Derivation algebras and automorphism groups of a class of deformative super $W$-algebras $W^s_λ(2,2)$
In this paper, the derivation algebras and automorphism groups of a class of deformative super $W$-algebras $W_λ(2,2)$ are determined.
math.RA↗
arXiv subjects
Hao Wang
Publications and source records attributed to Hao Wang.
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.
cs.CV↗
ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data
Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. However, they failed to discover the implicit semantic information, i.e., individual career progress patterns and social-relations, which are vital to comprehensive understanding of career development. Besides, how to visualize them for better human cognition is also challenging. To tackle these issues, we propose a visual analytics system ResumeVis to mine and visualize resume data. Firstly, a text-mining based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. By interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2500 online officer resumes demonstrate the effectiveness of our system. We provide a demonstration video.
cs.HC↗
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
cs.CL↗
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.
cs.CL↗
Hearing the birth of the lightest intermediate mass black holes with the second generation gravitational wave detectors
Intermediate mass black holes (IMBHs) with a mass between $10^{2}$ and $10^{5}$ times that of the sun, which bridges the {mass gap between the} stellar-mass black holes and the supermassive black holes, are crucial in understanding the evolution of the black holes. Although they are widely believed to exist, decisive evidence has long been absent. Motivated by the successful detection of massive stellar-mass black holes by advanced LIGO, through the gravitational wave radiation during the binary merger, in this work we investigate the prospect of detecting/identifying the lightest IMBHs (LIMBHs; the black holes $\gtrsim 100M_\odot$) with the second generation gravitational wave detectors. In general, the chance of hearing the birth of the LIMBHs is significantly higher than that to identify pre-merger IMBHs. The other formation channel of LIMBHs, where stars with huge mass/low-metallicity directly collapse, is likely "silent", so the merger-driven birth of the LIMBHs may be the only "observable" scenario in the near future. Moreover, the prospect of establishing the presence of (lightest) intermediate mass black holes in the O3 run and beyond of advanced LIGO is found quite promising, implying that such an instrument could make another breakthrough on astronomy in the near future. The joining of other detectors like advanced Virgo would only increase the detection rate.
astro-ph.HE↗
Room-temperature Strong Coupling of Au Nanorod-WSe2 Heterostructures
All-solid-state strong light-matter coupling systems with large vacuum Rabi splitting are great important for quantum information application, such as quantum manipulation, quantum information storage and processing. The monolayer transition metal dichalcogenides (TMDs) have been explored as excellent candidates for the strong light-matter interaction, due to their extraordinary exciton binding energies and remarkable optical properties. Here, for both of experimental and theoretical aspects, we explored resonance coupling effect between exciton and plasmonic nanocavity in heterostructures consisting of monolayer tungsten diselenide (WSe2) and an individual Au nanorod. We also study the influences on the resonance coupling of various parameters, including localized surface plasmon resonances of Au nanorods with varied topological aspects, separation between Au nanorod and monolayer WSe2 surface, and the thickness of WSe2. More importantly, the resonance coupling can approach the strong coupling regime at room-temperature by selecting appropriate parameters, where an anti-crossing behavior with the vacuum Rabi splitting strength of 98 meV was observed on the energy diagram.
cond-mat.mes-hall↗
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience. The re-training process effectively performs distance metric learning under the object persistency constraints, to modify the view-manifold of object representations. It reduces the effective distance between the representations of different views of the same object without compromising the distance between those of the views of different objects, resulting in the untangling of the view-manifolds between individual objects within the same category and across categories. This untangling enables the model to discriminate and recognize objects within the same category, independent of viewpoints. We found that this ability is not limited to the trained objects, but transfers to novel objects in both trained and untrained categories, as well as to a variety of completely novel artificial synthetic objects. This transfer in learning suggests the modification of distance metrics in view- manifolds is more general and abstract, likely at the levels of parts, and independent of the specific objects or categories experienced during training. Interestingly, the resulting transformation of feature representation in the deep networks is found to significantly better match human perceptual similarity judgment than AlexNet, suggesting that object persistence could be an important constraint in the development of perceptual similarity judgment in biological neural networks.
cs.CV↗
ZM-Net: Real-time Zero-shot Image Manipulation Network
Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.
cs.CV↗
Room-temperature vacuum Rabi splitting with active control in two-dimensional atomic crystals
Strong light-matter coupling manifested by vacuum Rabi splitting has attracted tremendous attention due to its fundamental importance in cavity quantum-electrodynamics research and great potentials in quantum information applications. A prerequisite for practical applications of the strong coupling in future quantum information processing and coherent manipulation is an all-solid-state system exhibiting room-temperature vacuum Rabi splitting with active control. Here we realized such a system in heterostructure consisting of monolayer WS2 and an individual plasmonic gold nanorod. By taking advantage of both of the small mode volume of the nanorod and large binding energy of the WS2 exciton, giant vacuum Rabi splitting energies of 91 meV ~ 133 meV can be obtained at ambient conditions, which only involve 5 ~ 18 excitons. The vacuum Rabi splitting can be dynamically tuned either by electrostatic gating or temperature scanning. These findings could pave the way towards active quantum optic devices operating at room temperature.
physics.optics↗
Lie super-bialgebra structures on a class of generalized super $W$-algebra $\mathfrak{L}$
In this paper, Lie super-bialgebra structures on a class of generalized super $W$-algebra $\mathfrak{L}$ are investigated. By proving the first cohomology group of $\mathfrak{L}$ with coefficients in its adjoint tensor module is trivial, namely, $H^1(\mathfrak{L},\mathfrak{L}\otimes {\mathfrak{L}})=0$, we obtain that all Lie super-bialgebra structures on $\mathfrak{L}$ are triangular coboundary.
math.RA↗
Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway
The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.
stat.AP↗
The Role of Big Data on Smart Grid Transition
Despite being popularly referred to as the ultimate solution for all problems of our current electric power system, smart grid is still a growing and unstable concept. It is usually considered as a set of advanced features powered by promising technological solutions. In this paper, we describe smart grid as a socio-technical transition and illustrate the evolutionary path on which a smart grid can be realized. Through this conceptual lens, we reveal the role of big data, and how it can fuel the organic growth of smart grid. We also provide a rough estimate of how much data will be potentially generated from different data sources, which helps clarify the big data challenges during the evolutionary process.
cs.CY↗
An all optical phase shifter and switch near 1550nm using tungsten disulfide (WS2) deposited tapered fiber
Optical phase shifters and switches play an important role for various optical applications including optical signal processing, sensing and communication. In this paper, we demonstrate a fiber all optical phase shifter using few-layer 2D material tungsten disulfide (WS2) deposited on a tapered fiber. WS2 absorbs injected 980 nm pump (control light) and generates heat which changes the refractive index of the tapered fiber due to thermo-optic effect and achieves a maximum phase shift of 6.1π near 1550 nm. The device has a loss of 3.7 dB. By constructing a Mach-Zehnder interferometer with WS2 based phase shifter in one arm, an all optical switch is also obtained with an extinction ratio of 15 dB and a rise time of 7.3 ms. This all fiber low-cost and compact optical phase shifter and switch demonstrates the potential of 2D transition metal dichalcogenides for all optical signal processing devices.
physics.optics↗
Analysis of the Residual Type and the Recovery Type a Posteriori Error Estimators for a Consistent Atomistic-to-Continuum Coupling Method in 1D
We consider the a posteriori error estimation for an atomistic-to-continuum cou- pling scheme for a generic one-dimensional many-body next-nearest-neighbour interaction model in 1D. We derive and rigorously prove the efficiency of the residual type estimator. We prove the equivalence between the residual type and the gradient recovery type estima- tor in the continuum region and propose a (novel) hybrid a a posteriori error estimator by combining the two types of estimators. Our numerical experiments illustrate the optimal con- vergence rate of the adaptive algorithms using these estimators whose efficiency factors are also presented.
math.NA↗
Reinforcement Learning Based Argument Component Detection
Argument component detection (ACD) is an important sub-task in argumentation mining. ACD aims at detecting and classifying different argument components in natural language texts. Historical annotations (HAs) are important features the human annotators consider when they manually perform the ACD task. However, HAs are largely ignored by existing automatic ACD techniques. Reinforcement learning (RL) has proven to be an effective method for using HAs in some natural language processing tasks. In this work, we propose a RL-based ACD technique, and evaluate its performance on two well-annotated corpora. Results suggest that, in terms of classification accuracy, HAs-augmented RL outperforms plain RL by at most 17.85%, and outperforms the state-of-the-art supervised learning algorithm by at most 11.94%.
cs.CL↗
Evaluating the bulk Lorentz factors of outflow material: lessons learned from the extremely-energetic outburst GRB 160625B
GRB 160625B is an extremely-bright outburst with well-monitored afterglow emission. The geometry-corrected energy is high up to $\sim 5.2\times10^{52}$ erg or even $\sim 8\times 10^{52}$ erg, rendering it the most energetic GRB prompt emission recorded so far. We analyzed the time-resolved spectra of the prompt emission and found that in some intervals there were likely thermal-radiation components and the high energy emission were characterized by significant cutoff. The bulk Lorentz factors of the outflow material are estimated accordingly. We found out that the Lorentz factors derived in the thermal-radiation model are consistent with the luminosity-Lorentz factor correlation found in other bursts as well as in GRB 090902B for the time-resolved thermal-radiation components. While the spectral cutoff model yields much lower Lorentz factors that are in tension with the constraints set by the electron pair Compoton scattering process. We then suggest that these spectral cutoffs are more likely related to the particle acceleration process and that one should be careful in estimating the Lorentz factors if the spectrum cuts at a rather low energy (e.g., $\sim$ tens MeV). The nature of the central engine has also been discussed and a stellar-mass black hole is likely favored.
astro-ph.HE↗
Pathwise Uniqueness of the Solutions of Stochastic Heat Equation with Square-root Coefficient
White-noise case stochastic heat equation was derived from Dawson-Watanabe superprocess. The pathwise uniqueness of their solutions with non-Lipschitz coefficients has attracted wide attention and in particular, the square-root coefficient case was listed as a long standing open problem in the famous literature (i.e. Perkins \cite{Perkins02} p217.) This short note gives an affirmative answer to this open problem. Our idea is using the $φ_k(x)$ function constructed by Yamada-Watanable to prove that \beqlb \lab{key} \E_μ| X_{t}(z)-Y_{t}(z) | = \lim_{k \ra \infty} \E_μ|\int_{\r}\frac{1}{\sqrt{ε_k}} ρ_{ε_k}(z-x)[ X_{t}(x)-Y_{t}(x)]dx |=0 . \eeqlb However, $\frac{1}{\sqrt{ε_k}} ρ_{ε_k}(x)=p_{ε_k}(x)$, the stable kernel, is not square-integrable uniformly in $k$ and we cannot directly use the heat kernel or stable kernel convolution transformation. This forces us to go a new way in which we have used the linear and reciprocal linear properties of $φ_k$ and $φ^{\prime \prime}_k$, respectively, making a $ρ_{ε_k}$ convolution transformation first, then, taking expectation and multiplying singularity factor $\frac{1}{\sqrt{ε_k}}$ and finally letting $k \ra \infty$ to estimate and derive (\ref{key}). In this way, we have avoided the non-uniform square integrability of the stochastic integral terms which involve the singularity. The same idea also can be used to prove the pathwise uniqueness of the nonnegative solutions of stochastic heat equation with $α$ coefficients ($1/2 \leq α$) since by a same idea we can construct the $φ_k(x)$ functions with $1/2 \leq α$.
math.PR↗