Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

BINTERNET: Smart Waste Management System

To make the cities greener, safer, and more efficient, Internet of Things (IoT) can play an important role. Improvement in safety and quality of life can be achieved by connecting devices, vehicles and infrastructure all around in a city. We present a waste collection management solution based on providing intelligence to waste bins, using an IOT prototype with sensors. It can read, collect, and transmit huge volume of data over the Internet. Such data, when put into a spatial-temporal context and processed by intelligent and optimized algorithms, can be used to dynamically manage waste collection mechanism. Simulations for several cases are carried out to investigate the benefits of such system over a traditional system

A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APEC-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

The Spread of the Lengthening Time Effect of Emotions in Memory: A Test in the Setting of the Central Tendency Effect

The aim of the present study was to test how the perception of an emotional stimulus colors the temporal context of judgment and modifies the participant’s perception of the current neutral duration. Participants were given two ready-set-go tasks consisting of a distribution of short (0.5–0.9 s) or long sample intervals (0.9–1.3 s) with an overlapping 0.9-s interval. Additional intervals were introduced in the temporal distribution. These were neutral for the two temporal tasks in a control condition and emotional for the short, but not the long temporal task in an emotion condition. The results indicated a replication of a kind of Vierordt’s law in the control condition, i.e., the temporal judgment toward the mean of the distribution of sample intervals (central tendency effect). However, there was a shift in the central tendency effect in the emotion condition indicating a general bias in the form of an overestimation of current intervals linked to the presence of a few emotional stimuli among the previous intervals. This finding is entirely consistent with timing mechanisms driven by prior duration context, particularly experience of prior emotional duration.

Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways. Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting ML models for tasks depends on many factors as they vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for tasks that use explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are (implicitly) ordered or time-dependent, potentially allowing a hidden `arrow of time’ to affect ML performance on non-temporal tasks. This research explores the interaction of ML and implicit order using two ML models to automatically classify (a non-temporal task) tweets (temporal data) under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when matching appropriate ML models to tasks, even when time is only implicitly included.

The Topographic Representation of Time and its Link With Temporal Context and Perception

Neuronal tuning and topography are mechanisms widely used in the brain to represent sensory information and also abstract features like time. In humans, temporal topography has been shown in a wide circuit of brain regions. However, it is unclear whether chronotopic maps are specific to vision, whether they map time in an absolute or relative fashion, to what extent they reflect objective or subjective time and whether they are influenced by temporal context. Here we asked human participants to reproduce the durations of sounds in two, partially overlapping, temporal contexts while we record high-spatial resolution fMRI. Both model-based and data driven analyses show the presence of auditory chronomaps in the auditory parabelt, intraparietal sulcus, and in supplementary motor area. Most importantly, when the same physical duration is presented in different temporal contexts, and thus perceived differently, different neuronal units respond to it. Those units are also spatially shifted according to the relative position of the perceived duration within each context. Finally, the pattern of activity is more similar within rather than across contexts suggesting their pivotal role in shaping the maps. These results highlight two important properties of chronomaps: their flexibility of representation and their dependency on the context.

The relationship between physiological arousal and multiple levels of temporal context in risky choice

Context-dependence is fundamental to risky monetary decision-making. A growing body of evidence suggests that temporal context, or recent events, alters risk-taking at a minimum of three timescales: immediate (e.g. trial-by-trial), neighborhood (e.g. a group of consecutive trials), and global (e.g. task-level). To examine context effects, we created a novel monetary choice set with intentional temporal structure in which option values shifted between multiple levels of value magnitude (“contexts”) several times over the course of the task. This structure allowed us to examine whether effects of each timescale were simultaneously present in risky choice behavior and the potential mechanistic role of arousal, an established correlate of risk-taking, in context-dependency. We found that risk-taking was sensitive to immediate, neighborhood, and global timescales, increasing following small (vs. large) outcome amounts, large positive (but not negative) shifts in context, and when cumulative earnings exceeded expectations. We quantified arousal with skin conductance responses, which were specifically related to the global timescale, increasing with cumulative earnings, suggesting that physiological arousal captures a task-level assessment of performance. We complimented this correlational analysis with a secondary reanalysis of risky monetary choices following the double-blind administration of propranolol and a placebo during a temporally unstructured choice task. We replicated our behavioral finding that risk-taking is context-sensitive at three timescales but found no change in temporal context-effects following propranolol administration. Our results demonstrate that risky decision-making is consistently dynamic at multiple timescales and that arousal is likely the consequence, rather than the cause, of temporal context in risky monetary decision-making.

The study of the contribution of explorers-travelers to the formation of the heritage of the Altai Krai and the Altai Republic by means of GIS technology

The paper considers possibilities of geoinformation technology tools use for studying and updating the contribution of researchers-travelers to the formation of historical, cultural and scientific heritage of the Altai Krai and the Altai Republic in 18-19 centuries. Inevitably the heritage is a valuable resource for development of cultural, social and scientific component of the society. There is an urgent need to study and actualize historical heritage, find optimal ways of its adaptation to changing tendencies and demands of today. The theme of promoting heritage in the development of domestic educational tourism is a popular one. Recreation has long ceased to be seen only as passive, there is a growing interest in cognitive tourism. Each region seeks to preserve and present its historical and cultural peculiarities, connections with outstanding personalities, to assess their contribution to the development of the territory. Geographic information systems (GIS) are the main tool of this study. They have functions of displaying information in a visual form, as well as allow a comprehensive and systematic storage and processing of data in a spatial and temporal context. Geoinformation mapping in the study and actualization of personal contribution to the study of the region will allow to establish travel routes, research discoveries, collections, publications, which in the 18-19 centuries contributed to the study of the territory of the Altai Krai and the Altai Republic. These territories are united by a common rich past. A new look at the heritage will allow to develop research knowledge about the history and culture of the regions as well as undoubtedly contribute to the development and enrichment of tourist products.