OpenRaiser/Intern-Atlas huggingface dataset:
Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferences
Venue: arXiv cs.IR 2024
Year: 2024

Diversity control is an important task to alleviate bias amplification and filter bubble problems. The desired degree of diversity may fluctuate based on users' daily moods or business strategies. However, existing methods for controlling diversity often lack flexibility, as diversity is decided during training and cannot be easily modified during inference. We propose \textbf{D3Rec} (\underline{D}isentangled \underline{D}iffusion model for \underline{D}iversified \underline{Rec}ommendation), an end-to-end method that controls the accuracy-diversity trade-off at inference. D3Rec meets our three desiderata by (1) generating recommendations based on category preferences, (2) controlling category preferences during the inference phase, and (3) adapting to arbitrary targeted category preferences. In the forward process, D3Rec removes category preferences lurking in user interactions by adding noises. Then, in the reverse process, D3Rec generates recommendations through denoising steps while reflecting desired category preferences. Extensive experiments on real-world and synthetic datasets validate the effectiveness of D3Rec in controlling diversity at inference.
ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the information from the evidence in a relationally logical form, and combine the above information with the original evidence to generate the context from which our fact-checking model provide verdicts for the input claims. We conducted empirical experiments to evaluate our approach on two multi-hop fact-checking datasets including HoVer and FEVEROUS, and achieved potential results results comparable to other state-of-the-art fact verification task methods.
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealistic data, \ie, open-loop simulation usually lacks the ability to assess dynamic decision-making. While the recent efforts of closed-loop simulation offer feedback-driven environments, they cannot process visual sensor inputs or produce outputs that differ from real-world data. To address these challenges, we propose DrivingSphere, a realistic and closed-loop simulation framework. Its core idea is to build 4D world representation and generate real-life and controllable driving scenarios. In specific, our framework includes a Dynamic Environment Composition module that constructs a detailed 4D driving world with a format of occupancy equipping with static backgrounds and dynamic objects, and a Visual Scene Synthesis module that transforms this data into high-fidelity, multi-view video outputs, ensuring spatial and temporal consistency. By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms, ultimately advancing the development of more reliable autonomous cars. The benchmark will be publicly released.
GROOT: Effective Design of Biological Sequences with Limited Experimental Data
Venue: arXiv cs.LG 2024
Year: 2024

Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from available data and using a surrogate model to guide optimization algorithms toward optimal outputs. However, existing methods struggle when labeled data is limited, as training the surrogate model with few labeled data points can lead to subpar outputs, offering no advantage over the training data itself. We address this challenge by introducing GROOT, a Graph-based Latent Smoothing for Biological Sequence Optimization. In particular, GROOT generates pseudo-labels for neighbors sampled around the training latent embeddings. These pseudo-labels are then refined and smoothed by Label Propagation. Additionally, we theoretically and empirically justify our approach, demonstrate GROOT's ability to extrapolate to regions beyond the training set while maintaining reliability within an upper bound of their expected distances from the training regions. We evaluate GROOT on various biological sequence design tasks, including protein optimization (GFP and AAV) and three tasks with exact oracles from Design-Bench. The results demonstrate that GROOT equalizes and surpasses existing methods without requiring access to black-box oracles or vast amounts of labeled data, highlighting its practicality and effectiveness. We release our code at https://anonymous.4open.science/r/GROOT-D554
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
Venue: arXiv cs.LG 2024
Year: 2024

Over an extensive duration, administrators and clinicians have endeavoured to predict Emergency Department (ED) visits with precision, aiming to optimise resource distribution. Despite the proliferation of diverse AI-driven models tailored for precise prognostication, this task persists as a formidable challenge, besieged by constraints such as restrained generalisability, susceptibility to overfitting and underfitting, scalability issues, and complex fine-tuning hyper-parameters. In this study, we introduce a novel Meta-learning Gradient Booster (Meta-ED) approach for precisely forecasting daily ED visits and leveraging a comprehensive dataset of exogenous variables, including socio-demographic characteristics, healthcare service use, chronic diseases, diagnosis, and climate parameters spanning 23 years from Canberra Hospital in ACT, Australia. The proposed Meta-ED consists of four foundational learners-Catboost, Random Forest, Extra Tree, and lightGBoost-alongside a dependable top-level learner, Multi-Layer Perceptron (MLP), by combining the unique capabilities of varied base models (sub-learners). Our study assesses the efficacy of the Meta-ED model through an extensive comparative analysis involving 23 models. The evaluation outcomes reveal a notable superiority of Meta-ED over the other models in accuracy at 85.7% (95% CI ;85.4%, 86.0%) and across a spectrum of 10 evaluation metrics. Notably, when compared with prominent techniques, XGBoost, Random Forest (RF), AdaBoost, LightGBoost, and Extra Tree (ExT), Meta-ED showcases substantial accuracy enhancements of 58.6%, 106.3%, 22.3%, 7.0%, and 15.7%, respectively. Furthermore, incorporating weather-related features demonstrates a 3.25% improvement in the prediction accuracy of visitors' numbers. The encouraging outcomes of our study underscore Meta-ED as a foundation model for the precise prediction of daily ED visitors.
Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of valuable high-frequency information in graph signals. These issues are particularly pronounced in heterophilic graphs. To address these challenges, we propose Dual-Frequency Filtering Self-aware Graph Neural Networks (DFGNN). DFGNN integrates low-pass and high-pass filters to extract smooth and detailed topological features, using frequency-specific constraints to minimize noise and redundancy in the respective frequency bands. The model dynamically adjusts filtering ratios to accommodate both homophilic and heterophilic graphs. Furthermore, DFGNN mitigates interference by aligning topological and attribute representations through dynamic correspondences between their respective frequency bands, enhancing overall model performance and expressiveness. Extensive experiments conducted on benchmark datasets demonstrate that DFGNN outperforms state-of-the-art methods in classification performance, highlighting its effectiveness in handling both homophilic and heterophilic graphs.
Recurrent Stochastic Configuration Networks with Incremental Blocks
Venue: arXiv cs.LG 2024
Year: 2024

Recurrent stochastic configuration networks (RSCNs) have shown promise in modelling nonlinear dynamic systems with order uncertainty due to their advantages of easy implementation, less human intervention, and strong approximation capability. This paper develops the original RSCNs with block increments, termed block RSCNs (BRSCNs), to further enhance the learning capacity and efficiency of the network. BRSCNs can simultaneously add multiple reservoir nodes (subreservoirs) during the construction. Each subreservoir is configured with a unique structure in the light of a supervisory mechanism, ensuring the universal approximation property. The reservoir feedback matrix is appropriately scaled to guarantee the echo state property of the network. Furthermore, the output weights are updated online using a projection algorithm, and the persistent excitation conditions that facilitate parameter convergence are also established. Numerical results over a time series prediction, a nonlinear system identification task, and two industrial data predictive analyses demonstrate that the proposed BRSCN performs favourably in terms of modelling efficiency, learning, and generalization performance, highlighting their significant potential for coping with complex dynamics.
Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, few-shot action recognition has achieved remarkable performance through spatio-temporal relation modeling. Although a wide range of spatial and temporal alignment modules have been proposed, they primarily address spatial or temporal misalignments at the video level, while the spatio-temporal relationships across different videos at the task level remain underexplored. Recent studies utilize class prototypes to learn task-specific features but overlook the spatio-temporal relationships across different videos at the task level, especially in the spatial dimension, where these relationships provide rich information. In this paper, we propose a novel Dual Motion-Guided Attention Learning method (called DMGAL) for few-shot action recognition, aiming to learn the spatio-temporal relationships from the video-specific to the task-specific level. To achieve this, we propose a carefully designed Motion-Guided Attention (MGA) method to identify and correlate motion-related region features from the video level to the task level. Specifically, the Self Motion-Guided Attention module (S-MGA) achieves spatio-temporal relation modeling at the video level by identifying and correlating motion-related region features between different frames within a video. The Cross Motion-Guided Attention module (C-MGA) identifies and correlates motion-related region features between frames of different videos within a specific task to achieve spatio-temporal relationships at the task level. This approach enables the model to construct class prototypes that fully incorporate spatio-temporal relationships from the video-specific level to the task-specific level. We validate the effectiveness of our DMGAL method by employing both fully fine-tuning and adapter-tuning paradigms. The models developed using these paradigms are termed DMGAL-FT and DMGAL-Adapter, respectively.
Visual-Semantic Graph Matching Net for Zero-Shot Learning
Venue: arXiv cs.CV 2024
Year: 2024

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process. To address the aforementioned challenges, this paper proposes a Visual-Semantic Graph Matching Net, termed as VSGMN, which leverages semantic relationships among classes to aid in visual-semantic embedding. VSGMN employs a Graph Build Network (GBN) and a Graph Matching Network (GMN) to achieve two-stage visual-semantic alignment. Specifically, GBN first utilizes an embedding-based approach to build visual and semantic graphs in the semantic space and align the embedding with its prototype for first-stage alignment. Additionally, to supplement unseen class relations in these graphs, GBN also build the unseen class nodes based on semantic relationships. In the second stage, GMN continuously integrates neighbor and cross-graph information into the constructed graph nodes, and aligns the node relationships between the two graphs under the class relationship constraint. Extensive experiments on three benchmark datasets demonstrate that VSGMN achieves superior performance in both conventional and generalized ZSL scenarios. The implementation of our VSGMN and experimental results are available at github: https://github.com/dbwfd/VSGMN
CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset
Venue: arXiv cs.CV 2024
Year: 2024

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categories, locations, and dynamics of changed objects (e.g., additions or disappearances). Many current methods attempt to leverage the long-sequence understanding and reasoning capabilities of multimodal large language models (MLLMs) for this task. However, without comprehensive data support, these approaches often alter the essential feature transmission pathways of MLLMs, disrupting the intrinsic knowledge within the models and limiting their potential in RSICC. In this paper, we propose a novel model, CCExpert, based on a new, advanced multimodal large model framework. Firstly, we design a difference-aware integration module to capture multi-scale differences between bi-temporal images and incorporate them into the original image context, thereby enhancing the signal-to-noise ratio of differential features. Secondly, we constructed a high-quality, diversified dataset called CC-Foundation, containing 200,000 image pairs and 1.2 million captions, to provide substantial data support for continue pretraining in this domain. Lastly, we employed a three-stage progressive training process to ensure the deep integration of the difference-aware integration module with the pretrained MLLM. CCExpert achieved a notable performance of $S^*_m=81.80$ on the LEVIR-CC benchmark, significantly surpassing previous state-of-the-art methods. The code and part of the dataset will soon be open-sourced at https://github.com/Meize0729/CCExpert.
Continual Task Learning through Adaptive Policy Self-Composition
Venue: arXiv cs.LG 2024
Year: 2024

Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challenge for current offline reinforcement learning (RL) algorithms. Specifically, an agent must be able to rapidly adapt to new tasks using newly collected trajectories (plasticity), while retaining knowledge from previously learned tasks (stability). However, systematic analyses of this setting are scarce, and it remains unclear whether conventional continual learning (CL) methods are effective in continual offline RL (CORL) scenarios. In this study, we develop the Offline Continual World benchmark and demonstrate that traditional CL methods struggle with catastrophic forgetting, primarily due to the unique distribution shifts inherent to CORL scenarios. To address this challenge, we introduce CompoFormer, a structure-based continual transformer model that adaptively composes previous policies via a meta-policy network. Upon encountering a new task, CompoFormer leverages semantic correlations to selectively integrate relevant prior policies alongside newly trained parameters, thereby enhancing knowledge sharing and accelerating the learning process. Our experiments reveal that CompoFormer outperforms conventional CL methods, particularly in longer task sequences, showcasing a promising balance between plasticity and stability.
Membership Inference Attack against Long-Context Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Recent advances in Large Language Models (LLMs) have enabled them to overcome their context window limitations, and demonstrate exceptional retrieval and reasoning capacities on longer context. Quesion-answering systems augmented with Long-Context Language Models (LCLMs) can automatically search massive external data and incorporate it into their contexts, enabling faithful predictions and reducing issues such as hallucinations and knowledge staleness. Existing studies targeting LCLMs mainly concentrate on addressing the so-called lost-in-the-middle problem or improving the inference effiencicy, leaving their privacy risks largely unexplored. In this paper, we aim to bridge this gap and argue that integrating all information into the long context makes it a repository of sensitive information, which often contains private data such as medical records or personal identities. We further investigate the membership privacy within LCLMs external context, with the aim of determining whether a given document or sequence is included in the LCLMs context. Our basic idea is that if a document lies in the context, it will exhibit a low generation loss or a high degree of semantic similarity to the contents generated by LCLMs. We for the first time propose six membership inference attack (MIA) strategies tailored for LCLMs and conduct extensive experiments on various popular models. Empirical results demonstrate that our attacks can accurately infer membership status in most cases, e.g., 90.66% attack F1-score on Multi-document QA datasets with LongChat-7b-v1.5-32k, highlighting significant risks of membership leakage within LCLMs input contexts. Furthermore, we examine the underlying reasons why LCLMs are susceptible to revealing such membership information.
Deegen: A JIT-Capable VM Generator for Dynamic Languages
Venue: arXiv cs.PL 2024
Year: 2024

Building a high-performance JIT-capable VM for a dynamic language has traditionally required a tremendous amount of time, money, and expertise. We present Deegen, a meta-compiler that allows users to generate a high-performance JIT-capable VM for their own language at an engineering cost similar to writing a simple interpreter. Deegen takes in the execution semantics of the bytecodes implemented as C++ functions, and automatically generates a two-tier VM execution engine with a state-of-the-art interpreter, a state-of-the-art baseline JIT, and the tier-switching logic that connects them into a self-adaptive system. We are the first to demonstrate the automatic generation of a JIT compiler, and the automatic generation of an interpreter that outperforms the state of the art. Our performance comes from a long list of optimizations supported by Deegen, including bytecode specialization and quickening, register pinning, tag register optimization, call inline caching, generic inline caching, JIT polymorphic IC, JIT IC inline slab, type-check removal and strength reduction, type-based slow-path extraction and outlining, JIT hot-cold code splitting, and JIT OSR-entry. These optimizations are either employed automatically, or guided by the language implementer through intuitive APIs. As a result, the disassembly of the Deegen-generated interpreter, baseline JIT, and the generated JIT code rivals the assembly code hand-written by experts in state-of-the-art VMs. We implement LuaJIT Remake (LJR), a standard-compliant Lua 5.1 VM, using Deegen. Across 44 benchmarks, LJR's interpreter is on average 179% faster than the official PUC Lua interpreter, and 31% faster than LuaJIT's interpreter. LJR's baseline JIT has negligible startup delay, and its execution performance is on average 360% faster than PUC Lua and only 33% slower (but faster on 13/44 benchmarks) than LuaJIT's optimizing JIT.
SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI
Venue: arXiv cs.CR 2024
Year: 2024

The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious activities. However, existing watermarking surveys focus more on traditional watermarks, overlooking AIGC-specific challenges. In this work, we propose a systematic investigation into AIGC watermarking and provide the first formal definition of AIGC watermarking. Different from previous surveys, we provide a taxonomy based on the core properties of the watermark which are summarized through comprehensive literature from various AIGC modalities. Derived from the properties, we discuss the functionality and security threats of AIGC watermarking. In the end, we thoroughly investigate the AIGC governance of different countries and practitioners. We believe this taxonomy better aligns with the practical demands for watermarking in the era of GenAI, thus providing a clearer summary of existing work and uncovering potential future research directions for the community.
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localization implementations or re-localization and loop closure detection pipelines within simultaneous localization and mapping (SLAM). More specifically, it corresponds to the system's ability to identify and match a previously visited location using computer vision tools. Towards developing novel techniques with enhanced accuracy and robustness, while motivated by the success presented in natural language processing methods, researchers have recently turned their attention to vision-language models, which integrate visual and textual data.