arXiv subjects

Chen Fang

Publications and source records attributed to Chen Fang.

At least 127 records · Page 7Linked to original sources

arXiv2018

A Robust Interferometry Against Imperfections Base on Weak Value Amplification

The optical interferometry has been widely used in various high precision applications. Usually, the minimum precision of an interferometry is limited by various technique noises in practice. To suppress such kind of noises, we propose a novel scheme, which combines the weak measurement with the standard interferometry. The proposed scheme dramatically outperforms the standard interferometry in the signal noise ratio and the robustness against noises caused by the optical elements' reflections and the offset fluctuation between two paths. A proof-of-principle experiment is demonstrated to validate the amplification theory.

physics.optics↗

arXiv2018

Difference Weak Measurement

We propose the difference weak measurement scheme, and illustrate its advantages for measuring small longitude phase-shift in high precision. Compared to the standard interferometry and standard weak measurement schemes, the proposed scheme has much higher resolution in present of various practical imperfections, such as alignment error and light intensity variation error. Moreover, we highlight the advantage of utilizing complex weak value, where its imaginary part can reduce the harmful effect induced by channel decoherence. Finally, we propose closed-loop scenario to solve the narrow dynamic range problem obsessing the current weak measurement schemes. Difference weak measurement scheme simultaneously fulfills the requirements of high precision, wide dynamic range and strong robustness, which makes it a powerfully practical tool for phase-shift measurement and other metrological tasks.

quant-ph↗

arXiv2017

Realization of low-energy Dirac fermions in (Ir$_{1-x}$Pt$_x$)Te$_2$ superconductors

Superconducting and topological states are two distinct quantum states of matter. Superconductors with topologically nontrivial electronic states provide a platform to study the interplay between them. Recent study revealed that a bulk superconductor PdTe2 is a topological Dirac semimetal with type-II Dirac fermions. However, the Dirac fermions in PdTe2 have no contribution to the superconducting pair because the Dirac points reside far below the Fermi level (EF). Here, we show that in the isostructural compounds (Ir1-xPtx)Te2, the type-II Dirac points can be adjusted to EF by element substitution at x ~ 0.1, for which the bulk superconductivity appears near 2 K. The (Ir1-xPtx)Te2 superconductor with the Dirac points at EF paves the way for studying the relevant exotic physical phenomena such as topological superconductivity in Dirac semimetals.

cond-mat.mtrl-sci↗

arXiv2017

Observation of bulk nodal lines in topological semimetal ZrSiS

ZrSiS is the most intensively studied topological nodal-line semimetal candidate, which is proposed to host multiple nodal lines in its bulk electronic structure. However, previous angle-resolved photoemission spectroscopy (ARPES) experiments with vacuum ultraviolet lights mainly probed the surface states. Here using bulk-sensitive soft X-ray ARPES, we acquire the bulk electronic states of ZrSiS without any interference from surface states. Our results clearly show two groups of three-dimensional bulk nodal lines located on high-symmetry planes and along high-symmetry lines in the bulk Brillouin zone, respectively. The nodal lines on high-symmetry planes are enforced to pin at the Fermi level by carrier compensation and constitute the whole Fermi surfaces. This means that the carriers in ZrSiS are entirely contributed by nodal-line fermions, suggesting that ZrSiS is a remarkable platform for studying physical properties related to nodal lines.

cond-mat.mtrl-sci↗

arXiv2017

A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images

Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD. Among the recent investigated state-of-the-art approaches, Gaussian discriminant analysis (GDA)-based approaches have been demonstrated to be more effective and accurate in the classification of AD, especially for delineating its prodromal stage of mild cognitive impairment (MCI). Moreover, among those binary classification investigations, the local feature extraction methods were mostly used, which made them hardly be applied to a practical computer aided diagnosis system. Therefore, this study presents a novel global feature extraction model taking advantage of the recent proposed GDA-based dual high-dimensional decision spaces, which can significantly improve the early diagnosis performance comparing to those local feature extraction methods. In the true test using 20% held-out data, for discriminating the most challenging MCI group from the cognitively normal control (CN) group, an F1 score of 91.06%, an accuracy of 88.78%, a sensitivity of 91.80%, and a specificity of 83.78% were achieved that can be considered as the best performance obtained so far.

stat.ML↗

arXiv2017

The Cultural Evolution of National Constitutions

We explore how ideas from infectious disease and genetics can be used to uncover patterns of cultural inheritance and innovation in a corpus of 591 national constitutions spanning 1789 - 2008. Legal "Ideas" are encoded as "topics" - words statistically linked in documents - derived from topic modeling the corpus of constitutions. Using these topics we derive a diffusion network for borrowing from ancestral constitutions back to the US Constitution of 1789 and reveal that constitutions are complex cultural recombinants. We find systematic variation in patterns of borrowing from ancestral texts and "biological"-like behavior in patterns of inheritance with the distribution of "offspring" arising through a bounded preferential-attachment process. This process leads to a small number of highly innovative (influential) constitutions some of which have yet to have been identified as so in the current literature. Our findings thus shed new light on the critical nodes of the constitution-making network. The constitutional network structure reflects periods of intense constitution creation, and systematic patterns of variation in constitutional life-span and temporal influence.

cs.SI↗

arXiv2017

Universal Style Transfer via Feature Transforms

Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures via simple feature coloring.

cs.CV↗

arXiv2017

$(d-2)$-dimensional edge states of rotation symmetry protected topological states

We study fourfold rotation invariant gapped topological systems with time-reversal symmetry in two and three dimensions ($d=2,3$). We show that in both cases nontrivial topology is manifested by the presence of the $(d-2)$-dimensional edge states, existing at a point in 2D or along a line in 3D. For fermion systems without interaction, the bulk topological invariants are given in terms of the Wannier centers of filled bands, and can be readily calculated using a Fu-Kane-like formula when inversion symmetry is also present. The theory is extended to strongly interacting systems through explicit construction of microscopic models having robust $(d-2)$-dimensional edge states.

cond-mat.mes-hall↗

arXiv2017

Visually-Aware Fashion Recommendation and Design with Generative Image Models

Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using `off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features. Furthermore, we show that our model can be used \emph{generatively}, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.

cs.CV↗

arXiv2017

Dirac and nodal line magnons in three-dimensional antiferromagnets

We study the topological properties of magnon excitations in three-dimensional antiferromagnets, where the ground state configuration is invariant under time-reversal followed by space-inversion ($PT$-symmetry). We prove that Dirac points and nodal lines, the former being the limiting case of the latter, are the generic forms of symmetry-protected band crossings between magnon branches. As a concrete example, we study a Heisenberg spin model for a "spin-web" compound, Cu$_3$TeO$_6$, and show the presence of the magnon Dirac points assuming a collinear magnetic structure. Upon turning on symmetry-allowed Dzyaloshinsky-Moriya interactions, which introduce a small non-collinearity in the ground state configuration, we find that the Dirac points expand into nodal lines with nontrivial $Z_2$-topological charge, a new type of nodal lines unpredicted in any materials so far.

cond-mat.mes-hall↗

arXiv2017

Topological spin excitations observed in a three-dimensional antiferromagnet

Band topology, or global wave-function structure that enforces novel properties in the bulk and on the surface of crystalline materials, is currently under intense investigations for both fundamental interest and its technological promises. While band crossing of non-trivial topological nature was first studied in three dimensions for electrons, the underlying physical idea is not restricted to fermionic excitations. In fact, experiments have confirmed the possibility to have topological band crossing of electromagnetic waves in artificial structures. Fundamental bosonic excitations in real crystals, however, have not been observed to exhibit the counterpart under ambient pressure and magnetic field, where the difficulty is in part because natural materials cannot be precisely engineered like artificial structures. Here, we use inelastic neutron scattering to reveal the presence of topological spin excitations (magnons) in a three-dimensional antiferromagnet, Cu3TeO6, which features a unique lattice of magnetic spin-1/2 Cu2+ ions. Beyond previous understanding, we find that the material's spin lattice possesses a variety of exchange interactions, with the interaction between the ninth-nearest neighbours being as strong as that between the nearest neighbours. Although theoretical analysis indicates that the presence of topological magnon band crossing is independent of model details, Cu3TeO6 turns out to be highly favourable for the experimental observation, as its optical magnons are spectrally sharp and intense due to the highly interconnected spin network and the large magnetic cell. The observed magnon band crossing generally has the form of a special type of Z2-topological nodal lines that are yet to be found in fermion systems, rendering magnon systems a fertile ground for exploring novel band topology.

cond-mat.mes-hall↗

arXiv2017

Discovery of ideal Weyl points with helicoid surface states

Weyl points, serving as monopoles in the momentum space and laying the foundation of topological gapless phases, have recently been experimentally demonstrated in various physical systems. However, none of the observed Weyl degeneracies are ideal: they either offset in energy or coexist with trivial dispersions at other momenta. The lack of an ideal Weyl system sets a serious limit to the further development of Weyl physics and potential applications. Here, by constructing a photonic metamaterial, we experimentally observe an ideal Weyl system, whose nodal frequencies are pinned by symmetries to exactly the same value. Benefitting from the ideal Weyl nodes, we are able to map out the complete evolution of the helicoid surface states spinning around the projections of each Weyl nodes. Our discovery provides an ideal photonic platform for Weyl systems and novel topological devices.

cond-mat.mtrl-sci↗

arXiv2017

Rotation Anomaly and Topological Crystalline Insulators

We show that in the presence of $n$-fold rotation symmetries and time-reversal symmetry, the number of fermion flavors must be a multiple of $2n$ ($n=2,3,4,6$) on two-dimensional lattices, a stronger version of the well-known fermion doubling theorem in the presence of only time-reversal symmetry. The violation of the multiplication theorems indicates anomalies, and may only occur on the surface of new classes of topological crystalline insulators. Put on a cylinder, these states have $n$ Dirac cones on the top and on the bottom surfaces, connected by $n$ helical edge modes on the side surface.

cond-mat.mes-hall↗

arXiv2017

Topological Spin Liquid with Symmetry-Protected Edge States

Topological spin liquids are robust quantum states of matter with long-range entanglement and possess many exotic properties such as the fractional statistics of the elementary excitations. Yet these states, short of local parameters like all topological states, are elusive for conventional experimental probes. In this work, we combine theoretical analysis and quantum Monte Carlo numerics on a frustrated spin model which hosts a $\mathbb Z_2$ topological spin liquid ground state, and demonstrate that the presence of symmetry-protected gapless edge modes is a characteristic feature of the state, originating from the nontrivial symmetry fractionalization of the elementary excitations. Experimental observation of these modes on the edge would directly indicate the existence of the topological spin liquids in the bulk, analogous to the fact that the observation of Dirac edge states confirmed the existence of topological insulators.

cond-mat.str-el↗

arXiv2017

Ground state degeneracy in quantum spin systems protected by crystal symmetries

We develop a no-go theorem for two-dimensional bosonic systems with crystal symmetries: if there is a half-integer spin at a rotation center, where the point-group symmetry is $\mathbb D_{2,4,6}$, such a system must have a ground-state degeneracy protected by the crystal symmetry. Such a degeneracy indicates either a broken-symmetry state or a unconventional state of matter. Comparing to the Lieb-Schultz-Mattis Theorem, our result counts the spin at each rotation center, instead of the total spin per unit cell, and therefore also applies to certain systems with an even number of half-integer spins per unit cell.

cond-mat.str-el↗

arXiv2017

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize. This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems.

cs.CV↗

arXiv2017

Rethinking Skip-thought: A Neighborhood based Approach

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.

cs.CL↗

arXiv2017

Trimming and Improving Skip-thought Vectors

The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a hypothesis that, given a current sentence, inferring the previous and inferring the next sentence provide similar supervision power, therefore only one decoder for predicting the next sentence is preserved in our trimmed skip-thought model. Second, we present a connection layer between encoder and decoder to help the model to generalize better on semantic relatedness tasks. Third, we found that a good word embedding initialization is also essential for learning better sentence representations. We train our model unsupervised on a large corpus with contiguous sentences, and then evaluate the trained model on 7 supervised tasks, which includes semantic relatedness, paraphrase detection, and text classification benchmarks. We empirically show that, our proposed model is a faster, lighter-weight and equally powerful alternative to the original skip-thought model.

cs.CL↗