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Lead chalcogenides are the promising thermoelectric (TE) materials having narrow band gap. The present work investigates the TE behaviour of PbSe in the temperature range 300-500 K. The transport properties of the sample have been studied using the Abinit and BoltzTrap code. The experimentally observed value of \textit{S} at 300 and 500 K is found to be $\sim$ 198 and 266 $\mu$V K$^{-1}$, respectively. The rate of increase in \emph{S} from 300 to 460 (460 to 500) K is found to be $\sim$ 0.4 (0.09). The temperature dependent electrical conductivity \textit{($\sigma$)} shows the increasing trend, with values of $\sim $ 0.35 $\times $ 10$^{3}$ and $\sim$ 0.58 $\times$ 10$^{3}$ $\Omega$$^{-1}$ m$^{-1}$ at 300 and 500 K, respectively. Further, the value of thermal conductivity \textit{($\kappa$)} at 300 (500) K is found to be 0.74 (1.07) W m$^{-1}$ K$^{-1}$. The value of \textit{$\kappa$} is found to be increasing upto 460 K and then starts decreasing. The dispersion plot indicates that PbSe is a direct band gap semiconductor with band gap value of 0.16 (0.27) eV considering spin-orbit coupling (without SOC). The partial density of states (PDOS) plot shows that Pb 6p and Se 4p states have a major contribution in the transport properties. The observed and calculated values of \textit{S} gives a good match for SOC case. The calculated \textit{$\sigma$} and electronic part of thermal conductivity (\textit{$\kappa{_e}$}) gives good match with the experimental data. The maximum power factor (PF) value of $\sim$ 4.3 $\times$ 10$^{-5}$ W/mK$^{2}$ is observed at 500 K. This work helps in understanding the TE behaviour of PbSe through a novel and insightful alliance of experimental measurements and DFT approach.

V5579 Sgr was a fast nova discovered in 2008 April 18.784 UT. We present the optical spectroscopic observations of the nova observed from the Castanet Tolosan, SMARTS and CTIO observatories spanning over 2008 April 23 to 2015 May 11. The spectra are dominated by hydrogen Balmer, Fe II and O I lines with P-Cygni profiles in the early phase, typical of an Fe II class nova. The spectra show He I and He II lines along with forbidden lines from N, Ar, S, and O in the nebular phase. The nova showed a pronounced dust formation episode that began about 20 days after the outburst. The dust temperature and mass were estimated using the WISE data from spectral energy distribution (SED) fits. The PAH-like features are also seen in the nova ejecta in the mid-IR Gemini spectra taken 522 d after the discovery. Analysis of the light curve indicates values of t$_2$ and t$_3$ about 9 and 13 days, respectively, placing the nova in the category of fast nova. The best fit cloudy model of the early decline phase JHK spectra obtained on 2008 May 3 and the nebular optical spectrum obtained on 2011 June 2 shows a hot white dwarf source with T$_{BB}$ $\sim$ 2.6 $\times$ 10$^5$ K having a luminosity of 9.8 $\times$ 10$^{36}$ ergs s$^{-1}$. Our abundance analysis shows that the ejecta is significantly enhanced relative to solar, O/H = 32.2, C/H = 15.5 and N/H = 40.0 in the early decline phase and O/H = 5.8, He/H = 1.5 and N/H = 22.0 in the nebular phase., Comment: 17 pages, 12 figures, 5 tables, Accepted for publication in PASA

We investigate the 1D stellar evolution of a 16.5 M$_{\odot}$ zero-age main-sequence star having different initial rotations. Starting from the pre-main-sequence, the models evolve up to the onset of the core collapse stage. The collapse of such a massive star can result in several kinds of energetic transients, such as Gamma-Ray Bursts (GRBs), Supernovae, etc. Using the simulation parameters, we calculate their free-fall timescales when the models reach the stage of the onset of core collapse. Estimating the free-fall timescale is crucial for understanding the duration for which the central engine can be fueled, allowing us to compare the free-fall timescale with the T$_{\rm 90}$ duration of GRBs. Our results indicate that, given the constraints of the parameters and initial conditions in our models, rapidly rotating massive stars might serve as potential progenitors of Ultra-Long GRBs (T$_{\rm 90}$ $>>$ 500 sec). In contrast, the non-rotating or slowly rotating models are more prone to explode as hydrogen-rich Type IIP-like core-collapse supernovae., Comment: 8 pages, 4 figures, 1 table, accepted

Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video. However, one major limitation of the NeRF-based avatars is the slow rendering speed due to the dense point sampling of NeRF, preventing them from broader utility on resource-constrained devices. We introduce LightAvatar, the first head avatar model based on neural light fields (NeLFs). LightAvatar renders an image from 3DMM parameters and a camera pose via a single network forward pass, without using mesh or volume rendering. The proposed approach, while being conceptually appealing, poses a significant challenge towards real-time efficiency and training stability. To resolve them, we introduce dedicated network designs to obtain proper representations for the NeLF model and maintain a low FLOPs budget. Meanwhile, we tap into a distillation-based training strategy that uses a pretrained avatar model as teacher to synthesize abundant pseudo data for training. A warping field network is introduced to correct the fitting error in the real data so that the model can learn better. Extensive experiments suggest that our method can achieve new SOTA image quality quantitatively or qualitatively, while being significantly faster than the counterparts, reporting 174.1 FPS (512x512 resolution) on a consumer-grade GPU (RTX3090) with no customized optimization., Comment: Appear in ECCV'24 CADL Workshop. Code: https://github.com/MingSun-Tse/LightAvatar-TensorFlow

Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines., Comment: Resubmitted to ApJL after addressing the referee's comments; comments are welcome

Nodal line semimetals, a class of topological quantum materials, exhibit a variety of novel phenomena due to their properties, such as bands touching on a one-dimensional line or a ring in the Brillouin zone and drumhead-like surface states. In addition, these semimetals are protected by the combined space-inversion and time-reversal ($\mathcal{PT}$) symmetry. In this study, we investigate the longitudinal DC conductivity of the Dirac nodal line semimetals for the broken $\mathcal{PT}$-symmetric system by the mass term. Here, using the quantum kinetic technique, we find the intrinsic (field-driven) and extrinsic (scattering-driven) contributions to the total DC conductivity due to interband effects. Interestingly, the resulting intrinsic conductivity is the Fermi sea contribution, while the extrinsic stems from the Fermi surface contribution. We show that at low chemical potential, the extrinsic part contributes more and dominates over the traditional Drude intraband term, while at the high chemical potential, the intrinsic conductivity contributes. Furthermore, the total DC response due to interband effects saturates at high chemical potential and its strength decreases with increasing mass value. Our findings suggest that the extrinsic contributions are rich enough to understand the overall feature of the response for the three-dimensional system., Comment: Accepted for publication in the Physical Review B

Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations., Comment: Pre Print

Single Event Latch-up (SEL) is one of the prime concerns for CMOS ICs used in space systems. Galactic Cosmic Rays or Solar Energetic Particles (SEP) may trigger the parasitic latch up circuit in CMOS ICs and cause increase in current beyond the safe limits thereby presenting a threat of permanent failure of the IC. Mitigation of the SEL is always a challenging task. The conventional mitigation approaches inherently introduce some response time which presents an uncertainty because during this response time the current may exceed the safe current limits. This paper presents a novel circuit based on MOSFETs which provides end-to-end complete solution of detecting SEL, limiting the current below the set threshold and executing power cycling to restore the normal functioning of the CMOS IC. The proposed circuit has been simulated in MULTISIM and the simulation results match very well with the expected behavior of (i)current limiting and (ii) the total time duration taken in power cycling to bring the SEL sensitive device back to its normal operational state. This circuit can be harnessed by spacecraft system designers to overcome the catastrophic threat of SEL posed by space radiation environment.

Understanding the origins of massive black hole seeds and their co-evolution with their host galaxy requires studying intermediate-mass black holes (IMBHs) and estimating their mass. However, measuring the mass of these IMBHs is challenging due to the high spatial resolution requirement. A spectrophotometric reverberation monitoring is performed for a low-luminosity Seyfert 1 galaxy NGC 4395 to measure the size of the broad line region (BLR) and black hole mass. The data were collected using the 1.3-m Devasthal fast optical telescope (DFOT) and 3.6-m Devasthal optical telescope (DOT) at ARIES, Nainital, over two consecutive days in March 2022. The analysis revealed strong emission lines in the spectra and light curves of merged 5100{\AA} spectroscopic continuum flux ($f_{\mathrm{5100}}$) with photometric continuum V-band and H$\alpha$, with fractional variabilities of 6.38\% and 6.31\% respectively. In comparison to several previous studies with lag estimation $<$ 90 minutes, our calculated H$\alpha$ lag supersedes by $125.0^{+6.2}_{-6.1}$ minutes using ICCF and {\small JAVELIN} methods. The velocity dispersion ($\sigma_{\mathrm{line}}$) of the broad line clouds is measured to be $544.7^{+22.4}_{-25.1}$ km s$^{-1}$, yielding a black hole mass of $\sim$ $2.2^{+0.2}_{-0.2}\times 10^{4}M_{\mathrm{\odot}}$ and an Eddington ratio of 0.06., Comment: 17 pages, 8 figures, accepted for publication in ApJ

We present near-simultaneous X-ray and optical polarization measurements in the high synchrotron peaked (HSP) blazar Mrk 421. The X-ray polarimetric observations were carried out using {\it Imaging X-ray Polarimetry Explorer} ({\it IXPE}) on 06 December 2023. During {\it IXPE} observations, we also carried out optical polarimetric observations using 104cm Sampurnanand telescope at Nainital and multi-band optical imaging observations using 2m Himalayan Chandra Telescope at Hanle. From model-independent analysis of {\it IXPE} data, we detected X-ray polarization with degree of polarization ($\Pi_X$) of 8.5$\pm$0.5\% and an electric vector position angle ($\Psi_X$) of 10.6$\pm$1.7 degrees in the 2$-$8 keV band. From optical polarimetry on 06 December 2023, in B, V, and R bands, we found values of $\Pi_B$ = 4.27$\pm$0.32\%, $\Pi_V$= 3.57$\pm$0.31\%, and $\Pi_R$= 3.13$\pm$0.25\%. The value of $\Pi_B$ is greater than that observed at longer optical wavelengths, with the degree of polarization suggesting an energy-dependent trend, gradually decreasing from higher to lower energies. This is consistent with that seen in other HSP blazars and favour a stratified emission region encompassing a shock front. The emission happening in the vicinity of the shock front will be more polarized due to the ordered magnetic field resulting from shock compression. The X-ray emission, involving high-energy electrons, originates closer to the shock front than the optical emission. The difference in the spatial extension could plausibly account for the observed variation in polarization between X-ray and optical wavelengths. This hypothesis is further supported by the broadband spectral energy distribution modeling of the X-ray and optical data., Comment: 9 pages, 3 figures. Accepted for publication in ApJ

The production of a pair of on-shell $Z$-bosons is an important process at the Large Hadron Collider. Owing to its large production cross section at the LHC, this process is very useful for SM precision studies, electroweak symmetry breaking sector as well as to unravel the possible new physics. In this work, we have performed the threshold resummation of the large logarithms that arise in the partonic threshold limit $z \to 1$, up to Next-to-Next-to-Leading Logarithmic (NNLL) accuracy. The presence of the two-loop contributions in the process dependent resummation coefficient $g_0$ makes the numerical computation a non-trivial task. After matching the resummed predictions to the Next-to-Next-to-Leading order (NNLO) fixed order results, we present the invariant mass distribution to NNLO+NNLL accuracy in QCD for the current LHC energies. We find that in the high invariant mass region ($Q=1$ TeV), while the NNLO corrections are as large as $83\%$ with respect to the leading order, the NNLL contribution enhances the cross section by additional few percent, about $4\%$ for $13.6$ TeV LHC. In this invariant mass region, the conventional scale uncertainties in the fixed order results get reduced from $3.4\%$ at NNLO to about $2.6\%$ at NNLO+NNLL, and this reduction is expected to be more for higher $Q$ values., Comment: 23 Pages, 8 Figures, 1 Table

Antiferromagnets that display very small stray magnetic field are ideal for spintronic applications. Of particular interest are non-collinear, chiral antiferromagnets of the type Mn3X (X=Sn, Ge), which display a large magnetotransport response that is correlated with their antiferromagnetic ordering. The ability to read out and manipulate this ordering is crucial for their integration into spintronic devices. These materials exhibit a tiny unbalanced magnetic moment such that a large external magnetic field can, in principle, be used to set the material into a single antiferromagnetic domain. However, in thin films of Mn3Sn, we find that such fields induce only a partial magnetic ordering. By detecting two orthogonal in-plane components of the magnetic order vector, we find that the non-switchable fraction has a unidirectional anisotropy. This also enables us to visualize switching along multiple easy axes in Mn3Sn. Studying the switching at the nanoscale allows us to correlate the pining behavior to crystal grain boundaries in the Mn3Sn nanowire structures.

We present our findings from the first long X-ray observation of the hard X-ray source Swift J0826.2-7033 with XMM-Newton, which has shown characteristics of magnetic accretion. The system appears to have a long orbital period (~7.8 h) accompanied by short timescale variabilities, which we tentatively interpret as the spin and beat periods of an intermediate polar. These short- and long-timescale modulations are energy-independent, suggesting that photoelectric absorption does not play any role in producing the variabilities. If our suspected spin and beat periods are true, then Swift J0826.2-7033 accretes via disc-overflow with an equal fraction of accretion taking place via disc and stream. The XMM-Newton and Swift-BAT spectral analysis reveals that the post-shock region in Swift J0826.2-7033 has a multi-temperature structure with a maximum temperature of ~43 keV, which is absorbed by a material with an average equivalent hydrogen column density of ~1.6$\times$10$^{22}$ cm$^{-2}$ that partially covers ~27% of the X-ray source. The suprasolar abundances, with hints of an evolved donor, collectively make Swift J0826.2-7033 an interesting target, which likely underwent a thermal timescale mass transfer phase., Comment: 10 pages, 7 figures. Accepted for publication in A&A

Nucleon structure functions, as measured in lepton-nucleon scattering, have historically provided a critical observable in the study of partonic dynamics within the nucleon. However, at very large parton momenta it is both experimentally and theoretically challenging to extract parton distributions due to the probable onset of non-perturbative contributions and the unavailability of high precision data at critical kinematics. Extraction of the neutron structure and the d-quark distribution have been further challenging due to the necessity of applying nuclear corrections when utilizing scattering data from a deuteron target to extract free neutron structure. However, a program of experiments has been carried out recently at the energy-upgraded Jefferson Lab electron accelerator aimed at significantly reducing the nuclear correction uncertainties on the d-quark distribution function at large partonic momentum. This allows leveraging the vast body of deuterium data covering a large kinematic range to be utilized for d-quark parton distribution function extraction. We present new data from experiment E12-10-002 carried out in Jefferson Lab Hall C on the deuteron to proton cross-section ratio at large BJorken-x. These results significantly improve the precision of existing data, and provide a first look at the expected impact on quark distributions extracted from global parton distribution function fits.

This paper addresses the problem of energy-efficient and safe routing of last-mile electric freight vehicles. With the rising environmental footprint of the transportation sector and the growing popularity of E-Commerce, freight companies are likely to benefit from optimal time-window-feasible tours that minimize energy usage while reducing traffic conflicts at intersections and thereby improving safety. We formulate this problem as a Bi-criterion Steiner Traveling Salesperson Problem with Time Windows (BSTSPTW) with energy consumed and the number of left turns at intersections as the two objectives while also considering regenerative braking capabilities. We first discuss an exact mixed-integer programming model with scalarization to enumerate points on the efficiency frontier for small instances. For larger networks, we develop an efficient local search-based heuristic, which uses several operators to intensify and diversify the search process. We demonstrate the utility of the proposed methods using benchmark data and real-world instances from Amazon delivery routes in Austin, US. Comparisons with state-of-the-art solvers shows that our heuristics can generate near-optimal solutions within reasonable time budgets, effectively balancing energy efficiency and safety under practical delivery constraints.

The proliferation of distributed energy resources has heightened the interactions between transmission and distribution (T&D) systems, necessitating novel analyses for the reliable operation and planning of interconnected T&D networks. A critical gap is an analysis approach that identifies and localizes the weak spots in the combined T\&D networks, providing valuable information to system planners and operators. The research goal is to efficiently model and simulate infeasible (i.e. unsolvable in general settings) combined positive sequence transmission and three-phase distribution networks with a unified solution algorithm. We model the combined T&D network with the equivalent circuit formulation. To solve the overall T&D network, we build a Gauss-Jacobi-Newton (GJN) based distributed primal dual interior point optimization algorithm capable of isolating weak nodes. We validate the approach on large combined T&D networks with 70k+ T and 15k+ D nodes and demonstrate performance improvement over the alternating direction method of multipliers (ADMM) method.

Prompt emission of GRB 230812B stands out as one of the most luminous events observed by both the Fermi-GBM and LAT. Prompt emission spectral analysis (both time-integrated and resolved) of this burst supports an additional thermal component together with a non-thermal, indicating the hybrid jet composition. The spectral parameters alpha, Ep, and kT of the best-fit Band+Blackbody model show a tacking behaviour with the intensity. Further, the low energy afterglow emission is consistent with the synchrotron emission from the external forward shock in the ISM medium. LAT detected very high energy emission (VHE) deviating from the synchrotron mechanism, possibly originating from the Lorentz boosting of prompt emission photons by accelerated electrons in the external shock via Inverse Compton (IC) or Synchrotron Self Compton (SSC) emission mechanisms. The comparison of the prompt and afterglow emission properties of this burst revealed that, unlike the bright prompt emission, the afterglow of GRB 230812B is fainter than the other SN-detected bright bursts (GRB 130427A and GRB 171010A) at a similar redshift., Comment: 10 pages, 8 figures and the revised version after addressing the referee's comments

We employed sodium mesitylene sulfonate crystals to investigate angle-dependent phonon resonance and thickness-dependent splitting in THz time-domain polarimetry. This crystal possesses a C2 space group, leading to a repetition pattern after 180deg rotations. Our experimental observations revealed intriguing behaviour: We observed a non-linear response when varying the angle from 10deg to 360deg in both 0.182mm and 1.266mm thick crystals. specifically, at 90deg and 270deg, dip resonance occurred, while at 180deg and 360deg, no phonon resonance was observed. For thick crystals, we observed the splitting of phonon modes. Our findings offer valuable insights into the phononic properties of this crystal as the angle varies., Comment: 9 pages, 9 figures

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. In this study, we show how physics-informed ML architectures can exactly enforce the detailed balance condition, by construction. Using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost., Comment: 20 pages, 11 figures

Physics-informed neural networks (PINNs) are a class of deep learning models that utilize physics as differential equations to address complex problems, including ones that may involve limited data availability. However, tackling solutions of differential equations with oscillations or singular perturbations and shock-like structures becomes challenging for PINNs. Considering these challenges, we designed an efficient wavelet-based PINNs (W-PINNs) model to solve singularly perturbed differential equations. Here, we represent the solution in wavelet space using a family of smooth-compactly supported wavelets. This framework represents the solution of a differential equation with significantly fewer degrees of freedom while still retaining in capturing, identifying, and analyzing the local structure of complex physical phenomena. The architecture allows the training process to search for a solution within wavelet space, making the process faster and more accurate. The proposed model does not rely on automatic differentiations for derivatives involved in differential equations and does not require any prior information regarding the behavior of the solution, such as the location of abrupt features. Thus, through a strategic fusion of wavelets with PINNs, W-PINNs excel at capturing localized nonlinear information, making them well-suited for problems showing abrupt behavior in certain regions, such as singularly perturbed problems. The efficiency and accuracy of the proposed neural network model are demonstrated in various test problems, i.e., highly singularly perturbed nonlinear differential equations, the FitzHugh-Nagumo (FHN), and Predator-prey interaction models. The proposed design model exhibits impressive comparisons with traditional PINNs and the recently developed wavelet-based PINNs, which use wavelets as an activation function for solving nonlinear differential equations., Comment: 17 pages, 12 figures

Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We develop a transformer-based conditional generative model for discrete point objects and their properties. We use it to build a model for populating cosmological simulations with gravitationally collapsed structures called dark matter halos. Specifically, we condition our model with dark matter distribution obtained from fast, approximate simulations to recover the correct three-dimensional positions and masses of individual halos. This leads to a first model that can recover the statistical properties of the halos at small scales to better than 3% level using an accelerated dark matter simulation. This trained model can then be applied to simulations with significantly larger volumes which would otherwise be computationally prohibitive with traditional simulations, and also provides a crucial missing link in making end-to-end differentiable cosmological simulations. The code, named GOTHAM (Generative cOnditional Transformer for Halo's Auto-regressive Modeling) is publicly available at \url{https://github.com/shivampcosmo/GOTHAM}., Comment: 6 pages, 2 figures. Accepted by the Structured Probabilistic Inference & Generative Modeling workshop of ICML 2024

Dialog systems, such as voice assistants, are expected to engage with users in complex, evolving conversations. Unfortunately, traditional automatic speech recognition (ASR) systems deployed in such applications are usually trained to recognize each turn independently and lack the ability to adapt to the conversational context or incorporate user feedback. In this work, we introduce a general framework for ASR in dialog systems that can go beyond learning from single-turn utterances and learn over time how to adapt to both explicit supervision and implicit user feedback present in multi-turn conversations. We accomplish that by leveraging advances in student-teacher learning and context-aware dialog processing, and designing contrastive self-supervision approaches with Ohm, a new online hard-negative mining approach. We show that leveraging our new framework compared to traditional training leads to relative WER reductions of close to 10% in real-world dialog systems, and up to 26% on public synthetic data., Comment: Presented at ICML 2024

Many IoT applications run on a wireless infrastructure supported by resource-constrained nodes which is popularly known as Low-Power and Lossy Networks (LLNs). Currently, LLNs play a vital role in digital transformation of industries. The resource limitations of LLNs restrict the usage of traditional routing protocols and therefore require an energy-efficient routing solution. IETF's Routing Protocol for Low-power Lossy Networks (RPL, pronounced 'ripple') is one of the most popular energy-efficient protocols for LLNs, specified in RFC 6550. In RPL, Destination Advertisement Object (DAO) control message is transmitted by a child node to pass on its reachability information to its immediate parent or root node. An attacker may exploit the insecure DAO sending mechanism of RPL to perform 'DAO insider attack' by transmitting DAO multiple times. This paper shows that an aggressive DAO insider attacker can drastically degrade network performance. We propose a Lightweight Mitigation Solution for DAO insider attack, which is termed as 'Li-MSD'. Li-MSD uses a blacklisting strategy to mitigate the attack and restore RPL performance, significantly. By using simulations, it is shown that Li-MSD outperforms the existing solution in the literature.

Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}., Comment: 12 pages and 8 figures. This is a Learning the Universe Publication

Many modern applications of Bayesian inference, such as in cosmology, are based on complicated forward models with high-dimensional parameter spaces. This considerably limits the sampling of posterior distributions conditioned on observed data. In turn, this reduces the interpretability of posteriors to their one- and two-dimensional marginal distributions, when more information is available in the full dimensional distributions. We show how to learn smooth and differentiable representations of posterior distributions from their samples using normalizing flows, which we train with an added evidence error loss term, to improve accuracy in multiple ways. Motivated by problems from cosmology, we implement a robust method to obtain one and two-dimensional posterior profiles. These are obtained by optimizing, instead of integrating, over other parameters, and are thus less prone than marginals to so-called projection effects. We also demonstrate how this representation provides an accurate estimator of the Bayesian evidence, with log error at the 0.2 level, allowing accurate model comparison. We test our method on multi-modal mixtures of Gaussians up to dimension 32 before applying it to simulated cosmology examples. Our code is publicly available at https://github.com/mraveri/tensiometer., Comment: 10 pages, 6 figures Tutorial available at https://tensiometer.readthedocs.io/en/latest/example_posterior_profiles.html

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets, for $Q^2$ values as small as 0.012 and 0.02 GeV$^2$, respectively, using the CEBAF Large Acceptance Spectrometer (CLAS). This is the archival paper of the EG4 experiment that summaries the previously reported results of the polarized structure functions $g_1$, $A_1F_1$, and their moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$, for both the proton and the deuteron. In addition, we report on new results on the neutron $g_1$ extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$ formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for $g_1$ and the Bjorken integral $\overline \Gamma_1^{p-n}$, and compared to available theoretical predictions. All of our results provide for the first time extensive tests of spin observable predictions from chiral effective field theory ($\chi$EFT) in a $Q^2$ range commensurate with the pion mass. They motivate further improvement in $\chi$EFT calculations from other approaches such as the lattice gauge method., Comment: 33 pages. 26 figures. Data table provided in supplementary material (30 pages)

This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.

Positron Annihilation Lifetime Spectroscopy (PALS) is a well-established non-destructive technique used for nanostructural characterization of porous materials. It is based on the annihilation of a positron and an electron. Mean positron lifetime in the material depends on the free voids size and molecular environment, allowing the study of porosity and structural transitions in the nanometer scale. We have developed a novel method enabling spatially resolved PALS, thus providing tomography of nanostructural characterization of an extended object. Correlating space (position) and structural (lifetime) information brings new insight in materials studies, especially in the characterization of the purity and pore distribution. For the first time, a porosity image using stationary positron sources for the simultaneous measurement of the porous polymers XAD4, silica aerogel powder IC3100, and polyvinyl toluene scintillator PVT by the J-PET tomograph is demonstrated

Image analysis in the euclidean space through linear hyperspaces is well studied. However, in the quest for more effective image representations, we turn to hyperbolic manifolds. They provide a compelling alternative to capture complex hierarchical relationships in images with remarkably small dimensionality. To demonstrate hyperbolic embeddings' competence, we introduce a light-weight hyperbolic graph neural network for image segmentation, encompassing patch-level features in a very small embedding size. Our solution, Seg-HGNN, surpasses the current best unsupervised method by 2.5\%, 4\% on VOC-07, VOC-12 for localization, and by 0.8\%, 1.3\% on CUB-200, ECSSD for segmentation, respectively. With less than 7.5k trainable parameters, Seg-HGNN delivers effective and fast ($\approx 2$ images/second) results on very standard GPUs like the GTX1650. This empirical evaluation presents compelling evidence of the efficacy and potential of hyperbolic representations for vision tasks., Comment: BMVC 2024

Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans., Comment: 10 pages, To be published in the International Conference on Human-Agent Interaction (HAI '24) proceedings

In cell-free massive MIMO systems with multiple distributed access points (APs) serving multiple users over the same time-frequency resources, downlink beamforming is done through spatial precoding. Precoding vectors can be optimally designed to use the minimum downlink transmit power while satisfying a quality-of-service requirement for each user. However, existing centralized solutions to beamforming optimization pose challenges such as high communication overhead and processing delay. On the other hand, distributed approaches either require data exchange over the network that scales with the number of antennas or solve the problem for cellular systems where every user is served by only one AP. In this paper, we formulate a multi-user beamforming optimization problem to minimize the total transmit power subject to per-user SINR requirements and propose a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve it. In our method, every AP solves an iterative optimization problem using its local channel state information. APs only need to share a real-valued vector of interference terms with the size of the number of users. Through simulation results, we demonstrate that our proposed algorithm solves the optimization problem within tens of ADMM iterations and can effectively satisfy per-user SINR constraints.

We calculate the valence quark transverse momentum-dependent parton distributions (TMDs) of the lightest pseudoscalar meson, pions, in isospin asymmetric nuclear matter at zero temperature by employing a light-cone quark model. The medium modifications in the pion unpolarized TMDs are induced through the effective quark masses computed using the chiral SU($3$) quark mean field model. The spin densities at different momentum fraction ($x$) have also been calculated at different baryonic densities., Comment: 7 pages and 4 figures

We have analyzed the prompt and afterglow characteristics of the intermediate luminosity burst ``GRB 210210A". Our prompt emission analysis indicates that GRB 210210A is among the softest long GRBs detected by the Swift-BAT. The time-integrated prompt emission spectrum of GRB 210210A is aptly described by a power law function with an exponential cutoff. The spectral peak energy (E$_{p,z}$) in rest-frame and the E$_{\rm \gamma, iso}$ for this GRB marginally satisfy the 2$\sigma$ Amati correlation, a common feature observed in low/intermediate luminosity GRBs. Notably, an early bump is observed in the Swift-XRT light curve (a rare feature); the optical afterglow light curve, on the other hand, appears to follow a power law decay. However, due to the lack of sufficient early optical observations, we cannot completely rule out the possibility of an early bump in the optical light curve. For the bump observed in the early X-ray light curve, we calculated parameters such as peak time, rise time, decay time, and bulk Lorentz factor ($\Gamma_{0}$ $\sim$ 156), which perfectly satisfy the correlation between the parameters of the onset of the afterglow in GRBs. Both the optical and X-ray (including our observations) light curves exhibit a chromatic break in the late afterglow. Based on the prompt and afterglow parameters, we confirm that the intermediate luminosity GRB 210210A favors a collapsar scenario and is possibly powered by a magnetar., Comment: 17 pages, 9 figures, Revised version after addressing the referee's comments

Early prediction of seizures and timely interventions are vital for improving patients' quality of life. While seizure prediction has been shown in software-based implementations, to enable timely warnings of upcoming seizures, prediction must be done on an edge device to reduce latency. Ideally, such devices must also be low-power and track long-term drifts to minimize maintenance from the user. This work presents SPIRIT: Stochastic-gradient-descent-based Predictor with Integrated Retraining and In situ accuracy Tuning. SPIRIT is a complete system-on-a-chip (SoC) integrating an unsupervised online-learning seizure prediction classifier with eight 14.4 uW, 0.057 mm2, 90.5 dB dynamic range, Zoom Analog Frontends. SPIRIT achieves, on average, 97.5%/96.2% sensitivity/specificity respectively, predicting seizures an average of 8.4 minutes before they occur. Through its online learning algorithm, prediction accuracy improves by up to 15%, and prediction times extend by up to 7x, without any external intervention. Its classifier consumes 17.2 uW and occupies 0.14 mm2, the lowest reported for a prediction classifier by >134x in power and >5x in area. SPIRIT is also at least 5.6x more energy efficient than the state-of-the-art.

Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting, where the model becomes too specialized in either the task or the characteristics of the training data, resulting in a loss of generalization. This paper introduces Selective Self-Rehearsal (SSR), a fine-tuning approach that achieves performance comparable to the standard supervised fine-tuning (SFT) while improving generalization. SSR leverages the fact that there can be multiple valid responses to a query. By utilizing the model's correct responses, SSR reduces model specialization during the fine-tuning stage. SSR first identifies the correct model responses from the training set by deploying an appropriate LLM as a judge. Then, it fine-tunes the model using the correct model responses and the gold response for the remaining samples. The effectiveness of SSR is demonstrated through experiments on the task of identifying unanswerable queries across various datasets. The results show that standard SFT can lead to an average performance drop of up to $16.7\%$ on multiple benchmarks, such as MMLU and TruthfulQA. In contrast, SSR results in close to $2\%$ drop on average, indicating better generalization capabilities compared to standard SFT., Comment: 14 pages, 8 figures

Traditional solar flare forecasting approaches have mostly relied on physics-based or data-driven models using solar magnetograms, treating flare predictions as a point-in-time classification problem. This approach has limitations, particularly in capturing the evolving nature of solar activity. Recognizing the limitations of traditional flare forecasting approaches, our research aims to uncover hidden relationships and the evolutionary characteristics of solar flares and their source regions. Our previously proposed Sliding Window Multivariate Time Series Forest (Slim-TSF) has shown the feasibility of usage applied on multivariate time series data. A significant aspect of this study is the comparative analysis of our updated Slim-TSF framework against the original model outcomes. Preliminary findings indicate a notable improvement, with an average increase of 5\% in both the True Skill Statistic (TSS) and Heidke Skill Score (HSS). This enhancement not only underscores the effectiveness of our refined methodology but also suggests that our systematic evaluation and feature selection approach can significantly advance the predictive accuracy of solar flare forecasting models., Comment: This is a preprint accepted at the 26th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2024)

Statistics that capture the directional dependence of the baryon distribution in the cosmic web enable unique tests of cosmology and astrophysical feedback. We use constrained oriented stacking of thermal Sunyaev-Zel'dovich (tSZ) maps to measure the anisotropic distribution of hot gas $2.5-40$ Mpc away from galaxy clusters embedded in massive filaments and superclusters. The cluster selection and orientation (at a scale of $\sim15$ Mpc) use Dark Energy Survey (DES) Year 3 data, while expanded tSZ maps from the Atacama Cosmology Telescope Data Release 6 enable a $\sim3\times$ more significant measurement of the extended gas compared to the technique's proof-of-concept. Decomposing stacks into cosine multipoles of order $m$, we detect a dipole ($m=1$) and quadrupole ($m=2$) at $8-10\sigma$, as well as evidence for $m=4$ signal at up to $6\sigma$, indicating sensitivity to late-time non-Gaussianity. We compare to the Cardinal simulations with spherical gas models pasted onto dark matter halos. The fiducial tSZ data can discriminate between two models that deplete pressure differently in low-mass halos (mimicking astrophysical feedback), preferring higher average pressure in extended structures. However, uncertainty in the amount of cosmic infrared background contamination reduces the constraining power. Additionally, we apply the technique to DES galaxy density and weak lensing to study for the first time their oriented relationships with tSZ. In the tSZ-to-lensing relation, averaged on 7.5 Mpc (transverse) scales, we observe dependence on redshift but not shape or radial distance. Thus, on large scales, the superclustering of gas pressure, galaxies, and total matter is coherent in shape and extent., Comment: 45 pages, 18 figures, submitted to ApJ

We study the impact of hole-doping on the Kitaev-Heisenberg model on the honeycomb lattice. We investigate the pairing tendencies and correlation functions in the framework of a $t-J-K$ model using density matrix renormalization group calculations on three-leg cylinders. In the case of the pure Kitaev model, which realizes a quantum spin-liquid phase at half-filling, we find that binding of two holes only occurs at low values of the hopping, where the holes are slow. We have theoretically verified that pair formation occurs in the limit of immobile holes, where the pure Kitaev model remains exactly solvable. When we instead fix the hopping at an intermediate, more realistic, value, and vary the Heisenberg and Kitaev interaction strengths, we find pairing tendencies only in the N\'eel phase. This is in contrast to prior mean-field calculations, highlighting the importance of accounting for the kinetic energy of dopants in generalized Kitaev models. Interestingly, we also find signatures of pair-density wave formation over the studied range of model parameters, namely a periodic modulation of the charge density as well as the spin-spin and pair-pair correlations in real space. Moreover, we present a comparative study of the different correlations as a function of doping. We finally discuss the potential for experimentally observing the studied physics in quantum materials and heterostructures., Comment: Main text: 12 pages, 7 figures. Supplemental material: 5 pages, 1 figure

Semi-analytic methods can generate baryon-corrected fields from N-body simulations (``baryonification'') and are rapidly becoming a ubiquitous tool in modeling structure formation on non-linear scales. We extend this formalism to consistently model the weak lensing and thermal Sunyaev-Zeldovich (tSZ) fields directly on the full-sky, with an emphasis on higher-order correlations. We use the auto- and cross- $N$th-order moments, with $N \in \{2, 3, 4\}$, as a summary statistic of the lensing and tSZ fields, and show that our model can jointly fit these statistics measured in IllustrisTNG to within measurement uncertainties, for scales above $\gtrsim 1 {\rm Mpc}$ and across multiple redshifts. The model predictions change only minimally when including additional information from secondary halo properties, such as halo concentration and ellipticity. Each individual moment is dependent on halos of different mass ranges and has different sensitivities to the model parameters. A simulation-based forecast on the ULAGAM simulation suite shows that the combination of all moments, measured from current and upcoming lensing and tSZ surveys, can jointly constrain cosmology and baryons to high precision. The lensing and tSZ field are sensitive to different combinations of the baryonification parameters, with degeneracy directions that are often orthogonal, and the combination of the two fields leads to significantly better constraints on both cosmology and astrophysics. Our pipeline for map-level baryonification is publicly available at https://github.com/DhayaaAnbajagane/Baryonification., Comment: 23 pages, 12 figures. Pipeline available at https://github.com/DhayaaAnbajagane/Baryonification

Twin Field Quantum key Distribution (TFQKD) has attracted recent interest due to the higher secret key capacity better than the fundamental repeaterless limit and extending the achievable distance. The key generation in TFQKD is based on the post selection of randomized phase slices. This paper describes a technique for enhancing the probability of choosing the phase slices by using four detectors at Charlie end placed after a 4x4 port beam-splitter network. Using theoretical modelling of secret keyrate and simulations using StrawberryFields, we observe an increase in secret key throughput when compared to conventional TFQKD.

We present the first comprehensive source catalog (UVIT DR1) of ultraviolet (UV) photometry in four far-UV (FUV $\sim$1300$-$1800 \AA) and five near-UV (NUV $\sim$2000$-$3000 \AA) filters of the Ultraviolet Imaging Telescope (UVIT) on board {\em AstroSat}. UVIT DR1 includes bright UV sources in 291 fields that UVIT detected during its first two years of pointed observation, encompassing an area of 58 square degrees. We used the {\sc ccdlab} pipeline to reduce the L1 data, source-extractor for source detection, and four photometric procedures to determine the magnitudes of the detected sources. We provided the 3$\sigma$ and 5$\sigma$ detection limits for all the filters of UVIT. We describe the details of observation, source extraction methods, and photometry procedures applied to prepare the catalog. In the final UVIT DR1 catalog, we have point sources, extended sources, clumps from nearby galaxies, There are 239,520 unique sources in the combined UVIT DR1, of which 70,488 sources have FUV magnitudes, and 211,410 have NUV magnitudes. We cross-matched and compared non-crowded sources of UVIT with the {\em Galaxy Evolution Explorer (GALEX)} and {\em Gaia} source catalogs. We provide a clean catalog of the unique sources in various UVIT filters that will help further multi-wavelength scientific analysis of the objects., Comment: 23 pages, 10 figures, 10 tables, accepted for publication in the Astrophysical Journal Supplement Series

The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

Contamination from singly ionized iron emission is one of the greatest obstacles to determining the intensity of emission lines in the UV and optical wavelength ranges. This study presents a comprehensive analysis of the FeII emission in the bright quasar RM 102, based on the most recent version of the CLOUDY software, with the goal of simultaneously reproducing UV and optical FeII emission. We employ a constant pressure model for the emitting clouds, instead of the customary constant density assumption. The allowed parameter range is broad, with metallicity up to 50 times the solar value and turbulent velocity up to 100 km s$^{-1}$ for a subset of models. We also consider geometrical effects that could enhance the visibility of the non-illuminated faces of the clouds, as well as additional mechanical heating. Our investigation reveals that the broad line region of RM 102 is characterized by highly metallic gas. The observed FeII features provide strong evidence for an inflow pattern geometry that favours the dark sides of clouds over isotropic emission. This study confirms the presence of chemically enriched gas in the broad line region of bright quasars, represented by RM 102, which is necessary to explain the strong FeII emission and its characteristic features. Additionally, we report that CLOUDY currently still lacks certain transitions in its atomic databases which prevents it from fully reproducing some observed FeII features in quasar spectra.

We have measured the flavor dependence of multiplicities for pi^+ and pi^- production in semi-inclusive deep-inelastic scattering (SIDIS) on proton and deuteron targets to explore a possible charge symmetry violation in fragmentation functions. The experiment used an electron beam with energies of 10.2 and 10.6 GeV at Jefferson Lab and the Hall-C spectrometers. The electron kinematics spanned the range 0.3

The recent detection of CN-functionalized aromatics partly addresses the long-standing mystery of the apparent absence of five- and six-membered rings in interstellar environments. N-heterocycles, which are crucial as the fundamental structures of nucleobases, have been a focus of these aromatic searches due to their biological significance. Although N-heterocycles have not been conclusively detected in astrophysical environments, their presence in chondrites and meteorites signifies their interstellar and circumstellar connection. Precise spectral data identifies the unique signatures of molecules, confirming their presence in space. In this light, the present work reports an extensive computational investigation on interstellar 3-pyrroline; a five-membered ring N-heterocycle. This includes an alternative formation route in cold interstellar environments and highly accurate rotational and vibrational spectroscopy. The results indicate that 3-pyrroline can form on dust grain surfaces from vinyl cyanide, as its formation from pyrrole through double hydrogenation may lead to the formation of pyrrole itself via a H$_{2}$ abstraction process. 3-pyrroline's rotational transition at 52.3 GHz offers a potential tool for its detection in cold interstellar regions. Additionally, the strongest infrared features of 3-pyrroline at 16.09 $\mu$m and $\sim$3.50 $\mu$m are observable with JWST. The provided data is crucial for laboratory identification and future interstellar observations of 3-pyrroline at both radio and IR wavelengths.

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system., Comment: NA

We use Principal Component Analysis (PCA) to analyze a volume-limited sample from the SDSS and explore how cosmic web environments affect the interrelations between various galaxy properties, such as $(u-r)$ colour, stellar mass, specific star formation rate, metallicity, concentration index, and $D4000$. Our analysis reveals that the first two principal components (PC1 and PC2) account for approximately $\sim 72\%$ of the data variance. We then classify galaxies into different cosmic web environments based on the eigenvalues of the deformation tensor and compare PC1 and PC2 across these environments, ensuring a mass-matched sample of equal size for each environment. We observe that PC1 displays clear bimodality across all cosmic web environments, with sheets and clusters showing distinct preferences for negative and positive PC1 values, respectively. PC2, on the other hand, shows a positively skewed unimodal distribution in all environments. A Kolmogorov-Smirnov (KS) test confirms that the distributions of PC1 and PC2 differ significantly across environments, with a confidence level exceeding $99.99\%$. Furthermore, we calculate the normalized mutual information (NMI) between the principal components and individual galaxy properties within different cosmic web environments. A two-tailed t-test reveals that for each relationship and each pair of environments, the null hypothesis is rejected with a confidence level $>99.99\%$. Our analysis confirms that cosmic web environments play a significant role in shaping the correlations between galaxy properties., Comment: 22 pages, 8 tables, 9 figures, comments are welcome