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To fulfill the low latency requirements of today's applications, deployment of RDMA in datacenters has become prevalent over the recent years. However, the in-order delivery requirement of RDMAs prevents them from leveraging powerful techniques that help improve the performance of datacenters, ranging from fine-grained load balancers to throughput-optimal expander topologies. We demonstrate experimentally that these techniques significantly deteriorate the performance in an RDMA network because they induce packet reordering. Furthermore, lifting the in-order delivery constraint enhances the flexibility of RDMA networks and enables them to employ these performance-enhancing techniques. To realize this, we propose an ordering layer, Eunomia, to equip RDMA NICs to handle packet reordering. Eunomia employs a hybrid-dynamic bitmap structure that efficiently uses the limited on-chip memory with the help of a customized memory controller and handles high degrees of packet reordering. We evaluate the feasibility of Eunomia through an FPGA-based implementation and its performance through large-scale simulations. We show that Eunomia enables a wide range of applications in RDMA datacenter networks, such as fine-grained load balancers which improve performance by reducing average flow completion times by 85% and 52% compared to ECMP and Conweave, respectively, or employment of RDMA in expander topologies like Jellyfish which allows up to 60% lower flow completion times and higher throughput gains compared to Fat tree.

Designing optoelectronic devices based on cesium lead chloride (CsPbCl${_3}$) perovskites requires accurate values of their optical constants. Unfortunately, experimental data for this material is very limited thus far. Therefore, here, we applied spectroscopic ellipsometry (SE) to measure the complex optical constants of thermally evaporated CsPbCl${_3}$ thin films with different thicknesses on Si/SiO${_2}$ substrates. The data were corroborated with scanning electron microscopy (SEM) images and absorption spectroscopy. An optical dispersion model was developed to derive the complex optical constants and film thicknesses. The Tauc-Lorentz model, in conjunction with two harmonic oscillators, was used to extract the required parameters. The extinction coefficient spectrum exhibited a sharp absorption edge at 411 nm, consistent with the absorption spectrum. In addition, the optical bandgap of the film was calculated from the absorption spectra and SE data. The experimental values agree well with the simulation results, with values of $\sim$ 2.99 eV for different film thicknesses. This work provides fundamental information for designing and modeling CsPbCl3-based optoelectronic devices., Comment: 15 pages

With the growing number of satellite-based Quantum Key Distribution (QKD) payload launches, it becomes essential to ensure compatibility across different platforms for satellite tracking and quantum signal acquisition. In this paper, the Technology Innovation Institute (TII) presents the development of the Abu Dhabi Quantum Optical Ground Station (ADQOGS) for secure free-space optical communications. With the know-how of GA-Synopta's experienced engineering team, we have developed a versatile multi-wavelength quantum acquisition and tracking system tailored to support various upcoming space-based QKD missions, crucial for the practical implementation of global quantum communication networks. This system is capable of handling multiple wavelengths, ranging from 600 nm to 1560 nm for downlink beacons and 1530 nm to 1610 nm for uplink beacons. It includes a free-space quantum module adequate to detect QKD signals at $780\pm10$ nm and $850\pm3$ nm and offers spatial and spectral filtering capabilities along with a motorized polarization correction system.

In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables different agents employing different neural network models to have unified representation. It proceeds by projecting the latent vectors of different models into a common space defined relative to a set of data samples called \textit{anchors}, whose number equals the dimension of the resulting space. A communication between different agents translates to a communication of semantic symbols sampled from this relative space. This approach, in addition to aligning the semantic representations of different agents, allows compressing the amount of information being exchanged, by appropriately selecting the number of anchors. Eventually, we introduce a novel anchor selection strategy, which advantageously determines prototypical anchors, capturing the most relevant information for the downstream task. Our numerical results show the effectiveness of the proposed approach allowing seamless communication between agents with radically different models, including differences in terms of neural network architecture and datasets used for initial training.

Metal-halide perovskites (MHPs) have exciting optoelectronic properties and are under investigation for various applications, such as photovoltaics, light-emitting diodes, and lasers. An essential step toward exploiting the full potential of this class of materials is their large-scale, on-chip integration with high-resolution, top-down patterning. The development of such patterning methods for perovskite films is challenging because of their ionic behavior and adverse reactions with the solvents used in standard lithography processes. Here, we introduce a versatile and precise method comprising photolithography and reactive ion etching (RIE) processes that can be tuned to accommodate different perovskite compositions and morphologies, including 3D, quasi-2D, and quasi-0D structures. Our method utilizes conventional photoresists at reduced temperatures to create micron-sized features down to 1 $\mu$m, providing high reproducibility from chip to chip. The patterning technique is validated through atomic force microscopy (AFM), X-ray diffraction (XRD), optical spectroscopy, and scanning electron microscopy (SEM). It enables the scalable and high-throughput on-chip monolithic integration of MHPs., Comment: 30 pages

Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as an additional cue to compensate for the loss of building height estimation using single-view imagery. We proposed a novel method that first localized a building and its shadow in the given satellite image. After localization, the shadow length is estimated using a regression model. To estimate the final height of each building, we utilize the principles of photogrammetry, specifically considering the relationship between the solar elevation angle, the vertical edge length of the building, and the length of the building's shadow. For the localization of buildings in our model, we utilized a modified YOLOv7 detector, and to regress the shadow length for each building we utilized the ResNet18 as backbone architecture. Finally, we estimated the associated building height using solar elevation with shadow length through analytical formulation. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin., Comment: 6 pages, 5 figures, 2 tables

Mass loss through a stellar wind is an important physical process that steers the evolution of massive stars and controls the properties of their end-of-life products, such as the supernova type and the mass of compact remnants. For an accurate mass loss determination, the inhomogeneities in the wind, known as clumping, needs to be taking into account. We aim to improve empirical estimates of mass loss and wind clumping for hot main-sequence massive stars, study the dependence of both properties on the metallicity, and compare the theoretical predictions to our findings. We analyzed the optical and UV spectra of 13 O-type stars in the Small Magellanic Cloud galaxy, which has a metallicity of $\sim 0.2\,Z_\odot$. We quantified the stellar atmosphere, outflow, and wind-clumping properties. To probe the role of metallicity, we compared our findings to studies of Galactic and Large Magellanic Cloud samples that were analyzed with similar methods. We find significant variations in the wind-clumping properties of the target stars, with clumping starting at flow velocities $0.01 - 0.23$ of the terminal wind velocity and reaching clumping factors $f_{\rm cl} = 2 - 30$. In the luminosity ($\log L / L_{\odot} = 5.0 - 6.0$) and metallicity ($Z/Z_{\odot} = 0.2 - 1$) range we considered, we find that the scaling of the mass loss $\dot{M}$ with metallicity $Z$ varies with luminosity. At $\log L/L_{\odot} = 5.75$, we find $\dot{M} \propto Z^m$ with $m = 1.02 \pm 0.30$, in agreement with pioneering work in the field. For lower luminosities, however, we obtain a significantly steeper scaling of $m > 2$. The monotonically decreasing $m(L)$ behavior adds a complexity to the functional description of the mass-loss rate of hot massive stars. Although the trend is present in the predictions, it is much weaker than we found here., Comment: 37 pages, 29 figures. Accepted for publication in Astronomy and Astrophysics

Data is the main fuel of a successful machine learning model. A dataset may contain sensitive individual records e.g. personal health records, financial data, industrial information, etc. Training a model using this sensitive data has become a new privacy concern when someone uses third-party cloud computing. Trained models also suffer privacy attacks which leads to the leaking of sensitive information of the training data. This study is conducted to preserve the privacy of training data in the context of customer churn prediction modeling for the telecommunications industry (TCI). In this work, we propose a framework for privacy-preserving customer churn prediction (PPCCP) model in the cloud environment. We have proposed a novel approach which is a combination of Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE). Synthetic data is generated from GANs, and aWOE is applied on the synthetic training dataset before feeding the data to the classification algorithms. Our experiments were carried out using eight different machine learning (ML) classifiers on three openly accessible datasets from the telecommunication sector. We then evaluated the performance using six commonly employed evaluation metrics. In addition to presenting a data privacy analysis, we also performed a statistical significance test. The training and prediction processes achieve data privacy and the prediction classifiers achieve high prediction performance (87.1\% in terms of F-Measure for GANs-aWOE based Na\"{\i}ve Bayes model). In contrast to earlier studies, our suggested approach demonstrates a prediction enhancement of up to 28.9\% and 27.9\% in terms of accuracy and F-measure, respectively., Comment: 26 pages, 14 tables, 13 figures

Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that is shared across modalities, we can improve interpretability and robustness and enable downstream tasks such as the generation of counterfactual outcomes. Separating the two types of information is challenging since they are often deeply entangled in many real-world applications. We propose Disentangled Self-Supervised Learning (DisentangledSSL), a novel self-supervised approach for learning disentangled representations. We present a comprehensive analysis of the optimality of each disentangled representation, particularly focusing on the scenario not covered in prior work where the so-called Minimum Necessary Information (MNI) point is not attainable. We demonstrate that DisentangledSSL successfully learns shared and modality-specific features on multiple synthetic and real-world datasets and consistently outperforms baselines on various downstream tasks, including prediction tasks for vision-language data, as well as molecule-phenotype retrieval tasks for biological data.

Among massive stars, binary interaction is the rule rather than the exception. The closest binaries, those with periods of less than about 10 days, undergo mass transfer during core-hydrogen burning, with many of them experiencing a nuclear-timescale contact phase. Current binary population synthesis models predict the mass-ratio distribution of contact binaries to be heavily skewed toward a mass ratio of unity, which is inconsistent with observations. It has been shown that effects of tidal deformation due to the Roche potential, as well as energy transfer in the common layers of a contact binary, alter the internal structure of close binary components. However, previous population studies neglected these effects. We model a population of massive binary stars that undergo mass transfer during core-hydrogen burning, while consistently considering the effects of tidal deformation and energy transfer in contact phases. We use the MESA binary-evolution code to compute large grids of models with primary star masses of $8\,M_\odot$ to $70\,M_\odot$ at Solar metallicity. We then perform a population synthesis study to predict distribution functions of the observational properties of close binary systems, focusing in particular on the mass and luminosity ratio distribution. We find that the effects of tidal deformation and energy transfer have a limited effect on the predicted mass-ratio distribution of massive contact binaries. Only a small fraction of the population has their mass ratio significantly shifted toward a more unequal configuration. However, we suggest that orbital hardening could affect the evolution of contact binaries and their progenitors, and we advocate for a homogeneous set of observed contact binary parameters., Comment: 12 pages, 10 Figures, (22 pages, 16 Figs. including appendices). Submitted to A&A

Context. Current implementations of mass loss for hot, massive stars in stellar evolution models include a sharp increase in mass loss when blue supergiants become cooler than Teff 20-22kK. This drastic mass-loss jump has been motivated by the potential presence of a so-called bistability ionisation effect, which may occur for line-driven winds in this temperature region due to recombination of important line-driving ions. Aims. We perform quantitative spectroscopy using UV (ULLYSES program) and optical (XShootU collaboration) data for 17 OB-supergiant stars in the LMC (covering the range Teff 14-32kK), deriving absolute constraints on global stellar, wind, and clumping parameters. We examine whether there are any empirical signs of a mass-loss jump in the investigated region, and we study the clumped nature of the wind. Methods. We use a combination of the model atmosphere code fastwind and the genetic algorithm code Kiwi-GA to fit synthetic spectra of a multitude of diagnostic spectral lines in the optical and UV. Results. We find no signs of any upward mass loss jump anywhere in the examined region. Standard theoretical comparison models, which include a strong bistability jump thus severely over predict the empirical mass-loss rates on the cool side of the predicted jump. Additionally, we find that on average about 40% of the total wind mass seems to reside in the diluted medium in between dense clumps. Conclusions. Our derived mass-loss rates suggest that for applications like stellar evolution one should not include a drastic bistability jump in mass loss for stars in the temperature and luminosity region investigated here. The derived high values of interclump density further suggest that the common assumption of an effectively void interclump medium (applied in the vast majority of spectroscopic studies of hot star winds) is not generally valid in this parameter regime.

The optimal training of a vision transformer for brain encoding depends on three factors: model size, data size, and computational resources. This study investigates these three pillars, focusing on the effects of data scaling, model scaling, and high-performance computing on brain encoding results. Using VideoGPT to extract efficient spatiotemporal features from videos and training a Ridge model to predict brain activity based on these features, we conducted benchmark experiments with varying data sizes (10k, 100k, 1M, 6M) and different model configurations of GPT-2, including hidden layer dimensions, number of layers, and number of attention heads. We also evaluated the effects of training models with 32-bit vs 16-bit floating point representations. Our results demonstrate that increasing the hidden layer dimensions significantly improves brain encoding performance, as evidenced by higher Pearson correlation coefficients across all subjects. In contrast, the number of attention heads does not have a significant effect on the encoding results. Additionally, increasing the number of layers shows some improvement in brain encoding correlations, but the trend is not as consistent as that observed with hidden layer dimensions. The data scaling results show that larger training datasets lead to improved brain encoding performance, with the highest Pearson correlation coefficients observed for the largest dataset size (6M). These findings highlight that the effects of data scaling are more significant compared to model scaling in enhancing brain encoding performance. Furthermore, we explored the impact of floating-point precision by comparing 32-bit and 16-bit representations. Training with 16-bit precision yielded the same brain encoding accuracy as 32-bit, while reducing training time by 1.17 times, demonstrating its efficiency for high-performance computing tasks.

Massive stars are predominantly born in stellar associations or clusters. Their radiation fields, stellar winds, and supernovae strongly impact their local environment. In the first few million years of a cluster's life, massive stars are dynamically ejected running away from the cluster at high speed. However, the production rate of dynamically ejected runaways is poorly constrained. Here we report on a sample of 55 massive runaway stars ejected from the young cluster R136 in the Large Magellanic Cloud. Astrometric analysis with Gaia reveals two channels of dynamically ejected runaways. The first channel ejects massive stars in all directions and is consistent with dynamical interactions during and after the birth of R136. The second channel launches stars in a preferred direction and may be related to a cluster interaction. We find that 23-33% of the most luminous stars initially born in R136 are runaways. Model predictions have significantly underestimated the dynamical escape fraction of massive stars. Consequently, their role in shaping and heating the interstellar and galactic medium, along with their role in driving galactic outflows, is far more important than previously thought., Comment: Published in Nature on 2024 October 09. 39 pages, 17 figures

This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset containing a total of 4,112 programming tasks was created by extracting tasks from various websites. Web scraping techniques were employed to collect this dataset of programming problems systematically. Specific HTML tags were tracked to extract key elements of each issue, including the title, problem description, input-output, examples, problem class, and complexity score. Examples from the dataset are provided in the appendix to illustrate the variety and complexity of tasks included. The dataset's effectiveness has been evaluated and benchmarked using two approaches; the first approach involved fine-tuning the FLAN-T5 small model on the dataset, while the second approach used in-context learning (ICL) with the GPT-4o mini. The performance was assessed using standard metrics: accuracy, recall, precision, and F1-score. The results indicated that in-context learning with GPT-4o-mini outperformed the FLAN-T5 model., Comment: This papaer has been accepted to The 3nd International conference on Machine Learning and Data Engineering (ICMLDE 2024)

Brain-body-computer interfaces aim to provide a fluid and natural way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (sEMG) signals have shown particular utility. However, much remains unknown about how sEMG is affected by various physiological and anatomical factors and how these confounds might affect gesture decoding across individuals or groups. In this article, we show that sEMG signals evince non-Euclidean graph data structure that is defined by a set of orthogonal axes and explain the signal distribution shift across individuals. We provide a dataset of upper limb sEMG signals and physiological measures of 91 adults as they perform 10 different hand gestures. Participants were selected to be representative of various age groups (18to 92 years) and BMI (healthy, overweight, and obese). Additional anatomical or physiological measures that might impact sEMG signals were also collected, such as skin hydration and elasticity. The article describes the inherent structure of sEMG data and provides methods to construct differentiable signal features that can be used with machine learning algorithms that use backpropagation. We then analyze how those parameters correlate with various physiological measures to probe if they can induce bias against (or towards) certain population groups. We find that higher frequencies in sEMG, although comprising less power than lower ones, provide better gesture decoding and show less bias with regard to demographic, circumstantial, and physiological confounds (such as age, skin hydration, and skin elasticity)., Comment: 24 pages

Massive stars are recognized for their high degree of multiplicity, yet the mass ratio regime below 0.1 remains insufficiently explored. It is therefore unknown whether extremely low-mass (possibly substellar) companions can form and survive in the direct UV-irradiated environment of massive stars. In this paper, we discuss VLT/SPHERE IFS (0".15 - 0".85) observations of six massive O- and early B-type stars in Sco OB1 and M17 that each have a low-mass candidate companion. Two targets have companions that are brown dwarf candidates. The other four have candidate companions in the low end of the stellar mass regime ($\leq$ 0.30 M$_\odot$). For three of these, we have obtained a second epoch observation. At least two sources exhibit similar proper motion to that of their central star. However, given the expected proper motion of background objects, this does not imply certain companionship. We show how future follow-up observations of the brown dwarf candidate companions in $J$, $H$ and $L$ bands should allow for an unambiguous confirmation of their nature., Comment: 16 pages, 6 figures, accepted for publication in AJ

Wolf-Rayet stars (WRs) are one of the final evolutionary stages of massive stars and immediate progenitors of stellar-mass black holes. Their multiplicity forms an important anchor point in single and binary population models for predicting gravitational-wave progenitors. Recent spectroscopic campaigns have suggested incompatible multiplicity fractions and period distributions for N- and C-rich Galactic WRs (WNs and WCs) at short as well as long orbital periods, in contradiction with evolutionary model predictions. In this work, we employed infrared interferometry using the $K$-band instrument GRAVITY at the VLTI to investigate the multiplicity of WRs at long periods and explore the nature of their companions. We present a survey of 39 Galactic WRs, including 11 WN, 15 WC and 13 H-rich WN (WNh) stars. We detected wide companions with GRAVITY for only four stars: WR 48, WR 89, WR 93 and WR 115. Combining with spectroscopic studies, we arrived at multiplicity fractions of $f^{\rm WN}_{\rm obs} = 0.55\pm0.15$, $f^{\rm WC}_{\rm obs} = 0.40\pm0.13$ and $f^{\rm WNh}_{\rm obs} = 0.23\pm0.12$. In addition, we also found other features in the GRAVITY dataset such as (i) a diffuse extended component in over half the WR sample; (ii) five known spectroscopic binaries resolved in differential phase data and (iii) spatially resolved winds in four stars: WR 16, WR 31a, WR 78 and WR 110. Our survey reveals a lack of intermediate (few 100s d) and long- (few years to decades) period WR systems. The 200-d peak in the period distributions of WR+OB and BH+OB binaries predicted by Case B mass-transfer binary evolution models is not seen in our data. The rich companionship of their O-type progenitors in this separation range suggest that the WR progenitor stars expand and interact with their companions, most likely through unstable mass-transfer, resulting in either a short-period system or a merger., Comment: Accepted in A&A

In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.

Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data, which can help mitigate privacy and data ownership issues. In FL, sub-optimal convergence caused by data heterogeneity is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. Through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression. To achieve this, we distill the global model resulting from each communication round. Subsequently, we use the synthetic data alongside the local data to enhance the generalization of local training. Extensive experiments show that the proposed method achieves state-of-the-art performance on both the BloodMNIST and Retina datasets, which contain label imbalance and domain shift, with an improvement in classification accuracy of up to 20%.

Current parking area perception algorithms primarily focus on detecting vacant slots within a limited range, relying on error-prone homographic projection for both labeling and inference. However, recent advancements in Advanced Driver Assistance System (ADAS) require interaction with end-users through comprehensive and intelligent Human-Machine Interfaces (HMIs). These interfaces should present a complete perception of the parking area going from distinguishing vacant slots' entry lines to the orientation of other parked vehicles. This paper introduces Multi-Task Fisheye Cross View Transformers (MT F-CVT), which leverages features from a four-camera fisheye Surround-view Camera System (SVCS) with multihead attentions to create a detailed Bird-Eye View (BEV) grid feature map. Features are processed by both a segmentation decoder and a Polygon-Yolo based object detection decoder for parking slots and vehicles. Trained on data labeled using LiDAR, MT F-CVT positions objects within a 25m x 25m real open-road scenes with an average error of only 20 cm. Our larger model achieves an F-1 score of 0.89. Moreover the smaller model operates at 16 fps on an Nvidia Jetson Orin embedded board, with similar detection results to the larger one. MT F-CVT demonstrates robust generalization capability across different vehicles and camera rig configurations. A demo video from an unseen vehicle and camera rig is available at: https://streamable.com/jjw54x., Comment: This paper is a preprint of a paper submitted to the 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024). If accepted, the copy of record will be available at IET Digital Library

In view of existing automatic driving is difficult to accurately and timely obtain the status and driving intention of other vehicles and the safety risk and urgency of autonomous vehicles in the absence of collision are evaluated. As a result, while vehicles generally maintain safe distances, accidents still frequently occur, particularly in merging areas. To ensure safety, improve road efficiency, this paper presents a pre-programmed technique for managing vehicles' spatiotemporal trajectories to proactively mitigate conflicts among vehicles. Firstly, the study focuses on the calculation of safe distances under varying spatiotemporal conditions, taking into account differences in vehicle speed. Subsequently, an evaluation model for vehicle conflict risk is developed, which incorporates critical parameters such as collision acceleration and emergency acceleration. The methodology further identifies the main line vehicles that are potentially in conflict with on-ramp vehicles and determines the target gap for the latter. Based on this selected target gap, a cooperative control method is formulated, enabling the pre-programming of vehicle trajectories. Using highway ramp merging as a case study, the paper introduces a mainline priority spatiotemporal cooperative control method and validates its efficacy through rigorous simulations. The analysis indicates that the average delay time can be reduced by 97.96%, and fuel consumption by 6.01%. The mainline priority strategy demonstrates increased speed, low latency and low fuel consumption.

Significant progress has been made toward understanding the formation of massive ($M > 8~$M$_{\odot}$) binaries in close orbits. For example, the detection of a very low velocity dispersion among the massive stars in the young region M17 and the measurement of a positive trend of velocity dispersion with age in Galactic clusters. The velocity dispersion observed in M17 could be explained either by the lack of binaries among the stars in this region or by larger binary separations than typically observed, but with a binary fraction similar to other young Galactic clusters. The latter implies that over time, the binary components migrate toward each other. We aim to determine the origin of the strikingly low velocity dispersion by determining the observed and intrinsic binary fraction of massive stars in M17 through multi-epoch spectroscopy. We performed a multi-epoch spectroscopic survey consisting of three epochs separated by days and months. We determine the radial velocity of each star at each epoch by fitting the stellar absorption profiles. We determine an observed binary fraction of 27% and an intrinsic binary fraction of 87%, consistent with that of other Galactic clusters. We conclude that the low velocity dispersion is due to a large separation among the young massive binaries in M17. Our result is in agreement with a migration scenario in which massive stars are born in binaries or higher order systems at large separation and harden within the first million years of evolution. Such an inward migration may either be driven by interaction with a remnant accretion disk, with other young stellar objects present in the system or by dynamical interactions within the cluster. Our results imply that possibly both dynamical interactions and binary evolution are key processes in the formation of gravitational wave sources., Comment: Accepted for publication in A&A. 18 pages, 8 figures. Appendices A and C will be available in electronic form at the CDS

The Rosetta spacecraft accompanied the comet 67P/C-G for nearly 2 years, collecting valuable data on the neutral and ion composition of the coma. The Rosetta Plasma Consortium (RPC) provided continuous measurements of the in situ plasma density while ROSINA-COPS monitored the neutral composition. In this work, we aim to estimate the composition of the cometary ionosphere at different heliocentric distances of the comet. Lauter et al. (2020) derived the temporal evolution of the volatile sublimation rates for 50 separated time intervals on the orbit of 67P/C-G using the COPS and DFMS data. We use these sublimation rates as inputs in a multifluid chemical-hydrodynamical model for 36 of the time intervals for heliocentric distances < 3 au. We compare the total ion densities obtained from our models with the local plasma density measured by the RPC instruments. We find that at the location of the spacecraft, our modeled ion densities match with the in situ measured plasma density within factors of 1 - 3 for many of the time intervals. We obtain the cometocentric distance variation of the ions H2O+ and H3O+ and the ion groups created respectively by the ionization and protonation of neutral species. We see that H3O+ is dominant at the spacecraft location for nearly all the time intervals while ions created due to protonation are dominant at low cometocentric distances for the intervals near perihelion. We also discuss our ion densities in the context of their detection by DFMS., Comment: Accepted for publication in Icarus

Foundation excavations are deepening, expanding, and approaching structures. Steel supports measure and manage axial force. The study regulates steel support structure power during deep excavation using a novel axial force management system for safety, efficiency, and structural integrity. Closed-loop control changes actuator output to maintain axial force based on force. In deep excavation, the servo system regulates unstable soil, side pressure, and structural demands. Modern engineering and tech are used. Temperature changes automatically adjust the jack to maintain axial force. Includes hydraulic jacks, triple-acting cylinders, temperature, and deformation sensors, and automatic control. Foundation pit excavation is dynamic, yet structure tension is constant. There is no scientific way to regulate axial force foundation pit excavation. The revolutionary Servo system adjusts temperature, compression, and axial force to deform pits. System control requires foundation pit direction detection and modification. This engineering method has performed effectively for deep foundation pit excavation at railway crossings and other infrastructure projects. The surrounding protective structure may reduce the steel support's axial stress, making deep foundation excavation safe and efficient. Keywords: Servo systems, Steel strut support design, Deformation control, Monitoring and control, Deep excavation projects., Comment: 6 pages,7 figures, 1 table, 2 graph, conference paper

Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the conditions of the Early Universe, remains sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign - an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the SMC - the lowest metallicity conditions in which multiplicity is probed to date (Z = 0.2 Zsun). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods P < 3 yr, (iii) dormant OB+BH binaries, and (iv) a legacy database of physical parameters of massive stars at low metallicity. The stars are observed with the LR02 setup of the giraffe instrument of the Very Large Telescope (3960-4570A, resolving power R=6200; typical signal-to-noise ratio S/N=70-100). This paper utilises the first 9 epochs obtained over a three-month time. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. The sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5

We constrain wind parameters of a sample of 18 O-type stars in the LMC, through analysis with stellar atmosphere and wind models including the effects of optically thick clumping. This allows us to determine the most accurate spectroscopic mass-loss and wind structure properties of massive stars at sub-solar metallicity to date and gain insight into the impact of metallicity on massive stellar winds. Combining high signal to noise (S/N) ratio spectroscopy in the UV and optical gives us access to diagnostics of multiple different physical processes in the stellar wind. We produce synthetic spectra using the stellar atmosphere modelling code FASTWIND, and reproduce the observed spectra using a genetic algorithm based fitting technique. We empirically constrain 15 physical parameters associated with the stellar and wind properties, including temperature, surface gravity, surface abundances, rotation, macroturbulence and wind parameters. We find, on average, mass-loss rates a factor of 4-5 lower than those predicted by Vink et al. 2001, in good agreement with predictions from Bjorklund et al. 2021, and the best agreement with those from Krticka et al. 2018. In the 'weak-wind' regime we find mass-loss rates orders of magnitude below any theoretical predictions. We find a positive correlation of clumping factors (fcl) with effective temperature with an average fcl = 14 +- 8 for the full sample. Above 38 kK an average 46 +- 24% of the wind velocity span is covered by clumps and the interclump density is 10-30% of the mean wind. Below an effective temperature of roughly 38 kK there must be additional light leakage for supergiants. For dwarf stars at low temperatures there is a statistical preference for very low clump velocity spans, however it is unclear if this can be physically motivated as there are no clearly observable wind signatures in UV diagnostics., Comment: 12 pages with 6 figures, plus 16 pages and 18 figures in appendix. Accepted for publication in A&A

Birkhoff polytope is the set of all bistochastic matrices (also known as doubly stochastic matrices). Bistochastic matrices form a special class of stochastic matrices where each row and column sums up to one. Permutation matrices and uniform matrices are special extreme cases of bistochastic matrices. In this paper, we define a scalar quantity called the matching factor on the Birkhoff polytope. Given a bistochastic matrix, we define the matching factor by taking the product of the squares of the Euclidean norms of each row and column and show that permutation matrices and uniform matrices maximize and minimize the matching factor, respectively. We also extend this definition of scalar matching factor to a larger class of matrices and show similar maximization and minimization properties.

Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches, it is essential to understand how uncertainty impacts these models. We propose LUMA, a unique benchmark dataset, featuring audio, image, and textual data from 50 classes, for learning from uncertain and multimodal data. It extends the well-known CIFAR 10/100 dataset with audio samples extracted from three audio corpora, and text data generated using the Gemma-7B Large Language Model (LLM). The LUMA dataset enables the controlled injection of varying types and degrees of uncertainty to achieve and tailor specific experiments and benchmarking initiatives. LUMA is also available as a Python package including the functions for generating multiple variants of the dataset with controlling the diversity of the data, the amount of noise for each modality, and adding out-of-distribution samples. A baseline pre-trained model is also provided alongside three uncertainty quantification methods: Monte-Carlo Dropout, Deep Ensemble, and Reliable Conflictive Multi-View Learning. This comprehensive dataset and its benchmarking tools are intended to promote and support the development, evaluation, and benchmarking of trustworthy and robust multimodal deep learning approaches. We anticipate that the LUMA dataset will help the ICLR community to design more trustworthy and robust machine learning approaches for safety critical applications.

A site-specific radio channel representation (SSCR) takes the surroundings of the communication system into account by considering the environment geometry, including buildings, vegetation, and mobile objects with their material and surface properties. We present methods for an SSCR that is spatially consistent, such that mobile transmitter and receiver cause a correlated time-varying channel impulse response and closely spaced antennas are correctly correlated. An SSCR is composed of a dynamically varying number of multipath components solely defined by the environment geometry and the material of the environmental objects. Hence, the environment geometry is the only natural scenario parameterization and specific calibration procedures shall be avoided. 5G and 6G physical layer technologies are increasingly able to exploit the properties of a wide range of environments from dense urban areas to railways, road transportation, industrial automation, and unmanned aerial vehicles. The channel impulse response in this wide range of scenarios has generally non-stationary statistical properties, i.e., the Doppler spectrum, power delay profile, K-factor and spatial correlation are all spatially variant (or time-variant for mobile receivers). SSCRs will enable research and development of emerging 5G and 6G technologies such as distributed multiple-input multiple-output systems, reconfigurable intelligent surfaces, multi-band communication, and joint communication and sensing. We highlight the state of the art and summarize research directions for future work towards an SSCR., Comment: 9 pages, 6 figures, to appear in IEEE Communication Magazine

Quantum key distribution (QKD) via satellite links is the only currently viable solution to create quantum-backed secure communication at a global scale. To achieve intercontinental coverage with available technology one must adopt a "flying trusted node" paradigm, in which users fully trust the satellite platform. Here, inspired by the concept of distributed secret sharing and the imminent projected launch of several QKD-equipped satellites, we propose a parallel trusted node approach, in which key distribution is mediated by several satellites in parallel. This has the effect of distributing the trust, removing single points of failure and reducing the necessary assumptions. In addition, we discuss the versatility that an optical ground station should provide to execute such a protocol and, in general, to be fully integrated into a multi-party global quantum network., Comment: 6 pages, 2 figures

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches., Comment: This work has been accepted for publication in 2024 European Conference on Networks and Communications (EuCNC) & 6G Summit

In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental learning offers a promising solution by adapting a deep network trained on specific disease classes to handle new diseases. However, catastrophic forgetting occurs, decreasing the performance of earlier classes when adapting the model to new data. Prior proposed methodologies to overcome this require perpetual storage of previous samples, posing potential practical concerns regarding privacy and storage regulations in healthcare. To this end, we propose a novel data-free class incremental learning framework that utilizes data synthesis on learned classes instead of data storage from previous classes. Our key contributions include acquiring synthetic data known as Continual Class-Specific Impression (CCSI) for previously inaccessible trained classes and presenting a methodology to effectively utilize this data for updating networks when introducing new classes. We obtain CCSI by employing data inversion over gradients of the trained classification model on previous classes starting from the mean image of each class inspired by common landmarks shared among medical images and utilizing continual normalization layers statistics as a regularizer in this pixel-wise optimization process. Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data.

The paper addresses a variant of the Dial-A-Ride problem with additional features. It is referred to as the DARP with driver preferences, which attempts to determine a solution more driver-oriented by designing a short trip in a specific direction that has to be finished at a destination of interest within a restricted time window. For this purpose, two solutions are considered. The first involves solving the new MILP exactly using the CPLEX software. The second is a new approach that employs an iterated local search as a general framework and exploits many heuristics. Numerical experiments indicate that the approach can efficiently solve the generated DARPDP instances in a reasonable time.

The main purpose of this paper is to obtain physically stable stellar models coupled with anisotropic matter distribution in the context of $f(\mathcal{R},\mathrm{T}^{2})$ theory. For this, we consider a static spherical geometry and formulate modified field equations containing various unknowns such as matter determinants and metric potentials. We then obtain a unique solution to these equations by employing Durgapal-Fuloria ansatz possessing a constant doublet. We also use matching criteria to calculate the values of these constants by considering the Schwarzschild exterior spacetime. Two different viable models of this modified theory are adopted to analyze the behavior of effective matter variables, anisotropy, energy conditions, compactness and redshift in the interiors of Her X-1, PSR J0348-0432, LMC X-4, SMC X-1, Cen X-3, and SAX J 1808.4-3658 star candidates. We also check the stability of these models by using three different physical tests. It is concluded that our considered stars satisfy all the physical requirements and are stable in this modified gravity for the considered parametric values., Comment: 28 pages, 10 figures

In order to predict the black hole mass distributions at high redshift, we need to understand whether very massive single stars ($M>40$ M$_\odot$) at low metallicity $Z$ lose their hydrogen-rich envelopes, like their metal-rich counterparts, or whether a binary companion is required to achieve this. To test this, we undertake a deep spectroscopic search for binary companions of the seven apparently single Wolf-Rayet (WR) stars in the Small Magellanic Cloud (SMC; $Z \simeq 1/5 Z_\odot$). For each of them, we acquired six high-quality VLT-UVES spectra spread over 1.5 years. By using the narrow N V lines in these spectra, we monitor radial velocity (RV) variations to search for binary motion. We find low RV variations between 6 and 23 km/s for the seven WR stars, with a median standard deviation of $5$ km/s. Our Monte Carlo simulations imply probabilities below ~5% for any of our target WR stars to have a binary companion more massive than ~5 M$_\odot$ at orbital periods of less than a year. We estimate that the probability that all our target WR stars have companions with orbital periods shorter than 10 yr is below ~10$^{-5}$, and argue that the observed modest RV variations may originate from intrinsic atmosphere or wind variability. Our findings imply that metal-poor massive stars born with $M \gtrsim 40$ M$_\odot$ can lose most of their hydrogen-rich envelopes via stellar winds or eruptive mass loss, which strongly constrains their initial mass - black hole mass relation. We also identify two of our seven target stars (SMC AB1 and SMC AB11) as runaway stars with a peculiar radial velocity of ~80 km/s. Moreover, with all five previously detected WR binaries in the SMC exhibiting orbital periods of below 20 d, a puzzling absence of intermediate-to-long-period WR binaries has emerged, with strong implications for the outcome of massive binary interaction at low metallicity., Comment: Accepted for publication in Astronomy & Astrophysics

Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an encoder and a decoder which were not jointly trained and it relies on a partition of the semantic (latent) space into atoms based on the the semantic meaning. In this work we explore the role of the semantic space partition in scenarios where the task structure involves a one-to-many mapping between the semantic space and the action space. In such scenarios, partitioning based on hard inference results results in loss of information which degrades the equalization performance. We propose a soft criterion to derive the atoms of the partition which leverages the soft decoder's output and offers a more comprehensive understanding of the semantic space's structure. Through empirical validation, we demonstrate that soft partitioning yields a more descriptive and regular partition of the space, consequently enhancing the performance of the equalization algorithm.

The spin evolution of stars in close binary systems can be strongly affected by tides. We investigate the rotational synchronisation of the stellar components for 69 SB1 systems and 14 SB2 B-type systems in the 30 Doradus region of the Large Magellanic Cloud using observations from the VFTS and BBC surveys. Their orbital periods range from a few to a few hundred days, while estimated primary masses for these systems are in the range 5-20 Msun with mass ratio ranges of approximately q=0.03-0.5 and q=0.6-1.0 for the SB1 and SB2 systems, respectively. Projected rotational velocities of the stellar components have been compared with their synchronous velocities derived from the orbital periods. We find that effectively all systems with orbital period of more than 10 days must be asynchronous, whilst all the systems with periods of less than 3 days are likely synchronised. In terms of the stellar fractional radius (r), our results imply that all systems with r<0.1 are asynchronous, with those having r>0.2 probably being synchronised. For the apparently synchronised systems our results are more consistent with synchronisation at the mean orbital angular velocity rather than with that at periastron., Comment: 13 pages, 5 figures. submitted to A&A

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of prominent LLMs; GPT-3.5-turbo, Llama2-7B-Chat, Bloomz 7B1 and Bloomz 3B, across 14 tasks using 15 Urdu datasets, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analysed. Our experiments show that SOTA models surpass all the encoder-decoder pre-trained language models in all Urdu NLP tasks with zero-shot learning. Our results further show that LLMs with fewer parameters, but more language specific data in the base model perform better than larger computational models, but low language data.

We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving. We propose a mathematical framework, which provides a modelling and a measure of the semantic distortion introduced in the communication when agents use distinct languages. We then propose a new approach to semantic channel equalization with proven effectiveness through numerical evaluations., Comment: Accepted for publication at 2024 IEEE ICMLCN

In recent years, the integration of communication and control systems has gained significant traction in various domains, ranging from autonomous vehicles to industrial automation and beyond. Multi-armed bandit (MAB) algorithms have proven their effectiveness as a robust framework for solving control problems. In this work, we investigate the use of MAB algorithms to control remote devices, which faces considerable challenges primarily represented by latency and reliability. We analyze the effectiveness of MABs operating in environments where the action feedback from controlled devices is transmitted over an unreliable communication channel and stored in a Geo/Geo/1 queue. We investigate the impact of queue sampling strategies on the MAB performance, and introduce a new stochastic approach. Its performance in terms of regret is evaluated against established algorithms in the literature for both upper confidence bound (UCB) and Thompson Sampling (TS) algorithms. Additionally, we study the trade-off between maximizing rewards and minimizing energy consumption.

Radio localization and sensing are anticipated to play a crucial role in enhancing radio resource management in future networks. In this work, we focus on millimeter-wave communications, which are highly vulnerable to blockages, leading to severe attenuation and performance degradation. In a previous work, we proposed a novel mechanism that senses the radio environment to estimate the angular position of a moving blocker with respect to the sensing node. Building upon this foundation, this paper investigates the benefits of cooperation between different entities in the network by sharing sensed data to jointly locate the moving blocker while mapping the interference profile to probe the radio environment. Numerical evaluations demonstrate that cooperative sensing can achieve a more precise location estimation of the blocker as it further allows accurate estimation of its distance rather than its relative angular position only, leading to effective assessment of the blocker direction, trajectory and possibly, its speed, and size., Comment: This work has been accepted for publication in 2024 European Conference on Networks and Communications (EuCNC) & 6G Summit. arXiv admin note: text overlap with arXiv:2306.17650