A French frigate has shot down two drones over the Red Sea that were believed to be approaching from the coast of Yemen, according to the French military. "The interception and destruction of these two identified threats" were carried out late on Saturday by the frigate Languedoc, which operates in the Red Sea, the general staff said in a press release on Sunday. The interceptions happened at 20:30 GMT and 22:30 GMT and were 110km (68 miles) from the Yemeni coast, it added. Yemen's Iran-backed Houthi rebels on Saturday threatened to attack any vessels heading to Israeli ports unless food and medicine were allowed into the besieged Gaza Strip, which has been devastated by more than two months of Israeli bombing. The Houthis said that all "ships linked to Israel or that will transport goods to Israeli ports" are not welcome in the Red Sea, a vital channel for global trade linked to the Suez Canal.

A fundamental trait of intelligence involves finding novel and creative solutions to address a given challenge or to adapt to unforeseen situations. Reflecting this, Quality-Diversity optimization is a family of Evolutionary Algorithms, that generates collections of both diverse and high-performing solutions. Among these, MAP-Elites is a prominent example, that has been successfully applied to a variety of domains, including evolutionary robotics. However, MAP-Elites performs a divergent search with random mutations originating from Genetic Algorithms, and thus, is limited to evolving populations of low-dimensional solutions. PGA-MAP-Elites overcomes this limitation using a gradient-based variation operator inspired by deep reinforcement learning which enables the evolution of large neural networks. Although high-performing in many environments, PGA-MAP-Elites fails on several tasks where the convergent search of the gradient-based variation operator hinders diversity. In this work, we present three contributions: (1) we enhance the Policy Gradient variation operator with a descriptor-conditioned critic that reconciles diversity search with gradient-based methods, (2) we leverage the actor-critic training to learn a descriptor-conditioned policy at no additional cost, distilling the knowledge of the population into one single versatile policy that can execute a diversity of behaviors, (3) we exploit the descriptor-conditioned actor by injecting it in the population, despite network architecture differences. Our method, DCG-MAP-Elites, achieves equal or higher QD score and coverage compared to all baselines on seven challenging continuous control locomotion tasks.

Segmentation of Arabic manuscripts into lines of text and words is an important step to make recognition systems more efficient and accurate. The problem of segmentation into text lines is solved since there are carefully annotated dataset dedicated to this task. However, To the best of our knowledge, there are no dataset annotating the word position of Arabic texts. In this paper, we present a new dataset specifically designed for historical Arabic script in which we annotate position in word level.

This short paper gives an overview of the XCSP3 solver implemented in Picat. Picat provides several constraint modules, and the Picat XCSP3 solver uses the sat module. The XCSP3 solver mainly consists of a parser implemented in Picat, which converts constraints from XCSP3 format to Picat. The solver demonstrates the strengths of Picat, a logic-based language, in parsing, modeling, and encoding constraints into SAT. The solver submitted to the 2022 XCSP competition is based on the one that won the 2019 XCSP competition.

With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions. However, facial expression recognition (FER) from NIR images presents more challenging problem than traditional FER due to the limitations imposed by the data scale and the difficulty of extracting discriminative features from incomplete visible lighting contents. In this paper, we give the first attempt to deep NIR facial expression recognition and proposed a novel method called near-infrared facial expression transformer (NFER-Former). Specifically, to make full use of the abundant label information in the field of VIS, we introduce a Self-Attention Orthogonal Decomposition mechanism that disentangles the expression information and spectrum information from the input image, so that the expression features can be extracted without the interference of spectrum variation. We also propose a Hypergraph-Guided Feature Embedding method that models some key facial behaviors and learns the structure of the complex correlations between them, thereby alleviating the interference of inter-class similarity. Additionally, we have constructed a large NIR-VIS Facial Expression dataset that includes 360 subjects to better validate the efficiency of NFER-Former. Extensive experiments and ablation studies show that NFER-Former significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

This short paper gives an overview of the XCSP3 solver implemented in Picat. Picat provides several constraint modules, and the Picat XCSP3 solver uses the sat module. The XCSP3 solver mainly consists of a parser implemented in Picat, which converts constraints from XCSP3 format to Picat. The solver demonstrates the strengths of Picat, a logic-based language, in parsing, modeling, and encoding constraints into SAT. The solver submitted to the 2022 XCSP competition is based on the one that won the 2019 XCSP competition.

The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey meaning, humor, and cultural significance. While meme analysis has been an active area of research, little work has been done on unsupervised multimodal topic modeling of memes, which is important for content moderation, social media analysis, and cultural studies. We propose \textsf{PromptMTopic}, a novel multimodal prompt-based model designed to learn topics from both text and visual modalities by leveraging the language modeling capabilities of large language models. Our model effectively extracts and clusters topics learned from memes, considering the semantic interaction between the text and visual modalities. We evaluate our proposed model through extensive experiments on three real-world meme datasets, which demonstrate its superiority over state-of-the-art topic modeling baselines in learning descriptive topics in memes. Additionally, our qualitative analysis shows that \textsf{PromptMTopic} can identify meaningful and culturally relevant topics from memes. Our work contributes to the understanding of the topics and themes of memes, a crucial form of communication in today's society.\\ \red{\textbf{Disclaimer: This paper contains sensitive content that may be disturbing to some readers.}}

Time series classification (TSC) is the most import task in time series mining as it has several applications in medicine, meteorology, finance cyber security, and many others. With the ever increasing size of time series datasets, several traditional TSC methods are no longer efficient enough to perform this task on such very large datasets. Yet, most recent papers on TSC focus mainly on accuracy by using methods that apply deep learning, for instance, which require extensive computational resources that cannot be applied efficiently to very large datasets. The method we introduce in this paper focuses on these very large time series datasets with the main objective being efficiency. We achieve this through a simplified representation of the time series. This in turn is enhanced by a distance measure that considers only some of the values of the represented time series. The result of this combination is a very efficient representation method for TSC. This has been tested experimentally against another time series method that is particularly popular for its efficiency. The experiments show that our method is not only 4 times faster, on average, but it is also superior in terms of classification accuracy, as it gives better results on 24 out of the 29 tested time series datasets. .

In this research, we are concerned with the applicability of virtual reality-based attention training as a tool for stress management. Mental stress is a worldwide challenge that is still far from being fully managed. This has maintained a remarkable research attention on developing and validating tools for detecting and managing stress. Technology-based tools have been at the heart of these endeavors, including virtual reality (VR) technology. Nevertheless, the potential of VR lies, to a large part, in the nature of the content being consumed through such technology. In this study, we investigate the impact of a special type of content, namely, attention training, on the feasibility of using VR for stress management. On a group of fourteen undergraduate engineering students, we conducted a study in which the participants got exposed twice to a stress inducer while their EEG signals were being recorded. The first iteration involved VR-based attention training before starting the stress task while the second time did not. Using multiple features and various machine learning models, we show that VR-based attention training has consistently resulted in reducing the number of recognized stress instances in the recorded EEG signals. This research gives preliminary insights on adopting VR-based attention training for managing stress, and future studies are required to replicate the results in larger samples.

While communication research frequently studies latent message features like moral appeals, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual sensitivity and are limited by the vocabularies developed for the original applications. In this paper, we present a novel approach to construct vec-tionary measurement tools that boost validated dictionaries with word embeddings through nonlinear optimization. By harnessing semantic relationships encoded by embeddings, vec-tionaries improve the measurement of latent message features by expanding the applicability of original vocabularies to other contexts. Vec-tionaries can also help extract semantic information from texts, especially those in short format, beyond the original vocabulary of a dictionary. Importantly, a vec-tionary can produce additional metrics to capture the valence and ambivalence of a latent feature beyond its strength in texts. Using moral appeals in COVID-19-related tweets as a case study, we illustrate the steps to construct the moral foundations vec-tionary, showcasing its ability to process posts missed by dictionary methods and to produce measurements better aligned with crowdsourced human assessments. Furthermore, additional metrics from the moral foundations vec-tionary unveiled unique insights that facilitated predicting outcomes such as message retransmission.