Proof-checking Bias in Labeling Methods

We introduce a typed natural deduction system designed to formally verify the presence of bias in automatic labeling methods. The system relies on a ”data-as-terms” and ”labels-as-types” interpretation of formulae, with derivability contexts encoding probability distributions on training data. Bias is understood as the divergence that expected probabilistic labeling by a classifier trained on opaque data displays from the fairness constraints set by a transparent dataset

Deep Reinforcement Learning for Robotic Approaching Behavior Influenced by User Activity and Disengagement

A robot intended to monitor human behavior must account for the user's reactions to minimize his/her perceived discomfort. The possibility of learning user interaction preferences and changing the robot's behavior accordingly may positively impact the perceived quality of the interaction with the robot. The robot should approach the user without causing any discomfort or interference. In this work, we contribute and implement a novel Reinforcement Learning (RL) approach for robot navigation toward a human user. Our implementation is a proof-of-concept that uses data gathered from real-world experiments to show that our algorithm works on the kind of data that it would run on in a realistic scenario. To the best of our knowledge, our work is one of the first attempts to provide an adaptive navigation algorithm that uses RL to account for non-deterministic phenomena

Introducing k-lingo: a k-depth Bounded Version of ASP System Clingo

Depth-Bounded Boolean Logics (DBBL for short) are well-understood frameworks to model rational agents equipped with limited deductive capabilities. These Logics use a parameter k ≥ 0 to limit the amount of virtual information, i.e., the information that the agent may temporarily assume throughout the deductive process. This restriction brings several advantageous properties over classical Propositional Logic, including polynomial decision procedures for deducibility and refutability. Inspired by DBBL, we propose a limited-depth version of the popular ASP system clingo, tentatively dubbed k-lingo after the bound k on virtual information. We illustrate the connection between DBBL and ASP through examples involving both proof-theoretical and implementative aspects. The paper concludes with some comments on future work, which include a computational complexity characterization of the system, applications to multi-agent systems and feasible approximations of probability functions

Ultraviolet generation in periodically poled Lithium Tantalate waveguides

We demonstrate ultraviolet generation in lithium tantalate channel waveguides for frequency doubling via quasi-phase-matching. The samples, proton exchanged and nanostructured by electric-field assisted surface periodic poling with domains as deep as 40 μm, yield continuous wave light at 365.4 nm with conversion efficiencies larger than 7.5% W-1 cm-2

Variability of near-surface circulation and sea surface salinity observed from Lagrangian drifters in the northern Bay of Bengal during the Waning 2015 Southwest Monsoon

Author Posting. © The Oceanography Society, 2016. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 29, no. 2 (2016): 124–133, doi:10.5670/oceanog.2016.45.A dedicated drifter experiment was conducted in the northern Bay of Bengal during the 2015 waning southwest monsoon. To sample a variety of spatiotemporal scales, a total of 36 salinity drifters and 10 standard drifters were deployed in a tight array across a freshwater front. The salinity drifters carried for the first time a revised sensor algorithm, and its performance during the 2015 field experiment is very encouraging for future efforts. Most of the drifters were quickly entrained in a mesoscale feature centered at about 16.5°N, 89°E and stayed close together during the first month of observations. While the eddy was associated with rather homogeneous temperature and salinity characteristics, much larger variability was found outside of it toward the coastline, and some of the observed salinity patches had amplitudes in excess of 1.5 psu. To particularly quantify the smaller spatiotemporal scales, an autocorrelation analysis of the drifter salinities for the first two deployment days was performed, indicating not only spatial scales of less than 5 km but also temporal variations of the order of a few hours. The hydrographic measurements were complemented by first estimates of kinematic properties from the drifter clusters, however, more work is needed to link the different observed characteristics.VH and LR were supported by ONR grant N00014- 13-1-0477 and NOAA GDP grant NA10OAR4320156. AM and SE were funded by ONR grant N00014‑13-1- 0451, and ED by ONR grant N00014-14-1-0235. BPK acknowledges financial support from the Ministry of Earth Sciences (MoES, Government of India)

Editorial: Explainable artificial intelligence models and methods in finance and healthcare

This article is a foreword to a special issue on "Explainable artificial intelligence models and methods in finance and healthcare" and introduces the main articles of the collection. The core topic of this special issue is explainability and trusting algorithmic output

EXPORTS North Atlantic eddy tracking

The EXPORTS North Atlantic field campaign (EXPORTS-NA) of May 2021 used a diverse array of ship-based and autonomous platforms to measure and quantify processes leading to carbon export in the open ocean. The success of this field program relied heavily on the ability to make measurements following a Lagrangian trajectory within a coherent, retentive eddy (Sections 1, 2). Identifying an eddy that would remain coherent and retentive over the course of a monthlong deployment was a significant challenge that the EXPORTS team faced. This report details the processes and procedures used by the primarily shore-based eddy tracking team to locate, track, and sample with autonomous assets such an eddy before and during EXPORTS-NA.This field deployment was funded by the NASA Ocean Biology and Biogeochemistry program and the National Science Foundation Biological and Chemical Oceanography programs. Initial gliders deployments were performed by the RRS Discovery and the authors thank the Porcupine Abyssal Plain – Sustained Observatory of the Natural Environment Research Council (NERC, UK), which is principally funded through the Climate Linked Atlantic Sector Science (CLASS) project supported by NERC National Capability funding (NE/R015953/1) and by IFADO (Innovation in the Framework of the Atlantic Deep Ocean) EAPA_165/2016. Technical assistance with glider deployment was provided by Marine Autonomous Robotic Systems (NOC). The authors thank Inia Soto Ramos for assistance in publishing this manuscript through the NASA Technical Memorandum series. This is PMEL contribution number 5372

An ocean coupling potential intensity index for tropical cyclones

© American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 40 (2013): 1878–1882, doi:10.1002/grl.50091.Timely and accurate forecasts of tropical cyclones (TCs, i.e., hurricanes and typhoons) are of great importance for risk mitigation. Although in the past two decades there has been steady improvement in track prediction, improvement on intensity prediction is still highly challenging. Cooling of the upper ocean by TC-induced mixing is an important process that impacts TC intensity. Based on detail in situ air-deployed ocean and atmospheric measurement pairs collected during the Impact of Typhoons on the Ocean in the Pacific (ITOP) field campaign, we modify the widely used Sea Surface Temperature Potential Intensity (SST_PI) index by including information from the subsurface ocean temperature profile to form a new Ocean coupling Potential Intensity (OC_PI) index. Using OC_PI as a TC maximum intensity predictor and applied to a 14 year (1998–2011) western North Pacific TC archive, OC_PI reduces SST_PI-based overestimation of archived maximum intensity by more than 50% and increases the correlation of maximum intensity estimation from r2 = 0.08 to 0.31. For slow-moving TCs that cause the greatest cooling, r2 increases to 0.56 and the root-mean square error in maximum intensity is 11 m s−1. As OC_PI can more realistically characterize the ocean contribution to TC intensity, it thus serves as an effective new index to improve estimation and prediction of TC maximum intensity.This work is supported by Taiwan’s National Science Council and National Taiwan University (grant numbers: NSC 101- 2111-M-002-002-MY2; NSC 101-2628-M-002-001-MY4; 102R7803) and US Office of Naval Research (ONR) under the Impact of Typhoons on Pacific (ITOP) program. PB’s support is provided by ONR under PE 0601153N through NRL Contract N00173-10-C-6019