arXiv subjects

Zhengjun Zhang

Publications and source records attributed to Zhengjun Zhang.

At least 19 recordsLinked to original sources

arXiv2026

When and Why Na\"ive Diversification Works: A Simple Diagnostic Strategy

We explain the long-standing puzzle of na\"ive diversification with a simple, testable condition: equal weighting is minimum-variance optimal when the forecast-error covariance matrix has a uniform eigenstructure. This "Golden Criterion" drives a two-stage adaptive strategy that dynamically blends naive and optimized weights based on the empirical distance from this condition. Applied to U.S. equity premium forecasting, the method delivers consistent out-of-sample gains in forecast accuracy, utility, and Sharpe ratios. Diversity-driven shrinkage dominates at short horizons, while optimized weights regain their edge at longer horizons, offering clear horizon-dependent guidance for portfolio construction.

econ.GN↗

arXiv2026

Technology Fundamentals and False Bubble Detection: Evidence from Dot-Com and AI Episodes

We show that widely used bubble tests, most prominently the PSY framework, suffer severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, thereby altering the asymptotic null distribution of the test statistic and causing the standard bubble test to overreject. We propose a technology-adjusted diagnostic that removes an estimated technology component from measures of productivity, IT-investment, and patents before testing the residual. The adjustment is conservative: because a boom can itself raise these technology measures, a rejection remains robust to such feedback, whereas a non-rejection only bounds residual explosiveness. Dot-com residual explosiveness concentrates in December 1999-March 2000; the 2020-2025 AI rally shows no residual explosiveness in our sample across baseline and sensitivity checks.

econ.GN↗

arXiv2026

Room-temperature magnetic p-n junctions for charge-and-spin diodes

Non-magnetic p-n junctions have been fundamental components in the silicon era, serving as the backbone for nearly all Si-based semiconductor devices, including transistors. To tackle challenges such as scaling limitations, excessive latency, and high-power consumption in Si-based electronics, we develop magnetic p-n junctions composed of a p-type amorphous magnetic semiconductor (p-AMS) and n-type Si. These charge-and-spin junctions exhibit typical diode characteristics for charge current, along with distinctive spin diode features. By manipulating spin-polarized space charges, we observed a giant magnetic enhancement of approximately 24.36% at a breakdown current of 5 mA, and an impressive 29-fold increase in magnetic moments for p-AMS. The observed spin behavior is attributed to space charge effects or carrier depletion in the p-AMS with extended hole states.

cond-mat.mtrl-sci↗

arXiv2026

CosmicWeb-21cm array: A New Radio Observation Array Design for 21cm Cosmology

This paper presents the CosmicWeb-21cm array, a novel radio interferometer designed to overcome the key challenges in 21 cm cosmology. Its core innovations include: (1) a multi-scale nested geometry combining a hexagonal core with logarithmic spiral arms for excellent UV coverage and calibration robustness; (2) an intelligent non-uniform frequency sampling strategy that adapts resolution to foreground and signal characteristics, reducing data volume while preserving information; and (3) a machine-learning-enhanced, physics-informed processing pipeline that achieves 99.7\% foreground removal efficiency; (4) a dual-polarization crossed dipole integrated with a dielectric lens and cryogenically cooled LNA, achieving stable beam patterns and low noise temperature ($<35$ K) across 50-250 MHz. These co-designed advances enable high sensitivity mapping of the Epoch of Reionization, dark energy constraints and cosmic-web structure.

astro-ph.IM↗

arXiv2025

Why the hyperbolic polaritons are hyperbolic?

Polaritons travelling along a hyperbolic medium's surface have recently sparked significant interest in nanophotonics for the unprecedented manipulation ability on light at the nanoscale in a planar way, promising potential nano-optical applications, especially in two-dimensional circuitry. Despite of being named hyperbolic polaritons, the hyperbolic nature has not been thoroughly revealed since an analytical description of the Iso-frequency contour is still elusive. In this work, we proposed an analytical form for describing the iso-frequency contour of the hyperbolic polaritons, showcasing their strictly hyperbolic nature. Such an analytical form is obtained based on the focusing behavior of the hyperbolic polaritons and verified by both the published data from commonly used hyperbolic media systems of the hyperbolic polaritons and our own experimental characterizations on a hyperbolic metamaterial film. By presenting a concise and intuitive physical image, this work may provide a groundbreaking methodology in developing novel hyperbolic polaritons based optical devices.

physics.optics↗

arXiv2025

Competing Accelerated Failure Time Models for Multiple Concurrent Failure Mechanisms

The rising prevalence of complex diseases characterised by multiple coexisting and interacting etiological processes poses critical challenges for survival analysis and precision medicine, particularly as population ageing renders mutually exclusive models increasingly untenable. We propose a competing accelerated failure time (cAFT) framework to understand the individual-specific temporal dynamics of disease competition and interaction based on a first-to-fail principle. Specifically, we introduce an individualised, time-varying winning probability to quantify the relative contributions of latent causes and provide an interpretable basis for patient stratification within distinct subtypes. Consistency and asymptotic normality are established for the maximum likelihood estimation of the parameters, with practical implementation via an expectation-maximisation (EM) algorithm. We illustrate the model's effectiveness and efficiency through numerical simulations and real-world applications, including biomarker discovery for 28-day survival in sepsis and overall survival in lung adenocarcinoma. Compared with standard AFT and Cox proportional hazards models, the cAFT model consistently improves predictive accuracy (C-index and iAUC gains of 5--10%) and reveals subtype-dependent gene effects within distinct biological pathways across heterogeneous patient subgroups. Conclusively, the cAFT model provides deeper insights into patient prognosis and potential personalised therapeutic strategies.

stat.ME↗

arXiv2025

Max-Linear Tail Regression

The relationship between a response variable and its covariates can vary significantly, especially in scenarios where covariates take on extremely high or low values. This paper introduces a max-linear tail regression model specifically designed to capture such extreme relationships. To estimate the regression coefficients within this framework, we propose a novel M-estimator based on extreme value theory. The consistency and asymptotic normality of our proposed estimator are rigorously established under mild conditions. Simulation results demonstrate that our estimation method outperforms the conditional least squares approach. We validate the practical applicability of our model through two case studies: one using financial data and the other using rainfall data.

stat.ME↗

arXiv2024

Extreme Limit Theory of Competing Risks under Power Normalization

Advanced science and technology provide a wealth of big data from different sources for extreme value analysis. Classical extreme value theory was extended to obtain an accelerated max-stable distribution family for modelling competing risk-based extreme data in Cao and Zhang (2021). In this paper, we establish probability models for power normalized maxima and minima from competing risks. The limit distributions consist of an extensional new accelerated max-stable and min-stable distribution family (termed as the accelerated p-max/p-min stable distribution), and its left-truncated version. The consistency and asymptotic normality are obtained for the maximum likelihood estimation of the parameters involved in the accelerated p-max and p-min stable distributions when it exists. The limit types of distributions are determined principally by the sample generating process and the interplay among the competing risks, which are illustrated by common examples. Further, the statistical inference concerning the maximum likelihood estimation and model diagnosis of this model was investigated. Numerical studies show first the efficient approximation of all limit scenarios as well as its comparable convergence rate in contrast with those under linear normalization, and then present the maximum likelihood estimation and diagnosis of accelerated p-max/p-min stable models for simulated data sets. Finally, two real datasets concerning annual maximum of ground level ozone and survival times of Stanford heart plant demonstrate the performance of our accelerated p-max and accelerated p-min stable models.

math.ST↗

arXiv2024

Exploring the Potential of Two-Dimensional Materials for Innovations in Multifunctional Electrochromic Biochemical Sensors: A Review

In this review, the current advancements in electrochromic sensors based on two-dimensional (2D) materials with rich chemical and physical properties are critically examined. By summarizing the current trends in and prospects for utilizing multifunctional electrochromic devices (ECDs) in environmental monitoring, food quality control, medical diagnosis, and life science-related investigations, we explore the potential of using 2D materials for rational design of ECDs with compelling electrical and optical properties for biochemical sensing applications.

physics.app-ph↗

arXiv2023

Spatio-temporal Joint Modelling on Moderate and Extreme Air Pollution in Spain

Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We firstly propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017--2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold.

stat.AP↗

arXiv2023

High-dimensional Clustering onto Hamiltonian Cycle

Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outliers. This paper proposes a new framework called High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above problems. First, HCHC combines global structure with local structure in one objective function for deep clustering, improving the labels as relative probabilities, to mine the similarities between different clusters while keeping the local structure in each cluster. Then, the anchors of different clusters are sorted on the optimal Hamiltonian cycle generated by the cluster similarities and mapped on the circumference of a circle. Finally, a sample with a higher probability of a cluster will be mapped closer to the corresponding anchor. In this way, our framework allows us to appreciate three aspects visually and simultaneously - clusters (formed by samples with high probabilities), cluster similarities (represented as circular distances), and outliers (recognized as dots far away from all clusters). The experiments illustrate the superiority of HCHC.

cs.AI↗

arXiv2021

Modeling and Decoupling Systemic Risk

Identifying systemic risk patterns in geopolitical, economic, financial, environmental, transportation, epidemiological systems and their impacts is the key to risk management. This paper proposes a new nonlinear time series model: autoregressive conditional accelerated Fréchet (AcAF) model and introduces two new endopathic and exopathic competing risk measures for better learning risk patterns, decoupling systemic risk, and making better risk management. The paper establishes the probabilistic properties of stationarity and ergodicity of the AcAF model. Simulation demonstrates the efficiency of the proposed estimators and the AcAF model's flexibility in modeling heterogeneous data. Empirical studies on the stock returns in S&P 500 and the cryptocurrency trading show the superior performance of the proposed model in terms of the identified risk patterns, endopathic and exopathic competing risks, being informative with greater interpretability, enhancing the understanding of the systemic risks of a market and their causes, and making better risk management possible.

stat.AP↗

arXiv2020

Ultrasensitive Field-Effect Biosensors Enabled by the Unique Electronic Properties of Graphene

This review provides a critical overview of current developments on nanoelectronic biochemical sensors based on graphene. Composed of a single layer of conjugated carbon atoms, graphene has outstanding high carrier mobility and low intrinsic electrical noise, but a chemically inert surface. Surface functionalization is therefore crucial to unravel graphene sensitivity and selectivity for the detection of targeted analytes. To achieve optimal performance of graphene transistors for biochemical sensing, the tuning of the graphene surface properties via surface functionalization and passivation is highlighted, as well as the tuning of its electrical operation by utilizing multifrequency ambipolar configuration and a high frequency measurement scheme to overcome the Debye screening to achieve low noise and highly sensitive detection. Potential applications and prospectives of ultrasensitive graphene electronic biochemical sensors ranging from environmental monitoring and food safety, healthcare and medical diagnosis, to life science research, are presented as well.

cond-mat.mtrl-sci↗

arXiv2020

Modeling Multivariate Time Series with Copula-linked Univariate D-vines

This paper proposes a novel multivariate time series model named Copula-linked univariate D-vines (CuDvine), which enables the simultaneous copula-based modeling of both temporal and cross-sectional dependence for multivariate time series. To construct CuDvine, we first build a semiparametric univariate D-vine time series model (uDvine) based on a D-vine. The uDvine generalizes the existing first-order copula-based Markov chain models to Markov chains of an arbitrary-order. Building upon uDvine, we construct CuDvine by linking multiple uDvines via a parametric copula. As a simple and tractable model, CuDvine provides flexible models for marginal behavior and temporal dependence of time series, and can also incorporate sophisticated cross-sectional dependence such as time-varying and spatio-temporal dependence for high-dimensional applications. Robust and computationally efficient procedures, including a sequential model selection method and a two-stage MLE, are proposed for model estimation and inference, and their statistical properties are investigated. Numerical experiments are conducted to demonstrate the flexibility of CuDvine, and to examine the performance of the sequential model selection procedure and the two-stage MLE. Real data applications on the Australian electricity price data demonstrate the superior performance of CuDvine to traditional multivariate time series models.

stat.ME↗

arXiv2020

Electrical probing of COVID-19 spike protein receptor binding domain via a graphene field-effect transistor

Here, in an effort towards facile and fast screening/diagnosis of novel coronavirus disease 2019 (COVID-19), we combined the unprecedently sensitive graphene field-effect transistor (Gr-FET) with highly selective antibody-antigen interaction to develop a coronavirus immunosensor. The Gr-FET immunosensors can rapidly identify (about 2 mins) and accurately capture the COVID-19 spike protein S1 (which contains a receptor binding domain, RBD) at a limit of detection down to 0.2 pM, in a real-time and label-free manner. Further results ensure that the Gr-FET immunosensors can be promisingly applied to screen for high-affinity antibodies (with binding constant up to 2*10^11 M^-1 against the RBD) at concentrations down to 0.1 pM. Thus, our developed electrical Gr-FET immunosensors provide an appealing alternative to address the early screening/diagnosis as well as the analysis and rational design of neutralizing-antibody locking methods of this ongoing public health crisis.

physics.app-ph↗

arXiv2018

Vocabulary-informed Extreme Value Learning

The novel unseen classes can be formulated as the extreme values of known classes. This inspired the recent works on open-set recognition \cite{Scheirer_2013_TPAMI,Scheirer_2014_TPAMIb,EVM}, which however can have no way of naming the novel unseen classes. To solve this problem, we propose the Extreme Value Learning (EVL) formulation to learn the mapping from visual feature to semantic space. To model the margin and coverage distributions of each class, the Vocabulary-informed Learning (ViL) is adopted by using vast open vocabulary in the semantic space. Essentially, by incorporating the EVL and ViL, we for the first time propose a novel semantic embedding paradigm -- Vocabulary-informed Extreme Value Learning (ViEVL), which embeds the visual features into semantic space in a probabilistic way. The learned embedding can be directly used to solve supervised learning, zero-shot and open set recognition simultaneously. Experiments on two benchmark datasets demonstrate the effectiveness of proposed frameworks.

cs.CV↗

arXiv2016

Three-dimensional bulk electronic structure of the Kondo lattice CeIn3 revealed by photoemission

We show the three-dimensional electronic structure of the Kondo lattice CeIn3 using soft x-ray angle resolved photoemission spectroscopy in the paramagnetic state. For the first time, we have directly observed the three-dimensional topology of the Fermi surface of CeIn3 by photoemission. The Fermi surface has a complicated hole pocket centred at the Γ-Z line and an elliptical electron pocket centred at the R point of the Brillouin zone. Polarization and photon-energy dependent photoemission results both indicate the nearly localized nature of the 4f electrons in CeIn3, consistent with the theoretical prediction by means of the combination of density functional theory and single-site dynamical meanfield theory. Those results illustrate that the f electrons of CeIn3, which is the parent material of CeMIn5 compounds, are closer to the localized description than the layered CeMIn5 compounds.

cond-mat.str-el↗

arXiv2015

Calculation and evaluation of cross-sections for p+184W reactions up to 200MeV

The cross-sections of proton-induced reactions on 184W at incident proton energy below 200MeV are calculated and analyzed including reaction cross-sections, elastic scattering angular distributions, energy spectra and double differential cross section. Nuclear theoretical models which integrate the optical model, distorted born wave approximation theory, the intra-nuclear cascade model, the exciton model, the Hauser-Feshbach theory and the evaporation model are used in the reactions. Theoretical results are compared with the existent experimental data.

nucl-th↗