arXiv subjects

Yang Jiang

Publications and source records attributed to Yang Jiang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Research on the Quantum confinement of Carriers in the Type-I Quantum Wells Structure

Quantum confinement is recognized to be an inherent property in low-dimensional structures. Traditionally it is believed that the carriers trapped within the well cannot escape due to the discrete energy levels. However, our previous research has revealed efficient carrier escape in low-dimensional structures, contradicting this conventional understanding. In this study, we review the energy band structure of quantum wells considering it as a superposition of the bulk material dispersion and quantization energy dispersion resulting from the quantum confinement across the whole Brillouin zone. By accounting for all wave vectors, we obtain a certain distribution of carrier energy at each quantization energy level, giving rise to the energy subbands. These results enable carriers to escape from the well under the influence of an electric field. Additionally, we have compiled a comprehensive summary of various energy band scenarios in quantum well structures, relevant to carrier transport. Such a new interpretation holds significant value in deepening our comprehension of low-dimensional energy bands, discovering new physical phenomena, and designing novel devices with superior performance.

cond-mat.mes-hall↗

arXiv2024

Probing Berry phase effect in topological surface states

We have observed the Berry phase effect associated with interband coherence in topological surface states (TSSs) using two-color high-harmonic spectroscopy. This Berry phase accumulates along the evolution path of strong field-driven election-hole quasiparticles in electronic bands with strong spin-orbit coupling. By introducing a secondary weak field, we perturb the evolution of Dirac fermions in TSSs and thus provide access to the Berry phase. We observe a significant shift in the oscillation phase of the even-order harmonics from the spectral interferogram. We reveal that such a modulation feature is linked to the geometric phase acquired in the nonperturbative dynamics of TSSs. Furthermore, we show that the overwhelming Berry phase effect can significantly deform the quantum paths of electron-hole pairs, thus enhancing the ability to harness electron spin using lightwaves in quantum materials with strong spin-orbit interactions.

physics.optics↗

arXiv2023

Probing Ultralight Tensor Dark Matter with the Stochastic Gravitational-Wave Background from Advanced LIGO and Virgo's First Three Observing Runs

Ultralight bosons are attractive dark-matter candidates and appear in various scenarios beyond standard model. They can induce superradiant instabilities around spinning black holes (BHs), extracting the energy and angular momentum from BHs, and then dissipated through monochromatic gravitational radiation, which become promising sources of gravitational wave detectors. In this letter, we focus on massive tensor fields coupled to BHs and compute the stochastic gravitational wave backgrounds emitted by these sources. We then undertake a search for this background within the data from LIGO/Virgo O1$\sim$ O3 runs. Our analysis reveals no discernible evidence of such signals, allowing us to impose stringent limits on the mass range of tensor bosons. Specifically, we exclude the existence of tensor bosons with masses ranging from $4.0\times10^{-14}$ to $2.0\times10^{-12}$ eV at $95\%$ confidence level.

astro-ph.CO↗

arXiv2023

Random resistive memory-based deep extreme point learning machine for unified visual processing

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. We show the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems. Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.

cs.LG↗

arXiv2023

An Accessible Toolkit for 360 VR Studies

Virtual reality is expected to play a significant role in the transformation of education and psychological studies. The possibilities for its application as a visual research method can be enhanced as established frameworks and toolkits are made more available to users, not just developers, advocates, and technical academics, enhancing its controlled study impact. With an accessible first design approach, we can overcome accessibility constraints and tap into new research potential. The open-sourced toolkit demonstrates how game engine technologies can be utilized to immerse participants in a 360-video environment with curated text displayed at pre-set intervals. Allowing for researchers to guide participants through virtual experiences intuitively through a desktop application while the study unfolds in the users VR headset.

cs.HC↗

arXiv2023

A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design

Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery.

cs.LG↗

arXiv2023

Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey

A human 3D avatar is one of the important elements in the metaverse, and the modeling effect directly affects people's visual experience. However, the human body has a complex topology and diverse details, so it is often expensive, time-consuming, and laborious to build a satisfactory model. Recent studies have proposed a novel method, implicit neural representation, which is a continuous representation method and can describe objects with arbitrary topology at arbitrary resolution. Researchers have applied implicit neural representation to human 3D avatar modeling and obtained more excellent results than traditional methods. This paper comprehensively reviews the application of implicit neural representation in human body modeling. First, we introduce three implicit representations of occupancy field, SDF, and NeRF, and make a classification of the literature investigated in this paper. Then the application of implicit modeling methods in the body, hand, and head are compared and analyzed respectively. Finally, we point out the shortcomings of current work and provide available suggestions for researchers.

cs.CV↗

arXiv2023

Search for stochastic gravitational-wave background from string cosmology with Advanced LIGO and Virgo's O1$\sim$O3 data

String cosmology models predict a relic background of gravitational-wave (GW) radiation in the early universe. The GW energy spectrum of radiated power increases rapidly with the frequency, and therefore it becomes a potential and meaningful observation object for high-frequency GW detector. We focus on the stochastic background generated by superinflation in string theory and search for such signal in the observing data of Advanced LIGO and Virgo O1$\sim$O3 runs in a Bayesian framework. We do not find the existence of the signal, and thus put constraints on the GW energy density. Our results indicate that at $f=100\,\text{Hz}$, the fractional energy density of GW background is less than $1.7\times10^{-8}$ and $2.1\times10^{-8}$ for dilaton-string and dilaton only cases respectively, and further rule out the parameter space restricted by the model itself due to the non-decreasing dilaton and stable cosmology background ($β$ bound).

gr-qc↗

arXiv2023

Upper limits on the Polarized Isotropic Stochastic Gravitational-Wave Background from Advanced LIGO-Virgo's First Three Observing Runs

Parity violation is expected to generate an asymmetry between the amplitude of left and right-handed gravitational-wave modes which leads to a circularly polarized stochastic gravitational-wave background (SGWB). Due to the three independent baselines in the LIGO-Virgo network, we focus on the amplitude difference in strain power characterized by Stokes' parameters and do maximum-likelihood estimation to constrain the polarization degree of SGWB. Our results indicate that there is no evidence for the circularly polarized SGWB in the data. Furthermore, by modeling the SGWB as a power-law spectrum, we place upper limit on the normalized energy density $Ω_\text{gw}(25\,\text{Hz})<5.3\times10^{-9}$ at $95\%$ confidence level after marginalizing over the polarization degree and spectral index.

astro-ph.CO↗

arXiv2022

High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desiring way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Besides, inertial navigation systems (INSs) have been used as a well-complementary dead reckoning (DR) sensor for GNSS receivers in kinematic scenarios resisting various interferences since early. But little work focuses on the case of whether the INS can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information, and integrated solution of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Real-world experiments demonstrate the proposed SDR regarding estimating time-of-arrival (TOA) and positioning accuracy.

eess.SP↗

arXiv2022

Research Scholar Interest Mining Method based on Load Centrality

In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results. For the important problems found in the research and application of reality, this paper also proposes a research scholar interest mining algorithm based on load centrality (LCBIM), which can accurately solve the problem according to the researcher's research papers and patent data. Graphs of creative algorithms in various fields of the study aggregated ideas, generated topic graphs by aggregating neighborhoods, used the generated topic information to construct with similar or similar topic spaces, and utilize keywords to construct one or more topics. The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node, which can analyze the field in the complete coverage principle. The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars from papers and corpus.

cs.DL↗

arXiv2022

Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression

Although melanoma occurs more rarely than several other skin cancers, patients' long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that provides potential concordance information to healthcare providers could help inform diagnostic, prognostic, and therapeutic decision-making for challenging melanoma cases. We present a melanoma concordance regression deep learning model capable of predicting the concordance rate of invasive melanoma or melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features corresponding to melanoma concordance were learned in a self-supervised manner with the contrastive learning method, SimCLR. We trained a SimCLR feature extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens originating from four distinct pathology labs. We trained a separate melanoma concordance regression model on 990 specimens with available concordance ground truth annotations from three pathology labs and tested the model on 211 specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the test set. We also investigated the performance of using the predicted concordance rate as a malignancy classifier, and achieved a precision and recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These results are an important first step for building an artificial intelligence (AI) system capable of predicting the results of consulting a panel of experts and delivering a score based on the degree to which the experts would agree on a particular diagnosis. Such a system could be used to suggest additional testing or other action such as ordering additional stains or genetic tests.

cs.CV↗

arXiv2022

Implications for Cosmic Domain Walls from LIGO-Virgo First Three Observing Runs

We put constraints on the normalized energy density in gravitaional waves from cosmic domain walls (DWs) by searching for the stochastic gravitational-wave background (SGWB) in the data of Advanced LIGO and Virgo's first three observing runs. By adopting a phenomenological broken power-law model, we obtain the upper limit of normalized energy density of SGWB generated by DWs in the peak frequency band $10\sim200$ Hz, and get the most stringent limitation at the peak frequency $f_*=35$ Hz, namely $Ω_\text{DW}(f_*=35\,\text{Hz})<1.4\times10^{-8}$ at $95\%$ confidence level (CL). Subsequently, we work out the constraints on the parameter space in the appealing realization of DW structure -- the heavy axion model which can avoid the so-called quality problem.

astro-ph.CO↗

arXiv2022

Constraints on the ultralight scalar boson from Advanced LIGO and Advanced Virgo's first three observing runs using the stochastic gravitational-wave background

Ultralight bosons are promising dark matter candidates and can trigger superradiant instabilities of spinning black holes (BHs), resulting in long-lived rotating "bosonic clouds" around the BHs and dissipating their energy through the emission of monochromatic gravitational waves (GWs). We focus on the scalar bosons minimally coupled with both isolated stellar-origin BHs (SBH) and their binary merger remnants, and perform Bayesian data analysis to search for the stochastic GW background from all the unstable modes that can trigger the superradiant instabilities using the data of Advanced LIGO and Advanced Virgo's first three observing runs. We find no evidence for such signal, and hence rule out the scalar bosons within the mass range $[1.5, 16]\times10^{-13}$ eV, $[1.9, 8.3]\times10^{-13}$ eV and $[1.3, 17]\times10^{-13}$ eV at $95\%$ confidence level for isolated SBHs having a uniform dimensionless spin distribution in $[0,1]$, $[0,0.5]$ and $[0.5,1]$, respectively.

astro-ph.CO↗

arXiv2022

Constraining the Gravitational-Wave Spectrum from Cosmological First-Order Phase Transitions Using Data from LIGO-Virgo First Three Observing Runs

We search for a first-order phase transition (PT) gravitational wave (GW) signal from Advanced LIGO and Advanced Virgo's first three observing runs. Due to the large theoretical uncertainties, four GW energy spectral shapes from bubble and sound wave collisions widely adopted in literature are investigated, separately. Our results indicate that there is no evidence for the existence of such GW signals, and therefore we give the upper limits on the amplitude of GW energy spectrum $Ω_\text{pt}(f_*)$ in the peak frequency range of $f_*\in [5,500]$ Hz for these four theoretical models, separately. We find that $Ω_\text{pt}(f_*\simeq 40\ \text{Hz})<1.3\times10^{-8}$ at $95\%$ credible level, and roughly $H_*/β\lesssim 0.1$ and $α\lesssim 1$ at $68\%$ credible level in the peak frequency range of $20\lesssim f_*\lesssim 100$ Hz corresponding to the most sensitive frequency band of Advanced LIGO and Advanced Virgo's first three observing runs, where $H_*$ is the Hubble parameter when PT happens, $β$ is the bubble nucleation rate and $α$ is the ratio of vacuum and relativistic energy density.

astro-ph.CO↗

arXiv2022

Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing

The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled video streaming network under a time-varying wireless network and limited computation capacity. "Enhancement" means that the client can improve the quality of the downloaded video segments via image processing modules. We aim to design a joint bitrate adaptation and client-side enhancement algorithm toward maximizing the quality of experience (QoE). We formulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL)-based framework, named ENAVS. As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming. Ultimately, we implement ENAVS and demonstrate extensive testbed results under real-world bandwidth traces and videos. The simulation shows that ENAVS is capable of delivering 5%-14% more QoE under the same bandwidth and computing power conditions as conventional ABR streaming.

eess.SY↗

arXiv2021

Formation of Ultra-High-Resistance Au/Ti/p-GaN Junctions and the Applications in AlGaN/GaN HEMTs

We report a dramatic current reduction, or a resistance increase, by a few orders of magnitude of two common-anode Au/Ti/pGaN Schottky junctions annealed within a certain annealing condition window (600 - 700 oC, 1 - 4 min). Results from similar common-anode Schottky junctions made of Au/p-GaN, Al/Ti/p-GaN and Au/Ti/graphene/p-GaN junctions demonstrated that all the three layers (Au, Ti and p-GaN) are essential for the increased resistance. Raman characterization of the p-GaN showed a decrease of the Mg-N bonding, i.e., the deactivation of Mg, which is consistent with the Hall measurement results. Moreover, this high-resistance junction structure was employed in p-GaN gate AlGaN/GaN HEMTs. It was shown to be an effective gate technology that was capable to boost the gate breakdown voltage from 9.9 V to 13.8 V with a negligible effect on the threshold voltage or the sub-threshold slope.

cond-mat.mtrl-sci↗

arXiv2020

Determination of the Gate Breakdown Mechanisms in p-GaN Gate HEMTs by Multiple-gate-sweep Measurements

In this work, we studied the gate breakdown mechanisms of p-GaN gate AlGaN/GaN HEMTs by a novel multiple-gate-sweep-based method. For the first time, three different breakdown mechanisms were observed and identified separately in the same devices: the metal/p-GaN junction breakdown, the p-GaN/AlGaN/GaN junction breakdown, and the passivation related breakdown. This method is an effective method to determine the breakdown mechanisms. The different BD mechanisms were further confirmed by scanning electron microscopy (SEM). Finally, the temperature dependences of the three BD mechanisms were measured and compared. This analysis method was also employed in the devices with a different passivation material and showed its applicability.

physics.app-ph↗