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In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with challenges related to cost and durability. To address these issues, our study introduces a novel approach to visuo-tactile sensing with an emphasis on economy and replacablity. Our proposed sensor, BeadSight, uses hydro-gel beads encased in a vinyl bag as an economical, easily replaceable sensing medium. When the sensor makes contact with a surface, the deformation of the hydrogel beads is observed using a rear camera. This observation is then passed through a U-net Neural Network to predict the forces acting on the surface of the bead bag, in the form of a pressure map. Our results show that the sensor can accurately predict these pressure maps, detecting the location and magnitude of forces applied to the surface. These abilities make BeadSight an effective, inexpensive, and easily replaceable tactile sensor, ideal for many robotics applications., Comment: BeadSight code is available at: https://github.com/Abraham190137/BeadSight 7 pages, 8 figures

CDG-1 is a tight grouping of four likely globular clusters in the Perseus cluster, and a candidate dark galaxy with little or no diffuse light. Here we provide new constraints on the luminosity of any underlying stellar emission, using HST/UVIS F200LP imaging. No diffuse emission is detected, with a 2$\sigma$ upper limit of F200LP>28.1 mag/arcsec$^2$ on the 5'' scale of CDG-1. This surface brightness limit corresponds to a 2$\sigma$ lower limit of >0.5 for the fraction of the total luminosity that is in the form of globular clusters. The most likely alternative, although improbable, is that CDG-1 is a chance grouping of four globular clusters in the halo of the Perseus galaxy IC312., Comment: To appear in Research Notes of the American Astronomical Society

Optical matter, a rapidly growing field, aims to develop novel methods for controlling and assembling mesoscale systems through fascinating light-matter interactions. Its traditional approaches often involve complex illumination fields with costly and unstable setups. These methods require strong gradient forces, high-intensity laser spots that could harm samples, and substrate support. This work introduces an alternative approach using magneto-optical particles and straightforward illumination to effectively create magneto-optical matter. Our numerical framework, grounded in Langevin dynamics and realistic collision phenomena, serves as a robust and universal tool for exploring various optomechanical designs. To illustrate the capabilities of the developed method, we examine the impact of thermal noise, collision types, geometric variations, and resonance excitation on a dimer system, generating multiple scenarios for favorable dimer formation and subsequent manipulation. It is found that anisotropic dimers tuned initially at magneto-optical resonance can bind in stable average positions even when particle-particle collisions are present. We believe our method and subsequent findings provide essential knowledge for studying optical binding and the dynamics of any small-scale cluster of electromagnetically interacting components., Comment: 14 pages; 10 figures

We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications., Comment: 12 pages, 6 figures

We report the results of an extensive set of simulations exploring the sensitivity of the BlackCAT CubeSat to long-duration gamma-ray bursts (GRBs). BlackCAT is a NASA APRA-funded CubeSat mission for the detection and real-time sub-arcminute localization of high-redshift ($z\gtrsim 3.5$) GRBs. Thanks to their luminous and long-lived afterglow emissions, GRBs are uniquely valuable probes of high-redshift star-forming galaxies and the intergalactic medium. In addition, each detected GRB with a known redshift serves to localize a region of high-redshift star formation in three dimensions, enabling deep follow-on searches for host galaxies and associated local and large-scale structure. We explore two distinct models for the GRB redshift distribution and luminosity function, both consistent with \textit{Swift} observations. We find that, for either model, BlackCAT is expected to detect a mean of 42 bursts per year on-orbit, with 6.7% to 10% of these at $z>3.5$. BlackCAT bursts will be localized to $r_{90} \lesssim 55^{\prime\prime}$ precision and reported to the community within seconds. Due to the mission orbit and pointing scheme, bursts will be located in the night sky and well-placed for deep multiwavelength follow-up observations. BlackCAT is on schedule to achieve launch readiness in 2025., Comment: 10 pages, 4 figures, 3 tables; Accepted for publication in ApJ

Studying the relative orientations of the orbits of exoplanets and wide-orbiting binary companions (semimajor axis greater than 100 AU) can shed light on how planets form and evolve in binary systems. Previous observations by multiple groups discovered a possible alignment between the orbits of visual binaries and the exoplanets that reside in them. In this study, using data from \textit{Gaia} DR3 and TESS, we confirm the existence of an alignment between the orbits of small planets $(R<6 R_\oplus)$ and binary systems with semimajor axes below 700 AU ($p=10^{-6}$). However, we find no statistical evidence for alignment between planet and binary orbits for binary semimajor axes greater than 700 AU, and no evidence for alignment of large, closely-orbiting planets (mostly hot Jupiters) and binaries at any separation. The lack of orbital alignment between our large planet sample and their binary companions appears significantly different from our small planet sample, even taking into account selection effects. Therefore, we conclude that any alignment between wide-binaries and our sample of large planets (predominantly hot Jupiters) is probably not as strong as what we observe for small planets in binaries with semimajor axes less than 700 AU. The difference in the alignment distribution of hot Jupiters and smaller planets may be attributed to the unique evolutionary mechanisms occuring in systems that form hot Jupiters, including potentially destabilizing secular resonances that onset as the protoplanetary disk dissipates and high-eccentricity migration occurring after the disk is gone., Comment: 16 pages, 10 figures, submitted to AJ. Email corresponding author for data files

Li-ion batteries contain excess anode area to improve manufacturability and prevent Li plating. These overhang areas in graphite electrodes are active but experience decreased Li+ flux during cycling. Over time, the overhang and the anode portions directly opposite to the cathode can exchange Li+, driven by differences in local electrical potential across the electrode, which artificially inflates or decreases the measured cell capacity. Here, we show that lithiation of the overhang is less likely to happen in silicon anodes paired with layered oxide cathodes. The large voltage hysteresis of silicon creates a lower driving force for Li+ exchange as lithium ions transit into the overhang, rendering this exchange highly inefficient. For crystalline Si particles, Li+ storage at the overhang is prohibitive, because the low potential required for the initial lithiation can act as thermodynamic barrier for this exchange. We use micro-Raman spectroscopy to demonstrate that crystalline Si particles at the overhang are never lithiated even after cell storage at 45 oC for four months. Since the anode overhang can affect the forecasting of cell life, cells using silicon anodes may require different methodologies for life estimation compared to those used for traditional graphite-based Li-ion batteries.

Episodic accretion in protostars leads to luminosity outbursts that end up heating their surroundings. This rise in temperature pushes the snow lines back, enabling the desorption of chemical species from dust grain surfaces, which may significantly alter the chemical history of the accreting envelope. However, a limited number of extensive chemical surveys of eruptive young stars have been performed thus far. In the present study, we carry out a large spectral survey of the binary Class I protostar L1551 IRS 5, known to be a FUor-like object, in the 3mm and 2mm bands with the IRAM-30m telescope. As a result, we detected more than 400 molecular lines. The source displays a great chemical richness with the detection of 75 species, including isotopologues. Among these species, there are 13 hydrocarbons, 25 N-bearing species, 30 O-bearing species, 15 S-bearing species, 12 deuterated molecules, and a total of 10 complex organic molecules (l-C4H2, CH3CCH, CH2DCCH, CH3CHO, CH3CN, CH3OCH3, CH3OCHO, CH3OH, CH2DOH, and HC5N). With the help of local thermodynamic equilibrium (LTE) and non-LTE models, we determined the column densities of most molecules as well as excitation and kinetic temperatures. While most of those molecules trace the cold envelope (< 20 K), the OCS and CH3OH emission arise from the warm (> 100 K) innermost (< 2'' ) regions. We compared the chemical inventory of L1551 IRS 5 and its column density ratios, including isotopic ratios, with other protostellar sources. A broad chemical diversity is seen among Class I objects. More observations with both single-dish telescopes and interferometers are needed to characterize the diversity in a larger sample of protostars, while more astrochemical models would help explain this diversity, in addition to the impact of luminosity outbursts on the chemistry of protostellar envelopes., Comment: 46 pages, 16 figures, Accepted for publications in A&A

Recent advancements in technology have led to the emergence of Cyber-Physical Systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of artificial intelligence (AI) and machine learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations., Comment: 8 Pages, 3 Figures

Recently, Pulsar Timing Arrays (PTAs) reported a signal at nanohertz frequencies consistent with a stochastic gravitational wave background. Here, I show that the Brownian motion of the Sun as a result of its random gravitational interactions with the cluster of thousands of unmodeled Main-belt asteroids of diameters <80km, combined with small inaccuracies in the estimated masses of larger asteroids, introduces timing noise for pulsars which is up to 20% of the reported signal at frequencies of ~1/(few-years). The asteroid contribution needs to be modeled better in order to obtain accurate inferences from the PTA signal., Comment: Accepted for publication in ApJL, 5 pages (final version)

Interactive video retrieval is a cooperative process between humans and retrieval systems. Large-scale evaluation campaigns, however, often overlook human factors, such as the effects of perception, attention, and memory, when assessing media retrieval systems. Consequently, their setups fall short of emulating realistic retrieval scenarios. In this paper, we design novel task presentation modes based on concepts in media memorability, implement the pipelines necessary for processing target video segments, and build a custom experimental platform for the final evaluation. In order to study the effects of different task representation schemes, we conduct a large crowdsourced experiment. Our findings demonstrate that the way in which the target of a video retrieval task is presented has a substantial influence on the difficulty of the retrieval task and that individuals can successfully retrieve a target video segment despite reducing or even altering the provided hints, opening up a discussion around future evaluation protocols in the domain of interactive media retrieval.

Salient object detection (SOD) remains an important task in computer vision, with applications ranging from image segmentation to autonomous driving. Fully convolutional network (FCN)-based methods have made remarkable progress in visual saliency detection over the last few decades. However, these methods have limitations in accurately detecting salient objects, particularly in challenging scenes with multiple objects, small objects, or objects with low resolutions. To address this issue, we proposed a Saliency Fusion Attention U-Net (SalFAU-Net) model, which incorporates a saliency fusion module into each decoder block of the attention U-net model to generate saliency probability maps from each decoder block. SalFAU-Net employs an attention mechanism to selectively focus on the most informative regions of an image and suppress non-salient regions. We train SalFAU-Net on the DUTS dataset using a binary cross-entropy loss function. We conducted experiments on six popular SOD evaluation datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method, SalFAU-Net, achieves competitive performance compared to other methods in terms of mean absolute error (MAE), F-measure, s-measure, and e-measure., Comment: 9 pages, 5 figures

We propose a mathematical framework to the study of scalar conservation laws with moving interfaces. This framework is developed on a LWR model with constraint on the flux along these moving interfaces. Existence is proved by means of a finite volume scheme. The originality lies in the local modification of the mesh and in the treatment of the crossing points of the trajectories.

We build a finite volume scheme for the scalar conservation law $\partial_t u + \partial_x (H(x, u)) = 0$ with bounded initial condition for a wide class of flux function $H$, convex with respect to the second variable. The main idea for the construction of the scheme is to use the theory of discontinuous flux. We prove that the resulting approximating sequence converges boundedly almost everywhere on $\mathopen]0, +\infty\mathclose[$ to the entropy solution.

In this paper, we propose a macroscopic model that describes the influence of a slow moving large vehicle on road traffic. The model consists of a scalar conservation law with a non-local constraint on the flux. The constraint level depends on the trajectory of the slower vehicle which is given by an ODE depending on the downstream traffic density. After proving well-posedness, we first build a finite volume scheme and prove its convergence, and then investigate numerically this model by performing a series of tests. In particular, the link with the limit local problem of [M. L. Delle Monache and P. Goatin, J. Differ. Equ. 257 (2014), 4015--4029] is explored numerically.

Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalogue and study such features and to correlate them with other basic galaxy properties the environment in which the galaxies are located and their interactions with other galaxies. Surveys such as SDSS, Pan-STARRS, HSC-SSP have made available very large samples of galaxies for gainful morphological studies. The availability of galaxy images and catalogues will increase manifold with future surveys like LSST. The volume of present and future data is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI based techniques like machine learning and deep learning for finding morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) data release DR18. We use a catalogue by Buta (2017) of ringed galaxies from the SDSS to train the network reaching good accuracy and recall, and generate a catalogue of 29420 galaxies of which 9805 have ring like structures with prediction confidence exceeding 90 percent. Using a catalogue of barred galaxy images identified by Abraham et. al. (2018) using deep learning techniques, we identify a set of 2087 galaxies with bars as well as rings. The catalogues should be very useful in understanding the origin of these important morphological structures., Comment: 19 pages, 13 figures, 1 Table. Submitted to ApJ

Molecular chirality has traditionally been viewed as a binary property where a molecule is classified as either chiral or achiral, yet in the recent decades mathematical methods for quantifying chirality have been explored. Here we use toy molecular systems to systematically compare the performance of two state of the art chirality measures (1) the Continuous Chirality Measure (CCM) and (2) the Chirality Characteristic ($\chi$). We find that both methods exhibit qualitatively similar behavior when applied to simple molecular systems such as a four-site molecule or the polymer double-helix, but we show that the CCM may be more suitable for evaluating the chirality of arbitrary molecules or abstract structures such as normal vibrational modes. We discuss a range of considerations for applying these methods to molecular systems in general, and we provide links to user-friendly codes for both methods. We aim this paper to serve as a concise resource for scientists attempting to familiarize themselves with these chirality measures or attempting to implement chirality measures in their own work.

We compute the Borel complexity of some classes of Banach spaces such as different versions of diameter two properties, spaces satisfying the Daugavet equation or spaces with an octahedral norm. In most of the above cases our computation is even optimal, which completes the research done during the last years around this topic for isomorphism classes of Banach spaces.

The $James \ Webb \ Space \ Telescope \ (JWST)$ recently reported a large population of UV luminous galaxies at high redshifts, $ z > 10$, as well as Lyman-$\alpha$ emitting (LAE) galaxies out to $z \sim 11$. We use the observed UV luminosities along with a data-driven approach at lower redshifts to place constraints on the observability of the intergalactic Lyman-$\alpha$ intensity, scattered in the form of Loeb-Rybicki haloes, during the pre-reionization and reionization epochs ($z \sim 9-16$). We forecast the sensitivity and resolution required to detect these intergalactic haloes, finding that individual haloes with LAE luminosities $> 10^{43}$ ergs/s are detectable at a few sigma level at $z \lesssim 11$, while stacking of $\sim 10$ haloes is expected to result in detections out to $z \sim 16$. Finding these haloes is expected to shed light on the neutral intergalactic hydrogen during cosmic reionization., Comment: 13 pages, 3 figures, 2 tables

In this study, we investigate the crucial role of measurement time ($t_m$), information gain and energy consumption in information engines (IEs) utilizing a von-Neumann measurement model. These important measurement parameters allow us to analyze the efficiency and power output of these devices. As the measurement time increases, the information gain and subsequently the extracted work also increase. However, there is a corresponding increase in the energetic cost. The efficiency of converting information into free energy diminishes as $t_m$ approaches both 0 and infinity, peaking at intermediate values of $t_m$. The power output (work extracted per times) also reaches a maximum at specific operational time regimes. By considering the product of efficiency and power as a performance metric, we can identify the optimal operating conditions for the IE.

This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction.

In this study, we assess the usability of interactive personal assistants (IPAs), such as Amazon Alexa, in a simulated kitchen smart home environment, with deaf and hard of hearing users. Participants engage in activities in a way that causes their hands to get dirty. With these dirty hands, they are tasked with two different input methods for IPAs: American Sign Language (ASL) in a Wizard-of-Oz design, and smart home apps with a touchscreen. Usability ratings show that participants significantly preferred ASL over touch-based apps with dirty hands, although not to a larger extent than in comparable previous work with clean hands. Participants also expressed significant enthusiasm for ASL-based IPA interaction in Netpromoter scores and in questions about their overall preferences. Preliminary observations further suggest that having dirty hands may affect the way people sign, which may pose challenges for building IPAs that natively support sign language input., Comment: To appear in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA 2024, May 11-16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3613905.3651075

With the recent advancements in intelligent personal assistants (IPAs), their popularity is rapidly increasing when it comes to utilizing Automatic Speech Recognition within households. In this study, we used a Wizard-of-Oz methodology to evaluate and compare the usability of American Sign Language (ASL), Tap to Alexa, and smart home apps among 23 deaf participants within a limited-domain smart home environment. Results indicate a slight usability preference for ASL. Linguistic analysis of the participants' signing reveals a diverse range of expressions and vocabulary as they interacted with IPAs in the context of a restricted-domain application. On average, deaf participants exhibited a vocabulary of 47 +/- 17 signs with an additional 10 +/- 7 fingerspelled words, for a total of 246 different signs and 93 different fingerspelled words across all participants. We discuss the implications for the design of limited-vocabulary applications as a stepping-stone toward general-purpose ASL recognition in the future., Comment: To appear in Proceedings of the Conference on Human Factors in Computing Systems CHI 24, May 11-16, Honolulu, HI, USA, 15 pages. https://doi.org/10.1145/3613904.3642094

We analyze consequences of trying to replace dark matter and dark energy with models of stochastic spacetime. In particular, we analyze the model put forth by Ref. [1], in which it is claimed that ``post-quantum classical gravity" (PQCG), a stochastic theory of gravity, leads to modified Newtonian dynamics (MOND) behavior on galactic scales that reproduces galactic rotation curves, and leads to dark energy. We show that this analysis has four basic problems: (i) the equations of PQCG do not lead to a new large scale force of the form claimed in the paper, (ii) the form claimed is not of the MONDian form anyhow and so does not correspond to observed galactic dynamics, (iii) the spectrum of fluctuations is very different from observations, and (iv) we also identify some theoretical problems in these models., Comment: 10 pages in double column format, 1 figure. V2: More details including analysis of fluctuations. V3: Further details. V4: Analysis of temporal correlations. V5: Updated towards version accepted for publication in JCAP

JWST is discovering a large population of $z>4$ supermassive black holes (SMBHs) that are overmassive with respect to the stellar content of their hosts. A previous study developed a physical model to interpret this overmassive population as the result of quasar feedback acting on a compact host galaxy. In this Note, we apply this model to JADES GN 1146115, a dormant supermassive black hole at $z=6.7$ whose mass is $\sim40\%$ of the host's mass in stars and accreting at $\sim2\%$ of the Eddington limit. The host has been forming stars at the low rate of $\sim 1 \, \rm M_\odot \,yr^{-1}$ for the past $\sim 100$ Myr. Our model suggests that this galactic system is on the verge of a resurgence of global star formation activity. This transition comes after a period of domination by the effect of its overmassive black hole, whose duration is comparable to typical quasar lifetimes., Comment: Accepted on Research Notes of the AAS. This is a concise note (3 pages, 1 figure) regarding the direct application of the model described in arXiv:2401.04159 to the source presented in arXiv:2403.03872

Clover fixates nitrogen from the atmosphere to the ground, making grass-clover mixtures highly desirable to reduce external nitrogen fertilization. Herbage containing clover additionally promotes higher food intake, resulting in higher milk production. Herbage probing however remains largely unused as it requires a time-intensive manual laboratory analysis. Without this information, farmers are unable to perform localized clover sowing or take targeted fertilization decisions. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. The energy-intensive nature of deep learning however limits deployment to practical edge devices such as smartphones. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We report that although pruned networks are accurate on controlled, high-quality images of the grass, they struggle to generalize to real-world smartphone images that are blurry or taken from challenging angles. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model. This hybrid approach allows us to reduce energy consumption while retaining a high accuracy. We evaluate our algorithm on two datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge device. We find that we reduce energy reduction with respect to state-of-the-art solutions by 50% on average with only 4% accuracy loss., Comment: The paper has been accepted to CVPR 2024 5th Workshop on Vision for Agriculture

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Human pose estimation faces hurdles in real-world applications due to factors like lighting changes, occlusions, and cluttered environments. We introduce a unique RGB-Thermal Nearly Paired and Annotated 2D Pose Dataset, comprising over 2,400 high-quality LWIR (thermal) images. Each image is meticulously annotated with 2D human poses, offering a valuable resource for researchers and practitioners. This dataset, captured from seven actors performing diverse everyday activities like sitting, eating, and walking, facilitates pose estimation on occlusion and other challenging scenarios. We benchmark state-of-the-art pose estimation methods on the dataset to showcase its potential, establishing a strong baseline for future research. Our results demonstrate the dataset's effectiveness in promoting advancements in pose estimation for various applications, including surveillance, healthcare, and sports analytics. The dataset and code are available at https://github.com/avinres/LWIRPOSE, Comment: Submitted in ICIP2024

This paper presents a mixed methods study on how deaf, hard of hearing and hearing viewers perceive live TV caption quality with captioned video stimuli designed to mirror TV captioning experiences. To assess caption quality, we used four commonly-used quality metrics focusing on accuracy: word error rate, weighted word error rate, automated caption evaluation (ACE), and its successor ACE2. We calculated the correlation between the four quality metrics and viewer ratings for subjective quality and found that the correlation was weak, revealing that other factors besides accuracy affect user ratings. Additionally, even high-quality captions are perceived to have problems, despite controlling for confounding factors. Qualitative analysis of viewer comments revealed three major factors affecting their experience: Errors within captions, difficulty in following captions, and caption appearance. The findings raise questions as to how objective caption quality metrics can be reconciled with the user experience across a diverse spectrum of viewers., Comment: To appear in Proceedings of the Conference on Human Factors in Computing Systems CHI 24, May 11-16, Honolulu, HI, USA, 16 pages. https://doi.org/10.1145/3613904.3641988

In this paper, we present several new linearizations of a quadratic binary optimization problem (QBOP), primarily using the method of aggregations. Although aggregations were studied in the past in the context of solving system of Diophantine equations in non-negative variables, none of the approaches developed produced practical models, particularly due to the large size of associate multipliers. Exploiting the special structure of QBOP we show that selective aggregation of constraints provide valid linearizations with interesting properties. For our aggregations, multipliers can be any non-zero real numbers. Moreover, choosing the multipliers appropriately, we demonstrate that the resulting LP relaxations have value identical to the corresponding non-aggregated models. We also provide a review of existing explicit linearizations of QBOP and presents the first systematic study of such models. Theoretical and experimental comparisons of new and existing models are also provided.

This paper investigates performance guarantees on coverage-based ergodic exploration methods in environments containing disturbances. Ergodic exploration methods generate trajectories for autonomous robots such that time spent in each area of the exploration space is proportional to the utility of exploring in the area. We find that it is possible to use techniques from reachability analysis to solve for optimal controllers that guarantee ergodic coverage and are robust against disturbances. We formulate ergodic search as a differential game between the controller optimizing for ergodicity and an external disturbance, and we derive the reachability equations for ergodic search using an extended-state Bolza-form transform of the ergodic problem. Contributions include the computation of a continuous value function for the ergodic exploration problem and the derivation of a controller that provides guarantees for coverage under disturbances. Our approach leverages neural-network-based methods to solve the reachability equations; we also construct a robust model-predictive controller for comparison. Simulated and experimental results demonstrate the efficacy of our approach for generating robust ergodic trajectories for search and exploration on a 1D system with an external disturbance force., Comment: 21 pages, 5 figures

On March 6 2023, the Gaia telescope has alerted a 2-magnitude burst from Gaia23bab, a Young Stellar Object in the Galactic plane. We observed Gaia23bab with the Large Binocular Telescope obtaining optical and near-infrared spectra close in time to the peak of the burst, and collected all public multi-band photometry to reconstruct the historical light curve. This latter shows three bursts in ten years (2013, 2017 and 2023), whose duration and amplitude are typical of EXor variables. We estimate that, due to the bursts, the mass accumulated on the star is about twice greater than if the source had remained quiescent for the same period of time. Photometric analysis indicates that Gaia23bab is a Class,II source with age < 1 Myr, spectral type G3-K0, stellar luminosity 4.0 L_sun, and mass 1.6 M_sun. The optical/near infrared spectrum is rich in emission lines. From the analysis of these lines we measured the accretion luminosity and the mass accretion rate L_acc(burst)=3.7 L_sun, M_acc(burst) 2.0 10 $^(-7) M_sun/yr, consistent with those of EXors. More generally, we derive the relationships between accretion and stellar parameters in a sample of EXors. We find that, when in burst, the accretion parameters become almost independent of the stellar parameters and that EXors, even in quiescence, are more efficient than classical T Tauri stars in assembling mass., Comment: Accepted by The Astrophysical Journal

The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions can not ensure the accuracy of recommended parties or candidates, which we show to remain below 40%. To tackle these limitations, this work introduces an adaptive questionnaire approach that selects subsequent questions based on users' previous answers, aiming to enhance recommendation accuracy while reducing the number of questions posed to the voters. Our method uses an encoder and decoder module to predict missing values at any completion stage, leveraging a two-dimensional latent space reflective of political science's traditional methods for visualizing political orientations. Additionally, a selector module is proposed to determine the most informative subsequent question based on the voter's current position in the latent space and the remaining unanswered questions. We validated our approach using the Smartvote dataset from the Swiss Federal elections in 2019, testing various spatial models and selection methods to optimize the system's predictive accuracy. Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version., Comment: 16 pages plus appendix, submitted to ECML/PKDD 2024

Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks., Comment: 19 pages, 7 figures, 3 tables, 1 Appendix table

Building a modular architecture with superconducting quantum computing chips is one of the means to achieve qubit scalability, allowing the screening, selection, replacement, and integration of individual qubit modules into large quantum systems. However, the nondestructive replacement of modules within a compact architecture remains a challenge. Liquid metals, specifically gallium alloys, can be alternatives to solid-state galvanic interconnects. This is motivated by their self-healing, self-aligning, and other desirable fluidic properties, potentially enabling the nondestructive replacement of modules at room temperatures, even after operating the entire system at millikelvin regimes. In this study, we present coplanar waveguide resonators (CPWRs) interconnected by gallium alloy droplets, achieving high internal quality factors up to nearly one million and demonstrating performance on par with the continuous solid-state CPWRs. Leveraging the desirable fluidic properties of gallium alloys at room temperature and their compact design, we envision a modular quantum system enabled by liquid metals.

Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as powerful solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Furthermore, the paper illustrates how quantum computing can support enhanced financial risk management analysis. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies by exploiting the quantum advantage. This research underscores the transformative impact of quantum algorithms on financial risk management. By embracing quantum technologies, organisations can enhance their capabilities to combat evolving threats and ensure the integrity and stability of financial systems.

The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the low-confidence regions of models and can be identified using epistemic uncertainty of ML approaches -- epistemic uncertainty in a machine learning-based malware detector is a result of a lack of similar training samples in regions of the problem space. In particular, a Bayesian formulation can capture the model parameters' distribution and quantify epistemic uncertainty without sacrificing model performance. To verify our hypothesis, we consider Bayesian learning approaches with a mutual information-based formulation to quantify uncertainty and detect adversarial malware in Android, Windows domains and PDF malware. We found, quantifying uncertainty through Bayesian learning methods can defend against adversarial malware. In particular, Bayesian models: (1) are generally capable of identifying adversarial malware in both feature and problem space, (2) can detect concept drift by measuring uncertainty, and (3) with a diversity-promoting approach (or better posterior approximations) lead to parameter instances from the posterior to significantly enhance a detectors' ability., Comment: Accepted to the 29th European Symposium on Research in Computer Security (ESORICS) 2024 Conference

We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and outer-approximate robust controllable sets using constrained zonotopes, when the additive uncertainty set is a symmetric, convex, and compact set. Our least-squares-based approach uses novel closed-form approximations of the Pontryagin difference between a constrained zonotopic minuend and a symmetric, convex, and compact subtrahend. Unlike existing approaches, our approach does not rely on convex optimization solvers, and is projection-free for ellipsoidal and zonotopic uncertainty sets. We also propose a least-squares-based approach to compute a convex, polyhedral outer-approximation to constrained zonotopes, and characterize sufficient conditions under which all these approximations are exact. We demonstrate the computational efficiency and scalability of our approach in several case studies, including the design of abort-safe rendezvous trajectories for a spacecraft in near-rectilinear halo orbit under uncertainty. Our approach can inner-approximate a 20-step robust controllable set for a 100-dimensional linear system in under 15 seconds on a standard computer., Comment: 22 pages, 6 figures

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup., Comment: Project website: https://droid-dataset.github.io/

This paper presents the first results of the study of high-energy electron and muon neutrino charged-current interactions in the FASER$\nu$ emulsion/tungsten detector of the FASER experiment at the LHC. A subset of the FASER$\nu$ volume, which corresponds to a target mass of 128.6~kg, was exposed to neutrinos from the LHC $pp$ collisions with a centre-of-mass energy of 13.6~TeV and an integrated luminosity of 9.5 fb$^{-1}$. Applying stringent selections requiring electrons with reconstructed energy above 200~GeV, four electron neutrino interaction candidate events are observed with an expected background of $0.025^{+0.015}_{-0.010}$, leading to a statistical significance of 5.2$\sigma$. This is the first direct observation of electron neutrino interactions at a particle collider. Eight muon neutrino interaction candidate events are also detected, with an expected background of $0.22^{+0.09}_{-0.07}$, leading to a statistical significance of 5.7$\sigma$. The signal events include neutrinos with energies in the TeV range, the highest-energy electron and muon neutrinos ever detected from an artificial source. The energy-independent part of the interaction cross section per nucleon is measured over an energy range of 560--1740 GeV (520--1760 GeV) for $\nu_e$ ($\nu_{\mu}$) to be $(1.2_{-0.7}^{+0.8}) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$ ($(0.5\pm0.2) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$), consistent with Standard Model predictions. These are the first measurements of neutrino interaction cross sections in those energy ranges.

Tactile information is a critical tool for dexterous manipulation. As humans, we rely heavily on tactile information to understand objects in our environments and how to interact with them. We use touch not only to perform manipulation tasks but also to learn how to perform these tasks. Therefore, to create robotic agents that can learn to complete manipulation tasks at a human or super-human level of performance, we need to properly incorporate tactile information into both skill execution and skill learning. In this paper, we investigate how we can incorporate tactile information into imitation learning platforms to improve performance on manipulation tasks. We show that incorporating visuo-tactile pretraining improves imitation learning performance, not only for tactile agents (policies that use tactile information at inference), but also for non-tactile agents (policies that do not use tactile information at inference). For these non-tactile agents, pretraining with tactile information significantly improved performance (for example, improving the accuracy on USB plugging from 20% to 85%), reaching a level on par with visuo-tactile agents, and even surpassing them in some cases. For demonstration videos and access to our codebase, see the project website: https://sites.google.com/andrew.cmu.edu/visuo-tactile-pretraining, Comment: 7 pages, 6 figures, submitted to ICRA 2025. Prior version named "Visuo-Tactile Pretraining for Cable Plugging"

Diffusion models have shown remarkable capability in generating visually impressive content from textual descriptions. However, these models are trained on vast internet data, much of which contains undesirable elements such as sensitive content, copyrighted material, and unethical or harmful concepts. Therefore, beyond generating high-quality content, it is crucial to ensure these models do not propagate these undesirable elements. To address this issue, we propose a novel method to remove undesirable concepts from text-to-image diffusion models by incorporating a learnable prompt into the cross-attention module. This learnable prompt acts as additional memory, capturing the knowledge of undesirable concepts and reducing their dependency on the model parameters and corresponding textual inputs. By transferring this knowledge to the prompt, erasing undesirable concepts becomes more stable and has minimal negative impact on other concepts. We demonstrate the effectiveness of our method on the Stable Diffusion model, showcasing its superiority over state-of-the-art erasure methods in removing undesirable content while preserving unrelated elements.

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.

Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a new subject. Our novel functional alignment procedure linearly maps all brain data to a shared-subject latent space, followed by a shared non-linear mapping to CLIP image space. We then map from CLIP space to pixel space by fine-tuning Stable Diffusion XL to accept CLIP latents as inputs instead of text. This approach improves out-of-subject generalization with limited training data and also attains state-of-the-art image retrieval and reconstruction metrics compared to single-subject approaches. MindEye2 demonstrates how accurate reconstructions of perception are possible from a single visit to the MRI facility. All code is available on GitHub., Comment: In Forty-first International Conference on Machine Learning, 2024. Code at https://github.com/MedARC-AI/MindEyeV2. Published as a conference paper at ICML 2024