Distributed Task Encoding

The rate region of the task-encoding problem for two correlated sources is characterized using a novel parametric family of dependence measures. The converse uses a new expression for the $\rho$-th moment of the list size, which is derived using the relative $\alpha$-entropy.Comment: 5 pages; accepted at ISIT 201

Type systems for distributed programs: session communication

Distributed systems are everywhere around us and guaranteeing their correctness is of paramount importance. It is natural to expect that these systems interact and communicate among them to achieve a common task. In this work, we develop techniques based on types and type systems for the verification of correctness, consistency and safety properties related to communication in complex distributed systems. We study advanced safety properties related to communication, like deadlock or lock freedom and progress. We study session types in the pi-calculus describing distributed systems and communication-centric computation. Most importantly, we de- fine an encoding of the session pi-calculus into the standard typed pi-calculus in order to understand the expressive power of these concurrent calculi. We show how to derive in the session pi-calculus basic properties, like type safety or complex ones, like progress, by exploiting this encoding

Distinct patterns of neural activity during memory formation of nonwords versus words

Research into the neural underpinnings of memory formation has focused on the encoding of familiar verbal information. Here, we address how the brain supports the encoding of novel information that does not have meaning. Electrical brain activity was recorded from the scalps of healthy young adults while they performed an incidental encoding task (syllable judgments) on separate series of words and "nonwords" (nonsense letter strings that are orthographically legal and pronounceable). Memory for the items was then probed with a recognition memory test. For words as well as nonwords, event-related potentials differed depending on whether an item would subsequently be remembered or forgotten. However, the polarity and timing of the effect varied across item type. For words, subsequently remembered items showed the Usually observed positive-going, frontally distributed modulation from around 600 msec after word onset. For nonwords, by contrast, a negative-going, spatially widespread modulation predicted encoding success from 1000 rnsec onward. Nonwords also showed a modulation shortly after item onset. These findings imply that the brain supports the encoding of familiar and unfamiliar letter strings in qualitatively different ways, including the engagement of distinct neural activity at different points in time. The processing of semantic attributes plays an important role in the encoding of words and the associated positive frontal modulation

Task-Oriented Communication for Multi-Device Cooperative Edge Inference

This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features of local samples to a powerful edge server for inference. While cooperative edge inference can overcome the limited sensing capability of a single device, it substantially increases the communication overhead and may incur excessive latency. To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i.e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server. Specifically, we leverage an information bottleneck (IB) principle to extract the task-relevant feature at each edge device and adopt a distributed information bottleneck (DIB) framework to formalize a single-letter characterization of the optimal rate-relevance tradeoff for distributed feature encoding. To admit flexible control of the communication overhead, we extend the DIB framework to a distributed deterministic information bottleneck (DDIB) objective that explicitly incorporates the representational costs of the encoded features. As the IB-based objectives are computationally prohibitive for high-dimensional data, we adopt variational approximations to make the optimization problems tractable. To compensate the potential performance loss due to the variational approximations, we also develop a selective retransmission (SR) mechanism to identify the redundancy in the encoded features of multiple edge devices to attain additional communication overhead reduction. Extensive experiments evidence that the proposed task-oriented communication scheme achieves a better rate-relevance tradeoff than baseline methods.Comment: This paper was accepted to IEEE Transactions on Wireless Communicatio

Modeling working memory: An interference model of complex span

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentall

Practice Induces Function-Specific Changes in Brain Activity

Practice can have a profound effect on performance and brain activity, especially if a task can be automated. Tasks that allow for automatization typically involve repeated encoding of information that is paired with a constant response. Much remains unknown about the effects of practice on encoding and response selection in an automated task.To investigate function-specific effects of automatization we employed a variant of a Sternberg task with optimized separation of activity associated with encoding and response selection by means of m-sequences. This optimized randomized event-related design allows for model free measurement of BOLD signals over the course of practice. Brain activity was measured at six consecutive runs of practice and compared to brain activity in a novel task.Prompt reductions were found in the entire cortical network involved in encoding after a single run of practice. Changes in the network associated with response selection were less robust and were present only after the third run of practice.This study shows that automatization causes heterogeneous decreases in brain activity across functional regions that do not strictly track performance improvement. This suggests that cognitive performance is supported by a dynamic allocation of multiple resources in a distributed network. Our findings may bear importance in understanding the role of automatization in complex cognitive performance, as increased encoding efficiency in early stages of practice possibly increases the capacity to otherwise interfering information

Age-related Differences in Prestimulus Subsequent Memory Effects Assessed with Event-related Potentials

Prestimulus subsequent memory effects (preSMEs)—differences in neural activity elicited by a task cue at encoding that are predictive of later memory performance—are thought to reflect differential engagement of preparatory processes that benefit episodic memory encoding. We investigated age differences in preSMEs indexed by differences in ERP amplitude just before the onset of a study item. Young and older adults incidentally encoded words for a subsequent memory test. Each study word was preceded by a task cue that signaled a judgment to perform on the word. Words were presented for either a short (300 msec) or long (1000 msec) duration with the aim of placing differential benefits on engaging preparatory processes initiated by the task cue. ERPs associated with subsequent successful and unsuccessful recollection, operationalized here by source memory accuracy, were estimated time-locked to the onset of the task cue. In a late time window (1000–2000 msec after onset of the cue), young adults demonstrated frontally distributed preSMEs for both the short and long study durations, albeit with opposite polarities in the two conditions. This finding suggests that preSMEs in young adults are sensitive to perceived task demands. Although older adults showed no evidence of preSMEs in the same late time window, significant preSMEs were observed in an earlier time window (500–1000 msec) that was invariant with study duration. These results are broadly consistent with the proposal that older adults differ from their younger counterparts in how they engage preparatory processes during memory encoding