arXiv subjects

Wei Zhao

Publications and source records attributed to Wei Zhao.

At least 253 records · Page 14Linked to original sources

arXiv2023

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.

cs.CL↗

arXiv2023

Unveiling the small-scale jets in the rapidly growing supermassive black hole IZw1

Accretion of black holes at near-Eddington or super-Eddington rates is the most powerful episode that drives black hole growth, and it may work in several types of objects. However, the physics of accretion and jet-disc coupling in such a state remains unclear, mainly because the associated jets are not easily detectable due to the extremely weak emission or possibly episodic nature of the jets. Only a few near/super-Eddington systems have demonstrated radio activity, and it remains unclear whether there is a jet and what are their properties, in super-Eddington active galactic nuclei (AGNs) (and ultraluminous X-ray sources). The deficit is mainly due to the complex radio mixing between the origins of jets and others, such as star formation activity, photo-ionized gas, accretion disk wind, and coronal activity. In this work, we conducted high-resolution very long baseline interferometry (VLBI) observations to explore the jets in the highly accreting narrow-line Seyfert I system IZw1. Our observations successfully revealed small-scale jets (with a linear size of $\sim45$ parsec) at both 1.5 and 5 GHz, based on the high radio brightness temperature, radio morphology, and spectral index distribution. Interestingly, the lack of a flat-spectrum radio core and knotty jet structures imply episodic ejections in IZw1, which resemble the ejection process in Galactic X-ray binaries that are in the canonical very high state. The high accretion rates and jet properties in the AGN IZw1 may support the AGN/XRB analogy in the extreme state.

astro-ph.HE↗

arXiv2023

Absolutely continuous curves in Finsler-like spaces

The present paper is devoted to the investigation of absolutely continuous curves in asymmetric metric spaces induced by Finsler structures. Firstly, for asymmetric spaces induced by Finsler manifolds, we show that three different kinds of absolutely continuous curves coincide when their domains are bounded closed intervals. As an application, a universal existence and regularity theorem for gradient flow is obtained in the Finsler setting. Secondly, we study absolutely continuous curves in Wasserstein spaces over Finsler manifolds and establish the Lisini structure theorem in this setting, which characterize the nature of absolutely continuous curves in Wasserstein spaces in terms of dynamical transference plans concentrated on absolutely continuous curves in base Finsler manifolds. Besides, a close relation between continuity equations and absolutely continuous curves in Wasserstein spaces is founded. Last but not least, we also consider nonsmooth ``Finlser-like" spaces, in which case most of the aforementioned results remain valid. Various model examples are constructed in this paper, which point out genuine differences between the asymmetric and symmetric settings.

math.DG↗

arXiv2023

Atom-referenced on-chip soliton microcomb

For the applications of the frequency comb in microresonators, it is essential to obtain a fully frequency-stabilized microcomb laser source. Here, we demonstrate an atom-referenced stabilized soliton microcomb generation system based on the integrated microring resonator. The pump light around $1560.48\,\mathrm{nm}$ locked to an ultra-low-expansion (ULE) cavity, is frequency-doubled and referenced to the atomic transition of $^{87}\mathrm{Rb}$. The repetition rate of the soliton microcomb is injection-locked to an atomic-clock-stabilized radio frequency (RF) source, leading to mHz stabilization at $1$ seconds. As a result, all comb lines have been frequency-stabilized based on the atomic reference and could be determined with very high precision reaching $\sim18\,\mathrm{Hz}$ at 1 second, corresponding to the frequency stability of $9.5\times10^{-14}$. Our approach provides an integrated and fully stabilized microcomb experiment scheme with no requirement of $f-2f$ technique, which could be easily implemented and generalized to various photonic platforms, thus paving the way towards the portable and ultraprecise optical sources for high precision spectroscopy.

physics.optics↗

arXiv2023

Design of the Tsinghua Tabletop Kibble Balance

The Kibble balance is a precision instrument for realizing the mass unit, the kilogram, in the new international system of units (SI). In recent years, an important trend for Kibble balance experiments is to go tabletop, in which the instrument's size is notably reduced while retaining a measurement accuracy of $10^{-8}$. In this paper, we report a new design of a tabletop Kibble balance to be built at Tsinghua University. The Tsinghua Kibble balance aims to deliver a compact instrument for robust mass calibrations from 10 g to 1 kg with a targeted measurement accuracy of 50 $μ$g or less. Some major features of the Tsinghua Kibble balance system, including the design of a new magnet, one-mode measurement scheme, the spring-compensated magnet moving mechanism, and magnetic shielding considerations, are discussed.

physics.ins-det↗

arXiv2023

Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

Recent self-supervised pre-training methods on Heterogeneous Information Networks (HINs) have shown promising competitiveness over traditional semi-supervised Heterogeneous Graph Neural Networks (HGNNs). Unfortunately, their performance heavily depends on careful customization of various strategies for generating high-quality positive examples and negative examples, which notably limits their flexibility and generalization ability. In this work, we present SHGP, a novel Self-supervised Heterogeneous Graph Pre-training approach, which does not need to generate any positive examples or negative examples. It consists of two modules that share the same attention-aggregation scheme. In each iteration, the Att-LPA module produces pseudo-labels through structural clustering, which serve as the self-supervision signals to guide the Att-HGNN module to learn object embeddings and attention coefficients. The two modules can effectively utilize and enhance each other, promoting the model to learn discriminative embeddings. Extensive experiments on four real-world datasets demonstrate the superior effectiveness of SHGP against state-of-the-art unsupervised baselines and even semi-supervised baselines. We release our source code at: https://github.com/kepsail/SHGP.

cs.LG↗

arXiv2023

Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving stochastic games. In most MARL studies, agents in a game are defined as teammates or enemies beforehand, and the relationships among the agents remain fixed throughout the game. However, in real-world problems, the agent relationships are commonly unknown in advance or dynamically changing. Many multi-party interactions start off by asking: who is on my team? This question arises whether it is the first day at the stock exchange or the kindergarten. Therefore, training policies for such situations in the face of imperfect information and ambiguous identities is an important problem that needs to be addressed. In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task. In the IDRL framework, a relation network is constructed to deduce the identities of other agents by observing the behaviors of the agents. A danger network is optimized to estimate the risk of false-positive identifications. Beyond that, we propose an intrinsic reward that balances the need to maximize external rewards and accurate identification. After identifying the cooperation-competition pattern among the agents, IDRL applies one of the off-the-shelf MARL methods to learn the policy. To evaluate the proposed method, we conduct experiments on Red-10 card-shedding game, and the results show that IDRL achieves superior performance over other state-of-the-art MARL methods. Impressively, the relation network has the par performance to identify the identities of agents with top human players; the danger network reasonably avoids the risk of imperfect identification. The code to reproduce all the reported results is available online at https://github.com/MR-BENjie/IDRL.

cs.AI↗

arXiv2023

On $ϕ$-$w$-Flat modules and Their Homological Dimensions

In this paper, we introduce and study the class of $ϕ$-$w$-flat modules which are generalizations of both $ϕ$-flat modules and $w$-flat modules. The $ϕ$-$w$-weak global dimension $ϕ$-$w$-w.gl.dim$(R)$ of a strongly $ϕ$-ring $R$ is also introduced and studied. We show that, for a strongly $ϕ$-ring $R$, $ϕ$-$w$-w.gl.dim$(R)=0$ if and only if $w$-$dim(R)=0$ if and only if $R$ is a $ϕ$-von Neumann ring. It is also proved that, for a strongly $ϕ$-ring $R$, $ϕ$-$w$-w.gl.dim$(R)\leq 1$ if and only if each nonnil ideal of $R$ is $ϕ$-$w$-flat, if and only if $R$ is a $ϕ$-PvMR, if and only if $R$ is a PvMR.

math.AC↗

arXiv2023

Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information

It is very difficult to forecast the production rate of oil wells as the output of a single well is sensitive to various uncertain factors, which implicitly or explicitly show the influence of the static, temporal and spatial properties on the oil well production. In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells. There are 3 basic modules in this stacking model, which are regarded as the encoders to extract the features from different types of data. One is Multi-Layer Perceptron, which is to analyze the static geological properties of the reservoir that might influence the well production rate. The other two are both LSTMs, which have the input in the form of two matrices rather than vectors, standing for the temporal and the spatial information of the target well. The difference of the two modules is that in the spatial information processing module we take into consideration the time delay of water flooding response, from the injection well to the target well. In addition, we use Symbolic Transfer Entropy to prove the superiorities of the stacking model from the perspective of Causality Discovery. It is proved theoretically and practically that the presented model can make full use of the model structure to integrate the characteristics of the data and the experts' knowledge into the process of machine learning, greatly improving the accuracy and generalization ability of prediction.

cs.LG↗

arXiv2023

On strongly $ϕ$-$w$-Flat modules and Their Homological Dimensions

In this paper, we introduce and study the notion of strongly $ϕ$-$w$-flat modules. The $ϕ$-$w$-weak global dimension $ϕ$-$w$-w.gl.dim$(R)$ of an NP-ring $R$ is also introduced and studied. We characterize $ϕ$-\Prufer\ $v$-multiplication strongly $ϕ$-rings in terms of strongly $ϕ$-$w$-flat modules.

math.AC↗

arXiv2023

Multifocal laser direct writing through spatial light modulation guided by scalable vector graphics

Multifocal laser direct writing (LDW) based on phase-only spatial light modulator (SLM) can realize flexible and parallel nanofabrication with high throughput potential. In this investigation, a novel approach of combining two-photon absorption, SLM and vector path guided by scalable vector graphics (SVG) has been developed and tested preliminarily, for fast, flexible and parallel nanofabrication. Three laser focuses are independently controlled with different paths, which are according to SVG, to optimize fabrication and promote time efficiency. The minimum structure width can be as low as 74 nm. Accompanied with a translation stage, a carp structure of 18.16 $μ$m by 24.35 $μ$m has been fabricated. This method shows the possibility of developing LDW techniques towards full-electrical system, and provides a potential way to efficiently engrave complex structures on nanoscales.

physics.ins-det↗

arXiv2023

DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence

Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation systems. In this work, we introduce DiscoScore, a parametrized discourse metric, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory. Our experiments encompass 16 non-discourse and discourse metrics, including DiscoScore and popular coherence models, evaluated on summarization and document-level machine translation (MT). We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level -- which is particularly problematic as systems are typically compared in this manner. DiscoScore, in contrast, achieves strong system-level correlation with human ratings, not only in coherence but also in factual consistency and other aspects, and surpasses BARTScore by over 10 correlation points on average. Further, aiming to understand DiscoScore, we provide justifications to the importance of discourse coherence for evaluation metrics, and explain the superiority of one variant over another. Our code is available at \url{https://github.com/AIPHES/DiscoScore}.

cs.CL↗

arXiv2023

Eliminating zeroth-order light of spatial light modulator with voltage optimization

The crucial zeroth-order light due to the pixelation effect of spatial light modulator (SLM) has been a serious issue in the field of light modulation, especially in applications with a high numerical aperture optical system. In this investigation, we report that by properly adjusting the high-level and low-level pixel voltages of an SLM, the zeroth-order light caused by the pixelation effect of SLM can be significantly eliminated. The method is further validated in an inverted fluorescence microscope. The experimental results show that the zeroth-order light can be inhibited up to 91.3%, accompanied by an improvement of the modulation efficiency from 77.5% to 92.6%.

physics.optics↗

arXiv2023

TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization

Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit diverse compute characteristics and demand strict latency and throughput requirements. Over the years, there has been significant attention in building hardware-efficient stream processing engines (SPEs) that support several query optimization, parallelization, and execution strategies to meet the performance requirements of large scale streaming analytics applications. However, in this work, we observe that these strategies often fail to generalize well on many real-world streaming analytics applications due to several inherent design limitations of current SPEs. We further argue that these limitations stem from the shortcomings of the fundamental design choices and the query representation model followed in modern SPEs. To address these challenges, we first propose TiLT, a novel intermediate representation (IR) that offers a highly expressive temporal query language amenable to effective query optimization and parallelization strategies. We subsequently build a compiler backend for TiLT that applies such optimizations on streaming queries and generates hardware-efficient code to achieve high performance on multi-core stream query executions. We demonstrate that TiLT achieves up to 326x (20.49x on average) higher throughput compared to state-of-the-art SPEs (e.g., Trill) across eight real-world streaming analytics applications. TiLT source code is available at https://github.com/ampersand-projects/tilt.git.

cs.DB↗

arXiv2023

Flow cytometry with anti-diffraction light sheet (ADLS) by spatial light modulation

Flow cytometry is a widespread and powerful technique, whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. We, herein, propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel-Gaussian beam array, the light sheet is 12 $μ$m wide, 12 $μ$m high, with a thickness of $~ 0.8 μ$m. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O(100 nm) to 10 $μ$m (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light-sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.

physics.optics↗

arXiv2023

Transition routes of electrokinetic flow in a divergent microchannel with bending walls

Electrokinetic flow can be generated as a highly coupled phenomenon among velocity field, electric conductivity field and electric field. It can exhibit different responses to AC electric fields in different frequency regimes, according to different instability/receptivity mechanisms. In this investigation, by both flow visualization and single-point laser-induced fluorescence (LIF) method, the response of AC electrokinetic flow and the transition routes towards chaos and turbulence have been experimentally investigated. It is found, when the AC frequency $f_f<30$ Hz, the interface responds at both the neutral frequency of the basic flow and the AC frequency. However, when $f_f>=30$ Hz, the interface responds only at the neutral frequency of the basic flow. Both periodic doubling and subcritical bifurcations have been observed in the transition of AC electrokinetic flow. We hope the current investigation can promote our current understanding on the ultrafast transition process of electrokinetic flow from laminar state to turbulence.

physics.flu-dyn↗

arXiv2023

PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection

In autonomous driving, 3D object detection based on multi-modal data has become an indispensable approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiDAR point and camera image, the fusion of the data for object detection encounters a series of problems. Most multi-modal detection methods perform even worse than LiDAR-only methods. In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection. Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed, which can convert image information including texture and semantic features by pseudo points. Thereafter, through a transformer-based Point Fusion Transition (PFT) module, the features of LiDAR points and pseudo points from image can be deeply fused under a unified point-based representation. The combination of these modules can conquer the major obstacle in feature fusion across modalities and realizes a complementary and discriminative representation for proposal generation. Extensive experiments on the KITTI dataset show the PTA-Det achieves a competitive result and support its effectiveness.

cs.CV↗

arXiv2023

1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track

OOD-CV challenge is an out-of-distribution generalization task. To solve this problem in object detection track, we propose a simple yet effective Generalize-then-Adapt (G&A) framework, which is composed of a two-stage domain generalization part and a one-stage domain adaptation part. The domain generalization part is implemented by a Supervised Model Pretraining stage using source data for model warm-up and a Weakly Semi-Supervised Model Pretraining stage using both source data with box-level label and auxiliary data (ImageNet-1K) with image-level label for performance boosting. The domain adaptation part is implemented as a Source-Free Domain Adaptation paradigm, which only uses the pre-trained model and the unlabeled target data to further optimize in a self-supervised training manner. The proposed G&A framework help us achieve the first place on the object detection leaderboard of the OOD-CV challenge. Code will be released in https://github.com/hikvision-research/OOD-CV.

cs.CV↗