arXiv subjects

Yu Shi

Publications and source records attributed to Yu Shi.

At least 181 records · Page 10Linked to original sources

arXiv2020

Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans

Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measurements and imaging biomarkers, comparing to (often partial) human measurements. This is probably the only viable means to enable the large-scale clinical oncology patient studies that utilize medical imaging. Deep learning approaches have shown robust segmentation performances for certain types of tumors, e.g., brain tumors in MRI imaging, when a training dataset with plenty of pixel-level fully-annotated tumor images is available. However, more than often, we are facing the challenge that only (very) limited annotations are feasible to acquire, especially for hard tumors. Pancreatic ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor segmentation tasks, yet critically important for clinical needs. Previous work on PDAC segmentation is limited to the moderate amounts of annotated patient images (n<300) from venous or venous+arterial phase CT scans. Based on a new self-learning framework, we propose to train the PDAC segmentation model using a much larger quantity of patients (n~=1,000), with a mix of annotated and un-annotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. A student model is trained on both manual and pseudo annotated multi-phase images. Experiment results show that our proposed method provides an absolute improvement of 6.3% Dice score over the strong baseline of nnUNet trained on annotated images, achieving the performance (Dice = 0.71) similar to the inter-observer variability between radiologists.

eess.IV↗

arXiv2020

Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set

Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur-edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7% (mean absolute percent error). The instant proportion of infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID-19 cases which were scanned periodically within the treatment cycle.

eess.IV↗

arXiv2020

Improving Readability for Automatic Speech Recognition Transcription

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to grammatical errors, disfluency, and other errata common in spoken communication. Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline. In this work, we propose a novel NLP task called ASR post-processing for readability (APR) that aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker. In addition, we describe a method to address the lack of task-specific data by synthesizing examples for the APR task using the datasets collected for Grammatical Error Correction (GEC) followed by text-to-speech (TTS) and ASR. Furthermore, we propose metrics borrowed from similar tasks to evaluate performance on the APR task. We compare fine-tuned models based on several open-sourced and adapted pre-trained models with the traditional pipeline method. Our results suggest that finetuned models improve the performance on the APR task significantly, hinting at the potential benefits of using APR systems. We hope that the read, understand, and rewrite approach of our work can serve as a basis that many NLP tasks and human readers can benefit from.

cs.CL↗

arXiv2020

The Collectivity of Heavy Mesons in Proton-Nucleus Collisions

Using a model based on the Color Glass Condensate framework and the dilute-dense factorization, we systematically study the azimuthal angular correlations between a heavy flavor meson and a light reference particle in proton-nucleus collisions. The obtained second harmonic coefficients (also known as the elliptic flows) for $J/ψ$ and $D^0$ agree with recent experimental data from the LHC. We also provide predictions for the elliptic flows of $Υ$ and $B$ meson, which can be measured in the near future at the LHC. This work can shed light on the physics origin of the collectivity phenomenon in the collisions of small systems.

hep-ph↗

arXiv2019

Entangled baryons: violation of Inequalities based on local realism assuming dependence of decays on hidden variables

Bell inequalities are consequences of local realism while violated by quantum mechanics. In particle physics, entangled high energy particles can be produced from a common source, and the decay of each particle plays the role of measurement. However, in a hidden variable theory, the decay could be determined by hidden variables. This loophole killed such approaches to Bell test in particle physics. It is a special form of measurement-setting or free-will loophole, which also exists in other systems. Using entangled baryons, we present new inequalities of local realism with the explicit assumption of the dependence of the decays on hidden variables, as well as the consideration of the statistical mixture of polarizations and the separation of local hidden variables for objects with spacelike distances. These violations closes the measurement-setting loophole once and for all. We propose to use the processes $η_c\to Λ\barΛ$ and $χ_{c0} \to Λ\barΛ$ to test our inequalities, and show that their violations are likely to be observed with the data already collected in BESIII.

hep-ph↗

arXiv2019

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked data. Therefore, we are motivated to study the problem of multi-view network embedding with a focus on the optimization objectives that are specific and important in embedding this type of network. In our practice of embedding real-world multi-view networks, we explicitly identify two such objectives, which we refer to as preservation and collaboration. The in-depth analysis of these two objectives is discussed throughout this paper. In addition, the mvn2vec algorithms are proposed to (i) study how varied extent of preservation and collaboration can impact embedding learning and (ii) explore the feasibility of achieving better embedding quality by modeling them simultaneously. With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding. These experiments further demonstrate that better embedding can be obtained by simultaneously modeling the two objectives, while not over-complicating the model or requiring additional supervision. The code and the processed datasets are available at http://yushi2.web.engr.illinois.edu/.

cs.SI↗

arXiv2019

Fundamental Limits of the Dew-Harvesting Technology

Dew-harvesting technology radiatively cools a condenser below the dewpoint to achieve condensation of the water vapor from the atmosphere. Due to its passive nature, this technology has attracted a broad interest, in particular in the context of the worldwide drinking-water scarcity. However, the fundamental limit of its performance has not yet been clarified. Moreover, the existing applications have been limited to humid areas. Here, we point out the upper bound of the performance of this technology by carefully considering the spectral directional atmospheric transmittance in a wide range of parameters such as the ambient temperature, the relative humidity, and the convection coefficient. Moreover, we highlight the potential of a condenser consisting of a selective emitter, which is capable of condensing water vapor under significantly more arid conditions as compared with the use of a blackbody emitter. For example, a near-ideal emitter could achieve a dew-harvesting mass flux of 13 gram per square metre per hour even at ambient temperature of 20 degrees Celsius with relative humidity of 40%, whereas the black emitter cannot operate. We provide a numerical design of such a selective emitter, consisting of six layers, optimized for dew-harvesting purposes.

physics.app-ph↗

arXiv2019

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with $\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.

cs.LG↗

arXiv2019

User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription

Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic meaning can simultaneously exist. User-guided clustering is hence of great practical value for HINs where users provide labels to a small portion of nodes. To cater to a broad spectrum of user guidance evidenced by different expected clustering results, carefully exploiting the signals residing in the data is potentially useful. Meanwhile, as one type of complex networks, HINs often encapsulate higher-order interactions that reflect the interlocked nature among nodes and edges. Network motifs, sometimes referred to as meta-graphs, have been used as tools to capture such higher-order interactions and reveal the many different semantics. We therefore approach the problem of user-guided clustering in HINs with network motifs. In this process, we identify the utility and importance of directly modeling higher-order interactions without collapsing them to pairwise interactions. To achieve this, we comprehensively transcribe the higher-order interaction signals to a series of tensors via motifs and propose the MoCHIN model based on joint non-negative tensor factorization. This approach applies to arbitrarily many, arbitrary forms of HIN motifs. An inference algorithm with speed-up methods is also proposed to tackle the challenge that tensor size grows exponentially as the number of nodes in a motif increases. We validate the effectiveness of the proposed method on two real-world datasets and three tasks, and MoCHIN outperforms all baselines in three evaluation tasks under three different metrics. Additional experiments demonstrated the utility of motifs and the benefit of directly modeling higher-order information especially when user guidance is limited.

cs.SI↗

arXiv2019

Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity

Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym-hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals. We develop a new framework, named HyperMine, that exploits multi-granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of context to the scenario of text-rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high-precision textual patterns. Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high-quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine.

cs.CL↗

arXiv2019

Quantum teleportation in terms of Dirac modes between accelerated observers

We have studied entanglement between two Dirac modes respectively observed by two independently accelerated observers. Due to Unruh effect, the entanglement degrades, but residual nonzero entanglement remains even when the accelerations of both observers are infinite. We have also investigated quantum teleportation using entangled Dirac modes, and calculated the fidelities in various cases. In general, the fidelity depends on the single-qubit state $γ|0\rangle +δ|1\rangle$ to be teleported, as well as which Bell state to use. In the limit that both accelerations are infinite, the average fidelity in using $|ϕ^\pm\rangle\equiv\frac{1}{2}(|00\rangle\pm |11\rangle) $ is $\frac{3}{4}$, while that in using $|ψ^\pm\rangle\equiv \frac{1}{2} (|01\rangle\pm |10\rangle) $ is $\frac{1}{2} + |γ|^2 |δ|^2$.

quant-ph↗

arXiv2019

Gradient Boosting With Piece-Wise Linear Regression Trees

Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of GBDT and improve the accuracy. We also propose some optimization tricks to substantially reduce the training time of PL Trees, with little sacrifice of accuracy. Moreover, we propose several implementation techniques to speedup our algorithm on modern computer architectures with powerful Single Instruction Multiple Data (SIMD) parallelism. The experimental results show that GBDT with PL Trees can provide very competitive testing accuracy with comparable or less training time.

cs.LG↗

arXiv2019

Overcoming the absorption limit in high-harmonic generation from crystals

Since the new millennium coherent extreme ultra-violet and soft x-ray radiation has revolutionized the understanding of dynamical physical, chemical and biological systems at the electron's natural timescale. Unfortunately, coherent laser-based upconversion of infrared photons to vacuum-ultraviolet and soft x-ray high-order harmonics in gaseous, liquid and solid targets is notoriously inefficient. In dense nonlinear media, the limiting factor is strong re-absorption of the generated high-energy photons. Here we overcome this limitation by allowing high-order harmonics generated from a periodic array of thin one-dimensional crystalline silicon ridge waveguides to propagate in the vacuum gaps between the ridges, thereby avoiding the high absorption loss of the bulk nonlinear material and resulting in a ~ 100-fold increase in propagation length. As the grating period is varied, each high-harmonic shows a different and marked modulation, indicating the onset of coherent addition which is otherwise suppressed in absorption-limited emission. By beating the absorption limit, our results pave the way for bright coherent short-wavelength sources and their implementation in nano-photonic devices.

physics.optics↗

arXiv2019

Shape of a skyrmion

We propose a method of determining the shape of a two-dimensional magnetic skyrmion, which can be parameterized as the position dependence of the orientation of the local magnetic moment, by using the expansion in terms of the eigenfunctions of the Schrödinger equation of a harmonic oscillator. A variational calculation is done, up to the next-to-next-to-leading order. This result is verified by a lattice simulation based on Landau-Lifshitz-Gilbert equation. Our method is also applied to the dissipative matrix in the Thiele equation as well as two interacting skyrmions in a bilayer system.

cond-mat.str-el↗

arXiv2019

Generation of magnetic skyrmions through pinning effect

Based on analytical estimation and lattice simulation, a proposal is made that magnetic skyrmions can be generated through the pinning effect in 2D chiral magnetic materials, in absence of an external magnetic field or magnetic anisotropy. In our simulation, stable magnetic skyrmions can be generated in the pinning areas. The properties of the skyrmions are studied for various values of ferromagnetic exchange strength and the Dzyaloshinskii-Moriya interaction strength.

cond-mat.str-el↗

arXiv2019

Multiparticle azimuthal angular correlations in $pA$ collisions

In the Color Glass Condensate formalism, we evaluate the 3-dipole correlator up to the $\frac{1}{N_c^4}$ order with $N_c$ being the number of colors, and compute the azimuthal cumulant $c_{123}$ for 3-particle productions. In addition, we discuss the patterns appearing in the $n$-dipole formula in terms of $\frac{1}{N_c}$ expansions. This allows us to conjecture the $N_c$ scaling of $c_n\{m\}$, which is crosschecked by our calculation of $c_2\{4\}$ in the dilute limit.

hep-ph↗

arXiv2019

PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks

As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.

cs.SI↗

arXiv2018

Schwinger effect of a relativistic boson entangled with a qubit

We use the concept of quantum entanglement to analyze the Schwinger effect on an entangled state of a qubit and a bosonic mode coupled with the electric field. As a consequence of the Schwinger production of particle-antiparticle pairs, the electric field decreases both the correlation and the entanglement between the qubit and the particle mode. This work exposes a profound difference between bosons and fermions. In the bosonic case, entanglement between the qubit and the antiparticle mode cannot be caused by the Schwinger effect on the preexisting entanglement between the qubit and the particle mode, but correlation can.

hep-th↗