Deep neural networks (DNNs) allow digital receivers to operate in complex environments by learning from data corresponding to the channel input-output relationship. Since communication channels change over time, DNN-aided receivers may be required to retrain periodically, which conventionally involves excessive pilot signaling at the cost of reduced spectral efficiency. In this paper, we study how one can obtain data for retraining deep receivers without sending pilots or relying on specific protocol redundancies, by combining self-supervision with active learning concepts. We focus on the recently proposed ViterbiNet receiver, which integrates into the Viterbi algorithm a DNN for learning the channel. To enable self-supervision, we use the soft-output Viterbi algorithm to evaluate the decision confidence for each of the detected symbols in a given word. Then, to overcome learning with erroneous data, we choose a subset of the recovered symbols to be used for retraining via active learning. The proposed method selects decision-directed data whose confidence is not too low to result in inaccurate labeling, yet not too high to preserve sufficient diversity of the data. We demonstrate that self-supervised symbol-level training yields a performance within a small gap of the Viterbi algorithm with instantaneous channel knowledge.

Robot-Robot-Human Interaction is an emerging field, holding the potential to reveal social effects involved in human interaction with more than one robot. We tested if an interaction between one participant and two non-humanoid robots can lead to negative feelings related to ostracism, and if it can impact fundamental psychological needs including control, belonging, meaningful existence, and self-esteem. We implemented a physical ball-tossing activity based on the Cyberball paradigm. The robots' ball-tossing ratio towards the participant was manipulated in three conditions: Exclusion (10%), Inclusion (33%), and Over-inclusion (75%). Objective and subjective measures indicated that the Exclusion condition led to an ostracism experience which involved feeling "rejected", "ignored", and "meaningless", with an impact on various needs including control, belonging, and meaningful existence. We conclude that interaction with more than one robot can form a powerful social context with the potential to impact psychological needs, even when the robots have no humanoid features.

Lock-based software transactional memory algorithms do not perform well in workloads with a high rate of context switches, which is caused for example by scheduling events or page faults. This occurs since threads that are switched-out by the operating system while holding locks block other threads from progressing, causing their transactions to abort repeatedly. We present here Lock Stealing, a novel contention management algorithm for minimizing the effect of context switches by enabling threads to acquire locks which are held by other threads. While some methods addressing this problem exist (e.g., schedctl in Solaris) they are best effort and only cover scheduling related context switches. In addition, they are platform specific and thus are not suitable or available in managed runtimes such as Java or .NET. In contrast, our approach is solely based on user-level code and is de-coupled from specific operating system events. We evaluate the performance of our approach on a set of benchmarks and observe improvements in both micro benchmarks and more elaborate test applications.

Quadtrees are a compact hierarchical method of representation of images. In this paper, we explore a number of hierarchical image representations as applied to binary images, of which quadtrees are a single exemplar. We discuss quadtrees, binary trees, and an adaptive hierarchical method. Extending these methods into the third dimension of time results in several other methods. All of these methods are discussed in terms of time complexity, worst case and average compression of random images, and compression results on binary images derived from natural scenes. The results indicate that quadtrees are the most effective for two-dimensional images, but the adaptive algorithms are more effective for dynamic image sequences.