arXiv subjects

Ke Zhang

Publications and source records attributed to Ke Zhang.

At least 235 records · Page 13Linked to original sources

arXiv2022

UV-driven Chemistry as a Signpost for Late-stage Planet Formation

The chemical reservoir within protoplanetary disks has a direct impact on planetary compositions and the potential for life. A long-lived carbon-and nitrogen-rich chemistry at cold temperatures (<=50K) is observed within cold and evolved planet-forming disks. This is evidenced by bright emission from small organic radicals in 1-10 Myr aged systems that would otherwise have frozen out onto grains within 1 Myr. We explain how the chemistry of a planet-forming disk evolves from a cosmic-ray/X-ray-dominated regime to an ultraviolet-dominated chemical equilibrium. This, in turn, will bring about a temporal transition in the chemical reservoir from which planets will accrete. This photochemical dominated gas phase chemistry develops as dust evolves via growth, settling and drift, and the small grain population is depleted from the disk atmosphere. A higher gas-to-dust mass ratio allows for deeper penetration of ultraviolet photons is coupled with a carbon-rich gas (C/O > 1) to form carbon-bearing radicals and ions. This further results in gas phase formation of organic molecules, which then would be accreted by any actively forming planets present in the evolved disk.

astro-ph.EP↗

arXiv2022

Gevrey regularity for the formally linearizable billiard of Treschev

Treschev made the remarkable discovery that there exists formal power series describing a billiard with locally linearizable dynamics. We show that if the frequency for the linear dynamics is Diophanine, the Treschev example is $(1+ α)$-Gevrey for some $α> 0$. Our proof is based on an iterative scheme that further clarifies the structure and symmetries underlying the original Treschev construction. Hopefully, Our result sheds a light on the more important question of whether this example is convergent.

math.DS↗

arXiv2022

MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images

Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application.

eess.IV↗

arXiv2022

CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization

Social media has increasingly played a key role in emergency response: first responders can use public posts to better react to ongoing crisis events and deploy the necessary resources where they are most needed. Timeline extraction and abstractive summarization are critical technical tasks to leverage large numbers of social media posts about events. Unfortunately, there are few datasets for benchmarking technical approaches for those tasks. This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date. CrisisLTLSum contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built CrisisLTLSum using a semi-automated cluster-then-refine approach to collect data from the public Twitter stream. Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks. Our dataset, code, and models are publicly available.

cs.CL↗

arXiv2022

A power-efficient integrated lithium niobate electro-optic comb generator

Integrated electro-optic (EO) frequency combs are essential components for future applications in optical communications, light detection and ranging, optical computation, sensing and spectroscopy. To date, broadband on-chip EO combs are typically generated in high-quality-factor micro-resonators, while the more straightforward and flexible non-resonant method, usually using single or cascaded EO phase modulators, often requires high driving power to realize a reasonably strong modulation index. Here, we show that the phase modulation efficiency of an integrated lithium niobate modulator could be dramatically enhanced by passing optical signals through the modulation electrodes for a total of 4 round trips, via multiple low-loss TE0/TE1 mode multiplexers and waveguide crossings, reducing electrical power consumption by more than one order of magnitude. Using devices fabricated from a wafer-scale stepper lithography process, we demonstrate a broadband optical frequency comb featuring 47 comb lines at a 25-GHz repetition rate, using a moderate RF driving power of 28 dBm (0.63 W). Leveraging the excellent tunability in repetition rate and operation wavelength, our power-efficient EO comb generator could serve as a compact low-cost solution for future high-speed data transmission, sensing and spectroscopy, as well as classical and quantum optical computation systems.

physics.optics↗

arXiv2022

Molecules with ALMA at Planet-forming Scales (MAPS). A Circumplanetary Disk Candidate in Molecular Line Emission in the AS 209 Disk

We report the discovery of a circumplanetary disk (CPD) candidate embedded in the circumstellar disk of the T Tauri star AS 209 at a radial distance of about 200 au (on-sky separation of 1."4 from the star at a position angle of $161^\circ$), isolated via $^{13}$CO $J=2-1$ emission. This is the first instance of CPD detection via gaseous emission capable of tracing the overall CPD mass. The CPD is spatially unresolved with a $117\times82$ mas beam and manifests as a point source in $^{13}$CO, indicating that its diameter is $\lesssim14$ au. The CPD is embedded within an annular gap in the circumstellar disk previously identified using $^{12}$CO and near-infrared scattered light observations, and is associated with localized velocity perturbations in $^{12}$CO. The coincidence of these features suggests that they have a common origin: an embedded giant planet. We use the $^{13}$CO intensity to constrain the CPD gas temperature and mass. We find that the CPD temperature is $\gtrsim35$ K, higher than the circumstellar disk temperature at the radial location of the CPD, 22 K, suggesting that heating sources localized to the CPD must be present. The CPD gas mass is $\gtrsim 0.095 M_{\rm Jup} \simeq 30 M_{\rm Earth}$ adopting a standard $^{13}$CO abundance. From the non-detection of millimeter continuum emission at the location of the CPD ($3σ$ flux density $\lesssim26.4~μ$Jy), we infer that the CPD dust mass is $\lesssim 0.027 M_{\rm Earth} \simeq 2.2$ lunar masses, indicating a low dust-to-gas mass ratio of $\lesssim9\times10^{-4}$. We discuss the formation mechanism of the CPD-hosting giant planet on a wide orbit in the framework of gravitational instability and pebble accretion.

astro-ph.EP↗

arXiv2022

Systematic Investigation of Millimeter-Wave Optic Modulation Performance in Thin-Film Lithium Niobate

Millimeter-wave (mmWave) band (30 - 300 GHz) is an emerging spectrum range for wireless communication, short-range radar and sensor applications. mmWave-optic modulators that could efficiently convert mmWave signals into optical domain are crucial components for long-haul transmission of mmWave signals through optical networks. At these ultrahigh frequencies, however, the modulation performances are highly sensitive to the transmission line loss as well as the velocity- and impedance-matching conditions, while precise measurements and modeling of these parameters are often non-trivial. Here we present a systematic investigation of the mmWave-optic modulation performances of thin-film lithium niobate modulators through theoretical modeling, electrical verifications and electro-optic measurements at frequencies up to 325 GHz. Based on our experimentally verified model, we demonstrate thin-film lithium niobate mmWave-optic modulators with a measured 3-dB electro-optic bandwidth of 170 GHz and a 6-dB bandwidth of 295 GHz. The device also shows a low RF half-wave voltage of 7.3 V measured at an ultrahigh modulation frequency of 250 GHz. This work provides a comprehensive guideline for the design and characterization of mmWave-optic modulators and paves the way toward future integrated mmWave photonic systems for beyond-5G communication and radar applications.

physics.optics↗

arXiv2022

Mapping the Design Space of Human-AI Interaction in Text Summarization

Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans' roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks. We first conducted a systematic literature review of 70 papers, developing a taxonomy of five interactions in AI-assisted text generation and relevant design dimensions. We designed text summarization prototypes for each interaction. We then interviewed 16 users, aided by the prototypes, to understand their expectations, experience, and needs regarding efficiency, control, and trust with AI in text summarization and propose design considerations accordingly.

cs.HC↗

arXiv2022

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivates us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) Transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818), which significantly outperforms AUC = 0.784 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 (CC view) and 0.769 (MLO view), respectively. The study demonstrates the potential of using Transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.

cs.CV↗

arXiv2022

Deep Compatible Learning for Partially-Supervised Medical Image Segmentation

Partially-supervised learning can be challenging for segmentation due to the lack of supervision for unlabeled structures, and the methods directly applying fully-supervised learning could lead to incompatibility, meaning ground truth is not in the solution set of the optimization problem given the loss function. To address the challenge, we propose a deep compatible learning (DCL) framework, which trains a single multi-label segmentation network using images with only partial structures annotated. We first formulate the partially-supervised segmentation as an optimization problem compatible with missing labels, and prove its compatibility. Then, we equip the model with a conditional segmentation strategy, to propagate labels from multiple partially-annotated images to the target. Additionally, we propose a dual learning strategy, which learns two opposite mappings of label propagation simultaneously, to provide substantial supervision for unlabeled structures. The two strategies are formulated into compatible forms, termed as conditional compatibility and dual compatibility, respectively. We show this framework is generally applicable for conventional loss functions. The approach attains significant performance improvement over existing methods, especially in the situation where only a small training dataset is available. Results on three segmentation tasks have shown that the proposed framework could achieve performance matching fully-supervised models.

cs.CV↗

arXiv2022

An Exploration of Post-Editing Effectiveness in Text Summarization

Automatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? In similar text generation tasks (e.g., machine translation), human-AI collaboration in the form of "post-editing" AI-generated text reduces human workload and improves the quality of AI output. Therefore, we explored whether post-editing offers advantages in text summarization. Specifically, we conducted an experiment with 72 participants, comparing post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text. This study sheds valuable insights on when post-editing is useful for text summarization: it helped in some cases (e.g., when participants lacked domain knowledge) but not in others (e.g., when provided summaries include inaccurate information). Participants' different editing strategies and needs for assistance offer implications for future human-AI summarization systems.

cs.CL↗

arXiv2022

ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

Cardiac segmentation is an essential step for the diagnosis of cardiovascular diseases. However, pixel-wise dense labeling is both costly and time-consuming. Scribble, as a form of sparse annotation, is more accessible than full annotations. However, it's particularly challenging to train a segmentation network with weak supervision from scribbles. To tackle this problem, we propose a new scribble-guided method for cardiac segmentation, based on the Positive-Unlabeled (PU) learning framework and global consistency regularization, and termed as ShapePU. To leverage unlabeled pixels via PU learning, we first present an Expectation-Maximization (EM) algorithm to estimate the proportion of each class in the unlabeled pixels. Given the estimated ratios, we then introduce the marginal probability maximization to identify the classes of unlabeled pixels. To exploit shape knowledge, we apply cutout operations to training images, and penalize the inconsistent segmentation results. Evaluated on two open datasets, i.e, ACDC and MSCMRseg, our scribble-supervised ShapePU surpassed the fully supervised approach respectively by 1.4% and 9.8% in average Dice, and outperformed the state-of-the-art weakly supervised and PU learning methods by large margins. Our code is available at https://github.com/BWGZK/ShapePU.

cs.CV↗

arXiv2022

Molecules with ALMA at Planet-forming Scales (MAPS) III: Characteristics of Radial Chemical Substructures

The Molecules with ALMA at Planet-forming Scales (MAPS) Large Program provides a detailed, high resolution (${\sim}$10-20 au) view of molecular line emission in five protoplanetary disks at spatial scales relevant for planet formation. Here, we present a systematic analysis of chemical substructures in 18 molecular lines toward the MAPS sources: IM Lup, GM Aur, AS 209, HD 163296, and MWC 480. We identify more than 200 chemical substructures, which are found at nearly all radii where line emission is detected. A wide diversity of radial morphologies - including rings, gaps, and plateaus - is observed both within each disk and across the MAPS sample. This diversity in line emission profiles is also present in the innermost 50 au. Overall, this suggests that planets form in varied chemical environments both across disks and at different radii within the same disk. Interior to 150 au, the majority of chemical substructures across the MAPS disks are spatially coincident with substructures in the millimeter continuum, indicative of physical and chemical links between the disk midplane and warm, elevated molecular emission layers. Some chemical substructures in the inner disk and most chemical substructures exterior to 150 au cannot be directly linked to dust substructure, however, which indicates that there are also other causes of chemical substructures, such as snowlines, gradients in UV photon fluxes, ionization, and radially-varying elemental ratios. This implies that chemical substructures could be developed into powerful probes of different disk characteristics, in addition to influencing the environments within which planets assemble. This paper is part of the MAPS special issue of the Astrophysical Journal Supplement.

astro-ph.EP↗

arXiv2022

Parallel Network with Channel Attention and Post-Processing for Carotid Arteries Vulnerable Plaque Segmentation in Ultrasound Images

Carotid arteries vulnerable plaques are a crucial factor in the screening of atherosclerosis by ultrasound technique. However, the plaques are contaminated by various noises such as artifact, speckle noise, and manual segmentation may be time-consuming. This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset. First, a parallel network with three independent scale decoders is utilized as our base segmentation network, pyramid dilation convolutions are used to enlarge receptive fields in the three segmentation sub-networks. Subsequently, the three decoders are merged to be rectified in channels by SENet. Thirdly, in test stage, the initially segmented plaque is refined by the max contour morphology post-processing to obtain the final plaque. Moreover, three loss function Dice loss, SSIM loss and cross-entropy loss are compared to segment plaques. Test results show that the proposed method with dice loss function yields a Dice value of 0.820, an IoU of 0.701, Acc of 0.969, and modified Hausdorff distance (MHD) of 1.43 for 30 vulnerable cases of plaques, it outperforms some of the conventional CNN-based methods on these metrics. Additionally, we apply an ablation experiment to show the validity of each proposed module. Our study provides some reference for similar researches and may be useful in actual applications for plaque segmentation of ultrasound carotid arteries.

eess.IV↗

arXiv2022

Recent advances and clinical applications of deep learning in medical image analysis

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss the major technical challenges and suggest the possible solutions in future research efforts.

cs.CV↗

arXiv2022

InsCon:Instance Consistency Feature Representation via Self-Supervised Learning

Feature representation via self-supervised learning has reached remarkable success in image-level contrastive learning, which brings impressive performances on image classification tasks. While image-level feature representation mainly focuses on contrastive learning in single instance, it ignores the objective differences between pretext and downstream prediction tasks such as object detection and instance segmentation. In order to fully unleash the power of feature representation on downstream prediction tasks, we propose a new end-to-end self-supervised framework called InsCon, which is devoted to capturing multi-instance information and extracting cell-instance features for object recognition and localization. On the one hand, InsCon builds a targeted learning paradigm that applies multi-instance images as input, aligning the learned feature between corresponding instance views, which makes it more appropriate for multi-instance recognition tasks. On the other hand, InsCon introduces the pull and push of cell-instance, which utilizes cell consistency to enhance fine-grained feature representation for precise boundary localization. As a result, InsCon learns multi-instance consistency on semantic feature representation and cell-instance consistency on spatial feature representation. Experiments demonstrate the method we proposed surpasses MoCo v2 by 1.1% AP^{bb} on COCO object detection and 1.0% AP^{mk} on COCO instance segmentation using Mask R-CNN R50-FPN network structure with 90k iterations, 2.1% APbb on PASCAL VOC objection detection using Faster R-CNN R50-C4 network structure with 24k iterations.

cs.CV↗

arXiv2022

CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data

Data augmentation has been proved effective in training deep models. Existing data augmentation methods tackle the fine-grained problem by blending image pairs and fusing corresponding labels according to the statistics of mixed pixels, which produces additional noise harmful to the performance of networks. Motivated by this, we present a simple yet effective cross ensemble knowledge distillation (CEKD) model for fine-grained feature learning. We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem. The proposed model can be trained in an end-to-end manner, and only requires image-level label supervision. Extensive experiments on widely used fine-grained benchmarks demonstrate the effectiveness of our proposed model. Specifically, with the backbone of ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06% and 1.16%.

cs.CV↗

arXiv2022

CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix. For augmentation of supervision, CycleMix adopts the mixup strategy with a dedicated design of random occlusion, to perform increments and decrements of scribbles. For regularization of supervision, CycleMix intensifies the training objective with consistency losses to penalize inconsistent segmentation, which results in significant improvement of segmentation performance. Results on two open datasets, i.e., ACDC and MSCMRseg, showed that the proposed method achieved exhilarating performance, demonstrating comparable or even better accuracy than the fully-supervised methods. The code and expert-made scribble annotations for MSCMRseg are publicly available at https://github.com/BWGZK/CycleMix.

eess.IV↗