arXiv subjects

Yan Zhou

Publications and source records attributed to Yan Zhou.

At least 91 records · Page 5Linked to original sources

arXiv2024

Risk Analysis in Customer Relationship Management via Quantile Region Convolutional Neural Network-Long Short-Term Memory and Cross-Attention Mechanism

Risk analysis is an important business decision support task in customer relationship management (CRM), involving the identification of potential risks or challenges that may affect customer satisfaction, retention rates, and overall business performance. To enhance risk analysis in CRM, this paper combines the advantages of quantile region convolutional neural network-long short-term memory (QRCNN-LSTM) and cross-attention mechanisms for modeling. The QRCNN-LSTM model combines sequence modeling with deep learning architectures commonly used in natural language processing tasks, enabling the capture of both local and global dependencies in sequence data. The cross-attention mechanism enhances interactions between different input data parts, allowing the model to focus on specific areas or features relevant to CRM risk analysis. By applying QRCNN-LSTM and cross-attention mechanisms to CRM risk analysis, empirical evidence demonstrates that this approach can effectively identify potential risks and provide data-driven support for business decisions.

cs.LG↗

arXiv2024

Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis

In the context of global trade, cross-border commodity pricing largely determines the competitiveness and market share of businesses. However, existing methodologies often prove inadequate, as they lack the agility and precision required to effectively respond to the dynamic international markets. Time series data is of great significance in commodity pricing and can reveal market dynamics and trends. Therefore, we propose a new method based on the hybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate prediction and optimization of cross-border commodity pricing strategies through in-depth analysis and optimization of time series data. Our model undergoes experimental validation across multiple datasets. The results show that our method achieves significant performance advantages on datasets such as UNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our model reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly better than other models. On the IMF and WITS datasets, our method also achieves similar excellent performance. These experimental results verify the effectiveness and reliability of our model in the field of cross-border commodity pricing. Overall, this study provides an important reference for enterprises to formulate more reasonable and effective cross-border commodity pricing strategies, thereby enhancing market competitiveness and profitability. At the same time, our method also lays a foundation for the application of deep learning in the fields of international trade and economic strategy optimization, which has important theoretical and practical significance.

cs.LG↗

arXiv2024

Chemical models of interstellar glycine and adenine precursor aminoacetonitrile (NH2CH2CN)

Aminoacetonitrile (AAN), also known as glycinenitrile, has been suggested as a possible precursor of glycine and adenine in the interstellar medium. Here we present the chemical modeling of AAN and its isomers in hot cores using the three-phase chemical model NAUTILUS with the addition of over 300 chemical reactions of the three AAN isomers and related species. Our models predicted a peak gas phase abundance of AAN reaching the order of 10-8, which is consistent with observation towards Sgr B2(N). Regarding the reaction pathways of AAN and its isomers, we found that AAN is primarily formed via free radical reactions on grain surfaces during the early evolutionary stages. Subsequently, it is thermally desorbed into the gas phase as the temperature rises and is then destroyed by positive ions and radicals in gas phase. The isomers of AAN are formed through the hydrogenation reaction of CH3NCN on the grain surface and via electron recombination reactions of ion C2H5N2+ in gas phase. We speculate that there is a possibility for NCCN and AAN to react with each other, eventually leading to the formation of adenine in hot cores. However, further investigation is required to understand the efficiency of grain surfaces in adenine formation, through theoretical calculations or laboratory experiments in future research.

astro-ph.GA↗

arXiv2024

Bimerons create bimerons: proliferation and aggregation induced by currents and magnetic fields

The aggregation of topological spin textures at nano and micro scales has practical applications in spintronic technologies. Here, the authors report the in-plane current-induced proliferation and aggregation of bimerons in a bulk chiral magnet. It is found that the spin-transfer torques can induce the proliferation and aggregation of bimerons only in the presence of an appropriate out-of-plane magnetic field. It is also found that a relatively small damping and a relatively large non-adiabatic spin-transfer torque could lead to more pronounced bimeron proliferation and aggregation. Particularly, the current density should be larger than a certain threshold in order to trigger the proliferation; namely, the bimerons may only be driven into translational motion under weak current injection. Besides, the authors find that the aggregate bimerons could relax into a deformed honeycomb bimeron lattice with a few lattice structure defects after the current injection. The results are promising for the development of bio-inspired spintronic devices that use a large number of aggregate bimerons. The findings also provide a platform for studying aggregation-induced effects in spintronic systems, such as the aggregation-induced lattice phase transitions.

cond-mat.mes-hall↗

arXiv2024

Transformation of a cellular skyrmion to polyomino-like structures

Topological spin structures with transformable shapes may have potential implications on data storage and computation. Here, we demonstrate that a square cellular skyrmion on an artificial grid pinning pattern can be manipulated by programmed current pulses. We find that parallel short pulses could result in the elongation of the skyrmion mainly in the current direction, while parallel long pulses are able to induce the elongation in the direction perpendicular to the current due to the intrinsic skyrmion Hall effect. Consequently, a programmed sequence of parallel pulses could lead to the transformation of the skyrmion to I-, L-, and Z-shaped polyomino-like structures without affecting the topological charge. In addition, we find that orthogonal pulses could lead to the transformation to more complex polyomino-like structures, including the T-shaped and irregular ones. Particularly, when a small T-shaped structure is formed, the topological charge of the system is found to be non-integer due to incomplete compensation of local topological charge densities; however, the T-shaped structure is stable on the attractive pinning pattern. Our results offer an effective way to create polyomino-like spin structures toward functional applications.

cond-mat.mes-hall↗

arXiv2024

Investigating Sulfur Chemistry in the HD 163296 disk

Sulfur chemistry in the formation process of low-mass stars and planets remains poorly understood. The protoplanetary disks (PPDs) are the birthplace of planets and its distinctive environment provides an intriguing platform for investigating models of sulfur chemistry. We analyzed the ALMA observations of CS 7-6 transitions in the HD 163296 disk and perform astrochemical modeling to explore its sulfur chemistry. We simulated the distribution of sulfur-containing molecules and compared it with observationally deduced fractional column densities. We have found that the simulated column density of CS is consistent with the observationally deduced fractional column densities, while the simulated column density of C$_2$S is lower than the observationally deduced upper limits on column densities. This results indicate that we have a good understanding of the chemical properties of CS and C$_2$S in the disk. We also investigated the influence of the C/O ratio on sulfur-containing molecules and found that the column densities of SO, SO$_2$, and H$_2$S near the central star are dependent on the C/O ratio. Additionally, we found that the $N$[CS]/$N$[SO] ratio can serve as a promising indicator of the disk's C/O ratio in the HD 163296. Overall, the disk of HD 163296 provides a favorable environment for the detection of sulfur-containing molecules.

astro-ph.EP↗

arXiv2024

CTC-based Non-autoregressive Textless Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) has achieved impressive translation quality, but it often faces the challenge of slow decoding due to the considerable length of speech sequences. Recently, some research has turned to non-autoregressive (NAR) models to expedite decoding, yet the translation quality typically lags behind autoregressive (AR) models significantly. In this paper, we investigate the performance of CTC-based NAR models in S2ST, as these models have shown impressive results in machine translation. Experimental results demonstrate that by combining pretraining, knowledge distillation, and advanced NAR training techniques such as glancing training and non-monotonic latent alignments, CTC-based NAR models achieve translation quality comparable to the AR model, while preserving up to 26.81$\times$ decoding speedup.

cs.CL↗

arXiv2024

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees

This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence. By employing a approach this article introduces an innovative theoretical framework to meticulously assess the effectiveness and performance of Meta RL algorithms. We present an explanation of generalization limits measuring how well these algorithms can adapt to learning tasks while maintaining consistent results. Our analysis delves into the factors that impact the adaptability of Meta RL revealing the relationship, between algorithm design and task complexity. Additionally we establish convergence assurances by proving conditions under which Meta RL strategies are guaranteed to converge towards solutions. We examine the convergence behaviors of Meta RL algorithms across scenarios providing a comprehensive understanding of the driving forces behind their long term performance. This exploration covers both convergence and real time efficiency offering a perspective, on the capabilities of these algorithms.

cs.LG↗

arXiv2024

Automatic News Generation and Fact-Checking System Based on Language Processing

This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rapid development of Natural Language Processing (NLP) and deep learning technologies, automatic news generation systems are capable of extracting key information from massive data and generating well-structured, fluent news articles. Meanwhile, by integrating fact-checking technology, the system can effectively prevent the spread of false news and improve the accuracy and credibility of news. This study details the key technologies involved in automatic news generation and factchecking, including text generation, information extraction, and the application of knowledge graphs, and validates the effectiveness of these technologies through experiments. Additionally, the paper discusses the future development directions of automatic news generation and fact-checking systems, emphasizing the importance of further integration and innovation of technologies. The results show that with continuous technological optimization and practical application, these systems will play an increasingly important role in the future news industry, providing more efficient and reliable news services.

cs.CL↗

arXiv2024

RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis

As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polarities from the given text-image pairs, has gained increasing concerns. Existing works encounter two limitations: (1) multi-level modality noise, i.e., instance- and feature-level noise; and (2) multi-grained semantic gap, i.e., coarse- and fine-grained gap. Both issues may interfere with accurate identification of aspect-sentiment pairs. To address these limitations, we propose a novel framework named RNG for JMASA. Specifically, to simultaneously reduce multi-level modality noise and multi-grained semantic gap, we design three constraints: (1) Global Relevance Constraint (GR-Con) based on text-image similarity for instance-level noise reduction, (2) Information Bottleneck Constraint (IB-Con) based on the Information Bottleneck (IB) principle for feature-level noise reduction, and (3) Semantic Consistency Constraint (SC-Con) based on mutual information maximization in a contrastive learning way for multi-grained semantic gap reduction. Extensive experiments on two datasets validate our new state-of-the-art performance.

cs.CL↗

arXiv2024

Deep Reinforcement Learning for Modelling Protein Complexes

AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a.k.a., protein complex modelling, PCM). However, there are still two challenges: 1) The huge combinatorial optimization space of $N^{N-2}$ ($N$ is the number of chains) for the PCM problem can easily lead to high computational cost. 2) The scales of protein complexes exhibit distribution shift due to variance in chain numbers, which calls for the generalization in modelling complexes of various scales. To address these challenges, we propose GAPN, a Generative Adversarial Policy Network powered by domain-specific rewards and adversarial loss through policy gradient for automatic PCM prediction. Specifically, GAPN learns to efficiently search through the immense assembly space and optimize the direct docking reward through policy gradient. Importantly, we design an adversarial reward function to enhance the receptive field of our model. In this way, GAPN will simultaneously focus on a specific batch of complexes and the global assembly rules learned from complexes with varied chain numbers. Empirically, we have achieved both significant accuracy (measured by RMSD and TM-Score) and efficiency improvements compared to leading PCM softwares.

cs.CE↗

arXiv2024

Magnetic Bimeron Traveling on the Domain Wall

Domain wall bimerons (DWBMs) are nanoscale spin textures residing within the magnetic domain walls of in-plane magnets. In this study, we employ both numerical and analytical methods to explore the stabilization of Néel-type domain wall bimerons and their dynamics when excited by spin-orbit torque. Our findings reveal two unique and intriguing dynamic mechanisms, which depend on the polarization direction of the spin current: In the first scenario, the magnetic domain wall serves as a track that confines the motion of the bimeron and effectively suppresses the skyrmion Hall effect. In the second scenario, pushing the magnetic domain wall triggers a rapid sliding of the bimeron along the wall. This process significantly enhances the dynamics of the bimeron, resulting in a velocity increase of approximately 40 times compared to skyrmions and bimeron solitons. Our results highlight the potential advantages of the skyrmion Hall effect in developing energy-efficient spintronic devices based on domain wall bimerons.

cond-mat.mes-hall↗

arXiv2024

fMRI Exploration of Visual Quality Assessment

Despite significant strides in visual quality assessment, the neural mechanisms underlying visual quality perception remain insufficiently explored. This study employed fMRI to examine brain activity during image quality assessment and identify differences in human processing of images with varying quality. Fourteen healthy participants underwent tasks assessing both image quality and content classification while undergoing functional MRI scans. The collected behavioral data was statistically analyzed, and univariate and functional connectivity analyses were conducted on the imaging data. The findings revealed that quality assessment is a more complex task than content classification, involving enhanced activation in high-level cognitive brain regions for fine-grained visual analysis. Moreover, the research showed the brain's adaptability to different visual inputs, adopting different strategies depending on the input's quality. In response to high-quality images, the brain primarily uses specialized visual areas for precise analysis, whereas with low-quality images, it recruits additional resources including higher-order visual cortices and related cognitive and attentional networks to decode and recognize complex, ambiguous signals effectively. This study pioneers the intersection of neuroscience and image quality research, providing empirical evidence through fMRI linking image quality to neural processing. It contributes novel insights into the human visual system's response to diverse image qualities, thereby paving the way for advancements in objective image quality assessment algorithms.

cs.MM↗

arXiv2024

WKB asymptotics of Stokes matrices, spectral curves and rhombus inequalities

We consider an $n\times n$ system of ODEs on $\mathbb{P}^1$ with a simple pole $A$ at $z=0$ and a double pole $u={\rm diag}(u_1, \dots, u_n)$ at $z=\infty$. This is the simplest situation in which the monodromy data of the system are described by upper and lower triangular Stokes matrices $S_\pm$, and we impose reality conditions which imply $S_-=S_+^\dagger$. We study leading WKB exponents of Stokes matrices in parametrizations given by generalized minors and by spectral coordinates, and we show that for $u$ on the caterpillar line (which corresponds to the limit $(u_{j+1}-u_j)/(u_j - u_{j-1}) \to \infty$ for $j=2, \cdots, n-1$), the real parts of these exponents are given by periods of certain cycles on the degenerate spectral curve $Γ(u_{\rm cat}(t), A)$. These cycles admit unique deformations for $u$ near the caterpillar line. Using the spectral network theory, we give for $n=2$, and $n=3$ exact WKB predictions for asymptotics of generalized minors in terms of periods of these cycles. Boalch's theorem from Poisson geometry implies that real parts of leading WKB exponents satisfy the rhombus (or interlacing) inequalities. We show that these inequalities are in correspondence with finite webs of the canonical foliation on the root curve $Γ^r(u, A)$, and that they follow from the positivity of the corresponding periods. We conjecture that a similar mechanism applies for $n>3$. We also outline the relation of the spectral coordinates with the cluster structures considered by Goncharov-Shen, and with ${\mathcal N}=2$ supersymmetric quantum field theories in dimension four associated with some simple quivers.

math-ph↗

arXiv2024

Structural stability of three dimensional transonic shock flows with an external force

We establish the existence and uniqueness of the transonic shock solution for steady isentropic Euler system with an external force in a rectangular cylinder under the three-dimensional perturbations for the incoming supersonic flow, the exit pressure and the external force. The external force has a stabilization effect on the transonic shocks in flat nozzles and the transonic shock is completely free, we do not require it passing through a fixed point. By utilizing the deformation-curl decomposition to decouple the hyperbolic and elliptic modes in the steady Euler system effectively and reformulating the Rankine-Hugoniot conditions, the transonic shock problem is reduced to a deformation-curl first order system for the velocity field with nonlocal terms supplementing with an unusual second order differential boundary condition on the shock front, an algebraic equation for determining the shock front and two transport equations for the Bernoulli's quantity and the first component of the vorticity.

math.AP↗

arXiv2024

Smooth axisymmetric transonic irrotational flows to the steady Euler equations with an external force

For a class of external forces, we prove the existence and uniqueness of smooth transonic flows to the one dimensional steady Euler system with an external force, which is subsonic at the inlet and flows out at supersonic speed after smoothly accelerating through the sonic point. We then investigate the structural stability of the one-dimensional smooth transonic flows with positive acceleration under axisymmetric perturbations of suitable boundary conditions, and establish the first existence and uniqueness result for smooth axisymmetric transonic irrotational flows. The key point lies on the analysis of a linear second order elliptic-hyperbolic mixed differential equation of Keldysh type with a singular term. Some weighted Sobolev spaces $H_r^m(D) (m=2,3,4)$ are introduced to deal with the singularities near the axis. Compared with the stability analysis in the two dimensional case by Weng and Xin (arXiv:2309.07468), there are several interesting new observations about the structure of the linear mixed type equation with a singular term which play crucial roles in establishing the $H^4_r(D)$ estimate.

math.AP↗

arXiv2024

Mirror Symmetry for log Calabi-Yau Surfaces II

We show that the ring of regular functions of every smooth affine log Calabi-Yau surface with maximal boundary has a vector space basis parametrized by its set of integer tropical points and a $\mathbb{C}$-algebra structure with structure coefficients given by the geometric construction of Keel-Yu. To prove this result, we first give a canonical compactification of the mirror family associated with a pair $(Y,D)$ constructed by Gross-Hacking-Keel where $Y$ is a smooth projective rational surface, $D$ is an anti-canonical cycle of rational curves and $Y\setminus D$ is the minimal resolution of an affine surface with, at worst, du Val singularities. Then, we compute periods for the compactified family using techniques from work of Ruddat-Siebert and use this to give a modular interpretation of the compactified mirror family.

math.AG↗

arXiv2024

Using AI Uncertainty Quantification to Improve Human Decision-Making

AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The majority of past research on AI and human decision-making has concentrated on model explainability and interpretability, with little focus on understanding the potential impact of UQ on human decision-making. We evaluated the impact on human decision-making for instance-level UQ, calibrated using a strict scoring rule, in two online behavioral experiments. In the first experiment, our results showed that UQ was beneficial for decision-making performance compared to only AI predictions. In the second experiment, we found UQ had generalizable benefits for decision-making across a variety of representations for probabilistic information. These results indicate that implementing high quality, instance-level UQ for AI may improve decision-making with real systems compared to AI predictions alone.

cs.AI↗