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Recommender systems, while a powerful decision making tool, are often operationalized as black box models, such that their AI algorithms are not accessible or interpretable by human operators. This in turn can cause confusion and frustration for the operator and result in unsatisfactory outcomes. While the field of explainable AI has made remarkable strides in addressing this challenge by focusing on interpreting and explaining the algorithms to human operators, there are remaining gaps in the human's understanding of the recommender system. This paper investigates the relative impact of using context, properties of the decision making task and environment, to align human and AI algorithm understanding of the state of the world, i.e. judgment, to improve joint human-recommender performance as compared to utilizing post-hoc algorithmic explanations. We conducted an empirical, between-subjects experiment in which participants were asked to work with an automated recommender system to complete a decision making task. We manipulated the method of transparency (shared contextual information to support shared judgment vs algorithmic explanations) and record the human's understanding of the task, the recommender system, and their overall performance. We found that both techniques yielded equivalent agreement on final decisions. However, those who saw task context had less tendency to over-rely on the recommender system and were able to better pinpoint in what conditions the AI erred. Both methods improved participants' confidence in their own decision making, and increased mental demand equally and frustration negligibly. These results present an alternative approach to improving team performance to post-hoc explanations and illustrate the impact of judgment on human cognition in working with recommender systems., Comment: arXiv admin note: text overlap with arXiv:2310.11370

Microelectronic magnetic sensors are essential in diverse applications, including automotive, industrial, and consumer electronics. Hall-effect devices hold the largest share of the magnetic sensor market, and they are particularly valued for their reliability, low cost and CMOS compatibility. This paper introduces a novel 3-axis Hall-effect sensor element based on an inverted pyramid structure, realized by leveraging MEMS micromachining and CMOS processing. The devices are manufactured by etching the pyramid openings with TMAH and implanting the sloped walls with n-dopants to define the active area. Through the use of various bias-sense detection modes, the device is able to detect both in-plane and out-of-plane magnetic fields within a single compact structure. In addition, the offset can be significantly reduced by one to three orders of magnitude by employing the current-spinning method. The device presented in this work demonstrated high in-plane and out-of-plane current- and voltage-related sensitivities ranging between 64.1 to 198 V A$^{-1}$ T$^{-1}$ and 14.8 to 21.4 mV V$^{-1}$ T$^{-1}$, with crosstalk below 3.7 %. The sensor exhibits a thermal noise floor which corresponds to approximately 0.5 $\mu$T/$\sqrt{Hz}$ at 1.31 V supply. This novel Hall-effect sensor represents a promising and simpler alternative to existing state-of-the-art 3-axis magnetic sensors, offering a viable solution for precise and reliable magnetic field sensing in various applications such as position feedback and power monitoring., Comment: 16 pages, 6 figures. Submitted to peer-review

The rapid and complex patterning of biosilica in diatom frustules is of great interest in nanotechnology, although it remains incompletely understood. Specific organic molecules, including long-chain polyamines, silaffins, and silacidins are essential in this process. The molecular structure of the synthesized polyamines significantly affects the quantity, size, and shape of silica precipitates. Experimental findings show that silica precipitation occurs at specific phosphate ion concentrations. We focus on the hypothesis that pattern formation in diatom valve structures is driven by phase separation of species-specific organic molecules. The resulting organic structures serve as templates for silica precipitation. We investigate the role of phosphate ions in self-assembly of organic molecules and analyze how the reaction between them affects the morphology of the organic template. Using mathematical and computational techniques, we gain an understanding of the range of patterns that can arise in a phase-separating system. By varying the degree of dissociation and the initial concentrations of reacting components we demonstrate that the resulting geometric features are highly dependent on these factors. This approach provides insights into the parameters controlling patterning. Additionally, we consider the effects of prepatterns, mimicking silica ribs that preexist the pores, on the final patterns.

Broad Absorption Line Quasars (BALQs) are actively accreting supermassive black holes that have strong outflows characterized by broad absorption lines in their rest-UV spectra. Variability in these absorption lines occurs over months to years depending on the source. WPVS 007, a low-redshift, low-luminosity Narrow-line Seyfert 1 (NLS1) shows strong variability over shorter timescales, providing a unique opportunity to study the driving mechanism behind this variability that may mimic longer scale variability in much more massive quasars. We present the first variability study using {the} spectral synthesis code SimBAL, which provides velocity-resolved changes in physical conditions of the gas using constraints from multiple absorption lines. Overall, we find WPVS 007 to have a highly ionized outflow with a large mass-loss rate and kinetic luminosity. We determine the primary cause of the absorption-line variability in WPVS 007 to be a change in covering fraction of the continuum by the outflow. This study is the first SimBAL analysis where multiple epochs of observation were fit simultaneously, demonstrating the ability of SimBAL to use the time-domain as an additional constraint in spectral models., Comment: 26 pages, 3 tables, 11 figures

Although the mass growth of supermassive black holes during the Epoch of Reionisation is expected to play a role in shaping the concurrent growth of their host-galaxies, observational evidence of feedback at z$\gtrsim$6 is still sparse. We perform the first multi-scale and multi-phase characterisation of black-hole driven outflows in the $z\sim6.6$ quasar J0923+0402 and assess how these winds impact the cold gas reservoir. We employ the SimBAL spectral synthesis to fit broad absorption line (BAL) features and find a powerful ionized outflow on $\lesssim210$ pc scale, with a kinetic power $\sim2-100$\% of the quasar luminosity. ALMA observations of [CII] emission allow us to study the morphology and kinematics of the cold gas. We detect high-velocity [CII] emission, likely associated with a cold neutral outflow at $\sim0.5-2$ kpc scale in the host-galaxy, and a bright extended [CII] halo with a size of $\sim15$ kpc. For the first time at such an early epoch, we accurately constrain the outflow energetics in both the ionized and the atomic neutral gas phases. We find such energetics to be consistent with expectations for an efficient feedback mechanism, and both ejective and preventative feedback modes are likely at play. The scales and energetics of the ionized and atomic outflows suggest that they might be associated with different quasar accretion episodes. The results of this work indicate that strong black hole feedback is occurring in quasars at $z\gtrsim6$ and is likely responsible for shaping the properties of the cold gas reservoir up to circum-galactic scales., Comment: Accepted for publication in ApJ

This paper presents an incremental replanning algorithm, dubbed LTL-D*, for temporal-logic-based task planning in a dynamically changing environment. Unexpected changes in the environment may lead to failures in satisfying a task specification in the form of a Linear Temporal Logic (LTL). In this study, the considered failures are categorized into two classes: (i) the desired LTL specification can be satisfied via replanning, and (ii) the desired LTL specification is infeasible to meet strictly and can only be satisfied in a "relaxed" fashion. To address these failures, the proposed algorithm finds an optimal replanning solution that minimally violates desired task specifications. In particular, our approach leverages the D* Lite algorithm and employs a distance metric within the synthesized automaton to quantify the degree of the task violation and then replan incrementally. This ensures plan optimality and reduces planning time, especially when frequent replanning is required. Our approach is implemented in a robot navigation simulation to demonstrate a significant improvement in the computational efficiency for replanning by two orders of magnitude., Comment: 8 pages,9 figures

We investigate the real-time estimation of human situation awareness using observations from a robot teammate with limited visibility. In human factors and human-autonomy teaming, it is recognized that individuals navigate their environments using an internal mental simulation, or mental model. The mental model informs cognitive processes including situation awareness, contextual reasoning, and task planning. In teaming domains, the mental model includes a team model of each teammate's beliefs and capabilities, enabling fluent teamwork without the need for explicit communication. However, little work has applied team models to human-robot teaming. We compare the performance of two current methods at estimating user situation awareness over varying visibility conditions. Our results indicate that the methods are largely resilient to low-visibility conditions in our domain, however opportunities exist to improve their overall performance., Comment: Under review, project page: https://jackkolb.com/tmm-hri

We present the optical-near infrared spectral energy distributions (SED) and near infrared variability properties of 30 low-redshift iron low-ionization Broad Absorption Line quasars (FeLoBALQs) and matched samples of LoBALQs and unabsorbed quasars. Significant correlations between the SED properties and accretion rate indicators found among the unabsorbed comparison sample objects suggest an intrinsic origin for SED differences. A range of reddening likely mutes these correlations among the FeLoBAL quasars. The restframe optical-band reddening is correlated with the location of the outflow, suggesting a link between the outflows and the presence of dust. We analyzed WISE variability and provide a correction for photometry uncertainties in an appendix. We found an anticorrelation between the variability amplitude and inferred continuum emission region size, and suggest that as the origin of the anticorrelation between variability amplitude and luminosity typically observed in quasars. We found that the LoBALQ optical emission line and other parameters are more similar to those of the unabsorbed continuum sample objects than the FeLoBALQs. Thus, FeLoBAL quasars are a special population of objects. We interpret the results using an accretion-rate scenario for FeLoBAL quasars. The high accretion rate FeLoBAL quasars are radiating powerfully enough to drive a thick, high-velocity outflow. Quasars with intermediate accretion rates may have an outflow, but it is not sufficiently thick to include FeII absorption. Low accretion rate FeLoBAL outflows originate in absorption in a failing torus, no longer optically thick enough to reprocess radiation into the near-IR., Comment: Accepted for publication in ApJ

In private computation, a user wishes to retrieve a function evaluation of messages stored on a set of databases without revealing the function's identity to the databases. Obead \emph{et al.} introduced a capacity outer bound for private nonlinear computation, dependent on the order of the candidate functions. Focusing on private \emph{quadratic monomial} computation, we propose three methods for ordering candidate functions: a graph edge-coloring method, a graph-distance method, and an entropy-based greedy method. We confirm, via an exhaustive search, that all three methods yield an optimal ordering for $f < 6$ messages. For $6 \leq f \leq 12$ messages, we numerically evaluate the performance of the proposed methods compared with a directed random search. For almost all scenarios considered, the entropy-based greedy method gives the smallest gap to the best-found ordering., Comment: 7 pages, 6 figures, and 1 table. An extended version of a paper accepted for presentation at the 2024 International Zurich Seminar on Information and Communication (IZS)

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI., Comment: PNAS Nexus, in press

AI recommender systems are sought for decision support by providing suggestions to operators responsible for making final decisions. However, these systems are typically considered black boxes, and are often presented without any context or insight into the underlying algorithm. As a result, recommender systems can lead to miscalibrated user reliance and decreased situation awareness. Recent work has focused on improving the transparency of recommender systems in various ways such as improving the recommender's analysis and visualization of the figures of merit, providing explanations for the recommender's decision, as well as improving user training or calibrating user trust. In this paper, we introduce an alternative transparency technique of structuring the order in which contextual information and the recommender's decision are shown to the human operator. This technique is designed to improve the operator's situation awareness and therefore the shared situation awareness between the operator and the recommender system. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of contextual information the participant had, the assessment technique of the figures of merit, and the reliability of the recommender system. We found that providing contextual information upfront improves the team's shared situation awareness by improving the human decision maker's initial and final judgment, as well as their ability to discern the recommender's error boundary. Additionally, this technique accurately calibrated the human operator's trust in the recommender. This work proposes and validates a way to provide model-agnostic transparency into AI systems that can support the human decision maker and lead to improved team performance., Comment: 26 pages, 12 figures, submitted to Springer's "Cognition, Technology, and Work" journal