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The increasing amount of heavy metal copper ions (Cu2+) in industrial emissions, poses a serious threat to human health, biological environment, and resource scarcity. Capacitive deionization (CDI) is considered as a green and efficient method for desalination. It is crucial to develop high-performance electrodes for efficient operation of CDI that go beyond conventional carbon and yield considerable environmental benefits. Here, metal organic frameworks (MOFs) derived carbon-loaded cobalt metaphosphate (NC-Co(PO3)2) was prepared by lowtemperature gas-solid phosphating for Cu2+ removal as CDI electrode for the first time. NC-Co(PO3)2 demonstrated superior electrode structure and function due to the synergistic effects of electric double layer coupling PO bonds, the binding tendency of metaphosphate groups with Cu2+, and interfacial redox reactions induced by the labile valence state of cobalt. The optimal electrosorption capacity of NC-Co(PO3)2 was 95.41 mg g-1 at 1 V in 50 mL Cu2+ solution with splendid cyclic regeneration capability. Moreover, NC-Co(PO3)2 exhibited excellent selectivity and outstanding electrosorption performance in the presence of multiple coexisting ions and this CDI system realized the purification of actual copper-containing wastewater. A series of characterizations further revealed the specific mechanism of Cu2+ in adsorption-desorption process. Our finding strongly supported NCCo(PO3)2 electrode can extend the CDI platform's capability for effectively removing and retrieving Cu2+ from wastewater., Funding Agencies|National Natural Science Foundation of China [51778156]; Internal research project of Guangzhou University [YJ2023029]; Talent Cultivation Program of Guangzhou University [YJ2021005]; Guangdong Natural Science Foundation [2022A1515010441]; Basic Innovation Project for Graduate Students of Guangzhou University [2022GDJC-M43]

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs., Comment: Work in progress

We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. We introduce a unique semantic feature loss that effectively compensates for the shortcomings of traditional depth and color losses in object optimization. Through a semantic-guided keyframe selection strategy, we prevent erroneous reconstructions caused by cumulative errors. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy, while ensuring real-time rendering capabilities.

SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM. However, they encounter tracking drift and mapping errors in real-world scenarios with dynamic interferences. To address these issues, we introduce DDN-SLAM, the first real-time dense dynamic neural implicit SLAM system integrating semantic features. To address dynamic tracking interferences, we propose a feature point segmentation method that combines semantic features with a mixed Gaussian distribution model. To avoid incorrect background removal, we propose a mapping strategy based on sparse point cloud sampling and background restoration. We propose a dynamic semantic loss to eliminate dynamic occlusions. Experimental results demonstrate that DDN-SLAM is capable of robustly tracking and producing high-quality reconstructions in dynamic environments, while appropriately preserving potential dynamic objects. Compared to existing neural implicit SLAM systems, the tracking results on dynamic datasets indicate an average 90% improvement in Average Trajectory Error (ATE) accuracy., Comment: 11pages, 4figures

Visual-inertial SLAM is crucial in various fields, such as aerial vehicles, industrial robots, and autonomous driving. The fusion of camera and inertial measurement unit (IMU) makes up for the shortcomings of a signal sensor, which significantly improves the accuracy and robustness of localization in challenging environments. This article presents PLE-SLAM, an accurate and real-time visual-inertial SLAM algorithm based on point-line features and efficient IMU initialization. First, we use parallel computing methods to extract features and compute descriptors to ensure real-time performance. Adjacent short line segments are merged into long line segments, and isolated short line segments are directly deleted. Second, a rotation-translation-decoupled initialization method is extended to use both points and lines. Gyroscope bias is optimized by tightly coupling IMU measurements and image observations. Accelerometer bias and gravity direction are solved by an analytical method for efficiency. To improve the system's intelligence in handling complex environments, a scheme of leveraging semantic information and geometric constraints to eliminate dynamic features and A solution for loop detection and closed-loop frame pose estimation using CNN and GNN are integrated into the system. All networks are accelerated to ensure real-time performance. The experiment results on public datasets illustrate that PLE-SLAM is one of the state-of-the-art visual-inertial SLAM systems.

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

Multilingual multimodal reasoning is a core component in achieving human-level intelligence. However, most existing benchmarks for multilingual multimodal reasoning struggle to differentiate between models of varying performance; even language models without visual capabilities can easily achieve high scores. This leaves a comprehensive evaluation of leading multilingual multimodal models largely unexplored. In this work, we introduce M4U, a novel and challenging benchmark for assessing the capability of multi-discipline multilingual multimodal understanding and reasoning. M4U contains 8,931 samples covering 64 disciplines across 16 subfields in Science, Engineering, and Healthcare in Chinese, English, and German. Using M4U, we conduct extensive evaluations of 21 leading Large Multimodal Models (LMMs) and Large Language Models (LLMs) with external tools. The evaluation results show that the state-of-the-art model, GPT-4o, achieves only 47.6% average accuracy on M4U. Additionally, we observe that the leading LMMs exhibit significant language preferences. Our in-depth analysis indicates that leading LMMs, including GPT-4o, suffer performance degradation when prompted with cross-lingual multimodal questions, such as images with key textual information in Chinese while the question is in German. We believe that M4U can serve as a crucial tool for systematically evaluating LMMs based on their multilingual multimodal reasoning capabilities and monitoring their development. The homepage, codes and data are public available., Comment: Work in progress

Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based sentences. We also convert the detected anatomy into textual prompts conveying anatomical comprehension to the LLM. Additionally, the clinical context prompts guide the LLM to emphasize interactivity and clinical requirements. By integrating anatomy-focused sentences and anatomy/clinical prompts, the pre-trained LLM can generate structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. We evaluate using language generation and clinical effectiveness metrics, demonstrating strong performance., Comment: Accepted by IEEE Conference on Multimedia Expo 2024

In this paper, we present an extensive study of the promotion of illicit goods and services on Twitter, a popular online social network(OSN). This study is made possible through the design and implementation of multiple novel tools for detecting and analyzing illicit promotion activities as well as their underlying campaigns. As the results, we observe that illicit promotion is prevalent on Twitter, along with noticeable existence on other three popular OSNs including Youtube, Facebook, and TikTok. Particularly, 12 million distinct posts of illicit promotion (PIPs) have been observed on the Twitter platform, which are widely distributed in 5 major natural languages and 10 categories of illicit goods and services, e.g., drugs, data leakage, gambling, and weapon sales. What are also observed are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging (IM) accounts that are embedded in PIPs and serve as next hops of communication, which strongly indicates that the campaigns underpinning PIPs are also of a large scale. Also, an arms race between Twitter and illicit promotion operators is also observed. On one hand, Twitter is observed to conduct content moderation in a continuous manner and almost 80% PIPs will get gradually unpublished within six months since posted. However, in the meantime, miscreants adopt various evasion tactics to masquerade their PIPs, which renders more than 90% PIPs keeping hidden from the detection radar for two months or longer.

SLAM systems based on Gaussian Splatting have garnered attention due to their capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatting SLAM systems usually struggle with large scene representation and lack effective loop closure detection. To address these issues, we introduce NGM-SLAM, the first 3DGS based SLAM system that utilizes neural radiance field submaps for progressive scene expression, effectively integrating the strengths of neural radiance fields and 3D Gaussian Splatting. We utilize neural radiance field submaps as supervision and achieve high-quality scene expression and online loop closure adjustments through Gaussian rendering of fused submaps. Our results on multiple real-world scenes and large-scale scene datasets demonstrate that our method can achieve accurate hole filling and high-quality scene expression, supporting monocular, stereo, and RGB-D inputs, and achieving state-of-the-art scene reconstruction and tracking performance., Comment: 9pages, 4 figures

To defend against conflict-based cache side-channel attacks, cache partitioning or remapping techniques were proposed to prevent set conflicts between different security domains or obfuscate the locations of such conflicts. But such techniques complicate cache design and may result in significant performance penalties. Therefore, there have been lightweight prefetching-based schemes proposed to introduce noise to confuse attackers' observation. However, we have validated experimentally that relying on prefetching to only introduce noise is insufficient, as attackers can still reliably distinguish the victim's cache accesses. This paper proposes a novel prefetching-based scheme, called PCG. It combines adding victim-irrelevant cache occupancy changes and reducing victim-relevant cache occupancy changes to disrupt attackers by generating noisy and indistinguishable cache access patterns. Additionally, PCG can either work independently or seamlessly be integrated with most of the commonly used prefetchers. We have implemented and evaluated PCG in both gem5 and the open-source RISC-V core BOOMv3. The evaluation results show the PCG's robust security superior to the existing solutions, while without resulting in significant performance degradation. According to the evaluation based on the SPEC CPU 2017 benchmark suite, PCG even shows an average performance improvement of about 1.64%. Moreover, it incurs only 1.26% overhead on hardware resource consumption., Comment: 12 pages, 9 figures, submitting to a journal

Fang Wen,1,* Shantong Jiang,1– 4,* Ping Yuan,1– 5,* Jinbo Liu,1– 4 Xiu Bai,1 Hongwei Zhao,1– 4,6,7 Xin Chen,1– 4,7 Yan-Bing Gong,5 Roland Asmar,8 Hongyu Wang1– 4,6,7 1Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China; 2Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China; 3Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China; 4Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China; 5Department of Scientific Research, Peking University Shougang Hospital, Beijing, People’s Republic of China; 6State Key Laboratory of Vascular Homeostasis and Remodeling, (Peking University), Beijing, People’s Republic of China; 7Heart and Vascular Health Research Center of Chengdu Medical College (HVHRC-CMC), Chengdu, People’s Republic of China; 8Foundation-Medical Research Institutes, Beirut, Lebanon*These authors contributed equally to this workCorrespondence: Hongyu Wang, Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, 100144, People’s Republic of China, Email dr.hongyuwang@foxmail.comAbstract: Cardiovascular disease(CVD) has become a major disease burden affecting people’s health in China. Blood vessels are very important for human health and are the “sentinel” for the development of many cardiovascular and cerebrovascular diseases. The key to effectively preventing fatal, disabling heart, brain and peripheral vascular events lies in controlling traditional and non-traditional risk factors for vascular health from the source, and early assessment and intervention of early vascular lesions. Since 2004, China government promoted the early detection technology of vascular lesions and vascular medicine, and proposed the Bei

The lack of attention to nonviolence in education is highly problematic, and this article elaborates nonviolence as a new direction in a time of crisis. First, nonviolence is conceptualized as holding tensions to contest violence and cultivate nonviolent relations in the everyday praxis of education in both inner work and outer work in engaging social differences. Second, an aesthetics-based approach at intrapersonal, interpersonal, and transpersonal levels is introduced to address violence and nonviolence in curriculum. Third, we conceptualize how embodied living and mindfulness are crucial components in understanding and practicing nonviolence. This article brings philosophical understanding, artistic attunement, and a meditative stance together to demonstrate what possibilities can be opened by embracing nonviolence in curriculum studies. Throughout the article, we argue that nonviolence not only opposes violence but is also a positive, integrative force that we should become attuned to in order to transform curriculum and education. In addition, we discuss three theoretical and practical implications of our work, as well as four domains for further research.

Using capacitive deionization to remove heavy metal ions from water has received much attention, but the inferior salt adsorption capacity (SAC) of electrode materials has always limited its practical application. Herein, N, S co-doped two-dimensional (2D) porous glucose derived carbon nanosheets (NSPGC) was successfully fabricated, utilizing the gas exfoliation by calcination of thiourea. The NSPGC demonstrates distinct 2D lamellas, high specific surface area (2529 m2 g-1), hierarchical pore structure and high wettability. In electrochemical tests, a high specific capacitance (127 F g-1) and electrons/ions transport performance can be achieved in the NSPGC, moreover it showed a prominent SAC of 206.57 mg g-1 and recoverability in 100 mg L-1 CuSO4 solution. Moreover, the density functional theory (DFT) calculation manifested the intrinsic affinity of Cu2+ improved by N, S co-doping, which played an essential role in enhancing the Cu2+ removal performance of CDI. Our work provided a new insight into the preparation of high-performance CDI electrode materials for Cu2+ removal and promoted the application of CDI in heavy metal wastewater., Funding Agencies|National Natural Science Foundation of China [2022A1515010441]; Talent Cultivation Program of Guangzhou University; Guangdong Natural Science Foundation [2022GDJC-M43]; "Basic Innovation" Project for Graduate Students of Guangzhou University [YJ2021005]; [51778156]

Fang Wen,1,* Shantong Jiang,1– 4,* Ping Yuan,1– 5,* Jinbo Liu,1– 4 Xiu Bai,1 Hongwei Zhao,1– 4,6,7 Xin Chen,1– 4,7 Yan-Bing Gong,5 Roland Asmar,8 Hongyu Wang1– 4,6,7 1Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China; 2Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China; 3Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China; 4Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China; 5Department of Scientific Research, Peking University Shougang Hospital, Beijing, People’s Republic of China; 6State Key Laboratory of Vascular Homeostasis and Remodeling, (Peking University), Beijing, People’s Republic of China; 7Heart and Vascular Health Research Center of Chengdu Medical College (HVHRC-CMC), Chengdu, People’s Republic of China; 8Foundation-Medical Research Institutes, Beirut, Lebanon*These authors contributed equally to this workCorrespondence: Hongyu Wang, Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, 100144, People’s Republic of China, Email dr.hongyuwang@foxmail.comAbstract: Cardiovascular disease(CVD) has become a major disease burden affecting people’s health in China. Blood vessels are very important for human health and are the “sentinel” for the development of many cardiovascular and cerebrovascular diseases. The key to effectively preventing fatal, disabling heart, brain and peripheral vascular events lies in controlling traditional and non-traditional risk factors for vascular health from the source, and early assessment and intervention of early vascular lesions. Since 2004, China government promoted the early detection technology of vascular lesions and vascular medicine, and proposed the Bei

Liujiazi Shao,1 Yang Liu,2 Junqiang Hao,1 Jiayi Li,1 Hongyu Wang,1 Fu-Shan Xue,1 Bijia Song,1* Lei Wan1*1Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People’s Republic of China; 2Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bijia Song, Email songbijia@163.com; Lei Wan, Email wanleimz@sina.comObjective: Our study aimed to demonstrate that the combination of sevoflurane inhalation with continuous intravenous anesthesia can effectively reduce the dosage of muscle relaxants, shorten extubation time under anesthesia while meeting the requirements of laparoscopic deep neuromuscular block (dNMB) in obese patients. Additionally, we sought to assess the potential reduction in postoperative residual muscle relaxants.Methods: Fifty-nine patients were randomly assigned. Anesthesia-related variables, such as anesthetics dosages, muscle relaxant effective time, clinical muscle relaxant time, muscle relaxant in vivo action time, muscle relaxant recovery time, body movement times, and extubation duration were recorded. Surgery-related variables (the Leiden-Surgical Rating Scale (L-SRS), duration of the procedure) were recorded. Pain was measured using the visual analog scale (VAS) score before leaving the PACU. The duration of the PACU stay and patients’ satisfaction levels in the PACU were also recorded.Results: Patients who inhaled sevoflurane during the operation required a lower dosage of muscle relaxant to achieve the same deep neuromuscular block (dNMB) effect. The time from stopping the rocuronium pump to T1 recovery of 90% was shorter, and the time for T1 to recover from 25% to 75% was faster among patients who inhaled sevoflurane during the operation. Furthermore, the sevoflurane combined with continuous intravenous anesthesia gro

The lack of attention to nonviolence in education is highly problematic, and this article elaborates nonviolence as a new direction in a time of crisis. First, nonviolence is conceptualized as holding tensions to contest violence and cultivate nonviolent relations in the everyday praxis of education in both inner work and outer work in engaging social differences. Second, an aesthetics-based approach at intrapersonal, interpersonal, and transpersonal levels is introduced to address violence and nonviolence in curriculum. Third, we conceptualize how embodied living and mindfulness are crucial components in understanding and practicing nonviolence. This article brings philosophical understanding, artistic attunement, and a meditative stance together to demonstrate what possibilities can be opened by embracing nonviolence in curriculum studies. Throughout the article, we argue that nonviolence not only opposes violence but is also a positive, integrative force that we should become attuned to in order to transform curriculum and education. In addition, we discuss three theoretical and practical implications of our work, as well as four domains for further research.

Due to a threat to human life from heavy metal ions pollution, unprecedented interest has been gained in the development of water purification technologies. Here, we explore another new approach to exploit a prospective carbon material for removing copper ions from aqueous solution based on rapid and easy capacitive deionization (CDI). Reasonable carbon materials modification with ideal composition and improved morphological structure is essential to additionally optimize the capabilities of CDI. We prepared a nitrogen-rich hierarchically porous carbon composites (CoFe-NC) with uniform cobalt (Co) and iron (Fe) doped metal in carbon skeleton by a simple impregnation and pyrolysis method, derived from zeolitic imidazolate framework-8, to use as highly effective CDI electrode for copper ions removal. The addition of Fe can facilitate the uniform dispersion of metals, and enable the formation of a stable carbon cage after pyrolysis. It can sufficiently expose active sites of the electrode materials and promote interfacial charge transfer, thus improving CDI electrosorption efficiency. CoFe-NC composites electrode can achieve outstanding deionization capacity (91.31 mg g-1) in 25 mg L-1 CuSO4 solu-tion. The carbon cage structure of CoFe-NC not only prevents aggregation of metals and avoids destruction of rich multistage pore system by pyrolysis, but also induces a faster ions transport rate. In addition, density functional theory calculations demonstrated that the co-doping of Co and Fe can remarkably increase the adsorption en-ergies of Cu2+ ions, leading to excellent selectivity, which indicates that CoFe-NC composites can be a desired CDI electrode material., Funding Agencies|National Natural Science Foundation of China [51778156]; Talent Cultivation Program of Guangzhou University [YJ2021005]; Guangdong Natural Science Foundation [2022A1515010441]; "Basic Innovation" Project for Graduate Students of Guangzhou University [2022GDJC-M43]

Previous studies have shown that the topological properties of a complex network, such as heterogeneity and average degree, affect the evolutionary game dynamics on it. However, traditional numerical simulations are usually time-consuming and demand a lot of computational resources. In this paper, we propose the method of dynamical approximate master equations (DAMEs) to accurately approximate the evolutionary outcomes on complex networks. We demonstrate that the accuracy of DAMEs supersedes previous standard pairwise approximation methods, and DAMEs require far fewer computational resources than traditional numerical simulations. We use prisoner's dilemma and snowdrift game on regular and scale-free networks to demonstrate the applicability of DAMEs. Overall, our method facilitates the investigation of evolutionary dynamics on a broad range of complex networks, and provides new insights into the puzzle of cooperation., Comment: 21 pages, 4 figures

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields., Comment: Summarized paper for the MICCAI INSTANCE 2022 Challenge

Hyperspectral target detection is good at finding dim and small objects based on spectral characteristics. However, existing representation-based methods are hindered by the problem of the unknown background dictionary and insufficient utilization of spatial information. To address these issues, this paper proposes an efficient optimizing approach based on low-rank representation (LRR) and graph Laplacian regularization (GLR). Firstly, to obtain a complete and pure background dictionary, we propose a LRR-based background subspace learning method by jointly mining the low-dimensional structure of all pixels. Secondly, to fully exploit local spatial relationships and capture the underlying geometric structure, a local region-based GLR is employed to estimate the coefficients. Finally, the desired detection map is generated by computing the ratio of representation errors from binary hypothesis testing. The experiments conducted on two benchmark datasets validate the effectiveness and superiority of the approach. For reproduction, the accompanying code is available at https://github.com/shendb2022/LRBSL-GLR., Comment: 4 pages, 3 figures, 1 table

This work deals with the CPU-GPU heterogeneous code acceleration of a finite-volume CFD solver utilizing multiple CPUs and GPUs at the same time. First, a high-level description of the CFD solver called SENSEI, the discretization of SENSEI, and the CPU-GPU heterogeneous computing workflow in SENSEI leveraging MPI and OpenACC are given. Then, a performance model for CPU-GPU heterogeneous computing requiring ghost cell exchange is proposed to help estimate the performance of the heterogeneous implementation. The scaling performance of the CPU-GPU heterogeneous computing and its comparison with the pure multi-CPU/GPU performance for a supersonic inlet test case is presented to display the advantages of leveraging the computational power of both the CPU and the GPU. Using CPUs and GPUs as workers together, the performance can be improved further compared to using pure CPUs or GPUs, and the advantages can be fairly estimated by the performance model proposed in this work. Finally, conclusions are drawn to provide 1) suggestions for application users who have an interest to leverage the computational power of the CPU and GPU to accelerate their own scientific computing simulations and 2) feedback for hardware architects who have an interest to design a better CPU-GPU heterogeneous system for heterogeneous computing.

Speaker verification is to judge the similarity of two unknown voices in an open set, where the ideal speaker embedding should be able to condense discriminant information into a compact utterance-level representation that has small intra-speaker distances and large inter-speaker distances.We propose a novel model named Voice Transformer(VOT) for speaker verification. The model consists of multiple parallel Transformers, and the outputs of these Transformers are adaptively combined. Deeply-Fused Semantic Memory Network(DFSMN)is integrated into the attention parts of these Transformers to capture long-distance information and enhance the local dependencies. Statistical pooling layers are incorporated to enhance overall performance without significantly increasing the number of parameters. We propose a new loss function called Additive Angular Margin Focal Loss(AAMF) to address the hard sample mining issue.We evaluate the proposed approach on the VoxCeleb1 and CN-Celeb2 datasets. The experimental results demonstrate that VOT achieves state-of-the-art results, outperforming nearly all existing models. The code is available on GitHub., Comment: 8 pages,4 figures,6 tables

Familiarity with audiences plays a significant role in shaping individual performance and experience across various activities in everyday life. This study delves into the impact of familiarity with non-playable character (NPC) audiences on player performance and experience in virtual reality (VR) exergames. By manipulating of NPC appearance (face and body shape) and voice familiarity, we explored their effect on game performance, experience, and exertion. The findings reveal that familiar NPC audiences have a positive impact on performance, creating a more enjoyable gaming experience, and leading players to perceive less exertion. Moreover, individuals with higher levels of self-consciousness exhibit heightened sensitivity to the familiarity with NPC audiences. Our results shed light on the role of familiar NPC audiences in enhancing player experiences and provide insights for designing more engaging and personalized VR exergame environments., Comment: 10 pages, 5 figures, IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2023

The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits., Comment: Work in progress

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Pompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available., Comment: 8 pages, 4 figures

A realistic material may possess defects, which often bring the material new properties that have practical applications. The boundary defects of a two-dimensional topologically ordered system are thought of as an alternative way of realizing topological quantum computation. To facilitate the study of such boundary defects, in this paper, we construct an exactly solvable Hamiltonian model of topological orders with gapped boundary junctions, where the boundary defects reside, by placing the Levin-Wen model on a disk, whose gapped boundary is separated into multiple segments by junctions. We find that the Hamiltonian of a gapped boundary junction is characterized by either a morphism between or a common Frobenius subalgebra of the two Frobenius algebras (in the input fusion category) characetrizing the two boundary segments joint by the junction. We derive a formula of the ground state degeneracy and an explicit ground-state basis of our model. We propose the notion of mobile and immobile charges on the boundary and find that they are quantum observables and label the ground-state basis. Our model is computation friendly., Comment: Version appearing in JHEP

In this paper, we construct an exactly solvable lattice Hamiltonian model to investigate the properties of a composite system consisting of multiple topological orders separated by gapped domain walls. There are interdomain elementary excitations labeled by a pair of anyons in different domains of this system; This system also has elementary excitations with quasiparticles in the gapped domain wall. Each set of elementary excitations corresponds to a basis of the ground states of this composite system on the torus, reflecting that the ground-state degeneracy matches the number of either set of elementary excitations. The characteristic properties of this composite system lie in the basis transformations, represented by the $S$ and $T$ matrices: The $S$ matrix encodes the mutual statistics between interdomain excitations and domain-wall quasiparticles, and the $T$ matrix encapsulates the topological spins of interdomain excitations. Our model realizes a spatial counterpart of a temporal phase transition triggered by anyon condensation, bringing the abstract theory of anyon condensation into manifestable spatial interdomain excitation states., Comment: 39 pages, 16 figures

In this paper, we construct an exactly solvable lattice Hamiltonian model to investigate the properties of a composite system consisting of multiple topological orders separated by gapped domain walls. There are interdomain elementary excitations labeled by a pair of anyons in different domains of this system; This system also has elementary excitations with quasiparticles in the gapped domain wall. Each set of elementary excitations corresponds to a basis of the ground states of this composite system on the torus, reflecting that the ground-state degeneracy matches the number of either set of elementary excitations. The characteristic properties of this composite system lie in the basis transformations, represented by the $S$ and $T$ matrices: The $S$ matrix encodes the mutual statistics between interdomain excitations and domain-wall quasiparticles, and the $T$ matrix encapsulates the topological spins of interdomain excitations. Our model realizes a spatial counterpart of a temporal phase transition triggered by anyon condensation, bringing the abstract theory of anyon condensation into manifestable spatial interdomain excitation states., Comment: 39 pages, 16 figures

Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with better parallelization. In this work, we present TorchScale, an open-source toolkit that allows researchers and developers to scale up Transformers efficiently and effectively. TorchScale has the implementation of several modeling techniques, which can improve modeling generality and capability, as well as training stability and efficiency. Experimental results on language modeling and neural machine translation demonstrate that TorchScale can successfully scale Transformers to different sizes without tears. The library is available at https://aka.ms/torchscale., Comment: Work in progress

A big convergence of model architectures across language, vision, speech, and multimodal is emerging. However, under the same name "Transformers", the above areas use different implementations for better performance, e.g., Post-LayerNorm for BERT, and Pre-LayerNorm for GPT and vision Transformers. We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for various tasks and modalities with guaranteed training stability. In this work, we introduce a Transformer variant, named Magneto, to fulfill the goal. Specifically, we propose Sub-LayerNorm for good expressivity, and the initialization strategy theoretically derived from DeepNet for stable scaling up. Extensive experiments demonstrate its superior performance and better stability than the de facto Transformer variants designed for various applications, including language modeling (i.e., BERT, and GPT), machine translation, vision pretraining (i.e., BEiT), speech recognition, and multimodal pretraining (i.e., BEiT-3)., Comment: Work in progress

The end-user programming of social robot behavior is usually limited by a predefined set of movements. We are proposing a puppeteering robotic interface that provides a more intuitive method of programming robot expressive movements. As the user manipulates the puppet of a robot, the actual robot replicates the movements, providing real-time visual feedback. Through this proposed interface, even with limited training, a novice user can design and program expressive movements efficiently. We present our preliminary user study results in this extended abstract., Comment: Presented at PD/EUP Workshop, 2022 (arXiv:cs/4404636)

The coming quantum computation is forcing us to reexamine the cryptosystems people use. We are applying graph colorings of topological coding to modern information security and future cryptography against supercomputer and quantum computer attacks in the near future. Many of techniques introduced here are associated with many mathematical conjecture and NP-problems. We will introduce a group of W-constraint (k,d)-total colorings and algorithms for realizing these colorings in some kinds of graphs, which are used to make quickly public-keys and private-keys with anti-quantum computing, these (k,d)-total colorings are: graceful (k,d)-total colorings, harmonious (k,d)-total colorings, (k,d)-edge-magic total colorings, (k,d)-graceful-difference total colorings and (k,d)-felicitous-difference total colorings. One of useful tools we used is called Topcode-matrix with elements can be all sorts of things, for example, sets, graphs, number-based strings. Most of parameterized graphic colorings/labelings are defined by Topcode-matrix algebra here. From the application point of view, many of our coloring techniques are given by algorithms and easily converted into programs.

Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify the residual connection in Transformer, accompanying with theoretically derived initialization. In-depth theoretical analysis shows that model updates can be bounded in a stable way. The proposed method combines the best of two worlds, i.e., good performance of Post-LN and stable training of Pre-LN, making DeepNorm a preferred alternative. We successfully scale Transformers up to 1,000 layers (i.e., 2,500 attention and feed-forward network sublayers) without difficulty, which is one order of magnitude deeper than previous deep Transformers. Remarkably, on a multilingual benchmark with 7,482 translation directions, our 200-layer model with 3.2B parameters significantly outperforms the 48-layer state-of-the-art model with 12B parameters by 5 BLEU points, which indicates a promising scaling direction., Comment: Work in progress

Making topological authentication from theory to practical application is an important and challenging task. More and more researchers pay attention on coming quantum computation, privacy data protection, lattices and cryptography. Research show the advantages of topological authentications through graph operations, various matrices, graph colorings and graph labelings are: related with two or more different mathematical areas, be not pictures, there are huge number of colorings and labelings, rooted on modern mathematics, diversity of asymmetric ciphers, simplicity and convenience, easily created, irreversibility, computational security, provable security, and so on. Topological authentications based on various graph homomorphisms, degree-sequence homomorphisms, graph-set homomorphisms. Randomly topological coding and topological authentications are based on Hanzi authentication, randomly adding-edge-removing operation, randomly leaf-adding algorithms, graph random increasing techniques, operation graphic lattice and dynamic networked models and their spanning trees and maximum leaf spanning trees. Realization of topological authentication is an important topic, we study: number-based strings generated from colored graphs, particular graphs (complete graphs, trees, planar graphs), some methods of generating public-keys. some techniques of topologically asymmetric cryptosystem are: W-type matching labelings, dual-type labelings, reciprocal-type labelings, topological homomorphisms, indexed colorings, graphic lattices, degree-sequence lattices, every-zero Cds-matrix groups of degree-sequences, every-zero graphic groups, graphic lattices having coloring closure property, self-similar networked lattices., Comment: arXiv admin note: text overlap with arXiv:2106.15254

Computational Fluid Dynamics (CFD) has been widely used as a tool to analyze physical phenomena involving fluids. To perform a CFD simulation, the governing equations are discretized to formulate a set of nonlinear algebraic equations. Typical spatial discretization schemes include finite-difference methods, finite-volume methods, and finite-element methods. Error introduced in the discretization process is called discretization error and defined as the difference between the exact solution to the discrete equations and the exact solution to the partial differential or integral equations. For most CFD simulations, discretization error accounts for the largest portion of the numerical error in the simulation. Discretization error has a complicated nonlinear relationship with the computational grid and the discretization scheme, which makes it especially difficult to estimate. Thus, it is important to study the discretization error to characterize numerical errors in a CFD simulation. Discretization error estimation is performed using the Error Transport Equations (ETE) in this work. The original nonlinear form of the ETE can be linearized to formulate the linearized ETE. Results of the two types of the ETE are compared. This work implements the ETE for finite-volume methods and Discontinuous Galerkin (DG) finite-element methods. For finite volume methods, discretization error estimates are obtained for both steady state problems and unsteady problems. The work on steady-state problems focuses on turbulent flow modelled by the Spalart-Allmaras (SA) model and Menter's $k-omega$ SST model. Higher-order discretization error estimates are obtained for both the mean variables and the turbulence working variables. The type of pseudo temporal discretization used for the steady-state problems does not have too much influence on the final converged solution. However, the temporal discretization scheme makes a significant difference for unsteady problems. Different temporal dis

In this paper, we propose a method to add constraints that are un-formulatable in generative adversarial networks (GAN)-based arbitrary size RAW Bayer image generation. It is shown theoretically that by using the transformed data in GAN training, it is able to improve the learning of the original data distribution, owing to the invariant of Jensen-Shannon (JS) divergence between two distributions under invertible and differentiable transformation. Benefiting from the proposed method, RAW Bayer pattern images can be generated by configuring the transformation as demosaicing. It is shown that by adding another transformation, the proposed method is able to synthesize high-quality RAW Bayer images with arbitrary size. Experimental results show that images generated by the proposed method outperform the existing methods in the Fr\'echet inception distance (FID) score, peak signal to noise ratio (PSNR), and mean structural similarity (MSSIM), and the training process is more stable. To the best knowledge of the authors, there is no open-source, large-scale image dataset in the RAW Bayer domain, which is crucial for research works aiming to explore the image signal processing (ISP) pipeline design for computer vision tasks. Converting the existing commonly used color image datasets to their corresponding RAW Bayer versions, the proposed method can be a promising solution to the RAW image dataset problem. We also show in the experiments that, by training object detection frameworks using the synthesized RAW Bayer images, they can be used in an end-to-end manner (from RAW images to vision tasks) with negligible performance degradation., Comment: 10 pages

Topological Coding consists of two different kinds of mathematics: topological structure and mathematical relation. The colorings and labelings of graph theory are main techniques in topological coding applied in asymmetric encryption system. Topsnut-gpws (also, colored graphs) have the following advantages: (1) Run fast in communication networks because they are saved in computer by popular matrices rather than pictures. (2) Produce easily text-based (number-based) strings for encrypt files. (3) Diversity of asymmetric ciphers, one public-key corresponds to more private-keys, or more public-keys correspond more private-keys. (4) Irreversibility, Topsnut-gpws can generate quickly text-based (number-based) strings with bytes as long as desired, but these strings can not reconstruct the original Topsnut-gpws. (5) Computational security, since there are many non-polynomial (NP-complete, NP-hard) algorithms in creating Topsnut-gpws. (6) Provable security, since there are many mathematical conjectures (open problems) in graph labelings and graph colorings. We are committed to create more kinds of new Topsnut-gpws to approximate practical applications and antiquantum computation, and try to use algebraic method and Topsnut-gpws to establish graphic group, graphic lattice, graph homomorphism etc., Comment: AMS-LaTeX v1.2, 247 pages with 121 figures. We present new colorings and labelings for topological coding

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies.

Wang C, Wang M, Xing B, et al. Onco Targets Ther. 2019;12:5189–5200. The Editor and Publisher of OncoTargets and Therapy wish to retract the published article. It came to our attention that images in Figure 4E had allegedly been duplicated, specifically:● Figure 4E panel Scramble 0h appears to be duplicated with Figure 4E panels CTBP1-RNAi and JAK1-RNAi 0h, with all images showing evidence of frame shifting.● Figure 4E panel CTBP1-RNAi 12h appears to be duplicated with Figure 4E JAK1-RNAi 24h. The authors were unable to provide a satisfactory explanation for the alleged duplication nor were they able to provide the raw data for their study. The Editor determined the findings of the study are no longer valid and requested for the paper to be retracted. Our decision-making was informed by our policy on publishing ethics and integrity and the COPE guidelines on retraction. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”. This retraction relates to this paper &nbsp

We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constrained by an autoencoder. Comparing with vanilla graph convolutional networks, the autoencoder step is added to reduce the information loss brought by Laplacian smoothing. We consider applying our model on both homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the autoencoder approximates to the adjacency matrix of the input graph by taking hidden layer representations as encoder and another one-layer graph convolutional network as decoder. For heterogeneous graphs, since there are multiple adjacency matrices corresponding to different types of edges, the autoencoder approximates to the feature matrix of the input graph instead, and changes the encoder to a particularly designed multi-channel pre-processing network with two layers. In both cases, the error occurred in the autoencoder approximation goes to the penalty term in the loss function. In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks. Further, we notice that our technique can be applied on graph attention network to improve the performance as well. This reveals the wide applicability of the proposed autoencoder technique., Comment: 23 pages, 4 figures

In this paper, a robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output LaTeX sequences, which can effectively and correctly focus on where each step of output should be concerned and has a positive effect on analyzing the two-dimensional structure of handwritten mathematical expressions and identifying different mathematical symbols in a long expression. With the addition of visualization, the model's recognition process is shown in detail. In addition, our model achieved 49.459% and 46.062% ExpRate on the public CROHME 2014 and CROHME 2016 datasets. The present model results suggest that the state-of-the-art model has better robustness, fewer errors, and higher accuracy., Comment: 6 figures, 5 tables, 20 pages

Since 2014, very deep convolutional neural networks have been proposed and become the must-have weapon for champions in all kinds of competition. In this report, a pipeline is introduced to perform the classification of smoking and calling by modifying the pretrained inception V3. Brightness enhancing based on deep learning is implemented to improve the classification of this classification task along with other useful training tricks. Based on the quality and quantity results, it can be concluded that this pipeline with small biased samples is practical and useful with high accuracy.

Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications.

Ferromagnet/two-dimensional transition-metal dichalcogenide (FM/2D TMD) interfaces provide attractive opportunities to push magnetic information storage to the atomically thin limit. Existing work has focused on FMs contacted with mechanically exfoliated or chemically vapor-deposition-grown TMDs, where clean interfaces cannot be guaranteed. Here, we report a reliable way to achieve contamination-free interfaces between ferromagnetic CoFeB and molecular-beam epitaxial MoSe2. We show a spin reorientation arising from the interface, leading to a perpendicular magnetic anisotropy (PMA), and reveal the CoFeB/2D MoSe2 interface allowing for the PMA development in a broader CoFeB thickness-range than common systems such as CoFeB/MgO. Using X-ray magnetic circular dichroism analysis, we attribute generation of this PMA to interfacial d-d hybridization and deduce a general rule to enhance its magnitude. We also demonstrate favorable magnetic softness and considerable magnetic moment preserved at the interface and theoretically predict the interfacial band matching for spin filtering. Our work highlights the CoFeB/2D MoSe2 interface as a promising platform for examination of TMD-based spintronic applications and might stimulate further development with other combinations of FM/2D TMD interfaces.

Buildings produce a large amount of carbon emissions in their life cycle, which intensifies greenhouse-gas effects and has become a great threat to the survival of humans and other species. Although many previous studies shed light on the calculation of carbon emissions, a systematic analysis framework is still missing. Therefore, this study proposes an analysis framework of carbon emissions based on building information modeling (BIM) and life cycle assessment (LCA), which consists of four steps: (1) defining the boundary of carbon emissions in a life cycle; (2) establishing a carbon emission coefficients database for Chinese buildings and adopting Revit, GTJ2018, and Green Building Studio for inventory analysis; (3) calculating carbon emissions at each stage of the life cycle; and (4) explaining the calculation results of carbon emissions. The framework developed is validated using a case study of a hospital project, which is located in areas in Anhui, China with a hot summer and a cold winter. The results show that the reinforced concrete engineering contributes to the largest proportion of carbon emissions (around 49.64%) in the construction stage, and the HVAC (heating, ventilation, and air conditioning) generates the largest proportion (around 53.63%) in the operational stage. This study provides a practical reference for similar buildings in analogous areas and for additional insights on reducing carbon emissions in the future.

We generalize the Electric-magnetic (EM) duality in the quantum double (QD) models to the case of topological orders with gapped boundaries. We also map the QD models with boundaries to the Levin-Wen (LW) models with boundaries. To this end, we Fourier transform and rewrite the extended QD model with a finite gauge group $G$ on a trivalent lattice with a boundary. Gapped boundary conditions of the model before the transformation are known to be characterized by the subgroups $K \subseteq G$. We find that after the transformation, the boundary conditions are then characterized by the Frobenius algebras $A_{G,K}$ in $\mathrm{Rep}_G$. An $A_{G,K}$ is the dual space of the quotient of the group algebra of $G$ over that of $K$, and $\mathrm{Rep}_G$ is the category of the representations of $G$. The EM duality on the boundary is revealed by mapping the $K$'s to $A_{G,K}$'s. We also show that our transformed extended QD model can be mapped to an extended LW model on the same lattice via enlarging the Hilbert space of the extended LW model. Moreover, our transformed extended QD model elucidates the phenomenon of anyon splitting in anyon condensation., Comment: 30+11 pages

Ferromagnet/two-dimensional transition-metal dichalcogenide (FM/2D TMD) interfaces provide attractive opportunities to push magnetic information storage to the atomically thin limit. Existing work has focused on FMs contacted with mechanically exfoliated or chemically vapor-deposition-grown TMDs, where clean interfaces cannot be guaranteed. Here, we report a reliable way to achieve contamination-free interfaces between ferromagnetic CoFeB and molecular-beam epitaxial MoSe2. We show a spin reorientation arising from the interface, leading to a perpendicular magnetic anisotropy (PMA), and reveal the CoFeB/2D MoSe2 interface allowing for the PMA development in a broader CoFeB thickness-range than common systems such as CoFeB/MgO. Using X-ray magnetic circular dichroism analysis, we attribute generation of this PMA to interfacial d-d hybridization and deduce a general rule to enhance its magnitude. We also demonstrate favorable magnetic softness and considerable magnetic moment preserved at the interface and theoretically predict the interfacial band matching for spin filtering. Our work highlights the CoFeB/2D MoSe2 interface as a promising platform for examination of TMD-based spintronic applications and might stimulate further development with other combinations of FM/2D TMD interfaces.

We generalize the Electric-magnetic (EM) duality in the quantum double (QD) models to the case of topological orders with gapped boundaries. We also map the QD models with boundaries to the Levin-Wen (LW) models with boundaries. To this end, we Fourier transform and rewrite the extended QD model with a finite gauge group $G$ on a trivalent lattice with a boundary. Gapped boundary conditions of the model before the transformation are known to be characterized by the subgroups $K \subseteq G$. We find that after the transformation, the boundary conditions are then characterized by the Frobenius algebras $A_{G,K}$ in $\mathrm{Rep}_G$. An $A_{G,K}$ is the dual space of the quotient of the group algebra of $G$ over that of $K$, and $\mathrm{Rep}_G$ is the category of the representations of $G$. The EM duality on the boundary is revealed by mapping the $K$'s to $A_{G,K}$'s. We also show that our transformed extended QD model can be mapped to an extended LW model on the same lattice via enlarging the Hilbert space of the extended LW model. Moreover, our transformed extended QD model elucidates the phenomenon of anyon splitting in anyon condensation., Comment: 30+11 pages