To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interacting with a wide and unseen range of everyday objects. We refer to these tasks as Building-wide Mobile Manipulation. To tackle these inherently long-horizon tasks, we introduce BUMBLE, a unified Vision-Language Model (VLM)-based framework integrating open-world RGBD perception, a wide spectrum of gross-to-fine motor skills, and dual-layered memory. Our extensive evaluation (90+ hours) indicates that BUMBLE outperforms multiple baselines in long-horizon building-wide tasks that require sequencing up to 12 ground truth skills spanning 15 minutes per trial. BUMBLE achieves 47.1% success rate averaged over 70 trials in different buildings, tasks, and scene layouts from different starting rooms and floors. Our user study demonstrates 22% higher satisfaction with our method than state-of-the-art mobile manipulation methods. Finally, we demonstrate the potential of using increasingly-capable foundation models to push performance further. For more information, see https://robin-lab.cs.utexas.edu/BUMBLE/
We study bias and discrimination in the context of Bumble, an online dating platform in India. Drawing on research in AI fairness and inclusion studies we analyze algorithmic bias and their propensity to reproduce bias. We conducted an experiment to identify and address the presence of bias in the matching algorithms Bumble pushes to its users in the form of profiles for potential dates in the real world. Dating apps like Bumble utilize algorithms that learn from user data to make recommendations. Even if the algorithm does not have intentions or consciousness, it is a system created and maintained by humans. We attribute moral agency of such systems to be compositely derived from algorithmic mediations, the design and utilization of these platforms. Developers, designers, and operators of dating platforms thus have a moral obligation to mitigate biases in the algorithms to create inclusive platforms that affirm diverse social identities.
1. Challenging calibration of complex models can be approached by using prior knowledge on the parameters. However, the natural choice of Bayesian inference can be computationally heavy when relying on Markov Chain Monte Carlo (MCMC) sampling. When the likelihood of the data is intractable, alternative Bayesian methods have been proposed. Approximate Bayesian Computation (ABC) only requires sampling from the data generative model, but may be problematic when the dimension of the data is high. 2. We studied alternative strategies to handle high dimensional data in ABC applied to the calibration of a spatially explicit foraging model for \textit{Bombus terrestris}. The first step consisted in building a set of summary statistics carrying enough biological meaning, i.e. as much as the original data, and then applying ABC on this set. Two ABC strategies, the use of regression adjustment leading to the production of ABC posterior samples, and the use of machine learning approaches to approximate ABC posterior quantiles, were compared with respect to coverage of model estimates and true parameter values. The comparison was made on simulated data as well as on data from two field studies.
Bumble bees astonished researchers by inventing a new way to reach a hidden reward, despite never being taught the trick。 The discovery adds to growing evidence that these tiny insects are far smarter and more adaptable than once believed
Foraging site constancy, or repeated return to the same foraging location, is a foraging strategy used by many species to decrease uncertainty, but it is often unclear exactly how the foraging site is identified. Here we focus on the context where food harvesting is first preceded by a separate exploration period. Foraging then consists of three distinct steps: (1) exploration of the landscape (site-generation), (2) selection of a foraging site (site-selection), and (3) exploitation (harvesting) through repeated trips between the foraging site and "home base". This type of foraging has received scant attention by modellers, leading to two main knowledge gaps. First, little is known about how organisms implement steps (1) and (2). Second, it is not known how the choice of implementation method affects the outcomes of step (3). We investigate these two gaps using an agent-based model, with bumble bees as our model organism, foraging in a patchy resource landscape of crop, wildflower, and empty cells. We tested two different site-generation methods (random and circular foray exploration behaviour) and four different site-selection methods (random and optimizing based on distance from
The purpose of this paper is to investigate vertical self-resonant (VSR) bifurcations from the distant retrograde orbit (DRO) family in the framework of the Earth-Moon circular restricted three-body problem (CR3BP). To this end, by using a classical corrector-predictor algorithm we compute the vertical stability of the DROs and identify fourteen vertical-critical DROs. We split them into three groups according to orbiting around the libration points $L_i$, $i=1,2,4,5$. (i) We first analyze six VSR bifurcations of higher order periods (of multiplicity from integer multiples of five to ten) associated with the DROs near the Moon. (ii) For the DROs that move near the Moon and additionally around the $L_1$ and $L_2$ libration points, we study six VSR bifurcations of multiplicity from five to ten as well. (iii) Within the DROs orbiting around the $L_4$ and $L_5$ libration points, two vertical single-turn branch points occur. In total, we generate 25 bifurcated families of spatial symmetric periodic solutions and present their orbital characteristics, including bridge families to the Butterfly, prograde orbits, quasi DROs and DROs. We also obtain branches whose members consist of long pe
As large language models (LLMs) become an important way of information access, there have been increasing concerns that LLMs may intensify the spread of unethical content, including implicit bias that hurts certain populations without explicit harmful words. In this paper, we conduct a rigorous evaluation of LLMs' implicit bias towards certain demographics by attacking them from a psychometric perspective to elicit agreements to biased viewpoints. Inspired by psychometric principles in cognitive and social psychology, we propose three attack approaches, i.e., Disguise, Deception, and Teaching. Incorporating the corresponding attack instructions, we built two benchmarks: (1) a bilingual dataset with biased statements covering four bias types (2.7K instances) for extensive comparative analysis, and (2) BUMBLE, a larger benchmark spanning nine common bias types (12.7K instances) for comprehensive evaluation. Extensive evaluation of popular commercial and open-source LLMs shows that our methods can elicit LLMs' inner bias more effectively than competitive baselines. Our attack methodology and benchmarks offer an effective means of assessing the ethical risks of LLMs, driving progress t
We report on the effects of cosmic ray interactions with the Kinetic Inductance Detector (KID) based focal plane array for the Terahertz Intensity Mapper (TIM). TIM is a NASA-funded balloon-borne experiment designed to probe the peak of the star formation in the Universe. It employs two spectroscopic bands, each equipped with a focal plane of four $\sim\,$900-pixel, KID-based array chips. Measurements of an 864-pixel TIM array shows 791 resonators in a 0.5$\,$GHz bandwidth. We discuss challenges with resonator calibration caused by this high multiplexing density. We robustly identify the physical positions of 788 (99.6$\,$%) detectors using a custom LED-based identification scheme. Using this information we show that cosmic ray events occur at a rate of 2.1$\,\mathrm{events/min/cm^2}$ in our array. 66$\,$% of the events affect a single pixel, and another 33$\,$% affect $<\,$5 KIDs per event spread over a 0.66$\,\mathrm{cm^2}$ region (2 pixel pitches in radius). We observe a total cosmic ray dead fraction of 0.0011$\,$%, and predict that the maximum possible in-flight dead fraction is $\sim\,$0.165$\,$%, which demonstrates our design will be robust against these high-energy event
When it comes to classifying child sexual abuse images, managing similar inter-class correlations and diverse intra-class correlations poses a significant challenge. Vision transformer models, unlike conventional deep convolutional network models, leverage a self-attention mechanism to capture global interactions among contextual local elements. This allows them to navigate through image patches effectively, avoiding incorrect correlations and reducing ambiguity in attention maps, thus proving their efficacy in computer vision tasks. Rather than directly analyzing child sexual abuse data, we constructed two datasets: one comprising clean and pornographic images and another with three classes, which additionally include images indicative of pornography, sourced from Reddit and Google Open Images data. In our experiments, we also employ an adult content image benchmark dataset. These datasets served as a basis for assessing the performance of vision transformer models in pornographic image classification. In our study, we conducted a comparative analysis between various popular vision transformer models and traditional pre-trained ResNet models. Furthermore, we compared them with est
Microwave Kinetic Inductance Detectors (MKIDs) have been demonstrated as capable phonon sensors when coupled to crystalline substrates, and have been proposed as detectors for next-generation rare-event searches such as for the direct detection of dark matter. These Kinetic Inductance Phonon Mediated (KIPM) detector designs, favoring large superconducting absorber volumes and high readout powers, are oftentimes limited in their sensitivity by low temperature amplifier noise introduced in the signal readout chain. We report here an effort to couple a wideband Kinetic Inductance Travelling Wave Parametric Amplifier (KI-TWPA), operated near the Standard Quantum Limit of minimal added amplifier noise, to sensors spanning a 70 MHz bandwidth at 3.5 GHz. This results in a ~5x improvement in the inferred detector energy resolution in the best sensor and highlights the potential of constructing O(100) meV resolving phonon-mediated particle detectors. We detail limitations introduced by lossy passive components, degraded RF responsivity, and microphysical noise sources like two-level systems (TLS), in achieving ultimate quantum-limited system noise levels.
Approximately half of the existing winged-insect species are of very small size (wing length about 0.3-4 mm); they are referred to as miniature insects. Yet until recently, much of what we know about the mechanics of insect flight was derived from studies on relatively large insects, such as hoverflies, honey bees and hawkmoths. Because of their very small size, many miniature insects fly at a Reynolds number (Re) on the order of 10 or less. At such a low Re, the viscous effect of the air is very large: A miniature insect moves through the air as would a bumble bee move through mineral oil. Miniature insects must use new flapping mode and new aerodynamic mechanisms to fly. Over the past decade, much work has been done in the study of the mechanics of flight in miniature insects: novel flapping modes have been discovered and new mechanisms of aerodynamic force generation have been revealed; progress has also been made on the fluid-mechanics related flight problems, such as flight power requirements and flight dynamic stability. This article reviews these developments and discusses potential future directions.
Superconducting nanowire single photon detectors (SNSPDs) are the highest-performing technology for time-resolved single-photon counting from the UV to the near-infrared. The recent discovery of single-photon sensitivity in micrometer-scale superconducting wires is a promising pathway to explore for large active area devices with application to dark matter searches and fundamental physics experiments. We present 8-pixel $1 mm^2$ superconducting microwire single photon detectors (SMSPDs) with $1\,\mathrm{μm}$-wide wires fabricated from WSi and MoSi films of various stoichiometries using electron-beam and optical lithography. Devices made from all materials and fabrication techniques show saturated internal detection efficiency at 1064 nm in at least one pixel, and the best performing device made from silicon-rich WSi shows single-photon sensitivity in all 8 pixels and saturated internal detection efficiency in 6/8 pixels. This detector is the largest reported active-area SMSPD or SNSPD with near-IR sensitivity published to date, and the first report of an SMSPD array. By further optimizing the photolithography techniques presented in this work, a viable pathway exists to realize lar
We report on the extension of the spectral sensitivity of superconducting nanowire single-photon detectors to a wavelength of 29 $μ$m. This represents the first demonstration of a time correlated single-photon counting detector at these long infrared wavelengths. We achieve saturated internal detection efficiency from 10 to 29 $μ$m, whilst maintaining dark count rates below 0.1 counts per second. Extension of superconducting nanowire single-photon detectors to this spectral range provides low noise and high timing resolution photon counting detection, effectively providing a new class of single-photon sensitive detector for these wavelengths. These detectors are important for applications such as exoplanet spectroscopy, infrared astrophysics, physical chemistry, remote sensing and direct dark-matter detection.
In this position paper we draw attention to safety risks against youth and young adults that originate through the combination of online and in-person interaction, and opportunities for AI to address these risks. Our context of study is social matching systems (e.g., Tinder, Bumble), which are used by young adults for online-to-offline interaction with strangers, and which are correlated with sexual violence both online and in-person. The paper presents early insights from an ongoing participatory AI design study in which young women build directly explainable models for detecting risk associated with discovered social opportunities, and articulate what AI should do once risk has been detected. We seek to advocate for participatory AI design as a way to directly incorporate youth and young adults into the design of a safer Internet. We also draw attention to challenges with the method.
In this position paper we intend to advocate for participatory design methods and mobile social matching apps as ripe contexts for exploring novel human-AI interactions that benefit marginalized groups. Mobile social matching apps like Tinder and Bumble use AI to introduce users to each other for rapid face-to-face meetings. These user discoveries and subsequent interactions pose disproportionate risk of sexual violence and other harms to marginalized user demographics, specifically women and the LGBTQIA+ community. We want to extend the role of AI in these apps to keep users safe while they interact with strangers across online and offline modalities. To do this, we are using participatory design methods to empower women and LGBTQIA+ individuals to envision future human-AI interactions that prioritize their safety during social matching app-use. In one study, stakeholders identifying as LGBTQIA+ or women are redesigning dating apps to mediate exchange of sexual consent and therefore mitigate sexual violence. In the other study, women are designing multi-purpose, opportunistic social matching apps that foreground women's safety.
Superconducting nanowire single photon detectors are a key technology for quantum information and science due to their high efficiency, low timing jitter, and low dark counts. In this work, we present a detector for single 1550 nm photons with up to 78% detection efficiency, timing jitter below 50 ps FWHM, 158 counts/s dark count rate - as well as a world-leading maximum count rate of 1.5 giga-counts/s at 3 dB compression. The PEACOQ detector (Performance-Enhanced Array for Counting Optical Quanta) comprises a linear array of 32 straight superconducting niobium nitride nanowires which span the mode of an optical fiber. This design supports high count rates with minimal penalties for detection efficiency and timing jitter. We show how these trade-offs can be mitigated by implementing independent read-out for each nanowire and by using a temporal walk correction technique to reduce count-rate dependent timing jitter. These detectors make quantum communication practical on a 10 GHz clock.
Entomologists, ecologists and others struggle to rapidly and accurately identify the species of bumble bees they encounter in their field work and research. The current process requires the bees to be mounted, then physically shipped to a taxonomic expert for proper categorization. We investigated whether an image classification system derived from transfer learning can do this task. We used Google Inception, Oxford VGG16 and VGG19 and Microsoft ResNet 50. We found Inception and VGG classifiers were able to make some progress at identifying bumble bee species from the available data, whereas ResNet was not. Individual classifiers achieved accuracies of up to 23% for single species identification and 44% top-3 labels, where a composite model performed better, 27% and 50%. We feel the performance was most hampered by our limited data set of 5,000-plus labeled images of 29 species, with individual species represented by 59 -315 images.
We present the direct imaging discovery of a low-mass companion to the nearby accelerating F star, HIP 5319, using SCExAO coupled with the CHARIS, VAMPIRES, and MEC instruments in addition to Keck/NIRC2 imaging. CHARIS $JHK$ (1.1-2.4 $μ$m) spectroscopic data combined with VAMPIRES 750 nm, MEC $Y$, and NIRC2 $L_{\rm p}$ photometry is best matched by an M3--M7 object with an effective temperature of T=3200 K and surface gravity log($g$)=5.5. Using the relative astrometry for HIP 5319 B from CHARIS and NIRC2 and absolute astrometry for the primary from $Gaia$ and $Hipparcos$ and adopting a log-normal prior assumption for the companion mass, we measure a dynamical mass for HIP 5319 B of $31^{+35}_{-11}M_{\rm J}$, a semimajor axis of $18.6^{+10}_{-4.1}$ au, an inclination of $69.4^{+5.6}_{-15}$ degrees, and an eccentricity of $0.42^{+0.39}_{-0.29}$. However, using an alternate prior for our dynamical model yields a much higher mass of 128$^{+127}_{-88}M_{\rm J}$. Using data taken with the LCOGT NRES instrument we also show that the primary HIP 5319 A is a single star in contrast to previous characterizations of the system as a spectroscopic binary. This work underscores the importance o
A large number of insect species feed primarily on a fluid diet. To do so, they must overcome the numerous challenges that arise in the design of high-efficiency, miniature pumps. Although the morphology of insect feeding structures has been described for decades, their dynamics remain largely unknown even in the most well studied species (e.g. fruit fly). Here, in the fluid dynamics video, we demonstrate in-vivo imaging and microsurgery to elucidate the design principles of feeding structures of the common house fly. Using high-resolution X-ray absorption microscopy, we record in-vivo flow of sucrose solutions through the body over many hours during fly feeding. Borrowing from microsurgery techniques common in neurophysiology, we are able to perturb the pump to a stall position and thus evaluate function under load conditions. Furthermore, fluid viscosity-dependent feedback is observed for optimal pump performance. As the gut of the fly starts to fill up, feedback from the stretch receptors in the cuticle dictates the effective flow rate. Finally, via comparative analysis between the housefly, blow fly, fruit fly and bumble bees, we highlight the common design principles and the r
New US rules would legalize quiet supersonic flights without the sonic boom