Bonobos are our closest relatives in the animal kingdom, sharing about 98.7 per cent of our DNA - and it seem they have picked up a few human-like characteristics along the way. A new study has revealed that the apes produce high-pitched'baby-like' cries when they are attacked, in order to attract comfort from others. These displays of distress are strategic, increasing their chances of receiving consolation from bonobo bystanders, scientists claim. They resemble those typically used by infants - such as pouting, whimpering and showing tantrums. The study by psychologists at Durham University reveals that adult bonobos are also less likely to be re-attacked by their former opponent when they display these'baby-like' signals following a conflict.

There are thousands of species on Earth that we still don't know much about -- but we now know that they are already teetering on the edge of extinction. A new study used machine learning to figure out just how threatened these lesser-known species are, and the results were grim. Some species of animals and plants are labeled "data deficient" because conservationists haven't been able to gather enough information about them to understand how they live or how many of them are left. It turns out that those "data deficient" species are unfortunately even more threatened than other species that are more well known (to scientists, at least). The data from this study came from the International Union for Conservation of Nature (IUCN), which maintains a global "Red List" that ranks species based on how threatened they are.

ATLANTA, GA - AUGUST 1: Michael Johnson of the US (L) poses for the press next to the clock after ... [ ] the men's Olympic 400m race at the Olympic Stadium in Atlanta, Georgia, 01 Aug. Johnson hurtled into history books in world record time as he completed an unprecedented Olympic double with the 400m and the 200m. Johnson clocked 19.32 sec to destroy Frankie Fredericks of Namibia (19.68) and Ato Boldon of Trinidad (19.80). Not just in terms of gadget miniaturization, medical nanotechnology, increasingly sophisticated industrial electromechanical units and the process of so-called shrinkflation that leads our candy bars be thinner or shorter at the same price, but also data - data is getting smaller too. Data is getting smaller in two key senses: a) we are breaking down the component parts of application data flows into smaller containerized elements to work inside similarly compartmentalized and containerized application services – and b) the time windows within which business needs to react to data events is reducing. This latter time constraint on data of course leads us to the reality of real-time data and the need to be able to work with it.

Sharjah Taxi, a subsidiary of Sharjah Asset Management Company, investment arm of the government of Sharjah, has launched the first-of-its-kind smart taxi in the Middle East, with the chief goal being to employ artificial intelligence services to be utilised in vehicle operations and safety. The smart vehicles have been fitted with sensors, cameras, a mobile data unit and other devices connected to an'integrated system for control mechanisms'. Intelligent transport systems use contemporary technology in the areas of surveillance, data collection, control and means of communication. The arrangement helps regulate the flow of traffic, simplify access to key places, oversee driver behaviour, lessen wrong practices, and reduce metre manipulations. As a result, the number of trips and total squandered kilometres are reduced, ultimately decreasing the rate of road accidents and pollution, whilst raising operational competence.

We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e.. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for our motivating application. Software in the form of R code is available at https://github.com/BiostatsKim/BayesMSMW .

In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independently train subparts of a new class of LLMs on different subsets of the data, eliminating the massive multi-node synchronization currently required to train LLMs. BTM learns a set of independent expert LMs (ELMs), each specialized to a different textual domain, such as scientific or legal text. These ELMs can be added and removed to update data coverage, ensembled to generalize to new domains, or averaged to collapse back to a single LM for efficient inference. New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use. Experiments show that BTM improves in- and out-of-domain perplexities as compared to GPT-style Transformer LMs, when controlling for training cost. Through extensive analysis, we show that these results are robust to different ELM initialization schemes, but require expert domain specialization; LM ensembles with random data splits do not perform well. We also present a study of scaling BTM into a new corpus of 64 domains (192B whitespace-separated tokens in total); the resulting LM (22.4B total parameters) performs as well as a Transformer LM trained with 2.5 times more compute. These gains grow with the number of domains, suggesting more aggressive parallelism could be used to efficiently train larger models in future work.

As the autonomous driving industry is slowly maturing, visual map localization is quickly becoming the standard approach to localize cars as accurately as possible. Owing to the rich data returned by visual sensors such as cameras or LiDARs, researchers are able to build different types of maps with various levels of details, and use them to achieve high levels of vehicle localization accuracy and stability in urban environments. Contrary to the popular SLAM approaches, visual map localization relies on pre-built maps, and is focused solely on improving the localization accuracy by avoiding error accumulation or drift. We define visual map localization as a two-stage process. At the stage of place recognition, the initial position of the vehicle in the map is determined by comparing the visual sensor output with a set of geo-tagged map regions of interest. Subsequently, at the stage of map metric localization, the vehicle is tracked while it moves across the map by continuously aligning the visual sensors' output with the current area of the map that is being traversed. In this paper, we survey, discuss and compare the latest methods for LiDAR based, camera based and cross-modal visual map localization for both stages, in an effort to highlight the strength and weakness of each approach.

Past approaches to translate a phrase in a language L1 to a language L2 using a dictionary-based approach require grammar rules to restructure initial translations. This paper introduces a novel method without using any grammar rules to translate a given phrase in L1, which does not exist in the dictionary, to L2. We require at least one L1-L2 bilingual dictionary and n-gram data in L2. The average manual evaluation score of our translations is 4.29/5.00, which implies very high quality.