--Self-handover, transferring an object between one's own hands, is a common but understudied bimanual action. While it facilitates seamless transitions in complex tasks, the strategies underlying its execution remain largely unexplored. Here, we introduce the first systematic taxonomy of self-handover, derived from manual annotation of over 12 hours of cooking activity performed by 21 participants. Our analysis reveals that self-handover is not merely a passive transition, but a highly coordinated action involving anticipatory adjustments by both hands. As a step toward automated analysis of human manipulation, we further demonstrate the feasibility of classifying self-handover types using a state-of-the-art vision-language model. These findings offer fresh insights into bimanual coordination, underscoring the role of self-handover in enabling smooth task transitions--an ability essential for adaptive dual-arm robotics. UMANS skillfully perform coordinated bimanual actions in everyday life. Among them, self-handover -- transferring an object between one's own hands without intermediate placement--is remarkably common, yet largely overlooked [1], [2]. We define self-handover as the transition from holding an object with one hand to either passing it to the other hand or engaging both hands in manipulation.

The details of second-order partial derivatives of rigid-body Inverse/Forward dynamics are provided. Several properties and identities using Spatial Vector Algebra are listed, along with their detailed derivations. The expressions build upon previous work by the author on first-order partial derivatives of inverse dynamics. The first/second-order derivatives are also extended for systems with external forces. Finally, the KKT Forward dynamics and Impact dynamics derivatives are derived.

There's a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some jobs altogether. But in a study of science laboratories I carried out with three colleagues at the University of Manchester, the introduction of automated processes that aim to simplify work--and free people's time--can also make that work more complex, generating new tasks that many workers might perceive as mundane. In the study, published in Research Policy, we looked at the work of scientists in a field called synthetic biology, or synbio for short. Synbio is concerned with redesigning organisms to have new abilities.

Simple linear regression is a statistical approach that allows us to study and summarize the relationship between two continuous quantitative variables. Simple linear regression is used in machine learning models, mathematics, statistical modeling, forecasting epidemics, and other quantitative fields. Out of the two variables, one variable is called the dependent variable, and the other variable is called the independent variable. Our goal is to predict the dependent variable's value based on the value of the independent variable. A simple linear regression aims to find the best relationship between X (independent variable) and Y (dependent variable).

This tutorial's code is available on Github and its full implementation as well on Google Colab. Simple linear regression is a statistical approach that allows us to study and summarize the relationship between two continuous quantitative variables. Simple linear regression is used in machine learning models, mathematics, statistical modeling, forecasting epidemics, and other quantitative fields. Out of the two variables, one variable is called the dependent variable, and the other variable is called the independent variable. Our goal is to predict the dependent variable's value based on the value of the independent variable.

A new layer-by-layer fabrication process allows researchers to create new and improved soft robot actuators with variable degrees of stiffness. Over the past decade, there has been a growing interest in developing soft robots that mimic nature to make them safer and more compliant with the physical world. Soft robots offer the promise of being able to interact more effectively with unknown objects and surroundings while operating with variable degrees of freedom. However, soft robots' inherent compliance often makes it difficult for them to exert forces on surrounding surfaces or withstand mechanical loading. To circumvent this problem, researchers are investigating and developing new technologies to control and tune the stiffness of soft robotics applications. Nowadays, these technologies are widely implemented to enhance the grasping capabilities of soft actuators or to provide a physical feedback in wearable devices.

On October 10th, our team hosted a live webinar--Simple Distributed Deep Learning Model Inference--with Xiangrui Meng, Software Engineer at Databricks. Model inference, unlike model training, is usually embarrassingly parallel and hence simple to distribute. However, in practice, complex data scenarios and compute infrastructure often make this "simple" task hard to do from data source to sink. In this webinar, we provided a reference end-to-end pipeline for distributed deep learning model inference using the latest features from Apache Spark and Delta Lake. While the reference pipeline applies to various deep learning scenarios, we focused on image applications, and demonstrated specific pain points and proposed solutions.

As we prepare for Scylla Summit 2019, we are producing a series of blogs highlighting this year's featured presenters. And a reminder, if you're not yet registered for Scylla Summit, please take the time to register now! Today we are speaking with Timo Mechler, Product Manager, and Charles Adetiloye, Machine Learning Platform Engineer, both of SmartDeployAI, who will be co-presenting the session Simplifying the Creation of ML Workflow Pipelines for IoT Application on Kubernetes with Scylla. You sure you can fit all that into a single Scylla Summit session? While the title is buzzword heavy, the technologies we are talking about are at the top of everybody's mind today in the Enterprise IT space today.

Chatbots enabled by artificial intelligence that can imitate human conversation are becoming commonplace tools. Google Duplex might be a big thing from the technology viewpoint but it is not yet ready for everyday use. Automation of services started with RPA (Robotic Process Automation), then for customer service, we started seeing chatbots on every website, and now with AI tool integration new generation chatbots are able to perform complex tasks as well. When it comes to the relationship between RPA, chatbot and artificial intelligence there is some widespread sense that all chatbots and automation tools are AI enabled. That might be because some startups are using the hype of artificial intelligence to sell their chatbots and automation tools, and no doubt there are many tricksters in the market.

Will I ever understand the nuances of the advanced analytics landscape? Well, maybe the better question is will the advanced analytics landscape ever stop changing? The advanced analytics landscape, into which I include Deep Learning (DL), Machine Learning (ML), Reinforcement Learning (RL) and Artificial Intelligence (AI), seems to be in a constant state of evolution. New advanced analytic algorithms and tool sets seem to be coming out of every university, every startup, every digital media company and every technology company. And many of these new advanced analytic algorithms and tool sets are open source, which means that they are available for others to build upon.