Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. In this paper, we show that the performance of an object detector in a single frame of a time-lapse sequence can be improved by including spatio-temporal features from the prior frames. We propose a method that leverages temporal information by integrating two additional spatial feature channels which capture stationary and non-stationary elements of the scene and consequently improve scene understanding and reduce the number of stationary false positives. The proposed technique achieves a significant improvement of 24\% in mean average precision (mAP@0.05:0.95) over the baseline (temporal feature-free, single frame) object detector on a large dataset of breeding tropical seabirds. We envisage our method will be widely applicable to other wildlife monitoring applications that use time-lapse imaging.

Some one million eligible voters in the Indian Ocean Mauritius will head out to vote on Sunday amid an explosive scandal that has implicated government figures in a covert wiretapping operation. Since independence from Britain in 1968, the southeast African country has maintained a strong, vibrant parliamentary democracy. This will be its 12th national election. Elections are usually deemed free and fair and turnout is normally high, at close to 80 percent. This time, however, the unusual drama caused by the leaked recordings has sparked national agitation and dominated the campaign season.

We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset - performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.

We introduce model editing with canonical examples, a setting in which (1) a single learning example is provided per desired behavior, (2) evaluation is performed exclusively out-of-distribution, and (3) deviation from an initial model is strictly limited. A canonical example is a simple instance of good behavior, e.g., The capital of Mauritius is Port Louis) or bad behavior, e.g., An aspect of researchers is coldhearted). The evaluation set contains more complex examples of each behavior (like a paragraph in which the capital of Mauritius is called for.) We create three datasets and modify three more for model editing with canonical examples, covering knowledge-intensive improvements, social bias mitigation, and syntactic edge cases. In our experiments on Pythia language models, we find that LoRA outperforms full finetuning and MEMIT. We then turn to the Backpack language model architecture because it is intended to enable targeted improvement. The Backpack defines a large bank of sense vectors--a decomposition of the different uses of each word--which are weighted and summed to form the output logits of the model. We propose sense finetuning, which selects and finetunes a few ($\approx$ 10) sense vectors for each canonical example, and find that it outperforms other finetuning methods, e.g., 4.8% improvement vs 0.3%. Finally, we improve GPT-J-6B by an inference-time ensemble with just the changes from sense finetuning of a 35x smaller Backpack, in one setting outperforming editing GPT-J itself (4.1% vs 1.0%).

Ocorian is a global leader in corporate and fiduciary services, fund administration and capital markets. Wherever our clients hold financial interests, or however they are structured, we provide compliant, tailored solutions that are individual to their needs. We manage over 17,000 structures for 8000 clients with a global footprint operating from 18 locations. Our scale offers all our people great opportunities to develop their knowledge and skills and to progress their careers. To supervise a team of officers who will carry out the review and updates of client files as per the AML/CFT and other applicable regulations of the jurisdiction of domiciliation of the client entity, input relevant information on the Enterprise Resource Planning ('ERP') software and Document Management System ('DMS'), and complete the file reviews as per the defined process and quality and within agreed timeline.

Movate (formerly CSS Corp), a digital technology and customer experience (CX) services provider, announced the launch of its new global delivery center in Atal Bihari Vajpayee Tower, Ébène, Mauritius. It will leverage highly skilled local talent with multilingual capabilities, especially in French and English, to deliver high-tech and high-touch services in Customer Experience Management and Enterprise Product Services. Movate plans to employ over 200 Mauritian skilled technology professionals and this move is a part of its global expansion strategy to address the growing client portfolios and their business needs globally. Movate expands its operations in Mauritius with a new delivery center; plans to employ over 200 Mauritian skilled technology professionals in the next one year. The new state-of-the-art facility at the Atal Bihari Vajpayee Tower is a 300-seater workspace.

Ocorian is a global leader in corporate and fiduciary services, fund administration and capital markets. Wherever our clients hold financial interests, or however they are structured, we provide compliant, tailored solutions that are individual to their needs. We manage over 17,000 structures for 8000 clients with a global footprint operating from 18 locations. Our scale offers all our people great opportunities to develop their knowledge and skills and to progress their careers. To supervise a team of officers who will carry out the review and updates of client files as per the AML/CFT and other applicable regulations of the jurisdiction of domiciliation of the client entity, input relevant information on the Enterprise Resource Planning ('ERP') software and Document Management System ('DMS'), and complete the file reviews as per the defined process and quality and within agreed timeline.

The startup will be utilising the fund to expand its operation to the US, Mauritius, and Singapore markets. With the current funding, Racetrack.ai is valued at $21 Mn. The funding will also be used to launch bot-based kiosks by early next year which will be installed at public places such as hospitals, airports, and shopping complexes to guide customers in order to get the right product/destination. Marvin is a chatbot that provides sales support to clients whereas Turing is an online and offline business accelerator that helps in better business planning and execution. This boost in our physical presence will open new doors for us to partner with intercontinental brands and decision-makers while helping us to create global footprint in the AI industry." It has over 29 corporate clients that include the holiday lifestyle firm Sterling Holidays, online home rental startup Nestaway, and Mantri Developers among others.

Racetrack.ai, an artificial intelligence startup providing solution for sales and support to businesses, has raised around $5 million (33.5 crore) in pre-Series A funding from a clutch of Indian and global investors including Murali Krishna, former President and founding member of Biocon. The Bangalore-based company plans to use the funds to expand its operations in the US, Mauritius and Singapore by tapping business verticals such as BFSI, health and hospitality. "This move will help us delve into newer business verticals like retail, banking and healthcare. This boost in our physical presence will open new doors for us to partner with intercontinental brands and decision makers while helping us to create global footprint in the AI industry,"Subrat Parida, Founder and CEO Racetrack.ai, in a statement. Racetrack offers has two flagship products: Marvin and Tu ring.

Artificial intelligence startup Racetrack.ai said it has secured $5million from a group of high net-worth individuals in a funding round that has valued it at $21 million. "With the fresh capital, we are now looking forward to announce our next overseas operation base in the USA, Mauritius and Singapore," Subrat Parida, founder of Bengaluru-based Racetrack.ai, "We further plan to raise our Series A by the end of this year." The 50-member startup, founded in 2015, offers two flagship products --a chatbot for sales and support, and a business accelerator that helps in better business planning and execution. "This move will help us delve into newer business verticals like retail, banking and healthcare.