arXiv subjects

Qiang Zhang

Publications and source records attributed to Qiang Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2024

A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning

Feature selection is an important process in machine learning and knowledge discovery. By selecting the most informative features and eliminating irrelevant ones, the performance of learning algorithms can be improved and the extraction of meaningful patterns and insights from data can be facilitated. However, most existing feature selection methods, when applied to large datasets, encountered the bottleneck of high computation costs. To address this problem, we propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We introduce a dimensionless quantity as a surrogate representation to summarize the distributional individuality of certain classes, based on this quantity we evaluate features and study the correlation among them. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation in comparison with other state-of-the-art feature selection methods.

cs.LG↗

arXiv2024

Feature Selection via Maximizing Distances between Class Conditional Distributions

For many data-intensive tasks, feature selection is an important preprocessing step. However, most existing methods do not directly and intuitively explore the intrinsic discriminative information of features. We propose a novel feature selection framework based on the distance between class conditional distributions, measured by integral probability metrics (IPMs). Our framework directly explores the discriminative information of features in the sense of distributions for supervised classification. We analyze the theoretical and practical aspects of IPMs for feature selection, construct criteria based on IPMs. We propose several variant feature selection methods of our framework based on the 1-Wasserstein distance and implement them on real datasets from different domains. Experimental results show that our framework can outperform state-of-the-art methods in terms of classification accuracy and robustness to perturbations.

cs.LG↗

arXiv2024

Study Features via Exploring Distribution Structure

In this paper, we present a novel framework for data redundancy measurement based on probabilistic modeling of datasets, and a new criterion for redundancy detection that is resilient to noise. We also develop new methods for data redundancy reduction using both deterministic and stochastic optimization techniques. Our framework is flexible and can handle different types of features, and our experiments on benchmark datasets demonstrate the effectiveness of our methods. We provide a new perspective on feature selection, and propose effective and robust approaches for both supervised and unsupervised learning problems.

cs.LG↗

arXiv2024

Deep Blind Super-Resolution for Satellite Video

Recent efforts have witnessed remarkable progress in Satellite Video Super-Resolution (SVSR). However, most SVSR methods usually assume the degradation is fixed and known, e.g., bicubic downsampling, which makes them vulnerable in real-world scenes with multiple and unknown degradations. To alleviate this issue, blind SR has thus become a research hotspot. Nevertheless, existing approaches are mainly engaged in blur kernel estimation while losing sight of another critical aspect for VSR tasks: temporal compensation, especially compensating for blurry and smooth pixels with vital sharpness from severely degraded satellite videos. Therefore, this paper proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixel-wise blur levels in a coarse-to-fine manner. Specifically, we employed multi-scale deformable convolution to coarsely aggregate the temporal redundancy into adjacent frames by window-slid progressive fusion. Then the adjacent features are finely merged into mid-feature using deformable attention, which measures the blur levels of pixels and assigns more weights to the informative pixels, thus inspiring the representation of sharpness. Moreover, we devise a pyramid spatial transformation module to adjust the solution space of sharp mid-feature, resulting in flexible feature adaptation in multi-level domains. Quantitative and qualitative evaluations on both simulated and real-world satellite videos demonstrate that our BSVSR performs favorably against state-of-the-art non-blind and blind SR models. Code will be available at https://github.com/XY-boy/Blind-Satellite-VSR

cs.CV↗

arXiv2024

Loophole-free test of local realism via Hardy's violation

Bell's theorem states that quantum mechanical description on physical quantity cannot be fully explained by local realistic theories, and lays solid basis for various quantum information applications. Hardy's paradox is celebrated to be the simplest form of Bell's theorem concerning its "All versus Nothing" way to test local realism. However, due to experimental imperfections, existing tests of Hardy's paradox require additional assumptions of experimental systems, which constitute potential loopholes for faithfully testing local realistic theories. Here, we experimentally demonstrate Hardy's nonlocality through a photonic entanglement source. By achieving a detection efficiency of $82.2\%$, a quantum state fidelity of $99.10\%$ and applying high speed quantum random number generators for measurement setting switching, the experiment is implemented in a loophole-free manner. During $6$ hours of running, a strong violation of $P_{\text{Hardy}}=4.646\times 10^{-4}$ up to $5$ standard deviations is observed with $4.32\times 10^{9}$ trials. A null hypothesis test shows that the results can be explained by local realistic theories with an upper bound probability of $10^{-16348}$. These testing results present affirmative evidence against local realism, and provide an advancing benchmark for quantum information applications based on Hardy's paradox.

quant-ph↗

arXiv2024

Exploiting Polarized Material Cues for Robust Car Detection

Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detection algorithms to meet the highly accurate perception demand for safety, due to the unstable/limited color information, which impedes the extraction of meaningful/discriminative features of cars. In this work, we present a novel learning-based car detection method that leverages trichromatic linear polarization as an additional cue to disambiguate such challenging cases. A key observation is that polarization, characteristic of the light wave, can robustly describe intrinsic physical properties of the scene objects in various imaging conditions and is strongly linked to the nature of materials for cars (e.g., metal and glass) and their surrounding environment (e.g., soil and trees), thereby providing reliable and discriminative features for robust car detection in challenging scenes. To exploit polarization cues, we first construct a pixel-aligned RGB-Polarization car detection dataset, which we subsequently employ to train a novel multimodal fusion network. Our car detection network dynamically integrates RGB and polarization features in a request-and-complement manner and can explore the intrinsic material properties of cars across all learning samples. We extensively validate our method and demonstrate that it outperforms state-of-the-art detection methods. Experimental results show that polarization is a powerful cue for car detection.

cs.CV↗

arXiv2024

From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization

This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline" data examples. While recent years have seen a flurry of work on applying various machine learning techniques to the offline optimization problem, the majority of these work focused on learning a surrogate of the unknown objective function and then applying existing optimization algorithms. While the idea of modeling the unknown objective function is intuitive and appealing, from the learning point of view it also makes it very difficult to tune the objective of the learner according to the objective of optimization. Instead of learning and then optimizing the unknown objective function, in this paper we take on a less intuitive but more direct view that optimization can be thought of as a process of sampling from a generative model. To learn an effective generative model from the offline data examples, we consider the standard technique of ``re-weighting", and our main technical contribution is a probably approximately correct (PAC) lower bound on the natural optimization objective, which allows us to jointly learn a weight function and a score-based generative model. The robustly competitive performance of the proposed approach is demonstrated via empirical studies using the standard offline optimization benchmarks.

cs.LG↗

arXiv2024

Optical skyrmions and other topological quasiparticles of light

Skyrmions are topologically stable quasiparticles that have been predicted and demonstrated in quantum fields, solid-state physics, and magnetic materials, but only recently observed in electromagnetic fields, triggering fast expanding research across different spectral ranges and applications. Here we review the recent advances in optical skyrmions within a unified framework. Starting from fundamental theories, including classification of skyrmionic states, we describe generation and topological control of different kinds of optical skyrmions in structured and time-dependent optical fields. We further highlight generalized classes of optical topological quasiparticles beyond skyrmions and outline the emerging applications, future trends, and open challenges. A complex vectorial field structure of optical quasiparticles with versatile topological characteristics emerges as an important feature in modern spin-optics, imaging and metrology, optical forces, structured light and topological and quantum technologies.

physics.optics↗

arXiv2024

Local distortion driven magnetic phase switching in pyrochlore $Yb_2(Ti_{1-x}Sn_x)_2O_7$

While it is commonly accepted that the disorder induced by magnetic ion doping in quantum magnets usually generates a rugged free-energy landscape resulting in slow or glassy spin dynamics, the disorder/distortion effects associated with non-magnetic ion sites doping are still illusive. Here, using AC susceptibility measurements, we show that the mixture of Sn/Ti on the non-magnetic ion sites of pyrochlore $Yb_2(Ti_{1-x}Sn_x)_2O_7$ induces an antiferromagnetic ground state despite both parent compounds, $Yb_2Ti_2O_7$, and $Yb_2Sn_2O_7$, order ferromagnetically. Local structure studies through neutron total scattering reveals the local distortion in the non-magnetic ion sites and its strong correlation with the magnetic phase switching. Our study, for the first time, demonstrates the local distortion as induced by the non-magnetic ion site mixture could be a new path to achieve magnetic phase switching, which has been traditionally obtained by external stimuli such as temperature, magnetic field, pressure, strain, light, etc.

cond-mat.str-el↗

arXiv2023

Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality

In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low-dimensional counterparts of a target high-dimensional task are generated via random embeddings. Since the exact relationship between the auxiliary (low-dimensional) tasks and the target is a priori unknown, a multiform evolutionary algorithm is developed for unifying all formulations into a single multi-task setting. The resultant joint optimization enables the target task to efficiently reuse solutions evolved across various low-dimensional searches via cross-form genetic transfers, hence speeding up overall convergence characteristics. To validate the overall efficacy of our proposed algorithmic framework, comprehensive experimental studies are carried out on well-known continuous benchmark functions as well as a set of practical problems in the hyper-parameter tuning of machine learning models and deep learning models in classification tasks and Predator-Prey games, respectively.

cs.NE↗

arXiv2023

Double and Quadruple Flat Bands tuned by Alternative magnetic Fluxes in Twisted Bilayer Graphene

Twisted bilayer graphene (TBG) can host the moiré energy flat bands with two-fold degeneracy serving as a fruitful playground for strong correlations and topological phases. However, the number of degeneracy is not limited to two. Introducing a spatially alternative magnetic field, we report that the induced magnetic phase becomes an additional controllable parameter and leads to an undiscovered generation of four-fold degenerate flat bands. This emergence stems from the band inversion at $Γ$ point near the Fermi level with a variation of both twisted angle and magnetic phase. We present the conditions for the emergence of multi-fold degenerate flat bands, which are associated with the eigenvalue degeneracy of a Birman-Schwinger operator. Using holomorphic functions, which explain the origin of the double flat bands in the conventional TBG, we can generate analytical wave functions in the magnetic TBG to show absolute flatness with four-fold degeneracy. Moreover, we identify an orbital-related intervalley coherent state as the many-body ground state at charge neutrality. In contrast, the conventional TBG has only two moiré energy flat bands, and the highly degenerate flat bands with additional orbital channels in this magnetic platform might bring richer correlation physics.

cond-mat.str-el↗

arXiv2023

The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning

In recent years, short Text Matching tasks have been widely applied in the fields ofadvertising search and recommendation. The difficulty lies in the lack of semantic information and word ambiguity caused by the short length of the text. Previous works have introduced complement sentences or knowledge bases to provide additional feature information. However, these methods have not fully interacted between the original sentence and the complement sentence, and have not considered the noise issue that may arise from the introduction of external knowledge bases. Therefore, this paper proposes a short Text Matching model that combines contrastive learning and external knowledge. The model uses a generative model to generate corresponding complement sentences and uses the contrastive learning method to guide the model to obtain more semantically meaningful encoding of the original sentence. In addition, to avoid noise, we use keywords as the main semantics of the original sentence to retrieve corresponding knowledge words in the knowledge base, and construct a knowledge graph. The graph encoding model is used to integrate the knowledge base information into the model. Our designed model achieves state-of-the-art performance on two publicly available Chinese Text Matching datasets, demonstrating the effectiveness of our model.

cs.CL↗

arXiv2023

Chiral Dirac fermion in a collinear antiferromagnet

In a Dirac semimetal, the massless Dirac fermion has zero chirality, leading to surface states connected adiabatically to a topologically trivial surface state as well as vanishing anomalous Hall effect (AHE). Recently, it is predicted that in the nonrelativistic limit of certain collinear antiferromagnets, there exists a type of chiral Dirac-like fermion, whose dispersion manifests four-fold degenerate crossing points formed by spin-degenerate linear bands, with topologically protected Fermi arcs. Such unconventional chiral fermion, protected by a hidden SU(2) symmetry in the hierarchy of an enhanced crystallographic group, namely spin space group, is not experimentally verified yet. Here, by angle-resolved photoemission spectroscopy measurements, we reveal the surface origin of the electron pocket at the Fermi surface in collinear antiferromagnet CoNb3S6. Combining with neutron diffraction and first-principles calculations, we suggest a multidomain collinear AFM configuration, rendering the the existence of the Fermi-arc surface states induced by chiral Dirac-like fermions. Our work provides spectral evidence of the chiral Dirac-like fermion caused by particular spin symmetry in CoNb3S6, paving an avenue for exploring new emergent phenomena in antiferromagnets with unconventional quasiparticle excitations.

cond-mat.mtrl-sci↗

arXiv2023

Ground Calibration Result of the Lobster Eye Imager for Astronomy

We report on results of the on-ground X-ray calibration of the Lobster Eye Imager for Astronomy (LEIA), an experimental space wide-field (18.6*18.6 square degrees) X-ray telescope built from novel lobster eye mirco-pore optics. LEIA was successfully launched on July 27, 2022 onboard the SATech-01 satellite. To achieve full characterisation of its performance before launch, a series of tests and calibrations have been carried out at different levels of devices, assemblies and the complete module. In this paper, we present the results of the end-to-end calibration campaign of the complete module carried out at the 100-m X-ray Test Facility at IHEP. The PSF, effective area and energy response of the detectors were measured in a wide range of incident directions at several X-ray line energies. The distributions of the PSF and effective areas are roughly uniform across the FoV, in large agreement with the prediction of lobster-eye optics. The mild variations and deviations from the prediction of idealized lobster-eye optics can be understood to be caused by the imperfect shapes and alignment of the micro-pores as well as the obscuration by the supporting frames, which can be well reproduced by MC simulations. The spatial resolution of LEIA defined by the FWHM of the focal spot ranges from 4-8 arcmin with a median of 5.7. The measured effective areas are in range of 2-3 $cm^2$ at ~1.25 keV across the entire FoV, and its dependence on photon energy is in large agreement with simulations. The gains of the CMOS sensors are in range of 6.5-6.9 eV/DN, and the energy resolutions in the range of ~120-140 eV at 1.25 keV and ~170-190 eV at 4.5 keV. These results have been ingested into the calibration database and applied to the analysis of the scientific data acquired by LEIA. This work paves the way for the calibration of the Wide-field X-Ray Telescope modules of the Einstein Probe mission.

astro-ph.IM↗

arXiv2023

Spiking Denoising Diffusion Probabilistic Models

Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks (ANNs). While previous research has primarily focused on enhancing the performance of SNNs in classification tasks, the generative potential of SNNs remains relatively unexplored. In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality. To fully exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net architecture, which achieves comparable performance to its ANN counterpart using only 4 time steps, resulting in significantly reduced energy consumption. Extensive experimental results reveal that our approach achieves state-of-the-art on the generative tasks and substantially outperforms other SNN-based generative models, achieving up to 12x and 6x improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 2.69% in a training-free manner. The SDDPM symbolizes a significant advancement in the field of SNN generation, injecting new perspectives and potential avenues of exploration. Our code is available at https://github.com/AndyCao1125/SDDPM.

cs.NE↗

arXiv2023

1002 km Twin-Field Quantum Key Distribution with Finite-Key Analysis

Quantum key distribution (QKD) holds the potential to establish secure keys over long distances. The distance of point-to-point QKD secure key distribution is primarily impeded by the transmission loss inherent to the channel. In the quest to realize a large-scale quantum network, increasing the QKD distance under current technology is of great research interest. Here we adopt the 3-intensity sending-or-not-sending twin-field QKD (TF-QKD) protocol with the actively-odd-parity-pairing method. The experiment demonstrates the feasibility of secure QKD over a 1002 km fibre channel considering the finite size effect. The secure key rate is $3.11\times10^{-12}$ per pulse at this distance. Furthermore, by optimizing parameters for shorter fiber distances, we conducted performance tests on key distribution for fiber lengths ranging from 202 km to 505 km. Notably, the secure key rate for the 202 km, the normal distance between major cities, reached 111.74 kbps.

quant-ph↗

arXiv2023

Influence Maximization based on Threshold Model in Hypergraphs

Influence Maximization problem has received significant attention in recent years due to its application in various do?mains such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence in hypergraphs, focusing on identifying a set of seeds that maximize influence in threshold models. Firstly, we extend the Message Passing method from pairwise networks to hypergraphs to accurately describe the activation process in threshold models. Then we introduce the concept of hyper?graph collective influence (HCI) to measure the influence of nodes. Subsequently, We design an algorithm, HCI-TM, to select the Influence Maximization Set, taking into account both node and hyperedge activation. Numerical simu?lations demonstrate that HCI-TM outperforms several competing algorithms in synthetic and real-world hypergraphs. Furthermore, we find that HCI can be used as a tool to predict the occurrence of cascading phenomena. Notably, we find that HCI-TM algorithm works better for larger average hyperdegrees in Erdos-Rényi (ER) hypergraphs and smaller power-law exponents in scale-free (SF) hypergraphs.

cs.SI↗

arXiv2023

Reconciling results of 2019 and 2020 stellar occultations on Pluto's atmosphere. New constraints from both the 5 September 2019 event and consistency analysis

A stellar occultation by Pluto on 5 September 2019 yielded positive detections at two separate stations. Using an approach consistent with comparable studies, we derived a surface pressure of $11.478 \pm 0.55~\mathrm{μbar}$ for Pluto's atmosphere from the observations of this event. In addition, to avoid potential method inconsistancies highlighted by Sicardy et al. when comparing with historical pressure measurements, we reanalyzed the data by 15 August 2018 and 17 July 2019 events, respectively. All the new measurements provide a bridge between the two different perspectives on the pressure variation since 2015: a rapid pressure drop from previous studies of the 15 August 2018 and 17 July 2019 events and a plateau phase from that of the 6 June 2020 event. The pressure measurement from the 5 September 2019 event aligns with those from 2016, 2018, and 2020, supporting the latter perspective. While the measurements from the 4 June 2011 and 17 July 2019 events suggest probable V-shaped pressure variations unaccounted for by the volatile transport model (VTM) from Meza et al., the VTM remains applicable on average. And, the validity of the V-shaped variations is debatable due to the stellar faintness of the 4 June 2011 event and the grazing single-chord geometry of the 17 July 2019 event. To reveal and understand all significant pressure variations of Pluto's atmosphere, it is essential to provide constraints on both short-term and long-term evolutions of the interacting atmosphere and surface by continuous pressure monitoring through occultation observations, whenever possible, complemented by frequent spectroscopy and photometry of the surface.

astro-ph.EP↗