kousoubris S. Raja SUMMARY: Carotidynia is an idiopathic syndrome characterized by pain over the carotid bifurcation without associated luminal pathologic process. The classification of carotidynia as a distinct disease rather than as a symptom has generated controversy in the literature. Recent reports, however, suggest that carotidynia is a distinct disease characterized by the presence of enhancing soft tissue in the carotid sheath. We describe findings of carotidynia on positron-emission tomography and CT that further support the classification of carotidynia as a distinct inflammatory disease.
Misconceptions about quantifying animal encounter and interaction processes

The ability to quantify when and where animals interact is key to the understanding of a plethora of ecological processes, from the structure of social communities and predator–prey relations to the spreading of pathogens and information. Despite the ubiquity of interaction processes among animals and the revolution in tracking technologies that now allows for the monitoring of multiple individuals simultaneously, a common theoretical framework with which to analyze movement data and extract interaction events is still lacking. Given the wide spectrum of mechanisms that governs how a biological organism detects the proximity of other organisms, most of the proposed theoretical approaches have been tailored to specific species or empirical situations and so far have been lacking a common currency with which to evaluate and compare findings across taxa. Here, we propose such general framework by borrowing techniques from statistical physics, specifically from the theory of reaction diffusion processes. Some of these techniques have already been employed to predict analytically pathogen transmission events between pairs of animals living within home ranges, but have not yet pervaded the movement ecology literature. Using both continuous and discrete variables, we present the mathematical framework and demonstrate its suitability to study interaction processes. By defining interactions whenever a token of information is transferred from one individual to another, we show that the probability of transferring information for the first time is equivalent to the first-passage probability of reacting in a multi-target environment. As interaction events reduce to encounter events when information transfer is perfectly efficient, we compare our formalism to a recently proposed approach to study encounters. Such approach takes the joint occupation probability of two animals over a region of interaction as a measure of the probability of encounter, rather than the first-encounter probability. We show the discrepancy of the two approaches by analytically comparing their predictions with continuous variables, while with discrete space–time variables, we quantify their difference over time. We conclude by pointing to some of the open problems that the reaction diffusion formalism, alternatively, the reaction motion formalism, as it should be more appropriately called, might be able to tackle.
High Efficient of Ca/Al-Graphite for Removal of Direct Orange

The layered double hydroxide-based material features of Ca/Al have improved according to the adsorption capacity and structure stabilization by transforming into composite Ca/Al-graphite. The composite was synthesized by co-precipitation method, and the chemical structure was characterized using X-ray Diffraction (XRD), Fourier Transform Infra-red (FT-IR), Brunauer-Emmet-Teller (BET) Surface Area, and Thermo-Gravimetry Differential Analyse (TG-DTA). The XRD analysis of Ca/Al-Graphite composite was noticed in 10.205°(003), 18.083°(012), 20.45°(004), 26.532°(002), 44.52°(101), 54.52°(004), and 77.38°(006). The TG-DTA analysis of the composite was noticed at 100°C as water molecule decomposition, 270°C as nitrate decomposition, and 700 and 760°C as graphite decomposition to the oxide form. BET surface area analysis of Ca/Al-Graphite composite achieved the highest surface area at 16.795 m 2 /g. According to the kinetic parameter, the adsorption of direct orange to composite follows the pseudo-second-order model. The isotherm parameter of direct orange adsorption onto the composite followed the Langmuir model and occurred spontaneously and endothermically. The regeneration study proved the composite effective in 3 cycles by adsorption percentage at the third cycle reached 73.559%.