Searcharxiv⌕ Search

arXiv subjects

Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 487 records · Page 27Linked to original sources
arXiv2021

Data Pricing in Machine Learning Pipelines

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data is critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.

cs.LG↗
arXiv2021

Semi-Autoregressive Transformer for Image Captioning

Current state-of-the-art image captioning models adopt autoregressive decoders, \ie they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning~(dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local . Based on Transformer, there are only a few modifications needed to implement SATIC. Experimental results on the MSCOCO image captioning benchmark show that SATIC can achieve a good trade-off without bells and whistles. Code is available at {\color{magenta}\url{https://github.com/YuanEZhou/satic}}.

cs.CV↗
arXiv2021

Notes on polytopes, amplitudes and boundary configurations for Grassmannian string integrals

We continue the study of positive geometries underlying the {\it Grassmannian string integrals}, which are a class of "stringy canonical forms", or stringy integrals, over the positive Grassmannian mod torus action, $G_+(k,n)/T$. The leading order of any such stringy integral is given by the canonical function of a polytope, which can be obtained using the Minkowski sum of the Newton polytopes for the regulators of the integral, or equivalently given by the so-called scattering-equation map. The canonical function of the polytopes for Grassmannian string integrals, or the volume of their dual polytopes, is also known as the generalized bi-adjoint $ϕ^3$ amplitudes. We compute all the linear functions for the facets which cut out the polytope for all cases up to $n=9$, with up to k=4 and their parity conjugate cases. The main novelty of our computation is that we present these facets in a manifestly gauge-invariant and cyclic way, and identify the boundary configurations of $G_+(k,n)/T$ corresponding to these facets, which have nice geometric interpretations in terms of $n$ points in $(k{-}1)$-dimensional space. All the facets and configurations we discovered up to $n=9$ directly generalize to all $n$, although new types are still needed for higher $n$.

hep-th↗
arXiv2021

An Effective and Robust Detector for Logo Detection

In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people's attention. Particularly, with the rise of e-commerce, the IP not only represents the product design and brands, but also represents the images/videos displayed on e-commerce platforms. Unfortunately, some attackers adopt some adversarial methods to fool the well-trained logo detection model for infringement. To overcome this problem, a novel logo detector based on the mechanism of looking and thinking twice is proposed in this paper for robust logo detection. The proposed detector is different from other mainstream detectors, which can effectively detect small objects, long-tail objects, and is robust to adversarial images. In detail, we extend detectoRS algorithm to a cascade schema with an equalization loss function, multi-scale transformations, and adversarial data augmentation. A series of experimental results have shown that the proposed method can effectively improve the robustness of the detection model. Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams. Code is available at https://github.com/jiaxiaojunQAQ/Robust-Logo-Detection.

cs.CV↗
arXiv2021

Effect of Alkyl-group Flexibility on the Melting Point of Imidazolium-based Ionic Liquids

The low melting point of room temperature ionic liquids is usually explained in terms of the presence of bulky, low symmetry and flexible ions, with the first two factors related to the lattice energy while an entropic effect is attributed to the latter. By means of molecular dynamics simulations, the melting points of 1-ethyl-3-methylimidazolium hexafluorophosphate and 1-decyl-3-methyl-imidazolium hexafluorophosphate were determined and the effect of the molecular flexibility over the melting point was explicitly computed by restraining the rotation of dihedral angles in both the solid and the liquid phase. The rotational flexibility over the bond between the ring and the alkyl chain affects the relative ordering of the anions around the cations and results in substantial effects over both the enthalpy and the entropy of melting. For the other dihedral angles of the alkyl group, the contributions are predominantly entropic and an alternating behavior was found. The flexibility of some dihedral angles has negligible effects on the melting point, while others can lead to differences in the melting point as large as 20 K. This alternating behavior is rationalized by the different probabilities of conformation defects in the crystal.

physics.chem-ph↗
arXiv2021

SimROD: A Simple Adaptation Method for Robust Object Detection

This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric augmentation method, a gradual self-labeling adaptation procedure, and a teacher-guided fine-tuning mechanism. Using our method, target domain samples can be leveraged to adapt object detection models without changing the model architecture or generating synthetic data. When applied to image corruptions and high-level cross-domain adaptation benchmarks, our method outperforms prior baselines on multiple domain adaptation benchmarks. SimROD achieves new state-of-the-art on standard real-to-synthetic and cross-camera setup benchmarks. On the image corruption benchmark, models adapted with our method achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6% AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method outperformed the best baseline performance by up to 8% AP50 on Comic dataset and up to 4% on Watercolor dataset.

cs.CV↗
arXiv2021

Uniqueness and stability of steady-state solution with finite energy to the fractal Burgers equation

The paper is concerned with the steady-state Burgers equation of fractional dissipation on the real line. We first prove the global existence of viscosity weak solutions to the fractal Burgers equation driven by the external force. Then the existence and uniqueness of solution with finite $H^{\fracα{2}}$ energy to the steady-state equation are established by estimating the decay of fractal Burgers' solutions. Furthermore, we show that the unique steady-state solution is nonlinearly stable, which means any viscosity weak solution of fractal Burgers equation, starting close to the steady-state solution, will return to the steady state as $t\rightarrow\infty$.

math.AP↗
arXiv2021

Uniqueness and asymptotic stability of time-periodic solution for the fractal Burgers equation

The paper is concerned with the time-periodic (T-periodic) problem of the fractal Burgers equation with a T-periodic force on the real line. Based on the Galerkin approximates and Fourier series (transform) methods, we first prove the existence of T-periodic solution to a linearized version. Then, the existence and uniqueness of T-periodic solution to the nonlinear equation are established by the contraction mapping argument. Furthermore, we show that the unique T-periodic solution is asymptotically stable. This analysis, which is carried out in energy space $ H^{1}(0,T;H^{\fracα{2}}(R))\cap L^{2}(0,T;\dot{H}^α)$ with $1<α<\frac{3}{2}$, extends the T-periodic viscid Burgers equation in \cite{5} to the T-periodic fractional case.

math.AP↗
arXiv2021

Squeezed spectra of bosons and antibosons with different in-medium masses

We study the influence of the in-medium mass difference between boson and antiboson on their spectra. The in-medium mass difference may lead to a difference between the transverse momentum spectra of boson and antiboson. This effect increases with the increasing in-medium mass difference between boson and antiboson. The difference between the transverse momentum spectra of boson and antiboson increases with the increasing expanding velocity of the source and decreases with the increasing transverse momentum in large transverse mass region (mT > 1:6 GeV). The interactions between the hadron and the medium may increase with the increasing temperature of the medium and the higher freeze-out temperature may lead to a larger mass difference between boson and antiboson, and may give rise to a larger difference between the transverse momentum spectra of boson and antiboson for higher freeze-out temperature.

nucl-th↗
arXiv2021

The Lithium Abundances from the Large Sky Area Multi-object Fiber Spectroscopic Telescope Medium-resolution Survey. I. The Method

Standard stellar evolution model predicts a severe depletion of lithium (Li) abundance during the first dredge-up process (FDU). Yet a small fraction of giant stars are still found to preserve a considerable amount of Li in their atmospheres after FDU. Those giants are usually identified as Li-rich by a widely used criterion, A(Li) $ > 1.5$\,{\it dex}. A large number of works dedicated to search for and investigate this minority of the giant family, and the amount of Li-rich giants has been largely expanded, especially in the era of big data. In this paper, we present a catalog of Li-rich giants found from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey with Li abundances derived from a template matching method developed for LAMOST low-resolution spectra. The catalog contains $10,535$ Li-rich giants with Li abundances from $\sim 1.5$\,{\it dex} to $\sim 4.9$\,{\it dex}. We also confirm that the ratio of Li-rich phenomenon among giant stars is about one percent, or for a more expression, $1.29\%$ from our statistically important sample. This is the largest Li-rich giant sample ever reported to date, which significantly exceeds amount of all the reported Li-rich giants combined. The catalog will help the community to better understand the Li-rich phenomenon in giant stars.

astro-ph.SR↗
arXiv2021

Effect of pressure on thermalization of one-dimensional nonlinear chains

Pressure plays a vital role in changing the transport properties of matter. To understand this phenomenon at a microscopic level, we here focus on a more fundamental problem, i.e., how pressure affects the thermalization properties of solids. As illustrating examples, we study the thermalization behavior of the monatomic chain and the mass-disordered chain of Fermi-Pasta-Ulam-Tsingou-$β$ under different strains in the thermodynamic limit. It is found that the pressure-induced change in nonintegrability results in qualitatively different thermalization processes for the two kinds of chains. However, for both cases, the thermalization time follows the same law -- it is inversely proportional to the square of the nonintegrability strength. This result suggests that pressure can significantly change the integrability of a system, which provides a new perspective for understanding the pressure-dependent thermal transport behavior.

cond-mat.stat-mech↗
arXiv2021

Thickness-Driven Quantum Anomalous Hall Phase Transition in Magnetic Topological Insulator Thin Films

The quantized version of anomalous Hall effect realized in magnetic topological insulators (MTIs) has great potential for the development of topological quantum physics and low-power electronic/spintronic applications. To enable dissipationless chiral edge conduction at zero magnetic field, effective exchange field arisen from the aligned magnetic dopants needs to be large enough to yield specific spin sub-band configurations. Here we report the thickness-tailored quantum anomalous Hall (QAH) effect in Cr-doped (Bi,Sb)2Te3 thin films by tuning the system across the two-dimensional (2D) limit. In addition to the Chern number-related metal-to-insulator QAH phase transition, we also demonstrate that the induced hybridization gap plays an indispensable role in determining the ground magnetic state of the MTIs, namely the spontaneous magnetization owning to considerable Van Vleck spin susceptibility guarantees the zero-field QAH state with unitary scaling law in thick samples, while the quantization of the Hall conductance can only be achieved with the assistance of external magnetic fields in ultra-thin films. The modulation of topology and magnetism through structural engineering may provide a useful guidance for the pursuit of QAH-based new phase diagrams and functionalities.

cond-mat.mes-hall↗
arXiv2021

II-VI Organic-Inorganic Hybrid Nanostructures with Greatly Enhanced Optoelectronic Properties, Perfectly Ordered Structures, and Over 15-Year Shelf Stability

Organic-inorganic hybrids may offer material properties not available from their inorganic components. However, they are typically less stable and disordered. Long-term stability study of the hybrid materials, over the anticipated lifespan of a real-world electronic device, is practically nonexistent. Disordering, prevalent in most nanostructure assemblies, is a prominent adversary to quantum coherence. A family of perfectly ordered II-VI based hybrid nanostructures has been shown to possess a number of unusual properties and potential applications. Here, using a prototype structure ZnTe(en)0.5 - a hybrid superlattice, and applying an array of optical, structural, surface, thermal, and electrical characterization techniques in conjunction with density-functional theory calculations, we have performed a comprehensive and correlative study of the crystalline quality, structural degradation, electronic, optical, and transport properties on samples from over 15-years old to the recently synthesized. The findings show that not only do they exhibit an exceptionally high level of crystallinity in both macroscopic and microscopic scale, comparable to high-quality binary semiconductors; and greatly enhanced material properties, compared to those of the inorganic constituents; but also, some of them over 15-years old remain as good in structure and property as freshly made ones. This study reveals (1) what level of structural perfectness is achievable in a complex organic-inorganic hybrid structure or a man-made superlattice, suggesting a non-traditional strategy to make periodically stacked heterostructures with abrupt interfaces; and (2) how the stability of a hybrid material is affected differently by its intrinsic attributes, primarily formation energy, and extrinsic factors, such as surface and defects.

cond-mat.mtrl-sci↗
arXiv2021

ATOMS:ALMA Three-millimeter Observations of Massive Star-forming regions -- III :Catalogues of candidate hot molecular cores and Hyper/Ultra compact HII regions

We have identified 453 compact dense cores in 3 mm continuum emission maps in the ATOMS (ALMA Three-millimeter Observations of Massive Star-forming regions) survey, and compiled three catalogues of high-mass star forming cores. One catalogue, referred to as H/UC-HII catalogue, includes 89 cores that enshroud hyper/ultra compact (H/UC) HII regions as characterized by associated compact H40alpha emission. A second catalogue, referred to as pure s-cHMC, includes 32 candidate Hot Molecular Cores (HMCs) showing rich spectra (N>20lines) of complex organic molecules (COMs) but not associated with H/UC-HII regions. The third catalogue, referred to as pure w-cHMC, includes 58 candidate HMCs with relatively low levels of COM richness and not associated with H/UC-HII regions. These three catalogues of dense cores provide an important foundation for future studies of the early stages of high-mass star formation across the Milky Way. We also find that nearly half of H/UC-HII cores are candidate HMCs. From the number counts of COM-containing and H/UC-HII cores, we suggest that the duration of high-mass protostellar cores showing chemically rich features is at least comparable to the lifetime of H/UC-HII regions. For cores in the H/UC-HII catalogue, the width of the H40alpha line increases as the core size decreases, suggesting that the non-thermal dynamical and/or pressure line-broadening mechanisms dominate on the smaller scales of the H/UC-HII cores.

astro-ph.GA↗
arXiv2021

Towards Harmonized Regional Style Transfer and Manipulation for Facial Images

Regional facial image synthesis conditioned on semantic mask has achieved great success using generative adversarial networks. However, the appearance of different regions may be inconsistent with each other when conducting regional image editing. In this paper, we focus on the problem of harmonized regional style transfer and manipulation for facial images. The proposed approach supports regional style transfer and manipulation at the same time. A multi-scale encoder and style mapping networks are proposed in our work. The encoder is responsible for extracting regional styles of real faces. Style mapping networks generate styles from random samples for all facial regions. As the key part of our work, we propose a multi-region style attention module to adapt the multiple regional style embeddings from a reference image to a target image for generating harmonious and plausible results. Furthermore, we propose a new metric "harmony score" and conduct experiments in a challenging setting: three widely used face datasets are involved and we test the model by transferring the regional facial appearance between datasets. Images in different datasets are usually quite different, which makes the inconsistency between target and reference regions more obvious. Results show that our model can generate reliable style transfer and multi-modal manipulation results compared with SOTAs. Furthermore, we show two face editing applications using the proposed approach.

cs.MM↗
arXiv2021

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.

cs.CV↗
arXiv2021

Updatable Learned Index with Precise Positions

Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index size, they cannot efficiently support update operations. Although recent studies have proposed some preliminary approaches to support update, they are at the cost of scarifying the lookup performance as they suffer from the overheads brought by imprecise predictions in the leaf nodes. In this paper, we propose LIPP, a brand new framework of learned index to address such issues. Similar with state-of-the-art learned index structures, LIPP is able to support all kinds of index operations, namely lookup query, range query, insert, delete, update and bulkload. Meanwhile, we overcome the limitations of previous studies by properly extending the tree structure when dealing with update operations so as to eliminate the deviation of location predicted by the models in the leaf nodes. Moreover, we further propose a dynamic adjustment strategy to ensure that the height of the tree index is tightly bounded and provide comprehensive theoretical analysis to illustrate it. We conduct an extensive set of experiments on several real-life and synthetic datasets. The results demonstrate that our method consistently outperforms state-of-the-art solutions, achieving by up to 4x for a broader class of workloads with different index operations.

cs.DB↗