bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Graph Learning and Nuclear Norm Maximization for Deep Cross-Domain Robust Label Propagation
Authors: Wang Wei , Li Hanyang , Shi Ke , Huang Chao , Cao Yang , Wang Cong , Cao Xiaochun
Published: July 2024

Domain adaptation aims to achieve label transfer from a labeled source domain to an unlabeled target domain, where the two domains exhibit different distributions. Existing methods primarily concentrate on designing a feature extractor to learn better domain-invariant features, along with developing an effective classifier for reliable predictions. In this paper, we introduce optimal graph learning to generate a cross-domain graph that effectively connects the two domains, and two domain-specific graphs to capture domain-specific structures. On the one hand, we incorporate the three graphs into the label propagation (LP) classifier to enhance its robustness to distribution difference. On the other hand, we leverage the three graphs to introduce graph embedding losses, promoting the learning of locally discriminative and domain-invariant features. Furthermore, we maximize the nuclear norm of predictions in LP to enhance class diversity, thereby improving its robustness to class imbalance problem. Correspondingly, we develop an efficient algorithm to solve the associated optimization problem. Finally, we integrate the proposed LP and graph embedding losses into a deep neural network, resulting in our proposed deep cross-domain robust LP. Extensive experiments conducted on three cross-domain benchmark datasets demonstrate that our proposed approach could outperform existing state-of-the-art domain adaptation methods.
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation
Authors: Wu Yong , Wang Yang , Qu Sanqing , Li Zhijun , Chen Guang
Published: July 2024

We consider the problem of user-adaptive 3D gaze estimation. The performance of person-independent gaze estimation is limited due to interpersonal anatomical differences. Our goal is to provide a personalized gaze estimation model specifically adapted to a target user. Previous work on user-adaptive gaze estimation requires some labeled images of the target person data to fine-tune the model at test time. However, this can be unrealistic in real-world applications, since it is cumbersome for an end-user to provide labeled images. In addition, previous work requires the training data to have both gaze labels and person IDs. This data requirement makes it infeasible to use some of the available data. To tackle these challenges, this paper proposes a new problem called efficient label-free user adaptation in gaze estimation. Our model only needs a few unlabeled images of a target user for the model adaptation. During offline training, we have some labeled source data without person IDs and some unlabeled person-specific data. Our proposed method uses a meta-learning approach to learn how to adapt to a new user with only a few unlabeled images. Our key technical innovation is to use a generalization bound from domain adaptation to define the loss function in meta-learning, so that our method can effectively make use of both the labeled source data and the unlabeled person-specific data during training. Extensive experiments validate the effectiveness of our method on several challenging benchmarks.
DTS-TPT: Dual Temporal-Sync Test-time Prompt Tuning for Zero-shot Activity Recognition
Authors: Yan Rui , Qu Hongyu , Shu Xiangbo , Li Wenbin , Tang Jinhui , Tan Tieniu
Published: July 2024

Finetuning the large vision-language models on video data with a set of learnable prompts has shown promising performance on zero-shot activity recognition but still requires extra video data and expensive training costs. Inspired by recent Test-time Prompt Tuning (TPT) on the image domain, this work attempts to extend TPT to video data for zero-shot activity recognition. However, monotonous spatial augmentation and short class names cannot meet the need to capture diverse and complicated semantics of human behavior during prompt tuning. To this end, this work proposes a Dual Temporal-Sync Test-time Prompt Tuning (DTS-TPT) framework for zero-shot activity recognition. DTS-TPT tunes the learnable prompts appended to text inputs on video feature sequences of different temporal scales in multiple steps during test time. In each tuning step, we minimize the semantic consistency among the predictions from video feature sequences randomly augmented via AugMix with both original class names and the corresponding description generated through LLM. Compared with the state-of-the-art methods, the proposed method improves the zero-shot top-1 accuracy by approximately 2% ~ 5% on popular benchmarks. The code is available at https://github.com/quhongyu/DTS-TPT.
3D Vision and Language Pretraining with Large-Scale Synthetic Data
Authors: Yang Dejie , Xu Zhu , Mo Wentao , Chen Qingchao , Huang Siyuan , Liu Yang
Published: July 2024

3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can bridge 3D scenes with natural language, which is an important technique for embodied intelligence. However, current 3D-VLP datasets are hindered by limited scene-level diversity and insufficient fine-grained annotations (only 1.2K scenes and 280K textual annotations in ScanScribe), primarily due to the labor-intensive of collecting and annotating 3D scenes. To overcome these obstacles, we construct SynVL3D, a comprehensive synthetic scene-text corpus with 10K indoor scenes and 1M descriptions at object, view, and room levels, which has the advantages of diverse scene data, rich textual descriptions, multi-grained 3D-text associations, and low collection cost. Utilizing the rich annotations in SynVL3D, we pre-train a simple and unified Transformer for aligning 3D and language with multi-grained pretraining tasks. Moreover, we propose a synthetic-to-real domain adaptation in downstream task fine-tuning process to address the domain shift. Through extensive experiments, we verify the effectiveness of our model design by achieving state-of-the-art performance on downstream tasks including visual grounding, dense captioning, and question answering. Codes are available at: https://github.com/idejie/3DSyn
Unified Unsupervised Salient Object Detection via Knowledge Transfer
Authors: Yuan Yao , Liu Wutao , Gao Pan , Dai Qun , Qin Jie
Published: July 2024

Recently, unsupervised salient object detection (USOD) has gained increasing attention due to its annotation-free nature. However, current methods mainly focus on specific tasks such as RGB and RGB-D, neglecting the potential for task migration. In this paper, we propose a unified USOD framework for generic USOD tasks. Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network. This mechanism starts with easy samples and progressively moves towards harder ones, to avoid initial interference caused by hard samples. Afterwards, the obtained saliency cues are utilized to train a saliency detector, and we employ a Self-rectify Pseudo-label Refinement (SPR) mechanism to improve the quality of pseudo-labels. Finally, an adapter-tuning method is devised to transfer the acquired saliency knowledge, leveraging shared knowledge to attain superior transferring performance on the target tasks. Extensive experiments on five representative SOD tasks confirm the effectiveness and feasibility of our proposed method. Code and supplement materials are available at https://github.com/I2-Multimedia-Lab/A2S-v3.
Multi-Attention Based Visual-Semantic Interaction for Few-Shot Learning
Authors: Zhao Peng , Wang Yin , Wang Wei , Mu Jie , Liu Huiting , Wang Cong , Cao Xiaochun
Published: July 2024

Few-Shot Learning (FSL) aims to train a model that can generalize to recognize new classes, with each new class having only very limited training samples. Since extracting discriminative features for new classes with few samples is challenging, existing FSL methods leverage visual and semantic prior knowledge to guide discriminative feature learning. However, for meta-learning purposes, the semantic knowledge of the query set is unavailable, so their features lack discriminability. To address this problem, we propose a novel Multi-Attention based Visual-Semantic Interaction (MAVSI) approach for FSL. Specifically, we utilize spatial and channel attention mechanisms to effectively select discriminative visual features for the support set based on its ground-truth semantics while using all the support set semantics for each query set sample. Then, a relation module with class prototypes of the support set is employed to supervise and select discriminative visual features for the query set. To further enhance the discriminability of the support set, we introduce a visual-semantic contrastive learning module to promote the similarity between visual features and their corresponding semantic features. Extensive experiments on four benchmark datasets demonstrate that our proposed MAVSI could outperform existing state-of-the-art FSL methods.
Zero-Shot Sketch Based Image Retrieval via Modality Capacity Guidance
Authors: Zhou Yanghong , Liu Dawei , Mok P. Y.
Published: July 2024

Zero-shot sketch-based image retrieval (ZS-SBIR), aiming to recognize and retrieve relevant photos based on freehand sketch queries that belong to unseen categories in the search set, has sparked considerable interest, benefiting from the rapid advancements in multimodal learning and feature representation research. Despite the recent improvements in performance, there are still rooms for refining feature representation and thus enhancing the generalization capabilities of the models. Most of the existing research efforts have primarily focused on learning the feature distribution of modalities within specific datasets, without considering the broader dataset-agnostic `population distribution' of relevant modalities. In this paper, we investigate the modality population distribution and apply such knowledge to guide feature learning. Specifically, we propose a modality capacity constraint loss to control the learning of population distribution for sketches and photos. This loss can be effectively combined with retrieval loss (e.g., triplet loss) or classification loss (e.g., InfoNCE loss) to enhance the performance of ZS-SBIR, through the fine-tuning process of pre-trained models like CLIP and DINO. Extensive experiment results have demonstrated our significant performance improvements, achieving an increase of 7.3%/3.2% and 19.9%/10.3% in terms of mAP@200/P@200 compared to the state-of-the-art models on CLIP and DINO, respectively, on the Sketchy-ext dataset (split 2). Data, code, and supplementary information are available at https://github.com/YHdian0716/ZS-SBIR-MCC.git.
SceneDiff: Generative Scene-Level Image Retrieval with Text and Sketch Using Diffusion Models
Authors: Zuo Ran , Hu Haoxiang , Deng Xiaoming , Gao Cangjun , Zhang Zhengming , Lai Yu-Kun , Ma Cuixia , Liu Yong-Jin , Wang Hongan
Published: July 2024

Jointly using text and sketch for scene-level image retrieval utilizes the complementary between text and sketch to describe the fine-grained scene content and retrieve the target image, which plays a pivotal role in accurate image retrieval. Existing methods directly fuse the features of sketch and text and thus suffer from the bottleneck of limited utilization for crucial semantic and structural information, leading to inaccurate matching with images. In this paper, we propose SceneDiff, a novel retrieval network that leverages a pre-trained diffusion model to establish a shared generative latent space, enabling a joint latent representation learning for both sketch and text features and precise alignment with the corresponding image. Specifically, we encode text, sketch and image features, and project them into the diffusion-based share space, conditioning the denoising process on sketch and text features to generate latent fusion features, while employing the pre-trained autoencoder for latent image features. Within this space, we introduce the content-aware feature transformation module to reconcile encoded sketch and image features with the diffusion latent space's dimensional requirements and preserve their visual content information. Then we augment the representation capability of the generated latent fusion features by integrating multiple samplings with partition attention, and utilize contrastive learning to align both direct fusion features and generated latent fusion features with corresponding image representations. Our method outperforms the state-of-the-art works through extensive experiments, providing a novel insight into the related retrieval field.
Markov Constraint as Large Language Model Surrogate
Authors: Bonlarron Alexandre , Régin Jean-Charles
Published: July 2024

This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP).

It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence.

The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions.

It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram).

The experimental results show that the generated text is valued in a similar way to the LLM perplexity function.

Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams.
Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control
Authors: Qu Yang , Ma Jinming , Wu Feng
Published: July 2024

Active voltage control presents a promising avenue for relieving power congestion and enhancing voltage quality, taking advantage of the distributed controllable generators in the power network, such as roof-top photovoltaics. While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelling approach to address this challenge, existing MARL approaches tend to overlook the constrained optimization nature of this problem, failing in guaranteeing safety constraints. In this paper, we formalize the active voltage control problem as a constrained Markov game and propose a safety-constrained MARL algorithm. We expand the primal-dual optimization RL method to multi-agent settings, and augment it with a novel approach of double safety estimation to learn the policy and to update the Lagrange-multiplier. In addition, we proposed different cost functions and investigated their influences on the behavior of our constrained MARL method. We evaluate our approach in the power distribution network simulation environment with real-world scale scenarios. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art MARL methods.
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning
Authors: Ren Tianyu , Zeng Xiao-Jun
Published: July 2024

The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation.
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Authors: Deng Jiewen , Jiang Renhe , Zhang Jiaqi , Song Xuan
Published: July 2024

Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments. Despite significant strides in ST modeling in recent years, there remains a need to emphasize harnessing the potential of information from different modalities. Robust MoST forecasting is more challenging because it possesses (i) high-dimensional and complex internal structures and (ii) dynamic heterogeneity caused by temporal, spatial, and modality variations. In this study, we propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL, which aims to uncover latent patterns from temporal, spatial, and modality perspectives while quantifying dynamic heterogeneity. Experiment results on two real-world MoST datasets verify the superiority of our approach compared with the state-of-the-art baselines. Model implementation is available at https://github.com/beginner-sketch/MoSSL.
Class-Specific Semantic Generation and Reconstruction Learning for Open Set Recognition
Authors: Haoyang Liu , Lin Yaojin , Li Peipei , Hu Jun , Hu Xuegang
Published: July 2024

Open set recognition is a crucial research theme for open-environment machine learning. For this problem, a common solution is to learn compact representations of known classes and identify unknown samples by measuring deviations from these known classes. However, the aforementioned methods (1) lack open training consideration, which is detrimental to the fitting of known classes, and (2) recognize unknown classes on an inadequate basis, which limits the accuracy of recognition. In this study, we propose an open reconstruction learning framework that learns a union boundary region of known classes to characterize unknown space. This facilitates the isolation of known space from unknown space to represent known classes compactly and provides a more reliable recognition basis from the perspective of both known and unknown space. Specifically, an adversarial constraint is used to generate class-specific boundary samples. Then, the known classes and approximate unknown space are fitted with manifolds represented by class-specific auto-encoders. Finally, the auto-encoders output the reconstruction error in terms of known and unknown spaces to recognize samples. Extensive experimental results show that the proposed method outperforms existing advanced methods and achieves new stateof-the-art performance. The code is available at https://github.com/Ashowman98/CSGRL.
Multiplex Graph Representation Learning via Bi-level Optimization
Authors: Huang Yudi , Mo Yujie , Liu Yujing , Nie Ci , Wen Guoqiu , Zhu Xiaofeng
Published: July 2024

Many multiplex graph representation learning (MGRL) methods have been demonstrated to 1) ignore the globally positive and negative relationships among node features; and 2) usually utilize the node classification task to train both graph structure learning and representation learning parameters, and thus resulting in the problem of edge starvation.

To address these issues, in this paper, we propose a new MGRL method based on the bi-level optimization. Specifically, in the inner level, we optimize the self-expression matrix to capture the globally positive and negative relationships among nodes, as well as complement them with the local relationships in graph structures. In the outer level, we optimize the parameters of the graph convolutional layer to obtain discriminative node representations. As a result, the graph structure optimization does not depend on the node classification task, which solves the edge starvation problem. Extensive experiments show that our model achieves the superior performance on node classification tasks on all datasets.
Zero-shot Learning for Preclinical Drug Screening
Authors: Li Kun , Liu Weiwei , Luo Yong , Cai Xiantao , Wu Jia , Hu Wenbin
Published: July 2024

Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. The results of experiments on two large drug response datasets showed that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery. The code is available at https://github.com/DrugD/MSDA.
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
Authors: Li Zhengdao , Cao Yong , Shuai Kefan , Miao Yiming , Hwang Kai
Published: July 2024

Graph classification benchmarks, vital for assessing and developing graph neural network (GNN) models, have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an important question: Do these benchmarks effectively distinguish the advancements of GNNs over other methodologies? If so, how do we quantitatively measure this effectiveness? In response, we first propose an empirical protocol based on a fair benchmarking framework to investigate the performance discrepancy between simple methods and GNNs. We further propose a novel metric to quantify the dataset effectiveness by considering both dataset complexity and model performance. To the best of our knowledge, our work is the first to thoroughly study and provide an explicit definition for dataset effectiveness in the graph learning area. Through testing across 16 real-world datasets, we found our metric to align with existing studies and intuitive assumptions. Finally, we explore the causes behind the low effectiveness of certain datasets by investigating the correlation between intrinsic graph properties and class labels, and we developed a novel technique supporting the correlation-controllable synthetic dataset generation. Our findings shed light on the current understanding of benchmark datasets, and our new platform could fuel the future evolution of graph classification benchmarks.
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
Authors: Liu Chen , He Shibo , Zhou Qihang , Li Shizhong , Meng Wenchao
Published: July 2024

Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts with scenarios of a few available samples, thereby limiting their performance. To overcome the limitation, we propose AnomalyLLM, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets. During the testing phase, anomalies are detected when the discrepancy between the features of the teacher and student networks is large. To circumvent the student network from learning the teacher network’s feature of anomalous samples, we devise two key strategies. 1) Prototypical signals are incorporated into the student network to consolidate the normal feature extraction. 2) We use synthetic anomalies to enlarge the representation gap between the two networks. AnomalyLLM demonstrates state-of-the-art performance on 15 datasets, improving accuracy by at least 14.5% in the UCR dataset.
Graph Contrastive Learning with Reinforcement Augmentation
Authors: Liu Ziyang , Wang Chaokun , Wu Cheng
Published: July 2024

Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings from augmented graphs, has become a prevailing method for extracting embeddings from graphs in an unsupervised manner. As an important procedure in GCL, graph data augmentation (GDA) directly affects the model performance on downstream tasks. Currently, the GCL methods typically treat GDA as independent events, neglecting its continuity. In this paper, we regard the GDA in GCL as a Markov decision process and propose a novel graph reinforcement augmentation framework for GCL. Based on this framework, we design a Graph Advantage Actor-Critic (GA2C) model. We conduct extensive experiments to evaluate GA2C on unsupervised learning, transfer learning, and semi-supervised learning. The experimental results demonstrate the performance superiority of GA2C over the state-of-the-art GCL models. Furthermore, we verify that GA2C is more efficient than the other GCL methods with learnable GDA and provide two examples of chemical molecular graphs from ZINC-2M to demonstrate that GA2C generates meaningful augmented views, where the edge weights reflect the importance of chemical bonds in the molecule.
Make Bricks with a Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity
Authors: Wang Binwu , Wang Pengkun , Zhou Zhengyang , Zhao Zhe , Xu Wei , Wang Yang
Published: July 2024

Traffic prediction plays a key role in various smart city applications, which can help traffic managers make traffic plans in advance, assist online ride-hailing companies in deploying vehicles reasonably, and provide early warning of congestion for safety authorities. While increasingly complex models achieve impressive prediction performance, there are concerns about the effectiveness of these models in handling large-scale road networks. Especially for researchers who don't have access to powerful GPU devices, the expensive memory burden limits the usefulness of these models. In this paper, we take the first step of learning on the large-scale spatio-temporal graph and propose a divide-and-conquer training strategy for Large Spatio-Temporal Graph Learning, namely LarSTL. The core idea behind this strategy is to divide the large graph into multiple subgraphs, which are treated as task streams to sequentially train the model to conquer each subgraph one by one. We introduce a novel perspective based on the continuous learning paradigm to achieve this goal. In order to overcome forgetting the knowledge learned from previous subgraphs, an experience-replay strategy consolidates the learned knowledge by replaying nodes sampled from previous subgraphs. Moreover, we configure specific feature adaptors for each subgraph to extract personalized features, and it is also beneficial to consolidate the learned knowledge from the perspective of parameters. We conduct experiments using multiple large-scale traffic network datasets on a V100 GPU with only 16GB memory, and the results demonstrate that our LarSTL can achieve competitive performance and high efficiency.
HeterGCL: Graph Contrastive Learning Framework on Heterophilic Graph
Authors: Wang Chenhao , Liu Yong , Yang Yan , Li Wei
Published: July 2024

Graph Contrastive Learning (GCL) has attracted significant research attention due to its self-supervised ability to learn robust node representations. Unfortunately, most methods primarily focus on homophilic graphs, rendering them less effective for heterophilic graphs. In addition, the complexity of node interactions in heterophilic graphs poses considerable challenges to augmentation schemes, coding architectures, and contrastive designs for traditional GCL. In this work, we propose HeterGCL, a novel graph contrastive learning framework with structural and semantic learning to explore the true potential of GCL on heterophilic graphs. Specifically, We abandon the random augmentation scheme that leads to the destruction of the graph structure, instead introduce an adaptive neighbor aggregation strategy (ANA) to extract topology-supervised signals from neighboring nodes at different distances and explore the structural information with an adaptive local-to-global contrastive loss. In the semantic learning module, we jointly consider the original nodes' features and the similarity between nodes in the latent feature space to explore hidden associations between nodes. Experimental results on homophilic and heterophilic graphs demonstrate that HeterGCL outperforms existing self-supervised and semi-supervised baselines across various downstream tasks.
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Authors: Wang Tianfu , Fan Qilin , Wang Chao , Yang Long , Ding Leilei , Yuan Nicholas Jing , Xiong Hui
Published: July 2024

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a flexible and generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at https://github.com/GeminiLight/flag-vne.
Layered Graph Security Games
Authors: Cerny Jakub , Ling Chun Kai , Kroer Christian , Iyengar Garud
Published: July 2024

Security games model strategic interactions in adversarial real-world applications. Such applications often involve extremely large but highly structured strategy sets (e.g., selecting a distribution over all patrol routes in a given graph). In this paper, we represent each player's strategy space using a layered graph whose paths represent an exponentially large strategy space. Our formulation entails not only classic pursuit-evasion games, but also other security games, such as those modeling anti-terrorism and logistical interdiction. We study two-player zero-sum games under two distinct utility models: linear and binary utilities. We show that under linear utilities, Nash equilibrium can be computed in polynomial time, while binary utilities may lead to situations where even computing a best-response is computationally intractable. To this end, we propose a practical algorithm based on incremental strategy generation and mixed integer linear programs. We show through extensive experiments that our algorithm efficiently computes epsilon-equilibrium for many games of interest. We find that target values and graph structure often have a larger influence on running times as compared to the size of the graph per se.
A Successful Strategy for Multichannel Iterated Prisoner’s Dilemma
Authors: Wang Zhen , Cao Zhaoheng , Shi Juan , Zhu Peican , Hu Shuyue , Chu Chen
Published: July 2024

Iterated prisoner’s dilemma (IPD) and its variants are fundamental models for understanding the evolution of cooperation in human society as well as AI systems. In this paper, we focus on multichannel IPD, and examine how an agent should behave to obtain generally high payoffs under this setting.

We propose a novel strategy that chooses to cooperate or defect by considering the difference in the cumulative number of defections between two agents.

We show that our proposed strategy is nice, retaliatory, and forgiving.

Moreover, we analyze the performance of our proposed strategy across different scenarios, including the self-play settings with and without errors, as well as when facing various opponent strategies. In particular, we show that our proposed strategy is invincible and never loses to any opponent strategy in terms of the expected payoff.

Last but not least, we empirically validate the evolutionary advantage of our strategy, and demonstrate its potential to serve as a catalyst for cooperation emergence.
Public Event Scheduling with Busy Agents
Authors: Li Bo , Li Lijun , Li Minming , Zhang Ruilong
Published: July 2024

We study a public event scheduling problem, where multiple public events are scheduled to coordinate the availability of multiple agents. The availability of each agent is determined by solving a separate flexible interval job scheduling problem, where the jobs are required to be preemptively processed. The agents want to attend as many events as possible, and their agreements are considered to be the total length of time during which they can attend these events. The goal is to find a schedule for events as well as the job schedule for each agent such that the total agreement is maximized.

We first show that the problem is NP-hard, and then prove that a simple greedy algorithm achieves 1/2-approximation when the whole timeline is polynomially bounded.

Our method also implies a (1-1/e)-approximate algorithm for this case. Subsequently, for the general timeline case, we present an algorithmic framework that extends a 1/alpha-approximate algorithm for the one-event instance to the general case that achieves 1/(alpha+1)-approximation. Finally, we give a polynomial time algorithm that solves the one-event instance, and this implies a 1/2-approximate algorithm for the general case.
Combinatorial Games with Incomplete Information
Authors: Li Junkang , Zanuttini Bruno , Ventos Véronique
Published: July 2024

Games with incomplete information model multi-agent interaction in which players do not have common knowledge of the game they play. We propose a minimal generalisation of combinatorial games to incorporate incomplete information, called combinatorial game with incomplete information (CGII). The most important feature of CGIIs is that all actions are public, which allows better visualisation of each player's knowledge and incomplete information. To further motivate the study of this new formalism, we show that computing optimal strategies for CGIIs has the same computational complexity as for general extensive-form games.
Optimizing Prosumer Policies in Periodic Double Auctions Inspired by Equilibrium Analysis
Authors: Manvi Bharat , Chandlekar Sanjay , Subramanian Easwar
Published: July 2024

We consider a periodic double auction (PDA) wherein the main participants are wholesale suppliers and brokers representing retailers. The suppliers are represented by a composite supply curve and the brokers are represented by individual bids. Additionally, the brokers can also participate in small-scale selling by placing individual asks; hence, they act as prosumers. Specifically, in a PDA, the prosumers who are net buyers have multiple opportunities to buy or sell multiple units of a commodity with the aim of minimising the cost of buying across multiple rounds of the PDA. Formulating optimal bidding strategies for such a PDA setting involves planning across current and future rounds while taking into account the bidding strategies of other agents. In this work, we propose Markov perfect Nash equilibrium (MPNE) policies for a setup where multiple prosumers with knowledge of the composite supply curve compete to procure commodities. Thereafter, the MPNE policies are used to develop an algorithm called MPNE-BBS for the case wherein the prosumers need to re-construct an approximate composite supply curve using past auction information. The efficacy of the proposed algorithm is demonstrated on the PowerTAC wholesale market simulator against several baselines and state-of-the-art bidding policies.
Enabling Sustainable Freight Forwarding Network via Collaborative Games
Authors: Tan Pang-Jin , Cheng Shih-Fen , Chen Richard
Published: July 2024

Freight forwarding plays a crucial role in facilitating global trade and logistics. However, as the freight forwarding market is extremely fragmented, freight forwarders often face the issue of not being able to fill the available shipping capacity. This recurrent issue motivates the creation of various freight forwarding networks that aim at exchanging capacities and demands so that the resource utilization of individual freight forwarders can be maximized. In this paper, we focus on how to design such a collaborative network based on collaborative game theory, with the Shapley value representing a fair scheme for profit sharing. Noting that the exact computation of Shapley values is intractable for large-scale real-world scenarios, we incorporate the observation that collaboration among two forwarders is only possible if their service routes and demands overlap. This leads to a new class of collaborative games called the Locally Collaborative Games (LCGs), where agents can only collaborate with their neighbors. We propose an efficient approach to compute Shapley values for LCGs, and numerically demonstrate that our approach significantly outperforms the state-of-the-art approach for a wide variety of network structures.
VSGT: Variational Spatial and Gaussian Temporal Graph Models for EEG-based Emotion Recognition
Authors: Liu Chenyu , Zhou Xinliang , Xiao Jiaping , Zhu Zhengri , Zhai Liming , Jia Ziyu , Liu Yang
Published: July 2024

Electroencephalogram (EEG), which directly reflects the emotional activity of the brain, has been increasingly utilized for emotion recognition. Most works exploit the spatial and temporal dependencies in EEG to learn emotional feature representations, but they still have two limitations to reach their full potential. First, prior knowledge is rarely used to capture the spatial dependency of brain regions. Second, the cross temporal dependency between consecutive time slices for different brain regions is ignored. To address these limitations, in this paper, we propose Variational Spatial and Gaussian Temporal (VSGT) graph models to investigate the spatial and temporal dependencies for EEG-based emotion recognition. The VSGT has two key components: Variational Spatial Encoder (VSE) and Gaussian Temporal Encoder (GTE). The VSE leverages the upper bound theorem to identify the dynamic spatial dependency based on prior knowledge by the variational Bayesian method. Besides, the GTE exploits the conditional Gaussian graph transform that computes comprehensive temporal dependency between consecutive time slices. Finally, the VSGT utilizes a recurrent structure to calculate the spatial and temporal dependencies for all time slices. Extensive experiments show the superiority of VSGT over state-of-the-art methods on multiple EEG datasets.
ReliaAvatar: A Robust Real-Time Avatar Animator with Integrated Motion Prediction
Authors: Qian Bo , Wei Zhenhuan , Li Jiashuo , Wei Xing
Published: July 2024

Efficiently estimating the full-body pose with minimal wearable devices presents a worthwhile research direction. Despite significant advancements in this field, most current research neglects to explore full-body avatar estimation under low-quality signal conditions, which is prevalent in practical usage. To bridge this gap, we summarize three scenarios that may be encountered in real-world applications: standard scenario, instantaneous data-loss scenario, and prolonged data-loss scenario, and propose a new evaluation benchmark. The solution we propose to address data-loss scenarios is integrating the full-body avatar pose estimation problem with motion prediction. Specifically, we present ReliaAvatar, a real-time, reliable avatar animator equipped with predictive modeling capabilities employing a dual-path architecture. ReliaAvatar operates effectively, with an impressive performance rate of 109 frames per second (fps). Extensive comparative evaluations on widely recognized benchmark datasets demonstrate ReliaAvatar's superior performance in both standard and low data-quality conditions. The code is available at https://github.com/MIV-XJTU/ReliaAvatar.
CoCoG: Controllable Visual Stimuli Generation Based on Human Concept Representations
Authors: Wei Chen , Zou Jiachen , Heinke Dietmar , Liu Quanying
Published: July 2024

A central question for cognitive science is to understand how humans process visual scenes, i.e, to uncover human low-dimensional concept representation space from high-dimensional visual stimuli. Generating visual stimuli with controlling concepts is the key. However, there are currently no generative models in AI to solve this problem. Here, we present the Concept based Controllable Generation (CoCoG) framework. CoCoG consists of two components, a simple yet efficient AI agent for extracting interpretable concept and predicting human decision-making in visual similarity judgment tasks, and a conditional generation model for generating visual stimuli given the concepts. We quantify the performance of CoCoG from two aspects, the human behavior prediction accuracy and the controllable generation ability. The experiments with CoCoG indicate that 1) the reliable concept embeddings in CoCoG allows to predict human behavior with 64.07% accuracy in the THINGS-similarity dataset; 2) CoCoG can generate diverse stimuli through the control of concepts; 3) CoCoG can manipulate human similarity judgment behavior by intervening key concepts.

CoCoG offers visual objects with controlling concepts to advance our understanding of causality in human cognition. The code of CoCoG framework is available at https://github.com/ncclab-sustech/CoCoG.
General Epistemic Abstract Argumentation Framework: Semantics and Complexity
Authors: Alfano Gianvincenzo , Greco Sergio , Parisi Francesco , Trubitsyna Irina
Published: July 2024

Epistemic Abstract Argumentation Framework (EAAF) extends Dung's framework (AAF)---a central formalism in AI for modeling disputes among agents---by allowing the representation of epistemic knowledge.

In particular, EAAF augments AAF with weak and strong epistemic attacks whose intuitive meaning is that

an argument a defeats an argument b by means of a weak (resp. strong) epistemic attack if a is true in every (resp. at least one) extension.

So far, the semantics of EAAF has been defined only for a restricted class of frameworks, namely acyclic EAAF, where epistemic attacks do not occur in any cycle.

In this paper, we provide an intuitive semantics for (general) EAAF that naturally extends that for AAF as well as that for acyclic EAAF.

After providing some fundamental properties and giving an algorithm that enables the computation of EAAF semantics, by relying on state-of-the-art AAF-solvers, we investigate the complexity of canonical argumentation problems.
Higher-Order Argumentation Frameworks: Principles and Gradual Semantics
Authors: Amgoud Leila , Doder Dragan , Lagasquie-Schiex Marie-Christine
Published: July 2024

The paper investigates how to evaluate elements in complex argumentation frameworks, where both arguments and attacks are weighted and might be attacked by arguments. We propose the first gradual semantics that assign a numerical value to every argument and attack. The value represents the acceptance (seriousness) degree of an argument (attack). We start by highlighting various technical challenges facing semantics in such complex settings, including how to deal with attacks vs arguments, and how to combine their values. We present principles that describe different strategies offered to semantics to meet such challenges. Then, we introduce various semantics per strategy. For instance, some semantics evaluate attacks and arguments in the same way while others, called hybrid, treat them differently. Finally, the principles are used to compare the plethora of novel semantics. The final result is a catalogue of semantics with different formal guarantees and behaviours.
Computational Aspects of Progression for Temporal Equilibrium Logic
Authors: Eiter Thomas , Soldà Davide
Published: July 2024

Temporal logic plays a crucial role in specifying and reasoning about dynamic systems, where temporal constraints and properties to be monitored are essential. Traditional approaches like LTL-monitoring assume monotonicity, which limits their applicability to scenarios involving non-monotonic temporal properties. We delve into complexity aspects of monitoring temporal specifications using non-monotonic Temporal Equilibrium Logic (TEL), a temporal extension of Answer Set Programming defined over Temporal Here and There Logic (THT) with a minimality criterion enforcing stable models. Notably, we study the complexity gap between monitoring properties in THT and TEL semantics, and the complexity of monitoring approximations based on progression, which is widely used in verification and in AI. In that, we pay particular attention to the fragment of temporal logic programs.
Quantitative Claim-Centric Reasoning in Logic-Based Argumentation
Authors: Hecher Markus , Mahmood Yasir , Meier Arne , Schmidt Johannes
Published: July 2024

Argumentation is a well-established formalism for nonmonotonic reasoning, with popular frameworks being Dung’s abstract argumentation (AFs) or logic-based argumentation (Besnard-Hunter’s framework). Structurally, a set of formulas forms support for a claim if it is consistent, subset-minimal, and implies the claim. Then, an argument comprises support and a claim. We observe that the computational task (ARG) of asking for support of a claim in a knowledge base is “brave”, since many claims with a single support are accepted. As a result, ARG falls short when it comes to the question of confidence in a claim, or claim strength. In this paper, we propose a concept for measuring the (acceptance) strength of claims, based on counting supports for a claim. Further, we settle classical and structural complexity of counting arguments favoring a given claim in propositional knowledge bases (KBs). We introduce quantitative reasoning to measure the strength of claims in a KB and to determine the relevance strength of a formula for a claim.