Assessment of Research Efficiency in China\u27s Universities Based on Data Envelopment Method

To investigate the static and dynamic trends of scientific research efficiency of different types of universities in China during the period of 2016 - 2020. Methods: Based on the scientific research data of six types of universities nationwide from 2016 to 2020, which are classified by the Compendium of Science and Technology Information of Higher Education Institutions published by the Department of Science and Technology of the Ministry of Education. We selected the full-time personnel (person-years) of research and development personnel and Internal expenditure for the year of science and technology funds (thousand yuan) as input evaluation indexes, and the number of Academic papers published abroad (articles), the number of international projects acceptance (item), the number of patent authorizations (item), and the number of Actual income of technology patents in the current year (thousands of yuan) as output evaluation indexes, and constructed the evaluation index system of scientific research efficiency of six types of universities nationwide. SPSS version 23.0 software was used for descriptive data statistics, and DEA-BCC model and DEA-Malmquist index model of DEAP2.1 software were used for static and dynamic evaluation of its scientific research efficiency from 2016 to 2020, respectively. Results ① Overall analysis: the level of scientific research efficiency of all types of universities is high, but the total factor productivity of scientific research shows a trend of rising and then declining during the 13th Five-Year Plan period, and the overall scientific research efficiency of universities has limited room for improvement. ② Comparative analysis: universities of comprehensive, science and technology, medicine and other universities have the highest level of scientific research efficiency, followed by universities of teacher training and lower universities of agriculture and forestry. ③ From 2017 - 2020, full-time personnel, internal expenditures of agriculture and forestry universities are input redundancy, patent authorization number and the actual income of agriculture and forestry universities are insufficient output. In 2020, full-time personnel, internal expenditures of normal universities are input redundancy, patent authorization number of normal universities is output insufficient. Conclusion: During the period of 2016 - 2020, all kinds of universities nationwide have achieved high research efficiency with high input and high output, which provides a strong reference for the national research management to allocate university research funds more scientifically and reasonably. This result to optimize the allocation of resources of university\u27s scientific research in China and improve the economic benefit of university\u27s scientific research has important theoretical and practical significance

An invariant set bifurcation theory for nonautonomous nonlinear evolution equations

In this paper we establish an invariant set bifurcation theory for the nonautonomous dynamical system $(\varphi_\lambda,\theta)_{X,\mathcal H}$ generated by the evolution equation \begin{equation}\label{e0}u_t+Au=\lambda u+p(t,u),\hspace{0.4cm} p\in \mathcal H=\mathcal H[f(\cdot,u)]\end{equation} on a Hilbert space $X$, where $A$ is a sectorial operator, $\lambda$ is the bifurcation parameter, $f(\cdot,u):\mathbb{R}\rightarrow X$ is translation compact, $f(t,0)\equiv0$ and $\mathcal H[f]$ is the hull of $f(\cdot,u)$. Denote by $\varphi_\lambda:=\varphi_\lambda(t,p)u$ the cocycle semiflow generated by the equation. Under some other assumptions on $f$, we show that as the parameter $\lambda$ crosses an eigenvalue $\lambda_0\in\mathbb{R}$ of $A$, the system bifurcates from $0$ to a nonautonomous invariant set $B_\lambda(\cdot)$ on one-sided neighborhood of $\lambda_0$. Moreover, $$\lim_{\lambda\rightarrow\lambda_0}H_{X^\alpha}\left(B_\lambda(p),0\right)=0,\hspace{0.4cm} p\in P,$$ where $H_{X^\alpha}(\cdot,\cdot)$ denotes the Hausdorff semidistance in $X^\alpha$ (here $X^\alpha$ ($\alpha\geq0$) defined below is the fractional power spaces associated with $A$). Our result is based on the pullback attractor bifurcation on the local central invariant manifolds $\mathcal {M}^\lambda_{\operatorname{loc}}(\cdot)$

An invariant set bifurcation theory for nonautonomous nonlinear evolution equations

In this paper we establish an invariant set bifurcation theory for the nonautonomous dynamical system (ϕλ, θ)X,H generated by the evolution equation ut + Au = λu + p(t, u), p ∈ H = H[ f(·, u)] (0.1) on a Hilbert space X, where A is a sectorial operator, λ is the bifurcation parameter, f(·, u) : R → X is translation compact, f(t, 0) ≡ 0 and H[ f ] is the hull of f(·, u). Denote by ϕλ := ϕλ(t, p)u the cocycle semiflow generated by the system. Under some other assumptions on f , we show that as the parameter λ crosses an eigenvalue λ0 ∈ R of A, the system bifurcates from 0 to a nonautonomous invariant set Bλ(·) on one-sided neighborhood of λ0. Moreover, lim λ→λ0 HXα (Bλ(p), 0) = 0, p ∈ P, where HXα (·, ·) denotes the Hausdorff semidistance in X (here X (α ≥ 0) defined below is the fractional power spaces associated with A). Our result is based on the pullback attractor bifurcation on the local central invariant manifolds Mλ loc(·)

FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, the federated learning (FL) framework allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using Generative Adversarial Network-based adversarial attack with the reconstructed benign traffic. We conduct experiments to show that existing defenses provide little protection that the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.Comment: 14 pages, 9 figures, submitted to TIF

SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation

As an important and challenging problem in computer vision, PAnoramic Semantic Segmentation (PASS) gives complete scene perception based on an ultra-wide angle of view. Usually, prevalent PASS methods with 2D panoramic image input focus on solving image distortions but lack consideration of the 3D properties of original $360^{\circ}$ data. Therefore, their performance will drop a lot when inputting panoramic images with the 3D disturbance. To be more robust to 3D disturbance, we propose our Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation (SGAT4PASS), considering 3D spherical geometry knowledge. Specifically, a spherical geometry-aware framework is proposed for PASS. It includes three modules, i.e., spherical geometry-aware image projection, spherical deformable patch embedding, and a panorama-aware loss, which takes input images with 3D disturbance into account, adds a spherical geometry-aware constraint on the existing deformable patch embedding, and indicates the pixel density of original $360^{\circ}$ data, respectively. Experimental results on Stanford2D3D Panoramic datasets show that SGAT4PASS significantly improves performance and robustness, with approximately a 2% increase in mIoU, and when small 3D disturbances occur in the data, the stability of our performance is improved by an order of magnitude. Our code and supplementary material are available at https://github.com/TencentARC/SGAT4PASS.Comment: Accepted by IJCAI 202

An Ionophore‐Based Anion‐Selective Optode Printed on Cellulose Paper

A general anion‐sensing platform is reported based on a portable and cost‐effective ion‐selective optode and a smartphone detector equipped with a color analysis app. In contrast to traditional anion‐selective optodes using a hydrophobic polymer and/or plasticizer to dissolve hydrophobic sensing elements, the new optode relies on hydrophilic cellulose paper. The anion ionophore and a lipophilic pH indicator are inkjet‐printed and adsorbed on paper and form a “dry” hydrophobic sensing layer. Porous cellulose sheets also allow the sensing site to be modified with dried buffer that prevents any sample pH dependence of the observed color change. A highly selective fluoride optode using an AlIII‐porphyrin ionophore is examined as an initial example of this new anion sensing platform for measurements of fluoride levels in drinking water samples. Apart from Lewis acid–base recognition, hydrogen bonding recognition is also compatible with this sensing platform.Cellulose paper as a sole substrate allows adsorption of a lipophilic anion ionophore and pH‐sensitive indicator dye to enable heterogeneous anion sensing via an anion‐proton co‐extraction mechanism. This platform also enables adsorption of a buffer salt as the sample pH adjuster to prevent pH dependence of the optical anion response.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138373/1/anie201706147-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138373/2/anie201706147_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138373/3/anie201706147.pd