arXiv subjects

Jing Zhu

Publications and source records attributed to Jing Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2022

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment

Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. We show that CAPER can improve upon many different existing network alignment algorithms by enforcing alignment consistency across multiple graph resolutions: nodes matched at finer levels should also be matched at coarser levels. CAPER also accelerates the use of slower network alignment methods, at the modest cost of linear-time coarsening and refinement steps, by allowing them to be run on smaller coarsened versions of the input graphs. Experiments show that CAPER can improve upon diverse network alignment methods by an average of 33% in accuracy and/or an order of magnitude faster in runtime.

cs.SI↗

arXiv2022

Optimal Dynamic Orchestration in NDN-based Computing Networks

Named Data Networking (NDN) offers promising advantages in deploying next-generation service applications over distributed computing networks. We consider the problem of dynamic orchestration over a NDN-based computing network, in which nodes can be equipped with communication, computation, and data producing resources. Given a set of services with function-chaining structures, we address the design of distributed online algorithm that controls each node to make adaptive decisions on flowing service requests, committing function implementations, and/or producing data. We design a Service Discovery Assisted Dynamic Orchestration (SDADO) algorithm that reduces the end-to-end (E2E) delay of delivering the services, while providing optimal throughput performance. The proposed algorithm hybrids queuing-based flexibility and topology-based discipline, where the topological information is not pre-available but obtained through our proposed service discovery mechanism. We provide throughput-optimality analysis for SDADO, and then provide numerical results that confirm our analysis and demonstrates reduced round-trip E2E delay.

cs.NI↗

arXiv2022

Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection

Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensure that no single data source can adversely bias the model at any point in time. In this study we show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization, paving the way for a low-cost and accessible deep learning-based clinical system for lung cancer screening. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is performed using all combinations of lung field segmentation, close cropping, and rib suppression operators. We show that this pre-processing pipeline results in deep learning models that successfully generalize an independent lung nodule dataset using ablation studies to assess the contribution of each operator in this pipeline. In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.

cs.CV↗

arXiv2021

NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases

Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques. However, such techniques focus primarily on the acquisition of positive (true) KB statements, even though negative (false) statements are often also important for discriminative reasoning over commonsense KBs. As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM). Importantly, as most KBs do not contain negatives, NegatER relies only on the positive knowledge in the LM and does not require ground-truth negative examples. Experiments demonstrate that, compared to multiple contrastive data augmentation approaches, NegatER yields negatives that are more grammatical, coherent, and informative -- leading to statistically significant accuracy improvements in a challenging KB completion task and confirming that the positive knowledge in LMs can be "re-purposed" to generate negative knowledge.

cs.AI↗

arXiv2021

ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation

Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects. To empower conventional CNNs in learning geometric clues of lanes, we propose a deep network named ContinuityLearner to better learn geometric prior within lane. Specifically, our proposed CNN-based paradigm involves a novel Context-encoding image feature learning network to generate class-dependent image feature maps and a new encoding layer to exploit the geometric continuity feature representation by fusing both spatial and visual information of lane together. The ContinuityLearner, performing on the geometric continuity feature of lanes, is trained to directly predict the lane in traffic scenarios with integrated and continuous instance semantic. The experimental results on the CULane dataset and the Tusimple benchmark demonstrate that our ContinuityLearner has superior performance over other state-of-the-art techniques in lane segmentation.

cs.CV↗

arXiv2021

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding

While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes. We present PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. Clarifying a point of contention in the literature, we show which step of PhUSION produces the different kinds of embeddings and what steps can be used by both. Moreover, by aggregating the PhUSION node embeddings, we obtain graph-level features that model information lost by previous graph feature learning and kernel methods. In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35 methods, we systematically reveal successful design choices for node and graph-level machine learning with embeddings.

cs.SI↗

arXiv2020

Electronic inhomogeneity and band structure on superstructural CuO2 planes of infinite-layer Sr0.94La0.06CuO2+y films

Scanning tunneling microscopy and spectroscopy are utilized to study the atomic-scale structure and electronic properties of infinite-layer Sr0.94La0.06CuO2+y films prepared on SrRuO3-buffered SrTiO3(001) substrate by ozone-assisted molecular beam epitaxy. Incommensurate structural supermodulation with a period of 24.5Å is identified on the CuO2-terminated surface, leading to characteristic stripes running along the 45o direction with respect to the Cu-O-Cu bonds. Spatially resolved tunneling spectra reveal substantial inhomogeneity on a nanometer length scale and emergence of in-gap states at sufficient doping. Despite the Fermi level shifting up to 0.7 eV, the charge-transfer energy gap of the CuO2 planes remains fundamentally unchanged at different doping levels. The occurrence of the CuO2 superstructure is constrained in the surface region and its formation is found to link with oxygen intake that serves as doping agent of holes in the epitaxial films.

cond-mat.supr-con↗

arXiv2020

Direct visualization of ambipolar Mott transition in cuprate CuO2 planes

Identifying the essence of doped Mott insulators is one of the major outstanding problems in condensed matter physics and the key to understanding the high-temperature superconductivity in cuprates. We report real space visualization of Mott transition in Sr1-xLaxCuO2+y cuprate films that cover the entire electron- and hole-doped regimes. Tunneling conductance measurements directly on the cooper-oxide (CuO2) planes reveal a systematic shift in the Fermi level, while the fundamental Mott-Hubbard band structure remains unchanged. This is further demonstrated by exploring atomic-scale electronic response of CuO2 to substitutional dopants and intrinsic defects in a sister compound Sr0.92Nd0.08CuO2. The results could be better explained in the framework of self-modulation doping, similar to that in semiconductor heterostructures, and form a basis for developing any microscopic theories for cuprate superconductivity.

cond-mat.supr-con↗

arXiv2020

Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation

The aim of session-based recommendation is to predict the users' next clicked item, which is a challenging task due to the inherent uncertainty in user behaviors and anonymous implicit feedback information. A powerful session-based recommender can typically explore the users' evolving interests (i.e., a combination of his/her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are two main drawbacks. First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively. Second, current studies typically think long-term and short-term interests as equally important, but the importance of them should be user-specific. Therefore, we propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn user's short-term interest by taking into account the contextual information and temporal signals simultaneously. Besides, we introduce a gated fusion method that adaptively integrates the user's long-term and short-term preferences to generate the hybrid interest representation. Experiments on the three real-world datasets show that PAN achieves obvious improvements than the state-of-the-art methods.

cs.IR↗

arXiv2020

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.

cs.DL↗

arXiv2019

Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization

The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust the UAV locations are essential to maximize performance. In addition to careful tracking of spatio-temporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve moving users may improve the overall system performance even in the presence of backhaul capacity limitations.

cs.NI↗

arXiv2019

Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment

Learning to predict scene depth and camera motion from RGB inputs only is a challenging task. Most existing learning based methods deal with this task in a supervised manner which require ground-truth data that is expensive to acquire. More recent approaches explore the possibility of estimating scene depth and camera pose in a self-supervised learning framework. Despite encouraging results are shown, current methods either learn from monocular videos for depth and pose and typically do so without enforcing multi-view geometry constraints between scene structure and camera motion, or require stereo sequences as input where the ground-truth between-frame motion parameters need to be known. In this paper we propose to jointly optimize the scene depth and camera motion via incorporating differentiable Bundle Adjustment (BA) layer by minimizing the feature-metric error, and then form the photometric consistency loss with view synthesis as the final supervisory signal. The proposed approach only needs unlabeled monocular videos as input, and extensive experiments on the KITTI and Cityscapes dataset show that our method achieves state-of-the-art results in self-supervised approaches using monocular videos as input, and even gains advantage to the line of methods that learns from calibrated stereo sequences (i.e. with pose supervision).

cs.CV↗

arXiv2019

Structure-Attentioned Memory Network for Monocular Depth Estimation

Monocular depth estimation is a challenging task that aims to predict a corresponding depth map from a given single RGB image. Recent deep learning models have been proposed to predict the depth from the image by learning the alignment of deep features between the RGB image and the depth domains. In this paper, we present a novel approach, named Structure-Attentioned Memory Network, to more effectively transfer domain features for monocular depth estimation by taking into account the common structure regularities (e.g., repetitive structure patterns, planar surfaces, symmetries) in domain adaptation. To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain. More specifically, in the SOM module, we develop a Memorable Bank of Filters (MBF) unit to learn a set of filters that memorize the structure-aware image-depth residual pattern, and also an Attention Guided Controller (AGC) unit to control the filter selection in the MBF given image features queries. Given the query image feature, the trained SOM module is able to adaptively select the best customized filters for cross-domain feature transferring with an optimal structural disparity between image and depth. In summary, we focus on addressing this structure-specific domain adaption challenge by proposing a novel end-to-end multi-scale memorable network for monocular depth estimation. The experiments show that our proposed model demonstrates the superior performance compared to the existing supervised monocular depth estimation approaches on the challenging KITTI and NYU Depth V2 benchmarks.

cs.CV↗

arXiv2019

Learning Object-specific Distance from a Monocular Image

Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving. Many attentions have been paid on the object detection task, but distance estimation only arouse few interests in the computer vision community. Observing that the traditional inverse perspective mapping algorithm performs poorly for objects far away from the camera or on the curved road, in this paper, we address the challenging distance estimation problem by developing the first end-to-end learning-based model to directly predict distances for given objects in the images. Besides the introduction of a learning-based base model, we further design an enhanced model with a keypoint regressor, where a projection loss is defined to enforce a better distance estimation, especially for objects close to the camera. To facilitate the research on this task, we construct the extented KITTI and nuScenes (mini) object detection datasets with a distance for each object. Our experiments demonstrate that our proposed methods outperform alternative approaches (e.g., the traditional IPM, SVR) on object-specific distance estimation, particularly for the challenging cases that objects are on a curved road. Moreover, the performance margin implies the effectiveness of our enhanced method.

cs.CV↗

arXiv2018

Oxygen-Octahedral-Tilting-Driven Topological Hall Effect in Ultrathin SrRuO3 Films

Topological spin textures as an emerging class of topological matter offer a medium for information storage and processing. The recently discovered topological Hall effect (THE) is considered as a fingerprint for electrically probing non-trivial spin-textures. But the origin of THE in oxides has remained elusive. Here we report an observation of the THE in ultrathin (unit cells. u.c.) 4d ferromagnetic SrRuO3 films grown on SrTiO3(001) substrates, which can be attributed to the chiral ordering of spin structure (i.e., skyrmion-like) in the single SrRuO3 layer without contacting 5d oxide SrIrO3 layer. It is revealed that the RuO6 octahedral tilting induced by local orthorhombic-to-tetragonal structural phase transition exists across the SrRuO3/SrTiO3 interface, which naturally breaks the inversion symmetry. Our theoretical calculations demonstrate that the Dzyaloshinskii-Moriya (DM) interaction arises owing to the broken inversion symmetry and strong spin-orbit interaction of 4d SrRuO3. This DM interaction can stabilize the Néel-type magnetic skyrmions, which in turn accounts for the observed THE in transport. The RuO6 octahedral tilting-induced DM interaction provides a pathway toward the electrical control of the topological spin textures and resultant THE, which is confirmed both experimentally and theoretically. Besides the fundamental significance, the understanding of THE in oxides and its electrical manipulation presented in this work could advance the low power cost topological electronic and spintronic applications.

cond-mat.mtrl-sci↗

arXiv2018

Will TCP work in mmWave 5G Cellular Networks?

The vast available spectrum in the millimeter wave (mmWave) bands offers the possibility of multi-Gbps data rates for fifth generation (5G) cellular networks. However, mmWave capacity can be highly intermittent due to the vulnerability of mmWave signals to blockages and delays in directional searching. Such highly variable links present unique challenges for adaptive control mechanisms in transport layer protocols and end-to-end applications. This paper considers the fundamental question of whether TCP - the most widely used transport protocol - will work in mmWave cellular systems. The paper provides a comprehensive simulation study of TCP considering various factors such as the congestion control algorithm, including the recently proposed TCP BBR, edge vs. remote servers, handover and multi- connectivity, TCP packet size and 3GPP-stack parameters. We show that the performance of TCP on mmWave links is highly dependent on different combinations of these parameters, and identify the open challenges in this area.

cs.NI↗

arXiv2018

An experimental measurement of the topological charge of orbital angular momentum beams through weak measurement

As a special experimental technique, weak measurement extracts very little information about the measured system and will not cause the measured state collapse. When coupling the orbital angular momentum (OAM) state with a well-defined pre-selected and post-selected system of a weak measurement process, there is an indirect coupling between position and topological charge (TC) of OAM state. Based on these ideas, we propose an experimental scheme that experimentally measure the TC of OAM beams from -14 to 14 through weak measurement.

physics.optics↗

arXiv2017

milliProxy: a TCP Proxy Architecture for 5G mmWave Cellular Systems

TCP is the most widely used transport protocol in the internet. However, it offers suboptimal performance when operating over high bandwidth mmWave links. The main issues introduced by communications at such high frequencies are (i) the sensitivity to blockage and (ii) the high bandwidth fluctuations due to Line of Sight (LOS) to Non Line of Sight (NLOS) transitions and vice versa. In particular, TCP has an abstract view of the end-to-end connection, which does not properly capture the dynamics of the wireless mmWave link. The consequence is a suboptimal utilization of the available resources. In this paper we propose a TCP proxy architecture that improves the performance of TCP flows without any modification at the remote sender side. The proxy is installed in the Radio Access Network, and exploits information available at the gNB in order to maximize throughput and minimize latency.

cs.NI↗