In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is "long": we allow the number of rows (columns) to grow polynomially in the number of columns (rows). We prove there exists a critical signal-to-noise ratio (depending on the dimensions of the matrix), and the extreme singular values and singular vectors exhibit a BBP type phase transition. As a main application, we investigate the tensor unfolding algorithm for the asymmetric rank-one spiked tensor model, and obtain an exact threshold, which is independent of the procedure of tensor unfolding. If the signal-to-noise ratio is above the threshold, tensor unfolding detects the signals; otherwise, it fails to capture the signals.

We propose a nonparametric factorization approach for sparsely observed tensors. The sparsity does not mean zero-valued entries are massive or dominated. Rather, it implies the observed entries are very few, and even fewer with the growth of the tensor; this is ubiquitous in practice. Compared with the existent works, our model not only leverages the structural information underlying the observed entry indices, but also provides extra interpretability and flexibility -- it can simultaneously estimate a set of location factors about the intrinsic properties of the tensor nodes, and another set of sociability factors reflecting their extrovert activity in interacting with others; users are free to choose a trade-off between the two types of factors. Specifically, we use hierarchical Gamma processes and Poisson random measures to construct a tensor-valued process, which can freely sample the two types of factors to generate tensors and always guarantees an asymptotic sparsity. We then normalize the tensor process to obtain hierarchical Dirichlet processes to sample each observed entry index, and use a Gaussian process to sample the entry value as a nonlinear function of the factors, so as to capture both the sparse structure properties and complex node relationships. For efficient inference, we use Dirichlet process properties over finite sample partitions, density transformations, and random features to develop a stochastic variational estimation algorithm. We demonstrate the advantage of our method in several benchmark datasets.

Agriculture is a sector that is always in the hype. It is one of the most essential parts to keep us all alive. As farmers deal with tough times in monitoring and harvesting crops, new technological trends such as drones are making their work hustle-free. Let's see the top 5 agriculture drones start-ups to know in 2021 Aerobotics is one of the agriculture drones start-ups that are based on farm management and pest management solutions. It offers AI-enabled pest detection, drone imagery services, disease detection, orchard, and yield management.

This week's publication of the Pandora Papers–which the International Consortium of International Journalists based on a trove of private data leaked from offshore tax havens–showcased the alarming extent of fraud and corruption in the world. While big data tech like graph analytics and machine learning can help to a shine light on bad actors, we'll always be playing catch up, fraud hunters tell Datanami. The sheer numbers behind the Pandora Papers, which the ICIJ published on October 3, 2021, are staggering. The ICIJ was provided with 11.9 million documents, including text files, PDFs, images, emails, and spreadsheets, from 14 offshore tax havens, totaling 2.9 TB of data. The documents contained information about 27,000 shell companies created to protect the assets of 29,000 beneficial owners, including 130 billionaires and 330 politicians from 90 countries.

At the outbreak of World War I, the French army was mobilised in the fashion of Napoleonic times. On horseback and equipped with swords, the cuirassiers wore bright tricolour uniforms topped with feathers--the same get-up as when they swept through Europe a hundred years earlier. Vast fields were filled with trenches, barbed wire, poison gas and machine gun fire--plunging the ill-equipped soldiers into a violent hellscape of industrial-scale slaughter. Only three decades after the first World War I bayonet charge across no man's land, the US was able to incinerate entire cities with a single (nuclear) bomb blast. And since the destruction of Hiroshima and Nagasaki in 1945, our rulers' methods of war have been made yet more deadly and "efficient".

Babylon, a world leading digital-first, value-based care company today announced a collaboration to explore opportunities to improve the accessibility, affordability and quality of healthcare for people across the world by using their combined AI, Machine Learning and Cloud technologies. The new collaboration has the aim of exploring opportunities to accelerate and enhance current AI and Machine Learning, utilizing them to shift the focus from sick care to preventative health care. "Babylon and Microsoft working together shows our combined commitment to build on our leading-edge digital health technologies and deliver better access and greater affordability for health systems and patients alike", said Ali Parsa, Chief Executive Officer, Babylon. "We share the same vision of healthcare and believe that by bringing our assets together we can further the digital health revolution, offer immediate access to all-in-one personalized care and we can enhance the consumer experience, improve patient outcomes and reduce overall costs." As part of this relationship, the two organizations will explore opportunities to innovate and deliver across product, cloud and AI research with the view toward increasing the impact of their complementary healthcare technologies and extending the Babylon healthcare platform.

The phrase "overcoming the odds" is an understatement for 24-year-old Joshua Burgess. Though born with congenital rubella syndrome, which has caused him to suffer from a number of health challenges over the years, he continues to break barriers. On September 28, Burgess participated in the prestigious UNESCO Information for All Programme's (IFAP) Second Artificial Intelligence for Information Accessibility (AI4IA) Conference, where he spoke about'Openness and Inclusivity for the Disabled Community in a New Era'. "My presentation reflected my views as a young, blind Jamaican also living with chronic hearing loss. It was important for me to note that, while I have benefited from artificial intelligence's (AI) ability to help me integrate into society, it is also important for us to recognise that it is not a one-size-fits-all. We must collaborate with key stakeholders to ensure openness, inclusivity, fairness, and accessibility for everyone," said Burgess.

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.

Machine question answering is an essential yet challenging task in natural language processing. Recently, Pre-trained Contextual Embeddings (PCE) models like Bidirectional Encoder Representations from Transformers (BERT) and A Lite BERT (ALBERT) have attracted lots of attention due to their great performance in a wide range of NLP tasks. In our Paper, we utilized the fine-tuned ALBERT models and implemented combinations of additional layers (e.g. attention layer, RNN layer) on top of them to improve model performance on Stanford Question Answering Dataset (SQuAD 2.0). We implemented four different models with different layers on top of ALBERT-base model, and two other models based on ALBERT-xlarge and ALBERT-xxlarge. We compared their performance to our baseline model ALBERT-base-v2 + ALBERT-SQuAD-out with details. Our best-performing individual model is ALBERT-xxlarge + ALBERT-SQuAD-out, which achieved an F1 score of 88.435 on the dev set. Furthermore, we have implemented three different ensemble algorithms to boost overall performance. By passing in several best-performing models' results into our weighted voting ensemble algorithm, our final result ranks first on the Stanford CS224N Test PCE SQuAD Leaderboard with F1 = 90.123.

Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.