Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery

This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed method is built on the fast ''dense inverse searching'' algorithm, which estimates the disparity of the stereo images. The overlapping image patches (arbitrary squared image segment) from the images at different scales are aligned based on the photometric consistency presumption. We propose a Bayesian framework to evaluate the probability of the optimized patch disparity at different scales. Moreover, we introduce a spatial Gaussian mixed probability distribution to address the pixel-wise probability within the patch. In-vivo and synthetic experiments show that our method can handle ambiguities resulted from the textureless surfaces and the photometric inconsistency caused by the Lambertian reflectance. Our Bayesian method correctly balances the probability of the patch for stereo images at different scales. Experiments indicate that the estimated depth has higher accuracy and fewer outliers than the baseline methods in the surgical scenario

Downregulated serum 14, 15-epoxyeicosatrienoic acid is associated with abdominal aortic calcification in patients with primary aldosteronism

Patients with primary aldosteronism (PA) have increased risk of target-organ damage, among which vascular calcification is an important indicator of cardiovascular mortality. 14, 15-Epoxyeicosatrienoic acid (14, 15-EET) has been shown to have beneficial effects in vascular remodeling. However, whether 14, 15-EET associates with vascular calcification in PA is unknown. Thus, we aimed to investigate the association between 14, 15-EET and abdominal aortic calcification (AAC) in patients with PA. Sixty-nine patients with PA and 69 controls with essential hypertension, matched for age, sex, and blood pressure, were studied. 14, 15-Dihydroxyeicosatrienoic acid (14, 15-DHET), the inactive metabolite from 14, 15-EET, was estimated to reflect serum 14, 15-EET levels. AAC was assessed by computed tomographic scanning. Compared with matched controls, the AAC prevalence was almost 1-fold higher in patients with PA (27 [39.1%] versus 14 [20.3%]; P=0.023), accompanied by significantly higher serum 14, 15-DHET levels (7.18±4.98 versus 3.50±2.07 ng/mL; P<0.001). Plasma aldosterone concentration was positively associated with 14, 15-DHET (β=0.444; P<0.001). Multivariable logistic analysis revealed that lower 14, 15-DHET was an independent risk factor for AAC in PA (odds ratio, 1.371; 95% confidence interval, 1.145–1.640; P<0.001), especially in young patients with mild hypertension and normal body mass index. In conclusion, PA patients exibited more severe AAC, accompanied by higher serum 14, 15-DHET levels. On the contrary, decreased 14, 15-EET was significantly associated with AAC prevalence in PA patients, especially in those at low cardiovascular risk

Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly

Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force

Nonlinearity in macroscopic mechanical system plays a crucial role in a wide variety of applications, including signal transduction and processing, synchronization, and building logical devices. However, it is difficult to generate nonlinearity due to the fact that macroscopic mechanical systems follow the Hooke's law and response linearly to external force, unless strong drive is used. Here we propose and experimentally realize a record-high nonlinear response in macroscopic mechanical system by exploring the anharmonicity in deforming a single chemical bond. We then demonstrate the tunability of nonlinear response by precisely controlling the chemical bonding interaction, and realize a cubic elastic constant of \mathversion{bold}$2 \times 10^{18}~{\rm N}/{\rm m^3}$, many orders of magnitude larger in strength than reported previously. This enables us to observe vibrational bistate transitions of the resonator driven by the weak Brownian thermal noise at 6~K. This method can be flexibly applied to a variety of mechanical systems to improve nonlinear responses, and can be used, with further improvements, to explore macroscopic quantum mechanics