arXiv subjects

Kan Chen

Publications and source records attributed to Kan Chen.

At least 91 records · Page 5Linked to original sources

arXiv2017

Exotic tetraquark states with the $qq\bar{Q}\bar{Q}$ configuration

In this work, we study systematically the mass splittings of the $qq\bar{Q}\bar{Q}$ ($q=u$, $d$, $s$ and $Q=c$, $b$) tetraquark states with the color-magnetic interaction by considering color mixing effects and estimate roughly their masses. We find that the color mixing effect is relatively important for the $J^P=0^+$ states and possible stable tetraquarks exist in the $nn\bar{Q}\bar{Q}$ ($n=u$, $d$) and $ns\bar{Q}\bar{Q}$ systems either with $J=0$ or with $J=1$. Possible decay patterns of the tetraquarks are briefly discussed.

hep-ph↗

arXiv2017

TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals

Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e.g. human actions) segments from untrimmed videos is an important step for large-scale video analysis. We propose a novel Temporal Unit Regression Network (TURN) model. There are two salient aspects of TURN: (1) TURN jointly predicts action proposals and refines the temporal boundaries by temporal coordinate regression; (2) Fast computation is enabled by unit feature reuse: a long untrimmed video is decomposed into video units, which are reused as basic building blocks of temporal proposals. TURN outperforms the state-of-the-art methods under average recall (AR) by a large margin on THUMOS-14 and ActivityNet datasets, and runs at over 880 frames per second (FPS) on a TITAN X GPU. We further apply TURN as a proposal generation stage for existing temporal action localization pipelines, it outperforms state-of-the-art performance on THUMOS-14 and ActivityNet.

cs.CV↗

arXiv2017

Query-guided Regression Network with Context Policy for Phrase Grounding

Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. State-of-the-art methods address the problem by ranking a set of proposals based on the relevance to each query, which are limited by the performance of independent proposal generation systems and ignore useful cues from context in the description. In this paper, we adopt a spatial regression method to break the performance limit, and introduce reinforcement learning techniques to further leverage semantic context information. We propose a novel Query-guided Regression network with Context policy (QRC Net) which jointly learns a Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and a Context Policy Network (CPN). Experiments show QRC Net provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Referit Game, with 14.25% and 17.14% increase over the state-of-the-arts respectively.

cs.CV↗

arXiv2017

AMC: Attention guided Multi-modal Correlation Learning for Image Search

Given a user's query, traditional image search systems rank images according to its relevance to a single modality (e.g., image content or surrounding text). Nowadays, an increasing number of images on the Internet are available with associated meta data in rich modalities (e.g., titles, keywords, tags, etc.), which can be exploited for better similarity measure with queries. In this paper, we leverage visual and textual modalities for image search by learning their correlation with input query. According to the intent of query, attention mechanism can be introduced to adaptively balance the importance of different modalities. We propose a novel Attention guided Multi-modal Correlation (AMC) learning method which consists of a jointly learned hierarchy of intra and inter-attention networks. Conditioned on query's intent, intra-attention networks (i.e., visual intra-attention network and language intra-attention network) attend on informative parts within each modality; a multi-modal inter-attention network promotes the importance of the most query-relevant modalities. In experiments, we evaluate AMC models on the search logs from two real world image search engines and show a significant boost on the ranking of user-clicked images in search results. Additionally, we extend AMC models to caption ranking task on COCO dataset and achieve competitive results compared with recent state-of-the-arts.

cs.CV↗

arXiv2017

Hidden-charm pentaquarks and their hidden-bottom and $B_c$-like partner states

In the framework of the color-magnetic interaction, we have systematically studied the mass splittings of the possible hidden-charm pentaquarks $qqqc\bar{c}$ ($q=u,d,s$) where the three light quarks are in a color-octet state. We find that i) the LHCb $P_c$ states fall in the mass region of the studied system; ii) most pentaquarks should be broad states since their $S$-wave open-charm decays are allowed while the lowest state is the $J^P=\frac12^-$ $Λ$-like pentaquark with probably the suppressed $η_cΛ$ decay mode only; and iii) the $J^P=\frac52^-$ states do not decay through $S$-wave and their widths are not so broad. The masses and widths of the two LHCb $P_c$ baryons are compatible with such pentaquark states. We also explore the hidden-bottom and $B_c$-like partners of the hidden-charm states and find the possible existence of the pentaquarks which are lower than the relevant hadronic molecules.

hep-ph↗

arXiv2016

Triply heavy tetraquark states with the $QQ\bar{Q}\bar{q}$ configuration

In the framework of the color-magnetic interaction, we systematically investigate the mass splittings of the $QQ\bar{Q}\bar{q}$ tetraquark states and estimated their rough masses in this work. These systems include the explicitly exotic states $cc\bar{b}\bar{q}$ and $bb\bar{c}\bar{q}$ and the hidden exotic states $cc\bar{c}\bar{q}$, $cb\bar{b}\bar{q}$, $bc\bar{c}\bar{q}$, and $bb\bar{b}\bar{q}$. If a state around the estimated mass region could be observed, its nature as a genuine tetraquark is favored. The strong decay patterns shown here will be helpful to the experimental search for these exotic states.

hep-ph↗

arXiv2016

Knowledge Graph Representation with Jointly Structural and Textual Encoding

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.

cs.CL↗

arXiv2016

$X(4140)$, $X(4270)$, $X(4500)$ and $X(4700)$ and their $cs\bar{c}\bar{s}$ tetraquark partners

In the simple color-magnetic interaction model, we investigate possible ground $cs\bar{c}\bar{s}$ tetraquark states in the diquark-antidiquark basis. We use several methods to estimate the mass spectrum and discuss possible assignment for the $X$ states observed in the $J/ψϕ$ channel. We find that assigning the Belle $X(4350)$ as a $0^{++}$ tetraquark is consistent with the tetraquark interpretation for the $X(4140)$ and $X(4270)$ while the interpretation of the $X(4500)$ and $X(4700)$ needs orbital or radial excitation. There probably exist several tetraquarks around 4.3 GeV that decay into $J/ψϕ$ or $η_cϕ$.

hep-ph↗

arXiv2016

Learning Word Embeddings from Intrinsic and Extrinsic Views

While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word embeddings are limited since not all words' meaning can be learned based on only context. Moreover, it is also difficult to learn the representation of the rare words due to data sparsity problem. In this work, we address these issues by learning the representations of words by integrating their intrinsic (descriptive) and extrinsic (contextual) information. To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries,Wiki link prediction, and document classification. Experiment results show that our model is powerful in both word and document modeling.

cs.CL↗

arXiv2016

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions.

cs.CV↗

arXiv2015

Light axial vector mesons

Inspired by the abundant experimental observation of axial-vector states, we study whether the observed axial-vector states can be categorized into the conventional axial-vector meson family. In this paper we carry out an analysis based on the mass spectra and two-body Okubo-Zweig-Iizuka-allowed decays. Besides testing the possible axial-vector meson assignments, we also predict abundant information for their decays and the properties of some missing axial-vector mesons, which are valuable for further experimental exploration of the observed and predicted axial-vector mesons.

hep-ph↗

arXiv2006

Conditional Probability as a Measure of Volatility Clustering in Financial Time Series

In the past few decades considerable effort has been expended in characterizing and modeling financial time series. A number of stylized facts have been identified, and volatility clustering or the tendency toward persistence has emerged as the central feature. In this paper we propose an appropriately defined conditional probability as a new measure of volatility clustering. We test this measure by applying it to different stock market data, and we uncover a rich temporal structure in volatility fluctuations described very well by a scaling relation. The scale factor used in the scaling provides a direct measure of volatility clustering; such a measure may be used for developing techniques for option pricing, risk management, and economic forecasting. In addition, we present a stochastic volatility model that can display many of the salient features exhibited by volatilities of empirical financial time series, including the behavior of conditional probabilities that we have deduced.

physics.soc-ph↗

arXiv2005

Growing Directed Networks: Organization and Dynamics

We study the organization and dynamics of growing directed networks. These networks are built by adding nodes successively in such a way that each new node has $K$ directed links to the existing ones. The organization of a growing directed network is analyzed in terms of the number of ``descendants'' of each node in the network. We show that the distribution $P(S)$ of the size, $S$, of the descendant cluster is described generically by a power-law, $P(S) \sim S^{-η}$, where the exponent $η$ depends on the value of $K$ as well as the strength of preferential attachment. We determine that, in the case of growing random directed networks without any preferential attachment, $η$ is given by $1+1/K$. We also show that the Boolean dynamics of these networks is stable for any value of $K$. However, with a small fraction of reversal in the direction of the links, the dynamics of growing directed networks appears to operate on ``the edge of chaos'' with a power-law distribution of the cycle lengths. We suggest that the growing directed network may serve as another paradigm for the emergence of the scale-free features in network organization and dynamics.

cond-mat.stat-mech↗

arXiv2005

Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations

While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,γ)=\frac{c^{1-γ}-1}{1-γ}$ with agent specific and time-dependent risk aversion index, $γ_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $γ_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $δ^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.

physics.soc-ph↗

arXiv2005

A general model for collaboration networks

In this paper, we propose a general model for collaboration networks. Depending on a single free parameter "{\bf preferential exponent}", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before of some collaboration networks. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.

cond-mat.stat-mech↗

arXiv2005

Log-Poisson Statistics and Extended Self-Similarity in Driven Dissipative Systems

The Bak-Chen-Tang forest fire model was proposed as a toy model of turbulent systems, where energy (in the form of trees) is injected uniformly and globally, but is dissipated (burns) locally. We review our previous results on the model and present our new results on the statistics of the higher-order moments for the spatial distribution of fires. We show numerically that the spatial distribution of dissipation can be described by Log-Poisson statistics which leads to extended self-similarity (ESS). Similar behavior is also found in models based on directed percolation; this suggests that the concept of Log-Poisson statistics of (appropriately normalized) variables can be used to describe scaling not only in turbulence but also in a wide range of driven dissipative systems.

cond-mat.stat-mech↗

arXiv2004

Evolutionary Dynamics in Complex Networks of Competing Boolean Agents

We investigate the dynamics of network minority games on Kauffman's NK networks (Kauffman nets), growing directed networks (GDNets), as well as growing directed networks with a small fraction of link reversals (GDRNets). We show that the dynamics and the associated phase structure of the game depend crucially on the structure of the underlying network. The dynamics on GDNets is very stable for all values of the connection number $K$, in contrast to the dynamics on Kauffman's NK networks, which becomes chaotic when $K>K_c=2$. The dynamics of GDRNets, on the other hand, is near critical. Under a simple evolutionary scheme, the network system with a "near" critical dynamics evolves to a high level of global coordination among its agents. In particular, the performance of the system is close to the optimum for the GDRNets; this suggests that criticality leads to the best performance. For Kauffman nets with $K>3$, the evolutionary scheme has no effect on the dynamics (it remains chaotic) and the performance of the MG resembles that of a random choice game (RCG).

cond-mat.other↗

arXiv2004

Evolutionary Dynamics and the Phase Structure of the Minority Game

We show that a simple evolutionary scheme, when applied to the minority game (MG), changes the phase structure of the game. In this scheme each agent evolves individually whenever his wealth reaches the specified bankruptcy level, in contrast to the evolutionary schemes used in the previous works. We show that evolution greatly suppresses herding behavior, and it leads to better overall performance of the agents. Similar to the standard non-evolutionary MG, the dependence of the standard deviation $σ$ on the number of agents $N$ and the memory length $m$ can be characterized by a universal curve. We suggest a Crowd-Anticrowd theory for understanding the effect of evolution in the MG.

cond-mat.stat-mech↗