Lycodon irwini is the latest species named after The Crocodile Hunter. Breakthroughs, discoveries, and DIY tips sent every weekday. Conservationists have discovered a previously unknown species of snake, slithering around one of Earth's most unique environments. In naming their new reptile, researchers decided to honor one of popular culture's most unique and beloved wildlife educators: the late, great Steve Irwin . The snake was discovered in the Nicobar Islands.

Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated content. However, these systems often falter with complex reasoning and consistency across diverse queries. In this work, we present Think-on-Graph 2.0, an enhanced RAG framework that aligns questions with the knowledge graph and uses it as a navigational tool, which deepens and refines the RAG paradigm for information collection and integration. The KG-guided navigation fosters deep and long-range associations to uphold logical consistency and optimize the scope of retrieval for precision and interoperability. In conjunction, factual consistency can be better ensured through semantic similarity guided by precise directives. ToG${2.0}$ not only improves the accuracy and reliability of LLMs' responses but also demonstrates the potential of hybrid structured knowledge systems to significantly advance LLM reasoning, aligning it closer to human-like performance. We conducted extensive experiments on four public datasets to demonstrate the advantages of our method compared to the baseline.

Since the inception of the SARS - CoV - 2 (COVID - 19) novel coronavirus, a lot of time and effort is being allocated to estimate the trajectory and possibly, forecast with a reasonable degree of accuracy, the number of cases, recoveries, and deaths due to the same. The model proposed in this paper is a mindful step in the same direction. The primary model in question is a Hybrid Holt's Model embedded with a Wavelet-based ANN. To test its forecasting ability, we have compared three separate models, the first, being a simple ARIMA model, the second, also an ARIMA model with a wavelet-based function, and the third, being the proposed model. We have also compared the forecast accuracy of this model with that of a modern day Vanilla LSTM recurrent neural network model. We have tested the proposed model on the number of confirmed cases (daily) for the entire country as well as 6 hotspot states. We have also proposed a simple adjustment algorithm in addition to the hybrid model so that daily and/or weekly forecasts can be meted out, with respect to the entirety of the country, as well as a moving window performance metric based on out-of-sample forecasts. In order to have a more rounded approach to the analysis of COVID-19 dynamics, focus has also been given to the estimation of the Basic Reproduction Number, $R_0$ using a compartmental epidemiological model (SIR). Lastly, we have also given substantial attention to estimating the shelf-life of the proposed model. It is obvious yet noteworthy how an accurate model, in this regard, can ensure better allocation of healthcare resources, as well as, enable the government to take necessary measures ahead of time.

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naive Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a generalization to Supervised Learning (SL) for Natural Language Generation, and thereby performs better (empirically) than SL, we use an RL-based approach for this task of QfS. Additionally, we also resolve the conflict of employing RL in Transformers with Teacher Forcing. We develop multiple Policy Gradient networks, trained on various reward signals: ROUGE, BLEU, and Semantic Similarity, which lead to a 10-point improvement over the State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5). We also show performance of our approach in zero-shot setting for another benchmark dataset (DebatePedia) -- our approach leads to results comparable to baselines, which were specifically trained on DebatePedia. To aid the RL training, we propose a better semantic similarity reward, enabled by a novel Passage Embedding scheme developed using Cluster Hypothesis. Lastly, we contribute a gold-standard test dataset to further research in QfS and Long-form Question Answering (LfQA).

Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to forecast one or multiple future events. In this work, we propose a new neural architecture for simultaneous multi-event forecasting of spatio-temporal point processes, utilizing transformers, augmented with normalizing flows and probabilistic layers. Our network makes batched predictions of complex history-dependent spatio-temporal distributions of future discrete events, achieving state-of-the-art performance on a variety of benchmark datasets including the South California Earthquakes, Citibike, Covid-19, and Hawkes synthetic pinwheel datasets. More generally, we illustrate how our network can be applied to any dataset of discrete events with associated markers, even when no underlying physics is known.

(Sivanantham and Seran, 2019). It is widely spoken in the southern state of Tamil Nadu in India, Combating offensive content is crucial for different Sri Lanka, Malaysia, and Singapore. Tamil is an entities involved in content moderation, which official language of Tamil Nadu, Sri Lanka, Singapore, includes social media companies as well as individuals and the Union Territory of Puducherry in (Kumaresan et al., 2021; Chakravarthi and India. Significant minority speak Tamil in the four Muralidaran, 2021). To this end, moderation is other South Indian states of Kerala, Karnataka, often restrictive with either usage of human content Andhra Pradesh, and Telangana, as well as the moderators, who are expected to read through Union Territory of the Andaman and Nicobar Islands the content and flag the offensive mentions (Arsht (Sakuntharaj and Mahesan, 2021, 2017, 2016; and Etcovitch, 2018). Alternatively, there are Thavareesan and Mahesan, 2019, 2020a,b, 2021).

The Institute of Chartered Accountants of India (ICAI) will introduce Artificial Intelligence and forensic science in its curriculum for the Chartered Accountants to detect financial fraud at a much earlier stage. In most cases, the fraud is detected only when they reach a substantial volume. This new curriculum aims to track such irregularity at a much earlier stage so that the big scams either do not happen or are detected at the initial stages. This is the first time when the institute will bring such big technological changes in their international courses. President of ICAI, Debashish Mitra, said: "We are introducing artificial intelligence, data analytics and new forensic standards in the new curriculum. The mission of ICAI is to provide a strong foundation of knowledge, skill, and professional value that enables students to grow as wholesome professionals and adapt to change throughout their professional career."

South Korea, a country of 50 million people, has set an example of successfully flattening the curve of new COVID-19 infections by conducting over 400,000 tests [13] (Figure 2). This was achieved by setting up drive-through testing, allowing at least 10,000 people to be tested per day. South Korea's foreign minister Kang Kyung-wha, in an interview with BBC News [2], said that "Testing is central because that leads to early detection, minimizes further spread, and quickly treats those found with the virus". Several countries are suffering from severe community spread because of their delays in testing [12], two of the prime examples being the United States and Italy. In the United States, among a population of 330 million, the number of confirmed cases is more than 230,000 with over 10,000 deaths and these numbers are growing exponentially (Figure 3), whereas in South Korea there are around 9976 confirmed cases and 169 deaths (as of April 2, 2020). Thus, early testing and repeated testing at regular intervals are two of the key strategies to ensure a low fatality rate. However, for countries with a large population (more than 100 million), it is difficult to adopt exhaustive testing schemes because of the limited number of available testing-kits and facilities. Testing a lot of people with mild or no symptoms would occupy the limited testing resources, which could otherwise be used for highrisk patients. However, it is also important to test individuals with mild or no symptoms to detect asymptomatic cases [10] and implement a method that systematically tests individuals for COVID-19.

India is a country in South Asia. It is the seventh-largest country by area, the second-most populous country (with over 1.2 billion people), and the most populous democracy in the world. It is bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast. It shares land borders with Pakistan to the west; China, Nepal, and Bhutan to the northeast; and Bangladesh and Myanmar to the east. In the Indian Ocean, India is in the vicinity of Sri Lanka and the Maldives. According to the International Monetary Fund (IMF), the Indian economy in 2017 was nominally worth US$2.611