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Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference. While several recent attempts have tried to accelerate diffusion-QL, the improvement in training and/or inference speed often results in degraded performance. In this paper, we introduce a dual policy approach, Diffusion Trusted Q-Learning (DTQL), which comprises a diffusion policy for pure behavior cloning and a practical one-step policy. We bridge the two polices by a newly introduced diffusion trust region loss. The diffusion policy maintains expressiveness, while the trust region loss directs the one-step policy to explore freely and seek modes within the region defined by the diffusion policy. DTQL eliminates the need for iterative denoising sampling during both training and inference, making it remarkably computationally efficient. We evaluate its effectiveness and algorithmic characteristics against popular Kullback-Leibler (KL) based distillation methods in 2D bandit scenarios and gym tasks. We then show that DTQL could not only outperform other methods on the majority of the D4RL benchmark tasks but also demonstrate efficiency in training and inference speeds. The PyTorch implementation is available at https://github.com/TianyuCodings/Diffusion_Trusted_Q_Learning.

In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and fine-grained event categories. Under this setting, we formulate the \textbf{E}vent-Level \textbf{F}inancial \textbf{S}entiment \textbf{A}nalysis~(\textbf{EFSA} for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing $12,160$ news articles and $13,725$ quintuples is publicized as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://anonymous.4open.science/r/EFSA-645E

Foundation models have revolutionized knowledge acquisition across domains, and our study introduces OmniArch, a paradigm-shifting approach designed for building foundation models in multi-physics scientific computing. OmniArch's pre-training involves a versatile pipeline that processes multi-physics spatio-temporal data, casting forward problem learning into scalable auto-regressive tasks, while our novel Physics-Informed Reinforcement Learning (PIRL) technique during fine-tuning ensures alignment with physical laws. Pre-trained on the comprehensive PDEBench dataset, OmniArch not only sets new performance benchmarks for 1D, 2D and 3D PDEs but also demonstrates exceptional adaptability to new physics via few-shot and zero-shot learning approaches. The model's representations further extend to inverse problem-solving, highlighting the transformative potential of AI-enabled Scientific Computing(AI4SC) foundation models for engineering applications and physics discovery., Comment: Work in Progress

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD., Comment: Accepted by WSDM 2024

Languages with gradual information-flow control combine static and dynamic techniques to prevent security leaks. Gradual languages should satisfy the gradual guarantee: programs that only differ in the precision of their type annotations should behave the same modulo cast errors. Unfortunately, Toro et al. [2018] identify a tension between the gradual guarantee and information security; they were unable to satisfy both properties in the language $\mathrm{GSL}_\mathsf{Ref}$ and had to settle for only satisfying information-flow security. Azevedo de Amorim et al. [2020] show that by sacrificing type-guided classification, one obtains a language that satisfies both noninterference and the gradual guarantee. Bichhawat et al. [2021] show that both properties can be satisfied by sacrificing the no-sensitive-upgrade mechanism, replacing it with a static analysis. In this paper we present a language design, $\lambda_{\mathtt{IFC}}^\star$, that satisfies both noninterference and the gradual guarantee without making any sacrifices. We keep the type-guided classification of $\mathrm{GSL}_\mathsf{Ref}$ and use the standard no-sensitive-upgrade mechanism to prevent implicit flows through mutable references. The key to the design of $\lambda_{\mathtt{IFC}}^\star$ is to walk back the decision in $\mathrm{GSL}_\mathsf{Ref}$ to include the unknown label $\star$ among the runtime security labels. We give a formal definition of $\lambda_{\mathtt{IFC}}^\star$, prove the gradual guarantee, and prove noninterference. Of technical note, the semantics of $\lambda_{\mathtt{IFC}}^\star$ is the first gradual information-flow control language to be specified using coercion calculi (a la Henglein), thereby expanding the coercion-based theory of gradual typing., Comment: 48 pages, 24 figures

Applying security patches in open source software timely is critical for ensuring the security of downstream applications. However, it is challenging to apply these patches promptly because notifications of patches are often incomplete and delayed. To address this issue, existing approaches employ deep-learning (DL) models to identify additional vulnerability patches by determining whether a code commit addresses a vulnerability. Nonetheless, these approaches suffer from low accuracy due to the imprecise context provided for the patches. To provide precise context for patches, we propose a multi-granularity slicing algorithm and an adaptive-expanding algorithm to accurately identify code related to the patches. Additionally, the precise context enables to design an iterative identification framework, CompVPD, which utilizes the human validation results, and substantially improve the effectiveness. We empirically compare CompVPD with four state-of-the-art/practice (SOTA) approaches in identifying vulnerability patches. The results demonstrate that CompVPD improves the F1 score by 20% compared to the best scores of the SOTA approaches. Additionally, CompVPD contributes to security practice by helping identify 20 vulnerability patches and 18 fixes for high-risk bugs from 2,500 recent code commits in five highly popular open-source projects.

Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense of the vulnerability ecosystem. However, it is challenging for existing work on affected package identification to achieve a high accuracy. One reason is that all existing work focuses on relatively smaller models, thus they cannot harness the knowledge and semantic capabilities of large language models. To address this limitation, we propose VulLibGen, the first method to use LLM for affected package identification. In contrast to existing work, VulLibGen proposes the novel idea to directly generate the affected package. To improve the accuracy, VulLibGen employs supervised fine-tuning (SFT), retrieval augmented generation (RAG) and a local search algorithm. The local search algorithm is a novel postprocessing algorithm we introduce for reducing the hallucination of the generated packages. Our evaluation results show that VulLibGen has an average accuracy of 0.806 for identifying vulnerable packages in the four most popular ecosystems in GitHub Advisory (Java, JS, Python, Go) while the best average accuracy in previous work is 0.721. Additionally, VulLibGen has high value to security practice: we submitted 60 pairs to GitHub Advisory (covers four ecosystems). 34 of them have been accepted and merged and 20 are pending approval. Our code and dataset can be found in the attachments., Comment: ACL 2024 Main Conference

To address security vulnerabilities arising from third-party libraries, security researchers maintain databases monitoring and curating vulnerability reports. Application developers can identify vulnerable libraries by directly querying the databases with their used libraries. However, the querying results of vulnerable libraries are not reliable due to the incompleteness of vulnerability reports. Thus, current approaches model the task of identifying vulnerable libraries as a named-entity-recognition (NER) task or an extreme multi-label learning (XML) task. These approaches suffer from highly inaccurate results in identifying vulnerable libraries with complex and similar names, e.g., Java libraries. To address these limitations, in this paper, we propose VulLibMiner, the first to identify vulnerable libraries from textual descriptions of both vulnerabilities and libraries, together with VulLib, a Java vulnerability dataset with their affected libraries. VulLibMiner consists of a TF-IDF matcher to efficiently screen out a small set of candidate libraries and a BERT-FNN model to identify vulnerable libraries from these candidates effectively. We evaluate VulLibMiner using four state-of-the-art/practice approaches of identifying vulnerable libraries on both their dataset named VeraJava and our VulLib dataset. Our evaluation results show that VulLibMiner can effectively identify vulnerable libraries with an average F1 score of 0.657 while the state-of-the-art/practice approaches achieve only 0.521.

Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data remains a challenging task. In many situations, however, the end goal is to localize the changes (shifts) in the causal mechanisms between related datasets instead of learning the full causal structure of the individual datasets. Some applications include root cause analysis, analyzing gene regulatory network structure changes between healthy and cancerous individuals, or explaining distribution shifts. This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables -- without estimating the entire DAG structure of each SCM. Prior work under this setting assumed linear models with Gaussian noises; instead, in this work we assume that each SCM belongs to the more general class of nonlinear additive noise models (ANMs). A key technical contribution of this work is to show that the Jacobian of the score function for the mixture distribution allows for the identification of shifts under general non-parametric functional mechanisms. Once the shifted variables are identified, we leverage recent work to estimate the structural differences, if any, for the shifted variables. Experiments on synthetic and real-world data are provided to showcase the applicability of this approach. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN., Comment: 36 pages, 18 figures. Published at NeurIPS 2023

Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals. To excavate better MUsic SEquence Representation from labeled audio, we propose a novel text-supervision pre-training method, namely MUSER. MUSER adopts an audio-spectrum-text tri-modal contrastive learning framework, where the text input could be any form of meta-data with the help of text templates while the spectrum is derived from an audio sequence. Our experiments reveal that MUSER could be more flexibly adapted to downstream tasks compared with the current data-hungry pre-training method, and it only requires 0.056% of pre-training data to achieve the state-of-the-art performance.

The current state-of-the-art Diffusion model has demonstrated excellent results in generating images. However, the images are monotonous and are mostly the result of the distribution of images of people in the training set, making it challenging to generate multiple images for a fixed number of individuals. This problem can often only be solved by fine-tuning the training of the model. This means that each individual/animated character image must be trained if it is to be drawn, and the hardware and cost of this training is often beyond the reach of the average user, who accounts for the largest number of people. To solve this problem, the Character Image Feature Encoder model proposed in this paper enables the user to use the process by simply providing a picture of the character to make the image of the character in the generated image match the expectation. In addition, various details can be adjusted during the process using prompts. Unlike traditional Image-to-Image models, the Character Image Feature Encoder extracts only the relevant image features, rather than information about the model's composition or movements. In addition, the Character Image Feature Encoder can be adapted to different models after training. The proposed model can be conveniently incorporated into the Stable Diffusion generation process without modifying the model's ontology or used in combination with Stable Diffusion as a joint model., Comment: 8 pages, 5 figures, 2023 International Conference on Artificial Intelligence,Database and Machine Learning

Underwater acoustic recognition for ship-radiated signals has high practical application value due to the ability to recognize non-line-of-sight targets. However, due to the difficulty of data acquisition, the collected signals are scarce in quantity and mainly composed of mechanical periodic noise. According to the experiments, we observe that the repeatability of periodic signals leads to a double-descent phenomenon, which indicates a significant local bias toward repeated samples. To address this issue, we propose a strategy based on cross-entropy to prune excessively similar segments in training data. Furthermore, to compensate for the reduction of training data, we generate noisy samples and apply smoothness-inducing regularization based on KL divergence to mitigate overfitting. Experiments show that our proposed data pruning and regularization strategy can bring stable benefits and our framework significantly outperforms the state-of-the-art in low-resource scenarios.

This paper presents the first machine-checked proof of noninterference for a language with gradual information-flow control, thereby establishing a rock solid foundation for secure programming languages that give programmers the choice between runtime versus compile-time enforcement. Along the way we uncovered a flaw in one of the noninterference proofs in the literature, and give a counterexample for one of the main lemmas. The particular language studied in this paper, $\lambda_{\mathtt{SEC}}^\star$, is based on the GLIO language of Azevedo de Amorim et al. [2020]. To make the design more accessible to other researchers, this paper contributes the first traditional semantics for the language, that is, we define compilation from $\lambda_{\mathtt{SEC}}^\star$ to a cast calculus and design a reduction semantics for the latter that includes blame tracking. In addition to the proof of noninterference, we also mechanize proofs of type safety, determinism, and that compilation preserves types., Comment: 32 pages, 18 figures

Generally, residual connections are indispensable network components in building CNNs and Transformers for various downstream tasks in CV and VL, which encourages skip shortcuts between network blocks. However, the layer-by-layer loopback residual connections may also hurt the model's robustness by allowing unsuspecting input. In this paper, we proposed a simple yet strong backdoor attack method - BadRes, where the residual connections play as a turnstile to be deterministic on clean inputs while unpredictable on poisoned ones. We have performed empirical evaluations on four datasets with ViT and BEiT models, and the BadRes achieves 97% attack success rate while receiving zero performance degradation on clean data. Moreover, we analyze BadRes with state-of-the-art defense methods and reveal the fundamental weakness lying in residual connections., Comment: 16pages, 9 figures

Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation. Such problems are especially severe on tasks with limited data, thus hindering the progress for MoE models to improve performance by scaling up. In this work, we propose Mixture of Expert Clusters - a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. We further propose a cluster-level expert dropout strategy specifically designed for the expert cluster structure. Our experiments reveal that MoEC could improve performance on machine translation and natural language understanding tasks, and raise the performance upper bound for scaling up experts under limited data. We also verify that MoEC plays a positive role in mitigating overfitting and sparse data allocation.

The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of parameters, MoE is hard to be deployed on cloud or mobile environment. The inference of MoE requires expert parallelism, which is not hardware-friendly and communication expensive. Especially for resource-limited downstream tasks, such sparse structure has to sacrifice a lot of computing efficiency for limited performance gains. In this work, we observe most experts contribute scarcely little to the MoE fine-tuning and inference. We further propose a general method to progressively drop the non-professional experts for the target downstream task, which preserves the benefits of MoE while reducing the MoE model into one single-expert dense model. Our experiments reveal that the fine-tuned single-expert model could preserve 99.3% benefits from MoE across six different types of tasks while enjoying 2x inference speed with free communication cost., Comment: under review

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet. In this work, we introduce $\textit{THE-X}$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks. $\textit{THE-X}$ proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and LayerNorm. Experiments reveal our proposed $\textit{THE-X}$ can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage., Comment: Findings of ACL 2022