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The financial sector is increasingly concerned with the physical risks of climate change, but economic and financial impact representations are still developing, particularly for chronic risks. Mexico's Central Bank conducted a comprehensive assessment using a suite of global models to evaluate both physical and transition risks. We present the analysis concerning with chronic physical risks, underlining innovations such as the use of a recent integrated assessment model that enables grid-cell level analysis and differentiates urban and non-urban areas, capturing the local effects of climate change more accurately. The model includes multiple damage functions and a probabilistic climate model for encompassing analyses and detailed economic impact insights. Under the Current Policies scenario, economic losses could exceed 35 percent of Mexico's GDP by 2100. Accounting for the urban heat island effect, losses could surpass 20 trillion (USD) in present value, over ten times Mexico's 2024 GDP. However, implementing a scenario aligned with the Paris Agreement significantly reduces these losses, showcasing the benefits of international mitigation efforts, though substantial residual impacts persist. This study emphasizes integrating chronic physical risks into financial evaluations, proposing new approaches, metrics, and methods that exploit detailed, spatially explicit measures to improve risk and loss estimation and facilitate communication., Comment: 19 pages, 7 figures

We report the spectro-temporal study of the neutron star low mass X-ray binary Cygnus X-2 using NICER and NuSTAR data while the source was in the normal branch (NB). We detect a normal branch oscillation (NBO) feature at ~ 5.41 Hz that appears in the middle portion of the NB branch. We note that the NBO appeared only in the 0.5-3 keV energy range, with maximum strength in the 1-2 keV energy band, but was absent in the 3-10 keV energy band of NuSTAR and NICER data. The energy spectrum of the source exhibits an emission feature at ~ 1 keV, previously identified as the Fe L transition in the outer region of the accretion disk. Upon considering both the Fe L and NBO features, we suggest that the originating location of the Fe L line and the NBOs may coincide and perhaps be due to the same underlying mechanism. Therefore, lags seen in the frequency/energy dependent lag spectra of Cygnus X-2 could be considered to be arising from a region of photoionized material far from the central source. We study the frequency and energy dependent lag spectra of the source, which exhibited a few milliseconds hard lag at the NBO frequency (12-15 ms) and a switch from hard to soft lags at 1 keV. The rms spectrum peaks at 1 keV and the covariance spectrum clearly resembles a thermal spectrum. We discuss the spectro-temporal behavior of the NBO and attempt to constrain its location of origin., Comment: Accepted for Publication in ApJ

The MOOSE (Monitoring Observations of SMC X-1 Excursions) program uses the Neutron Star Interior Composition Explorer Mission (NICER) to monitor the high mass X-ray binary SMC X-1 during its superorbital period excursions. Here we perform X-ray spectral analyses of 26 NICER observations of SMC X-1, taken at the tail-end of the excursion between 2021-04-01 and 2022-01-05. We use a single spectral model to fit spectra observed in high, intermediate and low states, using a combination of a partial covering fraction model, a black-body disc, and a power-law component. We find that the partial covering fraction varies significantly with the superorbital state during superorbital excursion. Our findings suggest that the low/high state in SMC X-1 is caused by a very high obscuration of the accretion disk., Comment: 10 pages 4 figures, accepted to MNRAS

This study explores the integration of auditory and tactile experiences in musical haptics, focusing on enhancing sensory dimensions of music through touch. Addressing the gap in translating auditory signals to meaningful tactile feedback, our research introduces a novel method involving a touch-sensitive recorder and a wearable haptic display that captures musical interactions via force sensors and converts these into tactile sensations. Previous studies have shown the potential of haptic feedback to enhance musical expressivity, yet challenges remain in conveying complex musical nuances. Our method aims to expand music accessibility for individuals with hearing impairments and deepen digital musical interactions. Experimental results reveal high accuracy ($98\%$ without noise, 93% with white noise) in melody recognition through tactile feedback, demonstrating effective transmission and perception of musical information. The findings highlight the potential of haptic technology to bridge sensory gaps, offering significant implications for music therapy, education, and remote musical collaboration, advancing the field of musical haptics and multi-sensory technology applications., Comment: This paper has been accepted for publication at ROBIO 2024 conference

The shape of deformable objects can change drastically during grasping by robotic grippers, causing an ambiguous perception of their alignment and hence resulting in errors in robot positioning and telemanipulation. Rendering clear tactile patterns is fundamental to increasing users' precision and dexterity through tactile haptic feedback during telemanipulation. Therefore, different methods have to be studied to decode the sensors' data into haptic stimuli. This work presents a telemanipulation system for plastic pipettes that consists of a Force Dimension Omega.7 haptic interface endowed with two electro-stimulation arrays and two tactile sensor arrays embedded in the 2-finger Robotiq gripper. We propose a novel approach based on convolutional neural networks (CNN) to detect the tilt of deformable objects. The CNN generates a tactile pattern based on recognized tilt data to render further electro-tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm, tilt recognition by users increased from 23.13\% with the downsized data to 57.9%, and the success rate during teleoperation increased from 53.12% using the downsized data to 92.18% using the tactile patterns generated by the CNN., Comment: Manuscript accepted to IEEE Telepresence 2024. arXiv admin note: text overlap with arXiv:2204.03521 by other authors

We use the rms and lag spectra of the type-C quasi-periodic oscillation (QPO) to study the properties of the Comptonisation region (aka corona) during the low/hard and hard-intermediate states of the main outburst and reflare of MAXI J1348$-$630. We simultaneously fit the time-averaged energy spectrum of the source and the fractional rms and phase-lag spectra of the QPO with the time-dependent Comptonization model vKompth. The data can be explained by two physically connected coronae interacting with the accretion disc via a feedback loop of X-ray photons. The best-fitting model consists of a corona of $\sim$10$^3$ km located at the inner edge of the disc and a second corona of $\sim$10$^4$ km horizontally extended and covering the inner parts of the accretion disc. The properties of both coronae during the reflare are similar to those during the low/hard state of the main outburst, reinforcing the idea that both the outburst and the reflare are driven by the same physical mechanisms. We combine our results for the type-C QPO with those from previous work focused on the study of type-A and type-B QPOs with the same model to study the evolution of the geometry of the corona through the whole outburst, including the reflare of MAXI J1348$-$630. Finally, we show that the sudden increase in the phase-lag frequency spectrum and the sharp drop in the coherence function previously observed in MAXI J1348$-$630 are due to the type-C QPO during the decay of the outburst and can be explained in terms of the geometry of the coronae., Comment: 18 pages, 8 figures, 1 table. Submitted to ApJ

This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By leveraging generative AI, the system automates critical aspects of production, including product blueprint design, component manufacturing, logistics, and assembly. A heterogeneous swarm of robots, each equipped with individual AI through integration with Large Language Models (LLMs), orchestrates the production process. The robotic system includes manipulator arms, delivery drones, and 3D printers capable of generating assembly blueprints. The system was evaluated using commercial and open-source LLMs, functioning through APIs and local deployment. A user study demonstrated that the system reduces the average production time to 119.10 minutes, significantly outperforming a team of expert human developers, who averaged 528.64 minutes (an improvement factor of 4.4). Furthermore, in the product blueprinting stage, the system surpassed human CAD operators by an unprecedented factor of 47, completing the task in 0.5 minutes compared to 23.5 minutes. This breakthrough represents a major leap towards fully autonomous manufacturing., Comment: submitted to IEEE conf

Image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D Thin-Plate Splines (TPS3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler's ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the TPS3D is the best candidate to achieve image consistency. Furthermore, our Smooth-TPS3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the Smooth-TPS method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load.

Within the context of creating new Socially Assistive Robots, emotion recognition has become a key development factor, as it allows the robot to adapt to the user's emotional state in the wild. In this work, we focused on the analysis of two voice recording Spanish datasets: ELRA-S0329 and EmoMatchSpanishDB. Specifically, we centered our work in the paralanguage, e.~g. the vocal characteristics that go along with the message and clarifies the meaning. We proposed the use of the DeepSpectrum method, which consists of extracting a visual representation of the audio tracks and feeding them to a pretrained CNN model. For the classification task, DeepSpectrum is often paired with a Support Vector Classifier --DS-SVC--, or a Fully-Connected deep-learning classifier --DS-FC--. We compared the results of the DS-SVC and DS-FC architectures with the state-of-the-art (SOTA) for ELRA-S0329 and EmoMatchSpanishDB. Moreover, we proposed our own classifier based upon Attention Mechanisms, namely DS-AM. We trained all models against both datasets, and we found that our DS-AM model outperforms the SOTA models for the datasets and the SOTA DeepSpectrum architectures. Finally, we trained our DS-AM model in one dataset and tested it in the other, to simulate real-world conditions on how biased is the model to the dataset.

In recent years, several models using Quaternion-Valued Convolutional Neural Networks (QCNNs) for different problems have been proposed. Although the definition of the quaternion convolution layer is the same, there are different adaptations of other atomic components to the quaternion domain, e.g., pooling layers, activation functions, fully connected layers, etc. However, the effect of selecting a specific type of these components and the way in which their interactions affect the performance of the model still unclear. Understanding the impact of these choices on model performance is vital for effectively utilizing QCNNs. This paper presents a statistical analysis carried out on experimental data to compare the performance of existing components for the image classification problem. In addition, we introduce a novel Fully Quaternion ReLU activation function, which exploits the unique properties of quaternion algebra to improve model performance., Comment: 17 pages, 6 figures

We present a comprehensive study based on multi-wavelength observations from the NuSTAR, NICER, Swift, Fermi, NEOWISE, and ATCA telescopes during the 2022 outburst of the black hole X-ray binary IGR J17091-3624. Our investigation concentrates on the heartbeat-like variability in the X-ray emission, with the aim of using it as a tool to unravel the origin of the non-thermal emission during the heartbeat state. Through X-ray timing and spectral analysis, we observe that the heartbeat-like variability correlates with changes in the disk temperature, supporting the disk radiation pressure instability scenario. Moreover, in addition to a Comptonization component, our time-averaged and phase-resolved spectroscopy reveal the presence of a power-law component that varies independently from the disk component. Combined with the radio to X-ray spectral energy distribution fitting, our results suggest that the power-law component could originate from synchrotron self-Compton radiation in the jet, which requires a strong magnetic field of about $B = (0.3$-$3.5)\times10^6$ G. Additionally, assuming that IGR J17091-3624 and GRS 1915+105 share the same radio-X-ray correlation coefficient during both the hard and the heartbeat states, we obtain a distance of $13.7\pm2.3$ kpc for IGR J17091-3624., Comment: 19 pages, 11 figures, 3 tables; accepted for publication in ApJ

We present our contribution to the 7th ABAW challenge at ECCV 2024, by utilizing a Dual-Direction Attention Mixed Feature Network (DDAMFN) for multitask facial expression recognition, we achieve results far beyond the proposed baseline for the Multi-Task ABAW challenge. Our proposal uses the well-known DDAMFN architecture as base to effectively predict valence-arousal, emotion recognition, and facial action units. We demonstrate the architecture ability to handle these tasks simultaneously, providing insights into its architecture and the rationale behind its design. Additionally, we compare our results for a multitask solution with independent single-task performance., Comment: 11 pages

This paper presents GazeRace, a novel system that leverages eye-tracking technology for intuitive drone control. Using the MediaPipe library, the system translates eye movements into precise drone commands, enabling effective remote piloting. In testing, GazeRace demonstrated an 18% reduction in drone trajectory length while maintaining competitive speed with traditional controls. The results suggest that this approach enhances control accuracy and reduces user frustration, offering a significant advancement in the field of human-computer interaction and drone navigation., Comment: Accepted in: IEEE International Conference on Systems, Man, and Cybernetics (SMC 2024)

Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of cancer in regions with limited access to mammography. Such AI systems are more likely to be useful in a clinical setting if their predictions can be explained to a radiologist. This work proposes an explainable AI model that provides interpretable predictions using a standard lexicon from the American College of Radiology's Breast Imaging and Reporting Data System (BI-RADS). The model is a deep neural network featuring a concept bottleneck layer in which known BI-RADS features are predicted before making a final cancer classification. This enables radiologists to easily review the predictions of the AI system and potentially fix errors in real time by modifying the concept predictions. In experiments, a model is developed on 8,854 images from 994 women with expert annotations and histological cancer labels. The model outperforms state-of-the-art lesion detection frameworks with 48.9 average precision on the held-out testing set, and for cancer classification, concept intervention is shown to increase performance from 0.876 to 0.885 area under the receiver operating characteristic curve. Training and evaluation code is available at https://github.com/hawaii-ai/bus-cbm., Comment: Submitted version of manuscript accepted at MICCAI 2024. This preprint has not undergone peer review or any post-submission improvements or corrections

We report on two epochs of simultaneous near-infrared (IR) and X-ray observations with a sub-second time resolution of the low mass X-ray binary black hole candidate Swift J1753.5--0127 during its long 2005--2016 outburst. Data were collected strictly simultaneously with VLT/ISAAC (K$_{S}$ band, 2.2 $\mu m$) and RXTE (2-15 keV) or \textit{XMM-Newton} (0.7-10 keV). A clear correlation between the X-ray and the IR variable emission is found during both epochs but with very different properties. In the first epoch, the near-IR variability leads the X-ray by $ \sim 130 \, ms$. This is the opposite of what is usually observed in similar systems. The correlation is more complex in the second epoch, with both anti-correlation and correlations at negative and positive lags. Frequency-resolved Fourier analysis allows us to identify two main components in the complex structure of the phase lags: the first component, characterised by a few seconds near-IR lag at low frequencies, is consistent with a combination of disc reprocessing and a magnetised hot flow; the second component is identified at high frequencies by a near-IR lag of $\approx$0.7 s. Given the similarities of this second component with the well-known constant optical/near-IR jet lag observed in other black hole transients, we tentatively interpret this feature as a signature of a longer-than-usual jet lag. We discuss the possible implications of measuring such a long jet lag in a radio-quiet black hole transient., Comment: 10 pages, 7 figures, accepted for publication in A&A

We present mid-infrared (MIR) spectral-timing measurements of the prototypical Galactic microquasar GRS 1915+105. The source was observed with the Mid-Infrared Instrument (MIRI) onboard JWST in June 2023 at a MIR luminosity L(MIR)~10^{36} erg/s exceeding past IR levels by about a factor of 10. By contrast, the X-ray flux is much fainter than the historical average, in the source's now-persistent 'obscured' state. The MIRI low-resolution spectrum shows a plethora of emission lines, the strongest of which are consistent with recombination in the hydrogen Pfund (Pf) series and higher. Low amplitude (~1%) but highly significant peak-to-peak photometric variability is found on timescales of ~1,000 s. The brightest Pf(6-5) emission line lags the continuum. Though difficult to constrain accurately, this lag is commensurate with light-travel timescales across the outer accretion disc or with expected recombination timescales inferred from emission line diagnostics. Using the emission line as a bolometric indicator suggests a moderate (~5-30% Eddington) intrinsic accretion rate. Multiwavelength monitoring shows that JWST caught the source close in-time to unprecedentedly bright MIR and radio long-term flaring. Assuming a thermal bremsstrahlung origin for the MIRI continuum suggests an unsustainably high mass-loss rate during this time unless the wind remains bound, though other possible origins cannot be ruled out. PAH features previously detected with Spitzer are now less clear in the MIRI data, arguing for possible destruction of dust in the interim. These results provide a preview of new parameter space for exploring MIR spectral-timing in XRBs and other variable cosmic sources on rapid timescales., Comment: Dedicated to the memory of our colleague, Tomaso Belloni. Submitted 2024 June 21; Comments welcome

This work proposes the Least Squares with Virtual Displacements (LSVD) method to obtain the shear strength of n soil samples for the cohesionless, frictionless and mixed resistance conditions. Finding a common tangent line of more than two samples may not have an exact solution, especially if there are measurement errors. LSVD is an iterative method to find the best common tangent line or function to the n soil samples. There are soils that can be modeled with a linear failure envelope but the LSVD can be formulated in different forms, adapting itself to other criterion too, like the logarithmic failure envelope. The tested data is analyzed with LSVD and compared between other commonly used methods, like p-q method and CTPAC. The soils' samples were subjected to random noise to simulate errors from measurements to study the method's results. Also with that analysis a common reference point can be found to compare the methods given that all of them take different points of the Mohr Circles.

{We herein report on the calculation of thermoluminescence (TL) kinetic parameters determined from the TL emission of synthetic GdAlO3 (GAO) phosphors prepared by the co-precipitation method. The sample, characterized by means of X-ray diffraction with an orthorhombic phase structure (space group Pnma (62), shows complex glow curves consisting of at least four groups of components peaked at 100, 140, 240, and 290 {\deg}C where the two lower overlapped temperature peaks are difficult to identify using the $T_M-T_{stop}$. The coexistence of a continuum in the trap distribution (linked to the lower temperature peaks) and a discrete trap system (associated with the components at temperatures higher than 200 {\deg}C) can be distinguished. The estimation of the TL kinetic parameters is performed using GlowFit, TLAnal, the spreadsheet Origin, Computing Glow Curve Deconvolution (CGCD), and various heating rate (VHR) methods. However, only CGCD appears as the suitable technique for such purpose since it provides information on the TL physical process supported by mathematical models based on a linear combination of functions related to the First Order Kinetic approach., Comment: 27 pages, 6 figures and 1 table

Multi-wavelength polarimetry and radio observations of Swift J1727.8-1613 at the beginning of its recent 2023 outburst suggested the presence of a bright compact jet aligned in the north-south direction, which could not be confirmed without high angular resolution images. Using the Very Long Baseline Array and the Long Baseline Array, we imaged Swift J1727.8-1613, during the hard/hard-intermediate state, revealing a bright core and a large, two-sided, asymmetrical, resolved jet. The jet extends in the north-south direction, at a position angle of $-0.60\pm0.07\deg$ East of North. At 8.4 GHz, the entire resolved jet structure is $\sim110 (d/2.7\,\text{kpc})/\sin i$ AU long, with the southern approaching jet extending $\sim80 (d/2.7\,\text{kpc})/\sin i$ AU from the core, where $d$ is the distance to the source and $i$ is the inclination of the jet axis to the line of sight. These images reveal the most resolved continuous X-ray binary jet, and possibly the most physically extended continuous X-ray binary jet ever observed. Based on the brightness ratio of the approaching and receding jets, we put a lower limit on the intrinsic jet speed of $\beta\geq0.27$ and an upper limit on the jet inclination of $i\leq74\deg$. In our first observation we also detected a rapidly fading discrete jet knot $66.89\pm0.04$ mas south of the core, with a proper motion of $0.66\pm0.05$ mas hour$^{-1}$, which we interpret as the result of a downstream internal shock or a jet-ISM interaction, as opposed to a transient relativistic jet launched at the beginning of the outburst., Comment: Accepted in ApJL

This paper presents the development of a novel ethical reasoning framework for robots. "Robots Can Feel" is the first system for robots that utilizes a combination of logic and human-like emotion simulation to make decisions in morally complex situations akin to humans. The key feature of the approach is the management of the Emotion Weight Coefficient - a customizable parameter to assign the role of emotions in robot decision-making. The system aims to serve as a tool that can equip robots of any form and purpose with ethical behavior close to human standards. Besides the platform, the system is independent of the choice of the base model. During the evaluation, the system was tested on 8 top up-to-date LLMs (Large Language Models). This list included both commercial and open-source models developed by various companies and countries. The research demonstrated that regardless of the model choice, the Emotions Weight Coefficient influences the robot's decision similarly. According to ANOVA analysis, the use of different Emotion Weight Coefficients influenced the final decision in a range of situations, such as in a request for a dietary violation F(4, 35) = 11.2, p = 0.0001 and in an animal compassion situation F(4, 35) = 8.5441, p = 0.0001. A demonstration code repository is provided at: https://github.com/TemaLykov/robots_can_feel, Comment: The paper is submitted to the IEEE conference

We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as those based on variational Bayes) at a much lower computational cost. We show this empirically on a range of filtering problems with outlier measurements, such as object tracking, state estimation in high-dimensional chaotic systems, and online learning of neural networks., Comment: 41st International Conference on Machine Learning (ICML 2024)

This research introduces the Bi-VLA (Vision-Language-Action) model, a novel system designed for bimanual robotic dexterous manipulation that seamlessly integrates vision for scene understanding, language comprehension for translating human instructions into executable code, and physical action generation. We evaluated the system's functionality through a series of household tasks, including the preparation of a desired salad upon human request. Bi-VLA demonstrates the ability to interpret complex human instructions, perceive and understand the visual context of ingredients, and execute precise bimanual actions to prepare the requested salad. We assessed the system's performance in terms of accuracy, efficiency, and adaptability to different salad recipes and human preferences through a series of experiments. Our results show a 100% success rate in generating the correct executable code by the Language Module, a 96.06% success rate in detecting specific ingredients by the Vision Module, and an overall success rate of 83.4% in correctly executing user-requested tasks., Comment: The paper was accepted to the IEEE SMC 2024

The collaborative robot market is flourishing as there is a trend towards simplification, modularity, and increased flexibility on the production line. But when humans and robots are collaborating in a shared environment, the safety of humans should be a priority. We introduce a novel wearable robotic system to enhance safety during Human-Robot Interaction (HRI). The proposed wearable robot is designed to hold a fiducial marker and maintain its visibility to a motion capture system, which, in turn, localizes the user's hand with good accuracy and low latency and provides vibrotactile feedback to the user's wrist. The vibrotactile feedback guides the user's hand movement during collaborative tasks in order to increase safety and enhance collaboration efficiency. A user study was conducted to assess the recognition and discriminability of ten designed vibration patterns applied to the upper (dorsal) and the down (volar) parts of the user's wrist. The results show that the pattern recognition rate on the volar side was higher, with an average of 75.64% among all users. Four patterns with a high recognition rate were chosen to be incorporated into our system. A second experiment was carried out to evaluate users' response to the chosen patterns in real-world collaborative tasks. Results show that all participants responded to the patterns correctly, and the average response time for the patterns was between 0.24 and 2.41 seconds., Comment: 14 pages, Eurohaptics 2024

We present the discovery, with the Neutron Star Interior Composition Explorer (NICER), that SRGA J144459.2-604207 is a 447.9 Hz accreting millisecond X-ray pulsar (AMXP), which underwent a four-week long outburst starting on 2024 February 15. The AMXP resides in a 5.22 hr binary, orbiting a low-mass companion donor with $M_d>0.1M_\odot$. We report on the temporal and spectral properties from NICER observations during the early days of the outburst, from 2024 February 21 through 2024 February 23, during which NICER also detected a type-I X-ray burst that exhibited a plateau lasting ~6 s. The spectra of the persistent emission were well described by an absorbed thermal blackbody and power-law model, with blackbody temperature $kT\approx0.9{\rm\,keV}$ and power-law photon index $\Gamma\approx1.9$. Time-resolved burst spectroscopy confirmed the thermonuclear nature of the burst, where an additional blackbody component reached a maximum temperature of nearly $kT\approx3{\rm\,keV}$ at the peak of the burst. We discuss the nature of the companion as well as the type-I X-ray burst., Comment: 11 pages, 4 figures, 3 tables. Accepted by ApJL

Black-hole X-ray binaries exhibit different spectral and timing properties in different accretion states. The X-ray outburst of a recently discovered and extraordinarily bright source, Swift$~$J1727.8$-$1613, has enabled the first investigation of how the X-ray polarization properties of a source evolve with spectral state. The 2$-$8 keV polarization degree was previously measured by the Imaging X-ray Polarimetry Explorer (IXPE) to be $\approx$ 4% in the hard and hard intermediate states. Here we present new IXPE results taken in the soft state, with the X-ray flux dominated by the thermal accretion-disk emission. We find that the polarization degree has dropped dramatically to $\lesssim$ 1%. This result indicates that the measured X-ray polarization is largely sensitive to the accretion state and the polarization fraction is significantly higher in the hard state when the X-ray emission is dominated by up-scattered radiation in the X-ray corona. The combined polarization measurements in the soft and hard states disfavor a very high or low inclination of the system., Comment: 9 pages, 7 figures

Binaries containing a compact object orbiting a supermassive black hole are thought to be precursors of gravitational wave events, but their identification has been extremely challenging. Here, we report quasi-periodic variability in X-ray absorption which we interpret as quasi-periodic outflows (QPOuts) from a previously low-luminosity active galactic nucleus after an outburst, likely caused by a stellar tidal disruption. We rule out several models based on observed properties and instead show using general relativistic magnetohydrodynamic simulations that QPOuts, separated by roughly 8.3 days, can be explained with an intermediate-mass black hole secondary on a mildly eccentric orbit at a mean distance of about 100 gravitational radii from the primary. Our work suggests that QPOuts could be a new way to identify intermediate/extreme-mass ratio binary candidates., Comment: Accepted for publication in Science Advances. We report a new supermassive black hole phenomenon that we call quasi-periodic outflows (QPOuts)

Quasi-periodic Eruptions (QPEs) represent a novel class of extragalactic X-ray transients that are known to repeat at roughly regular intervals of a few hours to days. Their underlying physical mechanism is a topic of heated debate, with most models proposing that they originate either from instabilities within the inner accretion flow or from orbiting objects. At present, our knowledge of how QPEs evolve over an extended timescale of multiple years is limited, except for the unique QPE source GSN 069. In this study, we present results from strategically designed Swift observing programs spanning the past three years, aimed at tracking eruptions from eRO-QPE1. Our main results are: 1) the recurrence time of eruptions can vary between 0.6 and 1.2 days, 2) there is no detectable secular trend in evolution of the recurrence times, 3) consistent with prior studies, their eruption profiles can have complex shapes, and 4) the peak flux of the eruptions has been declining over the past 3 years with the eruptions barely detected in the most recent Swift dataset taken in June of 2023. This trend of weakening eruptions has been reported recently in GSN 069. However, because the background luminosity of eRO-QPE1 is below our detection limit, we cannot verify if the weakening is correlated with the background luminosity (as is claimed to be the case for GSN 069). We discuss these findings within the context of various proposed QPE models., Comment: Resubmitted to ApJ Letters after implementing referee comments

This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain., Comment: This paper has been accepted for publication at the HRI2024 conference

This paper introduces CognitiveOS, the first operating system designed for cognitive robots capable of functioning across diverse robotic platforms. CognitiveOS is structured as a multi-agent system comprising modules built upon a transformer architecture, facilitating communication through an internal monologue format. These modules collectively empower the robot to tackle intricate real-world tasks. The paper delineates the operational principles of the system along with descriptions of its nine distinct modules. The modular design endows the system with distinctive advantages over traditional end-to-end methodologies, notably in terms of adaptability and scalability. The system's modules are configurable, modifiable, or deactivatable depending on the task requirements, while new modules can be seamlessly integrated. This system serves as a foundational resource for researchers and developers in the cognitive robotics domain, alleviating the burden of constructing a cognitive robot system from scratch. Experimental findings demonstrate the system's advanced task comprehension and adaptability across varied tasks, robotic platforms, and module configurations, underscoring its potential for real-world applications. Moreover, in the category of Reasoning it outperformed CognitiveDog (by 15%) and RT2 (by 31%), achieving the highest to date rate of 77%. We provide a code repository and dataset for the replication of CognitiveOS: link will be provided in camera-ready submission., Comment: The paper is submitted to the IEEE conference

The tidal disruption of a star around a supermassive black hole (SMBH) offers a unique opportunity to study accretion onto a SMBH on a human-timescale. We present results from our 1000+ days NICER, Swift and Chandra monitoring campaign of AT 2019avd, a nuclear transient with TDE-like properties. Our primary finding is that approximately 225 days following the peak of X-ray emission, there is a rapid drop in luminosity exceeding two orders of magnitude. This X-ray drop-off is accompanied by X-ray spectral hardening, followed by a 740-day plateau phase. During this phase, the spectral index decreases from 6.2+-1.1 to 2.3+-0.4, while the disk temperature remains constant. Additionally, we detect pronounced X-ray variability, with an average fractional root mean squared amplitude of 47%, manifesting over timescales of a few dozen minutes. We propose that this phenomenon may be attributed to intervening clumpy outflows. The overall properties of AT 2019avd suggest that the accretion disk evolves from a super-Eddington to a sub-Eddington luminosity state, possibly associated with a compact jet. This evolution follows a pattern in the hardness-intensity diagram similar to that observed in stellar-mass black holes, supporting the mass invariance of accretion-ejection processes around black holes., Comment: 21 pages, 11 figures, 1 table; accepted for publication in ApJ

In X-ray observations of hard state black hole X-ray binaries, rapid variations in accretion disc and coronal power-law emission are correlated and show Fourier-frequency-dependent time lags. On short (~0.1 s) time-scales, these lags are thought to be due to reverberation and therefore may depend strongly on the geometry of the corona. Low-frequency quasi-periodic oscillations (QPOs) are variations in X-ray flux that have been suggested to arise because of geometric changes in the corona, possibly due to General Relativistic Lense-Thirring precession. Therefore one might expect the short-term time lags to vary on the QPO time-scale. We performed novel spectral-timing analyses on NICER observations of the black hole X-ray binary MAXI J1820+070 during the hard state of its outburst in 2018 to investigate how the short-term time lags between a disc-dominated and a coronal power-law-dominated energy band vary on different time-scales. Our method can distinguish between variability due to the QPO and broadband noise, and we find a linear correlation between the power-law flux and lag amplitude that is strongest at the QPO frequency. We also introduce a new method to resolve the QPO signal and determine the QPO-phase-dependence of the flux and lag variations, finding that both are very similar. Our results are consistent with a geometric origin of QPOs, but also provide evidence for a dynamic corona with a geometry varying in a similar way over a broad range of time-scales, not just the QPO time-scale., Comment: 19 pages, 14 figures, 3 tables, published in MNRAS, includes link to reproduction package

We present a novel method for measuring the lags of (weak) variability components in neutron-star and black-hole low-mass X-ray binaries (LMXBs). For this we assume that the power and cross spectra of these sources consists of a number of components that are coherent in different energy bands, but are incoherent with one another. The technique is based on fitting simultaneously the power spectrum (PS) and the Real and Imaginary parts of the cross spectrum (CS) with a combination of Lorentzian functions. We show that, because the PS of LMXBs is insensitive to signals with a large Imaginary part and a small Real part in the CS, this approach allows us to uncover new variability components that are only detected in the CS. We also demonstrate that, contrary to earlier claims, the frequency of the type-C quasi-periodic oscillation (QPO) in the black-hole binary GRS 1915+105 does not depend on energy. Rather, the apparent energy dependence of the QPO frequency can be explained by the presence of a separate QPO component with a slightly higher frequency than that of the QPO, whose rms amplitude increases faster with energy than the rms amplitude of the QPO. From all the above we conclude that, as in the case of the PS, the CS of black-hole and neutron-star binaries can be fitted by a combination of Lorentzian components. Our findings provide evidence that the frequency-dependent part of the transfer function of these systems can be described by a combination of responses, each of them acting over relatively well-defined time scales. This conclusion challenges models that assume that the main contribution to the lags comes from a global, broadband, transfer function of the accreting system., Comment: 27 pages, 13 figures, 6 tables; accepted for publication in MNRAS

The high-frequency bump, characterized by a frequency exceeding ~30 Hz, represents a seldom-explored time-variability feature in the power density spectrum (PDS) of black-hole X-ray binaries. In the 2002, 2004, 2007 and 2010 outbursts of GX 339-4, the bump has been occasionally observed in conjunction with type-C quasi-periodic oscillations (QPOs). We systematically study the properties of the bump during these four outbursts observed by Rossi X-ray Timing Explorer (RXTE) in the 2-60 keV bands and detect the bump in 39 observations. While the frequencies of the type-C QPOs are in the range of ~0.1-9 Hz, the root-mean-square (rms) amplitude of the bump shows an evolution in the hardness ratio versus the type-C QPO frequency plot. By comparing the rms amplitude of the bump with the corona temperature and simultaneous radio jet flux of the source, as previously studied in GRS 1915+105, we establish that in the hard state of GX 339-4, the bump is always strong, with the measurements of the rms amplitude in the range of 4-10%. At the same time, the corona temperature is high and the radio flux is low. These findings indicate that, using the bump as a proxy, the majority of the accretion energy is directed towards the hot corona rather than being channeled into the radio jet. We discuss this phenomenon in terms of an inefficient energy transfer mechanism between the corona and jet in GX 339-4., Comment: 11 pages, 5 figures, accepted for publication in MNRAS

Black hole X-ray binaries (BH XRBs) are ideal targets to study the connection between accretion inflow and jet outflow. Here we present quasi-simultaneous, multi-wavelength observations of the Galactic black hole system MAXI J1820+070, throughout its 2018-2019 outburst. Our data set includes coverage from the radio through X-ray bands from 17 different instruments/telescopes, and encompasses 19 epochs over a 7 month time period, resulting in one of the most well-sampled multi-wavelength data sets of a BH XRB outburst to date. With our data, we compile and model the broad-band spectra of this source using a phenomenological model that includes emission from the jet, companion star, and accretion flow. This modeling allows us to track the evolution of the spectral break in the jet spectrum, a key observable that samples the jet launching region. We find that the spectral break location changes over at least $\approx3$ orders of magnitude in electromagnetic frequency over this period. Using these spectral break measurements, we link the full cycle of jet behavior, including the rising, quenching, and re-ignition, to the changing accretion flow properties as the source evolves through its different accretion states. Our analyses show a consistent jet behavior with other sources in similar phases of their outbursts, reinforcing that the jet quenching and recovery may be a global feature of BH XRB systems in outburst. Our results also provide valuable evidence supporting a close connection between the geometry of the inner accretion flow and the base of the jet., Comment: Accepted for publication in ApJ

To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unreliable inferences and uncertainty quantification. Unfortunately, existing methods for robustifying GPs break closed-form conditioning, which makes them less attractive to practitioners and significantly more computationally expensive. In this paper, we demonstrate how to perform provably robust and conjugate Gaussian process (RCGP) regression at virtually no additional cost using generalised Bayesian inference. RCGP is particularly versatile as it enables exact conjugate closed form updates in all settings where standard GPs admit them. To demonstrate its strong empirical performance, we deploy RCGP for problems ranging from Bayesian optimisation to sparse variational Gaussian processes.

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, $\pm 5 \: mm $ and $\pm 10^o$. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics, to overcome the error in the position and orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movement of the charger. Three controllers were designed based on impedance control to mimic the human patterns of charging an electric car. The recorded data from humans were used to calibrate the parameters of the impedance controllers: inertia $M_d$, damping $D_d$, and stiffness $K_d$. A robotic validation was performed, where the designed controllers were applied to the robot UR10. Using the proposed controllers and the human kinesthetic data, it was possible to successfully automate the operation of charging an electric car., Comment: Accepted to the 21st IEEE International Conference on Advanced Robotics (ICAR 2023). IEEE copyright

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the automation of the charging task research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, 5 mm, and 10 degrees. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics to overcome the error in the orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movements of the charger. Eventually, an algorithm was developed based on the human's best strategies to be applied to a robotic arm., Comment: Manuscript accepted to IEEE ROBIO 2023

We present a detailed analysis of the spectral and timing characteristics of a 7-Hz type-A quasi-periodic oscillation (QPO) detected in NICER observations of the black hole X-ray binary MAXI J1348-630 during its high-soft state. The QPO is broad and weak, with an integrated fractional rms amplitude of 0.9 per cent in the 0.5-10 keV band. Thanks to the large effective area of NICER, combined with the high flux of the source and a relatively long accumulative exposure time, we construct the first rms and phase-lag spectra for a type-A QPO. Our analysis reveals that the fractional rms amplitude of the QPO increases with energy from below 1 per cent at 1 keV to 3 per cent at 6 keV. The shape of the QPO spectrum is similar to that of the Comptonised component, suggesting that the Comptonised region is driving the variability. The phase lags at the QPO frequency are always soft taking the lowest energy as reference. By jointly fitting the time-averaged spectrum of the source and the rms and phase-lag spectra of the QPO with the time-dependent Comptonisation model vkompthdk, we find that the radiative properties of the type-A QPO can be explained by a vertically extended Comptonised region with a size of 2300 km., Comment: 7 pages, 5 figures, accepted for publication in MNRAS

The evolution of accreting X-ray binary systems is closely coupled to the properties of their donor stars. As a result, we can constrain the evolutionary track a system is by establishing the nature of its donor. Here, we present far-UV spectroscopy of the transient neutron-star low-mass X-ray binary Swift J1858 in three different accretion states (low-hard, high-hard and soft). All of these spectra exhibit anomalous N\,{\sc v}, C\,{\sc iv}, Si\,{\sc iv} and He\,{\sc ii} lines, suggesting that its donor star has undergone CNO processing. We also determine the donor's effective temperature, $T_{d} \simeq 5700$~K, and radius, $R_d \simeq 1.7~R_{\odot}$, based on photometric observations obtained during quiescence. Lastly, we leverage the transient nature of the system to set an upper limit of $\dot{M}_{\rm acc} \lesssim 10^{-8.5}~M_{\odot}~yr^{-1}$ on the present-day mass-transfer rate. Combining all these with the orbital period of the system, $P_{\rm orb} = 21.3$~hrs, we search for viable evolution paths. The initial donor masses in the allowed solutions span the range $1~M_{\odot} \lesssim M_{d,i} \lesssim 3.5~M_{\odot}$. All but the lowest masses in this range are consistent with the strong CNO-processing signature in the UV line ratios. The present-day donor mass in the permitted tracks are $0.5~M_{\odot}\lesssim M_{d,obs} \lesssim 1.3~M_{\odot}$, higher than suggested by recent eclipse modelling. Since $P_{\rm orb}$ is close to the so-called bifurcation period, both converging and diverging binary tracks are permitted. If Swift J1858 is on a converging track, it will end its life as an ultra-compact system with a sub-stellar donor star., Comment: Accepted for publication in MNRAS

We report on X-ray (NICER/NuSTAR/MAXI/Swift) and radio (MeerKAT) timing and spectroscopic analysis from a three-month monitoring campaign in 2022 of a high-intensity outburst of the dipping neutron star low-mass X-ray binary 1A 1744-361. The 0.5-6.8 keV NICER X-ray hardness-intensity and color-color diagrams of the observations throughout the outburst suggests that 1A 1744-361 spent most of its outburst in an atoll-state, but we show that the source exhibited Z-state-like properties at the peak of the outburst, similar to a small sample of other atoll-state sources. A timing analysis with NICER data revealed several instances of an $\approx8$ Hz quasi-periodic oscillation (QPO; fractional rms amplitudes of ~5%) around the peak of the outburst, the first from this source, which we connect to the normal branch QPOs (NBOs) seen in the Z-state. Our observations of 1A 1744-361 are fully consistent with the idea of the mass accretion rate being the main distinguishing parameter between atoll- and Z-states. Radio monitoring data by MeerKAT suggests that the source was at its radio-brightest during the outburst peak, and that the source transitioned from the 'island' spectral state to the 'banana' state within ~3 days of the outburst onset, launching transient jet ejecta. The observations present the strongest evidence for radio flaring, including jet ejecta, during the island-to-banana spectral state transition at low accretion rates (atoll-state). The source also exhibited Fe XXV, Fe XXVI K$\alpha$, and K$\beta$ X-ray absorption lines, whose origins likely lie in an accretion disk atmosphere., Comment: 30 pages, 13 figures, and 8 tables. Accepted by ApJ (before proofs)