arXiv subjects

Kan Chen

Publications and source records attributed to Kan Chen.

At least 55 records · Page 3Linked to original sources

arXiv2023

Phase shifts of the light pseudoscalar meson and heavy meson scattering in heavy meson chiral perturbation theory

We calculate the complete $T$ matrices of the elastic light pseudoscalar meson and heavy meson scattering to the third order in heavy meson chiral perturbation theory. We determine the low-energy constants by fitting the phase shifts and scattering lengths from lattice QCD simulations simultaneously and predict the phase shifts at the physical meson masses. The phase shifts in the $Dπ(I=1/2)$, $DK(I=0)$, $D\bar{K}(I=0)$, $D_s\bar{K}$, $Dη$ and $D_sη$ $S$ waves are so strong that bound states or resonances may be generated dynamically in all these channels. The $DK(I=0)$ channel corresponds to the well-known exotic state $D_{s0}^{*}(2317)$. The $DK(I=0)$ channel corresponds to the well-known exotic state $D_{s0}^{*}(2317)$. The coupled-channel $Dπ$, $Dη$ and $D_s\bar{K}$ scattering corresponds to $D_{0}^{*}(2400)$. The coupled-channel $Dπ$, $Dη$ and $D_s\bar{K}$ scattering corresponds to $D_{0}^{*}(2400)$. We also predict the scattering lengths and scattering volumes and observe good convergence in the scattering volumes. Our calculations provide a possibility to accurately investigate the exotic state in the light pseudoscalar meson and heavy meson interactions.

hep-ph↗

arXiv2022

A comparison between the $P_c$ and $P_{cs}$ systems

We construct the effective potentials of the $P_c$ and $P_{cs}$ states based on the SU(3)$_{\text{f}}$ symmetry and heavy quark symmetry. Then we perform the coupled-channel analysis of the lowest isospin $P_c$ and $P_{cs}$ systems. The coupled-channel effects play different roles in the $P_c$ and $P_{cs}$ systems. In the $P_c$ systems, this effect gives minor corrections to the masses of the $P_c$ states. In the $P_{cs}$ system, the $Λ_c\bar{D}_s-Ξ_c\bar{D}$ coupling will shift the mass of the $P_{cs}(4338)$ close to the $Ξ_c\bar{D}$ threshold. The $Λ_c\bar{D}^{(*)}_s-Ξ_c\bar{D}^{(*)}$ coupling will also produce extra $P_{cs}$ states. We discuss the correspondence between the $P_c$ and $P_{cs}$ states. Our results prefer that the SU(3) partners of the observed $P_{c}(4312)$, $P_{c}(4440)$, and $P_{c}(4457)$ in the $P_{cs}$ system have not been found yet.

hep-ph↗

arXiv2022

Measuring Tail Risks

Value at risk (VaR) and expected shortfall (ES) are common high quantile-based risk measures adopted in financial regulations and risk management. In this paper, we propose a tail risk measure based on the most probable maximum size of risk events (MPMR) that can occur over a length of time. MPMR underscores the dependence of the tail risk on the risk management time frame. Unlike VaR and ES, MPMR does not require specifying a confidence level. We derive the risk measure analytically for several well-known distributions. In particular, for the case where the size of the risk event follows a power law or Pareto distribution, we show that MPMR also scales with the number of observations $n$ (or equivalently the length of the time interval) by a power law, $\text{MPMR}(n) \propto n^η$, where $η$ is the scaling exponent. The scale invariance allows for reasonable estimations of long-term risks based on the extrapolation of more reliable estimations of short-term risks. The scaling relationship also gives rise to a robust and low-bias estimator of the tail index (TI) $ξ$ of the size distribution, $ξ= 1/η$. We demonstrate the use of this risk measure for describing the tail risks in financial markets as well as the risks associated with natural hazards (earthquakes, tsunamis, and excessive rainfall).

q-fin.RM↗

arXiv2022

Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation

Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the Weighted Energy Distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC.

stat.ML↗

arXiv2022

Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies

One central goal of design of observational studies is to embed non-experimental data into an approximate randomized controlled trial using statistical matching. Despite empirical researchers' best intention and effort to create high-quality matched samples, residual imbalance due to observed covariates not being well matched often persists. Although statistical tests have been developed to test the randomization assumption and its implications, few provide a means to quantify the level of residual confounding due to observed covariates not being well matched in matched samples. In this article, we develop two generic classes of exact statistical tests for a biased randomization assumption. One important by-product of our testing framework is a quantity called residual sensitivity value (RSV), which provides a means to quantify the level of residual confounding due to imperfect matching of observed covariates in a matched sample. We advocate taking into account RSV in the downstream primary analysis. The proposed methodology is illustrated by re-examining a famous observational study concerning the effect of right heart catheterization (RHC) in the initial care of critically ill patients. Code implementing the method can be found in the supplementary materials.

stat.ME↗

arXiv2022

Cross-Domain Adaptive Teacher for Object Detection

We address the task of domain adaptation in object detection, where there is a domain gap between a domain with annotations (source) and a domain of interest without annotations (target). As an effective semi-supervised learning method, the teacher-student framework (a student model is supervised by the pseudo labels from a teacher model) has also yielded a large accuracy gain in cross-domain object detection. However, it suffers from the domain shift and generates many low-quality pseudo labels (\textit{e.g.,} false positives), which leads to sub-optimal performance. To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap. Specifically, we employ feature-level adversarial training in the student model, allowing features derived from the source and target domains to share similar distributions. This process ensures the student model produces domain-invariant features. Furthermore, we apply weak-strong augmentation and mutual learning between the teacher model (taking data from the target domain) and the student model (taking data from both domains). This enables the teacher model to learn the knowledge from the student model without being biased to the source domain. We show that AT demonstrates superiority over existing approaches and even Oracle (fully-supervised) models by a large margin. For example, we achieve 50.9% (49.3%) mAP on Foggy Cityscape (Clipart1K), which is 9.2% (5.2%) and 8.2% (11.0%) higher than previous state-of-the-art and Oracle, respectively.

cs.CV↗

arXiv2022

$Σ_cΣ_c$ interactions in chiral effective field theory

We study the interactions of the $Σ_cΣ_c$ system in the framework of chiral effective theory. We consider the contact, one-pion and two-pion exchange interactions and bridge the low energy constants of the $Σ_cΣ_c$ system to those of the $Σ_c^{(*)}\bar{D}^{(*)}$ systems through the quark-level ansatz for the contact interaction. We explore the influence of intermediate channels in the two-pion exchange diagrams of the $Σ_cΣ_c$ system. We obtain a deep bound state $[Σ_cΣ_c]_{J=0}^{I=0}$ and a shallow bound state $[Σ_cΣ_c]_{J=1}^{I=1}$. As a byproduct, we further investigate the interactions of the $Λ_cΛ_c$ and $Λ_cΣ_c$ systems.

hep-ph↗

arXiv2022

Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm

Sparse Group LASSO (SGL) is a regularized model for high-dimensional linear regression problems with grouped covariates. SGL applies $l_1$ and $l_2$ penalties on the individual predictors and group predictors, respectively, to guarantee sparse effects both on the inter-group and within-group levels. In this paper, we apply the approximate message passing (AMP) algorithm to efficiently solve the SGL problem under Gaussian random designs. We further use the recently developed state evolution analysis of AMP to derive an asymptotically exact characterization of SGL solution. This allows us to conduct multiple fine-grained statistical analyses of SGL, through which we investigate the effects of the group information and $γ$ (proportion of $\ell_1$ penalty). With the lens of various performance measures, we show that SGL with small $γ$ benefits significantly from the group information and can outperform other SGL (including LASSO) or regularized models which do not exploit the group information, in terms of the recovery rate of signal, false discovery rate and mean squared error.

math.ST↗

arXiv2022

Manifestly exotic pentaquarks with a single heavy quark

Inspired by the observed $X(2900)$, we study systematically the mass spectra of the ground pentaquark states with the $qqqq\bar{Q}$ ($Q=c,b$; $q=n,s$; $n=u,d$) configuration in the framework of the Chromomagnetic Interaction model. We present a detailed analysis of their stabilities and decay behaviors. Our results indicate that there may exist narrow states or even stable states. We hope that the present study may inspire experimentalist's interest in searching for such a type of the exotic pentaquark state.

hep-ph↗

arXiv2021

Heavy flavor molecular states with strangeness

We proposed a unified framework to describe the interactions of the observed $T_{cc}$, $P_c$, and $P_{cs}$ within a quark level interaction in our previous work. In this work, we generalize our framework to the loosely bound hadronic molecules composed of heavy flavor di-hadrons with strangeness. We predict the possible $D^{(*)}D^{(*)}_s$ molecular states in the SU(3) limit with the masses of the $P_c$ states as the inputs. We also investigate the baryon-meson and baryon-baryon systems and consider the SU(3) breaking effect in their flavor wave functions. We generalize our isospin criterion of the formation of heavy flavor di-hadron molecules to the $U/V$ spin case. For a specific heavy flavor meson-meson, baryon-meson, or baryon-baryon system, the interactions for the states with the same flavor and spin matrix elements can be related by a generalized flavor-spin symmetry.

hep-ph↗

arXiv2021

MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving. Applying deep learning to this problem requires fusing heterogeneous world state in the form of rich perception signals and map information, and inferring highly multi-modal distributions over possible futures. In this paper, we present MultiPath++, a future prediction model that achieves state-of-the-art performance on popular benchmarks. MultiPath++ improves the MultiPath architecture by revisiting many design choices. The first key design difference is a departure from dense image-based encoding of the input world state in favor of a sparse encoding of heterogeneous scene elements: MultiPath++ consumes compact and efficient polylines to describe road features, and raw agent state information directly (e.g., position, velocity, acceleration). We propose a context-aware fusion of these elements and develop a reusable multi-context gating fusion component. Second, we reconsider the choice of pre-defined, static anchors, and develop a way to learn latent anchor embeddings end-to-end in the model. Lastly, we explore ensembling and output aggregation techniques -- common in other ML domains -- and find effective variants for our probabilistic multimodal output representation. We perform an extensive ablation on these design choices, and show that our proposed model achieves state-of-the-art performance on the Argoverse Motion Forecasting Competition and the Waymo Open Dataset Motion Prediction Challenge.

cs.CV↗

arXiv2021

Systematics of the heavy flavor hadronic molecules

With a quark level interaction, we give a unified description of the loosely bound molecular systems composed of the heavy flavor hadrons $(\bar{D},\bar{D}^*)$, $(Λ_c, Σ_c, Σ_c^*)$, and $(Ξ_c, Ξ_c^\prime,Ξ_c^*)$. Using the $P_c$ states as inputs to fix the interaction strength of light quark-quark pairs, we reproduce the observed $P_{cs}$ and $T_{cc}^+$ states and predict another narrow $T_{cc}^{\prime+}$ state with quantum numbers $[D^*D^*]_{J=1}^{I=0}$. If we require a satisfactory description of the $T_{cc}^+$ and $P_c$ states simultaneously, our framework prefers the assignments of the $P_{c}(4440)$ and $P_{c}(4457)$ as the $[Σ_c\bar{D}^*]_{J=1/2}^{I=1/2}$ and $[Σ_c\bar{D}^*]_{J=3/2}^{I=1/2}$ states, respectively. We propose the isospin criterion to explain naturally why the experimentally observed $T_{cc}$, $P_c$, and $P_{cs}$ molecular candidates prefer the lowest isospin numbers. We also predict the loosely bound states for the bottom di-hadrons.

hep-ph↗

arXiv2021

Edge of chaos as a guiding principle for modern neural network training

The success of deep neural networks in real-world problems has prompted many attempts to explain their training dynamics and generalization performance, but more guiding principles for the training of neural networks are still needed. Motivated by the edge of chaos principle behind the optimal performance of neural networks, we study the role of various hyperparameters in modern neural network training algorithms in terms of the order-chaos phase diagram. In particular, we study a fully analytical feedforward neural network trained on the widely adopted Fashion-MNIST dataset, and study the dynamics associated with the hyperparameters in back-propagation during the training process. We find that for the basic algorithm of stochastic gradient descent with momentum, in the range around the commonly used hyperparameter values, clear scaling relations are present with respect to the training time during the ordered phase in the phase diagram, and the model's optimal generalization power at the edge of chaos is similar across different training parameter combinations. In the chaotic phase, the same scaling no longer exists. The scaling allows us to choose the training parameters to achieve faster training without sacrificing performance. In addition, we find that the commonly used model regularization method - weight decay - effectively pushes the model towards the ordered phase to achieve better performance. Leveraging on this fact and the scaling relations in the other hyperparameters, we derived a principled guideline for hyperparameter determination, such that the model can achieve optimal performance by saturating it at the edge of chaos. Demonstrated on this simple neural network model and training algorithm, our work improves the understanding of neural network training dynamics, and can potentially be extended to guiding principles of more complex model architectures and algorithms.

cs.LG↗

arXiv2021

Heavy flavor pentaquarks with four heavy quarks

In this work, we carry out the study of heavy flavor pentatuarks with four heavy quarks, which have typical $QQQQ\bar q$ configuration. Within the Chromomagnetic Interaction model, the mass spectrum of these discussed $QQQQ\bar q$ pentaquarks is given. In addition to the mass spectrum analysis, we also illustrate their two-body strong decay behavior by estimating some ratios of decay channels. By these effort, we suggest that future experiment should pay attention to this kind of pentaquark.

hep-ph↗

arXiv2021

Fully heavy pentaquarks

Very recently, the LHCb Collaboration reported a fully charmed tetraquark state $X(6900)$ in the invariant mass spectrum of $J/ψ$ pairs. If one $J/ψ$ meson is replaced with a fully charmed baryon, we obtain a fully charmed pentaquark candidate. In this work, we perform a systematic study on the mass spectra of the S-wave fully heavy pentaquark $QQQQ\bar{Q}$ in the framework of the chromomagnetic interaction model. Based on our results in two different schemes, we further investigate the decay behaviors for them. We hope that our study will be helpful in searching for such types of exotic pentaquark states in experiments in the future.

hep-ph↗

arXiv2021

Video Question Answering with Phrases via Semantic Roles

Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models' application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string. To reduce the influence of language bias in VidQA datasets, we retrieve a video having a different answer for the same question. To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models. We further perform extensive analysis and ablative studies to guide future work.

cs.CV↗

arXiv2021

FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining

Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (i.e., a training recipe), overlooking superior architecture-recipe combinations. To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously. NARS utilizes an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking. Furthermore, to compensate for the enlarged search space, we leverage "free" architecture statistics (e.g., FLOP count) to pretrain the predictor, significantly improving its sample efficiency and prediction reliability. After training the predictor via constrained iterative optimization, we run fast evolutionary searches in just CPU minutes to generate architecture-recipe pairs for a variety of resource constraints, called FBNetV3. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors. For example, FBNetV3 matches both EfficientNet and ResNeSt accuracy on ImageNet with up to 2.0x and 7.1x fewer FLOPs, respectively. Furthermore, FBNetV3 yields significant performance gains for downstream object detection tasks, improving mAP despite 18% fewer FLOPs and 34% fewer parameters than EfficientNet-based equivalents.

cs.CV↗

arXiv2021

A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks

When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth. While many works have been focusing on understanding the loss dynamics by training neural networks with the gradient descent (GD), in this work, we consider a broad class of optimization algorithms that are commonly used in practice. For example, we show from a dynamical system perspective that the Heavy Ball (HB) method can converge to global minimum on mean squared error (MSE) at a linear rate (similar to GD); however, the Nesterov accelerated gradient descent (NAG) may only converges to global minimum sublinearly. Our results rely on the connection between neural tangent kernel (NTK) and finite over-parameterized neural networks with ReLU activation, which leads to analyzing the limiting ordinary differential equations (ODE) for optimization algorithms. We show that, optimizing the non-convex loss over the weights corresponds to optimizing some strongly convex loss over the prediction error. As a consequence, we can leverage the classical convex optimization theory to understand the convergence behavior of neural networks. We believe our approach can also be extended to other optimization algorithms and network architectures.

cs.LG↗