Fast Motion Research Articles

Accuracy and Reproducibility of Lipari-Szabo Order Parameters From Molecular Dynamics.

The Lipari-Szabo generalized order parameter probes the picosecond to nanosecond time scale motions of a protein and is useful for rationalizing a multitude of biological processes such as protein recognition and ligand binding. Although these fast motions are an important and intrinsic property of proteins, it remains unclear what simulation conditions are most suitable to reproduce methyl symmetry axis side chain order parameter data (Saxis2) from molecular dynamics simulations. In this study, we show that, while Saxis2 tends to converge within tens of nanoseconds, it is essential to run 10 to 20 replicas starting from configurations close to the experimental structure to obtain the best agreement with experimental Saxis2 values. Additionally, in a comparison of force fields, AMBER ff14SB outperforms CHARMM36m in accurately capturing these fast time scale motions, and we suggest that the origin of this performance gap is likely attributed to differences in side chain torsional parametrization and not due to differences in the global protein conformations sampled by the force fields. This study provides insight into obtaining accurate and reproducible Saxis2 values from molecular simulations and underscores the necessity of using replica simulations to compute equilibrium properties.

Cardiac motion in patients with ventricular tachycardia: potential implications for the treatment of patients candidates to stereotactic arrhythmia radio-ablation (STAR)

Abstract Background Stereotactic arrhythmia radio-ablation (STAR) has proven to be a valid alternative treatment for refractory ventricular tachycardia (VT) in patients who are unsuitable to undergo standard catheter ablation (CA). It consists in the application of external beam radiotherapy in a single dose of 25 Gy to the target areas. There is increased research activity on better understanding the complex and fast motion of the heart and on reducing radiation toxicity. Purpose In the context of an in silico study to evaluate the feasibility of STAR with protons in patients with VT, the initial findings related to cardiac motion on the first 10 patients are here presented. Methods Prior to CA, each patient underwent ECG gated CT scans in expiratory breath-hold, with and without contrast and reconstructed at 30% (systole) and 80% (diastole) of the cardiac R-R cycle. Contouring of the ablation target as a clinical target volume (CTV) for proton treatment planning was performed based on a standard electrophysiological study with 3D eletroanatomical mapping. Two different treatment plans were created: the first with only the diastolic CTV as target (gated treatment), the second including both diastolic and systolic CTVs to create an Internal Target Volume (ITV) in the hypothesis of non-gated treatment. Robust optimization was used to create the Planning Target Volume (PTV). Results The target volumes used for treatment planning are given in Table 1. The median CTV was 10.90 (2.93-36.94) cm3 for diastole and 14.26 (1.73-36.31) cm3 for systole. These volumes are somewhat smaller than those reported in patients treated previously with STAR: this difference may be due to the fact that no respiratory motion was included in the target contour and that the study population includes many patients referred for ventricular ectopic beats without structural heart disease, where the ablation target is expected to be smaller as compared to patients with VT in structural heart disease. Despite a small median difference between the diastolic and systolic CTV of 0.53 (0.03-9.61) cm3, the ITV approach resulted in a median increase in volume compared to the largest of the two CTVs of 29 % (1%-44%). When considering the proton range uncertainty and potential errors in patient positioning (PTV), the target is this time enlarged by a factor 2.9 (1.9-3.9). This data indicates that both cardiac motion and uncertainties in patient positioning before treatment will have a large impact on the treatment volume thus affecting the amount of surrounding tissue exposed to radiation. Conclusion The ablation target contour at systole and diastole are of similar magnitude but their non-overlap results in a large increase in treated volume when radiation delivery is not gated for cardiac motion. Further analysis shall investigate on a case-by-case basis the potential reduction in risk of healthy tissue toxicity with cardiac-gated proton delivery.Table 1

Agile robotic fish based on direct drive of continuum body

Fish-like agile movements, such as fast swimming and rapid turning, are essential for biomimetic underwater robots but challenging to achieve simultaneously. We present a self-contained robotic fish capable of swimming at a speed of 6.3 body length per second and pivot turning at an angular speed of 1450° per second. These fast motions, comparable to real fish, are realized by directly oscillating a flexible body using an electromagnetic motor, eliminating the need for transmission parts and simplifying the structure. This direct-drive (DD) method improves mechanical robustness and generates fish-like deformations and subsequent rapid swimming. The Strouhal and swimming numbers of the robot match typical values observed in nature. Moreover, the observed frequency peaks in swimming are similar to computed values using a model, which guides the design of the robot. These results illustrate the DD method as a promising framework for the creation of highly versatile biomimetic underwater robots.

Efficient Multi-floor Indoor Mapping with A Versatile Rotating LiDAR System

Abstract. Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D environmental map. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. To this end, an efficient multi-floor indoor mapping system is proposed in this paper utilizing a versatile rotating LiDAR platform. In the front end, measurements from motor, IMU and LiDAR are tightly integrated to track the fast motion state of system, based on iterative Error State Kalman Filter (ESKF). Then linear and planar features are extracted from the point cloud map voxelized with adaptive resolutions. A sliding-window-based batch optimization is performed to simultaneously optimize the states of local frames with reference to the map consistency. In the experiments, we investigated the influence of rotating speed on the mapping performance as well as the superiority of rotating mechanism when compared to standard LiDAR setup. Moreover, comparative studies with one of the SOTA work, FAST-LIO2, have shown the competitive mapping results in multi-floor indoor environments.

Observation of super-Alfvénic slippage of reconnecting magnetic field lines on the Sun

AbstractSlipping motions of magnetic field lines are a distinct signature of three-dimensional magnetic reconnection, a fundamental process driving solar and stellar flares. While being a key prediction of numerical experiments, the rapid super-Alfvénic field line slippage driven by the ‘slip-running’ reconnection has remained elusive in previous observations. New frontiers into exploring transient flare phenomena were introduced by recently designed high cadence observing programs of the Interface Region Imaging Spectrograph (IRIS). By exploiting high temporal resolution imagery (~2 s) of IRIS, here we reveal slipping motions of flare kernels at speeds reaching thousands of kilometres per second. The fast kernel motions are direct evidence of slip-running reconnection in quasi-separatrix layers, regions where magnetic field strongly changes its connectivity. Our results provide observational proof of theoretical predictions unaddressed for nearly two decades and extend the range of magnetic field configurations where reconnection-related phenomena can occur.

Development of Dual-Arm Human Companion Robots That Can Dance.

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human-robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.

Search region updating with hierarchical feature fusion for accurate thermal infrared tracking

Due to their resilience against lighting variations, thermal infrared (TIR) images demonstrate robust adaptability in diverse environments, enabling effective object tracking even in intricate scenarios. Nevertheless, TIR target tracking encounters challenges such as fast target motion and interference from visually similar objects, substantially compromising the tracking precision of TIR trackers. To surmount these challenges, we propose a method grounded in the strategy of search region updating and hierarchical feature fusion, tailored for the precise TIR target-tracking task. Specifically, to address the issue of fast motion causing the target to depart from the search region, we propose to update the current search region by leveraging historical frame information. Additionally, we employ a hierarchical feature fusion strategy to contend with interference from visually similar objects in the tracking scenario. This strategy enhances the ability to model and represent the target more accurately, thereby elevating the tracker’s capacity to discriminate between the target and similar objects. Furthermore, to tackle the challenge of inaccurate estimation of target bounding boxes, we introduce an enhanced Intersection over Union (IoU) loss function, which improvement facilitates a more precise prediction of target bounding boxes, resulting in superior target localization. Extensive experiments substantiate that our tracker exhibits a commendable level of competitiveness when compared to other trackers.

An adaptive learning based aberrance repressed multi-feature integrated correlation filter for Visual Object Tracking (VOT)

Target tracking via Correlation Filter (CF) is a hot research area of computer vision domain, and offers various credible benefits. Existing CF algorithms face challenges when there are target appearance variations due to background noise, scale and illumination changes, occlusion, and fast motion, which severely degrades the overall tracker performance. To get maximum benefits, an object tracker should perform well with the less computational burden in the presence of real time challenging situations. To address this issue, a novel visual object trackeris proposed based on multi feature fusion and adaptive learning technique with aberrance suppression. At first, multiple features i.e., Histogram of gradient (HOG), Color Naming (CN), saliency, and gray level intensities are combined using feature fusion technique. Further, based on the evaluation of final fused response map using Peak-to-Sidelobe Ratio (PSR), an adaptive learning strategy is integrated to improve the learning phase of tracker. Tracking results show that the proposed strategy beats the other modern CF trackers with Distance Precision (DP) scores of 88.2%, 85.9%, and 74.1% and 64.7% over OTB2013, OTB2015, and TempleColor128 and UAV123 datasets respectively.

The Evolution of the Acylation Mechanism in β-Lactamase and Rapid Protein Dynamics.

β-Lactamases are a class of well-studied enzymes that are known to have existed since billions of years ago, starting as a defense mechanism to stave off competitors and are now enzymes responsible for antibiotic resistance. Using ancestral sequence reconstruction, it is possible to study the crystal structure of a laboratory resurrected 2-3 billion year-old β-lactamase. Comparing the ancestral enzyme to its modern counterpart, a TEM-1 β-lactamase, the structural changes are minor, and it is probable that dynamic effects play an important role in the evolution of function. We used molecular dynamics simulations and employed transition path sampling methods to identify the presence of rate-enhancing dynamics at the femtosecond level in both systems, found that these fast motions are more efficiently coordinated in the modern enzyme, and examined how specific dynamics can pinpoint evolutionary effects that are essential for improving enzymatic catalysis.

Localization–delocalization transition for light particles in turbulence

Small bubbles in fluids rise to the surface due to Archimede's force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still an outstanding experimental and computational challenge due to their small scale, fast chaotic motion, and transient nature. Here we show that, under the influence of a modulated oscillatory forcing, the collective bubble behavior switches from a dynamically localized to a delocalized state. Additionally, we find that by varying the forcing frequency and amplitude, a remarkable resonant phenomenon between light particles and small-scale vortex filaments emerges, likening particle behavior to a forced damped oscillator. We discuss how these externally actuated bubbles can be used as a type of microscopic probe to investigate the space-time statistical properties of the smallest turbulence scales, allowing to quantitatively measure physical characteristics of vortex filaments. We develop a superposition model that is in excellent agreement with the simulation data of the particle dynamics which reveals the fraction of localized/delocalized particles as well as characteristics of the potential landscape induced by vortices in turbulence. Our approach paves the way for innovative ways to accurately measure turbulent properties and to the possibility to control light particles and bubble motions in turbulence with potential applications to oceanography, medical imaging, drug/gene delivery, chemical reactions, wastewater treatment, and industrial mixing.

Image of the solid-state rotary motion encoded in the dielectric response

The future development of advanced molecular systems with controlled rotation requires the development of an effective methodology for assessing the rotational performance of artificial machine components. We identified two patterns of the dielectric behavior for polar rotators in a static non-polar framework of sizable crystal showing relations between the spectral and molecular-level features of solid-state rotary motion. Various functionalization of phenylene rotors with a fluorine atom(s) changed rotational performance from high to low with rotational barriers ranging from 6.06 to 11.84 kcal mol-1. The meta-F-substitution favored rotator-rotator contacts allowing for the implementation of fast rotary motion. Contrary, the presence of rotator-stator contacts inhibited independent rotator dynamics leading to opposite spectral behavior in terms of temperature evolution of loss peak amplitude. Our observations, supported by an analysis based on an asymmetric double well-potential model, show that easily noticeable spectral differences encoded some molecular-level information important for the implementation of rotary motion.

Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception

Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks but are challenged by low-light or high dynamic range scenes, adverse weather, and fast motion. Novel sensors, such as event and thermal cameras, offer capabilities with the potential to address these scenarios, but they remain to be fully exploited. This paper introduces the Novel Sensors for Autonomous Vehicle Perception (NSAVP) dataset to facilitate future research on this topic. The dataset was captured with a platform including stereo event, thermal, monochrome, and RGB cameras as well as a high precision navigation system providing ground truth poses. The data was collected by repeatedly driving two ∼8 km routes and includes varied lighting conditions and opposing viewpoint perspectives. We provide benchmarking experiments on the task of place recognition to demonstrate challenges and opportunities for novel sensors to enhance critical AV perception tasks. To our knowledge, the NSAVP dataset is the first to include stereo thermal cameras together with stereo event and monochrome cameras. The dataset and supporting software suite is available at https://umautobots.github.io/nsavp .

EvHandPose: Event-Based 3D Hand Pose Estimation With Sparse Supervision.

Event camera shows great potential in 3D hand pose estimation, especially addressing the challenges of fast motion and high dynamic range in a low-power way. However, due to the asynchronous differential imaging mechanism, it is challenging to design event representation to encode hand motion information especially when the hands are not moving (causing motion ambiguity), and it is infeasible to fully annotate the temporally dense event stream. In this paper, we propose EvHandPose with novel hand flow representations in Event-to-Pose module for accurate hand pose estimation and alleviating the motion ambiguity issue. To solve the problem under sparse annotation, we design contrast maximization and hand-edge constraints in Pose-to-IWE (Image with Warped Events) module and formulate EvHandPose in a weakly-supervision framework. We further build EvRealHands, the first large-scale real-world event-based hand pose dataset on several challenging scenes to bridge the real-synthetic domain gap. Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based methods under all evaluation scenes, achieves accurate and stable hand pose estimation with high temporal resolution in fast motion and strong light scenes compared with RGB-based methods, generalizes well to outdoor scenes and another type of event camera, and shows the potential for the hand gesture recognition task.

Pitfalls in measurements of R1 relaxation rates of protein backbone 15N nuclei.

The dynamics of the backbone and side-chains of protein are routinely studied by interpreting experimentally determined 15N spin relaxation rates. R1(15N), the longitudinal relaxation rate, reports on fast motions and encodes, together with the transverse relaxation R2, structural information about the shape of the molecule and the orientation of the amide bond vectors in the internal diffusion frame. Determining error-free 15N longitudinal relaxation rates remains a challenge for small, disordered, and medium-sized proteins. Here, we show that mono-exponential fitting is sufficient, with no statistical preference for bi-exponential fitting up to 800MHz. A detailed comparison of the TROSY and HSQC techniques at medium and high fields showed no statistically significant differences. The least error-prone DD/CSA interference removal technique is the selective inversion of amide signals while avoiding water resonance. The exchange of amide with solvent deuterons appears to affect the rate R1 of solvent-exposed amides in all fields tested and in each DD/CSA interference removal technique in a statistically significant manner. In summary, the most accurate R1(15N) rates in proteins are achieved by selective amide inversion, without the addition of D2O. Importantly, at high magnetic fields stronger than 800MHz, when non-mono-exponential decay is involved, it is advisable to consider elimination of the shortest delays (typically up to 0.32s) or bi-exponential fitting.

Earth’s tectonic and plate boundary evolution over 1.8 billion years

Understanding the intricate relationships between the solid Earth and its surface systems in deep time necessitates comprehensive full-plate tectonic reconstructions that include evolving plate boundaries and oceanic plates. In particular, a tectonic reconstruction that spans multiple supercontinent cycles is important to understand the long-term evolution of Earth’s interior, surface environments and mineral resources. Here, we present a new full-plate tectonic reconstruction from 1.8 Ga to present that combines and refines three published models: one full-plate tectonic model spanning 1 Ga to present and two continental-drift models focused on the late Paleoproterozoic to Mesoproterozoic eras. Our model is constrained by geological and geophysical data, and presented as a relative plate motion model in a paleomagnetic reference frame. The model encompasses three supercontinents, Nuna (Columbia), Rodinia, and Gondwana/Pangea, and more than two complete supercontinent cycles, covering ∼40% of the Earth’s history. Our refinements to the base models are focused on times before 1.0 Ga, with minor changes for the Neoproterozoic. For times between 1.8 Ga and 1.0 Ga, the root mean square speeds for all plates generally range between 4 cm/yr and 7 cm/yr (despite short-term fast motion around 1.1 Ga), which are kinematically consistent with post-Pangean plate tectonic constraints. The time span of the existence of Nuna is updated to between 1.6 Ga (1.65 Ga in the base model) and 1.46 Ga based on geological and paleomagnetic data. We follow the base models to leave Amazonia/West Africa separate from Nuna (as well as Western Australia, which only collides with the remnants of Nuna after initial break-up), and South China/India separate from Rodinia. Contrary to the concept of a “boring billion”, our model reveals a dynamic geological history between 1.8 Ga and 0.8 Ga, characterized by supercontinent assembly and breakup, and continuous accretion events. The model is publicly accessible, providing a framework for future refinements and facilitating deep time studies of Earth’s system. We suggest that the model can serve as a valuable working hypothesis, laying the groundwork for future hypothesis testing.

Motion Artifact Correction for OCT Microvascular Images Based on Image Feature Matching.

Optical coherence tomography angiography (OCTA), a functional extension of optical coherence tomography (OCT), is widely employed for high-resolution imaging of microvascular networks. However, due to the relatively low scan rate of OCT, the artifacts caused by the involuntary bulk motion of tissues severely impact the visualization of microvascular networks. This study proposes a fast motion correction method based on image feature matching for OCT microvascular images. First, the rigid motion-related mismatch between B-scans is compensated through the image feature matching based on the improved oriented FAST and rotated BRIEF algorithm. Then, the axial motion within A-scan lines in each B-scan image is corrected according to the displacement deviation between the detected boundaries achieved by the Scharr operator in a non-rigid transformation manner. Finally, an optimized intensity-based Doppler variance algorithm is developed to enhance the robustness of the OCTA imaging. The experimental results demonstrate the effectiveness of the method.

Some Strong Limit Theorems in Averaging

The paper deals with the fast-slow motions setups in the discrete time Xε((n+1)ε)=Xε(nε)+εB(Xε(nε),ξ(n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$X^{\\varepsilon }((n+1){\\varepsilon })=X^{\\varepsilon }(n{\\varepsilon })+{\\varepsilon }B(X^{\\varepsilon }(n{\\varepsilon }),\\xi (n))$$\\end{document}, n=0,1,...,[T/ε]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=0,1,...,[T/{\\varepsilon }]$$\\end{document} and the continuous time dXε(t)dt=B(Xε(t),ξ(t/ε)),t∈[0,T]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{dX^{\\varepsilon }(t)}{dt}=B(X^{\\varepsilon }(t),\\xi (t/{\\varepsilon })),\\, t\\in [0,T]$$\\end{document} where B is a smooth in the first variable vector function and ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi $$\\end{document} is a sufficiently fast mixing stationary stochastic process. It is known since (Khasminskii in Theory Probab Appl 11:211–228, 1966) that if X¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\bar{X}}$$\\end{document} is the averaged motion then Gε=ε-1/2(Xε-X¯)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G^{\\varepsilon }={\\varepsilon }^{-1/2}(X^{\\varepsilon }-{\\bar{X}})$$\\end{document} weakly converges to a Gaussian process G. We will show that for each ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varepsilon }$$\\end{document} the processes ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi $$\\end{document} and G can be redefined on a sufficiently rich probability space without changing their distributions so that Esup0≤t≤T|Gε(t)-G(t)|2M=O(εδ),δ>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E\\sup _{0\\le t\\le T}|G^{\\varepsilon }(t)-G(t)|^{2\\,M} =O({\\varepsilon }^{{\\delta }}),\\,{\\delta }>0$$\\end{document} which gives also O(εδ/3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O({\\varepsilon }^{{\\delta }/3})$$\\end{document} Prokhorov distance estimate between the distributions of Gε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G^{\\varepsilon }$$\\end{document} and G. This provides also convergence estimates in the Kantorovich–Rubinstein (or Wasserstein) metrics. In the product case B(x,ξ)=Σ(x)ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B(x,\\xi )={\\Sigma }(x)\\xi $$\\end{document} we obtain also almost sure convergence estimates of the form sup0≤t≤T|Gε(t)-G(t)|=O(εδ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sup _{0\\le t\\le T}|G^{\\varepsilon }(t)-G(t)| =O({\\varepsilon }^{\\delta })$$\\end{document} a.s., as well as the Strassen’s form of the law of iterated logarithm for Gε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G^{\\varepsilon }$$\\end{document}. We note that our mixing assumptions are adapted to fast motions generated by important classes of dynamical systems.

Real-time monitoring of fast gas dynamics with a single-molecule resolution by frequency-comb-referenced plasmonic phase spectroscopy

Surface plasmon resonance (SPR) sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces, which makes them highly sensitive to biological or chemical molecular bindings to functional metallic surfaces. Metal nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit, thus strengthening photon-sample interactions. However, plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental noises. Here, we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase spectroscopy. The photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement effects. By utilizing a sharp plasmonic phase slope and a high heterodyne information carrier, a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole (in 10–11 RIU) in Allan deviation at 0.2 s; a faster motion up to 200 Hz was clearly resolved. This fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical, chemical, and physical events and interactions.

PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM

PurposeCommonly employed in polyp segmentation, single-image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages video-based deep learning, harnessing temporal information for superior segmentation performance with least parameter overhead, making it possibly suitable for edge devices.MethodsPolypNextLSTM employs a UNet-like structure with ConvNext-Tiny as its backbone, strategically omitting the last two layers to reduce parameter overhead. Our temporal fusion module, a Convolutional Long Short Term Memory (ConvLSTM), effectively exploits temporal features. Our primary novelty lies in PolypNextLSTM, which stands out as the leanest in parameters and the fastest model, surpassing the performance of five state-of-the-art image and video-based deep learning models. The evaluation of the SUN-SEG dataset spans easy-to-detect and hard-to-detect polyp scenarios, along with videos containing challenging artefacts like fast motion and occlusion.ResultsComparison against 5 image-based and 5 video-based models demonstrates PolypNextLSTM’s superiority, achieving a Dice score of 0.7898 on the hard-to-detect polyp test set, surpassing image-based PraNet (0.7519) and video-based PNS+ (0.7486). Notably, our model excels in videos featuring complex artefacts such as ghosting and occlusion.ConclusionPolypNextLSTM, integrating pruned ConvNext-Tiny with ConvLSTM for temporal fusion, not only exhibits superior segmentation performance but also maintains the highest frames per speed among evaluated models. Code can be found here: https://github.com/mtec-tuhh/PolypNextLSTM.