arXiv subjects

Jian He

Publications and source records attributed to Jian He.

70 records · Page 4Linked to original sources

arXiv2020

Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results: Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3\%, 97.7\%, and 67.3\% and average Normalized Surface Dice (NSD) scores of 90.6\%, 91.4\%, and 70.0\% for left lung, right lung, and infection, respectively. Conclusions: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

eess.IV↗

arXiv2020

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention, particularly when other chest imaging modalities are less available. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images. The evolution of viruses and the emergence of novel mutated viruses further result in substantial dataset shift, which greatly limits the performance of classification approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough or the confidence score estimated by the confidence prediction module is small enough, we accept the input as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to reinforce the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 18,619 non-viral pneumonia cases, and 18,774 healthy controls.

eess.IV↗

arXiv2020

A new characterization of n-exangulated categories with (n+2)-angulated structure

Herschend-Liu-Nakaoka introduced the notion of $n$-exangulated categories. It is not only a higher dimensional analogue of extriangulated categories defined by Nakaoka-Palu, but also gives a simultaneous generalization of $(n+2)$-angulated in the sense of Geiss-Keller-Oppermann and $n$-exact categories in the sense of Jasso. In this article, we show that an $n$-exangulated category has the structure of an $(n+2)$-angulated category if and only if for any object $X$ in the category, the morphism $0\to X$ is a trivial deflation and the morphism $X\to 0$ is a trivial inflation.

math.CT↗

arXiv2019

Multi-Principal-Element Approach to High-Performance Thermoelectric Materials

High-entropy alloys are characterized by high configurational entropy. Since the discovery of high-entropy alloys (HEA) in 2004, entropy engineering has provided a promising direction for exploiting composition, lattice disorder, band structure, and microstructure effects to advance thermoelectric performance. This review discusses the impact of entropy on thermoelectric properties and looks back at the role of multi-principal-element alloys, a weaker version of HEA, on the development of compositionally complex thermoelectric alloys in achieving high thermoelectric performance. The experimental and theoretical efforts in a wide range of material systems such as TAGS, LAST, half-Heusler, liquid-like copper chalcogenides, SnTe, and CuInTe2 chalcopyrites provide insights into the entropy engineering approach and also promise an emerging paradigm of high-entropy thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2019

Counting non-crossing permutations on surfaces of any genus

Given a surface with boundary and some points on its boundary, a polygon diagram is a way to connect those points as vertices of non-overlapping polygons on the surface. Such polygon diagrams represent non-crossing permutations on a surface with any genus and number of boundary components. If only bigons are allowed, then it becomes an arc diagram. The count of arc diagrams is known to have a rich structure. We show that the count of polygon diagrams exhibits the same interesting behaviours, in particular it is almost polynomial in the number of points on the boundary components, and the leading coefficients of those polynomials are the intersection numbers on the compactified moduli space of curves.

math.CO↗

arXiv2019

A Kalman particle filter for online parameter estimation with applications to affine models

In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous time state space model with discrete time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, this proposed algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in $L_p$ with rate of order $\mathcal{O}(N^{-\frac{1}{2}}+δ^{\frac{1}{2}})$, where $N$ is the number of particles for the parameters in the outer layer and $δ$ is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.

stat.CO↗

arXiv2017

High Thermoelectric Figure of Merit by Resonant Dopant in Half-Heusler Alloys

Half-Heusler alloys have been one of the benchmark high temperature thermoelectric materials owing to their thermal stability and promising figure of merit ZT. Simonson et al. early showed that small amounts of vanadium doped in Hf0.75Zr0.25NiSn enhanced the Seebeck coefficient and correlated the change with the increased density of states near the Fermi level. We herein report a systematic study on the role of vanadium (V), niobium (Nb), and tantalum (Ta) as prospective resonant dopants in enhancing the ZT of n-type half-Heusler alloys based on Hf0.6Zr0.4NiSn0.995Sb0.005. The V doping was found to increase the Seebeck coefficient in the temperature range 300-1000 K, consistent with a resonant doping scheme. In contrast, Nb and Ta act as normal n-type dopants, as evident by the systematic decrease in electrical resistivity and Seebeck coefficient. The combination of enhanced Seebeck coefficient due to the presence of V resonant states and the reduced thermal conductivity has led to a state-of-the-art ZT of 1.3 near 850 K in n-type (Hf0.6Zr0.4)0.99V0.01NiSn0.995Sb0.005 alloys.

cond-mat.mtrl-sci↗

arXiv2016

Electronic and Magnetic Properties of Pristine and Hydrogenated Borophene Nanoribbons

The groundbreaking works in graphene and graphene nanoribbons (GNRs) over the past decade, and the recent discovery of borophene draw immediate attention to the underexplored borophene nanoribbons (BNRs). We herein report a density functional theory (DFT) study of the geometric, electronic and magnetic properties of BNRs as a function of orientation (denoted as BxNRs and ByNRs with the orientation along x- and y-axis, respectively), ribbon width (Nx, Ny = 4 to 15), and hydrogenation effects. We found that the anisotropic quasi-planar geometric structure of BNRand its edge states profoundly govern the electronic and magnetic properties. Pristine ByNRs adopt a magnetic ground state, anti-ferromagnetic (AFM) or ferromagnetic (FM) depending on the ribbon width, while pristine BxNRs are non-magnetic (NM). Upon hydrogenation, all BNRs become NM. Interestingly, both pristine and hydrogenated ByNRs undergo a metal-semiconductor-metal transition at Ny = 7, while BxNRs remain metallic.

cond-mat.mtrl-sci↗

arXiv2016

Decreased aneurysmal subarachnoid hemorrhage incidence rate in elderly population than in middle aged population: a retrospective analysis of 8,144 cases in Mainland China

Purpose: Rupture of an intracranial aneurysm is the most common cause of subarachnoid haemorrhage (SAH), which is a life-threatening acute cerebrovascular event that typically affects working-age people. This study aims to investigate the aneurysmal SAH incidence rate in elderly population than in middle aged population in China. Materials and methods: Aneurysmal SAH cases were collected retrospectively from the archives of 21 hospitals in Mainland China. All the cases collected were from September 2016 and backward consecutively for a period of time up to 8 years. SAH was initially diagnosed by brain computed tomography, and CT angiography (CTA) or digital subtraction angiography (DSA) was followed and SAH was confirmed to be due to cerebral aneurysm. When for cases multiple bleeding occurred, the age of the first SAH was used in this study. The toltal incidence from all hospital at each age were summed together for females and males; then adjusted by the total population number at each age for females and males. The total population data was from the 2010 population census of the People's Republic of China. Results: In total there were 8,144 cases, with 4,861 females and 3,283 males. Our analysis shows for both females and males the relative aneurysmal SAH rate started to decrease after around 65 years old. The males the relative aneurysmal SAH rate might have started to decrease after around 55 years old. Conclusion: In contrast to previous reports, our data demonstrated a decreased aneurysmal subarachnoid hemorrhage incidence rate in elderly population than in middle aged population. Our data therefore support the hypothesis that aneurysms do not grow progressively once they form but probably either rupture or stabilize and that very elderly patients are at a reduced risk of rupture compared with atients who are younger with the same-sized aneurysms.

q-bio.TO↗

arXiv2015

The thermal and electrical properties of the promising semiconductor MXene Hf2CO2

In this work, we investigate the thermal and electrical properties of oxygen-functionalized M2CO2 (M = Ti, Zr, Hf) MXenes using first-principles calculations. Hf2CO2 is found to exhibit a thermal conductivity better than MoS2 and phosphorene. The room temperature thermal conductivity along the armchair direction is determined to be 86.25-131.2 Wm-1K-1 with a flake length of 5-100 um, and the corresponding value in the zigzag direction is approximately 42% of that in the armchair direction. Other important thermal properties of M2CO2 are also considered, including their specific heat and thermal expansion coefficients. The theoretical room temperature thermal expansion coefficient of Hf2CO2 is 6.094x10-6 K-1, which is lower than that of most metals. Moreover, Hf2CO2 is determined to be a semiconductor with a band gap of 1.657 eV and to have high and anisotropic carrier mobility. At room temperature, the Hf2CO2 hole mobility in the armchair direction (in the zigzag direction) is determined to be as high as 13.5x103 cm2V-1s-1 (17.6x103 cm2V-1s-1), which is comparable to that of phosphorene. Broader utilization of Hf2CO2 as a material for nanoelectronics is likely because of its moderate band gap, satisfactory thermal conductivity, low thermal expansion coefficient, and excellent carrier mobility. The corresponding thermal and electrical properties of Ti2CO2 and Zr2CO2 are also provided here for comparison. Notably, Ti2CO2 presents relatively low thermal conductivity and much higher carrier mobility than Hf2CO2, which is an indication that Ti2CO2 may be used as an efficient thermoelectric material.

cond-mat.mtrl-sci↗

arXiv2013

Significant ZT Enhancement in p-type Ti(Co,Fe)Sb-InSb Nanocomposites via a Synergistic High Mobility Electron Injection Energy filtering and Boundary Scattering Approach

It has been demonstrated that InSb nanoinclusions, which are formed in situ, can simultaneously improve all three individual thermoelectric properties of the n-type half Heusler compound (Ti,Zr,Hf)(Co,Ni)Sb [Xie et al., Acta Mater. 58, 4795 (2010)]. In the work presented herein, we have adopted the same approach to the p-type half Heusler compound Ti(Co,Fe)Sb. The results of resistivity, Seebeck coefficient, thermal conductivity, and Hall coefficient measurements indicate that the combined high mobility electron injection, low energy electron filtering, and boundary scattering, again, lead to a simultaneous improvement of all three individual thermoelectric properties: enhanced Seebeck coefficient and electrical conductivity as well as reduced lattice thermal conductivity. A figure of merit of ZT=0.33 was attained at 900 K for the sample containing 1 atomic percent InSb nanoinclusions, a 450 percent improvement over the nanoinclusion-free sample. This represents a rare case that the same nanostructuring approach successfully works for both p-type and n-type thermoelectric materials of the same class, hence pointing to a promising materials design route for higher performance half-Heusler materials in the future and hopefully will realize similar improvement in TE devices based on such half Heusler alloys.

cond-mat.mtrl-sci↗

arXiv2012

Genus Zero One Point Correlators of Subcritical Stein Manifolds

We determine the one point genus zero correlators of compactly supported forms of a subcritical Stein filling whose first Chern class vanishes. This is a step towards determining the full potential function of the filling. As an application, we proved that if a Kähler manifold $M^{2n}$ admits a subcritical polarization and $c_1$ vanishes in the subcritical complement, then $M$ is uniruled.

math.SG↗

arXiv2012

Gravitational Descendants and Linearized Contact Homology

In this paper we prove a recursion relation between the the one-point genus-0 gravitational descendants of a Stein domain $(M,\partial M)$. This relation is best described by the degree -2 map $D$ in the linearized contact homology of $\partial M$, arising from the Bourgeois--Oancea exact sequence between symplectic homology of $M$ and linearized contact homology of $\partial M$. All one-point genus-0 gravitational descendants can be reduce to the one-point Gromov--Witten invariants via iterates of $D$.

math.SG↗

arXiv2012

Correlators and Descendants of Subcritical Stein Manifolds

We determine contact homology algebra of a subcritical Stein-fillable contact manifold whose first Chern class vanishes. We also compute the genus-0 one point correlators and gravitational descendants of compactly supported closed forms of their subcritical Stein fillings. This is a step towards determining the full potential function of the filling as defined in \cite{EliashbergGiventalHofer}. These invariants also give a canonical presentation of the cylindrical contact homology. With respect to this presentation, we determine the degree-2 differential in the Bourgeois--Oancea exact sequence of \cite{Oancea}. As a further application, we proved that if a Kähler manifold $M^{2n}$ admits a subcritical polarization and $c_1$ vanishes in the subcritical complement, then $M$ is uniruled.

math.SG↗

arXiv2002

Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations

This paper presents a statistical framework for assessing wireless systems performance using hierarchical data mining techniques. We consider WCDMA (wideband code division multiple access) systems with two-branch STTD (space time transmit diversity) and 1/2 rate convolutional coding (forward error correction codes). Monte Carlo simulation estimates the bit error probability (BEP) of the system across a wide range of signal-to-noise ratios (SNRs). A performance database of simulation runs is collected over a targeted space of system configurations. This database is then mined to obtain regions of the configuration space that exhibit acceptable average performance. The shape of the mined regions illustrates the joint influence of configuration parameters on system performance. The role of data mining in this application is to provide explainable and statistically valid design conclusions. The research issue is to define statistically meaningful aggregation of data in a manner that permits efficient and effective data mining algorithms. We achieve a good compromise between these goals and help establish the applicability of data mining for characterizing wireless systems performance.

cs.CE↗

arXiv2002

BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)

We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execution as a problem of managing semistructured data. The data interchange problem is addressed by three techniques for processing semistructured data: validation, binding, and conversion. We present BSML and describe its application to a PSE for wireless communications system design.

cs.CE↗