arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 685 records · Page 38Linked to original sources

arXiv2023

Controllable Single Cooper Pair Splitting in Hybrid Quantum Dot Systems

Cooper pair splitters hold utility as a platform for investigating the entanglement of electrons in Cooper pairs, but probing splitters with voltage-biased Ohmic contacts prevents the retention of electrons from split pairs since they can escape to the drain reservoirs. We report the ability to controllably split and retain single Cooper pairs in a multi-quantum-dot device isolated from lead reservoirs, and separately demonstrate a technique for detecting the electrons emerging from a split pair. First, we identify a coherent Cooper pair splitting charge transition using dispersive gate sensing at GHz frequencies. Second, we utilize a double quantum dot as an electron parity sensor to detect parity changes resulting from electrons emerging from a superconducting island.

cond-mat.mes-hall↗

arXiv2023

Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objec-tive to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged overall PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available 1 to all researchers as per data share protocol

cs.CV↗

arXiv2023

Gate-tunable Superconductivity in Hybrid InSb-Pb Nanowires

We present a report on hybrid InSb-Pb nanowires that combine high spin-orbit coupling with a high critical field and a large superconducting gap. Material characterization indicates the Pb layer of high crystal quality on the nanowire side facets. Hard induced superconducting gaps and gate-tunable supercurrent are observed in the hybrid nanowires. These results showcase the promising potential of this material combination for a diverse range of applications in hybrid quantum transport devices.

cond-mat.mes-hall↗

arXiv2023

Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks

Intra-urban origin-destination (OD) network communities evolve throughout the day, indicating changing groups of closely connected regions. Under this variation, groups of regions with high consistency of community affiliation characterize the temporally stable structure of the evolution process, aiding in comprehending urban dynamics. However, how to quantify this consistency and identify these groups are open questions. In this study, we introduce the consensus OD network to quantify the consistency of community affiliation among regions. Furthermore, the temporally stable community decomposition method is proposed to identify groups of regions with high internal and low external consistency (named "stable groups"), where each group consists of temporally stable cores and attaching peripheries. Wuhan taxi data is used to verify our methods. On the hourly time scale, eleven stable groups containing 82.9% of regions are identified. This high percentage suggests that dynamic communities can be well organized via cores. Moreover, stable groups are spatially closed and more likely to distribute within a single district and separated by water bodies. Cores exhibit higher POI entropy and more healthcare and shopping services than peripheries. Our methods and empirical findings contribute to some practical issues, such as urban area division, polycentric evaluation and construction, and infectious disease control.

physics.soc-ph↗

arXiv2023

Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion

Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of urban applications. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation.

cs.AI↗

arXiv2023

AUC Optimization from Multiple Unlabeled Datasets

Weakly supervised learning aims to empower machine learning when the perfect supervision is unavailable, which has drawn great attention from researchers. Among various types of weak supervision, one of the most challenging cases is to learn from multiple unlabeled (U) datasets with only a little knowledge of the class priors, or U$^m$ learning for short. In this paper, we study the problem of building an AUC (area under ROC curve) optimization model from multiple unlabeled datasets, which maximizes the pairwise ranking ability of the classifier. We propose U$^m$-AUC, an AUC optimization approach that converts the U$^m$ data into a multi-label AUC optimization problem, and can be trained efficiently. We show that the proposed U$^m$-AUC is effective theoretically and empirically.

cs.LG↗

arXiv2023

Evaluation Mappings of Spatial Accelerator Based On Data Placement

The scheduling strategies of workloads are critical to fully exploiting the performance of spatial accelerators, accurate performance models are required to evaluate the mapping of workloads.Recent works proposed various cost-model to describe the dataflow of the spatial accelerator. However, they are less expressive about customized memory hierarchies and thus lead to inaccurate performance models. In this paper, we propose, PolyAcc, a framework for evaluating the mappings of workload on spatial accelerator based on data placement. The Data placement relation describes the temporal-spatial relation of data at different memory levels, which can accurately capture the runtime behavior of hardware units. Based on data placement relations, polyAcc accurately analyzes the data volume for different reuse patterns and estimate metrics, including data reuse, latency, and energy. Overall, polyAcc closely matches the ideal execution time and PE utilization for GEMM and Conv workloads, respectively achieves 0.82%, 18.8% improvements for execution time and energy consumption estimates in validation against Eyeriss architecture compared to the state-of-the-art framework.

eess.SY↗

arXiv2023

A preliminary study of photometric redshifts based on the Wide Field Survey Telescope

The Wide Field Survey Telescope (WFST) is a dedicated time-domain multi-band ($u$, $g$, $r$, $i$, and $z$) photometric survey facility under construction. In this paper, we present a preliminary study that assesses the quality of photometric redshifts based on WFST by utilizing mock observations derived with the galaxy catalog in the COSMOS/UltraVISTA field. We apply the template fitting technique to estimate photometric redshifts by using the ZEBRA photometric-redshift code and adopting a modified set of adaptive templates. We evaluate the bias (median relative offset between the output photometric redshifts and input redshifts), normalized median absolute deviation ($σ_{\rm NMAD}$) and outlier fraction ($f_{\rm outlier}$) of photometric redshifts in two typical WFST observational cases, the single 30-second exposure observations (hereafter shallow mode) and co-added 50-minute exposure observations (hereafter deep mode). We find bias$\la0.006$, $σ_{\rm NMAD}\la0.03$, and $f_{\rm outlier}\la5\%$ in the shallow mode and bias$\approx 0.005$, $σ_{\rm NMAD}\approx 0.06$, and $f_{\rm outlier}\approx 17\%$--$27\%$ in the deep mode, respectively, under various lunar phases. Combining the WFST mock observational data with that from the upcoming CSST and Euclid surveys, we demonstrate that the $z_{\rm phot}$ results can be significantly improved, with $f_{\rm outlier}\approx 1\%$ and $σ_{\rm NMAD}\approx 0.02$.

astro-ph.CO↗

arXiv2023

Extending regionalization algorithms to explore spatial process heterogeneity

In spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction, and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.

stat.ME↗

arXiv2023

GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding

Multi-view camera-based 3D detection is a challenging problem in computer vision. Recent works leverage a pretrained LiDAR detection model to transfer knowledge to a camera-based student network. However, we argue that there is a major domain gap between the LiDAR BEV features and the camera-based BEV features, as they have different characteristics and are derived from different sources. In this paper, we propose Geometry Enhanced Masked Image Modeling (GeoMIM) to transfer the knowledge of the LiDAR model in a pretrain-finetune paradigm for improving the multi-view camera-based 3D detection. GeoMIM is a multi-camera vision transformer with Cross-View Attention (CVA) blocks that uses LiDAR BEV features encoded by the pretrained BEV model as learning targets. During pretraining, GeoMIM's decoder has a semantic branch completing dense perspective-view features and the other geometry branch reconstructing dense perspective-view depth maps. The depth branch is designed to be camera-aware by inputting the camera's parameters for better transfer capability. Extensive results demonstrate that GeoMIM outperforms existing methods on nuScenes benchmark, achieving state-of-the-art performance for camera-based 3D object detection and 3D segmentation. Code and pretrained models are available at https://github.com/Sense-X/GeoMIM.

cs.CV↗

arXiv2023

Modeling Heat Capacity of Supercritical Fluids: A Phonon Theory Approach

Recent research shows that liquids and dense supercritical fluids support high frequency shear waves. Here, we proposed a general heat capacity model of supercritical fluids using the latest theoretical findings (the liquid phonon theory). In this theory, heat capacity cv of supercritical fluids is only related to the infinite-frequency shear modulus, Debye relaxation time and minimal length of the longitudinal mode in supercritical fluids rather than the system-specific structure and interactions. The present model quantitatively explains the heat capacities in the liquid-like region and the gas-like region above the critical point. At the same time, the heat capacities of 6 supercritical fluids (including monatomic fluids Ar, Ne and molecular fluids N2, CO, CO2, CH4) over a wide range of temperatures and pressures were calculated using the present model, yielding a high accuracy with no free-fitting parameters. This model has two advantages: (1) The contribution of intramolecular vibrations was considered, which makes the model applicable to molecular fluids and high temperature region; (2) This method starts from the quasi-harmonic Debye model. Thus, it does not require any adjustable parameters and thermal expansion coefficients. Furthermore, the present cv model of the rigid liquid-like supercritical region in this paper can be extended to liquid phase of molecular fluids.

cond-mat.other↗

arXiv2023

3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability

Shape generation is the practice of producing 3D shapes as various representations for 3D content creation. Previous studies on 3D shape generation have focused on shape quality and structure, without or less considering the importance of semantic information. Consequently, such generative models often fail to preserve the semantic consistency of shape structure or enable manipulation of the semantic attributes of shapes during generation. In this paper, we proposed a novel semantic generative model named 3D Semantic Subspace Traverser that utilizes semantic attributes for category-specific 3D shape generation and editing. Our method utilizes implicit functions as the 3D shape representation and combines a novel latent-space GAN with a linear subspace model to discover semantic dimensions in the local latent space of 3D shapes. Each dimension of the subspace corresponds to a particular semantic attribute, and we can edit the attributes of generated shapes by traversing the coefficients of those dimensions. Experimental results demonstrate that our method can produce plausible shapes with complex structures and enable the editing of semantic attributes. The code and trained models are available at https://github.com/TrepangCat/3D_Semantic_Subspace_Traverser

cs.CV↗

arXiv2023

Software refactoring and rewriting: from the perspective of code transformations

To refactor already working code while keeping reliability, compatibility and perhaps security, we can borrow ideas from micropass/nanopass compilers. By treating the procedure of software refactoring as composing code transformations, and compressing repetitive transformations with automation tools, we can often obtain representations of refactoring processes short enough that their correctness can be analysed manually. Unlike in compilers, in refactoring we usually only need to consider the codebase in question, so regular text processing can be extensively used, fully exploiting patterns only present in the codebase. Aside from the direct application of code transformations from compilers, many other kinds of equivalence properties may also be exploited. In this paper, two refactoring projects are given as the main examples, where 10-100 times simplification has been achieved with the application of a few kinds of useful transformations.

cs.SE↗

arXiv2023

DETRs with Collaborative Hybrid Assignments Training

In this paper, we provide the observation that too few queries assigned as positive samples in DETR with one-to-one set matching leads to sparse supervision on the encoder's output which considerably hurt the discriminative feature learning of the encoder and vice visa for attention learning in the decoder. To alleviate this, we present a novel collaborative hybrid assignments training scheme, namely $\mathcal{C}$o-DETR, to learn more efficient and effective DETR-based detectors from versatile label assignment manners. This new training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training the multiple parallel auxiliary heads supervised by one-to-many label assignments such as ATSS and Faster RCNN. In addition, we conduct extra customized positive queries by extracting the positive coordinates from these auxiliary heads to improve the training efficiency of positive samples in the decoder. In inference, these auxiliary heads are discarded and thus our method introduces no additional parameters and computational cost to the original detector while requiring no hand-crafted non-maximum suppression (NMS). We conduct extensive experiments to evaluate the effectiveness of the proposed approach on DETR variants, including DAB-DETR, Deformable-DETR, and DINO-Deformable-DETR. The state-of-the-art DINO-Deformable-DETR with Swin-L can be improved from 58.5% to 59.5% AP on COCO val. Surprisingly, incorporated with ViT-L backbone, we achieve 66.0% AP on COCO test-dev and 67.9% AP on LVIS val, outperforming previous methods by clear margins with much fewer model sizes. Codes are available at \url{https://github.com/Sense-X/Co-DETR}.

cs.CV↗

arXiv2023

PandA(Box) flies on Bluesky: maintainable and user-friendly fly scans with Mamba at HEPS

Purpose: Fly scans are indispensible in many experiments at the High Energy Photon Source (HEPS). PandABox, the main platform to implement fly scans at HEPS, needs to be integrated into Mamba, the experiment control system developed at HEPS based on Bluesky. Methods: In less than 600 lines of easily customisable and extensible backend code, provided are full control of PandABox's TCP server in native ophyd, automated configuration (also including wiring) of "PandA blocks" for constant-speed mapping experiments of various dimensions, as well as generation of scans deliberately fragmented to deal with hardware limits in numbers of exposure frames or sequencer table entries. Results: The upper-level control system for PandABox has been ported to Bluesky, enabling the combination of both components' flexibility in fly-scan applications. Based on this backend, a user-friendly Mamba frontend is developed for X-ray fluorescence (XRF) mapping experiments, which provides fully online visual feedback.

physics.ins-det↗

arXiv2023

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities' bird's-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.

cs.CV↗

arXiv2023

Strong tunable coupling between two distant superconducting spin qubits

Superconducting (or Andreev) spin qubits have recently emerged as an alternative qubit platform with realizations in semiconductor-superconductor hybrid nanowires. In these qubits, the spin degree of freedom is intrinsically coupled to the supercurrent across a Josephson junction via the spin-orbit interaction, which facilitates fast, high-fidelity spin readout using circuit quantum electrodynamics techniques. Moreover, this spin-supercurrent coupling has been predicted to facilitate inductive multi-qubit coupling. In this work, we demonstrate a strong supercurrent-mediated coupling between two distant Andreev spin qubits. This qubit-qubit interaction is of the longitudinal type and we show that it is both gate- and flux-tunable up to a coupling strength of 178 MHz. Finally, we find that the coupling can be switched off in-situ using a magnetic flux. Our results demonstrate that integrating microscopic spin states into a superconducting qubit architecture can combine the advantages of both semiconductors and superconducting circuits and pave the way to fast two-qubit gates between remote spins.

quant-ph↗

arXiv2023

Electronic transport and thermoelectricity in selenospinel Cu$_{6-x}$Fe$_{4+x}$Sn$_{12}$Se$_{32}$

We report a study of selenospinel Cu$_{6-x}$Fe$_{4+x}$Sn$_{12}$Se$_{32}$ ($x$ = 0, 1, 2) single crystals, which crystalize in a cubic structure with the $Fd\overline{3}m$ space group, and show typical semiconducting behavior. The large discrepancy between the activation energy for electrical conductivity $E_ρ$ (32.3 $\sim$ 69.8 meV), and for thermopower $E_\textrm{S}$ (3.2 $\sim$ 11.5 meV), indicates a polaronic transport mechanism between 350 and 50 K. With decreasing temperature, it evolves into variable-range hopping conduction. Furthermore, the heat capacity shows a hump around 25(5) K and diverges from the Debye $T^3$ law at low temperatures, indicating the observation of structural glassy features in these crystalline solids.

cond-mat.mtrl-sci↗