arXiv subjects

Ke Chen

Publications and source records attributed to Ke Chen.

At least 307 records · Page 17Linked to original sources

arXiv2019

Selection Algorithms with Small Groups

We revisit the selection problem, namely that of computing the $i$th order statistic of $n$ given elements, in particular the classic deterministic algorithm by grouping and partition due to Blum, Floyd, Pratt, Rivest, and Tarjan (1973). Whereas the original algorithm uses groups of odd size at least $5$ and runs in linear time, it has been perpetuated in the literature that using smaller group sizes will force the worst-case running time to become superlinear, namely $Ω(n \log{n})$. We first point out that the usual arguments found in the literature justifying the superlinear worst-case running time fall short of proving this claim. We further prove that it is possible to use group size smaller than $5$ while maintaining the worst case linear running time. To this end we introduce three simple variants of the classic algorithm, the repeated step algorithm, the shifting target algorithm, and the hyperpair algorithm, all running in linear time.

cs.DS↗

arXiv2019

Helicoidal excitonic phase in an electron-hole double layer system

We propose helicoidal excitonic phase in a Coulomb-coupled two-dimensional electron-hole double layer (EHDL) system with relativistic spin-orbit interaction. Previously, it was demonstrated that layered InAs/AlSb/GaInSb heterostructure is an ideal experimental platform for searching excitonic condensate phases, while its electron layer has non-negligible Rashba interaction. We clarify that due to the Rashba term, the spin-triplet (spin-1) exciton field in the EHDL system forms a helicoidal structure and the helicoid plane can be controlled by an in-plane Zeeman field. We show that due to small but finite Dirac term in the heavy hole layer the helicoidal structure of the excitonic field under the in-plane field results in a helicoidal {\it magnetic} order in the electron layer. Based on linearization analyses, we further calculate momentum-energy dispersions of low-energy Goldstone modes in the helicoidal excitonic phase. We discuss possible experimental probes of the excitonic phase in the EHDL system.

cond-mat.mes-hall↗

arXiv2019

ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views

In this paper, we introduce the ShopSign dataset, which is a newly developed natural scene text dataset of Chinese shop signs in street views. Although a few scene text datasets are already publicly available (e.g. ICDAR2015, COCO-Text), there are few images in these datasets that contain Chinese texts/characters. Hence, we collect and annotate the ShopSign dataset to advance research in Chinese scene text detection and recognition. The new dataset has three distinctive characteristics: (1) large-scale: it contains 25,362 Chinese shop sign images, with a total number of 196,010 text-lines. (2) diversity: the images in ShopSign were captured in different scenes, from downtown to developing regions, using more than 50 different mobile phones. (3) difficulty: the dataset is very sparse and imbalanced. It also includes five categories of hard images (mirror, wooden, deformed, exposed and obscure). To illustrate the challenges in ShopSign, we run baseline experiments using state-of-the-art scene text detection methods (including CTPN, TextBoxes++ and EAST), and cross-dataset validation to compare their corresponding performance on the related datasets such as CTW, RCTW and ICPR 2018 MTWI challenge dataset. The sample images and detailed descriptions of our ShopSign dataset are publicly available at: https://github.com/chongshengzhang/shopsign.

cs.CV↗

arXiv2019

Element Replacement Approach by Reaction with Lewis Acidic Molten Salts to Synthesize Nanolaminated MAX Phases and MXenes

Nanolaminated materials are important because of their exceptional properties and wide range of applications. Here, we demonstrate a general approach to synthesize a series of Zn-based MAX phases and Cl-terminated MXenes originating from the replacement reaction between the MAX phase and the late transition metal halides. The approach is a top-down route that enables the late transitional element atom (Zn in the present case) to occupy the A site in the pre-existing MAX phase structure. Using this replacement reaction between Zn element from molten ZnCl2 and Al element in MAX phase precursors (Ti3AlC2, Ti2AlC, Ti2AlN, and V2AlC), novel MAX phases Ti3ZnC2, Ti2ZnC, Ti2ZnN, and V2ZnC were synthesized. When employing excess ZnCl2, Cl terminated MXenes (such as Ti3C2Cl2 and Ti2CCl2) were derived by a subsequent exfoliation of Ti3ZnC2 and Ti2ZnC due to the strong Lewis acidity of molten ZnCl2. These results indicate that A-site element replacement in traditional MAX phases by late transition metal halides opens the door to explore MAX phases that are not thermodynamically stable at high temperature and would be difficult to synthesize through the commonly employed powder metallurgy approach. In addition, this is the first time that exclusively Cl-terminated MXenes were obtained, and the etching effect of Lewis acid in molten salts provides a green and viable route to prepare MXenes through an HF-free chemical approach.

cond-mat.mtrl-sci↗

arXiv2019

Emergence and percolation of rigid domains during colloidal glass transition

Using video microscopy, we measure local spatial constraints in disordered binary colloidal samples, ranging from dilute fluids to jammed glasses, and probe their spatial and temporal correlations to local dynamics during the glass transition. We observe the emergence of significant correlations between constraints and local dynamics within the Lindemann criterion, which coincides with the onset of glassy dynamics in supercooled liquids. Rigid domains in fluids are identified based on local constraints, and demonstrate a percolation transition near glass transition, accompanied by the emergence of dynamical heterogeneities. Our results show that the spatial constraints instead of the geometry of amorphous structures is the key that connects the complex spatial-temporal correlations in disordered materials.

cond-mat.soft↗

arXiv2019

Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation

Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.

cs.LG↗

arXiv2019

Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding

Human action recognition refers to automatic recognizing human actions from a video clip. In reality, there often exist multiple human actions in a video stream. Such a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are many meaningful human actions in reality but it would be extremely difficult to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem in a holistic way. Our framework holistically tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for knowledge transfer. Consequently, our framework leads to a joint latent ranking embedding for multi-label zero-shot human action recognition. A novel neural architecture of two component models and an alternate learning algorithm are proposed to carry out the joint latent ranking embedding learning. Thus, multi-label zero-shot recognition is done by measuring relatedness scores of action labels to a test video clip in the joint latent visual and semantic embedding spaces. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework.

cs.CV↗

arXiv2019

Observation of second sound in graphite at temperatures above 100 K

Wavelike thermal transport in solids, referred to as second sound, has until now been an exotic phenomenon limited to a handful of materials at low temperatures. This has restricted interest in its occurrence and in its potential applications. Through time-resolved optical measurements of thermal transport on 5-20 μm length scales in graphite, we have made direct observations of second sound at temperatures above 100 K. The results are in qualitative agreement with ab initio calculations that predict wavelike phonon hydrodynamics on ~ 1-μm length scale up to almost room temperature. The results suggest an important role of second sound in microscale transient heat transport in two-dimensional and layered materials in a wide temperature range.

cond-mat.mes-hall↗

arXiv2019

The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.

cs.SD↗

arXiv2019

Tooth morphometry using quasi-conformal theory

Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichmüller maps are used for establishing a 1-1 correspondence between tooth surfaces with prescribed anatomical landmarks. Then, a quasi-conformal statistical shape analysis model based on the Teichmüller mapping results is proposed for building a tooth classification scheme. We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries. Experimental results show that our method achieves much higher classification accuracy with respect to both gender and ancestry when compared to the existing methods. Furthermore, our model reveals the underlying tooth shape difference between different genders and ancestries in terms of the local geometric distortion and curvatures.

cs.CV↗

arXiv2018

Study of electron emission from 1D nanomaterials under super high field

Photoemission driven by a strong electric field of near-infrared or visible light, referred to as strong-field photoemission, produces attosecond electron pulses that are synchronized to the waveform of the incident light, and this principle lies at the heart of current attosecond technologies. However, full access to strong-field photoemission regimes at near-infrared wavelengths based on solid-state materials is restricted by space-charge screening and material damage at high optical-field strengths, which significantly hampers the realization of predicted attosecond technologies, such as ultra-sensitive optical phase modulation. Here, we demonstrate a new type of strong-field photoemission behaviour with extreme nonlinearity -- photoemission current scales follow a 40th power law of the optical-field strength, making use of sub-nanometric carbon nanotubes and 800 nm pulses. As a result, the total photoemission current depends on the carrier-envelope phase with a greatly improved photoemission current modulation depth of up to 100%, which has not previously been achieved. Time-dependent density functional calculations reveal the completely new behaviour of the optical-field induced tunnelling emission process directly from the valence band of the carbon nanotubes, which is an indication of full access to a strong-field photoemission regime. Furthermore, the nonlinear dynamics are observed to be tunable by changing the binding energy of the valence-band-maximum, as confirmed by Simpleman model calculations. We believe that such extreme nonlinear photoemission from nanotips offers a new means of producing extreme temporal-spatial resolved electron pulses. These results additionally provide a new design philosophy for attosecond electronics and optics by making use of tunable band structures in nanomaterials.

physics.optics↗

arXiv2018

Randomized sampling for basis functions construction in generalized finite element methods

In the framework of generalized finite element methods for elliptic equations with rough coefficients, efficiency and accuracy of the numerical method depend critically on the use of appropriate basis functions. This work explores several random sampling strategies that construct approximations to the optimal set of basis functions of a given dimension, and proposes a quantitative criterion to analyze and compare these sampling strategies. Numerical evidence shows that the best results are achieved by two strategies, Random Gaussian and Smooth boundary sampling.

math.OC↗

arXiv2018

Decoupling between thermodynamics and dynamics during rejuvenation in colloidal glasses

We rejuvenate well-aged quasi-2D binary colloidal glasses by thermal cycling, and systematically measure both the statistical responses and particle-level structural evolutions during rejuvenation. While the moduli and boson peak are continuously rejuvenated with increasing number of cycles, the mean square displacement (MSD) fluctuates significantly between different groups of thermal cycles. The decoupling between the thermodynamical and dynamical evolutions suggests different microscopic origins for different bulk properties of glasses. We find that a small fraction of structural rearrangements triggered by thermal cycling could alter the whole elastic continuum and lead to the significant thermodynamic rejuvenation, while localized defects could be activated and deactivated at the positions close to the rearrangements with significantly high mobility change and hence result in the fluctuated dynamics even with only about 10% of particles as fast regions. Our results offer a comprehensive picture for the microscopic mechanisms underlying bulk glass rejuvenation, which could be readily used to refine glass properties or to formulate further statistical theories in glassy systems.

cond-mat.soft↗

arXiv2018

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test platform. We built neural networks as our policy to map sensor readings to control signals on the UAV. Our experiment scenarios cover both simulation and reality. We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation, at the same time it ensures safety and keeps training cost acceptable.

cs.RO↗

arXiv2018

A Convex Geodesic Selective Model for Image Segmentation

Selective segmentation is an important application of image processing. In contrast to global segmentation in which all objects are segmented, selective segmentation is used to isolate specific objects in an image and is of particular interest in medical imaging -- permitting segmentation and review of a single organ. An important consideration is to minimise the amount of user input to obtain the segmentation; this differs from interactive segmentation in which more user input is allowed than selective segmentation. To achieve selection, we propose a selective segmentation model which uses the edge-weighted geodesic distance from a marker set as a penalty term. It is demonstrated that this edge-weighted geodesic penalty term improves on previous selective penalty terms. A convex formulation of the model is also presented, allowing arbitrary initialisation. It is shown that the proposed model is less parameter dependent and requires less user input than previous models. Further modifications are made to the edge-weighted geodesic distance term to ensure segmentation robustness to noise and blur. We can show that the overall Euler-Lagrange equation admits a unique viscosity solution. Numerical results show that the result is robust to user input and permits selective segmentations that are not possible with other models.

math.NA↗

arXiv2018

Multigrid Algorithm Based on Hybrid Smoothers for Variational and Selective Segmentation Models

Automatic segmentation of an image to identify all meaningful parts is one of the most challenging as well as useful tasks in a number of application areas. This is widely studied. Selective segmentation, less studied, aims to use limited user specified information to extract one or more interesting objects (instead of all objects). Constructing a fast solver remains a challenge for both classes of model. However our primary concern is on selective segmentation. In this work, we develop an effective multigrid algorithm, based on a new non-standard smoother to deal with non-smooth coefficients, to solve the underlying partial differential equations (PDEs) of a class of variational segmentation models in the level set formulation. For such models, non-smoothness (or jumps) is typical as segmentation is only possible if edges (jumps) are present. In comparison with previous multigrid methods which were shown to produce an acceptable {\it mean} smoothing rate for related models, the new algorithm can ensure a small and {\it global} smoothing rate that is a sufficient condition for convergence. Our rate analysis is by Local Fourier Analysis and, with it, we design the corresponding iterative solver, improving on an ineffective line smoother. Numerical tests show that the new algorithm outperforms multigrid methods based on competing smoothers.

math.NA↗

arXiv2018

Orientational Order in Disordered Colloidal Suspensions

Exploring structural order in disordered systems including liquids and glasses is an intriguing but challenging issue in condensed matter physics. Here we construct a new parameter based on the angular distribution function of particles and show that this new orientational order has significantly higher correlation with dynamic heterogeneity compared to a translational parameter based on the radial distribution functions in colloidal glasses. The gradual development of orientational and translational order in supercooled liquids shows that the higher correlation between orientational order and dynamics comes from the onset of glass transition and the orientational order would dominate the glassy dynamics after a simple liquid is considerably supercooled. Our results suggest that orientational order reflects the formation of amorphous order during glass transition while translational order is mainly a result of density increase.

cond-mat.soft↗

arXiv2018

Online learning in optical tomography: a stochastic approach

We study the inverse problem of radiative transfer equation (RTE) using stochastic gradient descent method (SGD) in this paper. Mathematically, optical tomography amounts to recovering the optical parameters in RTE using the incoming-outgoing pair of light intensity. We formulate it as a PDE-constraint optimization problem, where the mismatch of computed and measured outgoing data is minimized with same initial data and RTE constraint. The memory and computation cost it requires, however, is typically prohibitive, especially in high dimensional space. Smart iterative solvers that only use partial information in each step is called for thereafter. Stochastic gradient descent method is an online learning algorithm that randomly selects data for minimizing the mismatch. It requires minimum memory and computation, and advances fast, therefore perfectly serves the purpose. In this paper we formulate the problem, in both nonlinear and its linearized setting, apply SGD algorithm and analyze the convergence performance.

math.OC↗