A Modified KNN Algorithm for Activity Recognition in Smart Home

Nowadays, more and more elderly people cannot take care of themselves, and feel uncomfortable in daily activities. Smart home systems can help to improve daily life of elderly people. A smart home can bring residents a more comfortable living environment by recognizing the daily activities automatically. In this paper, in order to improve the accuracy of activity recognition in smart homes, we conduct some improvements in data preprocess and recognition phase, and more importantly, a novel sensor segmentation method and a modified KNN algorithm are proposed. The segmentation algorithm employs segment sensor data into fragments based on predefined activity knowledge, and then the proposed modified KNN algorithm uses center distances as a measure for classification. We also conduct comprehensive experiments, and the results demonstrate that the proposed method outperforms the other classifiers

Sirt1 Deletion Leads to Enhanced Inflammation and Aggravates Endotoxin-Induced Acute Kidney Injury

Bacterial endotoxin has been known to induce excessive inflammatory responses and acute kidney injury. In the present study, we used a mouse model of endotoxemia to investigate the role of Sirt1 in inflammatory kidney injury. We examined molecular and cellular responses in inducible Sirt1 knockout (Sirt1-/-) mice and wild type littermates (Sirt1+/+) in lipopolysaccharide (LPS)-induced kidney injury. Our studies demonstrated that Sirt1 deletion caused aggravated kidney injury, which was associated with increased inflammatory responses including elevated pro-inflammatory cytokine production, and increased ICAM-1 and VCAM-1 expression. Inflammatory signaling such as STAT3/ERK phosphorylation and NF-κB activation was markedly elevated in kidney tissues of Sirt1 knockout mice after LPS challenge. The results indicate that Sirt1 is protective against LPS-induced acute kidney injury by suppressing kidney inflammation and down-regulating inflammatory signaling

Protective effect of omeprazole on gastric mucosal of cirrhotic portal hypertension rats

AbstractObjectiveTo observe the protective effect of omeprazole on gastric mucosal of cirrhotic portal hypertension rats.MethodsAll rats were randomly divided into normal control group, cirrhosis and treatment group. Thioacetamide was used to establish rat model of cirrhotic portal hypertension. The necrotic tissue of gastric mucosa ulcer focus, degree of neutrophils infiltration at the ulcer margin, portal pressure, portal venous flow, abdominal aortic pressure, abdominal aortic blood flow at front end, gastric mucosal blood flow (GMBF), glycoprotein (GP) of gastric mucosa, basal acid secretion, H+back -diffusion, gastric mucosal damage index, NO, prostaglandin E2(PGE2) and tumor necrosis factor-α (TNF-α) were determined respectively, and the pathological changes of gastric mucosa were also observed by microscope.ResultsCompared with cirrhosis group and the control group, the ulcer bottom necrotic material, gastric neutrophil infiltration and UI of the treatment group were all decreased significantly (P<0.01), GMBF value, GP values, serum NO, PGE2, TNF-α were all significantly increased.ConclusionsOmeprazole has an important protective effect on gastric mucosal and it can increase gastric mucosal blood flow and related to many factors

Recent Advances in Metasurface Design and Quantum Optics Applications with Machine Learning, Physics-Informed Neural Networks, and Topology Optimization Methods

As a two-dimensional planar material with low depth profile, a metasurface can generate non-classical phase distributions for the transmitted and reflected electromagnetic waves at its interface. Thus, it offers more flexibility to control the wave front. A traditional metasurface design process mainly adopts the forward prediction algorithm, such as Finite Difference Time Domain, combined with manual parameter optimization. However, such methods are time-consuming, and it is difficult to keep the practical meta-atom spectrum being consistent with the ideal one. In addition, since the periodic boundary condition is used in the meta-atom design process, while the aperiodic condition is used in the array simulation, the coupling between neighboring meta-atoms leads to inevitable inaccuracy. In this review, representative intelligent methods for metasurface design are introduced and discussed, including machine learning, physics-information neural network, and topology optimization method. We elaborate on the principle of each approach, analyze their advantages and limitations, and discuss their potential applications. We also summarise recent advances in enabled metasurfaces for quantum optics applications. In short, this paper highlights a promising direction for intelligent metasurface designs and applications for future quantum optics research and serves as an up-to-date reference for researchers in the metasurface and metamaterial fields

Tractable Algorithm for Robust Time-Optimal Trajectory Planning of Robotic Manipulators under Confined Torque

In this paper, the problem of time optimal trajectory planning under confined torque and uncertain dynamics and torque parameters along a predefined geometric path is considered. It is shown that the robust optimal solution to such a problem can be obtained by solving a linear program. Thus a tractable algorithm is given for robust time-optimal path-tracking control under confined torque

Eliminating stray radiation inside large area imaging arrays

With increasing array size, it is increasingly important to control stray radiation inside the detector chips themselves. We demonstrate this effect with focal plane arrays of absorber coupled Lumped Element microwave Kinetic Inductance Detectors (LEKIDs) and lens-antenna coupled distributed quarter wavelength Microwave Kinetic Inductance Detectors (MKIDs). In these arrays the response from a point source at the pixel position is at a similar level to the stray response integrated over the entire chip area. For the antenna coupled arrays, we show that this effect can be suppressed by incorporating an on-chip stray light absorber. A similar method should be possible with the LEKID array, especially when they are lens coupled.Comment: arXiv admin note: substantial text overlap with arXiv:1707.0214

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model

Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability. In practice, a CTR prediction model is also commonly assessed with the ranking ability. To optimize the ranking ability, ranking loss (e.g., pairwise or listwise loss) can be adopted as they usually achieve better rankings than pointwise loss. Previous studies have experimented with a direct combination of the two losses to obtain the benefit from both losses and observed an improved performance. However, previous studies break the meaning of output logit as the click-through rate, which may lead to sub-optimal solutions. To address this issue, we propose an approach that can Jointly optimize the Ranking and Calibration abilities (JRC for short). JRC improves the ranking ability by contrasting the logit value for the sample with different labels and constrains the predicted probability to be a function of the logit subtraction. We further show that JRC consolidates the interpretation of logits, where the logits model the joint distribution. With such an interpretation, we prove that JRC approximately optimizes the contextualized hybrid discriminative-generative objective. Experiments on public and industrial datasets and online A/B testing show that our approach improves both ranking and calibration abilities. Since May 2022, JRC has been deployed on the display advertising platform of Alibaba and has obtained significant performance improvements.Comment: Accepted at KDD 202