Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.

Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

Polychromatic liquid crystal laser arrays towards display applications.

Band-edge liquid crystal lasers are of interest for a number of applications including laser projection displays. Herein, we demonstrate simultaneous red-green-blue lasing from a single liquid crystal sample by creating a two-dimensional laser array fabricated from dye-doped chiral nematic liquid crystals. By forming a pitch gradient across the cell, and optically pumping the sample using a lenslet array, a polychromatic laser array can be observed consisting simultaneously of red-green-blue colors. Specifically, the two-dimensional polychromatic array could be used to produce a laser-based display, with low speckle and wide color gamut, whereby no complex fabrication procedure is required to generate the individual 'pixels'.

Motor learning.

Although learning a motor skill, such as a tennis stroke, feels like a unitary experience, researchers who study motor control and learning break the processes involved into a number of interacting components. These components can be organized into four main groups. First, skilled performance requires the effective and efficient gathering of sensory information, such as deciding where and when to direct one's gaze around the court, and thus an important component of skill acquisition involves learning how best to extract task-relevant information. Second, the performer must learn key features of the task such as the geometry and mechanics of the tennis racket and ball, the properties of the court surface, and how the wind affects the ball's flight. Third, the player needs to set up different classes of control that include predictive and reactive control mechanisms that generate appropriate motor commands to achieve the task goals, as well as compliance control that specifies, for example, the stiffness with which the arm holds the racket. Finally, the successful performer can learn higher-level skills such as anticipating and countering the opponent's strategy and making effective decisions about shot selection. In this Primer we shall consider these components of motor learning using as an example how we learn to play tennis.

Multiple color reflection in a single unit cell using double-layer electrochromic reaction

Multiple color states have been realized in single unit cell using double electrochromic (EC) reaction. The precise control of bistability in EC compounds which can maintain several colors on the two separated electrodes allows this new type of pixel to be realized. The specific electrical driving gives a way to maintain both sides in the reduced EC states and this colors overlapping in the vertical view direction can achieve the black state. The four color states (G, B, W, BK) in one cell/pixel can make a valuable progress to achieve a high quality color devices such like electronic paper, outdoor billboard, smart window and flexible display using external light source. © 2012 Optical Society of America.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.