Transition-metal distribution in kagome antiferromagnet CoCu3(OH)6Cl2 revealed by resonant x-ray diffraction

The distribution of chemically similar transition-metal ions is a fundamental issue in the study of herbertsmithite-type kagome antiferromagnets. Using synchrotron radiation, we have performed resonant powder x-ray diffractions on newly synthesized CoCu3(OH)6Cl2, which provide an exact distribution of transition-metal ions in the frustrated antiferromagnet. Both magnetic susceptibility and specific heat measurements are quantitatively consistent with the occupation fractions determined by resonant x-ray diffraction. The distribution of transition-metal ions and residual magnetic entropy suggest a novel low temperature (T < 4 K) magnetism, where the interlayer triangular spins undergo a spin-glass freezing while the kagome spins still keep highly frustrated.Comment: 18 pages, 4 figures and 2 table

Convergence Analysis and Design of Multi-block ADMM via Switched Control Theory

We consider three challenges in multi-block Alternating Direction Method of Multipliers (ADMM): building convergence conditions for ADMM with any block (variable) sequence, finding available block sequences to be fit for ADMM, and designing useful parameter controllers for ADMM with unfixed parameters. To address these challenges, we develop a switched control framework for studying multi-block ADMM. First, since ADMM recursively and alternately updates the block-variables, it is converted into a discrete-time switched dynamical system. Second, we study exponential stability and stabilizability of the switched system for linear convergence analysis and design of ADMM by employing switched Lyapunov functions. Moreover, linear matrix inequalities conditions are proposed to ensure convergence of ADMM under arbitrary sequence, to find convergent sequences, and to design the fixed parameters. These conditions are checked and solved by employing semidefinite programming. Numerical experiments further verify the effectiveness of our proposed theories.Comment: 19 pages, 4 figure

Discrete solitons in waveguide arrays with long-range linearly coupled effect

We study the influences to the discrete soliton (DS) by introducing linearly long-range nonlocal interactions, which give rise to the off-diagonal elements of the linearly coupled matrix in the discrete nonlinear schrodinger equation to be filled by non-zero terms. Theoretical analysis and numerical simulations find that the DS under this circumstance can exhibit strong digital effects: the fundamental DS is a narrow one, which occupies nearly only one waveguide, the dipole and double-monopole solitons, which occupy two waveguides, can be found in self-focusing and -defocusing nonlinearities, respectively. Stable flat-top solitons and their stagger counterparts, which occupy a controllable number of waveguides, can also be obtained through this system. Such digital properties may give rise to additional data processing applications and have potential in fabricating digital optical devices in all-optical networks.Comment: 8 pages, and 6 figure

Intrinsic ultralow lattice thermal conductivity of the unfilled skutterudite FeSb3_3

It has been generally accepted that unfilled skutterudites process high lattice thermal conductivity ($\kappa_{l}$) that can be efficiently reduced upon filling. Here by using first principles Boltzmann-Peierls transport calculations, we find pure skutterudite of FeSb$_3$ with no filler in fact has an intrinsic ultralow $\kappa_{l}$ smaller than that of CoSb$_3$ by one order of magnitude. The value is even smaller than those of most of the fully filled skutterudites. This finding means that with FeSb$_3$ as a reference, filling does not necessarily lower $\kappa_{l}$. The ultralow $\kappa_{l}$ of FeSb$_3$ is a consequence of much softened optical phonon branches associated with the weakly bonded Sb$_4$ rings. They overlap more with heat-carrying acoustic phonons and significantly increase the phase space for three-phonon anharmonic scattering processes. This provides an alternative non-filling related mechanism for lowering the $\kappa_{l}$ of skutterudites.Comment: 6 pages, 5 figures + Supplementary informatio

Electrical Control of Strong Spin-Phonon Coupling in a Carbon Nanotube

We describe an approach to electrically control the strong interaction between a single electron spin and the vibrational motion of a suspended carbon nanotube resonator. The strength of the deflection-induced spin-phonon coupling is dependent on the wavefunction of the electron confined in a lateral carbon nanotube quantum dot. An electrical field along the nanotube shifts the effective center of the quantum dot, leading to the corresponding modification of the spin-phonon strength. Numerical simulations with experimentally reachable parameters show that high fidelity quantum state transfer between mechanical and spin qubits driven by electrical pulses is feasible. Our results form the basis for the fully electrical control of the coherent interconvertion between light and spin qubits and for manufacturing electrically driven quantum information processing systems.Comment: 4pages,3figure

Tell Me Where to Look: Guided Attention Inference Network

Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) first time make attention maps an explicit and natural component of the end-to-end training, (2) provide self-guidance directly on these maps by exploring supervision form the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on Pascal VOC 2012 val. and test set. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.Comment: Accepted in CVPR201

Question Guided Modular Routing Networks for Visual Question Answering

This paper studies the task of Visual Question Answering (VQA), which is topical in Multimedia community recently. Particularly, we explore two critical research problems existed in VQA: (1) efficiently fusing the visual and textual modalities; (2) enabling the visual reasoning ability of VQA models in answering complex questions. To address these challenging problems, a novel Question Guided Modular Routing Networks (QGMRN) has been proposed in this paper. Particularly, The QGMRN is composed of visual, textual and routing network. The visual and textual network serve as the backbones for the generic feature extractors of visual and textual modalities. QGMRN can fuse the visual and textual modalities at multiple semantic levels. Typically, the visual reasoning is facilitated by the routing network in a discrete and stochastic way by using Gumbel-Softmax trick for module selection. When the input reaches a certain modular layer, routing network newly proposed in this paper, dynamically selects a portion of modules from that layer to process the input depending on the question features generated by the textual network. It can also learn to reason by routing between the generic modules without additional supervision information or expert knowledge. Benefiting from the dynamic routing mechanism, QGMRN can outperform the previous classical VQA methods by a large margin and achieve the competitive results against the state-of-the-art methods. Furthermore, attention mechanism is integrated into our QGMRN model and thus can further boost the model performance. Empirically, extensive experiments on the CLEVR and CLEVR-Humans datasets validate the effectiveness of our proposed model, and the state-of-the-art performance has been achieved

Rethinking Classification and Localization for Object Detection

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.Comment: CVPR 202

Group velocity locked vector dissipative solitons in a high repetition rate fiber laser

Vectorial nature of dissipative solitons (DSs) with high repetition rates is studied for the first time in a normal-dispersion fiber laser. Despite the fact that the formed DSs are strongly chirped and the repetition rate is greater than 100 MHz, polarization locked and polarization rotating group velocity locked vector DSs can be formed under 129.3 MHz fundamental mode-locking and 258.6 MHz harmonic mode-locking of the fiber laser, respectively. The two orthogonally polarized components of these vector DSs possess distinctly different central wavelengths and travel together at the same group velocity in the laser cavity, resulting in a gradual spectral edge and small steps on the optical spectra, which can be considered as an auxiliary indicator of the group velocity locked vector DSs.Comment: 6 pages, 4 figure

Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge

This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition. In the front-end, Multi-channel Wiener filter (MWF) is designed to achieve background noise reduction. Different from traditional MWF, optimized parameter for the tradeoff between noise reduction and target signal distortion is built according to the desired noise reduction level. In the back-end, several techniques are taken advantage to improve the noisy Automatic Speech Recognition (ASR) performance including Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long short-term memory (LSTM) using medium vocabulary, Lattice rescoring with a big vocabulary language model finite state transducer, and ROVER scheme. Experimental results show the proposed system combining front-end and back-end is effective to improve the ASR performance.Comment: 5 pages, 1 figur