We utilize classical facts from topology to show that the classification problem in machine learning is always solvable under very mild conditions. Furthermore, we show that a softmax classification network acts on an input topological space by a finite sequence of topological moves to achieve the classification task. Moreover, given a training dataset, we show how topological formalism can be used to suggest the appropriate architectural choices for neural networks designed to be trained as classifiers on the data. Finally, we show how the architecture of a neural network cannot be chosen independently from the shape of the underlying data. To demonstrate these results, we provide example datasets and show how they are acted upon by neural nets from this topological perspective.

A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the criteria' time series since providing essential information for decision-making (e.g., an improvement of the criteria). To deal with this issue, we propose a new approach to rank the alternatives based on the criteria time-series features (tendency, variance, etc.). In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the \textit{tensors}, instead of the classical matrix representation used in MCDA. Consequently, we propose an extension for the TOPSIS method to handle a tensor rather than a matrix. Computational results reveal that it is possible to rank the alternatives from a new perspective by considering meaningful decision-making information.

Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of our students (learners across 38 African countries), in this work, we developed an AI Teaching Assistant (Kwame) that provides answers to students' coding questions from our SuaCode courses in English and French. Kwame is a Sentence-BERT(SBERT)-based question-answering (QA) system that we trained and evaluated using question-answer pairs created from our course's quizzes and students' questions in past cohorts. It finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that SBERT performed the worst for the duration of 6 secs per question but the best for accuracy and fine-tuning on our course data improved the result.

Simultaneous speech-to-speech translation is widely useful but extremely challenging, since it needs to generate target-language speech concurrently with the source-language speech, with only a few seconds delay. In addition, it needs to continuously translate a stream of sentences, but all recent solutions merely focus on the single-sentence scenario. As a result, current approaches accumulate latencies progressively when the speaker talks faster, and introduce unnatural pauses when the speaker talks slower. To overcome these issues, we propose Self-Adaptive Translation (SAT) which flexibly adjusts the length of translations to accommodate different source speech rates. At similar levels of translation quality (as measured by BLEU), our method generates more fluent target speech (as measured by the naturalness metric MOS) with substantially lower latency than the baseline, in both Zh <-> En directions.

Drones have already shown that they can reliably deliver vital shipments of blood across Rwanda, drop off prescriptions to senior citizens in Florida, and help quarantining families stay safe with contactless deliveries. Now they're going to be buzzing through the skies of rural North Carolina thanks to a novel delivery service devised by drug-maker Merck and drone-maker Volansi. The plan is simple: use Volansi's 7-foot long "Gemini" quadcopter to ferry packages of cold chain medicines -- such as vaccines, glaucoma treatments, insulin, and asthma inhalers -- from Merck's Wilson, NC drug lab to the nine regional hospitals that make up Vidant Healthplex-Wilson. This medical network serves more than 1.4 million people across 29 counties in eastern North Carolina. "We've seen the world's supply chain strained like never before from the impact of Coronavirus," said Hannan Parvizian, CEO of Volansi, said in a press statement.

'Hillbilly Elegy' author J.D. Vance responds to suggestion on'Tucker Carlson Tonight' The idea, mooted by some Democrats and liberals, of a South Africa-style Truth and Reconciliation Commission after President Trump's term of office of complete would be less about reconciliation than "revenge," author J.D. Vance told "Tucker Carlson Tonight" Monday. Former Labor Secretary Robert Reich tweeted Saturday that such a commission would "erase Trump's lies, comfort those who have been harmed by his hatefulness, and name every official, politician, executive, and media mogul whose greed and cowardice enabled this catastrophe." "This is torn from a page in a George Orwell novel ... because who can protest'truth and reconciliation'," stated Vance, the author of "Hillbilly Elegy." Vance added that the idea would not only damage the country, but shows how "whiny" liberal Democrats still are about Hillary Clinton's 2016 election loss. "Instead of trying to win the next election and moving on with the life of American democratic politics, they want to go backward and punish everybody," Vance said.

Encoding classical inputs into quantum states is considered a quantum feature map to map classical data into a quantum Hilbert space. This feature map provides opportunities to incorporate quantum advantages into machine learning algorithms to be performed on near-term intermediate-scale quantum computers. While the quantum feature map has demonstrated its capability when combined with linear classification models in some specific applications, its expressive power from the theoretical perspective remains unknown. We prove that the quantum feature map is a universal approximator of continuous functions under its typical settings in many practical applications. We also study the capability of the quantum feature map in the classification of disjoint regions. Our work enables an important theoretical analysis to ensure that quantum-enhanced machine learning algorithms based on quantum feature maps can handle a broad class of machine learning tasks. In light of this, one can design a quantum machine learning model with more powerful expressivity.

Research over the last five years shows a clear improvement in computer-aided detection (CAD), specifically in disease prediction from medical images [36, 61, 105, 130, 134] as well as from Electronic Health Records (EHR) [113], by using deep neural networks (DNN) and treating the problem as supervised classification or segmentation tasks. Recently, Topol [129] indicates that the need for diagnosis and reporting from image-based examinations far exceeds the current medical capacity of physicians in the US. This situation promotes the development of automatic image-based diagnosis as well as automatic reporting. Furthermore, the lack of specialist physicians is even more critical in resource-limited countries [111], and therefore the expected impacts of this technology would become even more relevant. However, the elaboration of high-quality medical reports from medical images, such as chest X-rays, computed tomography (CT) or magnetic resonance (MRI) scans, is a task that requires a trained radiologist with years of experience.

Tucker decomposition is one of the most popular models for analyzing and compressing large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single solver to compute the factor matrices and core tensor, and are not flexible enough to adapt with the diversities of the input data and the hardware. Moreover, to exploit highly efficient GEMM kernels, most Tucker decomposition implementations make use of explicit matricizations, which could introduce extra costs in terms of data conversion and memory usage. In this paper, we present a-Tucker, a new framework for input-adaptive and matricization-free Tucker decomposition of dense tensors. A mode-wise flexible Tucker decomposition algorithm is proposed to enable the switch of different solvers for the factor matrices and core tensor, and a machine-learning adaptive solver selector is applied to automatically cope with the variations of both the input data and the hardware. To further improve the performance and enhance the memory efficiency, we implement a-Tucker in a fully matricization-free manner without any conversion between tensors and matrices. Experiments with a variety of synthetic and real-world tensors show that a-Tucker can substantially outperform existing works on both CPUs and GPUs.

The news: Facebook is open-sourcing a new AI language model called M2M-100 that can translate between any pair among 100 languages. This is in contrast to previous multilingual models, which heavily rely on English as an intermediate. A Chinese to French translation, for example, typically passes from Chinese to English and then English to French, which increases the chance of introducing errors. Data curation: The model was trained on 7.5 billion sentence pairs. In order to compile a data set that large, the researchers relied heavily on automated curation.