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Qiang Li

Publications and source records attributed to Qiang Li.

At least 289 records · Page 16Linked to original sources
arXiv2023

Probing darK Matter Using free leptONs: PKMUON

We propose a new method to detect sub-GeV dark matter, through their scatterings from free leptons and the resulting kinematic shifts. Specially, such an experiment can detect dark matter interacting solely with muons. The experiment proposed here is to directly probe muon-philic dark matter, in a model-independent way. Its complementarity with the muon on target proposal, is similar to, e.g. XENON/PandaX and ATLAS/CMS on dark matter searches. Moreover, our proposal can work better for relatively heavy dark matter such as in the sub-GeV region. We start with a small device of a size around 0.1 to 1 meter, using atmospheric muons to set up a prototype. Within only one year of operation, the sensitivity on cross section of dark matter scattering with muons can already reach $σ_D\sim 10^{-19 (-20,\,-18)}\rm{cm}^{2}$ for a dark mater $\rm{M_D}=100\, (10,\,1000)$ MeV. We can then interface the device with a high intensity muon beam of $10^{12}$/bunch. Within one year, the sensitivity can reach $σ_D\sim 10^{-27 (-28,\,-26)}\rm{cm}^{2}$ for $\rm{M_D}=100\, (10,\,1000)$ MeV.

hep-ph↗
arXiv2023

Search for vector-like leptons at a Muon Collider

A feasibility study is performed for searching vector-like leptons at a muon collider, in the context of the "4321 model", an ultraviolet-complete model with rich collider phenomenology together with potential to explain recent existing some B physics measurements or anomalies. In this paper, we perform a Monte Carlo study with various machine learning techniques, and examine the projected sensitivity on vector-like leptons over a wide mass range at a TeV-scale muon collider. We find that a 3 TeV muon collider with only 10/fb of data can already be sensitive to cover the mass range of a vector-like lepton up to 1450 GeV.

hep-ph↗
arXiv2023

One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System

Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive MISO communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common row-structured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.

eess.SP↗
arXiv2023

Noise Perturbation for Saliency Prediction with Psychophysical Synthetic Images

Convolutional neural networks (CNNs) have achieved great success in natural image saliency prediction. The primary goal of this study is to investigate the performance of saliency prediction in CNN and classic models with psychophysical synthetic images under noise perturbation. Is it still as decent as natural images in terms of performance? In the meantime, it can be used to investigate the relationship between CNNs and human vision, mainly low-level vision functions. On the other hand, are CNNs exact replicas of human visual function? This study used CNNs, Fourier, and spectral models inspired by low-level vision systems to investigate saliency prediction on psychophysical synthetic images rather than natural images. According to our findings, saliency prediction models inspired by Fourier and spectral theory outperformed current pre-trained deep neural networks on psychophysical images with noise perturbation. However, psychophysical models were more unstable in noise than pre-trained deep neural networks. Meanwhile, we suggested that investigating CNNs with psychophysical methods could benefit visual neuroscience and artificial neural network studies.

q-bio.NC↗
arXiv2023

Ultrathin Magnesium-based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Among the materials considered for transmon qubits, tantalum (Ta) has emerged as a promising candidate, surpassing conventional counterparts in terms of coherence time. However, the presence of an amorphous surface Ta oxide layer introduces dielectric loss, ultimately placing a limit on the coherence time. In this study, we present a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer deposited on top of tantalum. Synchrotron-based X-ray photoelectron spectroscopy (XPS) studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, we demonstrate that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Based on the experimental data and computational modeling, we establish an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation. This work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, our findings pave the way for the realization of large-scale, high-performance quantum computing systems.

cond-mat.mtrl-sci↗
arXiv2023

Probing Inhomogeneous Cuprate Superconductivity by Terahertz Josephson Echo Spectroscopy

Inhomogeneities play a crucial role in determining the properties of quantum materials. Yet methods that can measure these inhomogeneities are few, and apply to only a fraction of the relevant microscopic phenomena. For example, the electronic properties of cuprate materials are known to be inhomogeneous over nanometer length scales, although questions remain about how such disorder influences supercurrents and their dynamics. Here, two-dimensional terahertz spectroscopy is used to study interlayer superconducting tunneling in near-optimally-doped La1.83Sr0.17CuO4. We isolate a 2 THz Josephson echo signal with which we disentangle intrinsic lifetime broadening from extrinsic inhomogeneous broadening. We find that the Josephson plasmons are only weakly inhomogeneously broadened, with an inhomogeneous linewidth that is three times smaller than their intrinsic lifetime broadening. This extrinsic broadening remains constant up to 0.7Tc, above which it is overcome by the thermally-increased lifetime broadening. Crucially, the effects of disorder on the Josephson plasma resonance are nearly two orders of magnitude smaller than the in-plane variations in the superconducting gap in this compound, which have been previously documented using Scanning Tunnelling Microscopy (STM) measurements. Hence, even in the presence of significant disorder in the superfluid density, the finite frequency interlayer charge fluctuations exhibit dramatically reduced inhomogeneous broadening. We present a model that relates disorder in the superfluid density to the observed lifetimes.

cond-mat.supr-con↗
arXiv2023

Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery

With the development of deep learning techniques, supervised learning has achieved performances surpassing those of humans. Researchers have designed numerous corresponding models for different data modalities, achieving excellent results in supervised tasks. However, with the exponential increase of data in multiple fields, the recognition and classification of unlabeled data have gradually become a hot topic. In this paper, we employed a Reinforcement Learning framework to simulate the cognitive processes of humans for effectively addressing novel class discovery in the Open-set domain. We deployed a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information, aiming to acquire a more comprehensive understanding of the feature space. Furthermore, this approach facilitated the incorporation of self-supervised learning to enhance model training. We employed a clustering method with varying constraint conditions, ranging from strict to loose, allowing for the generation of dependable labels for a subset of unlabeled data during the training phase. This iterative process is similar to human exploratory learning of unknown data. These mechanisms collectively update the network parameters based on rewards received from environmental feedback. This process enables effective control over the extent of exploration learning, ensuring the accuracy of learning in unknown data categories. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets. Our approach achieves competitive competitive results.

cs.AI↗
arXiv2023

High efficiency spin-decoupled modulation using chiral C2-symmetric meta-atoms

Orthogonal circularly polarized light is essential for multiplexing tunable metasurfaces. Mainstream spin-decoupled metasurfaces, consisting of numerous meta-atoms with mirror symmetry, rely on the cooperative modulation of the Pancharatnam-Berry (PB) phase and the propagation phase. This paper demonstrates spin-decoupled functionality through the synergistic utilization of planar chiral meta-atom phase response and PB phase. Based on the Jones calculus, it has been found that meta-atoms with chiral C2-symmetry owns a larger geometric parameter range with high cross-polarization ratio compared to those with mirror symmetry or higher symmetries at the same aspect ratio. This characteristic is advantageous in terms of enabling high-efficiency manipulation and enhancing the signal-to-noise ratio. As an example, 10 kinds of C2-symmetry chiral meta-atoms with a H-like shape are selected by the self-adaptive genetic algorithm to attain a full 2$π$ phase span with an interval of $π$/5. To mitigate the additional propagation phase change of the guided modes originated from the arrangement alternation upon the rotation of the meta-atoms, the enantiomer of chiral meta-atoms and its PB phase delay are adopted to minimize the difference between the actual and desired target phases. A polarization-insensitive metalens and a chiral virtual-moving metalens array are designed to demonstrate the spin-decoupled function with both high efficiency and signal-to-noise ratio. The work in this paper may trigger more exciting and interesting spin-decoupled multiplexing metasurfaces and broaden the prospect of chiroptical applications.

physics.optics↗
arXiv2023

Polarization-insensitive dual-wavelength dispersion tunable metalens achieved by global modulation method

SF$_6$ is widely used as a gas-insulator in high-voltage power electrical system. Detecting SF$_6$ leaks using unmanned aerial vehicle (UAV)-based thermal cameras allows efficient large-scale inspections during routine maintenance. The emergence of lightweight metalenses can increase the endurance of UAVs. Simultaneously controlling dispersion and polarization properties in metalens is significant for thermal camera applications. However, via a propagation phase modulation method in which the phase is tuned locally, it is difficult and time-consuming to obtain enough different nanostructures to control multiwavelength independently while maintain the polarization-insensitive property. To this end, by using a global modulation method, a polarization-insensitive dual-wavelength achromatic and super-chromatic metalens are designed respectively. The working wavelength is set at 10.6 and 12 $\rmμ$m to match the absorption peaks of SF$_6$ and one of its decompositions (SO$_2$F$_2$), respectively. According to the operating wavelengths, only the geometric parameters of two nanofins are required to be optimized (through genetic algorithm). Then they are superimposed on each other to form cross-shaped meta-atoms. In order to control the influence between the two crossed nanofins, an additional term $Δf$ is introduced into the phase equation to modify the shape of the wavefront, whereby the phase dispersion can be easily engineered. Compared with local modulation, the number of unique nanostructures that need to be optimized can be reduced to two (operating at dual wavelengths) by the Pancharatnam-Berry (PB) phase based global modulation method. Therefore, the proposed design strategy is expected to circumvent difficulties in the local design approaches and can find widespread applications in multiwavelength imaging and spectroscopy.

physics.optics↗
arXiv2023

Reaction dynamics for the Cl($^2$P) + XCl $\to$ XCl + Cl($^2$P) (X = H, D, Mu) reaction on a high-fidelity ground state potential energy surface

Globally accurate full-dimensional ground state potential energy surface (PES) for the Cl($^2$P) + XCl $\to$ HCl + Cl($^2$P) reaction, a prototypical heavy-light-heavy abstract reaction, is developed using permutation invariant polynomial neural network (PIP-NN) method and embedded atom neural network (EANN) method, with the corresponding total root mean square error (RMSE) being only 0.043 and 0.056 kcal/mol, respectively. The saddle point of this reaction system is found to be nonlinear. A full-dimensional approximate quantum mechanical method, ring-polymer molecular dynamics (RPMD) with Cayley propagator, is employed to calculate the thermal rate coefficients and kinetic isotopic effects of title reactions Cl($^2$P) + XCl $\to$ XCl + Cl($^2$P) (X = H, D, Mu) on both new PESs. The results reproduce the experimental results at high temperatures perfectly, but with moderate accuracy at lower temperatures. The similar kinetic behavior is supported by quantum dynamics using wave packet calculations as well.

physics.chem-ph↗
arXiv2023

A proposal to the '$\frac{1}{2}$ vs. $\frac{3}{2}$ puzzle'

We reconsider the semileptonic decays of $B\to D_1^{(\prime)}l\bar ν_l$. The previous theoretical calculations predict a significantly smaller rate for the semileptonic decay of $B$ to $D'_1(J_l=\frac{1}{2})$ compared with that to the $D_1(J_l=\frac{3}{2})$, which is not consistent with the current experimental data. This conflict is the so-called `$\frac{1}{2}$ vs. $\frac{3}{2}$ puzzle'. In this work, we propose a simple scheme to fix this problem, where we suppose that the strong eigenstates $D_1^{(\prime)}$ do not coincide with the eigenstate of the weak interaction, since no experimental results show the weak and the strong interactions have to share the same eigenstates. Within the framework of this tentative scheme, meson $B$ first weakly decays to {the weak eigenstates} $D_{α(β)}$ and then the latter are detected as the $D_1^{(\prime)}$ by the strong products $D^*π$. We predict that there exist two new particles $D_{α(β)}$ with $J^P=1^+$ which were not previously identified. The good performance of the new scheme in describing the experimental data may hint at new symmetry in the weak decays of $B_{q}$ to $1^+$ heavy-light mesons. To test the scheme proposed here, we suggest an experiment to detect the difference in the invariant mass spectra of $D_1$ reconstructed from the $B$ weak decay and from the strong decay products.

hep-ph↗
arXiv2023

Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification

Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep features for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated. Experimental results on two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms the state-of-the-arts for classifying a whole mammogram as malignant or not.

cs.CV↗
arXiv2023

Response toward Public Health Policy Ambiguity and Insurance Decisions

Adjustments to public health policy are common. This paper investigates the impact of COVID-19 policy ambiguity on specific groups' insurance consumption. The results show that sensitive groups' willingness to pay (WTP) for insurance is 12.2% above the benchmark. Groups that have experienced income disruptions are more likely to suffer this. This paper offers fresh perspectives on the effects of pandemic control shifts.

econ.GN↗
arXiv2023

Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail

With the development of Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple practical scenarios, including electricity transactions. However, the isolation of IoT data among several entities makes it difficult to achieve optimal allocation of data resources and convert data resources into real economic value, thus it is necessary to introduce the IoT data sharing mode to drive data circulation. To enhance the accuracy and fairness of IoT data sharing, the heterogeneity of participants is sufficiently considered, and data valuation and profit allocation in IoT data sharing are improved based on the background of electricity retail. Data valuation is supposed to be relevant to attributes of IoT data buyers, thus risk preferences of electricity retailers are applied as characteristic attributes and data premium rates are proposed to modify data value rates. Profit allocation should measure the marginal contribution shares of electricity retailers and data brokers fairly, thus asymmetric Nash bargaining model is used to guarantee that they could receive reasonable profits based on their specific contribution to the coalition of IoT data sharing. Considering the heterogeneity of participants comprehensively, the proposed IoT data sharing fits for a large coalition of IoT data sharing with multiple electricity retailers and data brokers. Finally, to demonstrate the applications of IoT data sharing in smart grids, case studies are utilized to validate the results of data value for electricity retailers with different risk preferences and the efficiency of profit allocation using asymmetric Nash bargaining model.

eess.SY↗
arXiv2023

Perpendicular in-plane negative magnetoresistance in ZrTe5

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. Our results provide new insights into the understanding of in-plane NMR in topological materials.

cond-mat.mes-hall↗
arXiv2023

Strong decays of $P_ψ^N(4312)^+$ to $J/ψ(η_c) p$ and $\bar D^{(*)}Λ_c$ within the Bethe-Salpeter framework

Based on the effective Lagrangian in the heavy quark limit, we calculate the one-boson-exchange interaction kernel of $P_ψ^N(4312)^+$ as the $\bar DΣ_c$ molecular state in isospin-$\frac12$. We present the Bethe-Salpeter equation and wave function for the constituent particles to be a (pseudo)scalar meson and a $\frac12$ baryon. By solving the Bethe-Salpeter equation, we obtain $P_ψ^N(4312)^+$ as the $\bar DΣ_c$ molecular state with $J^P=(\frac{1}{2})^-$. Combining the effective Lagrangian and the obtained BS wave function, the partial decay widths of $P_ψ^N(4312)^+$ to $J/ψp$, $η_c p$, $\bar D^{*0}Λ_c^+$ and $\bar D^0Λ_c^+$ are calculated to be $0.17$, $0.085$, $8.8$, and $0.026$ MeV, respectively, which are roughly consistent with the LHCb experimental measurements and some other theoretical researches. The obtain results indicate the fraction of $\bar D^{*0}Λ_c^+$ channel amounts to $\sim90\%$ of $P_ψ^N(4312)^+$, and is a highly promising channel to be discovered in the near future experiments. Our results favor the interpretation of $P_ψ^N(4312)^+$ as the $\bar DΣ_c$ molecular state with $J^P=(\frac{1}{2})^-$ and isospin $I=\frac12$.

hep-ph↗
arXiv2023

Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions

Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher's predictions of extra unlabeled data. However, such knowledge flow is typically unidirectional, having the performance vulnerable to the quality of teacher model. In this paper, we seek to robust 3D reconstruction of stereo endoscopic images by proposing a novel fashion of bidirectional learning between two learners, each of which can play both roles of teacher and student concurrently. Specifically, we introduce two self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel Supervision (APS), to learn a dual-branch convolutional neural network. The two branches predict two different disparity probability distributions for the same position, and output their expectations as disparity values. The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS). Moreover, each branch also learns confidences to dynamically refine its provided supervisions. In ACS, the predicted disparity is softened into a unimodal distribution, and the lower the confidence, the smoother the distribution. In APS, the incorrect predictions are suppressed by lowering the weights of those with low confidence. With the adaptive bidirectional learning, the two branches enjoy well-tuned supervisions, and eventually converge on a consistent and more accurate disparity estimation. The extensive and comprehensive experimental results on four public datasets demonstrate our superior performance over other state-of-the-arts with a relative decrease of averaged disparity error by at least 9.76%.

cs.CV↗
arXiv2023

Coherent control of an ultrabright single spin in hexagonal boron nitride at room temperature

Hexagonal boron nitride (hBN) is a remarkable two-dimensional (2D) material that hosts solid-state spins and has great potential to be used in quantum information applications, including quantum networks. However, in this application, both the optical and spin properties are crucial for single spins but have not yet been discovered simultaneously for hBN spins. Here, we realize an efficient method for arraying and isolating the single defects of hBN and use this method to discover a new spin defect with a high probability of 85%. This single defect exhibits outstanding optical properties and an optically controllable spin, as indicated by the observed significant Rabi oscillation and Hahn echo experiments at room temperature. First principles calculations indicate that complexes of carbon and oxygen dopants may be the origin of the single spin defects. This provides a possibility for further addressing spins that can be optically controlled.

physics.optics↗