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We show that \emph{Sorting by Strip Swaps} (SbSS) is NP-hard by a polynomial reduction of \emph{Block Sorting}. The key idea is a local gadget, a \emph{cage}, that replaces every decreasing adjacency $(a_i,a_{i+1})$ by a guarded triple $a_i,m_i,a_{i+1}$ enclosed by guards $L_i,U_i$, so the only decreasing adjacencies are the two inside the cage. Small \emph{hinge} gadgets couple adjacent cages that share an element and enforce that a strip swap that removes exactly two adjacencies corresponds bijectively to a block move that removes exactly one decreasing adjacency in the source permutation. This yields a clean equivalence between exact SbSS schedules and perfect block schedules, establishing NP-hardness.

Ensuring safe and realistic interactions between automated driving systems and vulnerable road users (VRUs) in urban environments requires advanced testing methodologies. This paper presents a test environment that combines a Vehiclein-the-Loop (ViL) test bench with a motion laboratory, demonstrating the feasibility of cyber-physical (CP) testing of vehicle-pedestrian and vehicle-cyclist interactions. Building upon previous work focused on pedestrian localization, we further validate a human pose estimation (HPE) approach through a comparative analysis of real-world (RW) and virtual representations of VRUs. The study examines the perception of full-body motion using a commercial monocular camera-based 3Dskeletal detection AI. The virtual scene is generated in Unreal Engine 5, where VRUs are animated in real time and projected onto a screen to stimulate the camera. The proposed stimulation technique ensures the correct perspective, enabling realistic vehicle perception. To assess the accuracy and consistency of HPE across RW and CP domains, we analyze the reliability of detections as well as variations in movement trajectories and joint estimation stability. The validation includes dynamic test scenarios where human avatars, both walking and cycling, are monitored under controlled conditions. Our results show a strong alignment in HPE between RW and CP test conditions for stable motion patterns, while notable inaccuracies persist under dynamic movements and occlusions, particularly for complex cyclist postures. These findings contribute to refining CP testing approaches for evaluating next-generation AI-based vehicle perception and to enhancing interaction models of automated vehicles and VRUs in CP environments.

Sixteen new physics informed machine learning models have been trained on binding energy residuals from modern mass models that leverage shape parameters and other physical features. The models have been trained on a subset of AME 2012 data and have been verified with a subset of the AME 2020 data. Among the machine learning approaches tested in this work, the preferred approach is the least squares boosted ensemble of trees which appears to have a superior ability to both interpolate and extrapolate binding energy residuals. The machine learning models for four mass models created from the ensemble of trees approach have been combined to create a composite model called the Four Model Tree Ensemble (FMTE). The FMTE model predicts binding energy values from AME 2020 with a standard deviation of 76 keV and a mean deviation of 34 keV for all nuclei with N > 7 and Z > 7. A comparison with new mass measurements for 33 isotopes not included in AME 2012 or AME 2020 indicates that the FMTE performs better than all mass models that were tested.

Generally, deep learning has contributed to this field a lot. The most representative deep neural network architectures in computer vision can be roughly divided into transformer-based and CNN-based models. Transformer is originally proposed for natural language processing, which has been transferred to vision tasks and achieves considerably satisfying performance recently. Specifically, vision transformer [1] first introduces attention mechanism into computer vision whose strategy of information interaction enlargers the effective receptive field of related models observably so that crucial information can be better obtained. Due to efficiency of this architecture, the variations of transformer are devised corresponding to specific demands, and there are two main categories in the thoughts about improvements on the variations, namely integration of transformer framework with other models which are for particular usages and modifications on the original architecture. With respect to the former, DS-TransUNet [2] is a typical example, which synthesizes dual transformer-based architectures and U-Net to realize a breakthrough in medical image segmentation. Besides, some works focus on improvements on architecture of transformer, for instance, Mix-ViT [3] tries to design a mix attention mechanism to create more sufficient passages for information interaction.

This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty to preserve the snippet that reduces their uncertainty to the utmost. To facilitate solving complex tasks that require multiple retrievals, SeaKR utilizes their self-aware uncertainty to choose among different reasoning strategies. Our experiments on both complex and simple Question Answering datasets show that SeaKR outperforms existing adaptive RAG methods. We release our code at https://github.com/THU-KEG/SeaKR.

Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is triggered (determining what to retrieve). However, current dynamic RAG methods fall short in both aspects. Firstly, the strategies for deciding when to retrieve often rely on static rules. Moreover, the strategies for deciding what to retrieve typically limit themselves to the LLM's most recent sentence or the last few tokens, while the LLM's real-time information needs may span across the entire context. To overcome these limitations, we introduce a new framework, DRAGIN, i.e., Dynamic Retrieval Augmented Generation based on the real-time Information Needs of LLMs. Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process. We evaluate DRAGIN along with existing methods comprehensively over 4 knowledge-intensive generation datasets. Experimental results show that DRAGIN achieves superior performance on all tasks, demonstrating the effectiveness of our method. We have open-sourced all the code, data, and models in GitHub: https://github.com/oneal2000/DRAGIN/tree/main

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other unsupervised learning methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.

Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of available data from electronic health records. Models have been proposed to address numerous ICU prediction tasks like the early detection of complications. While authors frequently report state-of-the-art performance, it is challenging to verify claims of superiority. Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular framework that allows researchers to define reproducible and comparable clinical ML experiments; we offer an end-to-end solution from cohort definition to model evaluation. The framework natively supports most open-access ICU datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future ICU datasets. Combined with a transparent preprocessing pipeline and extensible training code for multiple ML and deep learning models, YAIB enables unified model development. Our benchmark comes with five predefined established prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and length of stay) developed in collaboration with clinicians. Adding further tasks is straightforward by design. Using YAIB, we demonstrate that the choice of dataset, cohort definition, and preprocessing have a major impact on the prediction performance - often more so than model class - indicating an urgent need for YAIB as a holistic benchmarking tool. We provide our work to the clinical ML community to accelerate method development and enable real-world clinical implementations. Software Repository: https://github.com/rvandewater/YAIB.

The development of large high-quality datasets and high-performing models have led to significant advancements in the domain of Extractive Question Answering (EQA). This progress has sparked considerable interest in exploring unanswerable questions within the EQA domain. Training EQA models with unanswerable questions helps them avoid extracting misleading or incorrect answers for queries that lack valid responses. However, manually annotating unanswerable questions is labor-intensive. To address this, we propose AGent, a novel pipeline that automatically creates new unanswerable questions by re-matching a question with a context that lacks the necessary information for a correct answer. In this paper, we demonstrate the usefulness of this AGent pipeline by creating two sets of unanswerable questions from answerable questions in SQuAD and HotpotQA. These created question sets exhibit low error rates. Additionally, models fine-tuned on these questions show comparable performance with those fine-tuned on the SQuAD 2.0 dataset on multiple EQA benchmarks.