A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests

Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs). While numerous architectures have been proposed, so far there is still a limited understanding of how various design paradigms differ in terms of expressive power, nor is it clear what design principle achieves maximal expressiveness with minimal architectural complexity. Targeting these fundamental questions, this paper conducts a systematic study of general node-based subgraph GNNs through the lens of Subgraph Weisfeiler-Lehman Tests (SWL). Our central result is to build a complete hierarchy of SWL with strictly growing expressivity. Concretely, we prove that any node-based subgraph GNN falls into one of the six SWL equivalence classes, among which $\mathsf{SSWL}$ achieves the maximal expressive power. We also study how these equivalence classes differ in terms of their practical expressiveness such as encoding graph distance and biconnectivity. In addition, we give a tight expressivity upper bound of all SWL algorithms by establishing a close relation with localized versions of Folklore WL tests (FWL). Overall, our results provide insights into the power of existing subgraph GNNs, guide the design of new architectures, and point out their limitations by revealing an inherent gap with the 2-FWL test. Finally, experiments on the ZINC benchmark demonstrate that $\mathsf{SSWL}$-inspired subgraph GNNs can significantly outperform prior architectures despite great simplicity.Comment: 74 pages, 13 figure

Bulk compositions of the Chang’E-5 lunar soil: Insights into chemical homogeneity, exotic addition, and origin of landing site basalts

Lunar soil is a fine mixture of local rocks and exotic components. The bulk-rock chemical composition of the newly returned Chang’E-5 (CE-5) lunar soil was studied to understand its chemical homogeneity, exotic additions, and origin of landing site basalts. Concentrations of 48 major and trace elements, including many low-concentration volatile and siderophile elements, of two batches of the scooped CE-5 soil samples were simultaneously obtained by inductively coupled plasma mass spectrometry (ICP-MS) with minimal sample consumption. Their major and trace elemental compositions (except for Ni) are uniform at milligram levels (2–4 mg), matching measured compositions of basaltic glasses and estimates based on mineral modal abundances of basaltic fragments. This result indicates that the exotic highland and KREEP (K, rare earth elements, and P-rich) materials are very low (<5%) and the bulk chemical composition (except for Ni) of the CE-5 soil can be used to represent the underlying mare basalt. The elevated Ni concentrations reflect the addition of about 1 wt% meteoritic materials, which would not influence the other bulk composition except for some highly siderophile trace elements such as Ir. The CE-5 soil, which is overall the same as the underlying basalt in composition, displays low Mg# (34), high FeO (22.7 wt%), intermediate TiO2 (5.12 wt%), and high Th (5.14 µg/g) concentrations. The composition is distinct from basalts and soils returned by the Apollo and Luna missions, however, the depletion of volatile or siderophile elements such as K, Rb, Mo, and W in their mantle sources is comparable. The incompatible lithophile trace element concentrations (e.g., Ba, Rb, Th, U, Nb, Ta, Zr, Hf, and REE) of the CE-5 basalts are moderately high and their pattern mimics high-K KREEP. The pattern of these trace elements with K, Th, U, Nb, and Ta anomalies of the CE-5 basalts cannot be explained by the partial melting and crystallization of olivine, pyroxene, and plagioclase. Thus, the mantle source of the CE-5 landing site mare basalt could have contained KREEP components, likely as trapped interstitial melts. To reconcile these observations with the initial unradiogenic Sr and radiogenic Nd isotopic compositions of the CE-5 basalts, clinopyroxene characterized by low Rb/Sr and high Sm/Nd ratios could be one of the main minerals in the KREEP-bearing mantle source. Consequently, we propose that the CE-5 landing site mare basalts very likely originated from partial melting of a shallow and clinopyroxene-rich (relative to olivine and orthopyroxene) upper mantle cumulate with a small fraction (about 1–1.5 %) of KREEP-like materials

Retrieve Anyone: A General-purpose Person Re-identification Task with Instructions

Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately, which limits the applications in the real world. This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions.Our instruct-ReID is a more general ReID setting, where existing ReID tasks can be viewed as special cases by designing different instructions. We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting. Experimental results show that the baseline model trained on our OmniReID benchmark can improve +0.5%, +3.3% mAP on Market1501 and CUHK03 for traditional ReID, +2.1%, +0.2%, +15.3% mAP on PRCC, VC-Clothes, LTCC for clothes-changing ReID, +12.5% mAP on COCAS+ real2 for clothestemplate based clothes-changing ReID when using only RGB images, +25.5% mAP on COCAS+ real2 for our newly defined language-instructed ReID. The dataset, model, and code will be available at https://github.com/hwz-zju/Instruct-ReID

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.Comment: 30 pages,6 figure

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages