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Deep neural networks have demonstrated remarkable performance in medical image analysis. However, its susceptibility to spurious correlations due to shortcut learning raises concerns about network interpretability and reliability. Furthermore, shortcut learning is exacerbated in medical contexts where disease indicators are often subtle and sparse. In this paper, we propose a novel gaze-directed Vision GNN (called GD-ViG) to leverage the visual patterns of radiologists from gaze as expert knowledge, directing the network toward disease-relevant regions, and thereby mitigating shortcut learning. GD-ViG consists of a gaze map generator (GMG) and a gaze-directed classifier (GDC). Combining the global modelling ability of GNNs with the locality of CNNs, GMG generates the gaze map based on radiologists' visual patterns. Notably, it eliminates the need for real gaze data during inference, enhancing the network's practical applicability. Utilizing gaze as the expert knowledge, the GDC directs the construction of graph structures by incorporating both feature distances and gaze distances, enabling the network to focus on disease-relevant foregrounds. Thereby avoiding shortcut learning and improving the network's interpretability. The experiments on two public medical image datasets demonstrate that GD-ViG outperforms the state-of-the-art methods, and effectively mitigates shortcut learning. Our code is available at https://github.com/SX-SS/GD-ViG.

A warm corona has been widely proposed to explain the soft X-ray excess (SE) above the 2--10 keV power law extrapolation in AGNs. In actual spectral fittings, the warm coronal seed photon temperature ($T_{\rm s}$) is usually assumed to be far away from the soft X-ray, but $kT_{\rm s}$ can reach close to 0.1 keV in standard accretion disc model. In this study, we used Monte Carlo simulations to obtain radiation spectra from a slab-like warm corona and fitted the spectra using the spherical-geometry-based routine \textsc{thcomp} or a thermal component. Our findings reveal that high $T_{\rm s}$ can influence the fitting results. A moderately high $kT_{\rm s}$ (around 0.03 keV) can result in an apparent low-temperature and flat SE, while an extremely high $kT_{\rm s}$ (around 0.07 keV) can even produce an unobserved blackbody-like SE. Our conclusions indicate that, for spectral fittings of the warm coronal radiation (SE in AGNs), $kT_{\rm s}$ should be treated as a free parameter with an upper limit, and an accurate coronal geometry is necessary when $kT_{\rm s}>0.01$ keV., Comment: Accepted for publication in RAA

Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based sentences. We also convert the detected anatomy into textual prompts conveying anatomical comprehension to the LLM. Additionally, the clinical context prompts guide the LLM to emphasize interactivity and clinical requirements. By integrating anatomy-focused sentences and anatomy/clinical prompts, the pre-trained LLM can generate structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. We evaluate using language generation and clinical effectiveness metrics, demonstrating strong performance., Comment: Accepted by IEEE Conference on Multimedia Expo 2024

Multimodal large language models (MLLMs) have demonstrated remarkable problem-solving capabilities in various vision fields (e.g., generic object recognition and grounding) based on strong visual semantic representation and language reasoning ability. However, whether MLLMs are sensitive to subtle visual spoof/forged clues and how they perform in the domain of face attack detection (e.g., face spoofing and forgery detection) is still unexplored. In this paper, we introduce a new benchmark, namely SHIELD, to evaluate the ability of MLLMs on face spoofing and forgery detection. Specifically, we design true/false and multiple-choice questions to evaluate multimodal face data in these two face security tasks. For the face anti-spoofing task, we evaluate three different modalities (i.e., RGB, infrared, depth) under four types of presentation attacks (i.e., print attack, replay attack, rigid mask, paper mask). For the face forgery detection task, we evaluate GAN-based and diffusion-based data with both visual and acoustic modalities. Each question is subjected to both zero-shot and few-shot tests under standard and chain of thought (COT) settings. The results indicate that MLLMs hold substantial potential in the face security domain, offering advantages over traditional specific models in terms of interpretability, multimodal flexible reasoning, and joint face spoof and forgery detection. Additionally, we develop a novel Multi-Attribute Chain of Thought (MA-COT) paradigm for describing and judging various task-specific and task-irrelevant attributes of face images, which provides rich task-related knowledge for subtle spoof/forged clue mining. Extensive experiments in separate face anti-spoofing, separate face forgery detection, and joint detection tasks demonstrate the effectiveness of the proposed MA-COT. The project is available at https$:$//github.com/laiyingxin2/SHIELD

The Boltzmann distribution of a protein provides a roadmap to all of its functional states. Normalizing flows are a promising tool for modeling this distribution, but current methods are intractable for typical pharmacological targets; they become computationally intractable due to the size of the system, heterogeneity of intra-molecular potential energy, and long-range interactions. To remedy these issues, we present a novel flow architecture that utilizes split channels and gated attention to efficiently learn the conformational distribution of proteins defined by internal coordinates. We show that by utilizing a 2-Wasserstein loss, one can smooth the transition from maximum likelihood training to energy-based training, enabling the training of Boltzmann Generators for macromolecules. We evaluate our model and training strategy on villin headpiece HP35(nle-nle), a 35-residue subdomain, and protein G, a 56-residue protein. We demonstrate that standard architectures and training strategies, such as maximum likelihood alone, fail while our novel architecture and multi-stage training strategy are able to model the conformational distributions of protein G and HP35.

A state feedback control strategy is proposed for input-output (IO) decoupling of a class of fully output controllable Boolean control networks (BCNs). Some necessary and sufficient conditions for BCN IO-decoupling are presented. As an instrumental tool in our design, we introduce a canonical form for IO-decoupled BCNs along with some conditions guaranteeing its existence. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.

In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: Amharic<->German, Amharic<->English, and Amharic<->Urdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce., Comment: 16 pages, 9 tables, 5 figures