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In recent years, the breakthrough of Large Language Models (LLMs) offers new ideas for achieving universal methods on graph data. The common practice of converting graphs into natural language for LLMs, which refers to graph flattening, exhibits good generalizability and interpretability. However, the poor organization of the textual format results in poor performance in long-distance scenario understanding. Inspired by human cognitive reasoning habits, we propose a novel method for graph flattening to fit LLMs, termed as End-to-End DAG-Path prompting (EEDP). Experiments on real-world datasets show that EEDP enhances the reasoning performance of LLMs in long-distance scenarios while maintaining excellent performance in short-distance scenarios, demonstrating good robustness in the face of distance variations., Comment: 2024 1st International Conference on Computational Linguistics and Natural Language Processing (CLNLP 2024)

Rutile RuO$_2$ has been posited as a potential $d$-wave altermagnetism candidate, with a predicted significant spin splitting up to 1.4 eV. Despite accumulating theoretical predictions and transport measurements, direct spectroscopic observation of spin splitting has remained elusive. Here, we employ spin- and angle-resolved photoemission spectroscopy to investigate the band structures and spin polarization of thin-film and single-crystal RuO$_2$. Contrary to expectations of altermagnetism, our analysis indicates that RuO$_2$'s electronic structure aligns with those predicted under non-magnetic conditions, exhibiting no evidence of the hypothesized spin splitting. Additionally, we observe significant in-plane spin polarization of the low-lying bulk bands, which is antisymmetric about the high-symmetry plane and contrary to the $d$-wave spin texture due to time-reversal symmetry breaking in altermagnetism. These findings definitively challenge the altermagnetic order previously proposed for rutile RuO$_2$, prompting a reevaluation of its magnetic properties., Comment: 6 pages, 4 figures. Submitted to Physical Review Letters

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textit{actually} reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM's own style.

Anomaly detection is a long-standing challenge in manufacturing systems. Traditionally, anomaly detection has relied on human inspectors. However, 3D point clouds have gained attention due to their robustness to environmental factors and their ability to represent geometric data. Existing 3D anomaly detection methods generally fall into two categories. One compares scanned 3D point clouds with design files, assuming these files are always available. However, such assumptions are often violated in many real-world applications where model-free products exist, such as fresh produce (i.e., ``Cookie", ``Potato", etc.), dentures, bone, etc. The other category compares patches of scanned 3D point clouds with a library of normal patches named memory bank. However, those methods usually fail to detect incomplete shapes, which is a fairly common defect type (i.e., missing pieces of different products). The main challenge is that missing areas in 3D point clouds represent the absence of scanned points. This makes it infeasible to compare the missing region with existing point cloud patches in the memory bank. To address these two challenges, we proposed a unified, unsupervised 3D anomaly detection framework capable of identifying all types of defects on model-free products. Our method integrates two detection modules: a feature-based detection module and a reconstruction-based detection module. Feature-based detection covers geometric defects, such as dents, holes, and cracks, while the reconstruction-based method detects missing regions. Additionally, we employ a One-class Support Vector Machine (OCSVM) to fuse the detection results from both modules. The results demonstrate that (1) our proposed method outperforms the state-of-the-art methods in identifying incomplete shapes and (2) it still maintains comparable performance with the SOTA methods in detecting all other types of anomalies.

Educational resource understanding is vital to online learning platforms, which have demonstrated growing applications recently. However, researchers and developers always struggle with using existing general natural language toolkits or domain-specific models. The issue raises a need to develop an effective and easy-to-use one that benefits AI education-related research and applications. To bridge this gap, we present a unified, modularized, and extensive library, EduNLP, focusing on educational resource understanding. In the library, we decouple the whole workflow to four key modules with consistent interfaces including data configuration, processing, model implementation, and model evaluation. We also provide a configurable pipeline to unify the data usage and model usage in standard ways, where users can customize their own needs. For the current version, we primarily provide 10 typical models from four categories, and 5 common downstream-evaluation tasks in the education domain on 8 subjects for users' usage. The project is released at: https://github.com/bigdata-ustc/EduNLP.

In this paper, we reconsider two new iterative methods for solving absolute value equations (AVE), which is proposed by Ali and Pan (Jpn. J. Ind. Appl. Math. 40: 303--314, 2023). Convergence results of the two iterative schemes and new sufficient conditions for the unique solvability of AVE are presented. In addition, for a special case, the optimal iteration parameters of the two algorithms are analyzed, respectively. Numerical results demonstrate our claims., Comment: 13 pages

Altermagnetism (AM), a newly discovered magnetic state, ingeniously integrates the properties of ferromagnetism and antiferromagnetism, representing a significant breakthrough in the field of magnetic materials. Despite experimental verification of some typical AM materials, such as MnTe and MnTe$_2$, the pursuit of AM materials that feature larger spin splitting and higher transition temperature is still essential. Here, our research focuses on CrSb, which possesses N{\'e}el temperature of up to 700K and giant spin splitting near the Fermi level ($E_F$). Utilizing high-resolution angle-resolved photoemission spectroscopy and density functional theory calculations, we meticulously map the three-dimensional electronic structure of CrSb. Our photoemission spectroscopic results on both (0001) and (10$\overline{1}$0) cleavages of CrSb collaboratively reveal unprecedented details on AM-induced band splitting, and subsequently pin down its unique bulk $g$-wave symmetry through quantitative analysis of the angular and photon-energy dependence of spin splitting. Moreover, the observed spin splitting reaches the magnitude of 0.93~eV near $E_F$, the most substantial among all confirmed AM materials. This study not only validates the nature of CrSb as a prototype $g$-wave like AM material but also underscores its pivotal role in pioneering applications in spintronics.

Geometry Problem Solving (GPS), which is a classic and challenging math problem, has attracted much attention in recent years. It requires a solver to comprehensively understand both text and diagram, master essential geometry knowledge, and appropriately apply it in reasoning. However, existing works follow a paradigm of neural machine translation and only focus on enhancing the capability of encoders, which neglects the essential characteristics of human geometry reasoning. In this paper, inspired by dual-process theory, we propose a Dual-Reasoning Geometry Solver (DualGeoSolver) to simulate the dual-reasoning process of humans for GPS. Specifically, we construct two systems in DualGeoSolver, namely Knowledge System and Inference System. Knowledge System controls an implicit reasoning process, which is responsible for providing diagram information and geometry knowledge according to a step-wise reasoning goal generated by Inference System. Inference System conducts an explicit reasoning process, which specifies the goal in each reasoning step and applies the knowledge to generate program tokens for resolving it. The two systems carry out the above process iteratively, which behaves more in line with human cognition. We conduct extensive experiments on two benchmark datasets, GeoQA and GeoQA+. The results demonstrate the superiority of DualGeoSolver in both solving accuracy and robustness from explicitly modeling human reasoning process and knowledge application., Comment: IJCAI 2024 Accepted

Revealing the momentum-resolved electronic structure of infinite-layer nickelates is essential for understanding this new class of unconventional superconductors, but has been hindered by the formidable challenges in improving the sample quality. In this work, we report for the first time the angle-resolved photoemission spectroscopy of superconducting La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ films prepared by molecular beam epitaxy and ${\mathrm{\textit{in situ}}}$ atomic-hydrogen reduction. The measured Fermi topology closely matches theoretical calculations, showing a large Ni-$d_{x^2-y^2}$ derived Fermi sheet that evolves from hole-like to electron-like along $k_{z}$, and a three-dimensional (3D) electron pocket centered at Brillouin zone corner. The Ni-$d_{x^2-y^2}$ derived bands show a mass enhancement ($m^*/m_{\rm{DFT}}$) of 2-3,while the 3D electron band shows negligible band renormalization. Moreover, the Ni-$d_{x^2-y^2}$ derived states also display a band dispersion anomaly at higher binding energy, reminiscent of the waterfall feature and kinks observed in cuprates., Comment: 29 pages,13 figures

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://github.com/hly1998/BRCD., Comment: 12 pages, 19 figures, Proceedings of the ACM Web Conference 2024 (WWW '24)

Magnetic topological quantum materials display a diverse range of fascinating physical properties which arise from their intrinsic magnetism and the breaking of time-reversal symmetry. However, so far, few examples of intrinsic magnetic topological materials have been confirmed experimentally, which significantly hinder our comprehensive understanding of the abundant physical properties in this system. The kagome lattices, which host diversity of electronic structure signatures such as Dirac nodes, flat bands, and saddle points, provide an alternative and promising platform for in-depth investigations into correlations and band topology. In this article, drawing inspiration from the stacking configuration of MnBi$_2$Te$_4$, we conceive and then synthesize a high-quality single crystal EuTi$_3$Bi$_4$, which is a unique natural heterostructure consisting of both topological kagome layers and magnetic interlayers. We investigate the electronic structure of EuTi$_3$Bi$_4$ and uncover distinct features of anisotropic multiple Van Hove singularitie (VHS) that might prevent Fermi surface nesting, leading to the absence of a charge density wave (CDW). In addition, we identify the topological nontrivial surface states that serve as connections between different saddle bands in the vicinity of the Fermi level. Combined with calculations, we establish that, the effective time-reversal symmetry S=$\theta$$\tau_{1/2}$ play a crucial role in the antiferromagnetic ground state of EuTi$_3$Bi$_4$, which ensures the stability of the topological surface states and gives rise to their intriguing topological nature. Therefore, EuTi$_3$Bi$_4$ offers the rare opportunity to investigate correlated topological states in magnetic kagome materials., Comment: 9 pages, 4 figures

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow the proficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novel response-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness., Comment: Accepted by the ACM Web Conference 2024 (WWW '24)

Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio., Comment: 8 pages, 6 figures

A general method of separating isotopes by centrifuging dissolved chemical compounds in a liquid solution is introduced. This technique can be applied to almost all elements and leads to large separation factors. The method has been demonstrated in several isotopic systems including Ca, Mo, O, and Li with single-stage selectivities of 1.046-1.067 per unit mass difference (e.g., 1.434 in 40Ca/48Ca, 1.134 in 16O/18O), which are beyond the capabilities of various conventional methods of isotope enrichment. Equations are derived to model the process and the results agree with those of the experiments. The scalability of the technique has been demonstrated by performing a three-stage enrichment of 48Ca, and the scalability is more broadly supported through analogies to the gas centrifuge, whereby countercurrent centrifugation can further multiply the separation factor by 5-10 times per stage in a continuous process. Optimal centrifuge conditions and solutions can achieve both high-throughput and highly efficient isotope separation.

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process., Comment: Accepted by AAAI 2023

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learning. Besides, we treat subtask 2 as a generation problem with the solution pipeline including specially designed prompts, adversarial training, post-processing methods and ensemble learning. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition. Our code is available at https://github.com/bigdata-ustc/nl4opt.

Layered kagome-lattice materials with 3$d$ transition metals provide a fertile playground for studies on geometry frustration, band topology and other novel ordered states. A representative class of materials AV$_3$Sb$_5$ (A=K, Rb, Cs) have been proved to possess various unconventional phases such as superconductivity, non-trivial $\mathbb{Z}_2$ band topology, and electronic nematicity, which are intertwined with multiple interlaced charge density waves (CDW). However, the interplay among these novel states and their mechanisms are still elusive. Recently, the discovery of isostructural titanium-based single-crystals ATi$_3$Bi$_5$ (A=K, Rb, Cs), which demonstrate similar multiple exotic states but in the absence of the concomitant intertwined CDW, has been offering an ideal opportunity to disentangle these complex novel states in kagome-lattice. Here, we combine the high-resolution angle-resolved photoemission spectroscopy and first-principles calculations to systematically investigate the low-lying electronic structure of RbTi$_3$Bi$_5$. For the first time, we experimentally demonstrate the coexistence of flat bands and multiple non-trivial topological states, including type-II Dirac nodal lines and non-trivial $\mathbb{Z}_2$ topological surface states therein. Furthermore, our findings as well provide the hint of rotation symmetry breaking in RbTi$_3$Bi$_5$, suggesting the directionality of the electronic structure and possibility of emerging pure electronic nematicity in this new family of kagome compounds, which may provide important insights into the electronic nematic phase in correlated kagome metals., Comment: 6 pages, 5 figures

Machine learning systems are often applied to data that is drawn from a different distribution than the training distribution. Recent work has shown that for a variety of classification and signal reconstruction problems, the out-of-distribution performance is strongly linearly correlated with the in-distribution performance. If this relationship or more generally a monotonic one holds, it has important consequences. For example, it allows to optimize performance on one distribution as a proxy for performance on the other. In this paper, we study conditions under which a monotonic relationship between the performances of a model on two distributions is expected. We prove an exact asymptotic linear relation for squared error and a monotonic relation for misclassification error for ridge-regularized general linear models under covariate shift, as well as an approximate linear relation for linear inverse problems., Comment: 34 pages, 7 figures

Kagome superconductors AV$_3$Sb$_5$ (A = K, Rb, Cs) provide a fertile playground for studying intriguing phenomena, including non-trivial band topology, superconductivity, giant anomalous Hall effect and charge density wave (CDW). Recently, a $C_2$ symmetric nematic phase prior to the superconducting state in AV$_3$Sb$_5$ drew enormous attention due to its potential inheritance of the symmetry of the unusual superconductivity. However, direct evidence on the rotation symmetry breaking of the electronic structure in the CDW state from the reciprocal space is still rare, and the underlying mechanism remains ambiguous. The observation shows unconventional unidirectionality, indicative of rotation symmetry breaking from six-fold to two-fold. The interlayer coupling between adjacent planes with $\pi$-phase offset in the 2$\times$2$\times$2 CDW phase leads to the preferred two-fold symmetric electronic structure. These rarely observed unidirectional back-folded bands in KV$_3$Sb$_5$ may provide important insights into its peculiar charge order and superconductivity.

Deep learning based image reconstruction methods outperform traditional methods. However, neural networks suffer from a performance drop when applied to images from a different distribution than the training images. For example, a model trained for reconstructing knees in accelerated magnetic resonance imaging (MRI) does not reconstruct brains well, even though the same network trained on brains reconstructs brains perfectly well. Thus there is a distribution shift performance gap for a given neural network, defined as the difference in performance when training on a distribution $P$ and training on another distribution $Q$, and evaluating both models on $Q$. In this work, we propose a domain adaptation method for deep learning based compressive sensing that relies on self-supervision during training paired with test-time training at inference. We show that for four natural distribution shifts, this method essentially closes the distribution shift performance gap for state-of-the-art architectures for accelerated MRI.

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers., Comment: 9 pages, 12 figures