arXiv subjects

Xinxin Yang

Publications and source records attributed to Xinxin Yang.

14 recordsLinked to original sources

arXiv2026

High-Fidelity Quantum State Transfer in Multimode Resonators via Tunable Pulses

Quantum state transfer between distant nodes is essential for distributed quantum information processing. Existing protocols are typically optimized for specific coupling regimes, such as adiabatic dark-state transfer in the single-mode limit and pitch-and-catch schemes in the multimode regime, leaving the crossover between them without a simple and unified control strategy. Here we identify a minimal two-parameter control framework that enables high-fidelity quantum state transfer across this single-mode-to-multimode crossover in a multimode quantum channel. Using a pulse-shaped pitch-and-catch protocol controlled only by the pulse ramp rate and the emission-absorption delay, we achieve transfer fidelities exceeding 99.9%, extending pitch-and-catch protocols toward the single-mode limit without requiring dark-state protection or complex pulse design. We further demonstrate robustness against dissipation, disorder, detuning, and imperfect initialization under experimentally realistic conditions. These results provide a simple and broadly applicable framework for state transfer in multimode quantum channels, with relevance to circuit-QED and hybrid quantum-acoustic systems.

quant-ph↗

arXiv2026

AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising

In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist advertisers in bid optimization. This complexity is further compounded in multi-channel scenarios, where effective allocation of budgets and constraints across channels with distinct behavioral patterns becomes critical for optimizing return on investment. Current approaches predominantly rely on either optimization-based strategies or reinforcement learning techniques. However, optimization-based methods lack flexibility in adapting to dynamic market conditions, while reinforcement learning approaches often struggle to capture essential historical dependencies and observational patterns within the constraints of Markov Decision Process frameworks. To address these limitations, we propose AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control. The framework employs a high-level generative planner based on diffusion models to dynamically allocate budgets and constraints by effectively capturing historical context and temporal patterns. We introduce a constraint enforcement mechanism to ensure compliance with specified constraints, along with a trajectory refinement mechanism that enhances adaptability to environmental changes through the utilization of historical data. The system further incorporates a control-based bidding algorithm that synergistically combines historical knowledge with real-time information, significantly improving both adaptability and operational efficacy. Extensive experiments conducted on large-scale offline datasets and through online A/B tests demonstrate the effectiveness of AHBid, yielding a 13.57% increase in overall return compared to existing baselines.

cs.AI↗

arXiv2023

Multiple Testing under High-dimensional Dynamic Factor Model

Large-scale multiple testing under static factor models is widely used to detect sparse signals in high-dimensional data. However, static factor models are arguably too stringent because they ignore serial correlation, which seriously distorts error rate control in large-scale inference. In this manuscript, we propose a new multiple testing procedure under dynamic factor models that is robust to nonlinear serial dependence. The idea is to integrate a new sample-splitting strategy based on chronological order and a two-pass Fama--Macbeth regression to form a series of test statistics with marginal symmetry properties and then to use these properties to obtain a data-driven threshold. We show that our procedure can control the false discovery rate asymptotically under high-dimensional dynamic factor models. {As a byproduct of independent interest, we establish a new exponential-type deviation inequality for the sum of random variables over various functionals of linear and nonlinear processes.} Our numerical results, including a case study on hedge fund selection, demonstrate the advantage of our proposed method over several state-of-the-art methods.

math.ST↗

arXiv2022

6G Radio Requirements to Support Integrated Communication, Localization, and Sensing

6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.

eess.SP↗

arXiv2021

Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)

Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results indicate that properly trained learning algorithms can play a useful role in the diagnosis of LGESS.

eess.IV↗

arXiv2021

Terahertz rectennas on flexible substrates based on one-dimensional metal-insulator-graphene diodes

Flexible energy harvesting devices fabricated in scalable thin-film processes are important components in the field of wearable electronics and the Internet of Things. We present a flexible rectenna based on a one-dimensional junction metal-insulator-graphene diode, which offers low-noise power detection at terahertz (THz) frequencies. The rectennas are fabricated on a flexible polyimide film in a scalable process by photolithography using graphene grown by chemical vapor deposition. A one-dimensional junction area reduces the junction capacitance and enables operation in the D-band (110 - 170 GHz). The rectenna on polyimide shows a maximum voltage responsivity of 80 V/W at 167 GHz in free space measurements and minimum noise equivalent power of 80 pW/$\sqrt{\text{Hz}}$.

physics.app-ph↗

arXiv2020

Thermoelectric graphene photodetectors with sub-nanosecond response times at Terahertz frequencies

Ultrafast and sensitive (noise equivalent power <1 nWHz-1/2) light-detection in the Terahertz (THz) frequency range (0.1-10 THz) and at room-temperature is key for applications such as time-resolved THz spectroscopy of gases, complex molecules and cold samples, imaging, metrology, ultra-high-speed data communications, coherent control of quantum systems, quantum optics and for capturing snapshots of ultrafast dynamics, in materials and devices, at the nanoscale. Here, we report room-temperature THz nano-receivers exploiting antenna-coupled graphene field effect transistors integrated with lithographically-patterned high-bandwidth (~100 GHz) chips, operating with a combination of high speed (hundreds ps response time) and high sensitivity (noise equivalent power <120 pWHz-1/2) at 3.4 THz. Remarkably, this is achieved with various antenna and transistor architectures (single-gate, dual-gate), whose operation frequency can be extended over the whole 0.1-10 THz range, thus paving the way for the design of ultrafast graphene arrays in the far infrared, opening concrete perspective for targeting the aforementioned applications.

physics.app-ph↗

arXiv2020

Floquet and Anomalous Floquet Weyl Semimetals

The periodic driving of a quantum system can enable new topological phases without analogs in static systems. This provides a route towards preparing non-equilibrium quantum phases rooted into the non-equilibrium nature by periodic driving engineering. Motivated by the ongoing considerable interest in topological semimetals, we are interested in the novel topological phases in the periodically driven topological semimetals without a static counterpart. We propose to design non-equilibrium topological semimetals in the regime of weakly driving field where the spectrum width of shares the same magnitude with the driving frequency. We identify two novel types of non-equilibrium Weyl semimetals (i.e., Floquet and anomalous Floquet Weyl semimetals) that do not exhibit analogues in equilibrium. The proposed setup is shown to be experimentally feasible using the state-of-the-art techniques used to control ultracold atoms in optical lattices.

cond-mat.str-el↗

arXiv2020

Coherent transport through a resonant level coupled to random-matrix leads

We study the transport through a resonant level coupled to two leads with the latter being described by Wigner's random matrices. By taking appropriate thermodynamic limit before taking the long time limit, we obtain the stationary current as a function of voltage bias. The I-V curve is similar to that of single impurity Anderson model. On the other hand, the current matrix and initial density matrix in our model look like random matrices in the eigenbasis of Hamiltonian. They satisfy the description of eigenstate thermalization hypothesis (ETH) and nonequilibrium steady state hypothesis (NESSH), respectively. A statistical formula of current has been derived based on ETH and NESSH (J. Stat. Mech.: Theo. Exp., 093105 (2017)). We check this formula in our model and find it to predict the stationary current to a high precision. The shape of I-V curve is explained by the peak structure in the characteristic function of NESSH, which is reminiscent of the transmission coefficient.

cond-mat.mes-hall↗

arXiv2019

Testing High-dimensional Covariance Matrices under the Elliptical Distribution and Beyond

We develop tests for high-dimensional covariance matrices under a generalized elliptical model. Our tests are based on a central limit theorem (CLT) for linear spectral statistics of the sample covariance matrix based on self-normalized observations. For testing sphericity, our tests neither assume specific parametric distributions nor involve the kurtosis of data. More generally, we can test against any non-negative definite matrix that can even be not invertible. As an interesting application, we illustrate in empirical studies that our tests can be used to test uncorrelatedness among idiosyncratic returns.

math.ST↗

arXiv2019

Does carrier velocity saturation help to enhance fmax in graphene field-effect transistors?

It has been argued that current saturation in graphene field-effect transistors (GFETs) is needed to get the highest possible maximum oscillation frequency (fmax). This paper numerically investigates whether velocity saturation can help to get better current saturation and if that correlates with enhanced fmax. For such a purpose, we used a drift-diffusion simulator that includes several factors that influence output conductance, especially at short channel lengths and-or large drain bias: short-channel electrostatics, saturation velocity, graphene-dielectric interface traps, and self-heating effects. As a testbed for our investigation, we analyzed fabricated GFETs with high extrinsin cutoff frequency fT,x (34 GHz) and fmax (37 GHz). Our simulations allow for a microscopic (local) analysis of the channel parameteres such as carrier concentration, drift and saturation velocities. For biases far away from the Dirac voltage, where the channel behaves as unipolar, we confirmed that the higher is the drift velocity, as close as possible to the saturation velocity, the greater fmax is. However, the largest fmax is recorded at biases near the crossover between unipolar and bipolar behavior, where it does not hold that the highest drift velocity maximizes fmax. In fact, the position and magnitude of the largest fmax depend on the complex interplay between the carrier concentration and total velocity which, in turn, are impacted by the self-heating. Importantly, this effect was found to severely limit radio-frequency performance, reducing the maximum fmax from around 60 to 40 GHz.

physics.app-ph↗

arXiv2018

Semantic Human Matting

Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes them difficult to be applied to large scale data or time-sensitive scenarios. In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints learned from data and propose an automatic human matting algorithm (SHM). SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and fine details is challenging. We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha matte. We also construct a very large dataset with high quality annotations consisting of 35,513 unique foregrounds to facilitate the learning and evaluation of human matting. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods.

cs.CV↗

arXiv2018

Density matrix of chaotic quantum systems

The nonequilibrium dynamics in chaotic quantum systems denies a fully understanding up to now, even if thermalization in the long-time asymptotic state has been explained by the eigenstate thermalization hypothesis which assumes a universal form of the observable matrix elements in the eigenbasis of Hamiltonian. It was recently proposed that the density matrix elements have also a universal form, which can be used to understand the nonequilibrium dynamics at the whole time scale, from the transient regime to the long-time steady limit. In this paper, we numerically test these assumptions for density matrix in the models of spins.

cond-mat.stat-mech↗

arXiv2016

Social Politics: Agenda Setting and Political Communication on Social Media

Social media play an increasingly important role in political communication. Various studies investigated how individuals adopt social media for political discussion, to share their views about politics and policy, or to mobilize and protest against social issues. Yet, little attention has been devoted to the main actors of political discussions: the politicians. In this paper, we explore the topics of discussion of U.S. President Obama and the 50 U.S. State Governors using Twitter data and agenda-setting theory as a tool to describe the patterns of daily political discussion, uncovering the main topics of attention and interest of these actors. We examine over one hundred thousand tweets produced by these politicians and identify seven macro-topics of conversation, finding that Twitter represents a particularly appealing vehicle of conversation for American opposition politicians. We highlight the main motifs of political conversation of the two parties, discovering that Republican and Democrat Governors are more or less similarly active on Twitter but exhibit different styles of communication. Finally, by reconstructing the networks of occurrences of Governors' hashtags and keywords related to political issues, we observe that Republicans form a tight core, with a stronger shared agenda on many issues of discussion.

cs.SI↗