arXiv subjects

Ni Zhao

Publications and source records attributed to Ni Zhao.

12 recordsLinked to original sources

arXiv2026

R-Estimation with Right-Censored Data

This paper considers the problem of directly generalizing the R-estimator under a linear model formulation with right-censored outcomes. We propose a natural generalization of the rank and corresponding estimating equation for the R-estimator in the case of the Wilcoxon (i.e., linear-in-ranks) score function, and show how it can respectively be exactly represented as members of the classes of estimating equations proposed in Ritov (1990) and Tsiatis (1990). We then establish analogous results for a large class of bounded nonlinear-in-ranks score functions. Asymptotics and variance estimation are obtained as straightforward consequences of these representation results. The self-consistent estimator of the residual distribution function, and the mid-cumulative distribution function (and, where needed, a generalization of it), play critical roles in these developments.

stat.ME↗

arXiv2024

Subsampling-based Tests in Mediation Analysis

Testing for mediation effect poses a challenge since the null hypothesis (i.e., the absence of mediation effects) is composite, making most existing mediation tests quite conservative and often underpowered. In this work, we propose a subsampling-based procedure to construct a test statistic whose asymptotic null distribution is pivotal and remains the same regardless of the three null cases encountered in mediation analysis. The method, when combined with the popular Sobel test, leads to an accurate size control under the null. We further introduce a Cauchy combination test to construct p-values from different subsample splits, which reduces variability in the testing results and increases detection power. Through numerical studies, our approach has demonstrated a more accurate size and higher detection power than the competing classical and contemporary methods.

stat.ME↗

arXiv2024

Measuring magnetic field coil constants based on atomic magnetometry and fluxgate magnetometry

In a magnetic field detection system,to achieve high-sensitivity magnetic field measurement, it is necessary to use uniform magnetic field coils to provide a stable working environment, so the measurement of the magnetic field coilsconstant is of great significance. To accurately measure the magnetic field and compare the coil constant, we employed two different methods under good magnetic shielding conditions: the optically-pumped rubidium free-induction decay magnetometry and the fluxgate magnetometry. In terms of measuring coil constant, the optically-pumped rubidium FID magnetometer performs better than fluxgate magnetometer due to its high-sensitivity and good signal-to-noise ratio. We compare the magnetic field measured by the FID magnetometer with that of the fluxgate magnetometer and obtain a calibration factor of 0.9967. The calibration of fluxgate magnetometer with optically-pumped atomic magnetometer is realized. The method is simple and easy to operate for calibrating fluxgate magnetometer.

physics.atom-ph↗

arXiv2024

Comparison and analysis of methods for measuring the spin transverse relaxation time of rubidium atomic vapor

The spin transverse relaxation time (T_2) of atoms is an important indicator for precision measurement. Several methods have been proposed to characterize the T_2 of atoms. In this paper, the T_2 of rubidium (Rb) atomic vapor in the same cell was measured using four measuring methods, namely spin noise spectrum signal fitting, improved free induction decay (FID) signal fitting, w_m-broadening fitting, and magnetic resonance broadening fitting. Meanwhile, the T_2 of five different types of Rb atomic vapor cells were measured and characterized. A comparative analysis visualizes the characteristics of the different measuring methods and the effects of buffer gas on T_2 of Rb. We theoretically and experimentally analyzed the applicability of the different methods, and then demonstrated that the improved FID signal fitting method provides the most accurate measurement because of the clean environment in which the measurements were taken. Furthermore, we demonstrated and qualitatively analyzed the relationship between the atomic number density and the T_2 of Rb. This work provides analytical insight in selecting atomic vapor cells, and may shed light on the improvement of the sensitivity of atomic magnetometers.

physics.atom-ph↗

arXiv2023

Early ChatGPT User Portrait through the Lens of Data

Since its launch, ChatGPT has achieved remarkable success as a versatile conversational AI platform, drawing millions of users worldwide and garnering widespread recognition across academic, industrial, and general communities. This paper aims to point a portrait of early GPT users and understand how they evolved. Specific questions include their topics of interest and their potential careers; and how this changes over time. We conduct a detailed analysis of real-world ChatGPT datasets with multi-turn conversations between users and ChatGPT. Through a multi-pronged approach, we quantify conversation dynamics by examining the number of turns, then gauge sentiment to understand user sentiment variations, and finally employ Latent Dirichlet Allocation (LDA) to discern overarching topics within the conversation. By understanding shifts in user demographics and interests, we aim to shed light on the changing nature of human-AI interaction and anticipate future trends in user engagement with language models.

cs.HC↗

arXiv2023

Characterizing current noise of commercial constant-current sources by using of an optically-pumped rubidium atomic magnetometer

This paper introduces a method for characterizing the current noise of commercial constant-current sources(CCSs) using a free-induction-decay(FID) type optically-pumped rubidium atomic magnetometer driven by a radio-frequency(RF) magnetic field. We convert the sensitivity of the atomic magnetometer into the current noise of CCS by calibrating the coil constant. At the same time, the current noise characteristics of six typical commercial low-noise CCSs are compared. The current noise level of the KeySight Model B2961A is the lowest among the six tested CCSs, which is 36.233 0.022 nA / Hz1/2 at 1-25 Hz and 133.905 0.080 nA / Hz1/2 at 1-100 Hz respectively. The sensitivity of atomic magnetometer is dependent on the current noise level of the CCS. The CCS with low noise is of great significance for high-sensitivity atomic magnetometer. The research provides an important reference for promoting the development of high precision CCS, metrology and basic physics research.

physics.atom-ph↗

arXiv2023

A Dynamic Model for Frequency Response Optimization in Photovoltaic Visible Light Communication

Photovoltaic (PV) modules are recently employed in photovoltaic visible light communication (PVLC) for simultaneous energy harvesting and visible light communication. A PV-based receiver features large signal output, easy optical alignment, and self-powered operation. However, PV modules usually have a severe bandwidth limitation when used as passive photodetectors. In this paper, we systematically investigate the internal impedance dynamic of PV modules and how that affects their frequency response characteristics under different illuminances. We propose a simplified yet accurate dynamic PV mode AC detection model to capture the frequency response characteristics of a PVLC receiver. The model is validated with the impedance spectroscopy characterization methodologies. Experimental results show that a PV module's internal resistance and capacitance depend on incident illuminance, affecting PV's frequency response. The bandwidth is exacerbated under indoor environments with low illuminance levels due to the increment of internal resistance for PV modules. The RC constant can be reduced for PVLC receivers working near open-circuit voltage conditions by adding a moderate local light to decrease the internal resistance value. For practical implementation, PVLC receivers will employ a load for data recovery. We show that adjusting the forward bias conditions can simultaneously reduce the resistance and capacitance values. With the optimization of equivalent trans-impedance, the data rate of a Cadmium telluride (CdTe) PV module achieves a 3.8 times enhancement under 200 lux. We also demonstrate that the BER of a 5-Mbit/s eight-level pulse amplitude modulation (PAM8) signal can be reduced from 9.8*10-2 to 1.4*10-3 by maximizing the transimpedance gain-bandwidth product.

eess.SP↗

arXiv2023

A Multi-Pass Optically Pumped Rubidium Atomic Magnetometer with Free Induction Decay

A free-induction-decay (FID) type optically-pumped rubidium atomic magnetometer driven by a radio-frequency (RF) magnetic field is presented in this paper. Influences of parameters, such as the temperature of rubidium vapor cell, the power of pump beam, and the strength of RF magnetic field and static magnetic field on the amplitude and the full width at half maximum (FWHM) of the FID signal, have been investigated in the time domain and frequency domain. At the same time, the sensitivities of the magnetometer for the single-pass and the triple-pass probe beam cases have been compared by changing the optical path of the interaction between probe beam and atomic ensemble. Compared with the sensitivity of ~21.2 pT/Hz^(1/2) in the case of the single-pass probe beam, the amplitude of FID signal in the case of the triple-pass probe beam has been significantly enhanced, and the sensitivity has been improved to ~13.4 pT/Hz^(1/2). The research in this paper provids a reference for the subsequent study of influence of different buffer gas pressure on the FWHM and also a foundation for further improving the sensitivity of FID rubidium atomic magnetometer by employing a~polarization-squeezed light as probe beam, to achieve a sensitivity beyond the photo-shot-noise level.

physics.atom-ph↗

arXiv2020

Towards new forms of particle sensing and manipulation and 3D imaging on a smartphone for healthcare applications

Close to half of the world population have smartphones, while a typical flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting-edge healthcare applications, such as single-molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky and sophisticated devices. Most of the current healthcare applications are developed based on using the photon-sensitive components, such as CMOS sensors, flash & fill lights, lens modules, and LED lights in the screen, leaving the rest of the smart and high-performance sensors rarely explored. In this Perspective, we review recent progresses in advanced sensors in modern smartphones and discuss how those sensors have great, as yet unmet, promise to offer widespread and easy-to-implement solutions to many emerging healthcare applications, including nanoscale sensing, point-of-care testing, pollution monitoring, etc.

eess.IV↗

arXiv2020

Gradient Regularized Contrastive Learning for Continual Domain Adaptation

Human beings can quickly adapt to environmental changes by leveraging learning experience. However, the poor ability of adapting to dynamic environments remains a major challenge for AI models. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labeled source domain and a sequence of unlabeled target domains. There are two major obstacles in this problem: domain shifts and catastrophic forgetting. In this work, we propose Gradient Regularized Contrastive Learning to solve the above obstacles. At the core of our method, gradient regularization plays two key roles: (1) enforces the gradient of contrastive loss not to increase the supervised training loss on the source domain, which maintains the discriminative power of learned features; (2) regularizes the gradient update on the new domain not to increase the classification loss on the old target domains, which enables the model to adapt to an in-coming target domain while preserving the performance of previously observed domains. Hence our method can jointly learn both semantically discriminative and domain-invariant features with labeled source domain and unlabeled target domains. The experiments on Digits, DomainNet and Office-Caltech benchmarks demonstrate the strong performance of our approach when compared to the state-of-the-art.

cs.CV↗

arXiv2019

Feature Exploration for Knowledge-guided and Data-driven Approach Based Cuffless Blood Pressure Measurement

This study explores extended feature space that is indicative of blood pressure (BP) changes for better estimation of continuous BP in an unobtrusive way. A total of 222 features were extracted from noninvasively acquired electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject undergoing coronary angiography and/or percutaneous coronary intervention, during which intra-arterial BP was recorded simultaneously with the subject at rest and while administering drugs to induce BP variations. The association between the extracted features and the BP components, i.e. systolic BP (SBP), diastolic BP (DBP), mean BP (MBP), and pulse pressure (PP) were analyzed and evaluated in terms of correlation coefficient, cross sample entropy, and mutual information, respectively. Results show that the most relevant indicator for both SBP and MBP is the pulse full width at half maximum, and for DBP and PP, the amplitude between the peak of the first derivative of PPG (dPPG) to the valley of the second derivative of PPG (sdPPG) and the time interval between the peak of R wave and the sdPPG, respectively. As potential inputs to either the knowledge-guided model or data-driven method for cuffless BP calibration, the proposed expanded features are expected to improve the estimation accuracy of cuffless BP.

eess.SP↗

arXiv2018

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.

cs.LG↗