arXiv subjects

Wei Hao

Publications and source records attributed to Wei Hao.

At least 37 records · Page 2Linked to original sources

arXiv2023

The mass spectrum and strong decay properties of the charmed-strange mesons within Godfrey-Isgur model considering the coupled-channel effects

Motivated by the recently observed $D_{s0}(2590)$ state by LHCb, we investigate the mass spectrum and the strong decay properties of the charmed-strange mesons within Godfrey-Isgur model considering the coupled-channel effects. Our results support that $D_{s0}^*(2317)$ and $D_{s1}(2460)$ can be interpreted as the $D_{s}(1^3P_0)$ and $D_{s}(1^3P_1)$ states with larger $DK$ and $D^*K$ components, respectively, and $D_{s1}(2700)$, $D_{s1}(2536)$, $D^*_{s2}(2573)$, $D_{s1}^*(2860)$, $D_{s3}^*(2860)$, and $D_{sJ}^*(3040)$ can be well interpreted as the $D_s(2^3S_1)$, $D_s(1^1P_1)$, $D_s(1^3P_2)$, $D_s(1^3D_1)$, $D_s(1^3D_3)$, and $D_s(2^1P_1)$ states, respectively. Although, $D_{s0}(2590)$ mass is about 50 MeV less than our prediction for the $D_{s}(2^1S_0)$ state, its width is still in good agreement with the one of $D_{s}(2^1S_0)$. Therefore, $D_{s0}(2590)$ state needs to be further confirmed by the experimental measurements, and the more precise information about $D_{s0}(2590)$ will shed light on its assignment of $D_{s}(2^1S_0)$. Furthermore, we predict the masses and the strong decay properties of the charmed-strange mesons with masses around 3 GeV, which would be helpful to experimentally search for these states.

hep-ph↗

arXiv2023

A Class of Directed Acyclic Graphs with Mixed Data Types in Mediation Analysis

We propose a unified class of generalized structural equation models (GSEMs) with data of mixed types in mediation analysis, including continuous, categorical, and count variables. Such models extend substantially the classical linear structural equation model to accommodate many data types arising from the application of mediation analysis. Invoking the hierarchical modeling approach, we specify GSEMs by a copula joint distribution of outcome variable, mediator and exposure variable, in which marginal distributions are built upon generalized linear models (GLMs) with confounding factors. We discuss the identifiability conditions for the causal mediation effects in the counterfactual paradigm as well as the issue of mediation leakage, and develop an asymptotically efficient profile maximum likelihood estimation and inference for two key mediation estimands, natural direct effect and natural indirect effect, in different scenarios of mixed data types. The proposed new methodology is illustrated by a motivating epidemiological study that aims to investigate whether the tempo of reaching infancy BMI peak (delay or on time), an important early life growth milestone, may mediate the association between prenatal exposure to phthalates and pubertal health outcomes.

stat.ME↗

arXiv2023

MGit: A Model Versioning and Management System

Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" models through finetuning. This has led to an ecosystem where models are related to each other, sharing structure and often even parameter values. However, it is hard to manage these model derivatives: the storage overhead of storing all derived models quickly becomes onerous, prompting users to get rid of intermediate models that might be useful for further analysis. Additionally, undesired behaviors in models are hard to track down (e.g., is a bug inherited from an upstream model?). In this paper, we propose a model versioning and management system called MGit that makes it easier to store, test, update, and collaborate on model derivatives. MGit introduces a lineage graph that records provenance and versioning information between models, optimizations to efficiently store model parameters, as well as abstractions over this lineage graph that facilitate relevant testing, updating and collaboration functionality. MGit is able to reduce the lineage graph's storage footprint by up to 7x and automatically update downstream models in response to updates to upstream models.

cs.LG↗

arXiv2023

Mediation with External Summary Statistic Information (MESSI)

Environmental health studies are increasingly measuring endogenous omics data ($\boldsymbol{M}$) to study intermediary biological pathways by which an exogenous exposure ($\boldsymbol{A}$) affects a health outcome ($\boldsymbol{Y}$), given confounders ($\boldsymbol{C}$). Mediation analysis is frequently carried out to understand such mechanisms. If intermediary pathways are of interest, then there is likely literature establishing statistical and biological significance of the total effect, defined as the effect of $\boldsymbol{A}$ on $\boldsymbol{Y}$ given $\boldsymbol{C}$. For mediation models with continuous outcomes and mediators, we show that leveraging external summary-level information on the total effect improves estimation efficiency of the natural direct and indirect effects. Moreover, the efficiency gain depends on the asymptotic partial $R^2$ between the outcome ($\boldsymbol{Y}\mid\boldsymbol{M},\boldsymbol{A},\boldsymbol{C}$) and total effect ($\boldsymbol{Y}\mid\boldsymbol{A},\boldsymbol{C}$) models, with smaller (larger) values benefiting direct (indirect) effect estimation. We robustify our estimation procedure to incongenial external information by assuming the total effect follows a random distribution. This framework allows shrinkage towards the external information if the total effects in the internal and external populations agree. We illustrate our methodology using data from the Puerto Rico Testsite for Exploring Contamination Threats, where Cytochrome p450 metabolites are hypothesized to mediate the effect of phthalate exposure on gestational age at delivery. External information on the total effect comes from a recently published pooled analysis of 16 studies. The proposed framework blends mediation analysis with emerging data integration techniques.

stat.ME↗

arXiv2023

The assignments of the $B_s$ mesons within the screened potential model and $^3P_0$ model

We investigate the mass spectrum and the decay properties of the $B_s$ mesons within the screened nonrelativistic quark model and the $^3P_0$ model. Our results suggest that the $B_{sJ}(6064)$ and $B_{sJ}(6114)$ states, as the first solution of the recently LHCb measurements, could be explained as the $B_s(1^3D_3)$ and $B_s(1^3D_1)$, respectively. In addition, the $B_{sJ}(6109)$ and $B_{sJ}(6158)$ states, as the second solution of the LHCb measurements, could be explained as the $B'_{s2}(1D)$ and $B_{s1}(2P)$, respectively. Meanwhile, the $B_{s1}(5830)$ could be interpreted as the candidate of the $B_{s1}(1P)$. We also calculated the decay properties of the other excited $B_s$ mesons with the predicted masses, which should be helpful for the experimental searching in future.

hep-ph↗

arXiv2023

Monitoring and Adapting ML Models on Mobile Devices

ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation. However, once the models are deployed, it is hard for ML operators to track their accuracy, which can degrade unpredictably (e.g., due to data drift). We design the first end-to-end system for continuously monitoring and adapting models on mobile devices without requiring feedback from users. Our key observation is that often model degradation is due to a specific root cause, which may affect a large group of devices. Therefore, once the system detects a consistent degradation across a large number of devices, it employs a root cause analysis to determine the origin of the problem and applies a cause-specific adaptation. We evaluate the system on two computer vision datasets, and show it consistently boosts accuracy compared to existing approaches. On a dataset containing photos collected from driving cars, our system improves the accuracy on average by 15%.

cs.LG↗

arXiv2022

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

eess.IV↗

arXiv2022

Coupled Channel Effects for the Charmed-Strange Mesons

We make a systematic calculation of the spectra and hadronic decays of the $D_s$ system in a coupled channel framework, where the unquenched effects are induced by the $^3P_0$ model. In the calculation, the wave functions are obtained by using a nonrelativistic potential model and are handled precisely with Gaussian Expansion Method. Even though the fitting mainly focuses on the spectrum, our model agrees well with the experiments on both the spectra and the hadronic decays, suggesting that the coupled channel effect could result in a reasonable and coherent description of the $D_s$ mesons. Based on the calculation, we give a detailed analysis on various aspects of the excited states, especially $D_s(2317), D_s(2460), D_s(2536), D_s(2860), D_s(3040)$. We also predict that $D_s(2{}^3P_0)$ should be a $D^*K^*$ dominant molecule with mass $2894$~MeV, which is only $5$~MeV below the $D^*K^*$ threshold.

hep-ph↗

arXiv2022

A Tale of Two Models: Constructing Evasive Attacks on Edge Models

Full-precision deep learning models are typically too large or costly to deploy on edge devices. To accommodate to the limited hardware resources, models are adapted to the edge using various edge-adaptation techniques, such as quantization and pruning. While such techniques may have a negligible impact on top-line accuracy, the adapted models exhibit subtle differences in output compared to the original model from which they are derived. In this paper, we introduce a new evasive attack, DIVA, that exploits these differences in edge adaptation, by adding adversarial noise to input data that maximizes the output difference between the original and adapted model. Such an attack is particularly dangerous, because the malicious input will trick the adapted model running on the edge, but will be virtually undetectable by the original model, which typically serves as the authoritative model version, used for validation, debugging and retraining. We compare DIVA to a state-of-the-art attack, PGD, and show that DIVA is only 1.7-3.6% worse on attacking the adapted model but 1.9-4.2 times more likely not to be detected by the the original model under a whitebox and semi-blackbox setting, compared to PGD.

cs.CR↗

arXiv2022

Methods for Large-scale Single Mediator Hypothesis Testing: Possible Choices and Comparisons

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, H0:alpha*beta=0 (alpha: effect of the exposure on the mediator after adjusting for confounders; beta: effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for multiple mediation hypothesis testing. In addition to these existing methods, we developed the Sobel-comp method, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods in terms of the false positive rates under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the class of methods which uses a mixture reference distribution could best maintain the false positive rates at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We also provide guidelines for choosing the optimal mediation hypothesis testing method in practice.

stat.ME↗

arXiv2021

Water-Superstructured Solid Fuel Cells

Protonic ceramic fuel cells can be operated at low temperatures, but their performances relying on bulk ion transfer in solid electrolytes are usually limited by much lower proton conductivity than 0.1 S/cm below 600 °C. Herein, however, we report a strategy for Al2O3 insulator to become a protonic superconductor, namely, in-situ generation of superstructured-water in porous Al2O3 layer could realize the unprecedented water-mediated proton transfer on Al2O3 surface, attaining ultrahigh proton conductivity of 0.13 S/cm at 550 °C. With such a water-superstructured proton-superconductor, we created water-superstructured solid fuel cell, achieving very high power density of 1036 mW/cm2 and high open circuit voltage above 1.1 V at 550 °C with H2 fuel. This provides a general approach to develop protonic superconductors and solid fuel cells.

physics.chem-ph↗

arXiv2021

Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted cross validation tests. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images, with a level of accuracy on par with human domain expert labelers. More specifically, the model can achieve average defect identification F1 scores as high as 0.8, and, based on random cross validation, have low overall average (+/- standard deviation) defect size and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%, respectively. Further, our model predicts the expected material hardening to within 10-20 MPa (about 10% of total hardening), which is about the same error level as experiments. Our targeted evaluation tests also suggest the best path toward improving future models is not expanding existing databases with more labeled images but instead data additions that target weak points of the model domain, such as images from different microscopes, imaging conditions, irradiation environments, and alloy types. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images.

cs.CV↗

arXiv2021

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. This study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

cs.CV↗

arXiv2021

Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks

Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental problem for the full understanding of complicated interactions. The development of graph neural network (GNN) has greatly advanced the analysis of ordinary graphs with pair-wise relations. However, these methods could not be easily extended to the case of hypergraph. In this paper, we generalize the challenges of GNN in representing higher-order data in principle, which are edge- and node-level ambiguities. To overcome the challenges, we present SNALS that utilizes bipartite graph neural network with structural features to collectively tackle the two ambiguity issues. SNALS captures the joint interactions of a hyperedge by its local environment, which is retrieved by collecting the spectrum information of their connections. As a result, SNALS achieves nearly 30% performance increase compared with most recent GNN-based models. In addition, we applied SNALS to predict genetic higher-order interactions on 3D genome organization data. SNALS showed consistently high prediction accuracy across different chromosomes, and generated novel findings on 4-way gene interaction, which is further validated by existing literature.

cs.SI↗

arXiv2021

The resonances $X(4140)$, $X(4160)$, and $P_{cs}(4459)$ in the decay of $Λ_b\to J/ψΛϕ$

We study the decay of $Λ_b\to J/ψΛϕ$ by taking into account the intermediate resonances $X(4140)$, $X(4160)$, and $P_{cs}(4459)$. In addition to the peak of the $X(4140)$, we also find a bump structure around 4160 MeV followed by a cusp structure around $D^*_s\bar{D}^*_s$ threshold in the $J/ψϕ$ invariant mass distribution of the $Λ_b\to J/ψΛϕ$ process, which can be associated to the $D^*_s\bar{D}^*_s$ molecular state $X(4160)$. We also show that this reaction can also be used to confirm the existence of the hidden-charm pentaquark $P_{cs}(4459)$ with strangeness. We predict an enhancement structure close to the threshold in the $D^*_s\bar{D}^*_s$ invariant mass distribution of the $Λ_b\to D^*_s\bar{D}^*_sΛ$ process, which is the reflection of the $X(4160)$ and should not be misidentified with a new resonance. We strongly suggest experimentalists to further measure these processes, which can be useful to clarify the nature of $X(4140)$ and $X(4160)$ resonances, and to confirm the existence of the $P_{cs}(4459)$.

hep-ph↗

arXiv2020

Serving DNNs like Clockwork: Performance Predictability from the Bottom Up

Machine learning inference is becoming a core building block for interactive web applications. As a result, the underlying model serving systems on which these applications depend must consistently meet low latency targets. Existing model serving architectures use well-known reactive techniques to alleviate common-case sources of latency, but cannot effectively curtail tail latency caused by unpredictable execution times. Yet the underlying execution times are not fundamentally unpredictable - on the contrary we observe that inference using Deep Neural Network (DNN) models has deterministic performance. Here, starting with the predictable execution times of individual DNN inferences, we adopt a principled design methodology to successively build a fully distributed model serving system that achieves predictable end-to-end performance. We evaluate our implementation, Clockwork, using production trace workloads, and show that Clockwork can support thousands of models while simultaneously meeting 100ms latency targets for 99.9999% of requests. We further demonstrate that Clockwork exploits predictable execution times to achieve tight request-level service-level objectives (SLOs) as well as a high degree of request-level performance isolation.

cs.DC↗

arXiv2020

Canonical interpretation of the $X(4140)$ state within the $^3P_0$ model

Recently, LHCb collaboration has confirmed the state $X(4140)$, with a much larger width $Γ= 83\pm 21^{+21}_{-14}$ MeV than the previous experimental measurements, which has confused the understanding of its nature. We have investigated the possibility of the $χ_{c1}(3P)$ interpretation for the $X(4140)$, considering the mass and the strong decay properties.

hep-ph↗

arXiv2020

Planetary Systems in a Star Cluster II: intermediate-mass black holes and planetary systems

Most stars form in dense stellar environments. It is speculated that some dense star clusters may host intermediate-mass black holes (IMBHs), which may have formed from runaway collisions between high-mass stars, or from the mergers of less massive black holes. Here, we numerically explore the evolution of populations of planets in star clusters with an IMBH. We study the dynamical evolution of single-planet systems and free-floating planets, over a period of 100~Myr, in star clusters without an IMBH, and in clusters with a central IMBH of mass $100~M_\odot$ or $200~M_\odot$. In the central region ($r\lesssim 0.2$~pc), the IMBH's tidal influence on planetary systems is typically 10~times stronger than the average neighbour star. For a star cluster with a $200M_\odot$ IMBH, the region in which the IMBH's influence is stronger within the virial radius ($\sim 1$~pc). The IMBH quenches mass segregation, and the stars in the core tend to move towards intermediate regions. The ejection rate of both stars and planets is higher when an IMBH is present. The rate at which planets are expelled from their host star rate is higher for clusters with higher IMBH masses, for $t<0.5 t_{rh}$, while remains mostly constant while the star cluster fills its Roche lobe, similar to a star cluster without an IMBH. The disruption rate of planetary systems is higher in initially denser clusters, and for wider planetary orbits, but this rate is substantially enhanced by the presence of a central IMBH.

astro-ph.EP↗