In recent years, low-resource Machine Reading Comprehension (MRC) has made significant progress, with models getting remarkable performance on various language datasets. However, none of these models have been customized for the Urdu language. This work explores the semi-automated creation of the Urdu Question Answering Dataset (UQuAD1.0) by combining machine-translated SQuAD with human-generated samples derived from Wikipedia articles and Urdu RC worksheets from Cambridge O-level books. UQuAD1.0 is a large-scale Urdu dataset intended for extractive machine reading comprehension tasks consisting of 49k question Answers pairs in question, passage, and answer format. In UQuAD1.0, 45000 pairs of QA were generated by machine translation of the original SQuAD1.0 and approximately 4000 pairs via crowdsourcing. In this study, we used two types of MRC models: rule-based baseline and advanced Transformer-based models. However, we have discovered that the latter outperforms the others; thus, we have decided to concentrate solely on Transformer-based architectures. Using XLMRoBERTa and multi-lingual BERT, we acquire an F1 score of 0.66 and 0.63, respectively.

We study anomaly detection for the case when the normal class consists of more than one object category. This is an obvious generalization of the standard one-class anomaly detection problem. However, we show that jointly using multiple one-class anomaly detectors to solve this problem yields poorer results as compared to training a single one-class anomaly detector on all normal object categories together. We further develop a new anomaly detector called DeepMAD that learns compact distinguishing features by exploiting the multiple normal objects categories. This algorithm achieves higher AUC values for different datasets compared to two top performing one-class algorithms that either are trained on each normal object category or jointly trained on all normal object categories combined. In addition to theoretical results we present empirical results using the CIFAR-10, fMNIST, CIFAR-100, and a new dataset we developed called RECYCLE.

Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded. Researchers from the Max Planck Institute for Psycholinguistics and the University of Turin studied the songs of indri in the rainforests of the island country. They found that the lemurs' strange, wailing songs have the same kinds of universal, categorical rhythms found across human musical cultures. Outside of humans, having rhythm is a rare trait in mammals -- although it can be found elsewhere in the animal kingdom, perhaps most notably in songbirds. Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded.

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/

OceanX, a team of marine biologists, media and filmmakers, embarked on a quest in 2020 to explore the depths of the Red Sea where they not only found a giant shipwreck, but a massive creature that appeared to be larger than a human. While investigating the'Pella,' which sank in November 2011, at a depth of 2,800 feet, the group spotted what they thought could be'The Giant Squid.' 'I will never forget what happened next for as long as I live,' said OceanX science program lead Mattie Rodrigue in a video taken of the discovery. 'All of a sudden, as we're looking at the bow of the shipwreck, this massive creature comes into view, takes a look at the ROV [remotely operated vehicle] and curls its entire body around the bow of the wreck.' It was not until September 2021 did the team learn that the mysterious creature was'the giant form' of the purpleback flying squid, which typically grow up to two feet long. The OceanX team traveled to the Red Sea aboard the OceanXplorer, a research vessel with a 40-ton crane to launch submersibles, towed sonar arrays and other heavy equipment down into the depths.

Behind Sahej, hanging on the wall, Nayana could see rows of colorful masks, which, she learned, Sahej had carved and painted himself. On the first day of the new term, the teacher had asked Sahej about the masks, and the new student shyly gave a show-and-tell, explaining how the masks combined Indian gods and spirits with the powers of superheroes. Now, in an invitation-only room on her ShareChat, some of Nayana's classmates were gossiping about Sahej. From the way his room was furnished to the fact that his surname was hidden from public view in school records, these girls were certain Sahej was among the "vulnerable group" that the government mandated make up at least 15% of their student body. At private schools across India, such children were practically guaranteed spots and their tuition, books, and uniforms were covered by scholarships.

Afghanistan War veteran Rep. Mike Waltz, R-Fla., and Rep. Michael McCaul, R-Texas, weigh the consequences of Biden's overseas withdrawal. A senior al Qaeda leader has been killed in a drone strike in Syria, U.S. defense officials confirmed to Fox News Thursday. Salim Abu-Ahmad was killed in a U.S. airstrike near Idlib, Syria on Sept. 20. He was responsible for planning, funding, and approving trans-regional al Qaeda attacks. A U.S. Air Force MQ-1B Predator unmanned aerial vehicle (UAV), carrying a Hellfire missile flies over an air base after flying a mission in the Persian Gulf region.

At the Goodwood festival of speed near Chichester, the crowds gathered at the hill-climb circuit to watch the world's fastest cars roar past, as they do every year. But not far from the high-octane action, there was a new, and quieter, attraction: a display of the latest electric vehicles, from the £28,000 Mini Electric to the £2m Lotus Evija hypercar. Even here, at one of the biggest events in Britain's petrolhead calendar, it's clear the days of the internal combustion engine are numbered. As countries strive to meet stringent carbon-emission targets, and vehicle-makers phase out combustion engines, 145m electric vehicles are predicted to be on the roads within a decade, up from 11m last year. The car batteries they require, along with storage batteries for solar and wind power, have sent demand for metals soaring, taking mining firms to the bottom of the sea in the hunt for those metals.

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.

Deep learning is on the frontline in a new age of piracy, outwitting attacks with pre-emptive tech, explains Yarden Gross, CEO and co-founder of Orca AI. Almost a decade has passed since piracy raged off Somalia, and yet the danger posed by maritime hijackings is as present as ever. The global pandemic last year sparked a resurgence of attacks, with piracy incidents doubling across Asia, in a worrying uptick also seen in the Gulf of Mexico and West Africa. The fallout from coronavirus, including the loss of key security personnel, turned quarantined vessels into easy targets. This wave has since receded a little, with the International Maritime Bureau reporting a 44 per cent YoY dip in piracy and armed robbery incidents in 2021.