Matrix Completion with Noise via Leveraged Sampling

Many matrix completion methods assume that the data follows the uniform distribution. To address the limitation of this assumption, Chen et al. \cite{Chen20152999} propose to recover the matrix where the data follows the specific biased distribution. Unfortunately, in most real-world applications, the recovery of a data matrix appears to be incomplete, and perhaps even corrupted information. This paper considers the recovery of a low-rank matrix, where some observed entries are sampled in a \emph{biased distribution} suitably dependent on \emph{leverage scores} of a matrix, and some observed entries are uniformly corrupted. Our theoretical findings show that we can provably recover an unknown $n\times n$ matrix of rank $r$ from just about $O(nr\log^2 n)$ entries even when the few observed entries are corrupted with a small amount of noisy information. Empirical studies verify our theoretical results

Novel silica filled deep eutectic solvent based nanofluids for energy transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2- butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

Novel Silica Filled Deep Eutectic Solvent Based Nanofluids for Energy Transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2-butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

The Designs of Intelligent Bedroom Network Monitor System

AbstractThis system is composed of a 8-bits 51 kernel MCU, various security alarm, many household appliances control component. Host and slave, operators and controlled by popular GSM network and the 315MHz mature technique, communication frequency wireless network using the phone key-press mode, mutual communication operation mode and SMS operating mode, wireless remote operating mode of the three ways of manipulating the house appliances. Once in place, the system will alert sirens alarm, remote phone local language alarm 2 ways and alarm to subdue the illegal invasion and inform residents to take prompt emergency response thus maximum possible to reduce unnecessary losses. Because the system completely using wireless communication mode, makes the system is convenient in installation; Friendly man-machine interface that allows operation is simple; using universal communication protocol, making the system in expanding peripherals with relative ease. This design in safety, humanity, generality, practical wait for a respect to have breakthrough innovation

Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation

Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their resemblance to the original data. Nevertheless, precise control over similarity during sample generation presents a formidable challenge, often impeding the effective discovery of representative graph patterns. To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein. Within the ACGCL framework, we have devised a novel adversarial curriculum training methodology that facilitates progressive learning by sequentially increasing the difficulty of distinguishing the generated samples. Notably, this approach transcends the prevalent sparsity issue inherent in conventional curriculum learning strategies by adaptively concentrating on more challenging training data. Finally, a comprehensive assessment of ACGCL is conducted through extensive experiments on six well-known benchmark datasets, wherein ACGCL conspicuously surpasses a set of state-of-the-art baselines

Abortive Apoptosis and Its Profound Effects on Radiation, Chemical, and Oncogene-Induced Carcinogenesis

Traditionally apoptosis and the apoptotic machinery have been deemed as anticarcinogenic because of their presumed roles in eliminating damaged or unwanted cells. However, recent work from our laboratory and others have shown that the established paradigm is deeply flawed. The fundamental flaw is the assumption that apoptosis, once initiated, is irreversible and invariably leads to cell death. However, there is increasing evidence that cells can survive activation of the apoptotic cascade. This new revelation about abortive apoptotic cells can dramatically change our assessment of the biological roles of apoptosis. In this brief review, we will cover some of the original studies that report the "undead" apoptotic cells and how they lead to unexpected new roles for apoptotic factors in space radiation and other stress induced genetic instability and carcinogenesis. We will also review exciting new discoveries on the association among abortive apoptosis, spontaneous DNA double strand breaks, DNA damage response, and stemness of cancer cells

A Fusion-Denoising Attack on InstaHide with Data Augmentation

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye. In recent work, however, Carlini et al. show that it is possible to reconstruct private images from the encrypted dataset generated by InstaHide. Nevertheless, we demonstrate that Carlini et al.'s attack can be easily defeated by incorporating data augmentation into InstaHide. This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. The basic idea is to use a comparative network to identify encrypted images that are likely to correspond to the same private image, and then employ a fusion-denoising network for restoring the private image from the encrypted ones, taking into account the effects of data augmentation. Extensive experiments demonstrate the effectiveness of the proposed attack in comparison to Carlini et al.'s attack.Comment: 15 page

A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix

For an analytical expression of Vandermonde inverse matrix, a new derivation process based on Wronskian matrix and Lagrange interpolation polynomial basis is presented. Recursive formula and implementation cases for the direct formula of Vandermonde inverse matrix are given based on deriving the unified formula of Wronskian inverse matrix. For the calculation of symbol-type Vandermonde inverse matrix, the direct formula and recursive method are verified to be more efficient than Mathematica which is good at symbolic computation by comparing the computing time in Mathematica. The process and steps of recursive algorithm are relatively simple. The derivation process and idea both have very important values in theory and practice of Vandermonde and generalized Vandermonde inverse matrix

Analysis of Multiple Intracranial Aneurysms with Different Outcomes in the Same Patient After Endovascular Treatment

BACKGROUND: Aneurysm recanalization after coiling, with or without stent assistance, is a major issue in the endovascular management of intracranial aneurysms. Multiple intracranial aneurysms with different outcomes after endovascular treatment may represent a useful disease model in which patient-specific risk factors can be balanced to investigate possible features linked to aneurysm recanalization. In the present study, we evaluated the impact of aneurysm-specific, treatment-related, and hemodynamics-related factors on multiple aneurysms and to explore the reason why one aneurysm recanalized and the other did not. METHODS: Between 2010 and 2015, 763 multiple intracranial aneurysms in 326 patients were diagnosed by digital subtraction angiography. We retrospectively collected and analyzed 13 pairs of multiple aneurysms with different outcomes (recanalized or stable) in the same patient. Patient-specific models were constructed and analyzed by a computational fluid dynamics method. The virtual stent deployment method was used, and the coils were simulated by a porous medium model. Factors were evaluated for significance with respect to recanalization. RESULTS: Aneurysm size (P = 0.021), neck width (P = 0.027), ruptured aneurysms (P = 0.002), reduction ratio of averaged velocity (P = 0.008), and wall shear stress (P = 0.024) were significantly associated with aneurysm recanalization. By contrast, the aneurysm location, all of treatment-related factors (packing density, duration of follow-up, stent use, initial angiographic result) and the reduction ratio of averaged pressure were not significantly associated (P > 0.05). CONCLUSIONS: Small aneurysm size and neck width, unruptured aneurysm, and perianeurysmal hemodynamics with marked reduction may be important factors associated with the midterm durability of aneurysm embolization