Search

The Ti–B–N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN–TiB2 or TiN–TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN–TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN–TiB tie-line studied) exhibits the highest hardness H of 37.1±1.9 GPa combined with the highest fracture toughness KIC of 3.0±0.2 MPa·m1/2 among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2±2.1 GPa and KIC = 2.7±<0.1 MPa·m1/2.

This paper proposes the field separation theory for predicting the magnetic field of a quasi-Halbach array permanent-magnet motor considering the magnetization pattern and iron saturation. According to the proposed method, the air-gap field consists of a permanent-magnet field, winding current field, and equivalent saturation field. The equivalent permanent-magnet currents replacing the Halbach array with radial or parallel magnetization are introduced to obtain the linear analytical air-gap field of the permanent magnets. The winding current field relating to the slot shape and air-gap length can be directly determined using the linear analytical model. The equivalent saturation field is derived from the combination of the linear analytical model in the air gap and the magnetic circuit model in the iron region. The finite-element analysis of an 8-pole/9-slot Halbach array permanent-magnet motor and its prototype experiments are carried out to verify the effectiveness of the field separation theory, which is then used to analyze the harmonic magnetic field of Halbach array permanent-magnet motors to further improve the electromagnetic torque estimation., QC 20240502

This article presents a comprehensive method to predict the electromagnetic losses in surface-mounted permanent-magnet (PM) motors based on a hybrid analytical model (HAM). HAM will significantly improve the calculation speed compared with finite-element method (FEM) while keeping great accuracy, making it a competitive alternative for the analysis and optimization of PM motors. As the accurate field distribution is the bias of electromagnetic losses prediction, the magnet loss, sleeve loss, and copper loss can all be accurately obtained from their vector potential distribution using HAM with small computational burden. As for iron loss, the improved Jiles–Atherton (JA) model is proposed to build the relationship between the loss and the magnetic field in the iron region from the perspective of energy conversion. The predictions of magnet loss, sleeve loss, and copper loss using HAM agree well with the FEM. The experiment on surface-mounted PM prototype demonstrates the high accuracy of iron loss calculation using the combination of the improved JA model and HAM., QC 20240626

The Ti–B–N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN–TiB2 or TiN–TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN–TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN–TiB tie-line studied) exhibits the highest hardness H of 37.1±1.9 GPa combined with the highest fracture toughness KIC of 3.0±0.2 MPa·m1/2 among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2±2.1 GPa and KIC = 2.7±<0.1 MPa·m1/2.

This article proposed a nonlinear analytical model (NAM)-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis (FEA) and experimental results of the manufactured prototype., QC 20240426

This paper proposes the field separation theory for predicting the magnetic field of a quasi-Halbach array permanent-magnet motor considering the magnetization pattern and iron saturation. According to the proposed method, the air-gap field consists of a permanent-magnet field, winding current field, and equivalent saturation field. The equivalent permanent-magnet currents replacing the Halbach array with radial or parallel magnetization are introduced to obtain the linear analytical air-gap field of the permanent magnets. The winding current field relating to the slot shape and air-gap length can be directly determined using the linear analytical model. The equivalent saturation field is derived from the combination of the linear analytical model in the air gap and the magnetic circuit model in the iron region. The finite-element analysis of an 8-pole/9-slot Halbach array permanent-magnet motor and its prototype experiments are carried out to verify the effectiveness of the field separation theory, which is then used to analyze the harmonic magnetic field of Halbach array permanent-magnet motors to further improve the electromagnetic torque estimation., QC 20240502

This article presents a comprehensive method to predict the electromagnetic losses in surface-mounted permanent-magnet (PM) motors based on a hybrid analytical model (HAM). HAM will significantly improve the calculation speed compared with finite-element method (FEM) while keeping great accuracy, making it a competitive alternative for the analysis and optimization of PM motors. As the accurate field distribution is the bias of electromagnetic losses prediction, the magnet loss, sleeve loss, and copper loss can all be accurately obtained from their vector potential distribution using HAM with small computational burden. As for iron loss, the improved Jiles–Atherton (JA) model is proposed to build the relationship between the loss and the magnetic field in the iron region from the perspective of energy conversion. The predictions of magnet loss, sleeve loss, and copper loss using HAM agree well with the FEM. The experiment on surface-mounted PM prototype demonstrates the high accuracy of iron loss calculation using the combination of the improved JA model and HAM., QC 20240626

This article proposed a nonlinear analytical model (NAM)-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis (FEA) and experimental results of the manufactured prototype., QC 20240426

The Ti–B–N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN–TiB2 or TiN–TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN–TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN–TiB tie-line studied) exhibits the highest hardness H of 37.1±1.9 GPa combined with the highest fracture toughness KIC of 3.0±0.2 MPa·m1/2 among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2±2.1 GPa and KIC = 2.7±<0.1 MPa·m1/2.

This paper proposes the field separation theory for predicting the magnetic field of a quasi-Halbach array permanent-magnet motor considering the magnetization pattern and iron saturation. According to the proposed method, the air-gap field consists of a permanent-magnet field, winding current field, and equivalent saturation field. The equivalent permanent-magnet currents replacing the Halbach array with radial or parallel magnetization are introduced to obtain the linear analytical air-gap field of the permanent magnets. The winding current field relating to the slot shape and air-gap length can be directly determined using the linear analytical model. The equivalent saturation field is derived from the combination of the linear analytical model in the air gap and the magnetic circuit model in the iron region. The finite-element analysis of an 8-pole/9-slot Halbach array permanent-magnet motor and its prototype experiments are carried out to verify the effectiveness of the field separation theory, which is then used to analyze the harmonic magnetic field of Halbach array permanent-magnet motors to further improve the electromagnetic torque estimation., QC 20240502

The Ti–B–N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN–TiB2 or TiN–TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN–TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN–TiB tie-line studied) exhibits the highest hardness H of 37.1±1.9 GPa combined with the highest fracture toughness KIC of 3.0±0.2 MPa·m1/2 among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2±2.1 GPa and KIC = 2.7±<0.1 MPa·m1/2.

This article presents a comprehensive method to predict the electromagnetic losses in surface-mounted permanent-magnet (PM) motors based on a hybrid analytical model (HAM). HAM will significantly improve the calculation speed compared with finite-element method (FEM) while keeping great accuracy, making it a competitive alternative for the analysis and optimization of PM motors. As the accurate field distribution is the bias of electromagnetic losses prediction, the magnet loss, sleeve loss, and copper loss can all be accurately obtained from their vector potential distribution using HAM with small computational burden. As for iron loss, the improved Jiles–Atherton (JA) model is proposed to build the relationship between the loss and the magnetic field in the iron region from the perspective of energy conversion. The predictions of magnet loss, sleeve loss, and copper loss using HAM agree well with the FEM. The experiment on surface-mounted PM prototype demonstrates the high accuracy of iron loss calculation using the combination of the improved JA model and HAM., QC 20240626

This article proposed a nonlinear analytical model (NAM)-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis (FEA) and experimental results of the manufactured prototype., QC 20240426

The positronium lifetime imaging (PLI) reconstruction is a technique used in time-of-flight (TOF) positron emission tomography (PET) that involves measuring the lifespan of positronium, more preciselyortho-positronium (o-Ps), before annihilation, in addition to the traditional uptake image of PET tracer. PLI faces the challenge of limited time resolution in TOF PET systems, which introduces complexities in both statistics and physics. In this dissertation, we developed a maximum likelihood (ML) algorithm for PLI reconstruction and demonstrated that it can generate quantitatively accurate lifetime images for a 570 ps TOF PET system. Furthermore, we conducted investigations into the statistical properties of the algorithm, including the variability of the reconstruction results, the sensitivity of the algorithm to the number of acquired PLI events, and its robustness to hyperparameter choices. Our findings indicate that the proposed ML method produces stable images to enable reliable distinction of regions of interest in the images. Moreover, the number of PLI events required to produce quantitatively accurate images is computationally plausible. These results demonstrate the potential of the proposed method for advancing the capabilities of PLI. In real-world scenarios, in addition to o-Ps, other populations of positron that exhibit different lifetimes also exist. We subsequently introduced a novel two-component reconstruction model for PLI in TOF PET, incorporating both slow-decay and fast-decay components. This model enhances the accuracy of positronium imaging by providing a more detailed representation of positron lifetime distributions. We conducted simulation studies to evaluate the performance of our model and compared it with the existing single-component reconstruction model. The results demonstrate the superiority of the two-component model in capturing the intricacies of the positron populations, thus paving the way for more precise and informative PLI dia

The Ti–B–N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN–TiB2 or TiN–TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN–TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN–TiB tie-line studied) exhibits the highest hardness H of 37.1±1.9 GPa combined with the highest fracture toughness KIC of 3.0±0.2 MPa·m1/2 among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2±2.1 GPa and KIC = 2.7±<0.1 MPa·m1/2.

With the trend toward transportation electrification, the power density of electrical machines faces ever-increasing requirement owing to the stringent limit of weight, especially for aerospace applications. Conventionally, the reliability of electrical machines in such safety-critical application is guaranteed by considerable safety margins, i.e., the over-engineering approach, which prevents electrical machines from reaching higher power densities and leads to a design conflict. This paper proposes a reliability-oriented design approach for low-voltage electrical machines by integrating model-based lifetime prediction into a multi-objective optimization process. Accelerated thermal degradation tests are carried out on mainwall insulation and turn insulation, then the thermal degradation model is built to predict the lifetimes, accordingly. Thermal lifetime models are developed at several lifetime percentiles for both continuous duty and variable duty applications. Finally, a feasible reliability-oriented multi-objective optimization platform is established, based on which a study-case electrical machine for aerospace application is designed and optimized. The prototype is manufactured to verify the optimized performances., QC 20240626

The Ti-B-N system offers a wide range of possible meta(stable) phases, making it interesting for science and industry. However, the solubility for B within the face-centered cubic (fcc)-TiN lattice is rather limited and less studied, especially without forming B-rich phases. Therefore, we address how chemistries along the TiN-TiB2 or TiN-TiB tie-line influence this B-solubility. The variation between these two tie-lines is realized through non-reactive co-sputtering of a TiN, TiB2, and Ti target. We show that for variations along the TiN-TiB tie-line, even 8.9 at.% B (equivalent to 19.3 at.% non-metal fractions) can fully be incorporated into the fcc-TiNy lattice without forming other B-containing phases. The combination of detailed microstructural characterization through X-ray diffraction and transmission electron microscopy with ab initio calculations of fcc-Ti1-xNBx, fcc-TiN1-xBx, and fcc-TiN1-2xBx solid solutions indicates that B essentially substitutes N. The single-phase fcc-TiB0.17N0.69 (the highest B-containing sample along the TiN-TiB tie-line studied) exhibits the highest hardness H of 37.1 +/- 1.9 GPa combined with the highest fracture toughness K-IC of 3.0 +/- 0.2 MPam(1/2) among the samples studied. These are markedly above those of B-free TiN0.87 having H = 29.2 +/- 2.1 GPa and K-IC = 2.7 +/-<0.1 MPam(1/2)., Funding Agencies|TU Wien Bibliothek; Swedish Research Council [2022-06725]; Vienna Scientific Cluster (VSC) in Austria; RADIATE project; Swedish research council VR-RFI [2019-00191]; Austrian Science Fund (FWF) [P 33696]

This article presents a comprehensive method to predict the electromagnetic losses in surface-mounted permanent-magnet (PM) motors based on a hybrid analytical model (HAM). HAM will significantly improve the calculation speed compared with finite-element method (FEM) while keeping great accuracy, making it a competitive alternative for the analysis and optimization of PM motors. As the accurate field distribution is the bias of electromagnetic losses prediction, the magnet loss, sleeve loss, and copper loss can all be accurately obtained from their vector potential distribution using HAM with small computational burden. As for iron loss, the improved Jiles–Atherton (JA) model is proposed to build the relationship between the loss and the magnetic field in the iron region from the perspective of energy conversion. The predictions of magnet loss, sleeve loss, and copper loss using HAM agree well with the FEM. The experiment on surface-mounted PM prototype demonstrates the high accuracy of iron loss calculation using the combination of the improved JA model and HAM., QC 20240626

This article proposed a nonlinear analytical model (NAM)-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis (FEA) and experimental results of the manufactured prototype., QC 20240426

This paper proposes the field separation theory for predicting the magnetic field of a quasi-Halbach array permanent-magnet motor considering the magnetization pattern and iron saturation. According to the proposed method, the air-gap field consists of a permanent-magnet field, winding current field, and equivalent saturation field. The equivalent permanent-magnet currents replacing the Halbach array with radial or parallel magnetization are introduced to obtain the linear analytical air-gap field of the permanent magnets. The winding current field relating to the slot shape and air-gap length can be directly determined using the linear analytical model. The equivalent saturation field is derived from the combination of the linear analytical model in the air gap and the magnetic circuit model in the iron region. The finite-element analysis of an 8-pole/9-slot Halbach array permanent-magnet motor and its prototype experiments are carried out to verify the effectiveness of the field separation theory, which is then used to analyze the harmonic magnetic field of Halbach array permanent-magnet motors to further improve the electromagnetic torque estimation., QC 20240502

The effects of climate change depend on specific local circumstances, posing a challenge for worldwide research to comprehensively encompass the diverse impacts on various local social-ecological systems. Here we use a place-specific but cross-culturally comparable protocol to document climate change indicators and impacts as locally experienced and analyze their distribution. We collected first-hand data in 48 sites inhabited by Indigenous Peoples and local communities and covering all climate zones and nature-dependent livelihoods. We documented 1,661 site-agreed reports of change corresponding to 369 indicators. Reports of change vary according to climate zone and livelihood activity. We provide compelling evidence that climate change impacts on Indigenous Peoples and local communities are ongoing, tangible, widespread, and affect multiple elements of their social-ecological systems. Beyond potentially informing contextualized adaptation plans, our results show that local reports could help identify economic and non-economic loss and damage related to climate change impacts suffered by Indigenous Peoples and local communities.

The effects of climate change depend on specific local circumstances, posing a challenge for worldwide research to comprehensively encompass the diverse impacts on various local social-ecological systems. Here we use a place-specific but cross-culturally comparable protocol to document climate change indicators and impacts as locally experienced and analyze their distribution. We collected first-hand data in 48 sites inhabited by Indigenous Peoples and local communities and covering all climate zones and nature-dependent livelihoods. We documented 1,661 site-agreed reports of change corresponding to 369 indicators. Reports of change vary according to climate zone and livelihood activity. We provide compelling evidence that climate change impacts on Indigenous Peoples and local communities are ongoing, tangible, widespread, and affect multiple elements of their social-ecological systems. Beyond potentially informing contextualized adaptation plans, our results show that local reports could help identify economic and non-economic loss and damage related to climate change impacts suffered by Indigenous Peoples and local communities.

Global polls have shown that people in high-income countries generally report being more satisfied with their lives than people in low-income countries. The persistence of this correlation, and its similarity to correlations between income and life satisfaction within countries, could lead to the impression that high levels of life satisfaction can only be achieved in wealthy societies. However, global polls have typically overlooked small-scale, nonindustrialized societies, which can provide an alternative test of the consistency of this relationship. Here, we present results from a survey of 2,966 members of Indigenous Peoples and local communities among 19 globally distributed sites. We find that high average levels of life satisfaction, comparable to those of wealthy countries, are reported for numerous populations that have very low monetary incomes. Our results are consistent with the notion that human societies can support very satisfying lives for their members without necessarily requiring high degrees of monetary wealth.

Luminous red novae (LRNe) and their connection to common envelope evolution (CEE) remain elusive in astrophysics. Here, we present a radiation hydrodynamic model capable of simulating the light curves of material ejected during a CEE. For the first time, the radiation hydrodynamic model incorporates complete recombination physics for hydrogen and helium. The radiation hydrodynamic equations are solved with Guangqi. With time-independent ejecta simulations, we show that the peaks in the light curves are attributed to radiation-dominated ejecta, while the extended plateaus are produced by matter-dominated ejecta. To showcase our model's capability, we fit the light curve of AT2019zhd. The central mass object of $6M_{\odot}$ is assumed based on observations and scaling relations. Our model demonstrates that the ejecta mass of AT2019zhd falls within the range of $0.04M_{\odot}$ to $0.1M_{\odot}$. Additionally, we demonstrate that recombination energy and radiation force acceleration significantly impact the light curves, whereas dust formation has a limited effect during the peak and plateau phases., Comment: accepted by the ApJL

The advancement of Multi-modal Pre-training highlights the necessity for a robust Multi-Modal Knowledge Graph (MMKG) representation learning framework. This framework is crucial for integrating structured knowledge into multi-modal Large Language Models (LLMs) at scale, aiming to alleviate issues like knowledge misconceptions and multi-modal hallucinations. In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA). Building on this foundation, we propose a novel SNAG method that utilizes a Transformer-based architecture equipped with modality-level noise masking for the robust integration of multi-modal entity features in KGs. By incorporating specific training objectives for both MKGC and MMEA, our approach achieves SOTA performance across a total of ten datasets (three for MKGC and seven for MEMA), demonstrating its robustness and versatility. Besides, SNAG can not only function as a standalone model but also enhance other existing methods, providing stable performance improvements. Our code and data are available at: https://github.com/zjukg/SNAG., Comment: Ongoing work; 10 pages, 6 Tables, 2 Figures; Repo is available at https://github.com/zjukg/SNAG

Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information of entities into the discriminant models. The information from different modalities will work together to measure the triple plausibility. Existing MMKGC methods overlook the imbalance problem of modality information among entities, resulting in inadequate modal fusion and inefficient utilization of the raw modality information. To address the mentioned problems, we propose Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality information for MMKGC. AdaMF-MAT achieves multi-modal fusion with adaptive modality weights and further generates adversarial samples by modality-adversarial training to enhance the imbalanced modality information. Our approach is a co-design of the MMKGC model and training strategy which can outperform 19 recent MMKGC methods and achieve new state-of-the-art results on three public MMKGC benchmarks. Our code and data have been released at https://github.com/zjukg/AdaMF-MAT., Comment: Accepted by LREC-COLING 2024

Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks., Comment: Ongoing work; 16 pages, 9 Tables, 8 Figures; Code: https://github.com/lyyf2002/ASGEA

As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms, still faces several challenges. High knowledge barriers and limited scalability in current methods restrict the full exploitation of LLMs in mastering single-cell data, impeding direct accessibility and rapid iteration. To this end, we introduce ChatCell, which signifies a paradigm shift by facilitating single-cell analysis with natural language. Leveraging vocabulary adaptation and unified sequence generation, ChatCell has acquired profound expertise in single-cell biology and the capability to accommodate a diverse range of analysis tasks. Extensive experiments further demonstrate ChatCell's robust performance and potential to deepen single-cell insights, paving the way for more accessible and intuitive exploration in this pivotal field. Our project homepage is available at https://zjunlp.github.io/project/ChatCell., Comment: I have decided to temporarily withdraw this draft as I am in the process of making further revisions to improve its content. Code: https://github.com/zjunlp/ChatCell Dataset: https://huggingface.co/datasets/zjunlp/ChatCell-Instructions Demo: https://chat.openai.com/g/g-vUwj222gQ-chatcell

In this paper, we address the challenge of learning with limited fault data for power transformers. Traditional operation and maintenance tools lack effective predictive capabilities for potential faults. The scarcity of extensive fault data makes it difficult to apply machine learning techniques effectively. To solve this problem, we propose a novel approach that leverages the knowledge graph (KG) technology in combination with gradient boosting decision trees (GBDT). This method is designed to efficiently learn from a small set of high-dimensional data, integrating various factors influencing transformer faults and historical operational data. Our approach enables accurate safe state assessments and fault analyses of power transformers despite the limited fault characteristic data. Experimental results demonstrate that this method outperforms other learning approaches in prediction accuracy, such as artificial neural networks (ANN) and logistic regression (LR). Furthermore, it offers significant improvements in progressiveness, practicality, and potential for widespread application.

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm. We begin by defining KGs and MMKGs, then explore their construction progress. Our review includes two primary task categories: KG-aware multi-modal learning tasks, such as Image Classification and Visual Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph Completion and Entity Alignment, highlighting specific research trajectories. For most of these tasks, we provide definitions, evaluation benchmarks, and additionally outline essential insights for conducting relevant research. Finally, we discuss current challenges and identify emerging trends, such as progress in Large Language Modeling and Multi-modal Pre-training strategies. This survey aims to serve as a comprehensive reference for researchers already involved in or considering delving into KG and multi-modal learning research, offering insights into the evolving landscape of MMKG research and supporting future work., Comment: Ongoing work; 41 pages (Main Text), 55 pages (Total), 11 Tables, 13 Figures, 619 citations; Paper list is available at https://github.com/zjukg/KG-MM-Survey

MaxSAT is an optimization version of the famous NP-complete Satisfiability problem (SAT). Algorithms for MaxSAT mainly include complete solvers and local search incomplete solvers. In many complete solvers, once a better solution is found, a Soft conflict Pseudo Boolean (SPB) constraint will be generated to enforce the algorithm to find better solutions. In many local search algorithms, clause weighting is a key technique for effectively guiding the search directions. In this paper, we propose to transfer the SPB constraint into the clause weighting system of the local search method, leading the algorithm to better solutions. We further propose an adaptive clause weighting strategy that breaks the tradition of using constant values to adjust clause weights. Based on the above methods, we propose a new local search algorithm called SPB-MaxSAT that provides new perspectives for clause weighting on MaxSAT local search solvers. Extensive experiments demonstrate the excellent performance of the proposed methods.

The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation. However, existing methods still have some limitations: 1) repetitions, transpositions, and omissions in the output synthesized speech due to limited alignment constraints between audio and phoneme tokens; 2) challenges of fine-grained control over the synthesized speech with autoregressive (AR) language model; 3) infinite silence generation due to the nature of AR-based decoding, especially under the greedy strategy. To alleviate these issues, we propose ELLA-V, a simple but efficient LM-based zero-shot text-to-speech (TTS) framework, which enables fine-grained control over synthesized audio at the phoneme level. The key to ELLA-V is interleaving sequences of acoustic and phoneme tokens, where phoneme tokens appear ahead of the corresponding acoustic tokens. The experimental findings reveal that our model outperforms VALL-E in terms of accuracy and delivers more stable results using both greedy and sampling-based decoding strategies. The code of ELLA-V will be open-sourced after cleanups. Audio samples are available at https://ereboas.github.io/ELLAV/., Comment: Working in progress

To promote speech processing and recognition research in driving scenarios, we build on the success of the Intelligent Cockpit Speech Recognition Challenge (ICSRC) held at ISCSLP 2022 and launch the ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) Challenge. This challenge collects over 100 hours of multi-channel speech data recorded inside a new energy vehicle and 40 hours of noise for data augmentation. Two tracks, including automatic speech recognition (ASR) and automatic speech diarization and recognition (ASDR) are set up, using character error rate (CER) and concatenated minimum permutation character error rate (cpCER) as evaluation metrics, respectively. Overall, the ICMC-ASR Challenge attracts 98 participating teams and receives 53 valid results in both tracks. In the end, first-place team USTCiflytek achieves a CER of 13.16% in the ASR track and a cpCER of 21.48% in the ASDR track, showing an absolute improvement of 13.08% and 51.4% compared to our challenge baseline, respectively., Comment: Accepted at ICASSP 2024

This article proposed a nonlinear analytical model (NAM)-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis (FEA) and experimental results of the manufactured prototype., QC 20240426

This paper proposes the field separation theory for predicting the magnetic field of a quasi-Halbach array permanent-magnet motor considering the magnetization pattern and iron saturation. According to the proposed method, the air-gap field consists of a permanent-magnet field, winding current field, and equivalent saturation field. The equivalent permanent-magnet currents replacing the Halbach array with radial or parallel magnetization are introduced to obtain the linear analytical air-gap field of the permanent magnets. The winding current field relating to the slot shape and air-gap length can be directly determined using the linear analytical model. The equivalent saturation field is derived from the combination of the linear analytical model in the air gap and the magnetic circuit model in the iron region. The finite-element analysis of an 8-pole/9-slot Halbach array permanent-magnet motor and its prototype experiments are carried out to verify the effectiveness of the field separation theory, which is then used to analyze the harmonic magnetic field of Halbach array permanent-magnet motors to further improve the electromagnetic torque estimation., QC 20240502

This article presents a comprehensive method to predict the electromagnetic losses in surface-mounted permanent-magnet (PM) motors based on a hybrid analytical model (HAM). HAM will significantly improve the calculation speed compared with finite-element method (FEM) while keeping great accuracy, making it a competitive alternative for the analysis and optimization of PM motors. As the accurate field distribution is the bias of electromagnetic losses prediction, the magnet loss, sleeve loss, and copper loss can all be accurately obtained from their vector potential distribution using HAM with small computational burden. As for iron loss, the improved Jiles–Atherton (JA) model is proposed to build the relationship between the loss and the magnetic field in the iron region from the perspective of energy conversion. The predictions of magnet loss, sleeve loss, and copper loss using HAM agree well with the FEM. The experiment on surface-mounted PM prototype demonstrates the high accuracy of iron loss calculation using the combination of the improved JA model and HAM., QC 20240626

We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-TTS serves as a foundation model for speech generation and excels in speech in-context learning, achieving performance in speaker similarity and naturalness that matches ground truth human speech in both objective and subjective evaluations. With fine-tuning, we achieve even higher subjective scores across these metrics. Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild. Furthermore, we propose a self-distillation method for speech factorization, as well as a reinforcement learning approach to enhance model robustness, speaker similarity, and controllability. We additionally present a non-autoregressive (NAR) variant of the Seed-TTS model, named $\text{Seed-TTS}_\text{DiT}$, which utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS systems, $\text{Seed-TTS}_\text{DiT}$ does not depend on pre-estimated phoneme durations and performs speech generation through end-to-end processing. We demonstrate that this variant achieves comparable performance to the language model-based variant and showcase its effectiveness in speech editing. We encourage readers to listen to demos at \url{https://bytedancespeech.github.io/seedtts_tech_report}.

We propose Quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pre-trained language models. By leveraging quantum-inspired methods derived from quantum circuit structures, QuanTA enables efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA)--low-rank approximation may fail for complicated downstream tasks. Our approach is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations. Experiments demonstrate that QuanTA significantly enhances commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. Furthermore, QuanTA shows superior performance with fewer trainable parameters compared to other approaches and can be designed to integrate with existing fine-tuning algorithms for further improvement, providing a scalable and efficient solution for fine-tuning large language models and advancing state-of-the-art in natural language processing.

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover new knowledge triples in the given multi-modal knowledge graphs (MMKGs), which is achieved by collaborative modeling the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods tend to focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel MMKGC framework with Mixture of Modality Knowledge experts (MoMoK for short) to learn adaptive multi-modal embedding under intricate relational contexts. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve comprehensive decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MoMoK under complex scenarios., Comment: Work in progress. Code and data will be released at https://github.com/zjukg/MoMoK

Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enhance different downstream tasks, which is a key direction for KG management, maintenance, and applications. Existing works often focus on purely research questions in open domains, or they are not open source due to data security and privacy in real scenarios. Meanwhile, existing studies have not explored the training efficiency and transferability of KGP models in depth. To address these problems, We propose a framework MuDoK to achieve multi-domain collaborative pre-training and efficient prefix prompt tuning to serve diverse downstream tasks like recommendation and text understanding. Our design is a plug-and-play prompt learning approach that can be flexibly adapted to different downstream task backbones. In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research. We evaluated our approach based on constructed KPI benchmarks using diverse backbone models in heterogeneous downstream tasks. The experimental results show that our framework brings significant performance gains, along with its generality, efficiency, and transferability., Comment: Work in progress. Code and data will be open-sourced at https://github.com/zjukg/MuDoK

Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering. The advent of neural networks has initiated a significant shift in tackling these complexities though challenges in accuracy persist, especially for initial value problems. In this paper, we introduce the $\textit{Time-Evolving Natural Gradient (TENG)}$, generalizing time-dependent variational principles and optimization-based time integration, leveraging natural gradient optimization to obtain high accuracy in neural-network-based PDE solutions. Our comprehensive development includes algorithms like TENG-Euler and its high-order variants, such as TENG-Heun, tailored for enhanced precision and efficiency. TENG's effectiveness is further validated through its performance, surpassing current leading methods and achieving $\textit{machine precision}$ in step-by-step optimizations across a spectrum of PDEs, including the heat equation, Allen-Cahn equation, and Burgers' equation.

Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated in attribute descriptions, aids in identifying class-specific visual features, thus facilitating visual-semantic alignment and improving ZSL performance. However, real-world challenges such as distribution imbalances and attribute co-occurrence among instances often hinder the discernment of local variances in images, a problem exacerbated by the scarcity of fine-grained, region-specific attribute annotations. Moreover, the variability in visual presentation within categories can also skew attribute-category associations. In response, we propose a bidirectional cross-modal ZSL approach CREST. It begins by extracting representations for attribute and visual localization and employs Evidential Deep Learning (EDL) to measure underlying epistemic uncertainty, thereby enhancing the model's resilience against hard negatives. CREST incorporates dual learning pathways, focusing on both visual-category and attribute-category alignments, to ensure robust correlation between latent and observable spaces. Moreover, we introduce an uncertainty-informed cross-modal fusion technique to refine visual-attribute inference. Extensive experiments demonstrate our model's effectiveness and unique explainability across multiple datasets. Our code and data are available at: https://github.com/JethroJames/CREST, Comment: Ongoing work; 10 pages, 2 Tables, 9 Figures; Repo is available at: https://github.com/JethroJames/CREST

Multi-modal knowledge graphs (MMKG) store structured world knowledge containing rich multi-modal descriptive information. To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities. Existing MMKGC methods usually extract multi-modal features with pre-trained models and employ a fusion module to integrate multi-modal features with triple prediction. However, this often results in a coarse handling of multi-modal data, overlooking the nuanced, fine-grained semantic details and their interactions. To tackle this shortfall, we introduce a novel framework MyGO to process, fuse, and augment the fine-grained modality information from MMKGs. MyGO tokenizes multi-modal raw data as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MyGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 20 of the latest models, underlining its superior performance. Code and data are available at https://github.com/zjukg/MyGO, Comment: Working in progress; Repo is available at https://github.com/zjukg/MyGO

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However, prior LSS methods are conventionally investigated and evaluated on datasets within the same domain in clear weather. The robustness of LSS models in unseen scenes and all weather conditions is crucial for ensuring safety and reliability in real applications. To this end, we propose UniMix, a universal method that enhances the adaptability and generalizability of LSS models. UniMix first leverages physically valid adverse weather simulation to construct a Bridge Domain, which serves to bridge the domain gap between the clear weather scenes and the adverse weather scenes. Then, a Universal Mixing operator is defined regarding spatial, intensity, and semantic distributions to create the intermediate domain with mixed samples from given domains. Integrating the proposed two techniques into a teacher-student framework, UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations. We devote UniMix to two main setups: 1) unsupervised domain adaption, adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization, learning a model that generalizes well to unseen scenes in adverse weather. Extensive experiments validate the effectiveness of UniMix across different tasks and datasets, all achieving superior performance over state-of-the-art methods. The code will be released., Comment: Accepted by CVPR 2024

Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs). The core challenge of this task lies in understanding the complex semantic information regarding multiple types of time constraints (e.g., before, first) in questions. Existing end-to-end methods implicitly model the time constraints by learning time-aware embeddings of questions and candidate answers, which is far from understanding the question comprehensively. Motivated by semantic-parsing-based approaches that explicitly model constraints in questions by generating logical forms with symbolic operators, we design fundamental temporal operators for time constraints and introduce a novel self-improvement Programming method for TKGQA (Prog-TQA). Specifically, Prog-TQA leverages the in-context learning ability of Large Language Models (LLMs) to understand the combinatory time constraints in the questions and generate corresponding program drafts with a few examples given. Then, it aligns these drafts to TKGs with the linking module and subsequently executes them to generate the answers. To enhance the ability to understand questions, Prog-TQA is further equipped with a self-improvement strategy to effectively bootstrap LLMs using high-quality self-generated drafts. Extensive experiments demonstrate the superiority of the proposed Prog-TQA on MultiTQ and CronQuestions datasets, especially in the Hits@1 metric., Comment: Accepted by LREC-COLING 2024 (long paper)

In the age of neural natural language processing, there are plenty of works trying to derive interpretations of neural models. Intuitively, when gold rationales exist during training, one can additionally train the model to match its interpretation with the rationales. However, this intuitive idea has not been fully explored. In this paper, we propose a framework of utilizing interpretation methods and gold rationales to enhance models. Our framework is very general in the sense that it can incorporate various interpretation methods. Previously proposed gradient-based methods can be shown as an instance of our framework. We also propose two novel instances utilizing two other types of interpretation methods, erasure/replace-based and extractor-based methods, for model enhancement. We conduct comprehensive experiments on a variety of tasks. Experimental results show that our framework is effective especially in low-resource settings in enhancing models with various interpretation methods, and our two newly-proposed methods outperform gradient-based methods in most settings. Code is available at https://github.com/Chord-Chen-30/UIMER., Comment: EMNLP 2023

In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover overlong contexts while answering questions from open domains. This paper proposes a general and convenient method to covering longer contexts in Open-Domain Question-Answering tasks. It leverages a small encoder language model that effectively encodes contexts, and the encoding applies cross-attention with origin inputs. With our method, the origin language models can cover several times longer contexts while keeping the computing requirements close to the baseline. Our experiments demonstrate that after fine-tuning, there is improved performance across two held-in datasets, four held-out datasets, and also in two In Context Learning settings.

The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern for LLMs, as they may generate factually incorrect responses. In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Specifically, the test dataset is retrieved from a large knowledge graph with more than 10 million facts without expensive human efforts. Unlike conventional methods that evaluate LLMs based on generated responses, GraphEval streamlines the evaluation process by creating a judge model to estimate the correctness of the answers given by the LLM. Our experiments demonstrate that the judge model's factuality assessment aligns closely with the correctness of the LLM's generated outputs, while also substantially reducing evaluation costs. Besides, our findings offer valuable insights into LLM performance across different metrics and highlight the potential for future improvements in ensuring the factual integrity of LLM outputs. The code is publicly available at https://github.com/xz-liu/GraphEval.

Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal information within audio and text features, presenting substantial limitations for tasks such as audio retrieval and generation. To address this gap, we introduce T-CLAP, a temporal-enhanced CLAP model. We use Large Language Models~(LLMs) and mixed-up strategies to generate temporal-contrastive captions for audio clips from extensive audio-text datasets. Subsequently, a new temporal-focused contrastive loss is designed to fine-tune the CLAP model by incorporating these synthetic data. We conduct comprehensive experiments and analysis in multiple downstream tasks. T-CLAP shows improved capability in capturing the temporal relationship of sound events and outperforms state-of-the-art models by a significant margin., Comment: Preprint submitted to IEEE MLSP 2024

Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions. However, existing works still face a main challenge, concept overfitting. To tackle this challenge, we first analyze overfitting, categorizing it into concept-agnostic overfitting, which undermines non-customized concept knowledge, and concept-specific overfitting, which is confined to customize on limited modalities, i.e, backgrounds, layouts, styles. To evaluate the overfitting degree, we further introduce two metrics, i.e, Latent Fisher divergence and Wasserstein metric to measure the distribution changes of non-customized and customized concept respectively. Drawing from the analysis, we propose Infusion, a T2I customization method that enables the learning of target concepts to avoid being constrained by limited training modalities, while preserving non-customized knowledge. Remarkably, Infusion achieves this feat with remarkable efficiency, requiring a mere 11KB of trained parameters. Extensive experiments also demonstrate that our approach outperforms state-of-the-art methods in both single and multi-concept customized generation., Comment: 10 pages