Rigidity theorems for submetries in positive curvature

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Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting

In modern micromachining, there is a need to measure and monitor certain machining process parameters in process so as to detect tool wear in real time, to optimize the process parameters setup, and to render the machining process some level of smartness and intelligence. This paper presents the innovative design of a smart turning tool using two pieces of piezoelectric films to measure cutting and feed force in real time. The tool was tested on its performance through the calibration and cutting trials against the commercial dynamometer. The results show the smart turning tool has achieved the performance as designed

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Comment on "Superconducting gap anisotropy vs. doping level in high-T_c cuprates" by C. Kendziora et al, PRL 77, 727 (1996)

In a recent paper Kendziora et al concluded that the superconducting gap in overdoped Bi-2212 is isotropic. From data obtained from electronic Raman scattering measurements, their conclusion was based on the observation that pair breaking peaks occured at approximately the same frequency in different scattering geometries and that the normalized scattering intensity at low energies was strongly depleted. We discuss a different interpretation of the raw data and present new data which is consistent with a strongly anisotropic gap with nodes. The spectra can be successfully described by a model for Raman scattering in a d_{x^{2}-y^{2}} superconductor with spin fluctuations and impurity scattering included.Comment: 1 page revtex plus 1 postscript figur

Lattice ϕ4\phi^4 theory of finite-size effects above the upper critical dimension

We present a perturbative calculation of finite-size effects near $T_c$ of the $\phi^4$ lattice model in a $d$-dimensional cubic geometry of size $L$ with periodic boundary conditions for $d > 4$. The structural differences between the $\phi^4$ lattice theory and the $\phi^4$ field theory found previously in the spherical limit are shown to exist also for a finite number of components of the order parameter. The two-variable finite-size scaling functions of the field theory are nonuniversal whereas those of the lattice theory are independent of the nonuniversal model parameters.One-loop results for finite-size scaling functions are derived. Their structure disagrees with the single-variable scaling form of the lowest-mode approximation for any finite $\xi/L$ where $\xi$ is the bulk correlation length. At $T_c$, the large-$L$ behavior becomes lowest-mode like for the lattice model but not for the field-theoretic model. Characteristic temperatures close to $T_c$ of the lattice model, such as $T_{max}(L)$ of the maximum of the susceptibility $\chi$, are found to scale asymptotically as $T_c - T_{max}(L) \sim L^{-d/2}$, in agreement with previous Monte Carlo (MC) data for the five-dimensional Ising model. We also predict $\chi_{max} \sim L^{d/2}$ asymptotically. On a quantitative level, the asymptotic amplitudes of this large -$L$ behavior close to $T_c$ have not been observed in previous MC simulations at $d = 5$ because of nonnegligible finite-size terms $\sim L^{(4-d)/2}$ caused by the inhomogeneous modes. These terms identify the possible origin of a significant discrepancy between the lowest-mode approximation and previous MC data. MC data of larger systems would be desirable for testing the magnitude of the $L^{(4-d)/2}$ and $L^{4-d}$ terms predicted by our theory.Comment: Accepted in Int. J. Mod. Phys.

Superior removal of arsenic from water with zirconium metal-organic framework UiO-66

10.1038/srep16613Scientific Reports51661