More than one way to see it: Individual heuristics in avian visual computation

Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally

Secrecy Rate Analysis of UAV-Enabled mmWave Networks Using Matérn Hardcore Point Processes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Communications aided by low-altitude unmanned aerial vehicles (UAVs) have emerged as an effective solution to provide large coverage and dynamic capacity for both military and civilian applications, especially in unexpected scenarios. However, because of their broad coverage, UAV communications are prone to passive eavesdropping attacks. This paper analyzes the secrecy performance of UAVs networks at the millimeter wave band and takes into account unique features of air-to-ground channels and practical constraints of UAV deployment. To be specific, it explores the 3-D antenna gain in the air-to-ground links and uses the Matérn hardcore point process to guarantee the safety distance between the randomly deployed UAV base stations. In addition, we propose the transmit jamming strategy to improve the secrecy performance in which part of UAVs send jamming signals to confound the eavesdropper

Planning-Aware Communication for Decentralised Multi-Robot Coordination

© 2018 IEEE. We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomial-time belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance

Slaves, Soldiers, Citizens: African American Artifacts of the Civil War Era

Based on the exhibit Slaves, Soldiers, Citizens: African American Artifacts of the Civil War Era, this book provides the full experience of the exhibit, which was on display in Special Collections at Musselman Library November 2012- December 2013. It also includes several student essays based on specific artifacts that were part of the exhibit. Table of Contents: Introduction Angelo Scarlato, Lauren Roedner ’13 & Scott Hancock Slave Collars & Runaways: Punishment for Rebellious Slaves Jordan Cinderich ’14 Chancery Sale Poster & Auctioneer’s Coin: The Lucrative Business of Slavery Tricia Runzel ’13 Isaac J. Winters: An African American Soldier from Pennsylvania Who Fought at Petersburg Avery Lentz ’14 Basil Biggs: A Prominent African American in Gettysburg after the Battle Lauren Roedner ’13 Linton Ingram: A Former Slave Who Became a Notable African American Educator in Georgia Brian Johnson & Lincoln Fitch ’14 Uncle Tom’s Cabin Theatre Poster: Racism in Post-Emancipation Entertainment Michelle Seabrook ’13 Essay Bibliographies Grand Army of the Republic Exhibit Inventory Acknowledgmentshttps://cupola.gettysburg.edu/libexhibits/1001/thumbnail.jp

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201

Secrecy Rate Analysis of UAV-Enabled mmWave Networks Using Matern Hardcore Point Processes

Communications aided by low-altitude unmanned aerial vehicles (UAVs) have emerged as an effective solution to provide large coverage and dynamic capacity for both military and civilian applications, especially in unexpected scenarios. However, because of their broad coverage, UAV communications are prone to passive eavesdropping attacks. This paper analyzes the secrecy performance of UAVs networks at the millimeter wave band and takes into account unique features of air-to-ground channels and practical constraints of UAV deployment. To be specific, it explores the 3-D antenna gain in the air-to-ground links and uses the Matérn hardcore point process to guarantee the safety distance between the randomly deployed UAV base stations. In addition, we propose the transmit jamming strategy to improve the secrecy performance in which part of UAVs send jamming signals to confound the eavesdroppers. Simulation results verify our analysis and demonstrate the impact of different system parameters on the achievable secrecy rate. It is also revealed that optimizing the density of jamming UAVs will significantly improve security of UAV-enabled networks

Visualizing sound emission of elephant vocalizations: evidence for two rumble production types

Recent comparative data reveal that formant frequencies are cues to body size in animals, due to a close relationship between formant frequency spacing, vocal tract length and overall body size. Accordingly, intriguing morphological adaptations to elongate the vocal tract in order to lower formants occur in several species, with the size exaggeration hypothesis being proposed to justify most of these observations. While the elephant trunk is strongly implicated to account for the low formants of elephant rumbles, it is unknown whether elephants emit these vocalizations exclusively through the trunk, or whether the mouth is also involved in rumble production. In this study we used a sound visualization method (an acoustic camera) to record rumbles of five captive African elephants during spatial separation and subsequent bonding situations. Our results showed that the female elephants in our analysis produced two distinct types of rumble vocalizations based on vocal path differences: a nasally- and an orally-emitted rumble. Interestingly, nasal rumbles predominated during contact calling, whereas oral rumbles were mainly produced in bonding situations. In addition, nasal and oral rumbles varied considerably in their acoustic structure. In particular, the values of the first two formants reflected the estimated lengths of the vocal paths, corresponding to a vocal tract length of around 2 meters for nasal, and around 0.7 meters for oral rumbles. These results suggest that African elephants may be switching vocal paths to actively vary vocal tract length (with considerable variation in formants) according to context, and call for further research investigating the function of formant modulation in elephant vocalizations. Furthermore, by confirming the use of the elephant trunk in long distance rumble production, our findings provide an explanation for the extremely low formants in these calls, and may also indicate that formant lowering functions to increase call propagation distances in this species'

PAR1 Agonists Stimulate APC-Like Endothelial Cytoprotection and Confer Resistance to Thromboinflammatory Injury

Stimulation of protease-activated receptor 1 (PAR1) on endothelium by activated protein C (APC) is protective in several animal models of disease, and APC has been used clinically in severe sepsis and wound healing. Clinical use of APC, however, is limited by its immunogenicity and its anticoagulant activity. We show that a class of small molecules termed “parmodulins” that act at the cytosolic face of PAR1 stimulates APC-like cytoprotective signaling in endothelium. Parmodulins block thrombin generation in response to inflammatory mediators and inhibit platelet accumulation on endothelium cultured under flow. Evaluation of the antithrombotic mechanism showed that parmodulins induce cytoprotective signaling through Gβγ, activating a PI3K/Akt pathway and eliciting a genetic program that includes suppression of NF-κB–mediated transcriptional activation and up-regulation of select cytoprotective transcripts. STC1 is among the up-regulated transcripts, and knockdown of stanniocalin-1 blocks the protective effects of both parmodulins and APC. Induction of this signaling pathway in vivo protects against thromboinflammatory injury in blood vessels. Small-molecule activation of endothelial cytoprotection through PAR1 represents an approach for treatment of thromboinflammatory disease and provides proof-of-principle for the strategy of targeting the cytoplasmic surface of GPCRs to achieve pathway selective signaling