Machine Learning and Data Science Project Management From an Agile Perspective

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.

Sliding Mode Control for a DC Motor System with Dead-Zone

Due to the simple structure of DC motors, the natural decoupling between torque and speed, and its low cost the DC motors have been widely used in electromechanical systems, the paper deals with the experimental method of DC motor Coulomb friction identification that caused the dead nonlinear zone and proposed a nonlinear model of the DC motor, then a sliding mode strategy is developed to control the DC motor in high and low speed for bidirectional operation, The experimental implementation using Dspace 1104 demonstrate that the proposed sliding mode control can achieve favorable tracking performance against non-linearities for a DC motor.

A novel theoretical framework for simultaneous measurement of excitatory and inhibitory conductances

The firing of neurons throughout the brain is determined by the precise relations between excitatory and inhibitory inputs, and disruption of their balance underlies many psychiatric diseases. Whether or not these inputs covary over time or between repeated stimuli remains unclear due to the lack of experimental methods for measuring both inputs simultaneously. We developed a new analytical framework for instantaneous and simultaneous measurements of both the excitatory and inhibitory neuronal inputs during a single trial under current clamp recording. This can be achieved by injecting a current composed of two high frequency sinusoidal components followed by analytical extraction of the conductances. We demonstrate the ability of this method to measure both inputs in a single trial under realistic recording constraints and from morphologically realistic CA1 pyramidal model cells. Future experimental implementation of our new method will facilitate the understanding of fundamental questions about the health and disease of the nervous system.

Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

An Experimental Implementation of a Resilient Graphic Rendering Cluster

In this paper, we describe the challenge of developing a web front that will give an interactive and relatively immediate result without the overhead of complex grid scheduling, in the sense of the grid’s lack of interactivity and need for certificates that users simply do not own. In particular, the local system of issuing grid certificates is somewhat limited to a narrower community compared to that which we wanted to reach in order to popularize the grid, and our desired level of service availability exceeded the use of the cluster for grid purposes. Therefore, we have developed an interactive, scalable web front and back-end animation rendering frame dispatcher to access our cluster’s rendering power with low latency, low overhead and low performance penalty added to the cost of Persistence of Vision Ray rendering. The system is designed to survive temporary or catastrophic failures such as temporary power loss, load shedding, malfunction of rendering server cluster or client hardware, whether through an automatic or a manual restart, as long as the hardware that keeps the previous work and periodically dumped state of the automata is preserved.

Negative refractive index in a Doppler broadened three-level Λ-type atomic medium

We have achieved a negative refractive index with significantly reduced absorption in a three-level Λ-type atomic gas medium under Doppler broadening. It shows that the conditions for obtaining negative refractive index in the presence of Doppler broadening are very different from those of Doppler broadening absent. In particular, in order to obtain negative refractive index in the case of Doppler broadening the coupling laser intensity must be approximately ten times greater than that when the Doppler broadening is ignored. Meanwhile, the frequency band of negative refractive index with Doppler broadening is significantly expanded (about a hundred times) compared to that without Doppler broadening, however, the amplitude of negative refractive index decreases with increasing temperature (or Doppler width). Even in some cases as temperature (Doppler width) increases, the left-handedness of the material can disappear. In addition, we also show that the amplitude and the frequency band of negative refractive index can be changed by adjusting the intensity and the frequency of coupling laser. Our theoretical investigation can be useful for selection of laser parameters under different temperature conditions to achieve negative refractive index in experimental implementation.

Experimental critical quantum metrology with the Heisenberg scaling

Critical quantum metrology, which exploits quantum critical systems as probes to estimate a physical parameter, has gained increasing attention recently. However, the critical quantum metrology with a continuous quantum phase transition (QPT) is experimentally challenging since a continuous QPT only occurs at the thermodynamic limit. Here, we propose an adiabatic scheme on a perturbed Ising spin model with a first-order QPT. By introducing a small transverse magnetic field, we can not only encode an unknown parameter in the ground state but also tune the energy gap to control the evolution time of the adiabatic passage. Moreover, we experimentally implement the critical quantum metrology scheme using nuclear magnetic resonance techniques and show that at the critical point the precision achieves the Heisenberg scaling as 1/T. As a theoretical proposal and experimental implementation of the adiabatic scheme of critical quantum metrology and its advantages of easy implementation, inherent robustness against decays and tunable energy gap, our adiabatic scheme is promising for exploring potential applications of critical quantum metrology on various physical systems.