Adaptive process control in rubber industry

This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples

The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest

The common belief about the growing medium of livestreaming is that its value lies in its "live" component. In this paper, we leverage data from a large livestreaming platform to examine this belief. We are able to do this as this platform also allows viewers to purchase the recorded version of the livestream. We summarize the value of livestreaming content by estimating how demand responds to price before, on the day of, and after the livestream. We do this by proposing a generalized Orthogonal Random Forest framework. This framework allows us to estimate heterogeneous treatment effects in the presence of high-dimensional confounders whose relationships with the treatment policy (i.e., price) are complex but partially known. We find significant dynamics in the price elasticity of demand over the temporal distance to the scheduled livestreaming day and after. Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day. Over the post-livestream period, demand is still sensitive to price, but much less than the pre-livestream period. This indicates that the vlaue of livestreaming persists beyond the live component. Finally, we provide suggestive evidence for the likely mechanisms driving our results. These are quality uncertainty reduction for the patterns pre- and post-livestream and the potential of real-time interaction with the creator on the day of the livestream

Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification

The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification. The choice for image descriptor in a visual navigation system is not straightforward. Visual descriptors must be distinctive enough to allow for correct localisation while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work, we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment. We first describe a probabilistic approach to loop detection based on the standard’s suggested similarity metric. We then evaluate the performance of CDVS compression modes in terms of matching speed, feature extraction, and storage requirements and compare them with the state of the art SIFT descriptor for five different types of indoor floors. During the second part of this thesis we focus on the new paradigm to machine learning and computer vision called Deep Learning. Under this paradigm visual features are no longer extracted using fine-grained, highly engineered feature extractor, but rather using a Convolutional Neural Networks (CNN) that extracts hierarchical features learned directly from data at the cost of long training periods. In this context, we propose a method for speeding up the training of Convolutional Neural Networks (CNN) by exploiting the spatial scaling property of convolutions. This is done by first training a pre-train CNN of smaller kernel resolutions for a few epochs, followed by properly rescaling its kernels to the target’s original dimensions and continuing training at full resolution. We show that the overall training time of a target CNN architecture can be reduced by exploiting the spatial scaling property of convolutions during early stages of learning. Moreover, by rescaling the kernels at different epochs, we identify a trade-off between total training time and maximum obtainable accuracy. Finally, we propose a method for choosing when to rescale kernels and evaluate our approach on recent architectures showing savings in training times of nearly 20% while test set accuracy is preserved

Modeling cerebrocerebellar control in horizontal planar arm movements of humans and the monkey

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (leaves 215-236).In daily life, animals including humans make a wide repertoire of limb movements effortlessly without consciously thinking about joint trajectories or muscle contractions. These movements are the outcome of a series of processes and computations carried out by multiple subsystems within the central nervous system. In particular, the cerebrocerebellar system is central to motor control and has been modeled by many investigators. The bulk of cerebrocerebellar control involves both forward command and sensory feedback information inextricably combined. However, it is not yet clear how these types of signals are reflected in spiking activity in cerebellar cells in vivo. Segmentation of apparently continuous movements was first observed more than a century ago. Since then, submovements, which have been identified by non-smooth speed profiles, have been described in many types of movements. However, physiological origins of submovement have not been well understood. This thesis demonstrates that a currently proposed recurrent integrator PID (RIPID) cerebellar limb control model (Massaquoi 2006a) is consistent with average neural activity recorded in a monkey by developing the Recurrent Integrator-based Cerebellar Simple Spike (RICSS) model.(cont.) The RICSS formulation is consistent with known or plausible cerebrocerebellar and spinocerebellar neurocircuitry, including hypothetical classification of mossy fiber signals. The RICSS model accounts well for variety of cerebellar simple spike activity recorded from the monkey and outperforms any other existing models. The RIPID model is extended to include a simplified cortico-basal ganglionic loop to capture statistical characterization of intermittency observed in individual trials of the monkey. In order to extend the capability of the RIPID model to a larger workspace and faster movements, the model needs to be gainscheduled based on the local state information. A linear parameter varying (LPV) formulation, which shares a similar structure to that suggested by the RICSS model, is performed and its applicability was tested on human subjects performing double step tasks which requires rapid change in movement directions.by Kazutaka Takahashi.Ph.D

Top-Down Selection in Convolutional Neural Networks

Feedforward information processing fills the role of hierarchical feature encoding, transformation, reduction, and abstraction in a bottom-up manner. This paradigm of information processing is sufficient for task requirements that are satisfied in the one-shot rapid traversal of sensory information through the visual hierarchy. However, some tasks demand higher-order information processing using short-term recurrent, long-range feedback, or other processes. The predictive, corrective, and modulatory information processing in top-down fashion complement the feedforward pass to fulfill many complex task requirements. Convolutional neural networks have recently been successful in addressing some aspects of the feedforward processing. However, the role of top-down processing in such models has not yet been fully understood. We propose a top-down selection framework for convolutional neural networks to address the selective and modulatory nature of top-down processing in vision systems. We examine various aspects of the proposed model in different experimental settings such as object localization, object segmentation, task priming, compact neural representation, and contextual interference reduction. We test the hypothesis that the proposed approach is capable of accomplishing hierarchical feature localization according to task cuing. Additionally, feature modulation using the proposed approach is tested for demanding tasks such as segmentation and iterative parameter fine-tuning. Moreover, the top-down attentional traces are harnessed to enable a more compact neural representation. The experimental achievements support the practical complementary role of the top-down selection mechanisms to the bottom-up feature encoding routines

Organ preservation in rectal cancer:Capita Selecta

The standard operative treatment for patients with rectal cancer has a high risk for morbidity and (perioperative) mortality. Patients with an extensive tumor undergo preoperative (chemo)radiation to reduce the tumor and potential lymph node metastases. In 15-25% of patients the tumor has completely disappeared, also called a 'complete response'. Patients with a complete response may undergo organ preservation as an alternative treatment. This thesis describes several topics regarding organ preservation in rectal cancer. Methods are described to optimize the patient selection. In addition, a new follow-up schedule is proposed which is potentially more efficient and less burdensome for patients. Also, the oncological outcomes of patients after organ preservation are compared to patients after surgery. Finally, this thesis guides future research with an emphasis on exploration of new techniques to enable more accurate response prediction and assessment in rectal cancer