Diagnosis and Treatment of Chronic Ankle Instability

Ankle sprains involve up to 30% of all sport injuries. About 30% of patients may develop chronic ankle instability (CAI), which significantly limits their professional or recreational activities. The diagnosis of CAI relies on the understanding of anatomy and a thorough assessment of the patient. Imaging studies, including plain radiographs, sonography, MRI, and arthroscopic examinations, are beneficial in evaluating the extent and structures involved. Once conservative treatment has failed, surgery is indicated to restore ankle joint stability. Suture repair is sufficient, whether open or arthroscopically, if the remnant ligament quality is acceptable. Anatomical graft reconstruction is used for poor remnant quality or revision

Crack Risk Evaluation of Early Age Concrete Based on the Distributed Optical Fiber Temperature Sensing

Cracks often appear in concrete arch dams, due to the thermal stress and low tensile strength of early age concrete. There are three commonly used temperature controlling measures: controlling the casting temperature, burying cooling pipe, and protecting the surface. However, because of the difficulty to obtain accurate temperature and thermal stress field of the concrete, the rationality and economy of these measures are not assessed validly before and after construction. In this paper, a crack risk evaluation system for early age concrete is established, including distributed optical fiber temperature sensing (DTS), prediction of temperature and stress fields, and crack risk evaluation. Based on the DTS temperature data, the back-analysis method is applied to retrieve the thermal parameters of concrete. Then, the temperature and thermal stress of early age concrete are predicted using the reversed thermal parameters, as well as the laboratory test parameters. Finally, under the proposed cracking risk evaluation principle, the cracking risk level of each concrete block is given; the preliminary and later temperature controlling measures were recommended, respectively. The application of the proposed system in Xiluodu super high arch dam shows that this system works effectively for preventing cracks of early age concrete

Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network

Our project probes the relationship between temperatures and the blossom date of cherry trees. Through modeling, future flowering will become predictive, helping the public plan travels and avoid pollen season. To predict the date when the cherry trees will blossom exactly could be viewed as a multiclass classification problem, so we applied the multi-class Support Vector Classifier (SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), to formulate the problem. In the end, we evaluate and compare the performance of these approaches to find out which one might be more applicable in reality.Comment: 6 Pages, 6 Figure

Audio-Visual Instance Segmentation

In this paper, we propose a new multi-modal task, namely audio-visual instance segmentation (AVIS), in which the goal is to identify, segment, and track individual sounding object instances in audible videos, simultaneously. To our knowledge, it is the first time that instance segmentation has been extended into the audio-visual domain. To better facilitate this research, we construct the first audio-visual instance segmentation benchmark (AVISeg). Specifically, AVISeg consists of 1,258 videos with an average duration of 62.6 seconds from YouTube and public audio-visual datasets, where 117 videos have been annotated by using an interactive semi-automatic labeling tool based on the Segment Anything Model (SAM). In addition, we present a simple baseline model for the AVIS task. Our new model introduces an audio branch and a cross-modal fusion module to Mask2Former to locate all sounding objects. Finally, we evaluate the proposed method using two backbones on AVISeg. We believe that AVIS will inspire the community towards a more comprehensive multi-modal understanding

Moderate Exercise Enhances Endothelial Progenitor Cell Exosomes Release and Function

Purpose: Exercise has cardiovascular benefits which might be related to endothelial progenitor cells (EPC). Meanwhile, there is evidence suggesting that EPC-derived exosomes (EPC-EX) promote vascular repair and angiogenesis through their carried microRNA (miR)-126. In this study, we investigated whether exercise could increase the levels of circulating EPC-EX and their miR-126 cargo, and by which promote the protective function of EPC-EX on endothelial cells (EC). Methods: Plasma EPC-EX from sedentary, low, or moderate exercise mice, respectively, denoted as EPC-EXS, EPC-EXL, and EPC-EXM, were isolated using microbead-based sorting techniques and characterized by nanoparticle tracking analysis, Western blot, and quantitative real-time polymerase chain reaction assessments of biomarkers and miR-126. High glucose (25 mM) with hypoxia (1% O2) was used for inducing an EC injury model. The injured EC were treated by coculturing with vehicle, EPC-EXS, EPC-EXL, EPC-EXM, or EPC-EXM + anti–miR-126. After that, EC were used for flow cytometry analysis of apoptosis, assessments of tube formation and migration, and measurements of miR-126 level and its downstream sprouty-related protein-1 (SPRED1) and vascular endothelial growth factor (VEGF). Results: 1) Isolated EPC-EX positively expressed exosomal markers (CD63 and Tsg101) and EPC markers (CD34 and VEGFR2). 2) Exercise intensity dependently elevated plasma level of EPC, EPC-EX/EPC ratio, and miR-126 expression in EPC and EPC-EX. 3) Injured EC displayed apoptosis increment, angiogenic dysfunction and miR-126 reduction. 4) EPC-EXM had better effects than EPC-EXS and EPC-EXL on alleviating those changes of injured EC, accompanied with SPRED1 downregulation and VEGF upregulation. 5) The effects of EPC-EXM were abolished by miR-126 knockdown. Conclusions: Our data demonstrate that exercise can increase EPC-EX release and miR-126 level and enhance the effects of EPC-EX on protecting EC against injury through the SPRED1/VEGF pathway

PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems

Processing-in-memory (PIM), an increasingly studied neuromorphic hardware, promises orders of energy and throughput improvements for deep learning inference. Leveraging the massively parallel and efficient analog computing inside memories, PIM circumvents the bottlenecks of data movements in conventional digital hardware. However, an extra quantization step (i.e. PIM quantization), typically with limited resolution due to hardware constraints, is required to convert the analog computing results into digital domain. Meanwhile, non-ideal effects extensively exist in PIM quantization because of the imperfect analog-to-digital interface, which further compromises the inference accuracy. In this paper, we propose a method for training quantized networks to incorporate PIM quantization, which is ubiquitous to all PIM systems. Specifically, we propose a PIM quantization aware training (PIM-QAT) algorithm, and introduce rescaling techniques during backward and forward propagation by analyzing the training dynamics to facilitate training convergence. We also propose two techniques, namely batch normalization (BN) calibration and adjusted precision training, to suppress the adverse effects of non-ideal linearity and stochastic thermal noise involved in real PIM chips. Our method is validated on three mainstream PIM decomposition schemes, and physically on a prototype chip. Comparing with directly deploying conventionally trained quantized model on PIM systems, which does not take into account this extra quantization step and thus fails, our method provides significant improvement. It also achieves comparable inference accuracy on PIM systems as that of conventionally quantized models on digital hardware, across CIFAR10 and CIFAR100 datasets using various network depths for the most popular network topology.Comment: 25 pages, 12 figures, 8 table

Self-Reconstruction Property of a Special Speckle Pattern after Obstructing by an Opaque Obstruction

Numerical simulation of random optical field with special autocorrelation is performed. It can be seen that the speckle pattern with special autocorrelation is different from the general speckle pattern whose autocorrelation is a Gaussian function. Furthermore, we study the propagation property of the special speckle in free space after blocking by an opaque obstruction and find the self-healing effect of speckle. In particular, we investigate the influence of size and shape of obstruction and the coherence of speckle on the speckle’s reconstruction process. We find that it is affected by the size of obstruction and the coherence of the speckle but is almost not affected by the shape of obstruction. Our results can be applied to the imaging, optical communication, and so on