arXiv subjects

Chen Zhang

Publications and source records attributed to Chen Zhang.

At least 451 records · Page 25Linked to original sources

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

Efficient real-time spin readout of nitrogen-vacancy centers based on Bayesian estimation

In this work, to improve the spin readout efficiency of the nitrogen vacancy (NV) center, a real-time Bayesian estimation algorithm is proposed, which combines both the prior probability distribution and the fluorescence likelihood function established by the implementation of the NV center dynamics model. The theoretical surpass of the Cramer-Rao lower bound of the readout variance and the improvement of the readout efficiency in the simulation indicate that our approach is an appealing alternative to the conventional photon summation method. The Bayesian real-time estimation readout was experimentally realized by combining a high-performance acquisition and processing hardware, and the Rabi oscillation experiments divulged that the signal-to-noise ratio of our approach was improved by 28.6%. Therefore, it is anticipated that the employed Bayesian estimation readout will effectively present superior sensing capabilities of the NV ensemble, and foster the further development of compact and scalable quantum sensors and consequently novel quantum information processing devices on a monolithic platform.

quant-ph↗

arXiv2023

Measurement of the central Galactic black hole by extremely large mass-ratio inspirals

In the Galaxy, extremely large mass-ratio inspirals(X-MRIs) composed of brown dwarfs and the massive black hole at the Galactic Center are expected to be promising gravitational wave sources for space-borne detectors. In this work, we simulate the gravitational wave signals from twenty X-MRI systems by an axisymmetric Konoplya-Rezzolla-Zhidenko metric with varied parameters. We find that the mass, spin, and deviation parameters of the Kerr black hole could be determined accurately ( $\sim 10^{-5} - 10^{-6}$ ) with only one X-MRI event with a high signal-to-noise ratio. The measurement of the above parameters could be improved with more X-MRI observations.

gr-qc↗

arXiv2023

Moiré-induced bandgap tuning by varying electric dipole in InSe/CuSe vertical heterostructure

The stacked two layered materials with a lattice constant mismatch and/or with twist angle relative to each other can create a moiré pattern, modulating the electronic properties of the pristine materials. Here, we combine scanning tunneling microscopy/spectroscopy and density functional theory calculations to investigate the moiré potential induced bandgap tuning in InSe/CuSe vertical heterostructure synthesized by a two-step of molecular beam epitaxy. Scanning tunneling microscopy measurements demonstrate the heterostructure with a superlattice periodicity of ~3.48nm and a twist angle of about 11° between the monolayers. Scanning tunneling spectroscopy record on the different stacking sites of the heterostructure reveals the bandgap of the InSe is location-dependent and a variation of 400 meV is observed. Density functional theory calculations reveal that the moiré-induce electric dipole in the monolayer InSe is the key factor for tuning the bandgap. Besides, charge transfer between CuSe and InSe also contributes to the bandgap variation due to its stacking related. We also show that the moiré potential not only can tune the bandgap of InSe but also can vanish the Dirac nodal line of CuSe in some stackings. Our explorations provide valuable information in understanding the electronic properties of the twodimensional moiré materials.

cond-mat.mtrl-sci↗

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↗

arXiv2022

Detecting properties of echoes from inspiraling stage with ground-based detectors

The nature of black holes is one of most exciting issues in gravitational physics. If there is an exotic compact object as the compact as a black hole but without a horizon, gravitational wave echoes may be produced after the merger. In this work, we show that for extreme-mass-ratio binaries, even during the inspiraling phase of compact binary coalescence, the exists of hard surface of the exotic compact object will produce detectable signals on the gravitational waves. We predict that once the LIGO-Virgo-KAGRA, Einstein Telescope or Cosmic Explorer detect such kind of sources, our model shows that one can constrain the properties of surfaces of the compact objects in inspiraling stage better than the current level.

gr-qc↗

arXiv2022

PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. This calls for a dedicated network architecture for domain generalization. To tackle the multi-domain dialogue evaluation task, we propose a Panel of Experts (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning.

cs.CL↗

arXiv2022

End-to-end Emotion-Cause Pair Extraction via Learning to Link

Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. Such pipeline method, while intuitive, suffers from two critical issues, including error propagation across stages that may hinder the effectiveness, and high computational cost that would limit the practical application of the method. To tackle these issues, we propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.

cs.CL↗

arXiv2022

One-shot Machine Teaching: Cost Very Few Examples to Converge Faster

Artificial intelligence is to teach machines to take actions like humans. To achieve intelligent teaching, the machine learning community becomes to think about a promising topic named machine teaching where the teacher is to design the optimal (usually minimal) teaching set given a target model and a specific learner. However, previous works usually require numerous teaching examples along with large iterations to guide learners to converge, which is costly. In this paper, we consider a more intelligent teaching paradigm named one-shot machine teaching which costs fewer examples to converge faster. Different from typical teaching, this advanced paradigm establishes a tractable mapping from the teaching set to the model parameter. Theoretically, we prove that this mapping is surjective, which serves to an existence guarantee of the optimal teaching set. Then, relying on the surjective mapping from the teaching set to the parameter, we develop a design strategy of the optimal teaching set under appropriate settings, of which two popular efficiency metrics, teaching dimension and iterative teaching dimension are one. Extensive experiments verify the efficiency of our strategy and further demonstrate the intelligence of this new teaching paradigm.

cs.LG↗

arXiv2022

Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial role, we propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training. Specifically, we first design a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads. This encourages the generated pseudo labels to cover as many abnormal events as possible. We then devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage. Extensive experimental results demonstrate the proposed method performs favorably against state-of-the-art approaches on the UCF-Crime, TAD, and XD-Violence benchmark datasets.

cs.CV↗

arXiv2022

X-ray Performance of a Small Pixel Size sCMOS Sensor and the Effect of Depletion Depth

In recent years, scientific Complementary Metal Oxide Semiconductor (sCMOS) devices have been increasingly applied in X-ray detection, thanks to their attributes such as high frame rate, low dark current, high radiation tolerance and low readout noise. We tested the basic performance of a backside-illuminated (BSI) sCMOS sensor, which has a small pixel size of 6.5 um * 6.5 um. At a temperature of -20C, The readout noise is 1.6 e, the dark current is 0.5 e/pixel/s, and the energy resolution reaches 204.6 eV for single-pixel events. The effect of depletion depth on the sensor's performance was also examined, using three versions of the sensors with different deletion depths. We found that the sensor with a deeper depletion region can achieve a better energy resolution for events of all types of pixel splitting patterns, and has a higher efficiency in collecting photoelectrons produced by X-ray photons. We further study the effect of depletion depth on charge diffusion with a center-of-gravity (CG) model. Based on this work, a highly depleted sCMOS is recommended for applications of soft X-ray spectroscop.

physics.ins-det↗

arXiv2022

DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing

Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this paper, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students' learning interactions to capture the heterogeneous exercise-concept associations and interaction transitions by hypergraph modeling and directed graph modeling, respectively. To ensemble the dual graph models, we introduce the technique of online knowledge distillation, due to the fact that although the knowledge tracing model is expected to predict students' responses to the exercises related to different concepts, it is optimized merely with respect to the prediction accuracy on a single exercise at each step. With online knowledge distillation, the dual graph models are adaptively combined to form a stronger teacher model, which in turn provides its predictions on all exercises as extra supervision for better modeling ability. In the experiments, we compare DGEKT against eight knowledge tracing baselines on three benchmark datasets, and the results demonstrate that DGEKT achieves state-of-the-art performance.

cs.AI↗

arXiv2022

CATCH: Chasing All Transients Constellation Hunters Space Mission

In time-domain astronomy, a substantial number of transients will be discovered by multi-wavelength and multi-messenger observatories, posing a great challenge for follow-up capabilities. We have thus proposed an intelligent X-ray constellation, the Chasing All Transients Constellation Hunters (CATCH) space mission. Consisting of 126 micro-satellites in three types, CATCH will have the capability to perform follow-up observations for a large number of different types of transients simultaneously. Each satellite in the constellation will carry lightweight X-ray optics and use a deployable mast to increase the focal length. The combination of different optics and detector systems enables different types of satellites to have multiform observation capabilities, including timing, spectroscopy, imaging, and polarization. Controlled by the intelligent system, different satellites can cooperate to perform uninterrupted monitoring, all-sky follow-up observations, and scanning observations with a flexible field of view (FOV) and multi-dimensional observations. Therefore, CATCH will be a powerful mission to study the dynamic universe. Here, we present the current design of the spacecraft, optics, detector system, constellation configuration and observing modes, as well as the development plan.

astro-ph.IM↗

arXiv2022

TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method

Lyric-to-melody generation is an important task in automatic songwriting. Previous lyric-to-melody generation systems usually adopt end-to-end models that directly generate melodies from lyrics, which suffer from several issues: 1) lack of paired lyric-melody training data; 2) lack of control on generated melodies. In this paper, we develop TeleMelody, a two-stage lyric-to-melody generation system with music template (e.g., tonality, chord progression, rhythm pattern, and cadence) to bridge the gap between lyrics and melodies (i.e., the system consists of a lyric-to-template module and a template-to-melody module). TeleMelody has two advantages. First, it is data efficient. The template-to-melody module is trained in a self-supervised way (i.e., the source template is extracted from the target melody) that does not need any lyric-melody paired data. The lyric-to-template module is made up of some rules and a lyric-to-rhythm model, which is trained with paired lyric-rhythm data that is easier to obtain than paired lyric-melody data. Second, it is controllable. The design of template ensures that the generated melodies can be controlled by adjusting the musical elements in template. Both subjective and objective experimental evaluations demonstrate that TeleMelody generates melodies with higher quality, better controllability, and less requirement on paired lyric-melody data than previous generation systems.

cs.SD↗

arXiv2022

SDMuse: Stochastic Differential Music Editing and Generation via Hybrid Representation

While deep generative models have empowered music generation, it remains a challenging and under-explored problem to edit an existing musical piece at fine granularity. In this paper, we propose SDMuse, a unified Stochastic Differential Music editing and generation framework, which can not only compose a whole musical piece from scratch, but also modify existing musical pieces in many ways, such as combination, continuation, inpainting, and style transferring. The proposed SDMuse follows a two-stage pipeline to achieve music generation and editing on top of a hybrid representation including pianoroll and MIDI-event. In particular, SDMuse first generates/edits pianoroll by iteratively denoising through a stochastic differential equation (SDE) based on a diffusion model generative prior, and then refines the generated pianoroll and predicts MIDI-event tokens auto-regressively. We evaluate the generated music of our method on ailabs1k7 pop music dataset in terms of quality and controllability on various music editing and generation tasks. Experimental results demonstrate the effectiveness of our proposed stochastic differential music editing and generation process, as well as the hybrid representations.

cs.SD↗

arXiv2022

FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation or look at a single dialogue quality dimension. One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level. To this end, we are motivated to propose a multi-dimensional dialogue-level metric, which consists of three sub-metrics with each targeting a specific dimension. The sub-metrics are trained with novel self-supervised objectives and exhibit strong correlations with human judgment for their respective dimensions. Moreover, we explore two approaches to combine the sub-metrics: metric ensemble and multitask learning. Both approaches yield a holistic metric that significantly outperforms individual sub-metrics. Compared to the existing state-of-the-art metric, the combined metrics achieve around 16% relative improvement on average across three high-quality dialogue-level evaluation benchmarks.

cs.CL↗

arXiv2022

A Focused Study on Sequence Length for Dialogue Summarization

Output length is critical to dialogue summarization systems. The dialogue summary length is determined by multiple factors, including dialogue complexity, summary objective, and personal preferences. In this work, we approach dialogue summary length from three perspectives. First, we analyze the length differences between existing models' outputs and the corresponding human references and find that summarization models tend to produce more verbose summaries due to their pretraining objectives. Second, we identify salient features for summary length prediction by comparing different model settings. Third, we experiment with a length-aware summarizer and show notable improvement on existing models if summary length can be well incorporated. Analysis and experiments are conducted on popular DialogSum and SAMSum datasets to validate our findings.

cs.CL↗

arXiv2022

A Little Excitement Across the Horizon

We analyse numerically the transitions in an Unruh-DeWitt detector, coupled linearly to a massless scalar field, in radial infall in (3+1)-dimensional Schwarzschild spacetime. In the Hartle-Hawking and Unruh states, the transition probability attains a small local extremum near the horizon-crossing and is then moderately enhanced on approaching the singularity. In the Boulware state, the transition probability drops on approaching the horizon. The unexpected near-horizon extremum arises numerically from angular momentum superpositions, with a deeper physical explanation to be found.

gr-qc↗