搜索结果：bones

In this study, we propose the hypothesis that there is a significant difference in thermal cycling fatigue resistance between the bones of ectothermic and endothermic animals. We performed an experiment to test whether bones of endothermic animals, having potentially lost their ability to adapt to thermal cycling, exhibit reduced resistance to thermal fatigue compared to ectothermic animals, which may have retained this adaptive trait due to their environmental conditions. The change in stiffness were determined using shifts in the resonant peaks of the frequency spectrum obtained from Resonant Ultrasonic Spectroscopy (RUS). To achieve this, samples of compact (cortical) and spongy bone tissue were extracted and polished before undergoing a 29-day period of thermal cycling. The changes in the resonance frequencies were then observed. Changes in resonant frequencies imply corresponding changes in elastic constants. The primary findings indicated that bones from ectothermic species exhibited minimal changes in elastic properties compared to those from endothermic species, as evidenced by the smaller shifts in resonant peak magnitudes following thermal cycling.

Long and skinny molecular filaments running along Galactic spiral arms are known as "bones", since they make up the skeleton of the Milky Way. However, their origin is still an open question. Here, we compare spectral images of HI taken by FAST with archival CO and Herschel dust emission to investigate the conversion from HI to H$_2$ in two typical Galactic bones, CFG028.68-0.28 and CFG047.06+0.26. Sensitive FAST HI images and an improved methodology enabled us to extract HI narrow self-absorption (HINSA) features associated with CO line emission on and off the filaments, revealing the ubiquity of HINSA towards distant clouds for the first time. The derived cold HI abundances, [HI]/[H$_2$], of the two bones range from $\sim$(0.5 to 44.7)$\times10^{-3}$, which reveal different degrees of HI-H$_2$ conversion and are similar to that of nearby, low-mass star forming clouds, Planck Galactic cold clumps and a nearby active high-mass star forming region G176.51+00.20. The HI-H$_2$ conversion has been ongoing for 2.2 to 13.2 Myr in the bones, a timescale comparable to that of massive star formation therein. Therefore, we are witnessing young giant molecular clouds with rapid massive star f

Wireless sensing literature has long aspired to achieve X-ray-like vision at radio frequencies. Yet, state-of-the-art wireless sensing literature has yet to generate the archetypal X-ray image: one of the bones beneath flesh. In this paper, we explore MCT, a penetration-based RF-imaging system for imaging bones at mm-resolution, one that significantly exceeds prior penetration-based RF imaging literature. Indeed the long wavelength, significant attenuation and complex diffraction that occur as RF propagates through flesh, have long limited imaging resolution (to several centimeters at best). We address these concerns through a novel penetration-based synthetic aperture algorithm, coupled with a learning-based pipeline to correct for diffraction-induced artifacts. A detailed evaluation of meat models demonstrates a resolution improvement from sub-decimeter to sub-centimeter over prior art in RF penetrative imaging.

Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features' contributions to the model's outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators, and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-sou

Knee osteoarthritis is a degenerative joint disease that induces chronic pain and disability. Bone morphological analysis is a promising tool to understand the mechanical aspect of this disorder. This study proposes a 2D bone morphological analysis using manually segmented bones to explore morphological features related to distinct pain conditions. Furthermore, six semantic segmentation algorithms are assessed for extracting femur and tibia bones from X-ray images. Our analysis reveals that the morphology of the femur undergoes significant changes in instances where pain worsens. Conversely, improvements in pain may not manifest pronounced alterations in bone shape. The few-shot-learning-based algorithm, UniverSeg, demonstrated superior segmentation results with Dice scores of 99.69% for femur and 99.60% for tibia. Regarding pain condition classification, the zero-shot-learning-based algorithm, CP-SAM, achieved the highest accuracy at 66% among all models. UniverSeg is recommended for automatic knee bone segmentation, while SAM models show potential with prompt encoder modifications for optimized outcomes. These findings highlight the effectiveness of few-shot learning for semantic

Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-quality video segments and then enhance them to obtain high-quality content without violating the playback of the video stream. We introduce BONES, an NES control algorithm that jointly manages the network and computational resources to maximize the quality of experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization problem and solves it in an online manner with near-optimal performance, making it the first NES algorithm to provide a theoretical performance guarantee. Comprehensive experimental results indicate that BONES increases QoE by 5\% to 20\% over state-of-the-art algorithms with minimal overhead. Our code is available at https://github.com/UMass-LIDS/bones.

Real-time 3D human pose estimation is crucial for human-computer interaction. It is cheap and practical to estimate 3D human pose only from monocular video. However, recent bone splicing based 3D human pose estimation method brings about the problem of cumulative error. In this paper, the concept of virtual bones is proposed to solve such a challenge. The virtual bones are imaginary bones between non-adjacent joints. They do not exist in reality, but they bring new loop constraints for the estimation of 3D human joints. The proposed network in this paper predicts real bones and virtual bones, simultaneously. The final length of real bones is constrained and learned by the loop constructed by the predicted real bones and virtual bones. Besides, the motion constraints of joints in consecutive frames are considered. The consistency between the 2D projected position displacement predicted by the network and the captured real 2D displacement by the camera is proposed as a new projection consistency loss for the learning of 3D human pose. The experiments on the Human3.6M dataset demonstrate the good performance of the proposed method. Ablation studies demonstrate the effectiveness of the

We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation model, which captures motions of objects with a tree-structured bones. Our hierarchy system decomposes motions based on the granularity and reveals the correlations between parts without exploiting any prior structural knowledge. We further propose to regularize the bones to be positioned at the basis of motions, centers of parts, sufficiently covering related surfaces of the part. This is achieved by our bone occupancy function, which identifies whether a given 3D point is placed within the bone. Coupling the proposed components, our framework offers several clear advantages: (1) users can obtain animatable 3D models of the arbitrary objects in improved quality from their casual videos, (2) users can manipulate 3D models in an intuitive manner with minimal costs, and (3) users can interactively add or delete control points as necessary. The experimental results demonstrate the efficacy of our framework on diverse instances, in reconstruction quality, interpretability and easier manipulation. Our code is

Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. However, it is crucial that the patient's surface and internal organs are of high quality for any pose and shape estimates. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. Method: To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can benefit various medical applications. Results: We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. To further verify our findings, we conducted additional tests on publicly available datasets with

Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world. While real human hand represents one of the most intricate coordination between bones, muscle, tendon, and skin, state-of-the-art techniques unanimously focus on modeling only the skeleton of the hand. In this paper, we present NIMBLE, a novel parametric hand model that includes the missing key components, bringing 3D hand model to a new level of realism. We first annotate muscles, bones and skins on the recent Magnetic Resonance Imaging hand (MRI-Hand) dataset and then register a volumetric template hand onto individual poses and subjects within the dataset. NIMBLE consists of 20 bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin mesh. Via iterative shape registration and parameter learning, it further produces shape blend shapes, pose blend shapes, and a joint regressor. We demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks. By enforcing the inner bones and muscles to match anatomic and kinematic rules, NIMBLE can animate 3D hands to new poses at unpreceden

The standard differential scaling of proportions in limb long bones (length against circumference) is applied to a phylogenetically wide sample of the Proboscidea, Elephantidae and the Asian (Elephas maximus) and African elephant (Loxodonta africana). In order to investigate allometric patterns in proboscideans and terrestrial mammals with parasagittal limb kinematics, the computed slopes (slenderness exponents) are compared with published values for mammals and studied within a framework of theoretical models of long bone scaling under gravity and muscle forces. Limb bone allometry in E. maximus and the Elephantidae are congruent with adaptation to bending and/or torsion induced by muscular forces during fast locomotion, as in other mammals, whereas limb bones in L. africana appear adapted for coping with the compressive forces of gravity. Consequently, hindlimb bones are expected to be more compliant than forelimb bones in accordance with in vivo studies on elephant locomotory kinetics and kinematics, and the resultant negative limb compliance gradient in extinct and extant elephants, which contrasts to other mammals, suggests an important locomotory constraint preventing achieve

In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based neural networks to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images of the knee in individuals with varying grades of osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, the U-Net-based models were developed to reconstruct the bone profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. A second anomaly-aware network, which was compared to anomaly-naïve segmentation networks, was used to provide a final automated segmentation of the femoral, tibial and patellar bones and cartilages from the knee MR images containing a spectrum of bone anomalies. The anomaly-

In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of human wrist. We apply a metric called global spectral graph wavelet signature for representation of cortical surface of the carpal bone based on eigensystem of Laplace-Beltrami operator. Furthermore, we propose a heuristic and efficient way of aggregating local descriptors of a carpal bone surface to global descriptor. The resultant global descriptor is not only isometric invariant, but also much more efficient and requires less memory storage. We perform experiments on shape of the carpal bones of ten women and ten men from a publicly-available database. Experimental results show the excellency of the proposed GSGW compared to recent proposed GPS embedding approach for comparing shapes of the carpal bones across populations.

Dinosaur DNA may still be out of reach, but scientists are uncovering something almost as exciting—ancient blood vessels hidden inside fossilized bones。 In a massive Tyrannosaurus rex nicknamed Scotty, researchers discovered a network of preserved vessels within a rib that once fractured and began healing 66 million years ago。 Using powerful synchr

In order to better understand and analyze the currently widely used population-based metaheuristic optimization algorithms, , this paper proposes a novel computational intelligence algorithm called bare bones grey wolf optimizer (BBGWO) inspired by a bare bones mechanism. In the BBGWO, the complex updating mechanism of solutions is replaced by a random vector that obeys a normal distribution, whose mean and variance are derived by theoretically studying the probability distribution of the new solution of the original GWO. The corresponding theoretical analysis and simulation results verify the good optimization performance of the proposed BBGWO algorithm.