Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of ephedrine on intubating conditions following priming with rocuronium

Background: Onset of action of muscle relaxants is influenced by cardiac output and muscle blood flow. Ephedrine reduces the onset time of rocuronium. Onset is also shortened by priming. Accordingly, we hypothesized that priming combined with ephedrine is superior to either technique used separately.
Heavy Metal Pollution as a Biodiversity Threat

Heavy metals exert their toxic effects through different mechanisms. Lately, increasing attention has been focused on understanding the long-term ecological effects of chronically exposed populations and communities and their consequences to the ecosystem. The long-term exposure to heavy metals in the environment represents a threat to wild populations, affecting communities and putting ecosystem integrity at risk. Therefore, this type of exposure represents a threat to biodiversity. In the field, metal exposure is generally characterized by low doses and chronic exposures. This type of exposure exerts alterations across levels of biological organization. Distribution and abundance of populations, the community structure and the ecosystem dynamics may be altered. This chapter will focus on how chronically metal exposures in the field affect negatively populations and communities becoming a threat to biodiversity. Also, attention is put on the tools that enable to characterize and analyze the detrimental effects of heavy metal exposure on wild populations. Hence, the use and development of biomarkers in ecotoxi- cology will be discussed.
Feature-Cut: Video object segmentation through local feature correspondences

Accurately segmenting objects in video is a difficult and time consuming process in modern post-production houses. Automatic systems may work for a small number of frames, but will typically fail over longer video shots. This work proposes a semi-automatic, feature-based system to perform object segmentation over longer sequences. The user manually extracts masks from representative instances of the object, which are then propagated to the remaining unsegmented frames and used to bootstrap the automatic segmentation for these frames. The presented work dramatically reduces the manual workload required to segment a video sequence, allowing longer and more accurate object mattes.
Application-driven Educational Game to Assist Young Children in Learning English Vocabulary

This paper describes the development of an educational game, named My-Pet-Shop, to enhance young children’s learning of English vocabulary. The educational game is underpinned by an application-driven model, which consists of three components: application scenario, subject learning, and learning regulation. An empirical study is further conducted with 30 fourth-grade students to examine its influence on the aspects of performance, flow, self-regulation, and behavior related to learning. The results demonstrated that the system contributed to enhanced flow experience and better learning self-regulation when compared to using a quiz game system without the support of the application-driven model. In addition, behavior analysis revealed that the component of learning regulation played a critical role. Some implications about the application-driven model and its future development are also discussed.
Wine, flavonoids, and the "water of life".

In addition to its indirect effects on cognition, e.g., Wernicke-Korsakoff disease, cerebral trauma, and hepatic failure, ethanol is a direct neurotoxin.1 Heavy drinkers without evident nutritional deficiency demonstrate brain shrinkage and neuronal loss especially pronounced in frontal association cortex.2 Consistent with such damage, alcoholics without Wernicke-Korsakoff disease can have impaired working memory and executive function in the absence of clinical amnesia.3 A safe-dose threshold for such neurotoxicity has not been defined. Complicating the effects of ethanol on cognition are its double-edged cardiovascular effects. In most, but not all, cohort and case-control studies, low-to-moderate consumption of ethanol decreases the risk of ischemic stroke compared with nondrinking. Heavy consumption, however, increases stroke risk. Proposed mechanisms of protection include effects on lipoproteins, platelet function, fibrinogen, and, especially in red-wine drinkers, antioxidants. …
Membrane Contact Sites: Complex Zones for Membrane Association and Lipid Exchange

Lipid transport between membranes within cells involves vesicle and protein carriers, but as agents of nonvesicular lipid transfer, the role of membrane contact sites has received increasing attention. As zones for lipid metabolism and exchange, various membrane contact sites mediate direct associations between different organelles. In particular, membrane contact sites linking the plasma membrane (PM) and the endoplasmic reticulum (ER) represent important regulators of lipid and ion transfer. In yeast, cortical ER is stapled to the PM through membrane-tethering proteins, which establish a direct connection between the membranes. In this review, we consider passive and facilitated models for lipid transfer at PM–ER contact sites. Besides the tethering proteins, we examine the roles of an additional repertoire of lipid and protein regulators that prime and propagate PM–ER membrane association. We conclude that instead of being simple mediators of membrane association, regulatory components of membrane contact sites have complex and multilayered functions.
Water masses and mixing processes in the Southern Caribbean
upwelling system off Colombia

Abstract. The upwelling system off the southern Caribbean coast is probably the main nutrients source that support the biological productivity in this oligotrophic sea. The Subtropical Water Mass (SUW) that forms the subsurface salinity maximum in the Caribbean is the main source of upwelled waters in this system. Salinity and temperature vs. depth profiles from 4 oceanographic cruises in the western sector of the Caribbean upwelling system, showed that the salinity of upwelled SUW waters is ~ 0.2 g kg−1 lower than the SUW salinity in the central Caribbean and have a slight seasonal variation that agree with with the rainy/dry seasons in the region. Besides, the depth of these SUW waters in the continental slope (~ 100 m) is ~ 50 m shallower than the SUW depth in the rest of the Caribbean sea. The origin of these modified SUW waters was analyzed using the Mercator numerical model, which reproduces the main vertical characteristics of the subsurface salinity maximum. Modeled data showed that SUW waters upwelled off La Guajira peninsula come from the western Caribbean and arrive to the system transported by an intense Caribbean Coastal Undercurrent (CaCU, mean speed ~ 0.4 m s−1). The lower salinity observed in the upwelled SUW waters may be the result of intense vertical mixing processes with diluted surface waters in the Panama-Colombia gyro that could occur when the CaCU flows below this region before reaching the upwelling zones. The mixing processes in the SUW by double diffusion and mechanical turbulence driven by vertical shear of horizontal currents, were analyzed using the Turner angle and the Thorpe scale, respectively. Double diffusion by salt fingers was found between the SUW and the Central North Atlantic Waters (NACW) with diffusivity values ~ 10−5 m2 s−1. But the mechanical diffusivity was two orders of magnitude higher (10−3 m2 s−1.) than the double diffusivities in the entire water column, generating salt fluxes from the SUW towards surface and towards depth over 2 g kg−1 m d−1. Beyond modifying the salt content in coastal SUW waters, these mixing processes may also alter the nutrient content of upwelling waters, with effects still unknown to the upwelling ecosystem.
Statistical characterization of the large‐scale structure of the subauroral polarization stream

The subauroral polarization streams (SAPS) are latitudinally narrow regions of westward directed flows observed equatorward of the evening sector auroral oval. Previous studies have shown that SAPS generally occur during geomagnetically disturbed conditions and exhibit a strong dependence on geomagnetic activity. In this paper, we present the first comprehensive statistical study of SAPS using measurements from the U.S. midlatitude Super Dual Auroral Radar Network (SuperDARN) radars. The study period spans January 2011 to December 2014, and the results show that SuperDARN radars observe SAPS over a broad range of activity levels spanning storm time and nonstorm conditions. During relatively quiet conditions (−10 nT
Learning User Embeddings from Human Gaze for Personalised Saliency Prediction

Reusable embeddings of user behaviour have shown significant performance improvements for the personalised saliency prediction task. However, prior works require explicit user characteristics and preferences as input, which are often difficult to obtain. We present a novel method to extract user embeddings from pairs of natural images and corresponding saliency maps generated from a small amount of user-specific eye tracking data. At the core of our method is a Siamese convolutional neural encoder that learns the user embeddings by contrasting the image and personal saliency map pairs of different users. Evaluations on two public saliency datasets show that the generated embeddings have high discriminative power, are effective at refining universal saliency maps to the individual users, and generalise well across users and images. Finally, based on our model's ability to encode individual user characteristics, our work points towards other applications that can benefit from reusable embeddings of gaze behaviour.
Subgoal Diffuser: Coarse-to-fine Subgoal Generation to Guide Model Predictive Control for Robot Manipulation

Manipulation of articulated and deformable objects can be difficult due to their compliant and under-actuated nature. Unexpected disturbances can cause the object to deviate from a predicted state, making it necessary to use Model-Predictive Control (MPC) methods to plan motion. However, these methods need a short planning horizon to be practical. Thus, MPC is ill-suited for long-horizon manipulation tasks due to local minima. In this paper, we present a diffusion-based method that guides an MPC method to accomplish long-horizon manipulation tasks by dynamically specifying sequences of subgoals for the MPC to follow. Our method, called Subgoal Diffuser, generates subgoals in a coarse-to-fine manner, producing sparse subgoals when the task is easily accomplished by MPC and more dense subgoals when the MPC method needs more guidance. The density of subgoals is determined dynamically based on a learned estimate of reachability, and subgoals are distributed to focus on challenging parts of the task. We evaluate our method on two robot manipulation tasks and find it improves the planning performance of an MPC method, and also outperforms prior diffusion-based methods.
Function Trees: Transparent Machine Learning

The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as interpreting and explaining corresponding model predictions. A method is presented for representing a general multivariate function as a tree of simpler functions. This tree exposes the global internal structure of the function by uncovering and describing the combined joint influences of subsets of its input variables. Given the inputs and corresponding function values, a function tree is constructed that can be used to rapidly identify and compute all of the function's main and interaction effects up to high order. Interaction effects involving up to four variables are graphically visualized.
Undulatory swimming in suspensions and networks of flexible filaments

Many biological fluids are composed of suspended polymers immersed in a viscous fluid. A prime example is mucus, where the polymers are also known to form a network. While the presence of this microstructure is linked with an overall non-Newtonian response of the fluid, swimming cells and microorganisms similar in size to the network pores and polymer filaments instead experience the heterogeneous nature of the environment, interacting directly with the polymers as obstacles as they swim. To characterise and understand locomotion in these heterogeneous environments, we simulate the motion of an undulatory swimmer through suspensions and networks of elastic filaments, exploring the effects of filament and link compliance and filament concentration up to 20\% volume fraction. For compliant environments, the swimming speed increases with filament concentration to values about 10\% higher than in a viscous fluid. In stiffer environments, a non-monotonic dependence is observed, with an initial increase in speed to values 5\% greater than in a viscous fluid, followed by a dramatic reduction to speeds just a fraction of its value in a viscous fluid. Velocity fluctuations are also more pronounced in stiffer environments. We demonstrate that speed enhancements are linked to hydrodynamic interactions with the microstructure, while reductions are due to the filaments restricting the amplitude of the swimmer's propulsive wave. Unlike previous studies where interactions with obstacles allowed for significant enhancements in swimming speeds, the modest enhancements seen here are more comparable to those given by models where the environment is treated as a continuous viscoelastic fluid.
JaxUED: A simple and useable UED library in Jax

We present JaxUED, an open-source library providing minimal dependency implementations of modern Unsupervised Environment Design (UED) algorithms in Jax. JaxUED leverages hardware acceleration to obtain on the order of 100x speedups compared to prior, CPU-based implementations. Inspired by CleanRL, we provide fast, clear, understandable, and easily modifiable implementations, with the aim of accelerating research into UED. This paper describes our library and contains baseline results. Code can be found at https://github.com/DramaCow/jaxued.
3D Semantic MapNet: Building Maps for Multi-Object Re-Identification in 3D

We study the task of 3D multi-object re-identification from embodied tours. Specifically, an agent is given two tours of an environment (e.g. an apartment) under two different layouts (e.g. arrangements of furniture). Its task is to detect and re-identify objects in 3D - e.g. a"sofa"moved from location A to B, a new"chair"in the second layout at location C, or a"lamp"from location D in the first layout missing in the second. To support this task, we create an automated infrastructure to generate paired egocentric tours of initial/modified layouts in the Habitat simulator using Matterport3D scenes, YCB and Google-scanned objects. We present 3D Semantic MapNet (3D-SMNet) - a two-stage re-identification model consisting of (1) a 3D object detector that operates on RGB-D videos with known pose, and (2) a differentiable object matching module that solves correspondence estimation between two sets of 3D bounding boxes. Overall, 3D-SMNet builds object-based maps of each layout and then uses a differentiable matcher to re-identify objects across the tours. After training 3D-SMNet on our generated episodes, we demonstrate zero-shot transfer to real-world rearrangement scenarios by instantiating our task in Replica, Active Vision, and RIO environments depicting rearrangements. On all datasets, we find 3D-SMNet outperforms competitive baselines. Further, we show jointly training on real and generated episodes can lead to significant improvements over training on real data alone.
SportsNGEN: Sustained Generation of Multi-player Sports Gameplay

We present a transformer decoder based model, SportsNGEN, that is trained on sports player and ball tracking sequences that is capable of generating realistic and sustained gameplay. We train and evaluate SportsNGEN on a large database of professional tennis tracking data and demonstrate that by combining the generated simulations with a shot classifier and logic to start and end rallies, the system is capable of simulating an entire tennis match. In addition, a generic version of SportsNGEN can be customized to a specific player by fine-tuning on match data that includes that player. We show that our model is well calibrated and can be used to derive insights for coaches and broadcasters by evaluating counterfactual or what if options. Finally, we show qualitative results indicating the same approach works for football.
CrowdWatch: Dynamic Sidewalk Obstacle Detection Using Mobile Crowd Sensing

Pedestrians distracted by smartphones are easy to meet with various dangers when crossing or walking on the street, such as the obstacles on the sidewalk (e.g., temporary parking and road repairing). Existing works about pedestrian safety are mostly based on the sensing capabilities from a single device. The surrounding information that can be learned, however, is quite limited or incomplete. Therefore, in many cases the dangers cannot be detected and the pedestrians cannot be alerted. In this paper, a novel system called CrowdWatch is proposed, which leverages mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted walkers. To detect obstacles, we first study the regular rules of pedestrians’ avoidance behaviors from the aspects of turn-making and visual contexts. The Dempster–Shafer evidence theory is then used to fuse the behavior and visual contexts, and further calculate the confidence of obstacle existence. Afterwards, we leverage the features of pedestrians’ traces to characterize an appropriate dangerous area, which is used to alert distracted walkers. The conducted experiments with 36 participants and different obstacle settings indicate that the crowd-intelligence-based obstacle detection method is effective and the accuracy of reminding attains 83.3%.
Reassessing NIMBY: The demographics, politics, and geography of opposition to high-density residential infill

ABSTRACT Planners often attempt to accommodate growth in already developed areas. Opposition to high-density (i.e., at a higher unit density than surrounding development) residential infill in developed areas is, however, a long-established force in land use politics. We hypothesize that opposition to this development, as well as 6 specific concerns with this development’s impacts, are likely associated with a variety of ideological, demographic, geographical, and political characteristics and that these associations can tell planners much about the character of this opposition. We use a web survey of verified voters in local elections (n = 772) in medium- to medium-high-density ZIP codes to find relationships between these characteristics and respondents’ feelings toward a hypothetical high-density residential infill development. Our findings expose the varied character of responses to this development: respondents of some characteristics—for example, those who strongly value their communities’ attractiveness, vitality, walkability, and bikability—are more likely to have specific concerns while remaining open to the development, indicating the possibility of constructive conversations with these groups. On the other hand, the concerns of other groups, notably those of conservatives, may result from unfamiliarity or even prejudice.
Stochastic Geometry Models for Texture Synthesis of Machined Metallic Surfaces: Sandblasting and Milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.
UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Control

Nonprehensile manipulation through precise pushing is an essential skill that has been commonly challenged by perception and physical uncertainties, such as those associated with contacts, object geometries, and physical properties. For this, we propose a unified framework that jointly addresses system modeling, action generation, and control. While most existing approaches either heavily rely on a priori system information for analytic modeling, or leverage a large dataset to learn dynamic models, our framework approximates a system transition function via non-parametric learning only using a small number of exploratory actions (ca. 10). The approximated function is then integrated with model predictive control to provide precise pushing manipulation. Furthermore, we show that the approximated system transition functions can be robustly transferred across novel objects while being online updated to continuously improve the manipulation accuracy. Through extensive experiments on a real robot platform with a set of novel objects and comparing against a state-of-the-art baseline, we show that the proposed unified framework is a light-weight and highly effective approach to enable precise pushing manipulation all by itself. Our evaluation results illustrate that the system can robustly ensure millimeter-level precision and can straightforwardly work on any novel object.
Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks

Robot arms should be able to learn new tasks. One framework here is reinforcement learning, where the robot is given a reward function that encodes the task, and the robot autonomously learns actions to maximize its reward. Existing approaches to reinforcement learning often frame this problem as a Markov decision process, and learn a policy (or a hierarchy of policies) to complete the task. These policies reason over hundreds of fine-grained actions that the robot arm needs to take: e.g., moving slightly to the right or rotating the end-effector a few degrees. But the manipulation tasks that we want robots to perform can often be broken down into a small number of high-level motions: e.g., reaching an object or turning a handle. In this paper we therefore propose a waypoint-based approach for model-free reinforcement learning. Instead of learning a low-level policy, the robot now learns a trajectory of waypoints, and then interpolates between those waypoints using existing controllers. Our key novelty is framing this waypoint-based setting as a sequence of multi-armed bandits: each bandit problem corresponds to one waypoint along the robot's motion. We theoretically show that an ideal solution to this reformulation has lower regret bounds than standard frameworks. We also introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Results across benchmark simulations and two real-world experiments suggest that this proposed approach learns new tasks more quickly than state-of-the-art baselines. See videos here: https://youtu.be/MMEd-lYfq4Y