In the first study of its kind in cancer, researchers have applied artificial intelligence to measure the amount of muscle in patients with brain tumours to help improve prognosis and treatment. Dr Ella Mi, a clinical research fellow at Imperial College London (UK) told the NCRI Virtual Showcase, that using deep learning to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker. Furthermore, her research showed that the amount of muscle measured in this way could be used to predict how long a patient might survive their disease as it was an indicator of a patient's overall condition. Glioblastoma is an aggressive brain tumour that is very difficult to treat successfully. Average survival after diagnosis is 12-18 months and fewer than 5% of patients are still alive after five years.

Fabrizio Poltronieri is an artist who explores the relationship technology and deep-rooted philosophical concepts, such as chance. His current artwork involves Artificial Intelligence, applying machine and deep learning techniques to create and design narratives, moving images and objects. He is a self-taught programmer who started to code during his childhood. His first degree was in Maths, he has a Master Degree in Education and Culture and holds a PhD in Semiotics from the Pontifical Catholic University of São Paulo (PUC/SP). Poltronieri is an Associate Professor and permanent member of the IOCT (Institute of Creative Technologies) at De Montfort University, Leicester, UK, supervising PhD students and teaching creative code in the Digital Arts MA.

Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment to facilitate informed decisions that may sustain the economic viability of the farm. Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries. We develop drone-based field data and ground-based sky data collection systems to collect video imagery at multiple time points for use in crop health analysis. Extensive evaluation on the data set shows that it is possible to predict exposed fruit's inner temperature with high accuracy (0.02% MAPE). The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon. With 62.54% mIoU for segmentation and 13.46 MAE for counting accuracies in exposed fruit identification, this system is capable of giving informed feedback to growers to take precautionary action (e.g. irrigation) in identified crop field regions with higher risk of sunburn in the near future. Though this novel system is applied for cranberry health monitoring, it represents a pioneering step forward for efficient farming and is useful in precision agriculture beyond the problem of cranberry overheating.

The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supervised methods, especially those based on deep learning, are attractive for achieving better performance on the problem of query segmentation. However, the lack of labeled data is still a big challenge for training a deep segmentation network, and the problem of Out-of-Vocabulary (OOV) also adversely impacts the performance of query segmentation. Different from query segmentation task in an open domain, e-commerce scenario can provide external documents that are closely related to these queries. Thus, to deal with the two challenges, we employ the idea of distant supervision and design a novel method to find contexts in external documents and extract features from these contexts. In this work, we propose a BiLSTM-CRF based model with an attention module to encode external features, such that external contexts information, which can be utilized naturally and effectively to help query segmentation. Experiments on two datasets show the effectiveness of our approach compared with several kinds of baselines.

Reinforcement learning (RL) has achieved phenomenal success in games and robotics [,, ] in the past decade, which also stimulates the enthusiasm of extending these techniques in other areas including healthcare [, ], education [ ], autonomous driving [ ], recommendation systems [, ], etc. One of the major challenges in applying RL to these real-world applications, especially those involve high-stake environments, is the problem of o -policy evaluation (OPE): how one can evaluate a new policy before deployment, using only historical data collected from a di erent policy, known as the behavior policy. Indeed, for many practical applications, one may not have a faithful simulator of the domain from which su cient amount of data can be exploited to train the RL system, and it may not always be feasible to try out a new policy without causing unintended harms. For example, consider the problem of finding the best treatment plan for a patient, or testing the performance of an automated driving system, or suggesting a personalized curriculum for a student. In those tasks, conducting experimentation involves interactions with real people, thus it can be costly to collect data and even worse, a bad policy can be risky or unethical and may result in severe consequences. Therefore, it is important for the RL system to have the ability to predict how well a new policy would perform without having to deploy it first. While most existing works on OPE aim to provide accurate point estimates for short-horizon problems [,,, ] as well as long-or infinite-horizon problems [,,,,, ], it is equally important to quantify the uncertainty of the OPE point estimates for both safe exploration and optimistic planning.

A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approach uses Clopper-Pearson confidence intervals for the Binomial distribution in conjunction with the histogram binning approach to calibrated prediction. In addition, we demonstrate how our predicted confidences can be used to enable downstream guarantees in two settings: (i) fast DNN inference, where we demonstrate how to compose a fast but inaccurate DNN with an accurate but slow DNN in a rigorous way to improve performance without sacrificing accuracy, and (ii) safe planning, where we guarantee safety when using a DNN to predict whether a given action is safe based on visual observations. In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs. Due to the recent success of machine learning, there has been increasing interest in using predictive models such as deep neural networks (DNNs) in safety-critical settings, such as robotics (e.g., obstacle detection (Ren et al., 2015) and forecasting (Kitani et al., 2012)) and healthcare (e.g., diagnosis (Gulshan et al., 2016; Esteva et al., 2017) and patient care management (Liao et al., 2020)).

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.

Min-max optimization of an objective function $f: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}$ is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics, and deep learning. In many of these applications $f$ may be nonconvex-nonconcave, and finding a global min-max point may be computationally intractable. There is a long line of work that seeks computationally tractable algorithms for alternatives to the min-max optimization model. However, many of the alternative models have solution points which are only guaranteed to exist under strong assumptions on $f$, such as convexity, monotonicity, or special properties of the starting point. In this paper, we propose an optimization model, the $\varepsilon$-greedy adversarial equilibrium, which can serve as a computationally tractable alternative to the min-max optimization model. Roughly, we say a point $(x^\star, y^\star)$ is an $\varepsilon$-greedy adversarial equilibrium if $y^\star$ is an $\varepsilon$-approximate local maximum for $f(x^\star,\cdot)$, and $x^\star$ is an $\varepsilon$-approximate local minimum for a "greedy approximation" to the function $\max_z f(x, z)$ which can be efficiently estimated using second-order optimization algorithms. The existence follows from an algorithm that converges from any starting point to such a point in a number of evaluations to $f$, $\nabla_{y} f(x,y)$, and $\nabla^2_y f(x,y)$, that is polynomial in $1/\varepsilon$, the dimension $d$, and the bounds on $f$ and its Lipschitz constant. In addition to existence, our model retains many desirable properties of the min-max model. For instance, it empowers the min-player to make updates that take into account the max-player's response, and in the case of strong convexity/concavity it corresponds to a global min-max solution with duality gap $O(\epsilon^2)$.

We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dynamical system \emph{and} its parameters, we modularize the design of our engine using a discrete form of the governing equations of motion, similar to a traditional physics engine. We further reduce the dimension from 3D to 1D for each module, which allows efficient learning of system parameters using linear regression. The regression parameters correspond to physical quantities, such as spring stiffness or the mass of the rod, making the pipeline explainable. The approach significantly reduces the amount of training data required, and also avoids iterative identification of data sampling and model training. We compare the performance of the proposed engine with previous solutions, and demonstrate its efficacy on tensegrity systems, such as NASA's icosahedron.

We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a $\beta$-variational auto-encoder ($\beta$-VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability. Our code is available on https://github.com/IyatomiLab/GDCE-SSA