arXiv subjects

Yunlong Wang

Publications and source records attributed to Yunlong Wang.

At least 73 records · Page 4Linked to original sources

arXiv2023

RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions

Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is unclear how precisely the generated images express contexts and emotions from the input texts. We explored the emotional expressiveness of AI-generated images and developed RePrompt, an automatic method to refine text prompts toward precise expression of the generated images. Inspired by crowdsourced editing strategies, we curated intuitive text features, such as the number and concreteness of nouns, and trained a proxy model to analyze the feature effects on the AI-generated image. With model explanations of the proxy model, we curated a rubric to adjust text prompts to optimize image generation for precise emotion expression. We conducted simulation and user studies, which showed that RePrompt significantly improves the emotional expressiveness of AI-generated images, especially for negative emotions.

cs.HC↗

arXiv2023

CASIA-Iris-Africa: A Large-scale African Iris Image Database

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes. Research on iris biometrics has progressed tremendously, partly due to publicly available iris databases. Various databases have been available to researchers that address pressing iris biometric challenges such as constraint, mobile, multispectral, synthetics, long-distance, contact lenses, liveness detection, etc. However, these databases mostly contain subjects of Caucasian and Asian docents with very few Africans. Despite many investigative studies on racial bias in face biometrics, very few studies on iris biometrics have been published, mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain. Furthermore, most of these databases contain a relatively small number of subjects and labelled images. This paper proposes a large-scale African database named CASIA-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans. The database contains 28,717 images of 1023 African subjects (2046 iris classes) with age, gender, and ethnicity attributes that can be useful in demographically sensitive studies of Africans. Sets of specific application protocols are incorporated with the database to ensure the database's variability and scalability. Performance results of some open-source SOTA algorithms on the database are presented, which will serve as baseline performances. The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms. The database will be made available on our website: http://www.idealtest.org.

cs.CV↗

arXiv2022

Electric field tuning of magnetic states in single magnetic molecules

Single magnetic molecules may be the smallest functional magnets. An electric-field controllable spin state of magnetic molecules is of fundamental importance for applications while its realization remains challenging. To date the observed spin-electric interaction based on spin-orbit coupling or spin dipole coupling is useful to tune fine spin structures but too weak to flip the spin state. In this work, we propose a new mechanism to realize enhanced spin-electric coupling and flip the spin states by tuning the spin superexchange between local spins. Using first-principles calculations and Heisenberg Hamiltonian, we demonstrate this effect in a family of magnetic molecules, transition metallic Porphyrins. We show that their d-π and π-π spin superexchange couplings are determined by the relative energies of d and π electronic states, which are sensitive to the applied electric field. Therefore, applying electric field can tune a wide range of magnetic ground states, including ferromagnetic, ferrimagnetic, and antiferromagnetic configurations. This spin-electric coupling may provide a new approach for designing and controlling molecular spintronics.

cond-mat.mtrl-sci↗

arXiv2022

Novel superhard structures of high-pressure C-N compounds

Through machine learning force field accelerated structure search combined with first-principles calculations, we have studied the structures of new C-N compounds with different stoichiometric ratios, and found twelve new superhard C-N compounds, the energies of these structures are similar to c-C3N4 , which is possibly synthesized by high pressure experiment, the XRD of Pa-3(C4N) P3(C4N) and C2/m(C2N) are consistent with previous experimental data and can be used as the structural candidate. According to the macro hardness model, they are all superhard structures, with Vickers hardness over 40GPa, even, the hardness of Pa-3 (C4N) as high as 82.2GPa, and Pa-3 (C4N) combines high tensile and shear resistance. Compared with the hardness calculated by macro hardness model and bond resistance model, we obtained the relationship between the hardness and chemical concentration of C-N compounds under the two models, besides that, we also calculated the fracture toughness of these structures. According to Niu's model, P2_1/c(C4N) has the best fracture toughness, which is higher than WC in calculation, This also indicated the superior mechanical properties of the novel C-N compounds. Moreover, for nitrogen-rich structures, they have the potential to be used as high energy density, the energy density of Pa-3(CN3), P-3c1 (CN4), and I-42d (CN4) are 7.076kJ/g, 7.742kJ/g and 8.045kJ/g, which is close or higher than CL-20, therefore, the C-N compounds synthesized under high pressure have great potential as ideally superhard materials and high energy density materials(HEDMs).

cond-mat.mtrl-sci↗

arXiv2022

Noval Si-C Compounds with High Thermal Conductivity under TPa in Planetary Interior

Silicon carbide has excellent physical properties, such as high stability, thermal conductivity, and mechanical strength. It has been widely used in high-power devices, catalysis, material processing, and other fields and is of great significance in basic discipline research. We used crystal structure search and first principles calculation to predict four new silicon carbide structures stabilized within 3TPa. Pnma (SiC) replaces B1 (SiC) above 2.6TPa, and the new component Si3C2 becomes the most stable after 2.5TPa. P4/mbm phase Si3C2 under high pressure has electrode characteristics. The silicon-carbide structure is metallic in the study pressure range, and the electrons contribute most of the thermal conductivity, which is of great significance for the thermal evolution of silicon-carbide-like terrestrial planets. In addition, we propose a new silicon carbide planetary model and calculate the sound velocity of Si3C2 under TPa. Therefore, our research has deepened the understanding of silicon-carbide terrestrial planets' internal structure and thermal evolution, explored the complex silicon-carbide phase space, and enriched the silicon-carbide phase diagram.

cond-mat.mtrl-sci↗

arXiv2022

Perturbation Inactivation Based Adversarial Defense for Face Recognition

Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vulnerable to unseen adversarial attacks. Deep learning models are fairly robust to general perturbations, such as Gaussian noises. A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations. In this paper, a plug-and-play adversarial defense method, named perturbation inactivation (PIN), is proposed to inactivate adversarial perturbations for adversarial defense. We discover that the perturbations in different subspaces have different influences on the recognition model. There should be a subspace, called the immune space, in which the perturbations have fewer adverse impacts on the recognition model than in other subspaces. Hence, our method estimates the immune space and inactivates the adversarial perturbations by restricting them to this subspace. The proposed method can be generalized to unseen adversarial perturbations since it does not rely on a specific kind of adversarial attack method. This approach not only outperforms several state-of-the-art adversarial defense methods but also demonstrates a superior generalization capacity through exhaustive experiments. Moreover, the proposed method can be successfully applied to four commercial APIs without additional training, indicating that it can be easily generalized to existing face recognition systems. The source code is available at https://github.com/RenMin1991/Perturbation-Inactivate

cs.CV↗

arXiv2022

FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).

cs.IR↗

arXiv2022

Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings

One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise. Here, we experimentally demonstrate the $n$-setting linear HD quantum steering criterion. We verify the large violation of the steering inequalities without full-state tomography. The lower bound of the violation is $2.24\pm0.01$ in 11 dimensions, exceeding the bound ($V<2$) of 2-setting criteria. Hence, a higher strength of steering has been revealed. Moreover, we demonstrate the method for enhancing the noise robustness without increasing dimension, alternatively, by increasing measurement settings. Using the entanglement in 11 dimensions, we experimentally retrieve steering nonlocality with $63.4\pm1.4\%$ isotropic noise fraction, surpassing the $50\%$ limitation of 2-setting criteria. Our work offers the potential for practical one-sided device-independent quantum information processing that tolerates the noisy environment, lossy detection, and transcends the present transmission distance limitation.

quant-ph↗

arXiv2022

Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstable convergence. The core problems lie in that: 1) Domain-specific attributes, which are non-causal and only locally valid, are indeliberately mixed into global aggregation. 2) The one-stage optimizations of entangled attributes cannot simultaneously satisfy two conflicting objectives, i.e., generalization and personalization. To cope with these, we proposed disentangled federated learning (DFL) to disentangle the domain-specific and cross-invariant attributes into two complementary branches, which are trained by the proposed alternating local-global optimization independently. Importantly, convergence analysis proves that the FL system can be stably converged even if incomplete client models participate in the global aggregation, which greatly expands the application scope of FL. Extensive experiments verify that DFL facilitates FL with higher performance, better interpretability, and faster convergence rate, compared with SOTA FL methods on both manually synthesized and realistic attributes skew datasets.

cs.LG↗

arXiv2022

Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation

Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - to feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideations. We conducted formative and controlled user studies to understand the usage and usefulness of explanations to improve ideation diversity and quality. Users appreciated that explanation feedback helped focus their efforts and provided directions for improvement. This resulted in explanations improving diversity compared to no feedback or feedback with scores only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation and creativity support tools.

cs.HC↗

arXiv2022

SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations

Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, leading to large amounts of tracked data. However, much of that information is not salient for reflection and self-awareness. To tackle this burden for reflection, we created the SalienTrack framework, which aims to 1) identify salient tracking events, 2) select the salient details of those events, 3) explain why they are informative, and 4) present the details as manually elicited or automatically shown feedback. We implemented SalienTrack in the context of nutrition tracking. To do this, we first conducted a field study to collect photo-based mobile food tracking over 1-5 weeks. We then report how we used this data to train an explainable-AI model of salience. Finally, we created interfaces to present salient information and conducted a formative user study to gain insights about how SalienTrack could be integrated into an interface for reflection. Our key contributions are the SalienTrack framework, a demonstration of its implementation for semi-automated feedback in an important and challenging self-tracking context and a discussion of the broader uses of the framework.

cs.HC↗

arXiv2022

Multiparameter simultaneous optimal estimation with an SU(2) coding unitary evolution

In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal estimation of multiple parameters is significant but difficult. Using quantum control to optimize this $SU(2)$ coding unitary evolution is one of solutions. We propose a method, characterized by the nested cross-products of the coefficient vector $\mathbf{X}$ of $SU(2)$ generators and its partial derivative $\partial_\ell \mathbf{X}$, to investigate the control-enhanced quantum multiparameter estimation. Our work reveals that quantum control is not always functional in improving the estimation precision, which depends on the characterization of an $SU(2)$ dynamics with respect to the objective parameter. This characterization is quantified by the angle $α_\ell$ between $\mathbf{X}$ and $\partial_\ell \mathbf{X}$. For an $SU(2)$ dynamics featured by $α_\ell=π/2$, the promotion of the estimation precision can get the most benefits from the controls. When $α_\ell$ gradually closes to $0$ or $π$, the precision promotion contributed to by quantum control correspondingly becomes inconspicuous. Until a dynamics with $α_\ell=0$ or $π$, quantum control completely loses its advantage. In addition, we find a set of conditions restricting the simultaneous optimal estimation of all the parameters, but fortunately, which can be removed by using a maximally entangled two-qubit state as the probe state and adding an ancillary channel into the configuration. Lastly, a spin-$1/2$ system is taken as an example to verify the above-mentioned conclusions. Our proposal sufficiently exhibits the hallmark of control-enhancement in fulfilling the multiparameter estimation mission, and it is applicable to an arbitrary $SU(2)$ parametrization process.

quant-ph↗

arXiv2022

Verification of Kochen-Specker-type quantum contextuality with a single photon

Contextuality provides one of the fundamental characterizations of quantum phenomena, and can be used as a resource in lots of quantum information processing. In this paper, we summarize and derive some equivalent noncontextual inequalities from different noncontextual models of the proofs for Kochen-Specker theorem based on Greenberger-Horne-Zeilinger states. These noncontextual inequalities are equivalent up to some correlation items which hold both for noncontextual hidden variable theories and quantum mechanics. Therefore, using single-photon hyperentangled Greenberger-Horne-Zeilinger states encoded by spin, path and orbital angular momentum, we experimentally verify several state-dependent noncontextual models of the proofs for the Kochen-Specker theorem by testing an extreme simplest Mermin-like inequality.

quant-ph↗

arXiv2022

Multi-Domain Joint Training for Person Re-Identification

Deep learning-based person Re-IDentification (ReID) often requires a large amount of training data to achieve good performance. Thus it appears that collecting more training data from diverse environments tends to improve the ReID performance. This paper re-examines this common belief and makes a somehow surprising observation: using more samples, i.e., training with samples from multiple datasets, does not necessarily lead to better performance by using the popular ReID models. In some cases, training with more samples may even hurt the performance of the evaluation is carried out in one of those datasets. We postulate that this phenomenon is due to the incapability of the standard network in adapting to diverse environments. To overcome this issue, we propose an approach called Domain-Camera-Sample Dynamic network (DCSD) whose parameters can be adaptive to various factors. Specifically, we consider the internal domain-related factor that can be identified from the input features, and external domain-related factors, such as domain information or camera information. Our discovery is that training with such an adaptive model can better benefit from more training samples. Experimental results show that our DCSD can greatly boost the performance (up to 12.3%) while joint training in multiple datasets.

cs.CV↗

arXiv2021

PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving

The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360° mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset.

cs.CV↗

arXiv2021

Experimental Demonstration of Efficient High-dimensional Quantum Gates with Orbital Angular Momentum

Quantum gates are essential for the realization of quantum computer and have been implemented in various types of two-level systems. However, high-dimensional quantum gates are rarely investigated both theoretically and experimentally even that high-dimensional quantum systems exhibit remarkable advantages over two-level systems for some quantum information and quantum computing tasks. Here we experimentally demonstrate the four-dimensional X gate and its unique higher orders with the average conversion efficiency 93\%. All these gates are based on orbital-angular-momentum degree of freedom of single photons. Besides, a set of controlled quantum gates is implemented by use of polarization degree of freedom. Our work is an important step towards the goal of achieving arbitrary high-dimensional quantum circuit and paves a way for the implementation of high-dimensional quantum communication and computation.

quant-ph↗

arXiv2021

Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning

Iris segmentation is a deterministic part of the iris recognition system. Unreliable segmentation of iris regions especially the limbic area is still the bottleneck problem, which impedes more accurate recognition. To make further efforts on accurate and reliable iris segmentation, we propose a bilateral self-attention module and design Bilateral Transformer (BiTrans) with hierarchical architecture by exploring spatial and visual relationships. The bilateral self-attention module adopts a spatial branch to capture spatial contextual information without resolution reduction and a visual branch with a large receptive field to extract the visual contextual features. BiTrans actively applies convolutional projections and cross-attention to improve spatial perception and hierarchical feature fusion. Besides, Iris Segmentation Uncertainty Learning is developed to learn the uncertainty map according to prediction discrepancy. With the estimated uncertainty, a weighting scheme and a regularization term are designed to reduce predictive uncertainty. More importantly, the uncertainty estimate reflects the reliability of the segmentation predictions. Experimental results on three publicly available databases demonstrate that the proposed approach achieves better segmentation performance using 20% FLOPs of the SOTA IrisParseNet.

cs.CV↗

arXiv2021

Learning Instance-level Spatial-Temporal Patterns for Person Re-identification

Person re-identification (Re-ID) aims to match pedestrians under dis-joint cameras. Most Re-ID methods formulate it as visual representation learning and image search, and its accuracy is consequently affected greatly by the search space. Spatial-temporal information has been proven to be efficient to filter irrelevant negative samples and significantly improve Re-ID accuracy. However, existing spatial-temporal person Re-ID methods are still rough and do not exploit spatial-temporal information sufficiently. In this paper, we propose a novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to improve Re-ID accuracy. In our proposed framework, personalized information such as moving direction is explicitly considered to further narrow down the search space. Besides, the spatial-temporal transferring probability is disentangled from joint distribution to marginal distribution, so that outliers can also be well modeled. Abundant experimental analyses are presented, which demonstrates the superiority and provides more insights into our method. The proposed method achieves mAP of 90.8% on Market-1501 and 89.1% on DukeMTMC-reID, improving from the baseline 82.2% and 72.7%, respectively. Besides, in order to provide a better benchmark for person re-identification, we release a cleaned data list of DukeMTMC-reID with this paper: https://github.com/RenMin1991/cleaned-DukeMTMC-reID/

cs.CV↗