Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian multi-band fitting of alerts for kilonovae detection

In the era of multi-messenger astronomy, early classification of photometric alerts from wide-field and high-cadence surveys is a necessity to trigger spectroscopic follow-ups. These classifications are expected to play a key role in identifying potential candidates that might have a corresponding gravitational wave (GW) signature. Machine learning classifiers using features from parametric fitting of light curves are widely deployed by broker software to analyze millions of alerts, but most of these algorithms require as many points in the filter as the number of parameters to produce the fit, which increases the chances of missing a short transient. Moreover, the classifiers are not able to account for the uncertainty in the fits when producing the final score. In this context, we present a novel classification strategy that incorporates data-driven priors for extracting a joint posterior distribution of fit parameters and hence obtaining a distribution of classification scores. We train and test a classifier to identify kilonovae events which originate from binary neutron star mergers or neutron star black hole mergers, among simulations for the Zwicky Transient Facility observations with 19 other non-kilonovae-type events. We demonstrate that our method can estimate the uncertainty of misclassification, and the mean of the distribution of classification scores as point estimate obtains an AUC score of 0.96 on simulated data. We further show that using this method we can process the entire alert steam in real-time and bring down the sample of probable events to a scale where they can be analyzed by domain experts.
Active Collision Avoidance System for E-Scooters in Pedestrian Environment

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced LSTM network integrated with a state refinement module. This proactive model is designed to ensure unobstructed movement in areas with substantial pedestrian traffic without collisions. Results are validated on two public datasets, ETH and UCY, providing encouraging outcomes. Our model demonstrated proficiency in anticipating pedestrian paths and augmented scooter path planning, allowing for heightened adaptability in densely populated locales. This study shows the potential of melding pedestrian trajectory prediction with scooter motion planning. With the ubiquity of electric scooters in urban environments, such advancements have become crucial to safeguard all participants in urban transit.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences. Employing graph neural networks to capture structural patterns from syntactic dependency parsing has been confirmed as an effective approach for boosting ABSA. In most works, the topology of dependency trees or dependency-based attention coefficients is often loosely regarded as edges between aspects and opinions, which can result in insufficient and ambiguous syntactic utilization. To address these problems, we propose a new reinforced dependency graph convolutional network (RDGCN) that improves the importance calculation of dependencies in both distance and type views. Initially, we propose an importance calculation criterion for the minimum distances over dependency trees. Under the criterion, we design a distance-importance function that leverages reinforcement learning for weight distribution search and dissimilarity control. Since dependency types often do not have explicit syntax like tree distances, we use global attention and mask mechanisms to design type-importance functions. Finally, we merge these weights and implement feature aggregation and classification. Comprehensive experiments on three popular datasets demonstrate the effectiveness of the criterion and importance functions. RDGCN outperforms state-of-the-art GNN-based baselines in all validations.
Exploring the Robustness of stabilizing controls for stochastic quantum evolutions

In this work we analyze and bound the effect of modeling errors on the stabilization of pure states or subspaces for quantum stochastic evolutions. Different approaches are used for open-loop and feedback control protocols. For both, we highlight the key role of dynamical invariance of the target: if the perturbation preserves invariance, it is possible to prove that it also preserves its attractivity, under some additional assumptions. In addition, we prove boundedness in mean of the solutions of perturbed systems under open-loop protocols. For the feedback strategies, in the general case without assumptions on invariance, we provide bounds on the perturbation effect in expectation and in probability, as well as specific bounds for non-demolition nominal systems.
Development of time-of-flight particle identification for future Higgs factories

With the emergence of advanced Silicon (Si) sensor technologies such as LGADs, it is now possible to achieve exceptional time measurement precision below 50 ps. As a result, the implementation of time-of-flight (TOF) particle identification (PID) for charged hadrons at future $e^{+}e^{-}$ Higgs factory detectors has gained increasing attention. Other PID techniques require a gaseous tracker with excellent dE/dx resolution, or a Ring-imaging Cherenkov detector (RICH), which adds additional material in front of the calorimeter. TOF measurements can be implemented either in the outer layers of the tracker or in the electromagnetic calorimeter, and are thus particularly interesting as a PID method for detector concepts based on all-silicon trackers and optimised for particle-flow reconstruction. In this study, we will explore potential integration scenarios of a TOF measurement in a future Higgs factory detector, using the International Large Detector (ILD) as an example. We will focus on the challenges associated with crucial components of TOF PID, namely track length reconstruction and TOF measurements. The subsequent discussion will highlight the vital impact of precise track length reconstruction and various TOF measurement techniques, including recently developed machine learning approaches. We will evaluate the performance in terms of $\pi/K$ and $K/p$ separation as a function of momentum, and discuss potential physics applications.
Chandra’s Insights into SN 2023ixf

We report Chandra-ACIS observations of supernova (SN) 2023ixf in M101 on day 13 and 86 since the explosion. The X-rays in both epochs are characterized by high-temperature plasma from the forward shocked region as a result of circumstellar interaction. We are able to constrain the absorption column density at both Chandra epochs, which is much larger than that due to the Galactic and host absorption column, and we attribute it to absorption by the circumstellar matter in the immediate vicinity of SN 2023ixf. Combining our column density measurements with the published measurement on day 4, we show that the column density declines as t −2 between day 4 to day 13 and then evolves as t −1. The unabsorbed 0.3–10 keV luminosity evolves as t −1 during the Chandra epochs. On the Chandra first epoch observation, when the SN was 13 days old, we detect the Fe Kα fluorescent line at 6.4 keV indicating presence of cold material in the vicinity of the supernova. The line is absent on day 86, consistent with the decreased column density by a factor of 7 between the two epochs. Our analysis indicates that during 10–1.5 yr before explosion, the progenitor was evolving with a constant mass-loss rate of 5.6 × 10−4 M ⊙ yr−1.
Vector-Valued Gossip over w-Holonomic Networks

We study the weighted average consensus problem for a gossip network of agents with vector-valued states. For a given matrix-weighted graph, the gossip process is described by a sequence of pairs of adjacent agents communicating and updating their states based on the edge matrix weight. Our key contribution is providing conditions for the convergence of this non-homogeneous Markov process as well as the characterization of its limit set. To this end, we introduce the notion of"$w$-holonomy"of a set of stochastic matrices, which enables the characterization of sequences of gossiping pairs resulting in reaching a desired consensus in a decentralized manner. Stated otherwise, our result characterizes the limiting behavior of infinite products of (non-commuting, possibly with absorbing states) stochastic matrices.
All-Optical Phase Conjugation Using Diffractive Wavefront Processing

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing. Here, we present the design of a diffractive wavefront processor to approximate all-optical phase conjugation operation for input fields with phase aberrations. Leveraging deep learning, a set of passive diffractive layers was optimized to all-optically process an arbitrary phase-aberrated coherent field from an input aperture, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated the efficacy of this wavefront processor by 3D fabricating diffractive layers trained using deep learning and performing OPC on phase distortions never seen by the diffractive processor during its training. Employing terahertz radiation, our physical diffractive processor successfully performed the OPC task through a shallow spatially-engineered volume that axially spans tens of wavelengths. In addition to this transmissive OPC configuration, we also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and scalable nature, our diffractive wavefront processor can be used for diverse OPC-related applications, e.g., turbidity suppression and aberration correction, and is also adaptable to different parts of the electromagnetic spectrum, especially those where cost-effective wavefront engineering solutions do not exist.
A human brain atlas of chi-separation for normative iron and myelin distributions

Purpose: Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for healthy brain, and their abnormalities are often related to various neurological disorders. An advanced susceptibility mapping technique, which is referred to as chi-separation, has been recently proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This technique offers a new potential for generating iron map and myelin map of the brain. Utilizing this technique, this study constructs a normative chi-separation atlas from 106 healthy human brains. Methods: A total of 106 healthy volunteers (age range: 27 to 85, 34 males and 72 females) were recruited. The chi-separation maps were obtained by processing data acquired with a 3 T MRI with a 3D multi-echo gradient-echo sequence. The resulting chi-separation atlas was compared to well-known brain structures associated with iron and myelin distribution. Regions of interest (ROI) were defined using subcortical nuclei, thalamic nuclei, and white matter fiber tracts parcellation labels. Regional para- and diamagnetic susceptibility values were extracted. Results: The atlas reflects detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Paramagnetic and diamagnetic susceptibility values exhibited strong linear relationships with histochemical iron content (R2 = 0.99) and myelin water fraction (R2 = 0.63) from literatures, respectively. Conclusion: The atlas provides normative distributions of para- and diamagnetic susceptibility in the brain.
CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems

A Collaborative Artificial Intelligence System (CAIS) is a cyber-physical system that learns actions in collaboration with humans in a shared environment to achieve a common goal. In particular, a CAIS is equipped with an AI model to support the decision-making process of this collaboration. When an event degrades the performance of CAIS (i.e., a disruptive event), this decision-making process may be hampered or even stopped. Thus, it is of paramount importance to monitor the learning of the AI model, and eventually support its decision-making process in such circumstances. This paper introduces a new methodology to automatically support the decision-making process in CAIS when the system experiences performance degradation after a disruptive event. To this aim, we develop a framework that consists of three components: one manages or simulates CAIS's environment and disruptive events, the second automates the decision-making process, and the third provides a visual analysis of CAIS behavior. Overall, our framework automatically monitors the decision-making process, intervenes whenever a performance degradation occurs, and recommends the next action. We demonstrate our framework by implementing an example with a real-world collaborative robot, where the framework recommends the next action that balances between minimizing the recovery time (i.e., resilience), and minimizing the energy adverse effects (i.e., greenness).
Test the weak cosmic censorship conjecture via cold dark matter-black hole and ultralight dark matter-black hole

The weak cosmic censorship conjecture states that the black hole singularity is hidden inside the event horizon of the black hole, making it impossible for an external observer to measure. In this study, we investigate the weak cosmic censorship conjecture test of dark matter halo-black hole systems in both the cold dark matter model and ultralight dark matter model scenarios, with the aim of gaining insights into the influence of dark matter particles on the weak cosmic censorship conjecture. By examining the particle incident on an extremely or nearly extremal dark matter- black hole, as well as the scattering of a scalar field by an extreme or near-extreme dark matter- black hole. We find that the weak cosmic censorship conjecture does not violate the extreme and near-extreme dark matter-black hole systems for incident particles. When a scalar field is incident on an extreme dark matter-black hole system, the weak cosmic censorship conjecture can be violated by a scalar field pattern that satisfies $\dfrac{1}{2M\omega_{0}}<\dfrac{\omega}{m}<\dfrac{M\omega_{0}}{2M^{2}\omega_{0}^{2}+k_{i}}$ ($k_{i}<0$; where $k_{1}$ corresponds to the cold dark matter model and $k_{2}$ corresponds to the ultralight dark matter model). The weak cosmic censorship conjecture remains unviolated in the presence of a scalar field incident upon a nearly extreme dark matter-black hole system. This research will contribute to furthering our comprehension of the intricate interplay between dark matter and black holes.
Force-Constrained Visual Policy: Safe Robot-Assisted Dressing via Multi-Modal Sensing

Robot-assisted dressing could profoundly enhance the quality of life of adults with physical disabilities. To achieve this, a robot can benefit from both visual and force sensing. The former enables the robot to ascertain human body pose and garment deformations, while the latter helps maintain safety and comfort during the dressing process. In this letter, we introduce a new technique that leverages both vision and force modalities for this assistive task. Our approach first trains a vision-based dressing policy using reinforcement learning in simulation with varying body sizes, poses, and types of garments. We then learn a force dynamics model for action planning to ensure safety. Due to limitations of simulating accurate force data when deformable garments interact with the human body, we learn a force dynamics model directly from real-world data. Our proposed method combines the vision-based policy, trained in simulation, with the force dynamics model, learned in the real world, by solving a constrained optimization problem to infer actions that facilitate the dressing process without applying excessive force on the person. We evaluate our system in simulation and in a real-world human study with 10 participants across 240 dressing trials, showing it greatly outperforms prior baselines.
Lidar Annotation Is All You Need

In recent years, computer vision has transformed fields such as medical imaging, object recognition, and geospatial analytics. One of the fundamental tasks in computer vision is semantic image segmentation, which is vital for precise object delineation. Autonomous driving represents one of the key areas where computer vision algorithms are applied. The task of road surface segmentation is crucial in self-driving systems, but it requires a labor-intensive annotation process in several data domains. The work described in this paper aims to improve the efficiency of image segmentation using a convolutional neural network in a multi-sensor setup. This approach leverages lidar (Light Detection and Ranging) annotations to directly train image segmentation models on RGB images. Lidar supplements the images by emitting laser pulses and measuring reflections to provide depth information. However, lidar’s sparse point clouds often create difficulties for accurate object segmentation. Segmentation of point clouds requires time-consuming preliminary data preparation and a large amount of computational resources. The key innovation of our approach is the masked loss, addressing sparse ground-truth masks from lidar point clouds. By calculating loss exclusively where lidar points exist, the model learns road segmentation on images by using lidar points as ground truth. This approach allows for seamless blending of different ground-truth data types during model training. Experimental validation of the approach on benchmark datasets shows comparable performance to a high-quality image segmentation model. Incorporating lidar reduces the load on annotations and enables training of image-segmentation models without loss of segmentation quality. The methodology is tested by experiments with diverse datasets, both publicly available and proprietary. The strengths and weaknesses of the proposed method are also discussed. The work facilitates efficient use of point clouds for image model training by advancing neural network training for image segmentation.
Autonomous Advanced Aerial Mobility—An End-to-End Autonomy Framework for UAVs and Beyond

Developing aerial robots that can both safely navigate and execute assigned mission without any human intervention – i.e., fully autonomous aerial mobility of passengers and goods – is the larger vision that guides the research, design, and development efforts in the aerial autonomy space. However, it is highly challenging to concurrently operationalize all types of aerial vehicles that are operating fully autonomously sharing the airspace. Full autonomy of the aerial transportation sector includes several aspects, such as design of the technology that powers the vehicles, operations of multi-agent fleets, and process of certification that meets stringent safety requirements of aviation sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we present a comprehensive perspective on the emerging field of autonomous advanced aerial mobility, which involves the use of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft for various applications, such as urban air mobility, package delivery, and surveillance. The article proposes a scalable and extensible autonomy framework consisting of four main blocks: sensing, perception, planning, and controls. Furthermore, the article discusses the challenges and opportunities in multi-agent fleet operations and management, as well as the testing, validation, and certification aspects of autonomous aerial systems. Finally, the article explores the potential of monolithic models for aerial autonomy and analyzes their advantages and limitations. The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
Visitor constraints to physical activity in park and recreation areas: differences by race and ethnicity

Health benefits of physical activity are well recognized and documented, yet inactivity and obesity rates remain high in the U.S., particularly among racially and ethnically diverse populations. A greater understanding of factors that constrain physical activity in parks and recreation areas across various racial and ethnic groups may improve an agency’s ability to address obesity and sedentary lifestyles. Previous research has begun to document constraints to physical activity, as well as differences in both general leisure constraints and physical activity by race/ethnicity. However, additional work is needed to specifically examine constraints to physical activity of racially and ethnically diverse park visitors due to their increase in population proportion and risk for health issues. Therefore, this study examined differences in perceived constraints to physical activity in park and recreation areas among four racial and ethnic groups (Asian, Black/African American, non-Hispanic/Latino Whites, and Hispanic/Latinos of all races) while controlling for the effects of age, education, gender and recreation site location. Onsite questionnaires were administered to visitors in four park and recreation areas in the U.S. Two sites, one urban-proximate (city park) and one urban-distant (park/recreation area 50 miles or more from city), were selected in the Los Angeles and Minneapolis metropolitan areas. Results revealed similarities and differences among race and ethnic groups regarding constraints to physical activity in park and recreation areas and indicated a number of implications and opportunities for land managers. Time, family obligations and lack of energy were the greatest constraints for all groups. Therefore, efforts to reduce or at least address these constraints will be particularly important across all visitors. Differences emerged in 14 of the 18 constraint items among Asian, Black, Hispanic/Latino and White respondents when controlling for the effects of each recreation site location, and 10 differences remained when also controlling for age, education, and gender. In general, constraints were greater for racially/ethnically diverse respondents, and particularly for Hispanic/Latino visitors. As these populations have higher rates of obesity and sedentary lifestyles, it is critical that their constraints be addressed. In addition, land managers seeking to increase physical activity on their lands for the improved health benefits of visitors and potential visitors should attend to the constraints experienced by all.