While contrast-enhanced CT (CECT) is standard for assessing abdominal aortic aneurysms (AAA), the required iodinated contrast agents pose significant risks, including nephrotoxicity, patient allergies, and environmental harm. To reduce contrast agent use, recent deep learning methods have focused on generating synthetic CECT from non-contrast CT (NCCT) scans. However, most adopt a multi-stage pipeline that first generates images and then performs segmentation, which leads to error accumulation and fails to leverage shared semantic and anatomical structures. To address this, we propose a unified deep learning framework that generates synthetic CECT images from NCCT scans while simultaneously segmenting the aortic lumen and thrombus. Our approach integrates conditional diffusion models (CDM) with multi-task learning, enabling end-to-end joint optimization of image synthesis and anatomical segmentation. Unlike previous multitask diffusion models, our approach requires no initial predictions (e.g., a coarse segmentation mask), shares both encoder and decoder parameters across tasks, and employs a semi-supervised training strategy to learn from scans with missing segmentation labels, a common constraint in real-world clinical data. We evaluated our method on a cohort of 264 patients, where it consistently outperformed state-of-the-art single-task and multi-stage models. For image synthesis, our model achieved a PSNR of 25.61 dB, compared to 23.80 dB from a single-task CDM. For anatomical segmentation, it improved the lumen Dice score to 0.89 from 0.87 and the challenging thrombus Dice score to 0.53 from 0.48 (nnU-Net). These segmentation enhancements led to more accurate clinical measurements, reducing the lumen diameter MAE to 4.19 mm from 5.78 mm and the thrombus area error to 33.85% from 41.45% when compared to nnU-Net. Code is available at https://github.com/yuxuanou623/AortaDiff.git.

This paper presents a Spatiotemporal Tube (STT)-based control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances, guaranteeing the satisfaction of Temporal Reach-Avoid-Stay (T-RAS) specifications. The approach employs circular STT, characterized by smoothly time-varying center and radius, to define dynamic safe corridors that guide the robot from the start region to the goal while avoiding obstacles. In particular, we first develop a sampling-based synthesis algorithm to construct a feasible STT that satisfies the prescribed timing and safety constraints with formal guarantees. To ensure that the robot remains confined within this tube, we then design analytically a closed-form, approximation-free control law. The resulting controller is computationally efficient, robust to disturbances and {model uncertainties}, and requires no model approximations or online optimization. The proposed framework is validated through simulation studies on a differential-drive robot and benchmarked against state-of-the-art methods, demonstrating superior robustness, accuracy, and computational efficiency.

Social media has experienced incredible growth over the last decade, both in its scale and importance as a form of communication. The nature of social media means that anyone can post anything they desire, putting forward any position, whether it is enlightening, repugnant or anywhere between. Depending on the forum, such posts can be visible to many millions of people. Different forums have different definitions of inappropriate content and different processes for identifying it, but the scale of the medium means that automated methods are an important part of this task. Hate-speech is an important aspect of this inappropriate content. Hate-speech is a subjective and complex term with no single definition, however. Irrespective of the definition of the term or the problem, it is clear that automated methods for detecting hate-speech are necessary in some circumstances. In such cases it is critical that the methods employed are accurate, effective, and efficient. A variety of methods have been explored for the hate speech detection task, including traditional classifiers [6, 17, 32, 41,57], deep learning-based classifiers [1,7,8,46,59], or the combination of both approaches [7,25,37].

Even though online social movements can quickly become viral on social media, languages can be a barrier to timely monitoring and analyzing the underlying online social behaviors (OSB). This is especially true for under-resourced languages on social media like dialectal Arabic; the primary language used by Arabs on social media. Therefore, it is crucial to provide solutions to efficiently exploit resources from high-resourced languages to solve language-dependent OSB analysis in under-resourced languages. This paper proposes to localize content of resources in high-resourced languages into under-resourced Arabic dialects. Content localization goes beyond content translation that converts text from one language to another; content localization adapts culture, language nuances and regional preferences from one language to a specific language/dialect. Automating understanding of the natural and familiar day-to-day expressions in different regions, is the key to achieve a wider analysis of OSB especially for smart cities. In this paper, we utilize content-localization based neural machine translation to develop sentiment and hate classifiers for two low-resourced Arabic dialects: Levantine and Gulf. Not only this but we also leverage unsupervised learning to facilitate the analysis of sentiment and hate predictions by inferring hidden topics from the corresponding data and providing coherent interpretations of those topics in their native language/dialects. The experimental evaluations and proof-of-concept COVID-19 case study on real data have validated the effectiveness of our proposed system in precisely distinguishing sentiments and accurately identifying hate content in both Levantine and Gulf Arabic dialects. Our findings shed light on the importance of considering the unique nature of dialects within the same language and ignoring the dialectal aspect would lead to misleading analysis.

Social networking sites, blogs, and online articles are instant sources of news for internet users globally. However, in the absence of strict regulations mandating the genuineness of every text on social media, it is probable that some of these texts are fake news or rumours. Their deceptive nature and ability to propagate instantly can have an adverse effect on society. This necessitates the need for more effective detection of fake news and rumours on the web. In this work, we annotate four fake news detection and rumour detection datasets with their emotion class labels using transfer learning. We show the correlation between the legitimacy of a text with its intrinsic emotion for fake news and rumour detection, and prove that even within the same emotion class, fake and real news are often represented differently, which can be used for improved feature extraction. Based on this, we propose a multi-task framework for fake news and rumour detection, predicting both the emotion and legitimacy of the text. We train a variety of deep learning models in single-task and multi-task settings for a more comprehensive comparison. We further analyze the performance of our multi-task approach for fake news detection in cross-domain settings to verify its efficacy for better generalization across datasets, and to verify that emotions act as a domain-independent feature. Experimental results verify that our multi-task models consistently outperform their single-task counterparts in terms of accuracy, precision, recall, and F1 score, both for in-domain and cross-domain settings. We also qualitatively analyze the difference in performance in single-task and multi-task learning models.

Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical, high-risk applications where human lives are at risk. To address this issue, researchers and businesses have been focusing on finding ways to improve the interpretability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric. We demonstrate the effectiveness of both the technique and the metric by concretely defining all the concepts and through experimentation.

The K-means algorithm is among the most commonly used data clustering methods. However, the regular K-means can only be applied in the input space and it is applicable when clusters are linearly separable. The kernel K-means, which extends K-means into the kernel space, is able to capture nonlinear structures and identify arbitrarily shaped clusters. However, kernel methods often operate on the kernel matrix of the data, which scale poorly with the size of the matrix or suffer from the high clustering cost due to the repetitive calculations of kernel values. Another issue is that algorithms access the data only through evaluations of $K(x_i, x_j)$, which limits many processes that can be done on data through the clustering task. This paper proposes a method to combine the advantages of the linear and nonlinear approaches by using driven corresponding approximate finite-dimensional feature maps based on spectral analysis. Applying approximate finite-dimensional feature maps were only discussed in the Support Vector Machines (SVM) problems before. We suggest using this method in kernel K-means era as alleviates storing huge kernel matrix in memory, further calculating cluster centers more efficiently and access the data explicitly in feature space. These explicit feature maps enable us to access the data in the feature space explicitly and take advantage of K-means extensions in that space. We demonstrate our Explicit Kernel Minkowski Weighted K-mean (Explicit KMWK-mean) method is able to be more adopted and find best-fitting values in new space by applying additional Minkowski exponent and feature weights parameter. Moreover, it can reduce the impact of concentration on nearest neighbour search by suggesting investigate among other norms instead of Euclidean norm, includes Minkowski norms and fractional norms (as an extension of the Minkowski norms with p<1).

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual's intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

In Artificial Intelligence we often seek to identify an unknown target function of many variables $y=f(\mathbf{x})$ giving a limited set of instances $S=\{(\mathbf{x^{(i)}},y^{(i)})\}$ with $\mathbf{x^{(i)}} \in D$ where $D$ is a domain of interest. We refer to $S$ as the training set and the final quest is to identify the mathematical model that approximates this target function for new $\mathbf{x}$; with the set $T=\{ \mathbf{x^{(j)}} \} \subset D$ with $T \neq S$ (i.e. thus testing the model generalisation). However, for some applications, the main interest is approximating well the unknown function on a larger domain $D'$ that contains $D$. In cases involving the design of new structures, for instance, we may be interested in maximizing $f$; thus, the model derived from $S$ alone should also generalize well in $D'$ for samples with values of $y$ larger than the largest observed in $S$. In that sense, the AI system would provide important information that could guide the design process, e.g., using the learned model as a surrogate function to design new lab experiments. We introduce a method for multivariate regression based on iterative fitting of a continued fraction by incorporating additive spline models. We compared it with established methods such as AdaBoost, Kernel Ridge, Linear Regression, Lasso Lars, Linear Support Vector Regression, Multi-Layer Perceptrons, Random Forests, Stochastic Gradient Descent and XGBoost. We tested the performance on the important problem of predicting the critical temperature of superconductors based on physical-chemical characteristics.

Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In this paper, we proposed the CogniFNN framework, which is the first attempt at using fuzzy neural networks to extract non-linear and non-stationary characteristics for evaluations of English word embeddings against the corresponding cognitive datasets. In our experiment, we used 15 human cognitive datasets across three modalities: EEG, fMRI, and eye-tracking, and selected the mean square error and multiple hypotheses testing as metrics to evaluate our proposed CogniFNN framework. Compared to the recent pioneer framework, our proposed CogniFNN showed smaller prediction errors of both context-independent (GloVe) and context-sensitive (BERT) word embeddings, and achieved higher significant ratios with randomly generated word embeddings. Our findings suggested that the CogniFNN framework could provide a more accurate and comprehensive evaluation of cognitive word embeddings. It will potentially be beneficial to the further word embeddings evaluation on extrinsic natural language processing tasks.