A Comment on "Memory Effects in an Interacting Magnetic Nanoparticle System"

Recently, Sun et al reported that striking memory effects had been clearly observed in their new experiments on an interacting nanoparticle system [1]. They claimed that the phenomena evidenced the existence of a spin-glass-like phase and supported the hierarchical model. No doubt that a particle system may display spin-glass-like behaviors [2]. However, in our opinion, the experiments in Ref. [1] cannot evidence the existence of spin-glass-like phase at all. We will demonstrate below that all the phenomena in Ref. [1] can be observed in a non-interacting particle system with a size distribution. Numerical simulations of our experiments also display the same features.Comment: A comment on "Phys. Rev. Lett. 91, 167206

Elliptic blowup equations for 6d SCFTs. Part II: Exceptional cases

The building blocks of 6d $(1,0)$ SCFTs include certain rank one theories with gauge group $G=SU(3),SO(8),F_4,E_{6,7,8}$. In this paper, we propose a universal recursion formula for the elliptic genera of all such theories. This formula is solved from the elliptic blowup equations introduced in our previous paper. We explicitly compute the elliptic genera and refined BPS invariants, which recover all previous results from topological string theory, modular bootstrap, Hilbert series, 2d quiver gauge theories and 4d $\mathcal{N}=2$ superconformal $H_{G}$ theories. We also observe an intriguing relation between the $k$-string elliptic genus and the Schur indices of rank $k$ $H_{G}$ SCFTs, as a generalization of Lockhart-Zotto's conjecture at the rank one cases. In a subsequent paper, we deal with all other non-Higgsable clusters with matters

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1

A reagentless amperometric immunosensor based on nano-au and carbon nanotubes for detection of alphafetoprotein

In this paper, carboxyl-ferrocene (Fc-COOH) was explored to label alphafetoprotein antibody (anti-AFP), which was then mixed with AU nanoparticles (nano-Au) and multi-walled carbon nanotubes (MWCNTs) dispersed by chitosan (CS) to form the nano-Au/MWCNTs/anti-AFP-Fc chitosan composite. After that, the composite was immobilized onto the surface of the electrode to prepare a reagentless amperometric alphafetoprotein (AFP) immunosensor. The experimental results indicated that, the proposed immunosensor had good current response to AFP with a linear range from 0.1 to 50 ng/ml with a detection limit of 0.03 ng/ml. Moreover, the electrochemical properties and performance were investigated by the cyclic voltammetry. The results also found that, the immunosensor owned the advantages of high stability, simple preparation and operation and well regeneration performance.Key words: Multi-walled carbon nanotubes (MWCNTs), Au nanoparticles (nano-Au), chitosan (CS), amperometric immunosensor, alphafetoprotein (AFP)

Delay-dependent robust stability of stochastic delay systems with Markovian switching

In recent years, stability of hybrid stochastic delay systems, one of the important issues in the study of stochastic systems, has received considerable attention. However, the existing results do not deal with the structure of the diffusion but estimate its upper bound, which induces conservatism. This paper studies delay-dependent robust stability of hybrid stochastic delay systems. A delay-dependent criterion for robust exponential stability of hybrid stochastic delay systems is presented in terms of linear matrix inequalities (LMIs), which exploits the structure of the diffusion. Numerical examples are given to verify the effectiveness and less conservativeness of the proposed method

Relaxing the Irrevocability Requirement for Online Graph Algorithms

Online graph problems are considered in models where the irrevocability requirement is relaxed. Motivated by practical examples where, for example, there is a cost associated with building a facility and no extra cost associated with doing it later, we consider the Late Accept model, where a request can be accepted at a later point, but any acceptance is irrevocable. Similarly, we also consider a Late Reject model, where an accepted request can later be rejected, but any rejection is irrevocable (this is sometimes called preemption). Finally, we consider the Late Accept/Reject model, where late accepts and rejects are both allowed, but any late reject is irrevocable. For Independent Set, the Late Accept/Reject model is necessary to obtain a constant competitive ratio, but for Vertex Cover the Late Accept model is sufficient and for Minimum Spanning Forest the Late Reject model is sufficient. The Matching problem has a competitive ratio of 2, but in the Late Accept/Reject model, its competitive ratio is 3/2

Spectral salient object detection

© 2014 IEEE. Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection

A novel multi-objective evolutionary algorithm based on space partitioning

To design an e ective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs; 2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs; 3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion de ned in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks

Modelling combined intravenous thrombolysis and mechanical thrombectomy in acute ischaemic stroke: Understanding the relationship between stent retriever configuration and clot lysis mechanisms

Background: Combined intravenous thrombolysis and mechanical thrombectomy (IVT-MT) is a common treatment in acute ischaemic stroke, however the interaction between IVT and MT from a physiological standpoint is poorly understood. In this pilot study, we conduct numerical simulations of combined IVT-MT with various idealised stent retriever configurations to evaluate performance in terms of complete recanalisation times and lysis patterns. Methods: A 3D patient-specific geometry of a terminal internal carotid artery with anterior and middle cerebral arteries is reconstructed, and a thrombus is artificially implanted in the MCA branch. Various idealised stent retriever configurations are implemented by varying stent diameter and stent placement, and a configuration without a stent retriever provides a baseline for comparison. A previously validated multi-level model of thrombolysis is used, which incorporates blood flow, drug transport, and fibrinolytic reactions within a fibrin thrombus. Results: Fastest total recanalisation was achieved in the thrombus without a stent retriever, with lysis times increasing with stent retriever diameter. Two mechanisms of clot lysis were established: axial and radial permeation. Axial permeation from the clot front was the primary mechanism of lysis in all configurations, as it facilitated increased protein binding with fibrin fibres. Introducing a stent retriever channel allowed for radial permeation, which occurred at the fluid-thrombus interface, although lysis was much slower in the radial direction because of weaker secondary velocities. Conclusions: Numerical models can be used to better understand the complex physiological relationship between IVT and MT. Two different mechanisms of lysis were established, providing a basis towards improving the efficacy of combined treatments