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Robots, humanoid and otherwise, are being created with the underlying motivation in many cases that they will either replace or complement activities performed by humans. It has been many years since robots were starting to be designed to take over “dull, dirty, or dangerous” tasks (e.g., Singer 2009). Over time, roboticists and others within computing communities have extended their ambitions to create technology that seeks to emulate more complex ranges of human-like behavior, potentially including the ability to participate in complicated conversations. Regardless of how sophisticated its functionality is, a robot should arguably be encoded with ethical decision-making parameters, especially if it is going to interact with or could potentially endanger a human being. Yet of course determining the nature and specification of such parameters raises many longstanding and difficult philosophical questions. 

The current and next observation seasons will detect hundreds of gravitational waves (GWs) from compact binary systems coalescence at cosmological distances. When combined with independent electromagnetic measurements, the source redshift will be known, and we will be able to obtain precise measurements of the Hubble constant H0 via the distance–redshift relation. However, most observed mergers are not expected to have electromagnetic counterparts, which prevents a direct redshift measurement. In this scenario, one possibility is to use the dark sirens method that statistically marginalizes over all the potential host galaxies within the GW location volume to provide a probabilistic source redshift. Here we presented H0 measurements using two new dark sirens compared to previous analyses using DECam data: GW190924$\_$021846 and GW200202$\_$154313. The photometric redshifts of the possible host galaxies of these two events are acquired from the DECam Local Volume Exploration Survey (DELVE) carried out on the Blanco telescope at Cerro Tololo. The combination of the H0 posterior from GW190924$\_$021846 and GW200202$\_$154313 together with the bright siren GW170817 leads to $H_{0} = 68.84^{+15.51}_{-7.74}\, \rm {km\, s^{-1}\, Mpc^{-1}}$. Including these two dark sirens improves the 68  per cent confidence interval (CI) by 7  per cent over GW170817 alone. This demonstrates that the addition of well-localized dark sirens in such analysis improves the precision of cosmological measurements. Using a sample containing 10 well-localized dark sirens observed during the third LIGO/Virgo observation run, without the inclusion of GW170817, we determine a measurement of $H_{0} = 76.00^{+17.64}_{-13.45}\, \rm {km\, s^{-1}\, Mpc^{-1}}$.

 

We present a spectroscopic analysis of Eridanus IV (Eri IV) and Centaurus I (Cen I), two ultrafaint dwarf galaxies of the Milky Way. Using IMACS/Magellan spectroscopy, we identify 28 member stars of Eri IV and 34 member stars of Cen I. For Eri IV, we measure a systemic velocity of$v_{\mathrm{sys}}=-{31.5}_{-1.2}^{+1.3}\mathrm{km}{\mathrm{s}}^{-1}$, and velocity dispersion${\sigma }_v={6.1}_{-0.9}^{+1.2}\mathrm{km}{\mathrm{s}}^{-1}$. Additionally, we measure the metallicities of 16 member stars of Eri IV. We find a metallicity of$[\mathrm{Fe}/\mathrm{H}]=-{2.87}_{-0.07}^{+0.08}$, and resolve a dispersion ofσ_[Fe/H]=0.20 ± 0.09. The mean metallicity is marginally lower than all other known ultrafaint dwarf galaxies, making it one of the most metal-poor galaxies discovered thus far. Eri IV also has a somewhat unusual right-skewed metallicity distribution. For Cen I, we find a velocityv_sys= 44.9 ± 0.8 km s⁻¹, and velocity dispersion${\sigma }_v={4.2}_{-0.5}^{+0.6}\mathrm{km}{\mathrm{s}}^{-1}$. We measure the metallicities of 27 member stars of Cen I, and find a mean metallicity [Fe/H] = −2.57 ± 0.08, and metallicity dispersion${\sigma }_{[\mathrm{Fe}/\mathrm{H}]}={0.38}_{-0.05}^{+0.07}$. We calculate the systemic proper motion, orbit, and the astrophysical J-factor for each system, the latter of which indicates that Eri IV is a good target for indirect dark matter detection. We also find no strong evidence for tidal stripping of Cen I or Eri IV. Overall, our measurements confirm that Eri IV and Cen I are dark-matter-dominated galaxies with properties largely consistent with other known ultrafaint dwarf galaxies. The low metallicity, right-skewed metallicity distribution, and high J-factor make Eri IV an especially interesting candidate for further follow-up.

 

Our research team has been investigating methods for enabling robots to behave ethically while interacting with human beings. Our approach relies on two main sources of data for determining what counts as “ethical” behavior. The first are the views of average adults, which we refer to “folk morality”, and the second are the views of ethics experts. Yet the enterprise of identifying what should ground a robot’s decisions about ethical matters raises many fundamental metaethical questions. Here, we focus on one main metaethical question: would reason dedicate that it is more justifiable to base a robot’s decisions on folk morality or the guidance of ethics experts? The goal of this presentation is to highlight some of the arguments for and against each respective point of view, and the implications such arguments might have for the endeavor to encode ethical decision-making processes into robots. 

As robots are becoming more intelligent and more commonly used, it is critical for robots to behave ethically in human-robot interactions. However, there is a lack of agreement on a correct moral theory to guide human behavior, let alone robots. This paper introduces a robotic architecture that leverages cases drawn from different ethical frameworks to guide the ethical decision-making process and select the appropriate robotic action based on the specific situation. We also present an architecture implementation design used on a pill sorting task for older adults, where the robot needs to decide if it is appropriate to provide false encouragement so that the adults continue to be engaged in the training task. 

This paper examines the metaethical dimensions of the computing community’s efforts to program ethical decision- making abilities into robots. Arguments for and against that endeavor are outlined along with brief recommendations for the human-robot interaction realm. 

Abstract We present the discovery of DELVE 6, an ultra-faint stellar system identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on a maximum-likelihood fit to its structure and stellar population, we find that DELVE 6 is an old ( τ > 9.8 Gyr at 95% confidence) and metal-poor ([Fe/H] < −1.17 dex at 95% confidence) stellar system with an absolute magnitude of M V = − 1.5 − 0.6 + 0.4 mag and an azimuthally averaged half-light radius of r 1 / 2 = 10 − 3 + 4 pc. These properties are consistent with the population of ultra-faint star clusters uncovered by recent surveys. Interestingly, DELVE 6 is located at an angular separation of ∼10° from the center of the Small Magellanic Cloud (SMC), corresponding to a 3D physical separation of ∼20 kpc given the system’s observed distance ( D ⊙ = 80 kpc). This also places the system ∼35 kpc from the center of the Large Magellanic Cloud (LMC), lying within recent constraints on the size of the LMC’s dark matter halo. We tentatively measure the proper motion of DELVE 6 using data from Gaia, which we find supports a potential association between the system and the LMC/SMC. Although future kinematic measurements will be necessary to determine its origins, we highlight that DELVE 6 may represent only the second or third ancient ( τ > 9 Gyr) star cluster associated with the SMC, or one of fewer than two dozen ancient clusters associated with the LMC. Nonetheless, we cannot currently rule out the possibility that the system is a distant Milky Way halo star cluster. 

Ethical decision-making is difficult, certainly for robots let alone humans. If a robot's ethical decision-making process is going to be designed based on some approximation of how humans operate, then the assumption is that a good model of how humans make an ethical choice is readily available. Yet no single ethical framework seems sufficient to capture the diversity of human ethical decision making. Our work seeks to develop the computational underpinnings that will allow a robot to use multiple ethical frameworks that guide it towards doing the right thing. As a step towards this goal, we have collected data investigating how regular adults and ethics experts approach ethical decisions related to the use in a healthcare and game playing scenario. The decisions made by the former group is intended to represent an approximation of a folk morality approach to these dilemmas. On the other hand, experts were asked to judge what decision would result if a person was using one of several different types of ethical frameworks. The resulting data may reveal which features of the pill sorting and game playing scenarios contribute to similarities and differences between expert and non-expert responses. This type of approach to programming a robot may one day be able to rely on specific features of an interaction to determine which ethical framework to use in the robot's decision making.