We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network explicitly designed to discover and extract regulatory elements that drive target gene expression, enhancing the accuracy of the gene expression prediction. Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements. Specifically, we propose to decompose the epigenomic signals and the DNA sequence conditioned on the causal active regulatory elements, and apply an information bottleneck with the Beta distribution to combine their effects while filtering out non-causal components. Our experiments demonstrate that Seq2Exp outperforms existing baselines in gene expression prediction tasks and discovers influential regions compared to commonly used statistical methods for peak detection such as MACS3. The source code is released as part of the AIRS library (https://github.com/divelab/AIRS/).

To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Finally, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and DNA design.

Online market platforms serve as a central hub for sharing and deploying AI models among researchers, developers, and companies. In this context, watermarking techniques are essential to protect intellectual property (IP), preventing unauthorized use and duplication of large language models (LLMs). Two key challenges arise: (i) These platforms host diverse LLMs, yet current watermarking techniques are only tailored to specific models, such as fine-tuned or quantized LLMs. (ii) Efficient watermarking is critical. However, traditional methods require substantial data and costly hardware, which limits their feasibility. In this paper, we propose an efficient white-box LLM watermarking technique called ELLMark. This method treats LLMs as multi-layered matrices while embedding watermarks only relies on modifying the model's weights. To preserve LLMs' performance, it filters weights by correlations with the activation magnitudes and downstream tasks, then modifies weights as minimal as possible via histogram modulation. Notably, all phases are training-free with low hardware resources, making it efficient for online platforms. We conduct extensive experiments to evaluate the effectiveness of ELLMark on LLaMA-3, OPT, and Phi-3 LLMs. The results demonstrate that it achieves 100% success in watermark detection while preserving model performance. Moreover, the preprocessing, encoding, and decoding processes remain efficient, taking less than 7 minutes, 12 minutes, and 18 seconds, respectively, for models with 80B parameters. Lastly, it exhibits robustness against parameter overwriting, re-watermarking, forging, fine-tuning, and pruning attacks.

Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist. To address these challenges, we present PagPassGPT, a password guessing model constructed on a Generative Pretrained Transformer (GPT). It can perform pattern guided guessing by incorporating pattern structure information as background knowledge, resulting in a significant increase in the hit rate. Furthermore, we propose D&C-GEN to reduce the repeat rate of generated passwords, which adopts the concept of a divide-and-conquer approach. The primary task of guessing passwords is recursively divided into non-overlapping subtasks. Each subtask inherits the knowledge from the parent task and predicts succeeding tokens. In comparison to the state-of-the-art model, our proposed scheme exhibits the capability to correctly guess 12% more passwords while producing 25% fewer duplicates.

Web tables are valuable resources that can enrich existing knowledge bases. Researchers try to make the predictions of columns and column pairs in web tables into entities and relations of knowledge bases, which are known as the Column Type Annotation (CT) and Relation Extraction (RE) tasks in web table interpretation. Learning useful and accurate column vector representations plays the central role in solving the two tasks. A column’s semantics can be determined by three kinds of relationships: Column-Cell, Column-Table, Column-Column relationships. Existing works only rely on the Column-Cell relationship to determine the semantics of columns and result in suboptimal performances. In this paper, we propose to solve the above CT and RE tasks with the heterogeneous graph neural network technique. First, we construct heterogeneous graphs for web tables. Next, a new model named Tab-HGNN is proposed to consider all the three kinds of relationships and learn target column representations. Extensive experiments on real-world web table datasets demonstrate the effectiveness of the proposed Tab-HGNN model on the CT and RE tasks. It outperforms the competitive baselines by achieving up to +7.9% and +1.6% macro F1scores, +2.6% and +0.2% weighted F1scores on two CT datasets, respectively. It also achieves +0.3% macro F1scores, +0.1% weighted F1scores on one RE dataset.

Diagnosis of discrete event systems requires to decide whether the system model allows for certain types of executions to take place. Because this problem is hard, incomplete yet faster algorithms may be needed. This however can lead to a loss of precision. This paper presents a method to decide whether precision is maintained by such incomplete algorithms. To this end we define the Simulation, which is a modification of the model that simulates how the algorithm works. We then use the twin plant method to decide whether diagnosability is maintained despite the imprecision of the diagnostic algorithm. We illustrate the benefits of this approach on two diagnostic algorithms, namely Independent-Windows Algorithms and Chronicle-based Diagnosis.