@zimpenfish@social.rjp.isObviously I finally found the answer almost immediately after posting this.
You want a number MQTT Discovery with, OBVIOUSLY, state_class="measurement". It’s right there in the docs but finding the correct sequence of searches to get to the docs is a monstrous faff (HA docs are good but badly organised.)
@axbom@axbom.meGenerative AI can not generate its way out of prejudice
The concept of "generative" suggests that the tool can produce what it is asked to produce. In a study uncovering how stereotypical global health tropes are embedded in AI image generators, researchers found it challenging to generate images of Black doctors treating white children. They used Midjourney, a tool that after hundreds of attempts would not generate an output matching the prompt. I tried their experiment with…
@axbom@axbom.meGenerative AI can not generate its way out of prejudice
The concept of "generative" suggests that the tool can produce what it is asked to produce. In a study uncovering how stereotypical global health tropes are embedded in AI image generators, researchers found it challenging to generate images of Black doctors treating white children. They used Midjourney, a tool that after hundreds of attempts would not generate an output matching the prompt. I tried their experiment with…
@zimpenfish@social.rjp.isFound another great example of #HomeAssistant implementing the weird ideas of the developers in the face of common sense - people wanted hierarchical areas (heater IS-IN closest IS-IN bedroom IS-IN upstairs, etc.) Currently only one area is supported for a device.
Developers add ... the concept of floors which adds one level of hierarchy and that no-one wanted because it's a…
@arXiv_grqc_bot@mastoxiv.pageThis https://arxiv.org/abs/2403.02089 has been replaced.
link: https://scholar.google.com/scholar?q=a
@arXiv_csIR_bot@mastoxiv.pageLIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
Ziqi Yin, Shanshan Feng, Shang Liu, Gao Cong, Yew Soon Ong, Bin Cui
https://arxiv.org/abs/2403.07331
@arXiv_astrophGA_bot@mastoxiv.pageGalaxies in the Zone of Avoidance: Misclassifications using machine learning tools
P. Marchant Cort\'es, J. L. Nilo Castell\'on, M. V. Alonso, L. Baravalle, C. Villal\'on, M. A. Sgr\'o, I. V. Daza-Perilla, M. Soto, F. Milla Castro, D. Minniti, N. Masetti, C. Valotto, M. Lares
https://arxiv.org/abs/2403.03098 https://arxiv.org/pdf/2403.03098
arXiv:2403.03098v1 Announce Type: new
Abstract: Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. In this study, we explore the identification and classification of galaxies in the Zone of Avoidance (ZoA). In particular, we compare our results in the near-infrared with X-ray data. We analize the appearance of the objects classified as galaxies using machine learning by Zhang et al. (2021) in the Galactic disk and make a comparison with the visually confirmed galaxies from the VVV NIRGC (Baravalle et al. (2021). Our analysis, which includes the visual inspection of all sources catalogued as galaxies throughout the Galactic disk using machine learning techniques reveals significant differences. Only 4 galaxies were found in both the near-Infrared and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. The results indicate the difficulty of using machine learning methods for galaxy classification in the ZoA mainly due to the scarce information on galaxies behind the Galactic plane in the training set. They also stress the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.
@amino@fediverse.omaramin.meThe prioritisation and filtering system is the main reason I keep using TTRSS. I haven't found anything remotely as robust in the 6 years I've been using it. I'd be very happy if anyone knows of another option.
@carlos@social.perceptiveconstructs.com @carlos@perceptiveconstructs.com
@arXiv_mathGN_bot@mastoxiv.pageGraphs with tranches: consequences for topology and dynamics
Micha{\l} Kowalewski, Piotr Oprocha
https://arxiv.org/abs/2405.05407 https://
@arXiv_mathLO_bot@mastoxiv.pageThis https://arxiv.org/abs/2308.06789 has been replaced.
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@arXiv_astrophCO_bot@mastoxiv.pageThis https://arxiv.org/abs/2402.04800 has been replaced.
link: https://scholar.google.com/scholar?q=a
@arXiv_csRO_bot@mastoxiv.pageOpti-Acoustic Semantic SLAM with Unknown Objects in Underwater Environments
Kurran Singh, Jungseok Hong, Nicholas R. Rypkema, John J. Leonard
https://arxiv.org/abs/2403.12837
@arXiv_csHC_bot@mastoxiv.pageDynamic Human Trust Modeling of Autonomous Agents With Varying Capability and Strategy
Jason Dekarske (University of California, Davis), Zhaodan Kong (University of California, Davis), Sanjay Joshi (University of California, Davis)
https://arxiv.org/abs/2404.19291 https://arxiv.org/pdf/2404.19291
arXiv:2404.19291v1 Announce Type: new
Abstract: Objective We model the dynamic trust of human subjects in a human-autonomy-teaming screen-based task.
Background Trust is an emerging area of study in human-robot collaboration. Many studies have looked at the issue of robot performance as a sole predictor of human trust, but this could underestimate the complexity of the interaction.
Method Subjects were paired with autonomous agents to search an on-screen grid to determine the number of outlier objects. In each trial, a different autonomous agent with a preassigned capability used one of three search strategies and then reported the number of outliers it found as a fraction of its capability. Then, the subject reported their total outlier estimate. Human subjects then evaluated statements about the agent's behavior, reliability, and their trust in the agent.
Results 80 subjects were recruited. Self-reported trust was modeled using Ordinary Least Squares, but the group that interacted with varying capability agents on a short time order produced a better performing ARIMAX model. Models were cross-validated between groups and found a moderate improvement in the next trial trust prediction.
Conclusion A time series modeling approach reveals the effects of temporal ordering of agent performance on estimated trust. Recency bias may affect how subjects weigh the contribution of strategy or capability to trust. Understanding the connections between agent behavior, agent performance, and human trust is crucial to improving human-robot collaborative tasks.
Application The modeling approach in this study demonstrates the need to represent autonomous agent characteristics over time to capture changes in human trust.
@arXiv_csCG_bot@mastoxiv.pageEfficient computation of topological integral transforms
Vadim Lebovici, Steve Oudot, Hugo Passe
https://arxiv.org/abs/2405.02256 https://
@carlos@social.perceptiveconstructs.com @carlos@perceptiveconstructs.comhere's a nice sound I found on freesound.org, it's cc 0 so here's not only the link but it's attached
https://freesound.org/people/leeharebbeccah/sounds/399035/
#kitty
#c…
@arXiv_astrophGA_bot@mastoxiv.pageMicrolensing optical depth and event rate toward the Large Magellanic Cloud based on 20 years of OGLE observations
P. Mroz, A. Udalski, M. K. Szymanski, M. Kapusta, I. Soszynski, L. Wyrzykowski, P. Pietrukowicz, S. Kozlowski, R. Poleski, J. Skowron, D. Skowron, K. Ulaczyk, M. Gromadzki, K. Rybicki, P. Iwanek, M. Wrona, M. Ratajczak
https://arxiv.org/abs/2403.02398 https://arxiv.org/pdf/2403.02398
arXiv:2403.02398v1 Announce Type: new
Abstract: Measurements of the microlensing optical depth and event rate toward the Large Magellanic Cloud (LMC) can be used to probe the distribution and mass function of compact objects in the direction toward that galaxy - in the Milky Way disk, Milky Way dark matter halo, and the LMC itself. The previous measurements, based on small statistical samples of events, found that the optical depth is an order of magnitude smaller than that expected from the entire dark matter halo in the form of compact objects. However, these previous studies were not sensitive to long-duration events with Einstein timescales longer than 2.5-3 years, which are expected from massive ($10-100\,M_{\odot}$) and intermediate-mass ($10^2-10^5\,M_{\odot}$) black holes. Such events would have been missed by the previous studies and would not have been taken into account in calculations of the optical depth. Here, we present the analysis of nearly 20-year-long photometric monitoring of 78.7 million stars in the LMC by the Optical Gravitational Lensing Experiment (OGLE) from 2001 through 2020. We describe the observing setup, the construction of the 20-year OGLE dataset, the methods used for searching for microlensing events in the light curve data, and the calculation of the event detection efficiency. In total, we find 16 microlensing events (thirteen using an automated pipeline and three with manual searches), all of which have timescales shorter than 1 yr. We use a sample of thirteen events to measure the microlensing optical depth toward the LMC $\tau=(0.121 \pm 0.037)\times 10^{-7}$ and the event rate $\Gamma=(0.74 \pm 0.25)\times 10^{-7}\,\mathrm{yr}^{-1}\,\mathrm{star}^{-1}$. These numbers are consistent with lensing by stars in the Milky Way disk and the LMC itself, and demonstrate that massive and intermediate-mass black holes cannot comprise a significant fraction of dark matter.
@zimpenfish@social.rjp.is
@arXiv_astrophHE_bot@mastoxiv.pageSpectroscopy of a Sample of Unidentified Gamma-ray Fermi Sources
Alberto Ulgiati, Simona Paiano, Aldo Treves, Renato Falomo, Boris Sbarufatti, Fabio Pintore, Thomas D. Russell, Giancarlo Cusumano
https://arxiv.org/abs/2402.12081
@arXiv_csCR_bot@mastoxiv.pageThis https://arxiv.org/abs/2308.06466 has been replaced.
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@arXiv_mathLO_bot@mastoxiv.pageThis https://arxiv.org/abs/2308.06789 has been replaced.
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@arXiv_grqc_bot@mastoxiv.pageThis https://arxiv.org/abs/2401.14061 has been replaced.
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@zimpenfish@social.rjp.isAnyone know of an iOS app that overlays a photo on the camera to allow for alignment? MUST support the 0.5x, 2x, 3x modes (which, you will not be surprised to learn, none of the ones I've found thus far do.)
Tried "Before & After", "Stop Motion Factory", "Dittoed" (instacrashes!), "Onion Cam2", and "FrameApart". All useless.
@arXiv_astrophHE_bot@mastoxiv.pageSpectroscopy of a Sample of Unidentified Gamma-ray Fermi Sources
Alberto Ulgiati, Simona Paiano, Aldo Treves, Renato Falomo, Boris Sbarufatti, Fabio Pintore, Thomas D. Russell, Giancarlo Cusumano
https://arxiv.org/abs/2402.12081
@arXiv_astrophGA_bot@mastoxiv.pageAn optically-dark merging system at z~6 detected by JWST
Giulia Rodighiero, Andrea Enia, Laura Bisigello, Giorgia Girardi, Giovanni Gandolfi, Mahsa Kohandel, Andrea Pallottini, Nicolo' Badinelli, Andrea Grazian, Andrea Ferrara, Benedetta Vulcani, Alessandro Bianchetti, Antoninto Marasco, Francesco Sinigaglia, Marco Castellano, Paola Santini, Paolo Cassata
@arXiv_mathLO_bot@mastoxiv.pageThis https://arxiv.org/abs/2308.06789 has been replaced.
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@arXiv_physicsoptics_bot@mastoxiv.pageGeneration of Spatially Coherent Light at Extreme Ultraviolet Wavelengths
Randy A. Bartels, Ariel Paul, Hans Green, Henry C. Kapteyn, Margaret M. Murnane, Sterling Backus, Ivan P. Christov, Yanwei Liu, David Attwood, Chris Jacobsen
https://arxiv.org/abs/2403.19750
@zimpenfish@social.rjp.isFinally found the solder and managed, somehow, to solder on a USB-C socket (with fly leads, not SMC!) where the microUSB used to be (for the kitchen webcam.). Amazingly it works (although I am using an A to C cable to avoid any potential "whoops here's 100W" problems.)
(Not sure 380° was the right temperature but I'll look that up later.)
@arXiv_qbioNC_bot@mastoxiv.pageObject permanence in newborn chicks is robust against opposing evidence
Justin N. Wood, Tomer D. Ullman, Brian W. Wood, Elizabeth S. Spelke, Samantha M. W. Wood
https://arxiv.org/abs/2402.14641
@arXiv_csIR_bot@mastoxiv.pageThis https://arxiv.org/abs/2403.07331 has been replaced.
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@arXiv_grqc_bot@mastoxiv.pageIsotropic and anisotropic neutron star structure in 4D Einstein Gauss Bonnet Gravity
Gholam Hossein Bordbar, Mohammad Mazhari, Ahmad Poostforush
https://arxiv.org/abs/2402.14038 <…
@arXiv_astrophEP_bot@mastoxiv.pageBeyond Point Masses. IV. TNO Altjira is Likely a Hierarchical Triple Discovered Through Non-Keplerian Motion
Maia A Nelsen, Darin Ragozzine, Benjamin C. N. Proudfoot, William G. Giforos, Will Grundy
https://arxiv.org/abs/2403.12786
@zimpenfish@social.rjp.is@… Annoyingly I then found an octahedron net which is just 4 sheared squares and all my clever shenanigans for making the previous net are for NAUGHT (except personal satisifaction, I suppose.)
@arXiv_physicsoptics_bot@mastoxiv.pageGeneration of Spatially Coherent Light at Extreme Ultraviolet Wavelengths
Randy A. Bartels, Ariel Paul, Hans Green, Henry C. Kapteyn, Margaret M. Murnane, Sterling Backus, Ivan P. Christov, Yanwei Liu, David Attwood, Chris Jacobsen
https://arxiv.org/abs/2403.19750
@arXiv_csIR_bot@mastoxiv.pageThis https://arxiv.org/abs/2403.07331 has been replaced.
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@arXiv_astrophHE_bot@mastoxiv.pageSearching for precursor activity of Type IIn Supernovae
A. Reguitti, G. Pignata, A. Pastorello, R. Dastidar, D. E. Reichart, J. B. Haislip, V. V. Kouprianov
https://arxiv.org/abs/2403.10398
@zimpenfish@social.rjp.isToday we are discovering that RCON has no official multi-packet response support in the protocol even though a) the maximum response packet is 4096 bytes, b) several things which talk RCON (-glares at Minecraft-) return messages much, much longer than 4096 bytes and, most hilariously, c) I haven’t yet found an RCON client library which supports multi-packet / long responses.
@arXiv_astrophGA_bot@mastoxiv.pageThis https://arxiv.org/abs/2402.07855 has been replaced.
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