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Fan Xu

Publications and source records attributed to Fan Xu.

At least 145 records · Page 8Linked to original sources
arXiv2023

CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

The Chinese character riddle is a unique form of cultural entertainment specific to the Chinese language. It typically comprises two parts: the riddle description and the solution. The solution to the riddle is a single character, while the riddle description primarily describes the glyph of the solution, occasionally supplemented with its explanation and pronunciation. Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language. In this paper, we construct a \textbf{C}hinese \textbf{C}haracter riddle dataset named CC-Riddle, which covers the majority of common simplified Chinese characters. The construction process is a combination of web crawling, language model generation and manual filtering. In generation stage, we input the Chinese phonetic alphabet, glyph and meaning of the solution character into the generation model, which then produces multiple riddle descriptions. The generated riddles are then manually filtered and the final CC-Riddle dataset is composed of both human-written riddles and these filtered, generated riddles. In order to assess the performance of language models on the task of solving character riddles, we use retrieval-based, generative and multiple-choice QA strategies to test three language models: BERT, ChatGPT and ChatGLM. The test results reveal that current language models still struggle to solve Chinese character riddles. CC-Riddle is publicly available at \url{https://github.com/pku0xff/CC-Riddle}.

cs.CL↗
arXiv2023

Observation of integer and fractional quantum anomalous Hall effects in twisted bilayer MoTe2

The interplay between strong correlations and topology can lead to the emergence of intriguing quantum states of matter. One well-known example is the fractional quantum Hall effect, where exotic electron fluids with fractionally charged excitations form in partially filled Landau levels. The emergence of topological moiré flat bands provides exciting opportunities to realize the lattice analogs of both the integer and fractional quantum Hall states without the need for an external magnetic field. These states are known as the integer and fractional quantum anomalous Hall (IQAH and FQAH) states. Here, we present direct transport evidence of the existence of both IQAH and FQAH states in twisted bilayer MoTe2 (AA stacked). At zero magnetic field, we observe well-quantized Hall resistance of h/e2 around moiré filling factor ν = -1 (corresponding to one hole per moiré unit cell), and nearly-quantized Hall resistance of 3h/2e2 around ν = -2/3, respectively. Concomitantly, the longitudinal resistance exhibits distinct minima around ν = -1 and -2/3. The application of an electric field induces topological quantum phase transition from the IQAH state to a charge transfer insulator at ν = -1, and from the FQAH state to a generalized Wigner crystal state, further transitioning to a metallic state at ν = -2/3. Our study paves the way for the investigation of fractionally charged excitations and anyonic statistics at zero magnetic field based on semiconductor moiré materials.

cond-mat.mes-hall↗
arXiv2023

Possible origin of AT2021any: a failed GRB from a structured jet

Searching for afterglows not associated with any gamma-ray bursts (GRBs) is a longstanding goal of transient surveys. These surveys provide the very chance of discovering the so-called orphan afterglows. Recently, a promising orphan afterglow candidate, AT2021any, was found by the Zwicky Transient Facility. Here we perform multi-wavelength fitting of AT2021any with three different outflow models, namely the top-hat jet model, the isotropic fireball model, and the structured Gaussian jet model. Although the three models can all fit the observed light curve well, it is found that the structured Gaussian jet model presents the best result, and thus is preferred by observations. In the framework of the Gaussian jet model, the best-fit Lorentz factor is about 68, which indicates that AT2021any should be a failed GRB. The half-opening angle of the jet and the viewing angle are found to be 0.104 and 0.02, respectively, which means that the jet is essentially observed on-axis. The trigger time of the GRB is inferred to be about 1000 s before the first detection of the orphan afterglow. An upper limit of 21.4% is derived for the radiative efficiency, which is typical in GRBs.

astro-ph.HE↗
arXiv2023

Exploring Global and Local Information for Anomaly Detection with Normal Samples

Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce platforms, et al. However, in many realistic scenarios, only the samples following normal behavior are observed, while we can hardly obtain any anomaly information. To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples. The proposed method can be divided into a three-stage method. Firstly, the global similar normal scores and the local sparsity scores of unlabeled samples are computed separately. Secondly, potential anomaly samples are separated from the unlabeled samples corresponding to these two scores and corresponding weights are assigned to the selected samples. Finally, a weighted anomaly detector is trained by loads of samples, then the detector is utilized to identify else anomalies. To evaluate the effectiveness of the proposed method, we conducted experiments on three categories of real-world datasets from diverse domains, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods.

cs.LG↗
arXiv2023

Cooperative Multi-Cell Massive Access with Temporally Correlated Activity

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

eess.SP↗
arXiv2023

A generalized quantum cluster algebra of Kronecker type

The cluster multiplication formulas for a generalized quantum cluster algebra of Kronecker type are explicitly given. Furthermore, a positive bar-invariant $\mathbb{Z}[q^{\pm\frac{1}{2}}]$-basis of this algebra is constructed.

math.QA↗
arXiv2023

FMGNN: Fused Manifold Graph Neural Network

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while recent works extend the embedding models to hyperbolic or spherical spaces to achieve better performance on graphs with complex structures, such as hierarchical or ring structures. Fusing the embedding from different manifolds can further take advantage of the embedding capabilities over different graph structures. However, existing embedding fusion methods mostly focus on concatenating or summing up the output embeddings, without considering interacting and aligning the embeddings of the same vertices on different manifolds, which can lead to distortion and impression in the final fusion results. Besides, it is also challenging to fuse the embeddings of the same vertices from different coordinate systems. In face of these challenges, we propose the Fused Manifold Graph Neural Network (FMGNN), a novel GNN architecture that embeds graphs into different Riemannian manifolds with interaction and alignment among these manifolds during training and fuses the vertex embeddings through the distances on different manifolds between vertices and selected landmarks, geometric coresets. Our experiments demonstrate that FMGNN yields superior performance over strong baselines on the benchmarks of node classification and link prediction tasks.

cs.LG↗
arXiv2023

An analytic derivation of the empirical correlations of gamma-ray bursts

Empirical correlations between various key parameters have been extensively explored ever since the discovery of gamma-ray bursts (GRBs) and have been widely used as standard candles to probe the Universe. The Amati relation and the Yonetoku relation are two good examples that enjoyed special attention. The former reflects the connection between the peak photon energy (Ep) and the isotropic $γ$-ray energy release (Eiso), while the latter links Ep with the isotropic peak luminosity (Lp), both in the form of a power-law function. Most GRBs are found to follow these correlations well, but a theoretical interpretation is still lacking. Some obvious outliers may be off-axis GRBs and may follow correlations that are different from those at the on-axis. Here we present a simple analytical derivation for the Amati relation and the Yonetoku relation in the framework of the standard fireball model, the correctness of which is then confirmed by numerical simulations. The off-axis Amati relation and Yonetoku relation are also derived. They differ markedly from the corresponding on-axis relation. Our results reveal the intrinsic physics behind the radiation processes of GRBs, and they highlight the importance of the viewing angle in the empirical correlations of GRBs.

astro-ph.HE↗
arXiv2023

$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features

In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features. To address this issue, we propose a simple yet effective method to equalize the $l_2$ norms of sample features. Concretely, we $l_2$-normalize each sample feature before feeding them into batch normalization, and therefore the features are of the same magnitude. Since the proposed method combines the $l_2$ normalization and batch normalization, we name our method $L_2$BN. The $L_2$BN can strengthen the compactness of intra-class features and enlarge the discrepancy of inter-class features. The $L_2$BN is easy to implement and can exert its effect without any additional parameters or hyper-parameters. We evaluate the effectiveness of $L_2$BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The results demonstrate that the $L_2$BN can boost the generalization ability of various neural network models and achieve considerable performance improvements.

cs.CV↗
arXiv2022

Pseudo Redshifts of Gamma-Ray Bursts Derived from the L-T-E Correlation

The X-ray afterglow of many gamma-ray bursts (GRBs) exhibits a plateau phase before the normal power-law decay stage, which may be related to continued activities of the central engine. Tang et al. 2019 collected 174 such GRBs and confirmed the so called $L-T-E$ correlation which involves three key parameters, i.e., the isotropic $γ$-ray energy $E_{γ,\rm iso}$ of the prompt phase, the end time $T_{a}$ of the plateau phase and the corresponding X-ray luminosity $L_{X}$. In this study, the $L-T-E$ correlation is confirmed and updated as $L_{X} \propto T_{a}^{-0.99} E_{γ,\rm iso}^{0.86}$ with a large sample consisting of 210 plateau GRBs with known redshifts. The tight correlation is then applied to derive the pseudo redshift of other 130 plateau GRBs whose redshifts are not directly measured. Statistical analysis is also carried out on this pseudo redshift sample.

astro-ph.HE↗
arXiv2022

Fuzzy Positive Learning for Semi-supervised Semantic Segmentation

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach.

cs.CV↗
arXiv2022

Applications of spherical twist functors to Lie algebras associated to root categories of preprojective algebras

Let $Λ_Q$ be the preprojective algebra of a finite acyclic quiver $Q$ of non-Dynkin type and $D^b(\mathrm{rep}^n Λ_Q)$ be the bounded derived category of finite dimensional nilpotent $Λ_Q$-modules. We define spherical twist functors over the root category $\mathcal{R}_{Λ_Q}$ of $D^b(\mathrm{rep}^n Λ_Q)$ and then realize the Weyl group associated to $Q$ as certain subquotient of the automorphism group of the Ringel-Hall Lie algebra $\mathfrak{g}(\mathcal{R}_{Λ_Q})$ of $\mathcal{R}_{Λ_Q}$ induced by spherical twist functors. We also present a conjectural relation between certain Lie subalgebras of $\mathfrak{g}(\mathcal{R}_{Λ_Q})$ and $\mathfrak{g}(\mathcal{R}_Q)$, where $\mathfrak{g}(\mathcal{R}_Q)$ is the Ringe-Hall Lie algebra associated to the root category $\mathcal{R}_Q$ of $Q$.

math.RT↗
arXiv2022

Quantum cluster algebras associated to weighted projective lines

Let $\mathbb{X}_{\boldsymbol{p},\boldsymbolλ}$ be a weighted projective line. We define the quantum cluster algebra of $\mathbb{X}_{\boldsymbol{p},\boldsymbolλ}$ and realize its specialized version as the subquotient of the Hall algebra of $\mathbb{X}_{\boldsymbol{p},\boldsymbolλ}$ via the quantum cluster character map. Inspired by \cite{Chen2021}, we prove an analogue cluster multiplication formula between quantum cluster characters. As an application, we obtain the polynomial property of the cardinalities of Grassmannian varieties of exceptional coherent sheaves on $\mathbb{X}_{\boldsymbol{p},\boldsymbolλ}$ . In the end, we construct several bar-invariant $\mathbb{Z}[ν^{\pm}]$-bases for the quantum cluster algebra of the projective line $\mathbb{P}^1$ and show how it coincides with the quantum cluster algebra of the Kronecker quiver.

math.RT↗
arXiv2022

Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and sparsification, have been proposed to improve the communication efficiency of model aggregation. However, the information-theoretic minimum communication cost for a given distortion of gradient estimators is still unknown. In this paper, we study the fundamental limit of communication cost of model aggregation in distributed learning from a rate-distortion perspective. By formulating the model aggregation as a vector Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion function for the model aggregation problem, which reveals the minimum communication rate at a particular gradient distortion upper bound. We also analyze the communication cost at each iteration and total communication cost based on the sum-rate-distortion function with the gradient statistics of real-world datasets. It is found that the communication gain by exploiting the correlation between worker nodes is significant for SignSGD, and a high distortion of gradient estimator can achieve low total communication cost in gradient compression.

cs.IT↗
arXiv2022

PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction

Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the development of new models is restricted by the lack of dataset and benchmark training procedure. To the best of our knowledge, the existing open source datasets are far less to satisfy the needs of modern protein sequence-structure related research. To solve this problem, we present the first million-level protein structure prediction dataset with high coverage and diversity, named as PSP. This dataset consists of 570k true structure sequences (10TB) and 745k complementary distillation sequences (15TB). We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset. We validate the utility of this dataset for training by participating CAMEO contest in which our model won the first place. We hope our PSP dataset together with the training benchmark can enable a broader community of AI/biology researchers for AI-driven protein related research.

q-bio.BM↗
arXiv2022

Periodic repeating fast radio bursts: interaction between a magnetized neutron star and its planet in an eccentric orbit

Fast radio bursts (FRBs) are mysterious transient phenomena. The study of repeating FRBs may provide useful information about their nature due to their redetectability. The two most famous repeating sources are FRBs 121102 and 180916, with a period of 157 days and 16.35 days, respectively. Previous studies suggest that the periodicity of FRBs is likely associated with neutron star (NS) binary systems. Here we introduce a new model which proposes that periodic repeating FRBs are due to the interaction of a NS with its planet in a highly elliptical orbit. The periastron of the planet is very close to the NS so that it would be partially disrupted by tidal force every time it passes through the periastron. Fragments generated in the process could interact with the compact star through the Alfvén wing mechanism and produce FRBs. The model can naturally explain the repeatability of FRBs with a period ranging from a few days to several hundred days, but it generally requires that the eccentricity of the planet's orbit should be large enough. Taking FRBs 121102 and 180916 as examples, it is shown that the main features of the observed repeating behaviors can be satisfactorily accounted for.

astro-ph.HE↗
arXiv2022

Generalized quantum cluster algebras: the Laurent phenomenon and upper bounds

Generalized quantum cluster algebras introduced in [1] are quantum deformation of generalized cluster algebras of geometric types. In this paper, we prove that the Laurent phenomenon holds in these generalized quantum cluster algebras. We also show that upper bounds coincide with the corresponding generalized quantum upper cluster algebras under the "coprimality" condition.

math.QA↗