The effectiveness of Transformer-based methods in tabular data processing has been extensively validated. However, most existing models primarily adopt Transformer-based encoder architectures, which overly emphasize feature correlations while neglecting the unique explicit information representation characteristics of tabular data. The significantly higher information density of tabular data compared to textual data introduces noise that degrades performance. Consequently, directly applying language models to tabular data often yields suboptimal results. To address this limitation, we propose the Residual Attention Transformer (RAT), a Transformer-based mechanism specifically designed for tabular data. In subsequent sections, the Residual Attention Transformer will be referred to as RAT. RAT introduces residual connections into the self-attention mechanism, effectively preserving raw information while capturing dependencies with higher-order features, thus enhancing the richness of information representation. Additionally, we design a simplified Transformer output head, termed Light-Transformer, which consists of a lightweight Transformer block specifically dedicated to the output stage. This design not only significantly reduces the model's parameter count, but also greatly improves training and inference efficiency. Extensive experiments were conducted on six public datasets to evaluate our model. The results show that the RAT model consistently outperforms other models in various tasks, confirming its effectiveness and superiority.

OBJECTIVE: Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering. MATERIALS AND METHODS: We developed SBDH-Reader to extract 6 categories of granular SBDH data by prompting GPT-4o, including employment, housing, marital status, and substance use including alcohol, tobacco, and drug use. SBDH-Reader was developed using 7225 notes from 6382 patients in the MIMIC-III database (2001-2012) and externally validated using 971 notes from 437 patients at The University of Texas Southwestern Medical Center (UTSW; 2022-2023). We evaluated SBDH-Reader's performance against human-annotated ground truths based on precision, recall, F1, and confusion matrix. RESULTS: When tested on the UTSW validation set, SBDH-Reader achieved a macro-average F1 ranging from 0.94 to 0.98 across 6 SBDH categories. For clinically relevant adverse attributes, F1 ranged from 0.96 (employment; housing) to 0.99 (tobacco use). When extracting any adverse attributes across all SBDH categories, SBDH-Reader achieved an F1 of 0.97, recall of 0.97, and precision of 0.98 in the independent validation set. DISCUSSION: SBDH-Reader demonstrated strong performance in extracting structured SBDH data through effective prompt engineering of a general-purpose LLM, without the need for task-specific fine-tuning. Its modular design and adaptability to diverse datasets and documentation patterns support its applicability in real-world clinical settings. CONCLUSION: SBDH-Reader has the potential to serve as a scalable and effective method for collecting real-time, patient-level SBDH data to support clinical research and care.

Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (BED), but also plays a key part in facilitating downstream decision-making. Most recent BED methods use an amortized policy network to rapidly design experiments. However, the information gathered through these methods is suboptimal for down-the-line decision-making, as the experiments are not inherently designed with downstream objectives in mind. In this paper, we present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design, whilst inferring the downstream decision, thus effectively amortizing both tasks within a unified workflow. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.

Preference learning methods make use of models of human choice in order to infer the latent utilities that underlie human behavior. However, accurate modeling of human choice behavior is challenging due to a range of context effects that arise from how humans contrast and evaluate options. Cognitive science has proposed several models that capture these intricacies but, due to their intractable nature, work on preference learning has, in practice, had to rely on tractable but simplified variants of the well-known Bradley-Terry model. In this paper, we take one state-of-the-art intractable cognitive model and propose a tractable surrogate that is suitable for deployment in preference learning. We then introduce a mechanism for fitting the surrogate to human data and extend it to account for data that cannot be explained by the original cognitive model. We demonstrate on large-scale human data that this model produces significantly better inferences on static and actively elicited data than existing Bradley-Terry variants. We further show in simulation that when using this model for preference learning, we can significantly improve utility in a range of real-world tasks.

With the advancement in remote sensing satellite technology, object tracking in satellite videos has become an emerging research field. However, due to small object size, little appearance features, and poor distinguishability between targets and the background, traditional trackers with handcraft visual features achieve poor results in satellite videos. Deep neural networks have shown powerful potential for object tracking in ordinary videos but remain developing in satellite videos. In this letter, a Siamese network and a motion regression network are adopted to form a two-stream deep neural network (SRN) for satellite object tracking, which simultaneously utilizes appearance and motion features. Besides, a trajectory fitting motion (TFM) model based on history trajectories is also employed to further alleviate model drift. Comprehensive experiments demonstrate that the proposed method performs favorably compared with the state-of-the-art tracking methods.

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

Summary It is well known that virtual network (VN) embedding (VNE) aims to solve how to efficiently allocate physical resources to a VN. However, this issue has been proved to be an NP‐hard problem. Besides, as most of the existing approaches are based on heuristic algorithms, which is easy to fall into local optimal. To address the challenge, we formalize the problem as a mixed integer programming problem and propose a novel VNE method based on reinforcement learning in this article. And to solve the problem, we introduce a pointer network to generate virtual node mapping strategies through an attention mechanism, and design a reward function related to link resource consumption to build the connection between node mapping and link mapping stages of VNE. In addition, we present a policy gradient optimization mechanism to leverage the reward information obtained from the sampled solutions, and design an active search based process to automatically update the parameters of the neural network and to obtain near‐optimal embedding solution. The experimental results show that the proposed method can improve the performance in average physical node utilization and long‐term revenue to cost ratio comparing than that of the existing models.