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We present the long-term photometric and spectroscopic monitoring campaign of a transitioning SN~IIn/Ibn from $-$10.8 d to 150.7 d post $V$-band maximum. SN~2021foa shows prominent He i lines comparable in strength to the H$\alpha$ line around peak luminosity, placing SN~2021foa between the SN~IIn and SN~Ibn populations. The spectral comparison with SNe~IIn and SNe~Ibn shows that it resembles the SN~IIn population at pre-maximum, becomes intermediate between SNe~IIn/Ibn around maximum light, and similar to SN~1996al at late times. The photometric evolution shows a precursor at $-$50 d and a light curve shoulder around 17 d, which matches well with the light curve of the interacting IIns like SN~2016jbu. The peak luminosity and color evolution of SN 2021foa are consistent with most SNe~IIn and SNe~Ibn. SN~2021foa shows the unique case of a SN~IIn where the P-Cygni features in H$\alpha$ appear at later stages, either due to complex geometry of the CSM or an interaction of the ejecta with a CSM shell/disk (similar to SNe~2009ip and 2015bh). Temporal evolution of the H$\alpha$ profile favours a disk-like CSM geometry (CSM having both H and He) with a narrow (500 -- 1200 km s$^{-1}$) component, intermediate width (3000 -- 8000 km s$^{-1}$) and broad component in absorption. Hydrodynamical lightcurve modelling can well-reproduce the lightcurve by a two-component CSM structure with different densities ($\rho$ $\propto$ r$^{-2}$ -- $\rho$ $\propto$ r$^{-5}$), mass-loss rates (10$^{-3}$ -- 10$^{-1}$ M$_{\odot}$ yr$^{-1}$) assuming a wind velocity of 1000 km s$^{-1}$ and having a CSM mass of 0.18 M$_{\odot}$. The overall evolution supports the fact that indicates that SN~2021foa most likely originated from a LBV star transitioning to a WR star with the mass-loss rate increasing in the period from 5 to 0.5 years before the explosion or it could be due to a binary interaction., Comment: To be submitted to MNRAS in few days; 20 pages, 16 figures, 4 tables

In this paper, we deal with non-Baire rare sets in category bases which forms $\aleph_0$-independent family, where a rare set is a common generalization of both Luzin and Sierpinski set.

Understanding electrical characteristics and corresponding transport models at single molecular junctions is crucial. There have been many reports on organic compounds-based single molecular junctions. However, organometallic compounds-based single molecular junctions have not been explored yet. Re(I) organometallic compounds are known to exhibit intriguing photophysical properties scrutinized for photocatalysis, and light-emitting diodes but have not been explored in molecular electronics. In this work, a theoretical model study on the I-V characteristics of two Re(I)-carbonyl complexes bearing Re-P and Re-N-N linkage has been meticulously chosen. Tunneling and hopping transport in Au/Re(I)-complex/Au single-molecule junctions are governed by Landauer-formalism and the Marcus theory, respectively. Interestingly, variations in molecular architecture culminate in notable variations in junction functionality and mechanism of charge conduction. Physical parameters influencing the device characteristics such as dipole moment, molecule-electrode coupling strength, voltage division factor, and temperature have been extensively studied which offers modulation of the characteristics and device design. The dominant hopping current in Re complex bearing bipyridine linkage was found to be responsible for the observed asymmetric electrical (I-V) behavior. Our work paves the way for constructing various organometallic compounds-based molecular junctions to understand electronic functions and the underlying transport mechanisms., Comment: 17 pages, 8 figures

Synthesizing the voices of unseen speakers is a persisting challenge in multi-speaker text-to-speech (TTS). Most multi-speaker TTS models rely on modeling speaker characteristics through speaker conditioning during training. Modeling unseen speaker attributes through this approach has necessitated an increase in model complexity, which makes it challenging to reproduce results and improve upon them. We design a simple alternative to this. We propose SelectTTS, a novel method to select the appropriate frames from the target speaker and decode using frame-level self-supervised learning (SSL) features. We show that this approach can effectively capture speaker characteristics for unseen speakers, and achieves comparable results to other multi-speaker TTS frameworks in both objective and subjective metrics. With SelectTTS, we show that frame selection from the target speaker's speech is a direct way to achieve generalization in unseen speakers with low model complexity. We achieve better speaker similarity performance than SOTA baselines XTTS-v2 and VALL-E with over an 8x reduction in model parameters and a 270x reduction in training data., Comment: Submitted to IEEE Signal Processing Letters

Salient Object Detection (SOD) has traditionally relied on feature refinement modules that utilize the features of an ImageNet pre-trained backbone. However, this approach limits the possibility of pre-training the entire network because of the distinct nature of SOD and image classification. Additionally, the architecture of these backbones originally built for Image classification is sub-optimal for a dense prediction task like SOD. To address these issues, we propose a novel encoder-decoder-style neural network called SODAWideNet++ that is designed explicitly for SOD. Inspired by the vision transformers ability to attain a global receptive field from the initial stages, we introduce the Attention Guided Long Range Feature Extraction (AGLRFE) module, which combines large dilated convolutions and self-attention. Specifically, we use attention features to guide long-range information extracted by multiple dilated convolutions, thus taking advantage of the inductive biases of a convolution operation and the input dependency brought by self-attention. In contrast to the current paradigm of ImageNet pre-training, we modify 118K annotated images from the COCO semantic segmentation dataset by binarizing the annotations to pre-train the proposed model end-to-end. Further, we supervise the background predictions along with the foreground to push our model to generate accurate saliency predictions. SODAWideNet++ performs competitively on five different datasets while only containing 35% of the trainable parameters compared to the state-of-the-art models. The code and pre-computed saliency maps are provided at https://github.com/VimsLab/SODAWideNetPlusPlus., Comment: Accepted at ICPR 2024

We present radio observations of the long-duration gamma-ray burst (GRB) 221009A which has become known to the community as the Brightest Of All Time or the BOAT. Our observations span the first 475 days post-burst and three orders of magnitude in observing frequency, from 0.15 to 230GHz. By combining our new observations with those available in the literature, we have the most detailed radio data set in terms of cadence and spectral coverage of any GRB to date, which we use to explore the spectral and temporal evolution of the afterglow. By testing a series of phenomenological models, we find that three separate synchrotron components best explain the afterglow. The high temporal and spectral resolution allows us to conclude that standard analytical afterglow models are unable to explain the observed evolution of GRB 221009A. We explore where the discrepancies between the observations and the models are most significant and place our findings in the context of the most well-studied GRB radio afterglows to date. Our observations are best explained by three synchrotron emitting regions which we interpret as a forward shock, a reverse shock and an additional shock potentially from a cocoon or wider outflow. Finally, we find that our observations do not show any evidence of any late-time spectral or temporal changes that could result from a jet break but note that any lateral structure could significantly affect a jet break signature., Comment: 15 pages, 8 figures. Accepted to MNRAS

The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs) are pre-trained deep learning models used for natural language processing (NLP) tasks. The ability of LLMs to understand and generate human-like text makes them particularly useful for analysing social media data to detect and evaluate sentiments. We present a sentiment analysis framework utilising LLMs for longitudinal sentiment analysis of the Sinophobic sentiments expressed in X (Twitter) during the COVID-19 pandemic. The results show a significant correlation between the spikes in Sinophobic tweets, Sinophobic sentiments and surges in COVID-19 cases, revealing that the evolution of the pandemic influenced public sentiment and the prevalence of Sinophobic discourse. Furthermore, the sentiment analysis revealed a predominant presence of negative sentiments, such as annoyance and denial, which underscores the impact of political narratives and misinformation shaping public opinion. The lack of empathetic sentiment which was present in previous studies related to COVID-19 highlights the way the political narratives in media viewed the pandemic and how it blamed the Chinese community. Our study highlights the importance of transparent communication in mitigating xenophobic sentiments during global crises.

The emergence of cognition requires a framework that bridges evolutionary principles with neurocomputational mechanisms. This paper introduces the "thoughtseed" framework, proposing that cognition arises from the dynamic interaction of self-organizing units of embodied knowledge called "thoughtseeds." We leverage evolutionary theory, "neuronal packets," and the "Inner Screen" hypothesis within Free Energy Principle, and propose a four-level hierarchical model of the cognitive agent's internal states: Neuronal Packet Domains (NPDs), Knowledge Domains (KDs), thoughtseeds network, and meta-cognition. The dynamic interplay within this hierarchy, mediated by nested Markov blankets and reciprocal message passing, facilitates the emergence of thoughtseeds as coherent patterns of activity that guide perception, action, and learning. The framework further explores the role of the organism's Umwelt and the principles of active inference, especially the generative model at each nested level, in shaping the selection and activation of thoughtseeds, leading to adaptive behavior through surprise minimization. The "Inner Screen" is posited as the locus of conscious experience, where the content of the dominant thoughtseed is projected, maintaining a unitary conscious experience. Active thoughtseeds are proposed as the fundamental units of thought that contribute to the "content of consciousness." We present a mathematical framework grounded in active inference and dynamical systems theory. The thoughtseed framework represents an initial but promising step towards a novel, biologically-grounded model for understanding the organizing principles and emergence of embodied cognition, offering a unified account of cognitive phenomena, from basic physiological regulation to higher-order thought processes, and potentially bridge neuroscience and contemplative traditions., Comment: I will be referencing the pre-print for my poster/Spotlight Presentation at IWAI,https://iwaiworkshop.github.io/ on September -10, 2024 at Oxford, England. It has embedded 5 Figures and 2 Tables. it has mathematical equations 35+ mathematical equations, with explanations in Supllementary Section

Astrophysical population simulations predict that a subset of dynamically formed binary black holes (BBHs) may possess eccentricity $\gtrsim 0.1$ at a gravitational wave (GW) frequency of $10 \,\text{Hz}$. Presently, the LIGO-Virgo-KAGRA (LVK) Collaboration tests general relativity (GR) assuming that the binary eccentricity has decayed well before it enters the detector's frequency band. Previous works have shown that binary eccentricity can bias GR tests if unaccounted for. Here we develop two methods to extend parametrized tests of GR to eccentric binaries. The first method extends the standard null parametrized test for quasicircular binaries by adding fractional deviations at each post-Newtonian (PN) order in the eccentric part of the GW phasing (assuming the small-eccentricity limit). Simultaneous measurement of the circular and eccentric deviation parameters ($\delta\hat{\varphi}, \delta\hat{\varphi}^e$) allows us to constrain deviations from GR for eccentric binaries. While strong constraints on the deviation parameters are not achievable with LIGO's projected sensitivity, the multibanding of LISA and CE observations can constrain these deviations to $|\delta\hat{\varphi}_2| \lesssim 3 \times 10^{-3}$ and $|\delta\hat{\varphi}^e_2|\lesssim 2\times 10^{-2}$. The second method looks for GR deviations in the rate of periastron advance ($\Delta\alpha$). The parameter $\Delta\alpha$ ($\Delta\alpha^{\rm GR} \to 0$) can be constrained with LIGO to $|\Delta\alpha|\lesssim 4 \times 10^{-2}$ (with $1 \sigma$ confidence). Multiband sources observed by LISA and CE provide an improved constraint of $|\Delta\alpha|\lesssim 3\times 10^{-5}$. The space-based detector DECIGO provides the best constraint on $\Delta\alpha$ with $|\Delta\alpha|\lesssim 8 \times 10^{-6}$., Comment: 32 pages, 7 figures

Main-sequence Radio Pulse emitters (MRPs) are magnetic early-type stars that produce coherent radio emission observed in the form of periodic radio pulses. The emission mechanism behind is the Electron Cyclotron Maser Emission (ECME). Amongst all kinds of magnetospheric emission, ECME is unique due to its high directivity and intrinsically narrow bandwidth. The emission is also highly circularly polarized and the sign of polarization is opposite for the two magnetic hemispheres. This combination of properties makes ECME highly sensitive to the three-dimensional structures in the stellar magnetospheres. This is especially significant for late-B and A-type magnetic stars that do not emit other types of magnetospheric emission such as Halpha, the key probe used to trace magnetospheric densities. In this paper, we use ultra-wideband observation (0.4-2 GHz) of a late B-type MRP HD 133880 to demonstrate how we can extract information on plasma distribution from ECME. We achieve this by examining the differences in pulse arrival times ('lags') as a function of frequencies, and qualitatively comparing those with lags obtained by simulating ECME ray paths in hot stars' magnetospheres. This reveals that the stellar magnetosphere has a disk-like overdensity inclined to the magnetic equator with a centrally concentrated density that primarily affects the intermediate frequencies (400-800 MHz). This result, which is consistent with recent density model proposed for hotter centrifugally supported magnetospheres, lends support to the idea of a unifying model for magnetospheric operations in early-type stars, and also provides further motivation to fully characterize the ECME phenomenon in large-scale stellar magnetospheres., Comment: Accepted for publication in ApJ

We introduce and compare two domain decomposition based numerical methods, namely the Dirichlet-Neumann and Neumann-Neumann Waveform Relaxation methods (DNWR and NNWR respectively), tailored for solving partial differential equations (PDEs) incorporating time delay. Time delay phenomena frequently arise in various real-world systems, making their accurate modeling and simulation crucial for understanding and prediction. We consider a series of model problems, ranging from Parabolic, Hyperbolic to Neutral PDEs with time delay and apply the iterative techniques DNWR and NNWR for solving in parallel. We present the theoretical foundations, numerical implementation, and comparative performance analysis of these two methods. Through numerical experiments and simulations, we explore their convergence properties, computational efficiency, and applicability to various types of PDEs with time delay.

This paper proposes a new methodology for early validation of high-level requirements on cyber-physical systems with the aim of improving their quality and, thus, lowering chances of specification errors propagating into later stages of development where it is much more expensive to fix them. The paper presents a transformation of a real-world requirements specification of a medical device$-$a PCA pump$-$into an Event Calculus model that is then evaluated using answer set programming and the s(CASP) system. The evaluation under s(CASP) allowed deductive as well as abductive reasoning about the specified functionality of the PCA pump on the conceptual level with minimal implementation or design dependent influences, and led to fully-automatically detected nuanced violations of critical safety properties. Further, the paper discusses scalability and non-termination challenges that had to be faced in the evaluation and techniques proposed to (partially) solve them. Finally, ideas for improving s(CASP) to overcome its evaluation limitations that still persist as well as to increase its expressiveness are presented., Comment: Accepted for ICLP 2024

We examine the evaporation-induced coalescence of two droplets undergoing freezing by conducting numerical simulations employing the lubrication approximation. When two sessile drops undergo freezing in close vicinity over a substrate, they interact with each other through the gaseous phase and the simultaneous presence of evaporation/condensation. In an unsaturated environment, the evaporation flux over the two volatile sessile drops is asymmetric, with lower evaporation in the region between the two drops. This asymmetry in the evaporation flux generates an asymmetric curvature in each drop, which results in a capillary flow that drives the drops closer to each other, eventually leading to their coalescence. This capillary flow, driven by evaporation, competes with the upward movement of the freezing front, depending on the relative humidity in the surrounding environment. We found that higher relative humidity reduces the evaporative flux, delaying capillary flow and impeding coalescence by restricting contact line motion. For a constant relative humidity, the substrate temperature governs the coalescence phenomenon, and resulting condensation can accelerate this process. Interestingly, lower substrate temperatures are observed to facilitate faster propagation of the freezing front, which, in turn, restricts coalescence., Comment: 34 pages, 16 figures. arXiv admin note: text overlap with arXiv:2312.03271

With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software is becoming integral parts of these emerging sensing systems to reap the benefits of this progress. This paper looks into widefield Nitrogen Vacancy Center-based magnetometry and focuses on estimating the magnetic field from the Optically Detected Magnetic Resonances (ODMR) signal, a crucial output for various applications. Mapping the shifts of ODMR signals to phase estimation, a computationally efficient approaches are proposed. Involving Fourier Transform and Filtering as pre-processing steps, the suggested approaches involve linear curve fit or complex frequency estimation based on well-known super-resolution technique Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT). The existing methods in the quantum sensing literature take different routes based on Lorentzian fitting for determining magnetic field maps. To showcase the functionality and effectiveness of the suggested techniques, relevant results, based on experimental data are provided, which shows a significant reduction in computational time with the proposed method over existing methods, Comment: 4 pages, 3 figures, typos corrected

Large Language Models have demonstrated exceptional proficiency on coding tasks, but it is challenging to precisely evaluate their code reasoning ability. Existing benchmarks are insufficient as they are unrealistic and conflate semantic reasoning ability with performance on software engineering tasks. We introduce CRQBench, a benchmark of 100 C++ code reasoning questions and answers derived from contextualized code review comments. To curate CRQBench, we use an LLM assistant alongside human inspection, reducing manual effort. We conduct an evaluation of GPT-4 on CRQBench and find that it produces correct responses grounded in the given context for 65 of the 100 questions.

We experimentally investigate the evaporation dynamics of an array of sessile droplets arranged in different configurations. Utilizing a customized goniometer, we capture side and top view profiles to monitor the evolution of height, spread, contact angle, and volume of the droplets. Our results reveal that the lifetime of a droplet array surpasses that of an isolated droplet, attributed to the shielding effect induced by neighbouring droplets, which elevates the local vapour concentration, thereby reducing the evaporation rate. We found that lifetime increases as droplet separation distance decreases at a fixed configuration and substrate temperature. It is observed that the lifetimes increase with the number of droplets. We observe a decrease in lifetimes, following a power law trend with increasing substrate temperature, with the shielding effect diminishing at higher substrate temperatures due to natural convective effects. We also observe a generalised behavior for the centrally placed droplet across various separation distances and substrate temperatures. This arises from different droplet configurations and substrate temperatures, which modify the local vapour concentration around the droplets without significantly impacting the contact line dynamics. Additionally, the experimental results are compared with a diffusion-based theoretical model that incorporates the evaporative cooling effect to predict the lifetime of the central droplet within the array. We observe that the theoretical model satisfactorily predicts the lifetime of the droplet at room temperature. However, for high-temperature cases, the model slightly overpredicts the evaporative lifetimes., Comment: 38 pages, 9 figures, Accepted in Langmuir

The $\epsilon$ Lupi A (HD 136504) system stands out among magnetic massive binaries as the only short-period binary system in which both components have detectable magnetic fields. The proximity of the magnetospheres of the components leads to magnetospheric interactions, which are revealed as periodic pulses in the radio light curve of this system. In this work, we aim to investigate the magnetospheric interaction phenomenon in the X-ray domain. We observed this system with the XMM-Newton telescope, covering its orbital period. We observe variable X-ray emission with maximum flux near periastron, showing similarity with radio observations. The X-ray spectra show significantly elevated hard X-ray flux during periastron. We attribute the soft X-ray emission to individual magnetospheres, while the hard X-ray emission is explained by magnetospheric interaction, particularly due to magnetic reconnection. However, unlike in the radio, we do not find any significant short-term X-ray bursts. This exotic system can be an ideal target to study magnetospheric interactions in close binaries with organized magnetospheres., Comment: Accepted for publication in ApJ, 16 pages, 11 figures

Let $G=(V,E)$ be a simple graph. The concept of Inverse symmetric division deg index $(ISDD)$ was introduced in the chemical graph theory very recently. In spite of this, a few papers have already appeared with this index in the literature. Ghorbani et al. proposed Inverse symmetric division deg index and is defined as $$ISDD(G)=\sum\limits_{v_iv_j\in E(G)}\,\displaystyle{\frac{d_id_j}{d^2_i+d^2_j}},$$ where $d_i$ is the degree of the vertex $v_i$ in $G$. In this paper, we obtain some lower and upper bounds on the inverse symmetric division deg index $(ISDD)$ of graphs in terms of various graph parameters, with identifying extremal graphs. Moreover, we present two relations between the Inverse symmetric division deg index and the various topological indices of graphs. Finally, we give concluding remarks with future work.

Nowadays, various stakeholders involved in the analysis of electric power distribution grids face difficulties in the data acquisition related to the grid topology and parameters of grid assets. To mitigate the problem and possibly accelerate the accomplishment of grid studies without access to real data, we propose a novel approach for generating synthetic distribution grids (Syngrids) of (almost) arbitrary size replicating the characteristics of real medium- and low-voltage distribution networks. The method enables large-scale testing without incurring the burden of retrieving and pre-processing real-world data. The proposed algorithm exploits the publicly available information of Open Street Map (OSM). By leveraging geospatial data of real buildings and road networks, the approach allows to construct a Syngrid of chosen size with realistic topology and electrical parameters. It is shown that typical power-flow and short-circuit calculations can be performed on Syngrids ensuring convergence. Within the context of validating the effectiveness of the algorithm and the meaningful similarity of the output to real grids, the topological and electrical characteristics of a Syngrid are compared to their real-world counterparts. Finally, an open-source web platform named as Synthetic Grid Generator (SGG) and based on the proposed algorithm can be used by various stakeholders for the creation of synthetic grids., Comment: 8 pages, 8 figures

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and the difficult task of malware detection.

Quantum teleportation enables quantum information transmission, but requires distribution of entangled resource states. Unfortunately, decoherence, caused by environmental interference during quantum state storage, can degrade quantum states, leading to entanglement loss in the resource state and reduction of the fidelity of the teleported information. In this work, we investigate the use of error correction and error syndrome information in scheduling teleportation at a quantum network node in the presence of multiple teleportation requests and a finite rate of remote entanglement distribution. Specifically, we focus on the scenario where stored qubits undergo decoherence over time due to imperfect memories. To protect the qubits from the resulting errors, we employ quantum encodings, and the stored qubits undergo repeated error correction, generating error syndromes in each round. These error syndromes can provide additional benefits, as they can be used to calculate qubit-specific error likelihoods, which can then be utilized to make better scheduling decisions. By integrating error correction techniques into the scheduling process, our goal is to minimize errors and decoherence effects, thereby enhancing the fidelity and efficiency of teleportation in a quantum network setting.

Moving target defenses (MTD) are proactive security techniques that enhance network security by confusing the attacker and limiting their attack window. MTDs have been shown to have significant benefits when evaluated against traditional network attacks, most of which are automated and untargeted. However, little has been done to address an attacker who is aware the network uses an MTD. In this work, we propose a novel approach named MTDSense, which can determine when the MTD has been triggered using the footprints the MTD operation leaves in the network traffic. MTDSense uses unsupervised clustering to identify traffic following an MTD trigger and extract the MTD interval. An attacker can use this information to maximize their attack window and tailor their attacks, which has been shown to significantly reduce the effectiveness of MTD. Through analyzing the attacker's approach, we propose and evaluate two new MTD update algorithms that aim to reduce the information leaked into the network by the MTD. We present an extensive experimental evaluation by creating, to our knowledge, the first dataset of the operation of an IP-shuffling MTD in a software-defined network. Our work reveals that despite previous results showing the effectiveness of MTD as a defense, traditional implementations of MTD are highly susceptible to a targeted attacker., Comment: 12 pages, 12 figures, 3 tables

Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors. Robotics problems, however, pose fundamental difficulties for the application of RL, stemming from the complexity and cost of interacting with the physical world. This article provides a modern survey of DRL for robotics, with a particular focus on evaluating the real-world successes achieved with DRL in realizing several key robotic competencies. Our analysis aims to identify the key factors underlying those exciting successes, reveal underexplored areas, and provide an overall characterization of the status of DRL in robotics. We highlight several important avenues for future work, emphasizing the need for stable and sample-efficient real-world RL paradigms, holistic approaches for discovering and integrating various competencies to tackle complex long-horizon, open-world tasks, and principled development and evaluation procedures. This survey is designed to offer insights for both RL practitioners and roboticists toward harnessing RL's power to create generally capable real-world robotic systems., Comment: The first three authors contributed equally. Accepted to Annual Review of Control, Robotics, and Autonomous Systems

Crime forecasting is a critical component of urban analysis and essential for stabilizing society today. Unlike other time series forecasting problems, crime incidents are sparse, particularly in small regions and within specific time periods. Traditional spatial-temporal deep learning models often struggle with this sparsity, as they typically cannot effectively handle the non-Gaussian nature of crime data, which is characterized by numerous zeros and over-dispersed patterns. To address these challenges, we introduce a novel approach termed Spatial Temporal Multivariate Zero-Inflated Negative Binomial Graph Neural Networks (STMGNN-ZINB). This framework leverages diffusion and convolution networks to analyze spatial, temporal, and multivariate correlations, enabling the parameterization of probabilistic distributions of crime incidents. By incorporating a Zero-Inflated Negative Binomial model, STMGNN-ZINB effectively manages the sparse nature of crime data, enhancing prediction accuracy and the precision of confidence intervals. Our evaluation on real-world datasets confirms that STMGNN-ZINB outperforms existing models, providing a more reliable tool for predicting and understanding crime dynamics.

Considering the ever-evolving threat landscape and rapid changes in software development, we propose a risk assessment framework SRiQT (Software Risk Quantification through Trust). This framework is based on the necessity of a dynamic, data-driven, and adaptable process to quantify risk in the software supply chain. Usually, when formulating such frameworks, static pre-defined weights are assigned to reflect the impact of each contributing parameter while aggregating these individual parameters to compute resulting risk scores. This leads to inflexibility, a lack of adaptability, and reduced accuracy, making them unsuitable for the changing nature of the digital world. We adopt a novel perspective by examining risk through the lens of trust and incorporating the human aspect. Moreover, we quantify risk associated with individual software by assessing and formulating risk elements quantitatively and exploring dynamic data-driven weight assignment. This enhances the sensitivity of the framework to cater to the evolving risk factors associated with software development and the different actors involved in the entire process. The devised framework is tested through a dataset containing 9000 samples, comprehensive scenarios, assessments, and expert opinions. Furthermore, a comparison between scores computed by the OpenSSF scorecard, OWASP risk calculator, and the proposed SRiQT framework has also been presented. The results suggest that SRiQT mitigates subjectivity and yields dynamic data-driven weights as well as risk scores., Comment: 14 pages, 1 figure, 7 tables

Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and reduce the communication cost of data sharing. Thus, high-fidelity distilled data can support the efficient deployment of machine learning applications in distributed network environments. A naive way to construct a synthetic set in a distributed environment is to allow each client to perform local data distillation and to merge local distillations at a central server. However, the quality of the resulting set is impaired by heterogeneity in the distributions of the local data held by clients. To overcome this challenge, we introduce the first collaborative data distillation technique, called CollabDM, which captures the global distribution of the data and requires only a single round of communication between client and server. Our method outperforms the state-of-the-art one-shot learning method on skewed data in distributed learning environments. We also show the promising practical benefits of our method when applied to attack detection in 5G networks.

Given an embedded closed submanifold $\Sigma^n$ in the closed Riemannian manifold $M^{n + k}$, where $k < n + 2$, we define extrinsic global conformal invariants of $\Sigma$ by renormalizing the volume associated to the unique singular Yamabe metric with singular set $\Sigma$. In case $n$ is odd, the renormalized volume is an absolute conformal invariant, while if $n$ is even, there is a conformally invariant energy term given by the integral of a local Riemannian invariant. In particular, the volume gives a global conformal invariant of a knot embedding in the three-sphere. We extend the construction of energies for even $n$ to general codimension by considering formal solutions to the singular Yamabe problem; except that, for each fixed $n$, there are finitely many $k > n + 2$, which we identify, for which the smoothness of the formal solution is obstructed and we obtain instead a pointwise conformal invariant. We compute the new quantities in several cases., Comment: 28 pages

Measuring the properties of the intergalactic medium (IGM) and sources during the Epoch of Reionization (EoR) is of immense importance. We explore the prospects of probing the IGM and sources through redshifted 21-cm observations of individual ionized bubbles surrounding known luminous sources during the EoR. Accordingly, we simulate HI 21-cm maps, foreground contaminants and system noise which are specific to the uGMRT and SKA1-Low observations. Following the subtraction of the foreground from the total visibility, we employ a visibility-based matched filter technique to optimally combine the desired HI 21-cm signal while minimizing the system noise. Our analysis suggests that these ionized bubbles can be detected with more than $5 \sigma$ significance using approximately $\sim 2000$ and $\sim 3000$ hours of observation time with the uGMRT at redshift $7.1$ and $8.3$ respectively. The SKA1-Low should be able to detect these with more than $8 \sigma$ significance using just $\sim 100$ hrs of observations. Further, we investigate the impact of the foreground subtraction on the detectability and find the signal-to-noise ratio decreases when smaller bandwidth is used. More importantly, we show that the matched filtering method can measure ionized bubble radius and constrain HI-neutral fraction reasonably well, helping deeper insights into source properties and the intergalactic medium., Comment: 26 pages, 14 figures, 1 table

To address the rural broadband challenge and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity wireless x-haul and access platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment, base stations, edge, and cloud, ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as O-RAN, NextG, and agriculture applications. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA., Comment: 17 pages, 18 figures

Metal carbides, nitrides, or carbonitrides of early transition metals, better known as MXenes, possess notable structural, electrical, and magnetic properties. Analyzing electronic structures by calculating structural stability, band structure, density of states, Bader charge transfer, and work functions utilizing first principle calculations, we revealed that titanium nitride Mxenes, namely Ti$_2$N and Ti$_3$N$_2$, have excess anionic electrons in their pseudo-atomic structure inside the crystal lattice, making them MXene electrides. Bulk Ti$_3$N$_2$ has competing antiferromagnetic (AFM) and ferromagnetic(FM) configurations with slightly more stable AFM configurations, while the Ti$_2$N MXene is nonmagnetic. Although Ti$_3$N$_2$ favors AFM configurations with hexagonal crystal systems having $6/mmm$ point group symmetry, Ti$_3$N$_2$ does not support altermagnetism. The monolayer of the Ti$_3$N$_2$ MXene is a ferromagnetic electride. These unique properties of having non-nuclear interstitial anionic electrons in the electronic structure of titanium nitride MXene have not yet been reported in the literature. Density functional theory calculations show TiN is neither an electride, MXene, or magnetic.

We examine the impact of long-range Dzyaloshinskii-Moriya (DM) interaction in the extended XY model on the phase diagram as well as the static and dynamical properties of quantum and classical correlation functions. It is known that in the nearest-neighbor XY model with DM interaction, the transition from the gapless chiral phase to a gapped one occurs when the strengths of the DM interaction and anisotropy coincide. We exhibit that the critical line gets modified with the range of interactions which decay according to power-law. Specifically, instead of being gapless in the presence of a strong DM interaction, a gapped region emerges which grows with the increase of the moderate fall-off rate (quasi-long range regime) in the presence of a transverse magnetic field. The gapless chiral phase can also be separated from a gapped one by the decay patterns of quantum mutual information and classical correlation with distant sites of the ground state which are independent of the fall-off rate in the gapless zone. We observe that the corresponding critical lines that depend on the fall-off rate can also be determined from the effective central charge involved in the scaling of entanglement entropy. We illustrate that in a non-equilibrium setting, the relaxation dynamics of classical correlation, the decay rate of total correlation, and the growth rate of entanglement entropy can be employed to uncover whether the evolving Hamiltonian and the Hamiltonian corresponding to the initial state are gapped or gapless., Comment: 12 pages, 7 figures

The next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. We demonstrate the package's capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation., Comment: 14 pages, 8 figures

Wicking is a widely studied process across various natural and artificial systems. A more industrially relevant configuration is when the wicking liquid is allowed to evaporate, such as in the heat pipes and the variants, in order to regulate the operating temperature effectively. We consider a simpler case for investigation where liquid ethanol (kept in a container) climbs onto a thin filter paper oriented vertically and is exposed to laboratory conditions, facilitating mass loss via evaporation, thereby inducing the cooling effect. We use three filter papers having different permeability values and three diagnostics concurrently for each case in order to understand the entire dynamics; these are optical imaging, thermal imaging, and precision weighing scale. Results revealed a steady-state height considerably below Jurin's limit for all the cases, indicating evaporative mass loss as the primary limiting parameter rather than the conventional gravitational effect in such a process. Further, thermal imaging unveiled a non-uniform temperature distribution along the filter paper with a very peculiar temperature inversion near the mid-way of the wicking liquid. We use this feature to improve the Constant Evaporation Model (CEM by Fries et al) by addressing this non-linear behaviour as a consequence of the evaporation rate being a function of the vertical position; we term this newly proposed model Non-Constant Evaporation Model, (NCEM). In the NCEM, we test two different power-law relations for the evaporation rate and both seemingly capture the essential features of this dynamic process. However, both the models do not capture the process completely and further refinement is needed for complete understanding. Since evaporation rate is the limiting factor, we included it in defining two new non-dimensional numbers (Evaporation Height Number, EHN and Evaporation Time Number, ETN)., Comment: 40 pages, 19 figures

Realizing deterministic, high-fidelity entangling interactions--of the kind that can be utilized for efficient quantum information processing--between photons remains an elusive goal. Here, we address this long-standing issue by devising a protocol for creating and manipulating highly-entangled superpositions of well-controlled states of light by using an on-chip photonic system that has recently been shown to implement three-dimensional, non-Abelian quantum holonomy. Our calculations indicate that a subset of such entangled superpositions are maximally-entangled, volume-law states, and that the underlying entanglement can be distilled and purified for applications in quantum science. Specifically, we envisage that this entangling mechanism could be utilized for realizing two-qubit, universal, entangling quantum gates with linear photonic elements alone. Furthermore, this method should also--at least, in principle--be generalizable to systems of neutral atoms., Comment: 7 pages including references, 5 figures; no accompanying Supplemental Material

We investigate the ground state phases of an extended Bose-Hubbard ladder of unit filling via the density-matrix-renormalization-group method and, in particular, the effect of rung-hoppings. In contrast to a single-chain, a commensurate supersolid emerges, and based on the Luttinger parameter, we classify them into two types. The latter leads to a reentrant gapless behavior as the onsite interaction is increased while keeping all other parameters intact. A reentrant gapped transition is also found as a function of nearest-neighbor interactions. Further, we show that the string order characterizing the Haldane phase vanishes for a finite inter-chain hopping amplitude, however small it is. Finally, we propose two experimental platforms to observe our findings, using either dipolar atoms or polar molecules and Rydberg admixed atoms., Comment: 7 pages, 9 figures

Inverse problems have many applications in science and engineering. In Computer vision, several image restoration tasks such as inpainting, deblurring, and super-resolution can be formally modeled as inverse problems. Recently, methods have been developed for solving inverse problems that only leverage a pre-trained unconditional diffusion model and do not require additional task-specific training. In such methods, however, the inherent intractability of determining the conditional score function during the reverse diffusion process poses a real challenge, leaving the methods to settle with an approximation instead, which affects their performance in practice. Here, we propose a MAP estimation framework to model the reverse conditional generation process of a continuous time diffusion model as an optimization process of the underlying MAP objective, whose gradient term is tractable. In theory, the proposed framework can be applied to solve general inverse problems using gradient-based optimization methods. However, given the highly non-convex nature of the loss objective, finding a perfect gradient-based optimization algorithm can be quite challenging, nevertheless, our framework offers several potential research directions. We use our proposed formulation and develop empirically effective algorithms for solving noiseless and noisy image inpainting tasks. We validate our proposed algorithms with extensive experiments across diverse mask settings., Comment: 10 pages

In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.

Type Ibn supernovae (SNe Ibn) are rare stellar explosions powered primarily by interaction between the SN ejecta and H-poor, He-rich material lost by their progenitor stars. Multi-wavelength observations, particularly in the X-rays, of SNe Ibn constrain their poorly-understood progenitor channels and mass-loss mechanisms. Here we present Swift X-ray, ultraviolet, and ground-based optical observations of the Type Ibn SN 2022ablq -- only the second SN Ibn with X-ray detections to date. While similar to the prototypical Type Ibn SN 2006jc in the optical, SN 2022ablq is roughly an order of magnitude more luminous in the X-rays, reaching unabsorbed luminosities $L_X$ $\sim$ 3$\times$10$^{40}$ erg s$^{-1}$ between 0.2 - 10 keV. From these X-ray observations we infer time-varying mass-loss rates between 0.05 - 0.5 $M_\odot$ yr$^{-1}$ peaking 0.5 - 2 yr before explosion. This complex mass-loss history and circumstellar environment disfavor steady-state winds as the primary progenitor mass-loss mechanism. We also search for precursor emission from alternative mass-loss mechanisms, such as eruptive outbursts, in forced photometry during the two years before explosion. We find no statistically significant detections brighter than M $\approx$ -14 -- too shallow to rule out precursor events similar to those observed for other SNe Ibn. Finally, numerical models of the explosion of a $\sim$15 $M_\odot$ helium star that undergoes an eruptive outburst $\approx$1.8 years before explosion are consistent with the observed bolometric light curve. We conclude that our observations disfavor a Wolf-Rayet star progenitor losing He-rich material via stellar winds and instead favor lower-mass progenitor models, including Roche-lobe overflow in helium stars with compact binary companions or stars that undergo eruptive outbursts during late-stage nucleosynthesis stages., Comment: 23 pages, 8 figures, submitted to ApJ. Comments welcome

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy $\gamma$-ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the $\gamma$-ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The $\gamma$-ray luminosity above $0.5\,\mathrm{TeV}$ of both sources is $(2-3)\times 10^{35}\,\mathrm{erg}\,\mathrm{s}^{-1}$. This exceeds by more than a factor of 2 the luminosity of HESS J1646$-$458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the $\gamma$-ray emission from each source is extended with a significance of $>3\sigma$ and a Gaussian width of about $30\,\mathrm{pc}$. For 30 Dor C, a connection between the $\gamma$-ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the $\gamma$-ray signal from R136 are discussed., Comment: 10+11 pages, 4+6 figures. Corresponding authors: L. Mohrmann, N. Komin

We describe a one-parameter family of Euclidean wormhole solutions with the topology of a compact hyperbolic space times an interval in Einstein gravity minimally coupled to a massless scalar field in AdS$_{d+1}$ commonly referred to as Einstein-dilaton gravity. These solutions are locally described by the same metric and dilaton profile as the single-boundary Janus domain wall solutions in the same theory which are usually studied in the context of holographic RG flows. The wormholes compute the averaged product of partition functions of CFTs on either boundary deformed by different marginal couplings to the scalar operator dual to the dilaton. We observe that the renormalised volumes of these wormholes increase monotonically with the difference in the marginal couplings on the boundary thereby showing that the pair of CFTs on the boundaries get increasingly decorrelated as the difference in the marginal couplings increases. We use the partition functions of the three-dimensional wormhole solutions to determine the variance of the OPE data of local operators between the marginally deformed 2d CFTs and quantify how the variance decays with the difference in marginal couplings. In addition, a family of wormholes sourced by a thin shell of dust determine how the variance of the matrix elements of the dual line defect decays with the difference in marginal couplings. Applying the GKPW dictionary to wormholes, we compute averages of integrated dilaton correlators treating the wormhole amplitude as a functional of the dilaton sources. We observe that the crossed two-point correlators with a dilaton insertion on either boundary decay montonically with the difference in marginal couplings consistent with the observation that the CFTs increasingly decorrelate as the difference in marginal couplings grows., Comment: 32 pages + appendices

What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,'' systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia, causing damage to the environment and economy and losses of human and animal lives. An efficient method of forecasting floods is crucial to limit this damage. Techniques for flood prediction are currently based on hydrological, and hydrodynamic (physically-based) numerical models. Machine learning methods that include deep learning offer certain advantages over conventional physically based approaches, including flexibility and accuracy. Deep learning methods have been promising for predicting small to medium-sized climate extreme events over a short time horizon; however, large flooding events present a critical challenge. We present an ensemble-based machine learning approach that addresses large-scale extreme flooding challenges using a switching mechanism motivated by extreme-value theory for long-short-term-memory (LSTM) deep learning models. We use a multivariate and multi-step time-series prediction approach to predict streamflow for multiple days ahead in the major catchments of Australia. The ensemble framework also employs static information to enrich the time-series information, allowing for regional modelling across catchments. Our results demonstrate enhanced prediction of streamflow extremes, with notable efficacy for large flooding scenarios in the selected Australian catchments. Through comparative analysis, our methodology underscores the potential for deep learning models to revolutionise flood forecasting across diverse regions.

SqlCompose brings generative AI into the data analytics domain. SQL is declarative, has formal table schemas, and is often written in a non-linear manner. We address each of these challenges and develop a set of models that shows the importance of each problem. We first develop an internal SQL benchmark to perform offline tests at Meta. We evaluate how well the Public Llama model performs. We attain a BLEU score of 53% and 24% for single- and multi-line predictions, respectively. This performance is consistent with prior works on imperative languages. We then fine-tune Llama on our internal data and database schemas. SqlComposeSA substantially outperforms Llama by 16 percentage points on BLEU score. SQL is often written with multiple sub queries and in a non-sequential manner. We develop SqlComposeFIM which is aware of the context before and after the line(s) that need to be completed. This fill-in-the-middle model outperform SqlComposeFIM by 35 percentage points. We also measure how often the models get the correct table names, and SqlComposeFIM is able to do this 75% of the time. Aside from our scientific research, we also roll out SqlComposeFIM at Meta. SqlCompose is used on a weekly basis by over 10k users including data scientists and software engineers, less than 1% of users have disabled SqlCompose. We use the feedback from users to improve SqlCompose. Interesting positive themes include completing tedious or repetitive SQL clauses, suggesting boilerplate coding, and help in eliminate the need to remember difficult SQL syntax. The most significant negative themes was table and column name hallucinations, which has been reduced with the release of SqlComposeFIM. The SqlCompose models consistently outperform public and internal LLMs, despite being smaller (7 bn and 13 bn), which provides early indications that smaller specialist models can outperform larger general purpose models., Comment: 11 pages

We experimentally investigate the morphology and breakup of a droplet as it descends freely from a height and encounters an airstream. The size distributions of the child droplets are analysed using high-speed shadowgraphy and in-line holography techniques. We found that a droplet falling from various heights exhibits shape oscillations due to the intricate interplay between inertia and surface tension forces, leading to significant variations in the radial deformation of the droplet, influencing the breakup dynamics under an identical airstream condition. Specifically, the droplet undergoes vibrational breakup when introduced at a location slightly above the air nozzle. In contrast, as the release height of the droplet increases, keeping the Weber number defined based on the velocity of the airstream fixed, a dynamic interplay between the inertia of the droplet and the aerodynamic flow field comes into play, resulting in a sequence of breakup modes transitioning from vibrational breakup to retracting bag breakup, bag breakup, bag-stamen, retracting bag-stamen breakup, and eventually returning to vibrational breakup. Our experiments also reveal that the size distribution resulting from retracting bag breakup primarily arises from rim and node fragmentation, leading to a bimodal distribution. In contrast, bag and bag-stamen breakups yield a tri-modal size distribution due to the combined contributions of bag, rim, and node breakup mechanisms. Furthermore, we utilize a theoretical model that incorporates the effective Weber number, considering different release heights. This model accurately predicts the size distribution of the child droplets resulting from the various breakup modes observed in our experiments., Comment: 23 pages, 17 figures

Assessing the quality of Natural Language Generation (NLG) outputs, such as those produced by large language models (LLMs), poses significant challenges. Traditional approaches involve either resource-intensive human evaluations or automatic metrics, which often exhibit a low correlation with human judgment. In this study, we propose Review-Feedback-Reason (ReFeR), a novel evaluation framework for NLG using LLM agents. We rigorously test ReFeR using two pre-existing benchmark datasets on diverse NLG tasks. The proposed framework not only enhances the accuracy of NLG evaluation, surpassing previous benchmarks by $\sim$20\%, but also generates constructive feedback and significantly improves collective reasoning. This feedback is then leveraged for the creation of instruction-tuning datasets, which, when used to fine-tune smaller models like Mistral-7B, makes them extremely good evaluators, yielding a better correlation with human evaluations and performance nearly on par with GPT-3.5. We highlight the effectiveness of our methodology through its application on three reasoning benchmarks, where it outperforms most of the state-of-the-art methods, and also outperforms the reasoning capabilities of models like GPT-3.5 Turbo by $\sim$11.67\% and GPT-4 by $\sim$1\% on an average., Comment: Paper Under Review

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

In this paper, we address the challenge of differential privacy in the context of graph cuts, specifically focusing on the minimum $k$-cut and multiway cut problems. We introduce edge-differentially private algorithms that achieve nearly optimal performance for these problems. For the multiway cut problem, we first provide a private algorithm with a multiplicative approximation ratio that matches the state-of-the-art non-private algorithm. We then present a tight information-theoretic lower bound on the additive error, demonstrating that our algorithm on weighted graphs is near-optimal for constant $k$. For the minimum $k$-cut problem, our algorithms leverage a known bound on the number of approximate $k$-cuts, resulting in a private algorithm with optimal additive error $O(k\log n)$ for fixed privacy parameter. We also establish a information-theoretic lower bound that matches this additive error. Additionally, we give an efficient private algorithm for $k$-cut even for non-constant $k$, including a polynomial-time 2-approximation with an additive error of $\widetilde{O}(k^{1.5})$., Comment: 39 pages

In different nonlinear mediums, the wave trains carry energy and expose many amazing features. To describe a nonlinear phenomenon, a soliton is one that preserves its shape and amplitude even after the collision. Breather is one kind of soliton structure, which is a localized wave that periodically oscillates in amplitude. This article uses the reductive perturbation technique (RPT) to get the GE from a plasma system with four parts: cold positrons that can move, hot positrons and hot electrons that are spread out in a kappa pattern, and positive ions that can't move. Then, using the Hirota bilinear method (HBM), it is possible to obtain the multi-soliton and breather structures of GE. Breathers are fluctuating regional wave packets and significantly participate in hydrodynamics as well as optics; besides, their interaction can alter the dynamical characteristics of the wave fields. We also incorporate a detailed numerical simulation study based on a newly designed code by two of the co-authors. It is found that in our plasma system, soliton solutions, especially breather solutions, exist. Although superthermal (kappa-distributed) electrons and positrons play an important role in soliton structures, This type of analysis can also apply to the propagation of finite-amplitude waves in natural phenomena like the atmosphere, ocean, optic fibres, signal processing, etc. It should also be useful to study different electrostatic disturbances in space and laboratory plasmas, where immobile positive ions, superthermal electrons, superthermal hot positrons, and mobile cold positrons are the major plasma species., Comment: 34 pages, 26 figures

Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared white spaces such as those in the TVWS and CBRS spectrum bands offer opportunities to expand connectivity, innovate, and provide affordable access to high-speed Internet in under-served areas without additional cost to expensive licensed spectrum. However, the current methods to utilize these white spaces are inefficient due to very conservative models and spectrum policies, causing under-utilization of valuable spectrum resources. This hampers the full potential of innovative wireless technologies that could benefit farmers, small Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) operating in rural regions. This study explores the challenges faced by farmers and service providers when using shared spectrum bands to deploy their networks while ensuring maximum system performance and minimizing interference with other users. Additionally, we discuss how spatiotemporal spectrum models, in conjunction with database-driven spectrum-sharing solutions, can enhance the allocation and management of spectrum resources, ultimately improving the efficiency and reliability of wireless networks operating in shared spectrum bands.

We introduce a simple and efficient text-controllable high-fidelity music generation and editing model. It operates on sequences of continuous latent representations from a low frame rate 48 kHz stereo variational auto encoder codec that eliminates the information loss drawback of discrete representations. Based on a diffusion transformer architecture trained on a flow-matching objective the model can generate and edit diverse high quality stereo samples of variable duration, with simple text descriptions. We also explore a new regularized latent inversion method for zero-shot test-time text-guided editing and demonstrate its superior performance over naive denoising diffusion implicit model (DDIM) inversion for variety of music editing prompts. Evaluations are conducted on both objective and subjective metrics and demonstrate that the proposed model is not only competitive to the evaluated baselines on a standard text-to-music benchmark - quality and efficiency-wise - but also outperforms previous state of the art for music editing when combined with our proposed latent inversion. Samples are available at https://melodyflow.github.io.