arXiv subjects

Siyuan Zhang

Publications and source records attributed to Siyuan Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2022

Correlating atom probe tomography with X-Ray and electron spectroscopies to understand microstructure-activity relationships in electrocatalysts

The search for a new energy paradigm with net-zero carbon emissions requires new technologies for energy generation and storage that are at the crossroad between engineering, chemistry, physics, surface and materials sciences. To keep pushing the inherent boundaries of device performance and lifetime, we need to step away from a cook-and-look approach and aim to establish the scientific ground to guide the design of new materials. This requires strong efforts in establishing bridges between microscopy and spectroscopy techniques, across multiple scales. Here, we discuss how the complementarities of X-ray- and electron-based spectroscopies and atom probe tomography can be exploited in the study of surfaces and sub-surfaces to understand structure-property relationships in electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2022

Dynamic doping and Cottrell atmosphere optimize the thermoelectric performance of n-type PbTe

High thermoelectric energy conversion efficiency requires a large figure-of-merit, zT, over a broad temperature range. To achieve this, we optimize the carrier concentrations of n-type PbTe from room up to hot-end temperatures by co-doping Bi and Ag. Bi is an efficient n-type dopant in PbTe, often leading to excessive carrier concentration at room temperature. As revealed by density functional theory calculations, the formation of Bi and Ag defect complexes is exploited to optimize the room temperature carrier concentration. At elevated temperatures, we demonstrate the dynamic dissolution of Ag2Te precipitates in PbTe in situ by heating in a scanning transmission electron microscope. The release of n-type Ag interstitials with increasing temperature fulfills the requirement of higher carrier concentrations at the hot end. Moreover, as characterized by atom probe tomography, Ag atoms aggregate along parallel dislocation arrays to form Cottrell atmospheres. This results in enhanced phonon scattering and leads to a low lattice thermal conductivity. As a result of the synergy of dynamic doping and phonon scattering at decorated dislocations, an average zT of 1.0 is achieved in n-type Bi/Ag-codoped PbTe between 400 and 825 K. Introducing dopants with temperature-dependent solubility and strong interaction with dislocation cores enables simultaneous optimization of the average power factor and thermal conductivity, providing a new concept to exploit in the field of thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2022

Full-color three-loop three-point form factors in N=4 SYM

We present the detailed computation of full-color three-loop three-point form factors of both the stress-tensor supermultiplet and a length-three BPS operator in N=4 SYM. The integrands are constructed based on the color-kinematics (CK) duality and generalized unitarity method. An interesting observation is that the CK-dual integrands contain a large number of free parameters. We discuss the origin of these free parameters in detail and check that they cancel in the simplified integrands. We further perform the numerical evaluation of the integrals at a special kinematics point using public packages FIESTA and pySecDec based on the sector-decomposition approach. We find that the numerical computation can be significantly simplified by expressing the integrals in terms of uniformly transcendental basis, although the final three-loop computations still require large computational resources. Having the full-color numerical results, we verify that the non-planar infrared divergences reproduce the non-dipole structures, which firstly appear at three loops. As for the finite remainder functions, we check that the numerical planar remainder for the stress-tensor supermultiplet is consistent with the known result of the bootstrap computation. We also obtain for the first time the numerical results of the three-loop non-planar remainder for the stress-tensor supermultiplet as well as the three-loop remainder for the length-three operator.

hep-th↗

arXiv2021

Three-loop color-kinematics duality: A 24-dimensional solution space induced by new generalized gauge transformations

We obtain full-color three-loop three-point form factors of the stress-tensor supermultiplet and also of a length-3 half-BPS operator in N=4 SYM based on the color-kinematics duality and on-shell unitarity. The integrand results are verified by all planar and non-planar unitarity cuts, and they satisfy the minimal power-counting of loop momenta and diagrammatic symmetries. Interestingly, these three-loop solutions, while manifesting all dual Jacobi relations, contain a large number of free parameters; in particular, there are 24 free parameters for the form factor of stress-tensor supermultiplet. Such degrees of freedom are due to a new type of generalized gauge transformation associated with the operator insertion for form factors. We also perform numerical integration and obtain consistent full-color infrared divergences and the known planar remainder. The form factors we obtain can be understood as the N=4 SYM counterparts of three-loop Higgs plus three-gluon amplitudes in QCD and are expected to provide the maximally transcendental parts of the latter.

hep-th↗

arXiv2021

Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning

How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product.

cs.LG↗

arXiv2021

Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement

This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement. We aim to train a unique deep neural network (DNN) potentially performing well on unseen microphone arrays. The microphone array geometry shapes the network's parameters when training on a fixed microphone array, and thus restricts the generalization of the trained network to another microphone array. To resolve this problem, a single network is trained using data recorded by various microphone arrays of different geometries. We design three variants of our recently proposed narrowband network to cope with the agnostic number of microphones. Overall, the goal is to make the network learn the universal information for speech enhancement that is available for any array geometry, rather than learn the one-array-dedicated characteristics. The experiments on both simulated and real room impulse responses (RIR) demonstrate the excellent across-array generalization capability of the proposed networks, in the sense that their performance measures are very close to, or even exceed the network trained with test arrays. Moreover, they notably outperform various beamforming methods and other advanced deep-learning-based methods.

eess.AS↗

arXiv2021

Dopant-segregation to grain boundaries controls electrical conductivity of n-type NbCo(Pt)Sn half-Heusler alloy mediating thermoelectric performance

Science-driven design of future thermoelectric materials requires a deep understanding of the fundamental relationships between microstructure and transport properties. Grain boundaries in polycrystalline materials influence the thermoelectric performance through the scattering of phonons or the trapping of electrons due to space-charge effects. Yet, the current lack of careful investigations on grain boundary-associated features hinders further optimization of properties. Here, we study n-type NbCo1-xPtxSn half-Heusler alloys, which were synthesized by ball milling and spark plasma sintering (SPS). Post-SPS annealing was performed on one sample, leading to improved low-temperature electrical conductivity. The microstructure of both samples was examined by electron microscopy and atom probe tomography. The grain size increases from ~230 nm to ~2.38 μm upon annealing. Pt is found within grains and at grain boundaries, where it locally reduces the resistivity, as assessed by in situ four-point-probe electrical conductivity measurement. Our work showcases the correlation between microstructure and electrical conductivity, providing opportunities for future microstructural optimization by tuning the chemical composition at grain boundaries.

cond-mat.mtrl-sci↗

arXiv2021

Distributed-Memory Tensor Completion for Generalized Loss Functions in Python using New Sparse Tensor Kernels

Tensor computations are increasingly prevalent numerical techniques in data science, but pose unique challenges for high-performance implementation. We provide novel algorithms and systems infrastructure which enable efficient parallel implementation of algorithms for tensor completion with generalized loss functions. Specifically, we consider alternating minimization, coordinate minimization, and a quasi-Newton (generalized Gauss-Newton) method. By extending the Cyclops library, we implement all of these methods in high-level Python syntax. To make possible tensor completion for very sparse tensors, we introduce new multi-tensor primitives, for which we provide specialized parallel implementations. We compare these routines to pairwise contraction of sparse tensors by reduction to hypersparse matrix formats, and find that the multi-tensor routines are more efficient in theoretical cost and execution time in experiments. We provide microbenchmarking results on the Stampede2 supercomputer to demonstrate the efficiency of the new primitives and Cyclops functionality. We then study the performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset, considering both least squares and Poisson loss functions.

cs.DC↗

arXiv2021

Different Photostability of BiVO4 in Near-pH-Neutral Electrolytes

Photoelectrochemical water splitting is a promising route to produce hydrogen from solar energy. However, corrosion of semiconducting photoelectrodes remains a fundamental challenge for their practical application. The stability of BiVO4, one of the best performing photoanode materials, is systematically examined here using an illuminated scanning flow cell to measure its dissolution operando. The dissolution rates of BiVO4 under illumination depend on the electrolyte and decrease in the order: borate (pH=9.3) > phosphate (pH=7.2) > citrate (pH=7.0). BiVO4 exhibits an inherent lack of stability during the oxygen evolution reaction (OER), while hole-scavenging citrate electrolyte offers kinetic protection. The dissolution of Bi peaks at different potentials than the dissolution of V in phosphate buffer, whereas both ions dissolve simultaneously in borate buffer. The life cycle of a 90 nm BiVO4 film is monitored during one hour of light-driven OER in borate buffer. The photocurrent and dissolution rates show independent trends with time, highlighting the importance to measure both quantities operando. Dissolution rates are correlated to the surface morphology and chemistry characterized using electron microscopy, X-ray photoelectron spectroscopy and atom probe tomography. These correlative measurements further the understanding on corrosion processes of photoelectrodes down to the nanoscopic scale to facilitate their future developments.

cond-mat.mtrl-sci↗

arXiv2020

A feature-supervised generative adversarial network for environmental monitoring during hazy days

The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days. Its main idea is to train the model under the supervision of feature maps from the ground truth. Four key technical contributions are made in the paper. First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps. Second, the basic GAN formulation is modified by introducing perception loss, style loss, and feature regularization loss to generate better results. Third, multi-scale images are applied as the input to enhance the performance of discriminator. Finally, a hazy remote sensing dataset is created for testing our dehazing method and environmental detection. Extensive experimental results show that the proposed method has achieved better performance than current state-of-the-art methods on both synthetic datasets and real-world remote sensing images.

cs.CV↗

arXiv2020

Joint Embedding in Named Entity Linking on Sentence Level

Named entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document. It is difficult to link a mention when it appears multiple times in a document, since there are conflicts by the contexts around the appearances of the mention. In addition, it is difficult since the given training dataset is small due to the reason that it is done manually to link a mention to its mapping entity. In the literature, there are many reported studies among which the recent embedding methods learn vectors of entities from the training dataset at document level. To address these issues, we focus on how to link entity for mentions at a sentence level, which reduces the noises introduced by different appearances of the same mention in a document at the expense of insufficient information to be used. We propose a new unified embedding method by maximizing the relationships learned from knowledge graphs. We confirm the effectiveness of our method in our experimental studies.

cs.CL↗

arXiv2019

Could face-centered cubic titanium in cold-rolled commercially-pure titanium only be a Ti-hydride?

A face-centered cubic (FCC) phase in electro-polished specimens for transmission electron microscopy of commercially pure titanium has sometimes been reported. Here, a combination of atom-probe tomography, scanning transmission electron microscopy and low-loss electron energy loss spectroscopy is employed to study both the crystal structural and chemical composition of this FCC phase. Our results prove that the FCC phase is actually a TiHx (x>1) hydride, and not a new allotrope of Ti, in agreement with previous reports. The formation of the hydride is discussed.

cond-mat.mtrl-sci↗

arXiv2019

A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.

cs.CV↗

arXiv2019

Reproducible Performance Improvements to Monolayer MoS2 Transistors through Exposed Material Forming Gas Annealing

Metal-mediated exfoliation has been demonstrated as a promising approach for obtaining large-area flakes of 2D materials to fabricate prototypical nanoelectronics. However, several processing challenges related to organic contamination at the interfaces of the 2D material and the gate oxide must be overcome to realize robust devices with high yield. Here, we demonstrate an optimized process to realize high-performance field-effect transistor (FET) arrays from large-area (~5000 um2) monolayer MoS2 with a yield of 85 %. A central element of this process is an exposed material forming gas anneal (EM-FGA) that results in uniform FET performance metrics (i.e., field-effect mobilities, threshold voltages, and contact performance). Complementary analytical measurements show that the EM-FGA process reduces deleterious channel doping effects by decreasing organic contamination, while also reducing the prevalence of insulating molybdenum oxide, effectively improving the MoS2-gate oxide interface. The uniform FET performance metrics and high device yield achieved by applying the EM-FGA technique on large-area 2D material flakes will help advance the fabrication of complex 2D nanoelectronics devices and demonstrates the need for improved engineering of the 2D material-gate oxide interface.

cond-mat.mtrl-sci↗

arXiv2019

Rational Strain Engineering in Delafossite Oxides for Highly Efficient Hydrogen Evolution Catalysis in Acidic Media

The rational design of hydrogen evolution reaction (HER) electrocatalysts which are competitive with platinum is an outstanding challenge to make power-to-gas technologies economically viable. Here, we introduce the delafossites PdCrO$_2$, PdCoO$_2$ and PtCoO$_2$ as a new family of electrocatalysts for the HER in acidic media. We show that in PdCoO$_2$ the inherently strained Pd metal sublattice acts as a pseudomorphic template for the growth of a strained (by +2.3%) Pd rich capping layer under reductive conditions. The surface modification continuously improves the electrocatalytic activity by simultaneously increasing the exchange current density j$_0$ from 2 to 5 mA/cm$^2_{geo}$ and by reducing the Tafel slope down to 38 mV/decade, leading to overpotentials $η_{10}$ < 15 mV for 10 mA/cm$^2_{geo}$, superior to bulk platinum. The greatly improved activity is attributed to the in-situ stabilization of a $β$-palladium hydride phase with drastically enhanced surface catalytic properties with respect to pure or nanostructured palladium. These findings illustrate how operando induced electrodissolution can be used as a top-down design concept for rational surface and property engineering through the strain-stabilized formation of catalytically active phases.

physics.chem-ph↗

arXiv2018

Quantum Capacitance-Limited MoS2 Biosensors Enable Remote Label-Free Enzyme Measurements

We have demonstrated atomically thin, quantum capacitance-limited, field-effect transistors (FETs) that enable the detection of pH changes with ~75-fold higher sensitivity (4.4 V/pH) over the Nernst value of 59 mV/pH at room temperature when used as a biosensor. The transistors, which are fabricated from a monolayer of MoS2 with a room temperature ionic liquid (RTIL) in place of a conventional oxide gate dielectric, exhibit very low intrinsic noise resulting in a pH limit of detection (LOD) of 92x10^-6 at 10 Hz. This high device performance, which is a function of the structure of our device, is achieved by remotely connecting the gate to a pH sensing element allowing the FETs to be reused. Because pH measurements are fundamentally important in biotechnology, the low limit of detection demonstrated here will benefit numerous applications ranging from pharmaceutical manufacturing to clinical diagnostics. As an example, we experimentally quantified the function of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, at concentrations that are 5-fold lower than physiological values, and with sufficient time-resolution to allow the estimation of both steady-state and kinetic parameters in a single experiment. The high sensitivity, low LOD and fast turnaround time of the measurements will allow the development of early diagnostic tools and novel therapeutics to detect and treat neurological conditions years before currently possible.

q-bio.QM↗

arXiv2018

Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation

Instance segmentation in 3D images is a fundamental task in biomedical image analysis. While deep learning models often work well for 2D instance segmentation, 3D instance segmentation still faces critical challenges, such as insufficient training data due to various annotation difficulties in 3D biomedical images. Common 3D annotation methods (e.g., full voxel annotation) incur high workloads and costs for labeling enough instances for training deep learning 3D instance segmentation models. In this paper, we propose a new weak annotation approach for training a fast deep learning 3D instance segmentation model without using full voxel mask annotation. Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively. We evaluate our approach on several biomedical image datasets, and the experimental results show that (1) with full annotated boxes and a small amount of masks, our approach can achieve similar performance as the best known methods using full annotation, and (2) with similar annotation time, our approach outperforms the best known methods that use full annotation.

cs.CV↗

arXiv2017

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.

cs.CV↗