OTMatch: Improving Semi-Supervised Learning with Optimal Transport

Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning image predictions with specific classes generated through self-training techniques, thereby neglecting the inherent relationships that exist within these classes. In this paper, we present a new approach called OTMatch, which leverages semantic relationships among classes by employing an optimal transport loss function. By utilizing optimal transport, our proposed method consistently outperforms established state-of-the-art methods. Notably, we observed a substantial improvement of a certain percentage in accuracy compared to the current state-of-the-art method, FreeMatch. OTMatch achieves 3.18%, 3.46%, and 1.28% error rate reduction over FreeMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels per class, respectively. This demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting

Accurate few-shot object counting with Hough matching feature enhancement

IntroductionGiven some exemplars, few-shot object counting aims to count the corresponding class objects in query images. However, when there are many target objects or background interference in the query image, some target objects may have occlusion and overlap, which causes a decrease in counting accuracy.MethodsTo overcome the problem, we propose a novel Hough matching feature enhancement network. First, we extract the image feature with a fixed convolutional network and refine it through local self-attention. And we design an exemplar feature aggregation module to enhance the commonality of the exemplar feature. Then, we build a Hough space to vote for candidate object regions. The Hough matching outputs reliable similarity maps between exemplars and the query image. Finally, we augment the query feature with exemplar features according to the similarity maps, and we use a cascade structure to further enhance the query feature.ResultsExperiment results on FSC-147 show that our network performs best compared to the existing methods, and the mean absolute counting error on the test set improves from 14.32 to 12.74.DiscussionAblation experiments demonstrate that Hough matching helps to achieve more accurate counting compared with previous matching methods

Shared memory parallel computing procedures for nonlinear dynamic analysis of super high rise buildings

The proposed parallel state transformation procedures (PSTP) of fiber beam-column elements and multi-layered shell elements, combined with the parallel factorization of Jacobian (PF), are incorporated into a finite element program. In PSTP, elements are classified into different levels of workload prior to state determination in order to balance workload among different threads. In PF, the multi-threaded version of OpenBLAS is adopted to compute super-nodes. A case study on four super high-rise buildings, i.e. S1~S4, has demonstrated that the combination of PSTP and PF does not have any observable influence on computational accuracy. As number of elements and DOFs increases, the ratio of time consumed in the formation of the Jacobian to that consumed in the solution of algebraic equations tends to decrease. The introduction of parallel solver can yield a substantial reduction in computational cost. Combination of PSTP and PF can give rise to a further significant reduction. The acceleration ratios associated with PSTP do not exhibit a significant decrease as PGA level increases. Even PGA level is equal to 2.0g, PSTP still can result in acceleration ratios of 2.56 and 1.92 for S1 and S4, respectively. It is verified that it is more effective to accelerate analysis by reducing the time spent in solving algebraic equations rather than reducing that spent in the formation of the Jacobian for super high-rise buildings

Design of data acquisition card based on microwave auto-measurement system

This paper provides a brief introduction to the hardware configuration and the measuring principle of microwave auto-measurement system, and gives more details on the design of the data acquisition card based on the system. The card has a high accuracy which is owed to 12-bit high sampling AD converter and 12-bit multi-channel DA converter being applied, and it takes CPLD as the control core, USB as the interface, which achieves the speedy and reliable data transmission between the data acquisition card and the PC

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Improved the Accuracy of Seafloor Topography from Altimetry-Derived Gravity by the Topography Constraint Factor Weight Optimization Method

Gravity geologic method is one of the important to derive seafloor topography by using altimetry-gravity, and its committed step is gridding of regional gravity anomaly. Hence, we proposed a topography constraint factor weight optimization (TCFWO) method based on ordinary kriging method. This method fully considers the influence of topography factors on the construction of regional gravity grid besides horizontal distance. The results of regional gravity anomaly models constructed in the Markus-Wake seamount area show that the TCFWO method is better than ordinary kriging method. Then, the above two regional gravity models were applied to invert the seafloor topography. The accuracy of derived topographic models was evaluated by using the shipborne depth data and existing seafloor topography models, including ETOPO1 and V19.1 model. The experimental results show that the accuracy of ST_TCFWO (seafloor topography model inverted by TCFWO method) is better than ST_KR (seafloor topography model inverted by kriging method) and ETOPO1 model. Compared with the ST_KR, the accuracy of the ST_TCFWO has improved about 26%. In addition, the accuracy of seafloor topography is affected by the variation of depth, the distribution of control points and the type of terrain. In different depth layers, the ST_TCFWO has better advantages than ST_KR. In the sparse shipborne measurements area, the accuracy of ST_TCFWO is better than that of V19.1, ETOPO1 and ST_KR. Moreover, compared to other models, ST_TCFWO performs better in flat submarine plain or rugged seamount area

Electromagnetic Hysteresis Based Dynamics Model of an Electromagnetically Controlled Torque Coupling

This paper proposes a novel computationally efficient, easy-to-implement electromagnetic hysteresis based dynamics model of a kind of intelligent electromagnetic torque controlled coupling (EMTC), which, with drag torque under consideration, first models the electromagnetic hysteresis existing in the primary clutch with the classical Preisach model, and then models the transferred torques in the three friction elements of the center coupling in the slipping and locked modes, respectively. The performance of the model is verified by simulation and experiment jointly, which lays the basis for the development of advanced control algorithm

Delayed Thiol-Epoxy Photopolymerization: A General and Effective Strategy to Prepare Thick Composites

Photoinduced thiol-epoxy click polymerization possesses both the characteristics and advantages of photopolymerization and click reactions. However, the photopolymerization of pigmented or highly filled thiol-epoxy thick composites still remains a great challenge due to the light screening effect derived from the competitive absorption, reflection, and scattering of the pigments or functional fillers. In this article, we present a simple and versatile strategy to prepare thick composites via delayed thiol-epoxy photopolymerization. The irradiation of a small area with a light-emitting diode (LED) point light source at room temperature leads to the decomposition of a photobase generator and the released active basic species can uniformly disperse throughout the whole system, including unirradiated areas, via mechanical stirring. No polymerization was observed at room temperature and therefore the liquid formulations can be further processed with molds of arbitrary size and desired shapes. It is only by increasing the temperature that base-catalyzed thiol-epoxy polymerization occurs and controllable preparation of thick thiol-epoxy materials can be achieved. The formed networks display excellent uniformity in different radii and depths with comparable functionality conversions, similar Tg values, and thermal decomposition temperatures. The presented strategy can be applied to prepare thick composites with glass fibers possessing improved mechanical properties and dark composites containing 2 wt % carbon nanotubes