arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 883 records · Page 49Linked to original sources

arXiv2021

CT Image Harmonization for Enhancing Radiomics Studies

While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused by using non-standard reconstruction kernels. RadiomicGAN consists of hybrid neural blocks including both pre-trained and trainable layers adopted to learn radiomic feature distributions efficiently. A novel training approach, called Dynamic Window-based Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.

eess.IV↗

arXiv2021

The Inhomogeneity of Composition along the Magnetic Cloud Axis

It is generally accepted that CMEs result from eruptions of magnetic flux ropes, which are dubbed as magnetic clouds in interplanetary space. The composition (including the ionic charge states and elemental abundances) is determined prior to and/or during CME eruptions in the solar atmosphere, and does not alter during magnetic cloud propagation to 1 AU and beyond. It has been known that the composition is not uniform within a cross section perpendicular to magnetic cloud axis, and the distribution of ionic charge states within a cross section provides us an important clue to investigate the formation and eruption processes of flux ropes due to the freeze-in effect. The flux rope is a three dimensional magnetic structure intrinsically, and it remains unclear whether the composition is uniform along the flux rope axis as most magnetic clouds are only detected by one spacecraft. In this paper we report a magnetic cloud that was observed by ACE near 1 AU on 1998 March 4--6 and Ulysses near 5.4 AU on March 24--28 sequentially. At these times, both spacecraft were located around the ecliptic plane, and the latitudinal and longitudinal separations between them were $\sim$2.2$^{\circ}$ and $\sim$5.5$^{\circ}$, respectively. It provides us an excellent opportunity to explore the axial inhomogeneity of flux rope composition, as both spacecraft almost intersected the cloud center at different sites along its axis. Our study shows that the average values of ionic charge states exhibit significant difference along the axis for carbon, and the differences are relatively slight but still obvious for charge states of oxygen and iron, as well as the elemental abundances of iron and helium. Besides the means, the composition profiles within the cloud measured by both spacecraft also exhibit some discrepancies. We conclude that the inhomogeneity of composition exists along the cloud axis.

astro-ph.SR↗

arXiv2021

MFR 2021: Masked Face Recognition Competition

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multi-session, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

cs.CV↗

arXiv2021

Revamping Storage Class Memory With Hardware Automated Memory-Over-Storage Solution

Large persistent memories such as NVDIMM have been perceived as a disruptive memory technology, because they can maintain the state of a system even after a power failure and allow the system to recover quickly. However, overheads incurred by a heavy software-stack intervention seriously negate the benefits of such memories. First, to significantly reduce the software stack overheads, we propose HAMS, a hardware automated Memory-over-Storage (MoS) solution. Specifically, HAMS aggregates the capacity of NVDIMM and ultra-low latency flash archives (ULL-Flash) into a single large memory space, which can be used as a working or persistent memory expansion, in an OS-transparent manner. HAMS resides in the memory controller hub and manages its MoS address pool over conventional DDR and NVMe interfaces; it employs a simple hardware cache to serve all the memory requests from the host MMU after mapping the storage space of ULL-Flash to the memory space of NVDIMM. Second, to make HAMS more energy-efficient and reliable, we propose an "advanced HAMS" which removes unnecessary data transfers between NVDIMM and ULL-Flash after optimizing the datapath and hardware modules of HAMS. This approach unleashes the ULL-Flash and its NVMe controller from the storage box and directly connects the HAMS datapath to NVDIMM over the conventional DDR4 interface. Our evaluations show that HAMS and advanced HAMS can offer 97% and 119% higher system performance than a software-based hybrid NVDIMM design, while consuming 41% and 45% lower system energy, respectively.

cs.AR↗

arXiv2021

Bulk-boundary correspondence in disordered higher-order topological insulators

In this work, we study the disorder effects on the bulk-boundary correspondence of two-dimensional higher-order topological insulators (HOTIs). We concentrate on two cases: (i) bulk-corner correspondence, (ii) edge-corner correspondence. For the bulk-corner correspondence case, we demonstrate the existence of the mobility gaps and clarify the related topological invariant that characterizes the mobility gap. Furthermore, we find that, while the system preserves the bulk-corner correspondence in the presence of disorder, the corner states are protected by the mobility gap instead of the bulk gap. For the edge-corner correspondence case, we show that the bulk mobility gap and edge band gaps of HOTIs are no longer closed simultaneously. Therefore, a rich phase diagram is obtained, including various disorder-induced phase transition processes. Notably, a disorder-induced transition from the non-trivial to trivial phase is realized, distinguishing the HOTIs from the other topological states. Our results deepen the understanding of bulk-boundary correspondence and enrich the topological phase transitions of disordered HOTIs.

cond-mat.dis-nn↗

arXiv2021

M6: A Chinese Multimodal Pretrainer

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We propose a cross-modal pretraining method called M6, referring to Multi-Modality to Multi-Modality Multitask Mega-transformer, for unified pretraining on the data of single modality and multiple modalities. We scale the model size up to 10 billion and 100 billion parameters, and build the largest pretrained model in Chinese. We apply the model to a series of downstream applications, and demonstrate its outstanding performance in comparison with strong baselines. Furthermore, we specifically design a downstream task of text-guided image generation, and show that the finetuned M6 can create high-quality images with high resolution and abundant details.

cs.CL↗

arXiv2021

Flat Band Induced Negative Magnetoresistance in Multi-Orbital Kagome Metal

Electronic flat band systems are a fertile platform to host correlation-induced quantum phenomena such as unconventional superconductivity, magnetism and topological orders. While flat band has been established in geometrically frustrated structures, such as the kagome lattice, flat band-induced correlation effects especially in those multi-orbital bulk systems are rarely seen. Here we report negative magnetoresistance and signature of ferromagnetic fluctuations in a prototypical kagome metal CoSn, which features a flat band in proximity to the Fermi level. We find that the magnetoresistance is dictated by electronic correlations via Fermi level tuning. Combining with first principles and model calculations, we establish flat band-induced correlation effects in a multi-orbital electronic system, which opens new routes to realize unconventional superconducting and topological states in geometrically frustrated metals.

cond-mat.str-el↗

arXiv2021

Non-invasive time-sorting in radio-frequency compressed ultrafast electron diffraction

We demonstrate a non-invasive time-sorting method for ultrafast electron diffraction (UED) experiments with radio-frequency (rf) compressed electron beams. We show that electron beam energy and arrival time at the sample after rf compression are strongly correlated such that the arrival time jitter may be corrected through measurement of the beam energy. The method requires minimal change to the infrastructure of most of the UED machines and is applicable to both keV and MeV UED. In our experiment with ~3 MeV beam, the timing jitter after rf compression is corrected with 35 fs root-mean-square (rms) accuracy, limited by the 3x10^-4 energy stability. For keV UED with high energy stability, sub-10 fs accuracy in time-sorting should be readily achievable. This time-sorting technique allows us to retrieve the 2.5 THz oscillation related to coherent A1g phonon in laser excited Bismuth film and extends the temporal resolution of UED to a regime far beyond the 100-200 fs rms jitter limitation.

physics.acc-ph↗

arXiv2021

Disorder-induced vibrational anomalies from crystalline to amorphous solids

The origin of boson peak -- an excess of density of states over Debye's model in glassy solids -- is still under intense debate, among which some theories and experiments suggest that boson peak is related to van-Hove singularity. Here we show that boson peak and van-Hove singularity are well separated identities, by measuring the vibrational density of states of a two-dimensional granular system, where packings are tuned gradually from a crystalline, to polycrystals, and to an amorphous material. We observe a coexistence of well separated boson peak and van-Hove singularities in polycrystals, in which the van-Hove singularities gradually shift to higher frequency values while broadening their shapes and eventually disappear completely when the structural disorder $η$ becomes sufficiently high. By analyzing firstly the strongly disordered system ($η=1$) and the disordered granular crystals ($η=0$), and then systems of intermediate disorder with $η$ in between, we find that boson peak is associated with spatially uncorrelated random flucutations of shear modulus $δG/\langle G \rangle$ whereas the smearing of van-Hove singularities is associated with spatially correlated fluctuations of shear modulus $δG/\langle G \rangle$.

cond-mat.soft↗

arXiv2021

Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication

Deep convolutional neural networks have made outstanding contributions in many fields such as computer vision in the past few years and many researchers published well-trained network for downloading. But recent studies have shown serious concerns about integrity due to model-reuse attacks and backdoor attacks. In order to protect these open-source networks, many algorithms have been proposed such as watermarking. However, these existing algorithms modify the contents of the network permanently and are not suitable for integrity authentication. In this paper, we propose a reversible watermarking algorithm for integrity authentication. Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift. As shown in the experiments, the influence of embedding reversible watermarking on the classification performance is less than 0.5% and the parameters of the model can be fully recovered after extracting the watermarking. At the same time, the integrity of the model can be verified by applying the reversible watermarking: if the model is modified illegally, the authentication information generated by original model will be absolutely different from the extracted watermarking information.

cs.CR↗

arXiv2021

FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods.

eess.IV↗

arXiv2021

Revisiting Pulsar Velocities Using Gaia Data Release 2

Precise measurements of neutron star (NS) velocities provide critical clues to the supernova physics and evolution of binary systems. Based on Gaia Data Release 2 (DR2), we selected a sample of 24 young (<3 Myr) pulsars with precise parallax measurements and measured the velocity of their local standard of rest (LSR) and the velocity dispersion among their respective local stellar groups. The median velocity difference between thus calculated LSRs and the Galactic rotation model is ~7.6km/s, small compared to the typical velocity dispersion of ~27.5km/s. For pulsars off the Galactic plane, such differences grow significantly to as large as ~40 km/s. More importantly, the velocity dispersion of stars in the local group of low-velocity pulsars can be comparable to their transverse velocities, suggesting that the intrinsic velocities of NS progenitors should be taken into account when we consider their natal kicks and binary evolution. We also examined the double NS systems J0737- 3039A/B, and measured its transverse velocity to be 26(+18, -13) km/s assuming nearby Gaia sources being representative of its birth environment. This work demonstrated the feasibility and importance of using Gaia data to study the velocity of individual systems and velocity distribution of NSs.

astro-ph.HE↗

arXiv2021

Three-dimensional dynamics of a pair of deformable bubbles rising initially in line. Part 1: Moderately inertial regimes

The buoyancy-driven motion of two identical gas bubbles released in line in a liquid at rest is examined with the help of highly resolved simulations, focusing on moderately inertial regimes in which the path of an isolated bubble is vertical. Assuming first an axisymmetric evolution, equilibrium configurations of the bubble pair are determined as a function of the buoyancy-to-viscous and buoyancy-to-capillary force ratios which define the Galilei ($Ga$) and Bond ($Bo$) numbers of the system, respectively. The three-dimensional solutions reveal that this axisymmetric equilibrium is actually never reached. Instead, provided $Bo$ stands below a critical $Ga$-dependent threshold determining the onset of coalescence, two markedly different evolutions are observed. At the lower end of the explored $(Ga,\,Bo)$-range, the tandem follows a Drafting-Kissing-Tumbling scenario which eventually yields a planar side-by-side motion. For larger $Ga$, the trailing bubble drifts laterally and gets out of the wake of the leading bubble, barely altering the path of the latter. In this second scenario, the late configuration is characterized by a significant inclination of the tandem ranging from $30^\circ$ to $40^\circ$ with respect to the vertical. Bubble deformation has a major influence on the evolution of the system. It controls the magnitude of vortical effects in the wake of the leading bubble, hence the strength of the attractive force acting on the trailing bubble. It also governs the strength and even the sign of the lateral force acting on this bubble, a mechanism of particular importance when the tandem is released with a small angular deviation.

physics.flu-dyn↗

arXiv2021

Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of 3-dimensional structures is time-consuming and tedious, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 $\pm$ 1.5% of Dice score and the segmentation of tumors alone reached 61.8 $\pm$ 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds.

eess.IV↗

arXiv2021

Landau Levels and van der Waals Interfaces of Acoustics in Moiré Phononic Lattices

Moiré lattices which consist of parallel but staggered periodic lattices have been extensively explored due to their salient physical properties, such as van Hove singularities[1, 2], commensurable incommensurable transitions[3], non-Abelian gauge potentials[4], fractional quantum Hall effects[5-7], van der Waals interfaces[8, 9] and unconventional superconductivity[10, 11]. However, there are limited demonstrations of such concepts for classical wave systems. Here, we realized gauge fields in one-dimensional Moiré phononic lattices consisting of two superimposed periodic patterns which mismatched with each other along one direction. Benefiting from gauge fields, we generated Landau level flat bands near the Dirac cone and experimentally measured their spatial localization in pressure-field distributions. Then, by mismatching lattices along both directions, we constructed two-dimensional Moiré phononic lattices with van der Waals interfaces. We found that acoustic waves efficiently transported along van der Waals interfaces behaving as metallic networks. As mismatched lattices are well-controllable, our study offers a novel path to manipulate sound waves which are inaccessible in traditional periodic acoustic systems, and can be easily extended to mechanics, optics, electromagnetics and electronics.

physics.app-ph↗

arXiv2021

Multifunctional Superconducting Nanowire Quantum Sensors

Superconducting nanowire single photon detectors (SNSPDs) offer high-quantum-efficiency and low-dark-count-rate single photon detection. In a growing number of cases, large magnetic fields are being incorporated into quantum microscopes, nanophotonic devices, and sensors for nuclear and high-energy physics that rely on SNSPDs, but superconducting devices generally operate poorly in large magnetic fields. Here, we demonstrate robust performance of amorphous SNSPDs in magnetic fields of up to $\pm 6$ T with a negligible dark count rate and unchanged quantum efficiency at typical bias currents. Critically, we also show that in the electrothermal oscillation regime, the SNSPD can be used as a magnetometer with sensitivity of better than 100 $\mathrm{μT/\sqrt{Hz}}$ and as a thermometer with sensitivity of 20 $\mathrm{μK/\sqrt{Hz}}$ at 1 K. Thus, a single photon detector integrated into a quantum device can be used as a multifunctional quantum sensor capable of describing the temperature and magnetic field on-chip simply by varying the bias current to change the operating modality from single photon detection to thermometry or magnetometry.

quant-ph↗

arXiv2021

Round-robin differential phase-time-shifting protocol for quantum key distribution: theory and experiment

Quantum key distribution (QKD) allows the establishment of common cryptographic keys among distant parties. Many of the QKD protocols that were introduced in the past involve the challenge of monitoring the signal disturbance over the communication line, in order to evaluate the information leakage to a potential eavesdropper. Recently, a QKD protocol that circumvents the need for monitoring signal disturbance, has been proposed and demonstrated in initial experiments. Here, we propose a new version of this so-called round-robin differential phase-shifting (RRDPS) protocol, in which both time and phase degrees-of-freedom are utilized to enlarge the Hilbert space dimensionality, without increasing experimental complexity or relaxing security assumptions. We derive the security proofs of the round-robin differential phase-time-shifting (RRDPTS) protocol in the collective attack scenario and benchmark the new protocol against RRDPS for different experimental parameters. Furthermore, a proof-of-concept experiment of the RRDPTS protocol, using weak coherent pulses and decoy-state method, is demonstrated over 80 km of fiber link. Our results show that the RRDPTS protocol can achieve higher secret key rate in comparison with the RRDPS, in the condition of high quantum bit error rate.

quant-ph↗

arXiv2021

Experimental Test on Edwards Volume Ensemble of Tapped Granular Packings

Using X-ray tomography, we experimentally investigate granular packings subject to mechanical tapping for three types of beads with different friction coefficients. We validate Edwards volume ensemble in these three-dimensional granular systems and establish a granular version of thermodynamic zeroth law. Within Edwards framework, we also explicitly clarify how friction influences granular statistical mechanics as modifying the density of states, which allows us to determine the entropy as a function of packing fraction and friction subsequently. Additionally, we obtain a granular jamming phase diagram based on geometric coordination number and packing fraction.

cond-mat.soft↗