A robotic feeding system must be able to acquire a variety of foods. Prior bite acquisition works consider single-arm spoon scooping or fork skewering, which do not generalize to foods with complex geometries and deformabilities. For example, when acquiring a group of peas, skewering could smoosh the peas while scooping without a barrier could result in chasing the peas on the plate. In order to acquire foods with such diverse properties, we propose stabilizing food items during scooping using a second arm, for example, by pushing peas against the spoon with a flat surface to prevent dispersion. The added stabilizing arm can lead to new challenges. Critically, this arm should stabilize the food scene without interfering with the acquisition motion, which is especially difficult for easily breakable high-risk food items like tofu. These high-risk foods can break between the pusher and spoon during scooping, which can lead to food waste falling out of the spoon. We propose a general bimanual scooping primitive and an adaptive stabilization strategy that enables successful acquisition of a diverse set of food geometries and physical properties. Our approach, CARBS: Coordinated Acquisition with Reactive Bimanual Scooping, learns to stabilize without impeding task progress by identifying high-risk foods and robustly scooping them using closed-loop visual feedback. We find that CARBS is able to generalize across food shape, size, and deformability and is additionally able to manipulate multiple food items simultaneously. CARBS achieves 87.0% success on scooping rigid foods, which is 25.8% more successful than a single-arm baseline, and reduces food breakage by 16.2% compared to an analytical baseline. Videos can be found at https://sites.google.com/view/bimanualscoop-corl22/home .

This paper presents a compact, matrix-based representation of neural networks in a self-contained tutorial fashion. Specifically, we develop neural networks as a composition of several vector-valued functions. Although neural networks are well-understood pictorially in terms of interconnected neurons, neural networks are mathematical nonlinear functions constructed by composing several vector-valued functions. Using basic results from linear algebra, we represent a neural network as an alternating sequence of linear maps and scalar nonlinear functions, also known as activation functions. The training of neural networks requires the minimization of a cost function, which in turn requires the computation of a gradient. Using basic multivariable calculus results, the cost gradient is also shown to be a function composed of a sequence of linear maps and nonlinear functions. In addition to the analytical gradient computation, we consider two gradient-free training methods and compare the three training methods in terms of convergence rate and prediction accuracy.

It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis.

Quantum kernel methods, i.e., kernel methods with quantum kernels, offer distinct advantages as a hybrid quantum-classical approach to quantum machine learning (QML), including applicability to Noisy Intermediate-Scale Quantum (NISQ) devices and usage for solving all types of machine learning problems. Kernel methods rely on the notion of similarity between points in a higher (possibly infinite) dimensional feature space. For machine learning, the notion of similarity assumes that points close in the feature space should be close in the machine learning task space. In this paper, we discuss the use of variational quantum kernels with task-specific quantum metric learning to generate optimal quantum embeddings (a.k.a. quantum feature encodings) that are specific to machine learning tasks. Such task-specific optimal quantum embeddings, implicitly supporting feature selection, are valuable not only to quantum kernel methods in improving the latter's performance, but they can also be valuable to non-kernel QML methods based on parameterized quantum circuits (PQCs) as pretrained embeddings and for transfer learning. This further demonstrates the quantum utility, and quantum advantage (with classically-intractable quantum embeddings), of quantum kernel methods.

A personal, guided tour to the best scoops and stories every day in The Wall Street Journal. Dr. Frederick P. Brooks Jr. liked building things, first laying foundations for modern computer systems at International Business Machines Corp. and later at the University of North Carolina, where he started the computer-science department. Dr. Brooks managed the development of IBM's System/360 family of compatible mainframe computers and then the software system that went with them during the 1960s. The computers became some of IBM's most popular models of the era, offering customers a choice of big or small computers with different processing speeds that could be used for both business and scientific tasks. The system was easy to expand since all the hardware ran off the same software, a departure from other systems that required software reprogramming when computers were added.

Deep Learning is one of the cornerstones of modern Artificial Intelligence advancements. The increasingly powerful computation machines called GPUs have made it possible to build vision and language models that in some (a lot) instances outperform humans. The free availability of GPU resources in the cloud has democratised (a little bit) the access to this technological revolution of our days and has bestowed upon everyone the power to develop systems that solve daily problems, enhance their business capabilities or even nurture one's soul by creating fantastic pieces of art. Language models such as GPT-3 and BERT, diffusion models such as DALL-E and computer vision models such as Visual Transformers have really had a huge impact in the field demonstrating the immense possibilities of AI and specifically Deep Learning. No doubt Deep Learning is one of the hottest topics in AI right now.

Bigdata a buzzword itself suggest how big and voluminous your data is. To accommodate those data you require a huge storage.In recent times, big data has acquired almost every sector of the world. Even the current market trends are of Bigdata and analytics. BigData is just like an ocean in which you have many areas to learn and earn from it. Python programming is a general-purpose programming language that is open source, flexible, robust and simple.

The Korea Advanced Institute of Science and Technology (KAIST) was established in 1971 by the Korean government as the nation's first research-intensive graduate school for science, engineering and technology. It has now grown into one of the world's best universities, delivering top notch education and research programs for undergraduate and graduate students. KAIST encourages interdisciplinary and convergent research across a wide spectrum of disciplines, as well as strong collaborations with industry and global institutions.

Four University of South Australia researchers have been awarded ARC Discovery grants collectively worth $1.8 million, for projects starting in 2023. The project will investigate the ways in which existing Australian induction policies support "precariously employed" early career teachers – those on casual and short-term contracts – to effectively manage student classroom behaviour. "We hope to propose alternative policy and practice recommendations to support the transition of insecure replacement teachers within the profession," Prof Sullivan says. "The benefits of this research include improving teachers' classroom management practices; the retention of new teachers; improving teacher workforce development; and building a healthier education system." Australia's 117,000 km of concrete sewer pipes are currently internally corroding at a depth rate of 1-3 mm per annum.

Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual learning settings. We also introduce a new method called Critical Freezing based on the observations of our tool. Experiments on ResNet articulate how catastrophic forgetting happens, particularly showing which components of this famous network are forgetting. Our new continual learning algorithm defeats various recent techniques by a significant margin, proving the capability of the investigation. Critical freezing not only attacks catastrophic forgetting but also exposes explainability.