arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 451 records · Page 25Linked to original sources

arXiv2022

Stoichiometric Control and Optical Properties of BaTiO3 Thin Films Grown by Hybrid MBE

BaTiO3 is a technologically relevant material in the perovskite oxide class with above room temperature ferroelectricity and a very large electro optical coefficient, making it highly suitable for emerging electronic and photonic devices. An easy, robust, straightforward, and scalable growth method is required to synthesize epitaxial BaTiO3 thin films with sufficient control over the film stoichiometry to achieve reproducible thin film properties. Here we report the growth of BaTiO3 thin films by hybrid molecular beam epitaxy. A self regulated growth window is identified using complementary information obtained from reflection high energy electron diffraction, the intrinsic film lattice parameter, film surface morphology, and scanning transmission electron microscopy. Subsequent optical characterization of the BaTiO3 films by spectroscopic ellipsometry revealed refractive index and extinction coefficient values closely resembling those of stoichiometric bulk BaTiO3 crystals for films grown inside the growth window. Even in the absence of a lattice parameter change of BaTiO3 thin films, degradation of optical properties was observed, accompanied by the appearance of a wide optical absorption peak in the infrared spectrum, attributed to optical transitions involving defect states present. Therefore, the optical properties of BaTiO3 can be utilized as a much finer and more straightforward probe to determine the stoichiometry level present in BaTiO3 films.

cond-mat.mtrl-sci↗

arXiv2022

High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single scan. This property also makes it attractive for retrospectively synthesizing contrast-weighted images. In general, contrast-weighted images like T1-weighted, T2-weighted, etc., can be synthesized directly from parameter maps through spin-dynamics simulation (i.e., Bloch or Extended Phase Graph models). However, these approaches often exhibit artifacts due to imperfections in the mapping, the sequence modeling, and the data acquisition. Here we propose a supervised learning-based method that directly synthesizes contrast-weighted images from the MRF data without going through the quantitative mapping and spin-dynamics simulation. To implement our direct contrast synthesis (DCS) method, we deploy a conditional Generative Adversarial Network (GAN) framework and propose a multi-branch U-Net as the generator. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. In-vivo experiments demonstrate excellent image quality compared to simulation-based contrast synthesis and previous DCS methods, both visually as well as by quantitative metrics. We also demonstrate cases where our trained model is able to mitigate in-flow and spiral off-resonance artifacts that are typically seen in MRF reconstructions and thus more faithfully represent conventional spin echo-based contrast-weighted images.

eess.IV↗

arXiv2022

Studying triangle singularity through spin observables

In this work, we study the spin density matrix element $ρ_{00}$ of the $ϕ$ in the decay $J/ψ\rightarrow ηπϕ$. In previous studies, a band around 1.4 GeV on the $π^0ϕ$ distribution in Dalitz plot was reported by the BESIII Collaboration. This structure may be caused by the production of a resonance or the triangle singularity mechanism. We find that the predictions of the spin density matrix elements of the final $ϕ$ based on these mechanisms show distinct features. Thus the measurement of the spin density matrix elements of the $ϕ$ in this reaction may offer an alternative way to study the triangle singularity and to clarify the reaction mechanisms, i.e. resonance production or kinematic effects. This work also shows the potential of spin observables in studying kinematic singularities.

hep-ph↗

arXiv2022

Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection

Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision. Recently, significant breakthrough has been made in multi-view 3D detection tasks. However, the unprecedented detection performance of these vision BEV (bird's-eye-view) detection models is accompanied with enormous parameters and computation, which make them unaffordable on edge devices. To address this problem, in this paper, we propose a structured knowledge distillation framework, aiming to improve the efficiency of modern vision-only BEV detection models. The proposed framework mainly includes: (a) spatial-temporal distillation which distills teacher knowledge of information fusion from different timestamps and views, (b) BEV response distillation which distills teacher response to different pillars, and (c) weight-inheriting which solves the problem of inconsistent inputs between students and teacher in modern transformer architectures. Experimental results show that our method leads to an average improvement of 2.16 mAP and 2.27 NDS on the nuScenes benchmark, outperforming multiple baselines by a large margin.

cs.CV↗

arXiv2022

TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task

This paper describes the joint submission of Alibaba and Soochow University, TSMind, to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English-German and English-Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR's WMT19 English-German news translation system and MBART50 for English-Chinese as our pre-trained models. Considering the task's condition of limited use of training data, we follow the data augmentation strategies proposed by WeTS to boost our TS model performance. The difference is that we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.

cs.CL↗

arXiv2022

Thermally Induced Structural Evolution and Nanoscale Interfacial Dynamics in Layered Metal Chalcogenides

Layered chalcogenides including Bi2Te3, Sb2Te3, Bi-Sb-Te ternary alloys and heterostructures are known as great thermoelectric, topological insulators and recently highlighted as plasmonic building blocks beyond noble metals. Here, we conduct a joint in situ transmission electron microscopy (in situ TEM) and density functional theory (DFT) calculations to investigate the temperature dependent nanoscale dynamics, interfacial properties and further identifying the role of native defects and edge configurations in anisotropic sublimations of Bi2Te3-Sb2Te3 in-plane heterostructure and Sb2-xBixTe3 alloy. Structural dynamics including edge evolution, formation, expansion, and coalescence of thermally induced polygonal nanopores are reported. The nanopores appear to be initiated by preferential dissociation of chalcogenide species (Te) from the center, heterointerface and edges in the heterostructure and only from the outer edges in the alloy counterpart. This results in a reduced thermal stability and significantly different sublimation pathways of the heterostructure. Furthermore, triangular and quasi hexagonal configurations are observed to be the dominant nanopores configurations in the heterostructure. Additionally, our DFT calculations provide a mechanistic understanding on the role of native defects and edge formation energies, revealing the antisite defects TeBi to be the dominant native defect in a Te-rich condition and playing a key role on the defects assisted sublimation. These findings significantly impact our understanding of controlling the nanoscale sublimation dynamics and can ultimately assist us in designing tunable low-dimensional chalcogenides.

cond-mat.mtrl-sci↗

arXiv2022

Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets

Creating separable representations via representation learning and clustering is critical in analyzing large unstructured datasets with only a few labels. Separable representations can lead to supervised models with better classification capabilities and additionally aid in generating new labeled samples. Most unsupervised and semisupervised methods to analyze large datasets do not leverage the existing small amounts of labels to get better representations. In this paper, we propose a spatiotemporal clustering paradigm that uses spatial and temporal features combined with a constrained loss to produce separable representations. We show the working of this method on the newly published dataset ReaLSAT, a dataset of surface water dynamics for over 680,000 lakes across the world, making it an essential dataset in terms of ecology and sustainability. Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation. We then show how we can learn even better representation using a constrained loss with few labels. We conclude by showing how our method, using few labels, can pick out new labeled samples from the unlabeled data, which can be used to augment supervised methods leading to better classification.

cs.LG↗

arXiv2022

CLAD: A Contrastive Learning based Approach for Background Debiasing

Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification. However, unlike humans, CNNs leverage spurious features, such as background information to make decisions. This tendency creates different problems in terms of robustness or weak generalization performance. Through our work, we introduce a contrastive learning-based approach (CLAD) to mitigate the background bias in CNNs. CLAD encourages semantic focus on object foregrounds and penalizes learning features from irrelavant backgrounds. Our method also introduces an efficient way of sampling negative samples. We achieve state-of-the-art results on the Background Challenge dataset, outperforming the previous benchmark with a margin of 4.1\%. Our paper shows how CLAD serves as a proof of concept for debiasing of spurious features, such as background and texture (in supplementary material).

cs.CV↗

arXiv2022

Consistency test of the fine-structure constant from the whole ionization history

In cosmology, the fine-structure constant can affect the whole ionization history. However, the previous works confine themselves to the recombination epoch and give various strong constraints on the fine-structure constant. In this paper, we also take the reionization epoch into consideration and do a consistency test of the fine-structure constant from the whole ionization history. From the data combination of Planck 2018, BAO data, SNIa samples, SFR density from UV and IR measurements, and the $Q_\text{HII}$ constraints, we find the constraint on the fine-structure constant during the recombination epoch is $α_{\text{rec}}/α_{\text{EM}}=1.001494^{+0.002041}_{-0.002063}$ and its counterpart during the reionization epoch is $α_{\text{rei}}/α_{\text{EM}}=0.854034^{+0.031678}_{-0.027209}$ at 68$\%$ C.L.. They are not consistent with each other by $4.64σ$. A conservative explanation for such a discrepancy is that there are some issues in the data we used. We prefer a calibration of some important parameters involved in reconstructing the reionization history.

astro-ph.CO↗

arXiv2022

Quantum Interference and Coherent Population Trapping in a Double Quantum Dot

Quantum interference is a natural consequence of wave-particle duality in quantum mechanics, and is widely observed at the atomic scale. One interesting manifestation of quantum interference is coherent population trapping (CPT), first proposed in three-level driven atomic systems and observed in quantum optical experiments. Here, we demonstrate CPT in a gate-defined semiconductor double quantum dot (DQD), with some unique twists as compared to the atomic systems. Specifically, we observe CPT in both driven and non-driven situations. We further show that CPT in a driven DQD could be used to generate adiabatic state transfer. Moreover, our experiment reveals a non-trivial modulation to the CPT caused by the longitudinal driving field, yielding an odd-even effect and a tunable CPT.

cond-mat.mes-hall↗

arXiv2022

Monolith: Real Time Recommendation System With Collisionless Embedding Table

Building a scalable and real-time recommendation system is vital for many businesses driven by time-sensitive customer feedback, such as short-videos ranking or online ads. Despite the ubiquitous adoption of production-scale deep learning frameworks like TensorFlow or PyTorch, these general-purpose frameworks fall short of business demands in recommendation scenarios for various reasons: on one hand, tweaking systems based on static parameters and dense computations for recommendation with dynamic and sparse features is detrimental to model quality; on the other hand, such frameworks are designed with batch-training stage and serving stage completely separated, preventing the model from interacting with customer feedback in real-time. These issues led us to reexamine traditional approaches and explore radically different design choices. In this paper, we present Monolith, a system tailored for online training. Our design has been driven by observations of our application workloads and production environment that reflects a marked departure from other recommendations systems. Our contributions are manifold: first, we crafted a collisionless embedding table with optimizations such as expirable embeddings and frequency filtering to reduce its memory footprint; second, we provide an production-ready online training architecture with high fault-tolerance; finally, we proved that system reliability could be traded-off for real-time learning. Monolith has successfully landed in the BytePlus Recommend product.

cs.IR↗

arXiv2022

An Efficient Piggybacking Design Framework with Sub-packetization $l\le r$ for All-Node Repair

Piggybacking design has been widely applied in distributed storage systems since it can greatly reduce the repair bandwidth with small sub-packetization. Compared with other existing erasure codes, piggybacking is more convenient to operate and the I/O cost is lower. In this paper, we propose a new efficient design which can further reduce the repair bandwidth with the sub-packetization $l\le r$ where $r = n-k$. Generally, we let $l\le 8$. Compared with other analogous designs, our design has lower $l$ and the value of $l$ is more flexible. Moreover, our design can repair all nodes with small repair bandwidth. Therefore our piggybacking design is more feasible.

cs.IT↗

arXiv2022

Entropy Enhanced Multi-Agent Coordination Based on Hierarchical Graph Learning for Continuous Action Space

In most existing studies on large-scale multi-agent coordination, the control methods aim to learn discrete policies for agents with finite choices. They rarely consider selecting actions directly from continuous action spaces to provide more accurate control, which makes them unsuitable for more complex tasks. To solve the control issue due to large-scale multi-agent systems with continuous action spaces, we propose a novel MARL coordination control method that derives stable continuous policies. By optimizing policies with maximum entropy learning, agents improve their exploration in execution and acquire an excellent performance after training. We also employ hierarchical graph attention networks (HGAT) and gated recurrent units (GRU) to improve the scalability and transferability of our method. The experiments show that our method consistently outperforms all baselines in large-scale multi-agent cooperative reconnaissance tasks.

cs.MA↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- XII: Fragmentation and multi-scale gas kinematics in protoclusters G12.42+0.50 and G19.88-0.53

We present new continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey for the two protoclusters, G12.42+0.50 and G19.88-0.53. The 3 mm continuum maps reveal seven cores in each of the two globally contracting protoclusters. These cores satisfy the radius-mass relation and the surface mass density criteria for high-mass star formation. Similar to their natal clumps, the virial analysis of the cores suggests that they are undergoing gravitational collapse ($\rm α_{vir} << 2$). The clump to core scale fragmentation is investigated and the derived core masses and separations are found to be consistent with thermal Jeans fragmentation. We detect large-scale filamentary structures with velocity gradients and multiple outflows in both regions. Dendrogram analysis of the H$^{13}$CO$^{+}$ map identifies several branch and leaf structures with sizes $\sim$ 0.1 and 0.03 pc, respectively. The supersonic gas motion displayed by the branch structures is in agreement with the Larson power-law indicating that the gas kinematics at this spatial scale is driven by turbulence. The transition to transonic/subsonic gas motion is seen to occur at spatial scales of $\sim$0.1 pc indicating the dissipation of turbulence. In agreement with this, the leaf structures reveal gas motions that deviate from the slope of Larson's law. From the large-scale converging filaments to the collapsing cores, the gas dynamics in G12.42+0.50 and G19.88-0.53 show scale-dependent dominance of turbulence and gravity and the combination of these two driving mechanisms needs to be invoked to explain massive star formation in the protoclusters.

astro-ph.GA↗

arXiv2022

LAMOST meets Gaia: The Galactic Open Clusters

Open Clusters are born and evolve along the Milky Way plane, on them is imprinted the history of the Galactic disc, including the chemical and dynamical evolution. Chemical and dynamical properties of open clusters can be derived from photometric, spectroscopic, and astrometric data of their member stars. Based on the photometric and astrometric data from the Gaia mission, the membership of stars in more than 2000 Galactic clusters has been identified in the literature. The chemical and kinematical properties, however, are still poorly known for many of these clusters. In synergy with the large spectroscopic survey LAMOST (data release 8) and Gaia (data release 2), we report a new comprehensive catalogue of 386 open clusters. This catalogue has homogeneous parameter determinations of radial velocity, metallicity, and dynamical properties, such as orbit, eccentricity, angular momenta, total energy, and 3D Galactic velocity. These parameters allow the first radial velocity determination and the first spectroscopic [Fe/H] determination for 44 and 137 clusters, respectively. The metallicity distribution of majority clusters shows falling trends in the parameter space of the Galactocentric radius, the total energy, and the Z component of angular momentum -- except for two old groups that show flat tails in their own parameter planes. Cluster populations of ages younger and older than 500 Myrs distribute diversely on the disc. The latter has a spatial consistency with the Galactic disc flare. The 3-D spatial comparison between very young clusters (< 100 Myr) and nearby molecular clouds revealed a wide range of metallicity distribution along the Radcliffe gas cloud wave, indicating a possible inhomogeneous mixing or fast star formation along the wave. This catalogue would serve the community as a useful tool to trace the chemical and dynamical evolution of the Milky Way.

astro-ph.GA↗

arXiv2022

WheaCha: A Method for Explaining the Predictions of Models of Code

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction. In this paper, we present a new method, called WheaCha, for explaining the predictions of code models. Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways. Specifically, WheaCha divides an input program into "wheat" (i.e., the defining features that are the reason for which models predict the label that they predict) and the rest "chaff" for any prediction of a learned code model. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show (1) HuoYan is efficient - taking on average under twenty seconds to compute the wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to predict input programs is made of simple syntactic or even lexical properties (i.e., identifier names); (3) Based on wheat, we present a novel approach to explaining the predictions of code models through the lens of training data.

cs.LG↗

arXiv2022

On how to avoid exacerbating spurious correlations when models are overparameterized

Overparameterized models fail to generalize well in the presence of data imbalance even when combined with traditional techniques for mitigating imbalances. This paper focuses on imbalanced classification datasets, in which a small subset of the population -- a minority -- may contain features that correlate spuriously with the class label. For a parametric family of cross-entropy loss modifications and a representative Gaussian mixture model, we derive non-asymptotic generalization bounds on the worst-group error that shed light on the role of different hyper-parameters. Specifically, we prove that, when appropriately tuned, the recently proposed VS-loss learns a model that is fair towards minorities even when spurious features are strong. On the other hand, alternative heuristics, such as the weighted CE and the LA-loss, can fail dramatically. Compared to previous works, our bounds hold for more general models, they are non-asymptotic, and, they apply even at scenarios of extreme imbalance.

cs.LG↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- XI. From inflow to infall in hub-filament systems

We investigate the presence of hub-filament systems in a large sample of 146 active proto-clusters, using H$^{13}$CO$^{+}$ J=1-0 molecular line data obtained from the ATOMS survey. We find that filaments are ubiquitous in proto-clusters, and hub-filament systems are very common from dense core scales ($\sim$0.1 pc) to clump/cloud scales ($\sim$1-10 pc). The proportion of proto-clusters containing hub-filament systems decreases with increasing dust temperature ($T_d$) and luminosity-to-mass ratios ($L/M$) of clumps, indicating that stellar feedback from H{\sc ii} regions gradually destroys the hub-filament systems as proto-clusters evolve. Clear velocity gradients are seen along the longest filaments with a mean velocity gradient of 8.71 km s$^{-1}$pc$^{-1}$ and a median velocity gradient of 5.54 km s$^{-1}$pc$^{-1}$. We find that velocity gradients are small for filament lengths larger than $\sim$1~pc, probably hinting at the existence of inertial inflows, although we cannot determine whether the latter are driven by large-scale turbulence or large-scale gravitational contraction. In contrast, velocity gradients below $\sim$1~pc dramatically increase as filament lengths decrease, indicating that the gravity of the hubs or cores starts to dominate gas infall at small scales. We suggest that self-similar hub-filament systems and filamentary accretion at all scales may play a key role in high-mass star formation.

astro-ph.GA↗