arXiv subjects

Yi Chen

Publications and source records attributed to Yi Chen.

At least 235 records · Page 13Linked to original sources

arXiv2022

Observation of a multitude of correlated states at the surface of bulk 1T-TaSe$_2$ crystals

The interplay between electron-electron interactions and structural ordering can yield exceptionally rich correlated electronic phases. We have used scanning tunneling microscopy to investigate bulk 1T-TaSe2 and have uncovered surprisingly diverse correlated surface states thereof. These surface states exhibit the same in-plane charge density wave ordering but dramatically different electronic ground states ranging from insulating to metallic. The insulating variety of surface state shows signatures of a decoupled surface Mott layer. The metallic surface states, on the other hand, exhibit zero-bias peaks of varying strength that suggest Kondo phases arising from coupling between the Mott surface layer and the metallic bulk of 1T-TaSe2. The surface of bulk 1T-TaSe2 thus constitutes a rare realization of the periodic Anderson model covering a wide parameter regime, thereby providing a model system for accessing different correlated phenomena in the same crystal. Our results highlight the central role played by strong correlations in this material family.

cond-mat.str-el↗

arXiv2022

An electron-spin qubit platform assembled atom-by-atom on a surface

Creating a quantum-coherent architecture at the atomic scale has long been an ambition in quantum science and nanotechnology. This ultimate length scale requires the use of fundamental quantum properties of atoms, such as the spin of electrons, which naturally occurs in many solid-state environments and allows high-fidelity operations and readout by electromagnetic means. Despite decades of effort, however, it remains a formidable task to realize an atomic-scale quantum architecture where multiple electron spin qubits can be precisely assembled, controllably coupled, and coherently operated. Electron spin qubits created in dopants in semiconductors and color centers in insulators, for example, can be well controlled individually6-8 but are difficult to couple together into a circuit. On the other hand, multiple magnetic atoms and molecules on surfaces can be coupled to each other by building sophisticated atomic structures using a scanning tunneling microscope (STM), but coherent operation has so far been limited to a single qubit in the tunnel junction. Here we demonstrate an atomic-scale qubit platform by showing atom-by-atom construction, coherent operations, and readout of multiple electron-spin qubits on a surface. To enable the coherent control of remote qubits that are outside the tunnel junction, we complement each electron spin with a local magnetic field gradient from a nearby single-atom magnet. To enable readout of remote qubits, we employ a sensor qubit in the tunnel junction and implement pulsed double electron spin resonance. Using these methods, we demonstrate fast single-, two-, and three-qubit operations in an all-electrical fashion. Our work marks the creation of an Angstrom-scale qubit platform, where quantum functionalities using electron spin arrays, built atom-by-atom on a surface, are now within reach.

cond-mat.mes-hall↗

arXiv2022

Anisotropic hyperfine interaction of surface-adsorbed single atoms

Hyperfine interactions between electron and nuclear spins have been widely used in material science, organic chemistry, and structural biology as a sensitive probe to the local chemical environment through spatial identification of nuclear spins. With the nuclear spins identified, the isotropic and anisotropic components of the hyperfine interactions in turn offer unique insight into the electronic ground-state properties of the paramagnetic centers. However, traditional ensemble measurements of hyperfine interactions average over a macroscopic number of spins with different geometrical locations and nuclear isotopes. Here, we use a scanning tunneling microscope (STM) combined with electron spin resonance (ESR) to measure hyperfine spectra of hydrogenated-titanium (Ti) atoms on MgO/Ag(100) and thereby determine the isotropic and anisotropic hyperfine interactions at the single-atom level. By combining vector-field ESR spectroscopy with STM-based atom manipulation, we characterize the full hyperfine tensor of individual Ti-47 and Ti-49 atoms and identify significant spatial anisotropy of hyperfine interaction for both isotopes when they are adsorbed at low-symmetry binding sites. Density functional theory calculations reveal that the large hyperfine anisotropy arises from a highly anisotropic distribution of the ground-state electron spin density. Our work highlights the power of ESR-STM-enabled single-atom hyperfine spectroscopy as a powerful tool in revealing ground-state electronic structures and atomic-scale chemical environments with nano-electronvolt resolution.

cond-mat.mes-hall↗

arXiv2022

Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that either equally divides the transmission time slots among users or maximizing some ratios without physcial meanings, we propose to use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness. Since it is difficult to directly optimize 5TUDR, we first cast the problem into the stochastic game framework and subsequently propose a Multi-Agent Reinforcement Learning (MARL)-based algorithm to perform distributed optimization on the resource block group (RBG) allocation. Furthermore, each MARL agent is designed to take information measured by network counters from multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the input states while the RBG allocation as action with a proposed reward function designed to maximize 5TUDR. Extensive simulation is performed to show that the proposed MARL-based scheduler can achieve fair scheduling while maintaining good average network throughput as compared to conventional schedulers.

cs.OS↗

arXiv2022

Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis

Terahertz (0.1-10 THz) communications are envisioned as a key technology for sixth generation (6G) wireless systems. The study of underlying THz wireless propagation channels provides the foundations for the development of reliable THz communication systems and their applications. This article provides a comprehensive overview of the study of THz wireless channels. First, the three most popular THz channel measurement methodologies, namely, frequency-domain channel measurement based on a vector network analyzer (VNA), time-domain channel measurement based on sliding correlation, and time-domain channel measurement based on THz pulses from time-domain spectroscopy (THz-TDS), are introduced and compared. Current channel measurement systems and measurement campaigns are reviewed. Then, existing channel modeling methodologies are categorized into deterministic, stochastic, and hybrid approaches. State-of-the-art THz channel models are analyzed, and the channel simulators that are based on them are introduced. Next, an in-depth review of channel characteristics in the THz band is presented. Finally, open problems and future research directions for research studies on THz wireless channels for 6G are elaborated.

cs.IT↗

arXiv2022

DICP: Doppler Iterative Closest Point Algorithm

In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. We jointly optimize the Doppler velocity objective function and the geometric objective function which sufficiently constrains the point cloud alignment problem even in feature-denied environments. Furthermore, the correspondence matches used for the alignment are improved by pruning away the points from dynamic targets which generally degrade the ICP solution. We evaluate our method on data collected from real sensors and from simulation. Our results show that with the added Doppler velocity residual terms, our method achieves a significant improvement in registration accuracy along with faster convergence, on average, when compared to classical point-to-plane ICP that solely relies on geometric residuals.

cs.RO↗

arXiv2022

Running Newton Coupling, Scale Identification and Black Hole Thermodynamics

We discuss the quantum improvement of black hole solutions in the context of asymptotic safety. The Newton coupling in this formulation depends on an energy scale, which must be identified with some length scale in order to study physical consequences to black holes. However, no physical principle has so far been known for the identification. Here we propose that the consistency of the first law of thermodynamics is the principle that should determine physically sensible scale identification, at least close to the horizon. We show that this leads to a natural solution that the Newton coupling should be a function of the horizon area and find a universal formula for the quantum entropy, which agrees with the standard Bekenstein-Hawking entropy for constant Newton coupling, for Kerr black holes and other higher-dimensional black holes. This suggests that the Newton coupling is a function of the area near the horizon, and also away to infinity, where the quantum effects may not be so important.

hep-th↗

arXiv2022

Can We Do Better Than Random Start? The Power of Data Outsourcing

Many organizations have access to abundant data but lack the computational power to process the data. While they can outsource the computational task to other facilities, there are various constraints on the amount of data that can be shared. It is natural to ask what can data outsourcing accomplish under such constraints. We address this question from a machine learning perspective. When training a model with optimization algorithms, the quality of the results often relies heavily on the points where the algorithms are initialized. Random start is one of the most popular methods to tackle this issue, but it can be computationally expensive and not feasible for organizations lacking computing resources. Based on three different scenarios, we propose simulation-based algorithms that can utilize a small amount of outsourced data to find good initial points accordingly. Under suitable regularity conditions, we provide theoretical guarantees showing the algorithms can find good initial points with high probability. We also conduct numerical experiments to demonstrate that our algorithms perform significantly better than the random start approach.

cs.LG↗

arXiv2022

Channel Measurement, Characterization and Modeling for Terahertz Indoor Communications Above 200 GHz

Terahertz (THz) communications are envisioned as a promising technology for sixth-generation (6G) and beyond systems, owing to its unprecedented multi-gigahertz (GHz) bandwidth. In this paper, channel measurement campaigns in indoor scenarios at 201-209~GHz are reported. Four different communication scenarios including 90 transmitter-receiver pairs are measured in two channel measurement campaigns of a meeting room and an office room, respectively. The two measurement campaigns contains four scenarios, namely, a meeting room, cubicle area, hallway and non-line-of-sight (NLoS) case. The propagation of multi-path components (MPCs) in the four scenarios is characterized by the power-delay-angular profiles. Based on them, the temporal and spatial consistency for varying receiver locations in the complex hallway and NLoS scenarios are verified. To characterize, the large-scale best-direction and omni-directional path losses in indoor scenarios are separately analyzed and modeled by the close-in (CI) model. Furthermore, the small-scale channel parameters, e.g., the number of clusters, delay spread, angular spread, and cluster time-of-arrival are analyzed and modeled by proper distributions. As a general framework, a ray-tracing-statistical hybrid model is proposed for wireless propagation at 201-209~GHz, although, admittedly, the measurement results and analysis reveal that the channel characteristics in various indoor scenarios exhibit noticeable differences that need tailored parameter settings.

cs.IT↗

arXiv2022

Ultrafast disinfection of SARS-CoV-2 viruses

The wide use of surgical masks has been proven effective for mitigating the spread of respiration diseases, such as COVID-19, alongside social distance control, vaccines, and other efforts. With the newly reported variants, such as Delta and Omicron, a higher spread rate had been found compared to the initial strains. People might get infected even by inhaling fewer loading of viruses. More frequent sterilization of surgical masks is needed to protect the wearers. However, it is challenging to sterilize the commodity surgical masks with a fast and effective method. Herein, we reported the sterilization of the SARS-CoV-2 viruses within an ultra-short time, while retaining the mask performance. Silver thin film is coated on commercial polyimide film by physical vapor deposition and patterned by laser scribing to form a Joule heating electrode. Another layer of the gold thin film was coated onto the opposite side of the device to promote the uniformity of the Joule heating through nano-heat transfer regulation. As a result, the surgical mask can be heated to inactivation temperature within a short time and with high uniformity. By Joule-heating the surgical mask with the temperature at 90 °C for 3 minutes, the inactivation of the SARS-CoV-2 showed an efficacy of 99.89%. Normal commodity surgical masks can be sterilized faster, more frequently, and efficiently against SARS-CoV-2 viruses and the new invariants.

physics.med-ph↗

arXiv2022

Jet energy spectrum and substructure in $e^+e^-$ collisions at 91.2 GeV with ALEPH Archived Data

The first measurements of energy spectra and substructure of anti-$k_{T}$ jets in hadronic $Z^0$ decays in $e^+e^-$ collisions are presented. The archived $e^+e^-$ annihilation data at a center-of-mass energy of 91.2 GeV were collected with the ALEPH detector at LEP in 1994. In addition to inclusive jet and leading dijet energy spectra, various jet substructure observables are analyzed as a function of jet energy which includes groomed and ungroomed jet mass to jet energy ratios, groomed momentum sharing, and groomed jet radius. The results are compared with perturbative QCD calculations and predictions from the SHERPA, HERWIG v7.1.5, PYTHIA 6, PYTHIA 8, and PYQUEN event generators. The jet energy spectra agree with perturbative QCD calculations which include the treatment of logarithms of the jet radius and threshold logarithms. None of the event generators give a fully satisfactory description of the data.

hep-ex↗

arXiv2022

A Preliminary Research on Space Situational Awareness Based on Event Cameras

Event camera is a new type of sensor that is different from traditional cameras. Each pixel is triggered asynchronously by an event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement is higher than a certain threshold, the event is output. Compared with traditional cameras, event cameras have the advantages of high temporal resolution, low latency, high dynamic range, low bandwidth and low power consumption. We carried out a series of observation experiments in a simulated space lighting environment. The experimental results show that the event camera can give full play to the above advantages in space situational awareness. This article first introduces the basic principles of the event camera, then analyzes its advantages and disadvantages, then introduces the observation experiment and analyzes the experimental results, and finally, a workflow of space situational awareness based on event cameras is given.

cs.CV↗

arXiv2022

Event-Based Dense Reconstruction Pipeline

cs.CV↗

arXiv2022

Evidence for a spinon Kondo effect in cobalt atoms on single-layer 1T-TaSe$_2$

Quantum spin liquids (QSLs) are highly entangled, disordered magnetic states that arise in frustrated Mott insulators and host exotic fractional excitations such as spinons and chargons. Despite being charge insulators some QSLs are predicted to exhibit gapless itinerant spinons that yield metallic behavior in the spin channel. We have deposited isolated magnetic atoms onto single-layer (SL) 1T-TaSe$_2$, a gapless QSL candidate, to experimentally probe how itinerant spinons couple to impurity spin centers. Using scanning tunneling spectroscopy we observe the emergence of new, impurity-induced resonance peaks at the 1T-TaSe$_2$ Hubbard band edges when cobalt adatoms are positioned to have maximal spatial overlap with the Hubbard band charge distribution. These resonance peaks disappear when the spatial overlap is reduced or when the magnetic impurities are replaced with non-magnetic impurities. Theoretical simulations using a modified Anderson impurity model integrated with a gapless quantum spin liquid show that these resonance peaks are consistent with a Kondo resonance induced by spinons combined with spinon-chargon binding effects that arise due to QSL gauge-field fluctuations.

cond-mat.str-el↗

arXiv2022

Research on Event Accumulator Settings for Event-Based SLAM

Event cameras are a new type of sensors that are different from traditional cameras. Each pixel is triggered asynchronously by event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement of brightness is higher than a certain threshold, an event is output. Compared with traditional cameras, event cameras have the advantages of high dynamic range and no motion blur. Accumulating events to frames and using traditional SLAM algorithm is a direct and efficient way for event-based SLAM. Different event accumulator settings, such as slice method of event stream, processing method for no motion, using polarity or not, decay function and event contribution, can cause quite different accumulating results. We conducted the research on how to accumulate event frames to achieve a better event-based SLAM performance. For experiment verification, accumulated event frames are fed to the traditional SLAM system to construct an event-based SLAM system. Our strategy of setting event accumulator has been evaluated on the public dataset. The experiment results show that our method can achieve better performance in most sequences compared with the state-of-the-art event frame based SLAM algorithm. In addition, the proposed approach has been tested on a quadrotor UAV to show the potential of applications in real scenario. Code and results are open sourced to benefit the research community of event cameras.

cs.RO↗

arXiv2022

Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels for samples in the wrongly labeled set. However, current methods always hurt the informative hard samples due to the similar loss distribution between the hard samples and the noisy ones. In this paper, we proposed PGDF (Prior Guided Denoising Framework), a novel framework to learn a deep model to suppress noise by generating the samples' prior knowledge, which is integrated into both dividing samples step and semi-supervised step. Our framework can save more informative hard clean samples into the cleanly labeled set. Besides, our framework also promotes the quality of pseudo-labels during the semi-supervised step by suppressing the noise in the current pseudo-labels generating scheme. To further enhance the hard samples, we reweight the samples in the cleanly labeled set during training. We evaluated our method using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world datasets WebVision and Clothing1M. The results demonstrate substantial improvements over state-of-the-art methods.

cs.CV↗

arXiv2022

Direct reconstruction of tissue conductivity with deconvolution in magneto-acousto-electrical tomography (MAET): theory and numerical simulation

Magneto-acousto-electrical tomography (MAET), a combination of ultrasound imaging and electrical impedance tomography (EIT), offers both high resolution (in comparison to EIT) and high contrast (in comparison to ultrasound imaging). It is used to map the internal conductivity distribution of an imaging object. However, conductivity reconstruction in MAET is a challenge, so conventional MAET is mainly devoted to mapping the conductivity interface. This is primarily because integration byparts is used in the theory derivation, and the simplified measurement formula suggests the voltage is proportional to the conductivity gradient, which leads to an error in the measurement formula. In this study, the measurement signal is expressed as the convolution of acoustic velocity and conductivity distribution without using integration by parts, which retains the low-frequency term in the measurement signal. Based on the convolution formula, we subsequently propose a direct conductivity reconstruction scheme with deconvolution by utilizing the low-frequency component. We verify the proposed method based on two two-dimension models and quantify the L2 errors of reconstructed conductivity. Besides, we analyze factors influencing the reconstructed accuracy such as reconstructed regularization parameter ultrasound frequency, and noise. We also demonstrate that the spatial resolution is not influenced by the duration of excitation ultrasound. With the contributions of the proposed method, conductivity imaging appears to be feasible for application to the early diagnosis in the future.

physics.med-ph↗

arXiv2021

Harnessing the Quantum Behavior of Spins on Surfaces

The desire to control and measure individual quantum systems such as atoms and ions in a vacuum has led to significant scientific and engineering developments in the past decades that form the basis of today's quantum information science. Single atoms and molecules on surfaces, on the other hand, are heavily investigated by physicists, chemists, and material scientists in search of novel electronic and magnetic functionalities. These two paths crossed in 2015 when it was first clearly demonstrated that individual spins on a surface can be coherently controlled and read out in an all-electrical fashion. The enabling technique is a combination of scanning tunneling microscopy (STM) and electron spin resonance (ESR), which offers unprecedented coherent controllability at the Angstrom length scale. This review aims to illustrate the essential ingredients that allow the quantum operations of single spins on surfaces. Three domains of applications of surface spins, namely quantum sensing, quantum control, and quantum simulation, are discussed with physical principles explained and examples presented. Enabled by the atomically-precise fabrication capability of STM, single spins on surfaces might one day lead to the realization of quantum nanodevices and artificial quantum materials at the atomic scale.

physics.app-ph↗