Robust Chip (United States)

For decades, robotics researchers have pursued various tasks for multi-robot systems, from cooperative manipulation to search and rescue. These tasks are multi-robot extensions of classical robotic tasks and often optimized on dimensions such as speed or efficiency. As robots transition from commercial and research settings into everyday environments, social task aims such as engagement or entertainment become increasingly relevant. This work presents a designerly contribution—building a multi-robot task in which the main aim is to enthrall and interest. In this task, the goal is for a human to be drawn to move alongside and participate in a dynamic, expressive robot flock. Towards this aim, the research team created algorithms for robot movements and engaging interaction modes such as gestures and sound. The contributions are as follows: (1) a novel group navigation algorithm involving human and robot agents, (2) a gesture responsive algorithm for real-time, human–robot flocking interaction, (3) a weight mode characterization system for modifying flocking behavior, and (4) a method of encoding a choreographer’s preferences inside a dynamic, adaptive, learned system. An experiment was performed to understand individual human behavior while interacting with the flock under three conditions: weight modes selected by a human choreographer, a learned model, or subset list. Results from the experiment indicated that the perception of the experience was not influenced by the weight mode selection. This work elucidates how differing task aims such as engagement manifest in multi-robot system design and execution, and broadens the domain of multi-robot tasks.

Denne artikkelen er et resultat av 20 års arbeid med barn, unge og familier hvor forfatteren har utforsket mulighetene som ligger i de mange ulike praksisene som tilhører den narrative terapitradisjonen. Bruken av terapeutiske brev og dokumenter er blant disse, og denne artikkelen har til hensikt å presentere noen av ideene og holdningene som ligger til grunn for denne litterære praksisen. Artikkelen beskriver samt gir et konkret eksempel på en terapeutisk brevkampanje igangsatt i samarbeid med en mor, «Ellen», som befant seg i en kritisk livssituasjon. Ellen har lest teksten og bidratt med kommentarer. Alle navn er anonymisert.

Esta pesquisa, cujo contexto é a geografia cultural, teve o objetivo de discutir as relações paisagístico-culturais a partir do Rio Manoel Alves como um componente integrador da paisagem cultural do município de Morro Grande, no extremo sul do estado de Santa Catarina, um dos sete municípios que compõem o território do Geoparque Mundial da Unesco Caminhos dos Cânions do Sul. O percurso metodológico desta escrita adotou a pesquisa bibliográfica como um dos principais procedimentos, com foco nas apropriações e ocupações culturais e históricas do patrimônio abiótico local. Além disso, uma saída de campo foi realizada com o intuito de identificar, inicialmente in loco, os principais elementos constitutivos da paisagem morro-grandense e como as materialidades se constituíram a partir desses ambientes. Com base nas proposições de Carl Sauer para uma paisagem como uma relação harmônica entre as barreiras naturais e a sociedade, temos em Morro Grande robustas relações entre o meio social e o natural, sendo este considerado um determinante da paisagem cultural. Com os resultados, foi possível concluir que há espaço para a discussão da existência de uma paisagem cultural em Morro Grande e que esta contém um potencial como patrimônio.

Purpose: This study aims to identify an effective strategy for Indonesia to counter China's gray zone tactics in the South China Sea. Theoretical Reference: Employing a quantitative method with a descriptive approach rooted in the positivist philosophy, this research follows the principles outlined by Sugiyono (2011). This method is chosen for investigating specific populations or samples, with a focus on quantitative/statistical data analysis to test predetermined hypotheses. Method: The research employs multiple regression analysis to assess the significance of Soft Power Diplomacy and Smart Power Diplomacy strategies as essential national resources for persuading China to undertake desired actions in addressing the challenges posed by China's Gray Zone in the South China Sea. Smart power, as defined by Jr J.N. (2007), involves integrating soft and hard power for a comprehensive strategy. According to R. Wilson (2008), smart power effectiveness lies in combining a robust military force with investments in alliances, partnerships, and cooperation. Result and Conclusion: The findings indicate that both Soft Power Diplomacy and Smart Power Diplomacy are influential tools and strategies within Indonesia's foreign policy context to effectively navigate and counteract China's Gray Zone activities in the South China Sea. Implications of Research: The identified strategies hold significant implications for shaping Indonesia's defense diplomacy against China's gray zone tactics, emphasizing the importance of integrating soft and hard power elements for a more comprehensive and efficient approach. Originality/Value: This research contributes to the field by highlighting the relevance and effectiveness of Soft Power Diplomacy and Smart Power Diplomacy as key components in Indonesia's defense diplomacy strategy, providing a nuanced understanding of countering China's Gray Zone activities in the South China Sea.

Introdução: A monitoria é uma abordagem pedagógica que coloca os estudantes no centro do processo de aprendizagem, estimulando-os a pensar criticamente. O ensino discente com foco na aprendizagem baseada em problemas, permite que o aprendizado seja contínuo e prepara os alunos para enfrentar os desafios do mundo. Objetivo: Relatar a experiência acerca da monitoria e sua contribuição no desenvolvimento de habilidades médicas. Relato de Experiência: Trata-se de um relato de experiência, realizado por monitoras da disciplina de Habilidades Médicas, no período de agosto a outubro de 2023, para alunos do quarto período do curso

Separation assurance is an important facet of the safety guarantee in the the National Airspace System (NAS). Separation requirement for aircraft are defined by horizontal and vertical separation buffers depending on the type of aircraft and type of airspace. The violation of these standards or encroachment into the separation buffers are indicative of reduced safety margins in the airspace. We analyze the buffer encroachment trends in the terminal airspace of 24 major airports in the U.S. over a period of 21 months from April 2019 to December 2020. The analysis measures the duration of encroachments within the terminal airspace for 15-minute epochs and the trends across the airports and their temporal evolution. The period at the beginning of the Covid-19 pandemic in mid-March 2020 and the slow recovery over the remainder of the year is of special interest as it provides a natural experiment for evaluating the impact of unusually low traffic density on aviation operations. Our analysis indicates that the airports show significant variability in the encroachment levels. Some airports like Dallas/Fort Worth International Airport (DFW) and Charlotte Douglas International Airport (CLT) show encroachment levels higher than expected during the reduced traffic period. Furthermore, the trends reveal spikes in encroachment levels during the pre-pandemic period at other airports like Atlanta International Airport (ATL) and Denver International Airport (DEN). The observed trends suggest the utility for real-time measurement of buffer encroachments across airports as a proxy for monitoring safety margin levels and aid the decision-making of different stakeholders.

Prior work has demonstrated techniques for fingerprinting devices based on their network traffic or transmitted signals, which use software artifacts or characteristics of the underlying protocol. However these approaches are not robust or applicable in many real-world scenarios. In this paper we explore the feasibility of device fingerprinting under challenging realistic settings, by identifying artifacts in the transmitted signals caused by devices' unique hardware “imperfections”. We develop RF-DCN, a novel Deep Complex-valued Neural Network (DCN) that operates on raw RF signals and is completely agnostic of the underlying applications and protocols. We introduce two DCN variations: a retrofitted Convolutional DCN (CDCN) originally created for acoustic signals, and a novel Recurrent DCN (RDCN) for modeling time series. Our work demonstrates the feasibility of operating on raw I/Q data collected within a narrowband spectrum from open air captures across vastly different modulation schemes. In contrast to prior work, we do not utilize knowledge of the modulation scheme or protocol intricacies such as carrier frequencies. We conduct an extensive experimental evaluation on large and diverse datasets as part of a DARPA red team evaluation, and investigate the effects of different environmental factors as well as neural network architectures and hyperparameters on our system's performance. Our novel RDCN consistently outperforms all baseline neural network architectures, is robust to noise, and can identify a target device even when numerous devices are concurrently transmitting within the band of interest under the same or different protocols. While our experiments demonstrate the applicability of our techniques under challenging conditions where other neural network architectures break down, we identify additional challenges in signal-based fingerprinting and provide guidelines for future explorations.

The near-terminal operations of the aircraft highly rely on the ATC command. Accurate prediction for aircraft trajectory can help the safety operation in the National Airspace System (NAS). This paper presents a novel simulation framework that considers the voice communication between the pilot and Air Traffic Controllers (ATC) to assist the trajectory simulations. The simulation is based on NATS, a newly developed open access trajectory simulation platform. The framework utilizes a state-of-the-art voice-to-text engine specifically tuned for ATC environment to translate the audio data into text. A Natural Language Understanding (NLU) processes the text and extract key information such as aircraft call sign, related operation command etc. This information can them be used to update the NATS simulation code, resulting in a simulated trajectory augmented with voice commands. The framework will be referred as Voice-Communication Augmented Simulation (VCAS). The current VCAS can utilize the command in leveling change and runway assignments. The proposed method is validated with real trajectory data from Sherlock data warehouse.

Abstract The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.