Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.

Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method -- called WECHSEL -- to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.

From Zac Efron to Margot Robbie, many of the world's most beautiful celebrities are known for their square faces. Now, a new study claims that people with this face shape are seen as more aggressive than those with oval faces, such as Rihanna and Ben Affleck. Researchers from the University of New South Wales measured the facial-width-to-height ratio (FWHR) of 17,607 passport images of male and female faces, before asking people to rate them for aggression. The results revealed that faces with a high FWHR (square faces) were rated as more aggressive than people with low FWHR (oval faces) – particularly if they belonged to young men. From Zac Efron to Margot Robbie, many of the world's most beautiful celebrities are known for their square faces Researchers from the University of New South Wales measured the facial-width-to-height ratio (FWHR) of 17,607 passport images of male and female faces, before asking people to rate them for aggression.

Covid-19 can cause lasting cognitive and mental health issues, including brain fog, fatigue and even post-traumatic stress disorder. To better understand the scale of the problem, researchers at the University of Cambridge analysed 46 people who were hospitalised due to the infection between March and July 2020. The participants underwent cognitive tests on average six months after their initial illness. These results were compared against those of more than 66,000 people from the general population. Those hospitalised with covid-19 scored worse on verbal analogical reasoning tests, which assess an individual's ability to recognise relationships between ideas and think methodically. They also recorded slower processing speeds. Previous studies suggest glucose is less efficiently used by the part of the brain responsible for attention, complex problem-solving and working memory after covid-19. Scores and reaction speeds improved over time, however, any recovery was gradual at best, according to the researchers. This cognitive impairment probably has multiple causes, including inadequate blood supply to the brain, blood vessel blockage and microscopic bleeds caused by SARS-CoV-2 virus, as well as damage triggered by an overactive immune system, they added. "Around 40,000 people have been through intensive care with covid-19 in England alone and many more will have been very sick, but not admitted to hospital," Adam Hampshire at Imperial College London said in a statement. "This means there is a large number of people out there still experiencing problems with cognition many months later." The biological mechanism behind a rare and severe covid-19 response seen in some children may have been uncovered by researchers at the Murdoch Children's Research Institute in Melbourne, Australia. Doctors have so far been unable to identify why some children develop multisystem inflammatory syndrome (MIS) in response to covid-19, which can cause symptoms such as fever, abdominal pain and heart disease.

Twilio powers real-time business communications and data solutions that help companies and developers worldwide build better applications and customer experiences. Although we're headquartered in San Francisco, we have presence throughout South America, Europe, Asia and Australia. We're on a journey to becoming a globally anti-racist, anti-oppressive, anti-bias company that actively opposes racism and all forms of oppression and bias. At Twilio, we support diversity, equity & inclusion wherever we do business. We employ thousands of Twilions worldwide, and we're looking for more builders, creators, and visionaries to help fuel our growth momentum.

The Transformer architecture has led to significant gains in machine translation. However, most studies focus on only sentence-level translation without considering the context dependency within documents, leading to the inadequacy of document-level coherence. Some recent research tried to mitigate this issue by introducing an additional context encoder or translating with multiple sentences or even the entire document. Such methods may lose the information on the target side or have an increasing computational complexity as documents get longer. To address such problems, we introduce a recurrent memory unit to the vanilla Transformer, which supports the information exchange between the sentence and previous context. The memory unit is recurrently updated by acquiring information from sentences, and passing the aggregated knowledge back to subsequent sentence states. We follow a two-stage training strategy, in which the model is first trained at the sentence level and then finetuned for document-level translation. We conduct experiments on three popular datasets for document-level machine translation and our model has an average improvement of 0.91 s-BLEU over the sentence-level baseline. We also achieve state-of-the-art results on TED and News, outperforming the previous work by 0.36 s-BLEU and 1.49 d-BLEU on average.

The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND's design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69\% character error rate and 27.05% mixed error rate.

Bayesian optimisation (BO) has been widely used to solve problems with expensive function evaluations. In multi-objective optimisation problems, BO aims to find a set of approximated Pareto optimal solutions. There are typically two ways to build surrogates in multi-objective BO: One surrogate by aggregating objective functions (by using a scalarising function, also called mono-surrogate approach) and multiple surrogates (for each objective function, also called multi-surrogate approach). In both approaches, an acquisition function (AF) is used to guide the search process. Mono-surrogate has the advantage that only one model is used, however, the approach has two major limitations. Firstly, the fitness landscape of the scalarising function and the objective functions may not be similar. Secondly, the approach assumes that the scalarising function distribution is Gaussian, and thus a closed-form expression of the AF can be used. In this work, we overcome these limitations by building a surrogate model for each objective function and show that the scalarising function distribution is not Gaussian. We approximate the distribution using Generalised extreme value distribution. The results and comparison with existing approaches on standard benchmark and real-world optimisation problems show the potential of the multi-surrogate approach.

There used to be a time where video games were sneered at and overlooked by the culturati as lowbrow schlock but games are, and always have been, a lively and responsive form of artistic expression. It's not always immediately clear when a game was made in Australia, which makes it a little harder to celebrate homegrown hits – which we should do, because we have a thriving community of developers who punch well above their weight. The Australian independent games scene is vibrant, dynamic and overdue an apology. As I have (graciously, selflessly) decided, we're all going to yank games from the declasse and appreciate them properly – so here are 10 great Australian-made games, all variously ruminative, charming, effervescent, sincere, generous, visceral, cheeky, and beautiful. Glad we have that sorted.