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This study uses Joint Species Distribution Models (JSDMs) and Deep Neural Networks (DNNs) to explore how wild carnivores and free-ranging dogs interact in the Udanti-Sitanadi Tiger Reserve (USTR) in Central India. The research focuses on key species like the Striped Hyena, Grey Wolf, Golden Jackal, and Indian Fox, revealing significant overlaps in habitat with free-ranging dogs, especially in densely populated areas like the Sitanadi region of the tiger reserve. These overlaps pose serious risks to wildlife through competition for resources, predation, and the spread of diseases. The study shows that the Striped Hyena prefers gentle slopes and forested areas, while the Grey Wolf tends to avoid cropland and thrives in regions with higher rainfall that supports a stable prey base. The Golden Jackal, more adaptable than the others, favors west-facing slopes and stable temperatures, whereas the Indian Fox is mainly found in the less disturbed, mountainous Kuladighat region. Additionally, the study highlights the potential impacts of climate change, predicting that the Grey Wolf could face habitat extinction under more severe scenarios. These findings underscore the urgent need for conservation strategies tailored to address both dog wild carnivore interactions and the growing challenges posed by climate change, focusing on protecting the critical habitats of vulnerable species like the Striped Hyena and Grey Wolf.

The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs.

Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI). Previous studies have utilized various EEG features, such as subband power and connectivity patterns to differentiate these conditions. However, artifacts in EEG signals can obscure crucial information, necessitating advanced signal processing techniques. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. The study utilizes scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to check for the efficacy of the proposed method: the online BrainLat dataset (comprising AD, FTD, and healthy controls (HC)) and the in-house IITD-AIIA dataset (including subjects with AD, MCI, and HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes on both datasets. The best results were achieved by using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yields accuracies of 94.17$\%$ and 77.72$\%$ on the BrainLat and IITD-AIIA datasets, respectively. This highlights the potential of this approach for early and accurate differentiation of neurodegenerative disorders.

The search for the electromagnetic counterparts to gravitational wave (GW) events has been rapidly gathering pace in recent years thanks to the increasing number and capabilities of both gravitational wave detectors and wide field survey telescopes. Difficulties remain, however, in detecting these counterparts due to their inherent scarcity, faintness and rapidly evolving nature. To find these counterparts, it is important that one optimises the observing strategy for their recovery. This can be difficult due to the large number of potential variables at play. Such follow-up campaigns are also capable of detecting hundreds or potentially thousands of unrelated transients, particularly for GW events with poor localisation. Even if the observations are capable of detecting a counterpart, finding it among the numerous contaminants can prove challenging. Here we present the Gravitational wave Electromagnetic RecovRY code (GERry) to perform detailed analysis and survey-agnostic quantification of observing campaigns attempting to recover electromagnetic counterparts. GERry considers the campaign's spatial, temporal and wavelength coverage, in addition to Galactic extinction and the expected counterpart light curve evolution from the GW 3D localisation volume. It returns quantified statistics that can be used to: determine the probability of having detected the counterpart, identify the most promising sources, and assess and refine strategy. Here we demonstrate the code to look at the performance and parameter space probed by current and upcoming wide-field surveys such as GOTO & VRO., Comment: 19 pages, 11 figures, submitted to SPIE Astronomical Telescopes + Instrumentation 2024

The Gravitational-wave Optical Transient Observer (GOTO) is a project dedicated to identifying optical counter-parts to gravitational-wave detections using a network of dedicated, wide-field telescopes. After almost a decade of design, construction, and commissioning work, the GOTO network is now fully operational with two antipodal sites: La Palma in the Canary Islands and Siding Spring in Australia. Both sites host two independent robotic mounts, each with a field-of-view of 44 square degrees formed by an array of eight 40 cm telescopes, resulting in an instantaneous 88 square degree field-of-view per site. All four telescopes operate as a single integrated network, with the ultimate aim of surveying the entire sky every 2-3 days and allowing near-24-hour response to transient events within a minute of their detection. In the modern era of transient astronomy, automated telescopes like GOTO form a vital link between multi-messenger discovery facilities and in-depth follow-up by larger telescopes. GOTO is already producing a wide range of scientific results, assisted by an efficient discovery pipeline and a successful citizen science project: Kilonova Seekers., Comment: 8 pages, 5 figures, submitted to SPIE Astronomical Telescopes + Instrumentation 2024

This article presents a series of opposed flow flame spread experiments, conducted using cast cylindrical PMMA (acrylic) rods, 80 mm long and of diameters 1 mm and 0.5 mm, in normal gravity and microgravity environments. The experiments are primarily conducted for molar oxygen levels of 21%, 23% and 40% at 1 atmosphere pressure and opposed flow speed ranging from 0 cm/s to 25 cm/s. Experiments are also conducted in normal gravity for oxygen levels 21% to 60% to study the effect of oxygen level. At near ambient oxygen levels, the flame shape in microgravity resembles a mushroom and there are fluctuations at the leading edge due to sporadic fuel jets emanating from bursting bubbles at the fuel surface. The flame spreads faster in microgravity, which is determined to be due to increased preheat length. Preheat length is measured for flame spreading over 1 mm diameter fuel rod under no flow condition using fine thermocouples and is found to be 0.98 cm in microgravity and 0.34 cm in normal gravity. It is found that for scaling analysis, including Stefan flow velocity in the definition of reference velocity results in a reasonable estimate of the preheat length of a flame spreading in quiescent microgravity environment. At high oxygen levels (> 35%) the flame becomes turbulent and spreads at nearly the same rate in normal gravity and microgravity environments and the flame spread rates are not affected by external flow speed.

In this paper, we discuss the capable and isoclinic properties of the tensor square in the context of multiplicative Lie algebras. We also developed the concept of isoclinic extensions and proved several results for multiplicative Lie algebras. Consequently, we demonstrate that covers of a multiplicative Lie algebra are mutually isoclinic.

Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained embeddings may exhibit proximity or disparity based on their original training objectives, they might not fully align with the unique characteristics of enterprise-specific data, leading to suboptimal alignment with the retrieval goals of enterprise environments. In this paper, we propose a methodology to fine-tune pre-trained embedding models specifically for enterprise environments. By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of information retrieval solutions. We discuss the process of fine-tuning, its effect on retrieval accuracy, and the potential benefits for enterprise information management. Our findings demonstrate the efficacy of fine-tuned embedding models in improving the precision and relevance of search results in enterprise settings.

It was shown in arXiv:1208.5742 that any smooth knot can be represented by an \"ubercrossing projection, i.e. a knot projection with no crossings aside from a single multi-crossing. We extend this idea to Legendrian knots and investigate \"ubercrossing and petal projections in the front and Lagrangian projections. We show that any Legendrian knot with an \"ubercrossing projection in the front projection is smoothly isotopic to the unknot and we demonstrate how to compute the $tb$ and rotation numbers for petal projections in the Lagrangian projection., Comment: 9 pages, 6 figures

In this paper, our focus lies on the Merton's jump diffusion model, employing jump processes characterized by the compound Poisson process. Our primary objective is to forecast the drift and volatility of the model using a variety of methodologies. We adopt an approach that involves implementing different drift, volatility, and jump terms within the model through various machine learning techniques, traditional methods, and statistical methods on price-volume data. Additionally, we introduce a path-dependent Monte Carlo simulation to model cryptocurrency prices, taking into account the volatility and unexpected jumps in prices., Comment: 21 pages

This paper presents a numerical investigation into the phenomenon of flame spread over thin circular ducts in normal gravity and microgravity environments. Flame spread over such geometry is of significant interest due to its relevance in various practical applications, including tubes for flow purpose in medical system, fire safety in spacecrafts, ducts as well as wiring tubes. This study comprises of a comprehensive investigation of key parameters affecting flame spread rate, including fuel radius and opposed flow speed in normal gravity and microgravity environments. A 2-D axisymmetric flame spread model accounted for char and numerical simulations were performed which revealed valuable insights into the underlying mechanisms governing flame spread over such geometry. The results computed from the numerical model is compared with the experimentally observed flame spread rate to validate the numerical model which can be used to gain a comprehensive understanding of the underlying physical phenomena. As the radius of circular duct increases the flame spread rate increases both in normal gravity and microgravity environments. The conduction heat feedback and radiation heat gain coming from hot char through gas phase at inner core region are the two major mechanisms which controls the flame spread phenomena over the circular duct fuels. The flame spread rate at different flow ranging from quiescent (0 cm/s) to 30 cm/s is also evaluated and 21 % oxygen and found a non-monotonic increasing decreasing trend of flame spread rate at different opposed flow speed in both normal gravity and microgravity environments., Comment: 38 pages, 17 figures