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Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in LMM research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions, which are further divided into short and long-answer categories. ALM-bench design ensures a comprehensive assessment of a model's ability to handle varied levels of difficulty in visual and linguistic reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations. Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark is publicly available., Comment: A Multilingual Multimodal cultural benchmark for 100 languages

We report on the onset of a coronal mass ejection (CME) using spectroscopic observations in 5303 {\AA} coronal emission line with the Visible Emission Line Coronagraph (VELC) onboard ADITYA-L1, the recently launched first Indian space solar mission. The CME was observed on 16 July 2024 in association with a X1.9 class soft X-ray flare from heliographic location S05W85. The VELC observations were near the west limb of Sun during the CME. The results obtained helped to constrain the onset time of the CME. In addition, they indicate ${\approx}$50% decrease in the coronal intensity near the source region of the CME due to mass depletion, ${\approx}$15% enhancement in the emission line width, and redshifted Doppler velocity of about ${\approx}10$ km/s. The non-thermal velocity associated with the line broadening is ${\approx}24.87$ km/s.

Enhancing and preserving the readability of document images, particularly historical ones, is crucial for effective document image analysis. Numerous models have been proposed for this task, including convolutional-based, transformer-based, and hybrid convolutional-transformer architectures. While hybrid models address the limitations of purely convolutional or transformer-based methods, they often suffer from issues like quadratic time complexity. In this work, we propose a Mamba-based architecture for document binarisation, which efficiently handles long sequences by scaling linearly and optimizing memory usage. Additionally, we introduce novel modifications to the skip connections by incorporating Difference of Gaussians (DoG) features, inspired by conventional signal processing techniques. These multiscale high-frequency features enable the model to produce high-quality, detailed outputs.

Affect and cognitive load influence many user behaviors. In this paper, we propose Motion as Emotion, a novel method that utilizes fine differences in hand motion to recognise affect and cognitive load in virtual reality (VR). We conducted a study with 22 participants who used common free-hand gesture interactions to carry out tasks of varying difficulty in VR environments. We find that the affect and cognitive load induced by tasks are associated with significant differences in gesture features such as speed, distance and hand tension. Standard support vector classification (SVC) models could accurately predict two levels (low, high) of valence, arousal and cognitive load from these features. Our results demonstrate the potential of Motion as Emotion as an accurate and reliable method of inferring user affect and cognitive load from free-hand gestures, without needing any additional wearable sensors or modifications to a standard VR headset.

The magnetic field strength and its topology play an important role in understanding the formation, evolution, and dynamics of the solar corona. Also, it plays a significant role in addressing long-standing mysteries such as coronal heating problem, origin and propagation of coronal mass ejections, drivers of space weather, origin and acceleration of solar wind, and so on. Despite having photospheric magnetograms for decades, we do not have reliable observations of coronal magnetic field strengths today. To measure the coronal magnetic field precisely, the spectropolarimetry channel of the Visible Emission Line Coronagraph (VELC) on board the Aditya-L1 mission is designed. Using the observations of coronal emission line Fe XIII [10747{\AA~}], it is possible to generate full Stokes maps (I, Q, U, and V) that help in estimating the Line-of-Sight (LOS) magnetic field strength and to derive the magnetic field topology maps of solar corona in the Field of View (FOV) (1.05 -- 1.5~R$_{\odot}$). In this article, we summarize the instrumental details of the spectropolarimetry channel and detailed calibration procedures adopted to derive the modulation and demodulation matrices. Furthermore, we have applied the derived demodulation matrices to the observed data in the laboratory and studied their performance., Comment: 12 pages, 5 Figures, Published in Journal of Experimental Astronomy

Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset's reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event., Comment: Accepted to Findings of EMNLP 2023

Although the principal physical behaviour of a material is inherently connected to its fundamental crystal structure, the behaviours observed in the real-world are often driven by the microstructure, which for many polycrystalline materials, equates to the size and shape of the constituent crystal grains. Here we highlight a cutting edge synthesis route to the controlled engineering of grain structures in thin films and the simplification of associated 3-dimensional problems to less complex 2D ones. This has been applied to the actinide ceramic, uranium dioxide, to replicate structures typical in nuclear fission fuel pellets, in order to investigate the oxidation and subsequent transformation of cubic UO$_{2}$ to orthorhombic U$_{3}$O$_{8}$. This article shows how this synthesis approach could be utilised to investigate a range of phenomena, affected by grain morphology, and highlights some unusual results in the oxidation behaviour of UO$_{2}$, regarding the phase transition to U$_{3}$O$_{8}$., Comment: 21 pages, 3 figures

Aditya-L1 is the first Indian space mission to explore the Sun and solar atmosphere with seven multi-wavelength payloads, with Visible Emission Line Coronagraph (VELC) being the prime payload. It is an internally occulted coronagraph with four channels to image the Sun at 5000 \AA~ in the field of view 1.05 - 3 \rsun, and to pursue spectroscopy at 5303 \AA, 7892 \AA~ and 10747 \AA~ channels in the FOV (1.05 - 1.5 \rsun). In addition, spectropolarimetry is planned at 10747 \AA~ channel. Therefore, VELC has three sCMOS detectors and one InGaAs detector. In this article, we aim to describe the technical details and specifications of the detectors achieved by way of thermo-vacuum calibration at the CREST campus of the Indian Institute of Astrophysics, Bangalore, India. Furthermore, we report the estimated conversion gain, full-well capacity, and readout noise at different temperatures. Based on the numbers, it is thus concluded that it is essential to operate the sCMOS detectors and InGaAs detectors at $-5^{\circ}$ and $-17^{\circ}$ C, respectively, at the spacecraft level., Comment: Accepted for publication in Experimental Astronomy; 13 Pages, 5 Figures and 8 Tables