arXiv subjects

Yan Wang

Publications and source records attributed to Yan Wang.

At least 793 records · Page 44Linked to original sources

arXiv2022

Topmetal-M: a novel pixel sensor for compact tracking applications

The Topmetal-M is a large area pixel sensor (18 mm * 23 mm) prototype fabricated in a new 130 nm high-resistivity CMOS process in 2019. It contains 400 rows * 512 columns square pixels with the pitch of 40 μm. In Topmetal-M, a novel charge collection method combing the Monolithic Active Pixel Sensor (MAPS) and the Topmetal sensor has been proposed for the first time. Both the ionized charge deposited by the particle in the sensor and along the track over the sensor can be collected. The in-pixel circuit mainly consists of a low-noise charge sensitive amplifier to establish the signal for the energy reconstruction, and a discriminator with a Time-to-Amplitude Converter (TAC) for the Time of Arrival (TOA) measurement. With this mechanism, the trajectory, particle hit position, energy and arrival time of the particle can be measured. The analog signal from each pixel is accessible through time-shared multiplexing over the entire pixel array. This paper will discuss the design and preliminary test results of the Topmetal-M sensor.

physics.ins-det↗

arXiv2022

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the benchmark of model robustness. Existing benchmarks mainly focus on evaluating defenses, but there are no comprehensive studies of how architecture design and training techniques affect robustness. Comprehensively benchmarking their relationships is beneficial for better understanding and developing robust DNNs. Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e.g., data augmentation) towards diverse noises (adversarial, natural, and system noises). Extensive experiments substantiated several insights for the first time, e.g., (1) adversarial training is effective for the robustness against all noises types for Transformers and MLP-Mixers; (2) given comparable model sizes and aligned training settings, CNNs > Transformers > MLP-Mixers on robustness against natural and system noises; Transformers > MLP-Mixers > CNNs on adversarial robustness; (3) for some light-weight architectures, increasing model sizes or using extra data cannot improve robustness. Our benchmark presents: (1) an open-source platform for comprehensive robustness evaluation; (2) a variety of pre-trained models to facilitate robustness evaluation; and (3) a new view to better understand the mechanism towards designing robust DNNs. We will continuously develop to this ecosystem for the community.

cs.CV↗

arXiv2022

Verification of Operational Numerical Weather Prediction Model Forecasts of Precipitation Using Satellite Rainfall Estimates over Africa

Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate related diseases and public health. Numerical Weather Models (NWM) are an important component of this work, due to their complete spatial coverage, high resolution, and ability to forecast into the future. In this study, we seek to evaluate the spatiotemporal skill of short-term rainfall forecasts of rainfall across Africa. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in-situ observations and are commonly used in analysis of African rainfall. We found that the model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter-tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at one week aggregation time are improved against daily forecasts.

physics.ao-ph↗

arXiv2022

External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data

The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular injury can augment severity grading for improving clinical decision support and outcome prediction. However, accurate segmentation of splenic vascular injury is challenging for the following reasons: 1) Splenic vascular injury can be highly variant in shape, texture, size, and overall appearance; and 2) Data acquisition is a complex and expensive procedure that requires intensive efforts from both data scientists and radiologists, which makes large-scale well-annotated datasets hard to acquire in general. In light of these challenges, we hereby design a novel framework for multi-phase splenic vascular injury segmentation, especially with limited data. On the one hand, we propose to leverage external data to mine pseudo splenic masks as the spatial attention, dubbed external attention, for guiding the segmentation of splenic vascular injury. On the other hand, we develop a synthetic phase augmentation module, which builds upon generative adversarial networks, for populating the internal data by fully leveraging the relation between different phases. By jointly enforcing external attention and populating internal data representation during training, our proposed method outperforms other competing methods and substantially improves the popular DeepLab-v3+ baseline by more than 7% in terms of average DSC, which confirms its effectiveness.

eess.IV↗

arXiv2021

Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation

3D human pose and shape recovery from a monocular RGB image is a challenging task. Existing learning based methods highly depend on weak supervision signals, e.g. 2D and 3D joint location, due to the lack of in-the-wild paired 3D supervision. However, considering the 2D-to-3D ambiguities existed in these weak supervision labels, the network is easy to get stuck in local optima when trained with such labels. In this paper, we reduce the ambituity by optimizing multiple initializations. Specifically, we propose a three-stage framework named Multi-Initialization Optimization Network (MION). In the first stage, we strategically select different coarse 3D reconstruction candidates which are compatible with the 2D keypoints of input sample. Each coarse reconstruction can be regarded as an initialization leads to one optimization branch. In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism. Finally, a Consistency Estimation Network (CEN) is proposed to find the best result from mutiple candidates by evaluating if the visual evidence in RGB image matches a given 3D reconstruction. Experiments demonstrate that our Multi-Initialization Optimization Network outperforms existing 3D mesh based methods on multiple public benchmarks.

cs.CV↗

arXiv2021

Enhanced phonon blockade in a weakly-coupled hybrid system via mechanical parametric amplification

We propose how to achieve strong phonon blockade (PB) in a hybrid spin-mechanical system in the weak-coupling regime. We demonstrate the implementation of magnetically-induced two-phonon interactions between a mechanical cantilever resonator and an embedded nitrogen-vacancy (NV) center, which, combined with parametric amplification of the mechanical motion, produces significantly enlarged anharmonicity in the eigenenergy spectrum. In the weak-driving regime, we show that strong PB appears in the hybrid system along with a large mean phonon number, even in the presence of strong mechanical dissipation. We also show flexible tunability of phonon statistics by controlling the strength of mechanical parametric amplification. Our work opens up prospects for the implementation of an efficient single-phonon source, with potential applications in quantum phononics and phononic quantum networks.

quant-ph↗

arXiv2021

Simple Harish-Chandra modules over the super affine-Virasoro algebras

In this paper, we classify all simple Harish-Chandra modules over the super affine-Virasoro algebra $\widehat{\mathcal{L}}=\mathcal{W}\ltimes(\mathfrak{g}\otimes \mathcal{A})\oplus \mathbb{C}C$, where $\mathcal{A}=\mathbb{C}[t^{\pm 1}]\otimesΛ(1)$ is the tensor superalgebra of the Laurent polynomial algebra in even variable $t$ and the Grassmann algebra in odd variable $ξ$, $\mathcal{W}$ is the Lie superalgebra of superderivations of $\mathcal{A}$, and $\mathfrak{g}$ is a finite-dimensional perfect Lie superalgebra.

math.RT↗

arXiv2021

Quantum Algorithms for Ground-State Preparation and Green's Function Calculation

We propose quantum algorithms for projective ground-state preparation and calculations of the many-body Green's functions directly in frequency domain. The algorithms are based on the linear combination of unitary (LCU) operations and essentially only use quantum resources. To prepare the ground state, we construct the operator ${\exp}(-τ\hat{H}^2)$ using Hubbard-Stratonovich transformation by LCU and apply it on an easy-to-prepare initial state. Our projective state preparation procedure saturates the near-optimal scaling, $\mathcal{O}(\frac{1}{γΔ} \log \frac{1}{γη})$, of other currently known algorithms, in terms of the spectral-gap lower bound $Δ$, the additive error $η$ in the state vector, and the overlap lower bound $γ$ between the initial state and the exact ground state. It is straightforward to combine our algorithm with spectral-gap amplification technique to achieve quadratically improved scaling $\mathcal{O}(1/\sqrtΔ)$ for ground-state preparation of frustration-free Hamiltonians, which we demonstrate with numerical results of the $q$-deformed XXZ chain. To compute the Green's functions, including the single-particle and other response functions, we act on the prepared ground state with the retarded resolvent operator $R(ω+ iΓ; \hat{H})$ in the LCU form derived from the Fourier-Laplace integral transform (FIT). Our resolvent algorithm has $\mathcal{O}(\frac{1}{Γ^2} \log\frac{1}{Γε})$ complexity scaling for the frequency resolution $Γ$ of the response functions and the targeted error $ε$, while classical algorithms for FIT usually have polynomial scaling over the error $ε$. To illustrate the complexity scaling of our algorithms, we provide numerical results for their application to the paradigmatic Fermi-Hubbard model on a one-dimensional lattice with different numbers of sites.

quant-ph↗

arXiv2021

New Discovery Modes for a Light Charged Higgs Boson at the LHC

At the Large Hadron Collider (LHC), both the ATLAS and CMS Collaborations have been searching for light charged Higgs bosons via top (anti)quark production and decays channels, like $pp\to t \bar{t}$ with one top (anti)quark decaying into a charged Higgs boson and a $b$ (anti)quark, when the decay is kinematically open (i.e., when $m_{H^\pm}\lesssim m_t$). In this paper, we propose new searches at the LHC involving light charged Higgs bosons via their pair production channels like $pp\to H^\pm h/A$ and $pp\to H^+ H^-$ in the 2-Higgs Doublet Model (2HDM) Type-I and -X scenarios. By focusing on the case where the heavy $H$ state plays the role of the Standard Model (SM)-like Higgs boson with a mass near 125 GeV, we study the aforementioned Higgs boson pair production channels and investigate their bosonic decays, such as $H^\pm \to W^{\pm } h$ and/or $H^\pm \to W^{\pm } A$. We demonstrate that for a light charged Higgs boson state, with $m_{H^\pm}\lesssim m_t$, at the LHC, such di-Higgs production and decay channels can give rise to signatures with event rates much larger than those emerging from $pp\to t\bar{t}\to t\bar{b} H^-$ + c.c.. We specifically study $h/A\to b\bar b$ and $τ^+τ^-$ decays. We, therefore, claim that the discussed combination of new production and decay modes can result in an alternative discovery channel for charged Higgs bosons lighter than the top (anti)quark at the LHC within the above two 2HDM Types. Finally, in order to motivate experimentalists in ATLAS and CMS to search for such signatures, we propose 16 Benchmark Points (BPs) which are compatible with both theoretical and experimental constraints.

hep-ph↗

arXiv2021

New Light $H^\pm$ Discovery Channels at the LHC

A light charged Higgs boson has been searched for at the Large Hadron Collider (LHC) via top (anti)quark decay, i.e., $t \to b H^+$, if kinematically allowed. In this contribution, we propose new channels for light charged Higgs boson searches via the pair productions $pp\to H^\pm h/A$ and $pp\to H^+ H^-$ at the LHC in the context of the Two-Higgs Doublet Model (2HDM) Type-I. By focusing on a case where the heavy H state is the Standard Model (SM)-like one already observed, we investigate the production of the aforementioned charged Higgs bosons and their bosonic decay channels, namely, $H^\pm \to W^\pm h$ and/or $H^\pm \to W^\pm A$. We demonstrate that such production and decay channels can yield substantial alternative discovery channels for $H^\pm$ bosons at the LHC. Finally, we propose eight benchmark points (BPs) to motivate the search for such signatures.

hep-ph↗

arXiv2021

Mathematical modeling of shear-activated targeted nanoparticle drug delivery for the treatment of aortic diseases

The human aorta is a high-risk area for vascular diseases, which are commonly restored by thoracic endovascular aortic repair. In this paper, we report a promising shear-activated targeted nanoparticle drug delivery strategy to assist in the treatment of coarctation of the aorta and aortic aneurysm. Idealized three-dimensional geometric models of coarctation of the aorta and aortic aneurysm are designed, respectively. The unique hemodynamic environment of the diseased aorta is used to improve nanoparticle drug delivery. Micro-carriers with nanoparticle drugs would be targeting activated to release nanoparticle drugs by local abnormal shear stress rate (SSR). Coarctation of the aorta provides a high SSR hemodynamic environment, while the aortic aneurysm is exposed to low SSR. We propose a method to calculate the SSR thresholds for the diseased aorta. Results show that the upstream near-wall area of the diseased location is an ideal injection location for the micro-carriers, which could be activated by the abnormal SSR. Released nanoparticle drugs would be successfully targeted delivered to the aortic diseased wall. Besides, the high diffusivity of the micro-carriers and nanoparticle drugs has a significant impact on the surface drug concentrations of the diseased aortic walls, especially for aortic aneurysms. This study preliminary demonstrates the feasibility of shear-activated targeted nanoparticle drug delivery in the treatment of aortic diseases and provides a theoretical basis for developing the drug delivery system and novel therapy.

physics.med-ph↗

arXiv2021

Searching for $H^\pm\to W^{\pm} + 4γ$ signals in the 2HDM Type-I at the LHC

We analyze a new signature of a charged Higgs boson in the Type-I realization of the 2-Higgs Doublet Model (2HDM), where the mass of the charged Higgs boson satisfies the condition $M_{H^{\pm}}<M_{t}+M_{b}$ and the theoretical parameter space is consistent with the latest experimental constraints from Large Hadron Collider (LHC) and other direct as well as indirect searches. In the surviving regions, it is found that the bosonic decay mode of a charged Higgs boson is dominated by $H^{\pm} \rightarrow W^{\pm*}h$. At the same time, the light neutral Higgs boson $h$ dominantly decays into two photons. We thus find that the production process $pp \rightarrow H^{\pm}h \rightarrow W^{\pm*}h h \rightarrow l^{\pm}ν+4γ$ is almost background free. Since the $W^{\pm}$ could be either off-shell or on-shell, furthering a previous phenomenological analysis, we examine closely detector effects onto the above signature and demonstrate that it is indeed promising to study it at the LHC with both $\sqrt{s}= 13$ TeV and $\sqrt{s} = 14$ TeV, assuming an integrated luminosity of 300 $fb^{-1}$.

hep-ph↗

arXiv2021

Systematic error tolerant multiqubit holonomic entangling gates

Quantum holonomic gates hold built-in resilience to local noises and provide a promising approach for implementing fault-tolerant quantum computation. We propose to realize high-fidelity holonomic $(N+1)$-qubit controlled gates using Rydberg atoms confined in optical arrays or superconducting circuits. We identify the scheme, deduce the effective multi-body Hamiltonian, and determine the working condition of the multiqubit gate. Uniquely, the multiqubit gate is immune to systematic errors, i.e., laser parameter fluctuations and motional dephasing, as the $N$ control atoms largely remain in the much stable qubit space during the operation. We show that $C_N$-NOT gates can reach same level of fidelity at a given gate time for $N\leq5$ under a suitable choice of parameters, and the gate tolerance against errors in systematic parameters can be further enhanced through optimal pulse engineering. In case of Rydberg atoms, the proposed protocol is intrinsically different from typical schemes based on Rydberg blockade or antiblockade. Our study paves a new route to build robust multiqubit gates with Rydberg atoms trapped in optical arrays or with superconducting circuits. It contributes to current efforts in developing scalable quantum computation with trapped atoms and fabricable superconducting devices.

quant-ph↗

arXiv2021

Unidirectional acoustic metamaterials based on nonadiabatic holonomic quantum transformations

Nonadiabatic holonomic quantum transformations (NHQTs) have attracted wide attention and have been applied in many aspects of quantum computation, whereas related research is usually limited to the field of quantum physics. Here we bring NHQTs into constructing a unidirectional acoustic metamaterial (UDAM) for shaping classical beams. The UDAM is made up of an array of three-waveguide couplers, where the propagation of acoustic waves mimics the evolution of NHQTs. The excellent agreement among analytical predictions, numerical simulations, and experimental measurements confirms the great applicability of NHQTs in acoustic metamaterial engineering. The present work extends research on NHQTs from quantum physics to the field of classical waves for designing metamaterials with simple structures and may pave a new way to design UDAMs that would be of potential applications in acoustic isolation, communication, and stealth.

physics.class-ph↗

arXiv2021

MHFC: Multi-Head Feature Collaboration for Few-Shot Learning

Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). (2) Meta-test. Apply the trained FEM to acquire the novel data's features and recognize them. FSL relies heavily on the design of the FEM. However, various FEMs have distinct emphases. For example, several may focus more attention on the contour information, whereas others may lay particular emphasis on the texture information. The single-head feature is only a one-sided representation of the sample. Besides the negative influence of cross-domain (e.g., the trained FEM can not adapt to the novel class flawlessly), the distribution of novel data may have a certain degree of deviation compared with the ground truth distribution, which is dubbed as distribution-shift-problem (DSP). To address the DSP, we propose Multi-Head Feature Collaboration (MHFC) algorithm, which attempts to project the multi-head features (e.g., multiple features extracted from a variety of FEMs) to a unified space and fuse them to capture more discriminative information. Typically, first, we introduce a subspace learning method to transform the multi-head features to aligned low-dimensional representations. It corrects the DSP via learning the feature with more powerful discrimination and overcomes the problem of inconsistent measurement scales from different head features. Then, we design an attention block to update combination weights for each head feature automatically. It comprehensively considers the contribution of various perspectives and further improves the discrimination of features. We evaluate the proposed method on five benchmark datasets (including cross-domain experiments) and achieve significant improvements of 2.1%-7.8% compared with state-of-the-arts.

cs.CV↗

arXiv2021

The Gravitational-Wave Physics II: Progress

It has been a half-decade since the first direct detection of gravitational waves, which signifies the coming of the era of the gravitational-wave astronomy and gravitational-wave cosmology. The increasing number of the detected gravitational-wave events has revealed the promising capability of constraining various aspects of cosmology, astronomy, and gravity. Due to the limited space in this review article, we will briefly summarize the recent progress over the past five years, but with a special focus on some of our own work for the Key Project ``Physics associated with the gravitational waves'' supported by the National Natural Science Foundation of China. In particular, (1) we have presented the mechanism of the gravitational-wave production during some physical processes of the early Universe, such as inflation, preheating and phase transition, and the cosmological implications of gravitational-wave measurements; (2) we have put constraints on the neutron star maximum mass according to GW170817 observations; (3) we have developed a numerical relativity algorithm based on the finite element method and a waveform model for the binary black hole coalescence along an eccentric orbit.

gr-qc↗

arXiv2021

Co-degree threshold for rainbow perfect matchings in uniform hypergraphs

Let $k$ and $n$ be two integers, with $k\geq 3$, $n\equiv 0\pmod k$, and $n$ sufficiently large. We determine the $(k-1)$-degree threshold for the existence of a rainbow perfect matchings in $n$-vertex $k$-uniform hypergraph. This implies the result of Rödl, Ruciński, and Szemerédi on the $(k-1)$-degree threshold for the existence of perfect matchings in $n$-vertex $k$-uniform hypergraphs. In our proof, we identify the extremal configurations of closeness, and consider whether or not the hypergraph is close to the extremal configuration. In addition, we also develop a novel absorbing device and generalize the absorbing lemma of Rödl, Ruciński, and Szemerédi.

math.CO↗

arXiv2021

Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation

Recently, the interpretability of deep learning has attracted a lot of attention. A plethora of methods have attempted to explain neural networks by feature visualization, saliency maps, model distillation, and so on. However, it is hard for these methods to reveal the intrinsic properties of neural networks. In this work, we studied the 1-D optimal piecewise linear approximation (PWLA) problem, and associated it with a designed neural network, named lattice neural network (LNN). We asked four essential questions as following: (1) What are the characters of the optimal solution of the PWLA problem? (2) Can an LNN converge to the global optimum? (3) Can an LNN converge to the local optimum? (4) Can an LNN solve the PWLA problem? Our main contributions are that we propose the theorems to characterize the optimal solution of the PWLA problem and present the LNN method for solving it. We evaluated the proposed LNNs on approximation tasks, forged an empirical method to improve the performance of LNNs. The experiments verified that our LNN method is competitive with the start-of-the-art method.

cs.LG↗