Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks

On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data

In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexity, previous results studied a relaxed setting where the server has access to some additional public but unlabeled data. We continue in this direction. Specifically, we consider the problem under the standard setting instead of the large margin setting studied before. Under different mild assumptions on the underlying data distribution, we propose two approaches that are based on the Massart noise model and self-supervised learning and show that it is possible to achieve sample complexities that are only linear in the dimension and polynomial in other terms for both private and public data, which significantly improve the previous results. Our methods could also be used for other private PAC learning problems.Comment: To appear in The 14th Asian Conference on Machine Learning (ACML 2022

Truthful Generalized Linear Models

In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy when reporting data. Compared with the classical setting, here we aim to design mechanisms that can both incentivize most agents to truthfully report their data and preserve the privacy of individuals' reports, while their outputs should also close to the underlying parameter. In the first part of the paper, we consider the case where the covariates are sub-Gaussian and the responses are heavy-tailed where they only have the finite fourth moments. First, motivated by the stationary condition of the maximizer of the likelihood function, we derive a novel private and closed form estimator. Based on the estimator, we propose a mechanism which has the following properties via some appropriate design of the computation and payment scheme for several canonical models such as linear regression, logistic regression and Poisson regression: (1) the mechanism is $o(1)$-jointly differentially private (with probability at least $1-o(1)$); (2) it is an $o(\frac{1}{n})$-approximate Bayes Nash equilibrium for a $(1-o(1))$-fraction of agents to truthfully report their data, where $n$ is the number of agents; (3) the output could achieve an error of $o(1)$ to the underlying parameter; (4) it is individually rational for a $(1-o(1))$ fraction of agents in the mechanism ; (5) the payment budget required from the analyst to run the mechanism is $o(1)$. In the second part, we consider the linear regression model under more general setting where both covariates and responses are heavy-tailed and only have finite fourth moments. By using an $\ell_4$-norm shrinkage operator, we propose a private estimator and payment scheme which have similar properties as in the sub-Gaussian case.Comment: To appear in The 18th Conference on Web and Internet Economics (WINE 2022

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

Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in $\ell_p^d$ space; (3) DP-SCO with heavy-tailed data over a constrained and bounded set in $\ell_p^d$ space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set rather than the dimension of the space. Moreover, we also show the bound for strongly convex functions is optimal up to a logarithmic factor. For problems (2) and (3), we propose several novel algorithms and provide the first theoretical results for both cases when $1<p<2$ and $2\leq p\leq \infty$

Effect of combined administration of carboprost tromethamine and ergometrine on uterine atony-induced postpartum hemorrhage

Purpose: To determine the efficacy of the combined use of carboprost tromethamine and ergometrine in the prevention and treatment of postpartum hemorrhage induced by uterine atony.Methods: A total of 66 pregnant women with postpartum hemorrhage due to uterine atony who were treated in Fuyang Women's and Children's Hospital from February 2019 to January 2022 were randomly and equally assigned to control and combination groups, respectively, based on the order of admission. The control group was treated with 0.2 mg of ergometrine maleate via intramuscular injection in the buttocks. In the combination group, the patients were also given 250 μg of carboprost tromethamine via cervical injection in addition to ergometrine. The two groups were compared in terms of volume of postpartum vaginal bleeding and hemoglobin levels, coagulation function index, clinical effectiveness and incidence of adverse reactions.Results: There was a significant difference in total treatment effectiveness between the two groups (69.70 vs 90.91%; ꭓ2 = 4.694, p = 0.03) with the combination group showing higher effectiveness. The volume of bleeding in the combination group at 2 h and 24 h after delivery were significantly lower than the corresponding values for the control group (p &lt; 0.05). Comparison at 24 h postpartum showed significantly lower hemoglobin level in the combination group than in the control group (p &lt; 0.05). Posttreatment levels of prothrombin time (PT) and thrombin time (TT) in the two groups were lower than the pre-treatment values, but the post-treatment levels in the combination group were lower than those in the control group (p &lt; 0.05).Conclusion: Combined administration of carboprost tromethamine and ergometrine may be a viable&nbsp;treatment strategy for uterine atony-induced postpartum hemorrhage. It has acceptable level of safety. However, further clinical trials are required prior to application in clinical practice

Combining the Silhouette and Skeleton Data for Gait Recognition

Gait recognition, a promising long-distance biometric technology, has aroused intense interest in computer vision. Existing works on gait recognition can be divided into appearance-based methods and model-based methods, which extract features from silhouettes and skeleton data, respectively. However, since appearance-based methods are greatly affected by clothing changing and carrying condition, and model-based methods are limited by the accuracy of pose estimation approaches, gait recognition remains challenging in practical applications. In order to integrate the advantages of such two approaches, a two-branch neural network (NN) is proposed in this paper. Our method contains two branches, namely a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. In addition, two new modules are proposed in the GCN-based branch for better gait representation. First, we present a simple yet effective fully connected graph convolution operator to integrate the multi-scale graph convolutions and alleviate the dependence on natural human joint connections. Second, we deploy a multi-dimension attention module named STC-Att to learn spatial, temporal and channel-wise attention simultaneously. We evaluated the proposed two-branch neural network on the CASIA-B dataset. The experimental results show that our method achieves state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image Understandin