A long standing challenge in the engineering sciences is accurately modelling physical systems, most notably when these are described by partial differential equations (PDEs). Highresolution simulations are critical in fields such as automotive and structural engineering [1, 2], where precise modelling of subtle physical behaviours is essential to inform engineering decisions. However, repeated evaluations of high-fidelity simulations using traditional numerical solvers, such as the Finite Element Method (FEM), has high computational costs and significant time requirements. This limitation poses challenges in applications like optimal design, where iterative simulations across varied parameter sets are necessary to achieve optimal configurations, making the process both slow and resource-intensive [3]. With the growing reliance on simulation-based predictions, ensuring computational efficiency alongside accuracy in high-fidelity simulations is paramount. To address some of these challenges, researchers have proposed using super-resolution (SR) techniques, from the field of computer vision [4, 5], to learn a mapping from low-resolution (LR) images to high-resolution (HR) images.

In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable when the problems are ill-conditioned. A typical way to solve ill-conditioned problems is regularization, and a classical regularization method is the Tikhonov regularization. It is shown that the Tikhonov regularization can be seen as a low-order case of our method. We find that the proposed method is superior to the Tikhonov regularization in approximating some ill-conditioned inverse problems, such as robot localization problems. The proposed method overcomes the over-smoothing problem in the Tikhonov regularization as it can use more than one term in the approximation of the matrix inverse, and an explanation for the over-smoothing of the Tikhonov regularization is given. Moreover, one a priori criterion which improves the numerical stability of the ill-conditioned problem is proposed to obtain an optimal regularization matrix. As most of the regularization solutions are biased, we also provide two bias-correction techniques for the proposed high-order regularization. The simulation and experiment results using a sensor network in a 3D environment are discussed, demonstrating the performance of the proposed method.

HR has always been viewed as a paper-intensive, non-innovative area, where salary decisions are made, and people get hired or fired. Adding to the challenges comes the manual work involved in making sense of the reams of information that HR deals with daily. According to CareerBuilder survey, one-third of employers (34 per cent) do not use technology automation for recruiting candidates, 44 per cent do not automate onboarding and 60 per cent do not automate human capital management activities such as payroll, benefits and personnel administration. It is kind of ironic that in the digital era, HR suffers from overly administrative processes. This bureaucracy in the workplace often keeps HR from connecting with employees as people.

One of the beauties of cloud solutions is the opportunity they offer for immediate feedback towards product improvement. Oracle understands this challenge very well, having implemented thousands of enhancements over the past year--with the vast majority of them based on customer opinion. Never let it be said that your voice doesn't matter. The suggestions and ideas offered by customers are among the most important building blocks of a modern HR solution. Oracle's ongoing dialogue with customers ensures that the resulting software resolves the real-world issues that HR professionals grapple with daily.

Each small business has a unique set of problems, but all of them revolve around affordability, and time. With machine learning, small businesses can save on operations cost, make better business decisions, make more profits and save time through automation of workforces, better sales & marketing techniques, more engaged employees, more satisfied customers & demand forecasting through emotion gauging and predictive modeling. To know more about how these ideas work, keep reading this post. It consists of data and algorithms. The algorithms are setup to capture, store, sort and analyze data to solve business problems.

Human resources as a function has experienced significant changes in the last decade due to the evolution of technologies. Today, artificial intelligence (AI) is reshaping the way companies hire, manage and engage with their workforce. Advanced data-driven technology is rapidly making its way into the HR industry as businesses are focusing more on creating an employee-oriented corporate culture. Recruitment is no more a tedious process for HR practitioners as it no longer entails time-consuming activities such as manually screening the resumes of the prospective candidates, making phone calls or replying to candidates via emails. These mundane errands are now managed by smart technologies designed to replicate human conversation, thus enabling HR experts to contemplate the bigger picture.