arXiv subjects

Wei Tang

Publications and source records attributed to Wei Tang.

At least 127 records · Page 7Linked to original sources

arXiv2024

Large low-field magnetocaloric response in a ferromagnetic gadolinium orthophosphate

Bulk magnetic and thermodynamic measurements, along with mean-field calculations, were conducted on the ferromagnetic K3Gd5(PO4)6 powders. No magnetic ordering was observed until 2 K, while the application of an external field B > 1 T resulted in the splitting of the Gd3+ ground state multiplet and induced a non-cooperative Schottky effect. The average nearest-neighbor exchange strength |J1/kB| is determined to be 0.017 K, which leads to a remarkably large low field magnetic entropy change ΔSm = 36.2 J kg-1 K-1 under applied field change B = 2 T at temperature T = 2 K, as well as a maximum adiabatic temperature change Tad = 10.9 K. We contend that ferromagnetic gadolinium orthophosphates serve as a promising reservoir for exploring advanced magnetic refrigerants applicable under low magnetic fields.

cond-mat.mtrl-sci↗

arXiv2024

Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items and the behavior space of users, or overly simplistic control strategies (e.g., whether to rank or directly present existing results). To bridge these gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning that uses LLMs as surrogate users, thereby guiding the recommendation process and invoking external tools to generate a recommendation list that aligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. The process factors in the nuances of the context and user preferences. The LLM then invokes external tools based on a user's attribute instructions and probes different segments of the item pool. We consider two types of attribute-oriented tools: rank tools and retrieval tools. Through the integration of LLMs, ToolRec enables conventional recommender systems to become external tools with a natural language interface. Extensive experiments verify the effectiveness of ToolRec, particularly in scenarios that are rich in semantic content.

cs.IR↗

arXiv2024

Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes

Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performance. In this work, we show that the widely used heatmaps are underestimated in the efficiency of 6-Dof grasp generation. Therefore, we propose an effective local grasp generator combined with grasp heatmaps as guidance, which infers in a global-to-local semantic-to-point way. Specifically, Gaussian encoding and the grid-based strategy are applied to predict grasp heatmaps as guidance to aggregate local points into graspable regions and provide global semantic information. Further, a novel non-uniform anchor sampling mechanism is designed to improve grasp accuracy and diversity. Benefiting from the high-efficiency encoding in the image space and focusing on points in local graspable regions, our framework can perform high-quality grasp detection in real-time and achieve state-of-the-art results. In addition, real robot experiments demonstrate the effectiveness of our method with a success rate of 94% and a clutter completion rate of 100%. Our code is available at https://github.com/THU-VCLab/HGGD.

cs.RO↗

arXiv2024

Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment

In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic inconsistency, mainly due to missing modal attributes, poses a significant challenge. Traditional approaches rely on attribute interpolation, but this often introduces modality noise, distorting the original semantics. Moreover, the lack of a universal theoretical framework limits advancements in achieving semantic consistency. This study introduces a novel approach, DESAlign, which addresses these issues by applying a theoretical framework based on Dirichlet energy to ensure semantic consistency. We discover that semantic inconsistency leads to model overfitting to modality noise, causing performance fluctuations, particularly when modalities are missing. DESAlign innovatively combats over-smoothing and interpolates absent semantics using existing modalities. Our approach includes a multi-modal knowledge graph learning strategy and a propagation technique that employs existing semantic features to compensate for missing ones, providing explicit Euler solutions. Comprehensive evaluations across 60 benchmark splits, including monolingual and bilingual scenarios, demonstrate that DESAlign surpasses existing methods, setting a new standard in performance. Further testing with high rates of missing modalities confirms its robustness, offering an effective solution to semantic inconsistency in real-world MMKGs.

cs.IR↗

arXiv2024

Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes

The crux of semi-supervised temporal action localization (SS-TAL) lies in excavating valuable information from abundant unlabeled videos. However, current approaches predominantly focus on building models that are robust to the error-prone target class (i.e, the predicted class with the highest confidence) while ignoring informative semantics within non-target classes. This paper approaches SS-TAL from a novel perspective by advocating for learning from non-target classes, transcending the conventional focus solely on the target class. The proposed approach involves partitioning the label space of the predicted class distribution into distinct subspaces: target class, positive classes, negative classes, and ambiguous classes, aiming to mine both positive and negative semantics that are absent in the target class, while excluding ambiguous classes. To this end, we first devise innovative strategies to adaptively select high-quality positive and negative classes from the label space, by modeling both the confidence and rank of a class in relation to those of the target class. Then, we introduce novel positive and negative losses designed to guide the learning process, pushing predictions closer to positive classes and away from negative classes. Finally, the positive and negative processes are integrated into a hybrid positive-negative learning framework, facilitating the utilization of non-target classes in both labeled and unlabeled videos. Experimental results on THUMOS14 and ActivityNet v1.3 demonstrate the superiority of the proposed method over prior state-of-the-art approaches.

cs.CV↗

arXiv2024

A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection

Diabetic retinopathy (DR) is a leading global cause of blindness. Early detection of hard exudates plays a crucial role in identifying DR, which aids in treating diabetes and preventing vision loss. However, the unique characteristics of hard exudates, ranging from their inconsistent shapes to indistinct boundaries, pose significant challenges to existing segmentation techniques. To address these issues, we present a novel supervised contrastive learning framework to optimize hard exudate segmentation. Specifically, we introduce a patch-wise density contrasting scheme to distinguish between areas with varying lesion concentrations, and therefore improve the model's proficiency in segmenting small lesions. To handle the ambiguous boundaries, we develop a discriminative edge inspection module to dynamically analyze the pixels that lie around the boundaries and accurately delineate the exudates. Upon evaluation using the IDRiD dataset and comparison with state-of-the-art frameworks, our method exhibits its effectiveness and shows potential for computer-assisted hard exudate detection. The code to replicate experiments is available at github.com/wetang7/HECL/.

eess.IV↗

arXiv2024

Code Reviewer Recommendation Based on a Hypergraph with Multiplex Relationships

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing stateof-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.

cs.SE↗

arXiv2023

Context Disentangling and Prototype Inheriting for Robust Visual Grounding

Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the target from other objects, particularly for the targets that have the same category as others. However, most previous methods underestimate such information. Moreover, they are usually designed for the standard scene (without any novel object), which limits their generalization to the open-vocabulary scene. In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes. Specifically, the context disentangling disentangles the referent and context features, which achieves better discrimination between them. The prototype inheriting inherits the prototypes discovered from the disentangled visual features by a prototype bank to fully utilize the seen data, especially for the open-vocabulary scene. The fused features, obtained by leveraging Hadamard product on disentangled linguistic and visual features of prototypes to avoid sharp adjusting the importance between the two types of features, are then attached with a special token and feed to a vision Transformer encoder for bounding box regression. Extensive experiments are conducted on both standard and open-vocabulary scenes. The performance comparisons indicate that our method outperforms the state-of-the-art methods in both scenarios. {The code is available at https://github.com/WayneTomas/TransCP.

cs.CV↗

arXiv2023

FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion

Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on multiple aligned KGs can improve performance, previous methods that rely on transferring raw data among KGs raise privacy concerns. To address this challenge, we propose a new federated learning framework that implicitly aggregates knowledge from multiple KGs without demanding raw data exchange and entity alignment. We treat each KG as a client that trains a local language model through textbased knowledge representation learning. A central server then aggregates the model weights from clients. As natural language provides a universal representation, the same knowledge thus has similar semantic representations across KGs. As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing. Extensive experiments on a benchmark dataset demonstrate that our method substantially improves KGC on multilingual KGs, achieving comparable performance to state-of-the-art alignment-based models without requiring any labeled alignments or raw user data sharing. Our codes will be publicly available.

cs.CL↗

arXiv2023

Deeply Coupled Cross-Modal Prompt Learning

Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models to downstream tasks has gained significant attention recently. Existing prompt-tuning methods in cross-modal learning, however, either solely focus on language branch, or learn vision-language interaction in a shallow mechanism. In this context, we propose a Deeply coupled Cross-modal Prompt learning (DCP) method based on CLIP. DCP flexibly accommodates the interplay between vision and language with a Cross-Modal Prompt Attention (CMPA) mechanism, which enables the mutual exchange of respective representation through a well-connected multi-head attention module progressively and strongly. We then conduct comprehensive few-shot learning experiments on 11 image classification datasets and analyze the robustness to domain shift as well. Thorough experimental analysis evidently demonstrates the superb few-shot generalization and compelling domain adaption capacity of a well-executed DCP. The code can be found at https://github.com/GingL/CMPA.

cs.CV↗

arXiv2023

What Large Language Models Bring to Text-rich VQA?

Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this work, we focus on investigating the advantages and bottlenecks of LLM-based approaches in addressing this problem. To address the above concern, we separate the vision and language modules, where we leverage external OCR models to recognize texts in the image and Large Language Models (LLMs) to answer the question given texts. The whole framework is training-free benefiting from the in-context ability of LLMs. This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets. Besides, based on the ablation study, we find that LLM brings stronger comprehension ability and may introduce helpful knowledge for the VQA problem. The bottleneck for LLM to address text-rich VQA problems may primarily lie in visual part. We also combine the OCR module with MLLMs and pleasantly find that the combination of OCR module with MLLM also works. It's worth noting that not all MLLMs can comprehend the OCR information, which provides insights into how to train an MLLM that preserves the abilities of LLM.

cs.CV↗

arXiv2023

Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response

Classic mechanism/information design imposes the assumption that agents are fully rational, meaning each of them always selects the action that maximizes her expected utility. Yet many empirical evidence suggests that human decisions may deviate from this full rationality assumption. In this work, we attempt to relax the full rationality assumption with bounded rationality. Specifically, we formulate the bounded rationality of an agent by adopting the quantal response model (McKelvey and Palfrey, 1995). We develop a theory of rationality-robust information design in the canonical setting of Bayesian persuasion (Kamenica and Gentzkow, 2011) with binary receiver action. We first identify conditions under which the optimal signaling scheme structure for a fully rational receiver remains optimal or approximately optimal for a boundedly rational receiver. In practice, it might be costly for the designer to estimate the degree of the receiver's bounded rationality level. Motivated by this practical consideration, we then study the existence and construction of robust signaling schemes when there is uncertainty about the receiver's bounded rationality level.

cs.GT↗

arXiv2023

RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning

Sequential processes in real-world often carry a combination of simple subsystems that interact with each other in certain forms. Learning such a modular structure can often improve the robustness against environmental changes. In this paper, we propose recurrent independent Grid LSTM (RigLSTM), composed of a group of independent LSTM cells that cooperate with each other, for exploiting the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability on the basis of the recent Grid LSTM for the tasks where some factors differ between training and evaluation. Specifically, at each time step, only a fraction of cells are activated, and the activated cells select relevant inputs and cells to communicate with. At the end of one time step, the hidden states of the activated cells are updated by considering the relevance between the inputs and the hidden states from the last and current time steps. Extensive experiments on diversified sequential modeling tasks are conducted to show the superior generalization ability when there exist changes in the testing environment. Source code is available at \url{https://github.com/ziyuwwang/rig-lstm}.

cs.LG↗

arXiv2023

A holistic review on fatigue properties of additively manufactured metals

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superior to that of conventionally manufactured metals, but the fatigue performance is still a thorny problem that may hinder the replacement of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. This article reviews the state-of-art published data on the fatigue properties of AM metals, principally including $S$--$N$ data and fatigue crack growth data. The AM techniques utilized to generate samples in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, WAAM). Further, the fatigue properties of AM metallic materials that involve titanium alloys, aluminum alloys, stainless steel, nickel-based alloys, magnesium alloys, and high entropy alloys, are systematically overviewed. In addition, summary figures or tables for the published data on fatigue properties are presented for the above metals, the AM techniques, and the influencing factors (manufacturing parameters, e.g., built orientation, processing parameter, and post-processing). The effects of build direction, particle, geometry, manufacturing parameters, post-processing, and heat-treatment on fatigue properties, when available, are provided and discussed. The fatigue performance and main factors affecting the fatigue behavior of AM metals are finally compared and critically analyzed, thus potentially providing valuable guidance for improving the fatigue performance of AM metals.

cond-mat.mtrl-sci↗

arXiv2023

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.

cs.LG↗

arXiv2023

Kac-Moody symmetries in one-dimensional bosonic systems

In conformal field theories, when the conformal symmetry is enhanced by a global Lie group symmetry, the original Virasoro algebra can be extended to Kac-Moody algebra. In this paper, we extend the lattice construction of the Kac-Moody generators introduced in Wang et al., [Phys. Rev. B. 106, 115111 (2022)] to continuous systems and apply it to one-dimensional continuous boson systems. We justify this microscopic construction of Kac-Moody generators in two ways. First, through phenomenological bosonization, we express the microscopic construction in terms of the boson operators in the bosonization context, which can be related to the Kac-Moody generators in conformal field theories. Second, we study the behavior of the Kac-Moody generators in the integrable Lieb-Liniger model, and reveal its underlying particle-hole excitation picture through Bethe ansatz solutions. Finally, we test the computation of the Kac-Moody generator in the continuous matrix product state simulations, paving the way for more challenging non-integrable systems.

cond-mat.str-el↗

arXiv2023

LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels

In real-world applications, perfect labels are rarely available, making it challenging to develop robust machine learning algorithms that can handle noisy labels. Recent methods have focused on filtering noise based on the discrepancy between model predictions and given noisy labels, assuming that samples with small classification losses are clean. This work takes a different approach by leveraging the consistency between the learned model and the entire noisy dataset using the rich representational and topological information in the data. We introduce LaplaceConfidence, a method that to obtain label confidence (i.e., clean probabilities) utilizing the Laplacian energy. Specifically, it first constructs graphs based on the feature representations of all noisy samples and minimizes the Laplacian energy to produce a low-energy graph. Clean labels should fit well into the low-energy graph while noisy ones should not, allowing our method to determine data's clean probabilities. Furthermore, LaplaceConfidence is embedded into a holistic method for robust training, where co-training technique generates unbiased label confidence and label refurbishment technique better utilizes it. We also explore the dimensionality reduction technique to accommodate our method on large-scale noisy datasets. Our experiments demonstrate that LaplaceConfidence outperforms state-of-the-art methods on benchmark datasets under both synthetic and real-world noise.

cs.LG↗

arXiv2023

The Influence of Dimensionality on the Charge Density Wave Transition and Its Application on Mid-infrared Photodetection

Two-dimensional charge density wave (CDW) materials received much attention for high responsivity and broadband photodetection in recent years, due to their collective electron transport and narrow bandgap. However, the high dark current density problem hinders their real application. Here we report a sharp CDW transition in quasi-1D (TaSe4)2I, and apply it for broadband photodetection. Especially at mid-infrared region, the device shows both high photo responsivity of 1.18e3 A/W and large light on-off ratio of 80, which is superior than 2D CDW TaS2 and most reported low-dimensional materials. The fact for such high performance lies on two aspects. One is the much lower dark current density resulted from the pseudo gap associated with 1D Luttinger liquid state, which is supported by finite size scaling of nonlinear I-V at variable temperatures and occurrence of 1D structural phase transition consolidated by In-situ Raman spectroscopy. The other is the high photocurrent associated with the Frohlich superconductivity state, manifested by an ultrasensitive switching, which can be only accessible in 1D CDW materials, in agreement with our density functional theory calculation. Our work thus reveals the pivotal role of dimensionality in CDW phase transition, and paves a way for implementing highly sensitive broadband photodetector.

cond-mat.mtrl-sci↗