Efficient Asymmetric Co-Tracking using Uncertainty Sampling

Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to update the object model. However, these approaches are heavily invested in the efficiency and effectiveness of their detectors. Evaluating a massive number of samples for each frame (e.g., obtained by a sliding window) forces the detector to trade the accuracy in favor of speed. Furthermore, misclassification of borderline samples in the detector introduce accumulating errors in tracking. In this study, we propose a co-tracking based on the efficient cooperation of two detectors: a rapid adaptive exemplar-based detector and another more sophisticated but slower detector with a long-term memory. The sampling labeling and co-learning of the detectors are conducted by an uncertainty sampling unit, which improves the speed and accuracy of the system. We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response. Experiments demonstrate the efficiency and effectiveness of the proposed tracker against its baselines and its superior performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Probability of informed trading and volatility for an ETF

We use the new procedure developed by Easley et al. to estimate the Probability of Informed Trading (PIN), based on the volume imbalance: Volume-Synchronized Probability of Informed Trading (VPIN). Unlike the previous method, this one does not require the use of numerical methods to estimate unobservable parameters. We also relate the VPIN metric to volatility measures. However, we use most efficient estimators of volatility which consider the number of jumps. Moreover, we add the VPIN to a Heterogeneous Autoregressive model of Realized Volatility to further investigate its relation with volatility. For the empirical analysis we use data on the exchange traded fund (SPY)

Speech rhythms and multiplexed oscillatory sensory coding in the human brain

Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations

Active Collaboration of Classifiers for Visual Tracking

Recently, discriminative visual trackers obtain state-of-the-art performance, yet they suffer in the presence of different real-world challenges such as target motion and appearance changes. In a discriminative tracker, one or more classifiers are employed to obtain the target/nontarget label for the samples, which in turn determine the target’s location. To cope with variations of the target shape and appearance, the classifier(s) are updated online with different samples of the target and the background. Sample selection, labeling, and updating the classifier are prone to various sources of errors that drift the tracker. In this study, we motivate, conceptualize, realize, and formalize a novel active co-tracking framework, step by step to demonstrate the challenges and generic solutions for them. In this framework, not only classifiers cooperate in labeling the samples but also exchange their information to robustify the labeling, improve the sampling, and realize efficient yet effective updating. The proposed framework is evaluated against state-of-the-art trackers on public dataset and showed promising results

A short note on the problematic concept of excess demand in asset pricing models with mean-variance optimization

Referring to asset pricing models where demand is proportional to excess returns and said to be derived from a mean-variance optimization problem, the note formulates what probably is common knowledge but hardly ever made an explicit subject of discussion. This is an insufficient distinction between the desired holding of the risky asset on the part of the speculative agents, which is the solution to the optimization problem and usually directly presented as excess demand, and the desired change in this holding, which is what should reasonably constitute the excess demand on the market. The note arrives at the conclusion that in models with a market maker the story of the maximization of expected wealth should be dropped