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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

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/$c^2$, as well as upper limits on dark photon kinetic mixing and axion-like particle axioelectric coupling for masses between 1.2 and 23.3 eV/$c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross-section sensitivity was achieved., Comment: 7 pages + title and references, 4 figures, and 1 table

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

The van der Waals (vdW) heterostructure of emerging two-dimensional (2D) quantum materials, with control over their quantum geometries, crystal symmetries, spin-orbit coupling, and magnetic anisotropies, provides a new platform for generating unconventional nonlinear Hall effects, spin polarization and efficiently controlling the magnetization dynamics for non-volatile spin-based computing. However, so far, the generation of a large out-of-plane spin polarization is limited to achieve energy-efficient field-free magnetization switching and spin dynamics measurements in all-2D vdW heterostructure are so far missing, where the interplay between spins and magnetization dynamics should enable the design of ultrafast spintronic devices. Here, we demonstrate magnetization dynamics and energy-efficient field-free spin-orbit torque (SOT) switching of out-of-plane magnet Fe3GaTe2 due to unconventional Berry curvature-induced out-of-plane spin polarization from a topological Weyl semimetal TaIrTe4 in a vdW heterostructure at room temperature. We observed a large non-linear 2nd harmonic Hall signal at room temperature and evaluated the SOT-induced magnetization dynamics with a large damping-like torque. Deterministic field-free SOT magnetization switching in vdW heterostructure of TaIrTe4/Fe3GaTe2 is observed at room temperature with a low current and power density, which is an order of magnitude better than that of conventional systems. From the magnetization switching experiments, a large SOT efficiency and a very large spin Hall conductivity. These findings on all-vdW heterostructures offer a promising route to energy-efficient and external field-free ultrafast spintronic technologies.

In the present work we will give an explicit solution the problem of divergence of propgator of gauge-invariant Siegel-Zwiebach (SZ) action in Fierz-Pauli (FP) gauge (also called van Dam-Veltman-Zakharov discontinuity) by connecting it's Green's functions to that of Transverse-Traceless (TT) gauge using improved finite-field-dependent BRST (FFBRST) method., Comment: 13 Pages

As an IT-enabled multi-passenger mobility service, microtransit has the potential to improve accessibility, reduce congestion, and enhance flexibility in transportation options. However, due to its heterogeneous impacts on different communities and population segments, there is a need for better tools in microtransit forecast and revenue management, especially when actual usage data are limited. We propose a novel framework based on an agent-based mixed logit model estimated with microtransit usage data and synthetic trip data. The framework involves estimating a lower-branch mode choice model with synthetic trip data, combining lower-branch parameters with microtransit data to estimate an upper-branch ride pass subscription model, and applying the nested model to evaluate microtransit pricing and subsidy policies. The framework enables further decision-support analysis to consider diverse travel patterns and heterogeneous tastes of the total population. We test the framework in a case study with synthetic trip data from Replica Inc. and microtransit data from Arlington Via. The lower-branch model result in a rho-square value of 0.603 on weekdays and 0.576 on weekends. Predictions made by the upper-branch model closely match the marginal subscription data. In a ride pass pricing policy scenario, we show that a discount in weekly pass (from $25 to $18.9) and monthly pass (from $80 to $71.5) would surprisingly increase total revenue by $102/day. In an event- or place-based subsidy policy scenario, we show that a 100% fare discount would reduce 80 car trips during peak hours at AT&T Stadium, requiring a subsidy of $32,068/year.

This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for compute-efficient speech enhancement models running on resource-constrained hardware platforms. It leverages the slow evolution characteristic of RNN hidden states over steps, and updates only a selected set of neurons at each step by adding a newly proposed select gate to the RNN model. This select gate allows the computation cost of the conventional RNN to be reduced during network inference. As a realization of the DG-RNN, we further propose the Dynamic Gated Recurrent Unit (D-GRU) which does not require additional parameters. Test results obtained from several state-of-the-art compute-efficient RNN-based speech enhancement architectures using the DNS challenge dataset, show that the D-GRU based model variants maintain similar speech intelligibility and quality metrics comparable to the baseline GRU based models even with an average 50% reduction in GRU computes., Comment: Accepted to Interspeech 2024

In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provide the model with better guidance based on the ordinal characteristics of the data and improve the overall performance of the models. For our experiments, we employ a ResNet34-based model with transfer learning to predict $\geq$M-class flares by utilizing the shape-based features of magnetograms of active region (AR) patches spanning from $-$90$^{\circ}$ to $+$90$^{\circ}$ of solar longitude as our input data. We use a composite skill score (CSS) as our evaluation metric, which is calculated as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare our models' performance. The primary contributions of this work are as follows: (i) We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction, (ii) We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions, and evaluate and compare performance. (iii) Our candidate model, optimized with the proposed loss function, shows an improvement of $\sim$7%, $\sim$4%, and $\sim$3% for AR patches within $\pm$30$^\circ$, $\pm$60$^\circ$, and $\pm$90$^\circ$ of solar longitude, respectively in terms of CSS, when compared with standard BCE. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between $\pm$60$^{\circ}$ to $\pm$90$^{\circ}$) with a CSS=0.34 (TSS=0.50 and HSS=0.23), expanding the scope of AR-based models for solar flare prediction. This advances the reliability of solar flare forecasts, leading to more effective prediction capabilities., Comment: 10 Pages, 8 Figures. This manuscript is accepted to be published at DSAA 2024 conference. arXiv admin note: substantial text overlap with arXiv:2406.11054

This paper presents results from Kepler photometric light curves and high-resolution spectroscopic study of a super Li-rich giant KIC11087027. Using the light curve analysis, we measured the star's rotational period P$_{\rm rot}$=30.4$\pm$0.1~days, which translates to rotational velocity V$_{\rm rot}$=19.5 $\pm$ 1.7~km s$^{-1}$. Star's location in the HR-diagram, derived values of $^{12}C/^{13}C$ = 7$\pm$1 and $[C/N]=-0.95\pm 0.2$, and the inferred asteroseismic parameters from secondary calibration based on spectra suggest star is a low-mass red clump giant in the He-core burning phase. Using Gaia data, we found evidence of variation in radial velocity and proper motion, indicative of presence of an unresolved binary. The large V$_{\rm rot}$ is probably a result of tidal synchronization combined with the after-effects of He-flash, in which the size of the star is reduced significantly. The simultaneous presence of features like high rotation, very high Li abundance, strong dust shell, and strong flares in a single star is relatively uncommon, suggesting that the star experiencing tidal synchronization has recently undergone He-flash. The results pose a question whether the binary interaction, hence the high rotation, is a prerequisite for dredging-up of the high amounts of Li from the interior to the photosphere during or immediately after the He-flash event., Comment: 9 pages, 3 figures, 2 tables, accepted

This paper presents a novel multi-channel speech enhancement approach, FoVNet, that enables highly efficient speech enhancement within a configurable field of view (FoV) of a smart-glasses user without needing specific target-talker(s) directions. It advances over prior works by enhancing all speakers within any given FoV, with a hybrid signal processing and deep learning approach designed with high computational efficiency. The neural network component is designed with ultra-low computation (about 50 MMACS). A multi-channel Wiener filter and a post-processing module are further used to improve perceptual quality. We evaluate our algorithm with a microphone array on smart glasses, providing a configurable, efficient solution for augmented hearing on energy-constrained devices. FoVNet excels in both computational efficiency and speech quality across multiple scenarios, making it a promising solution for smart glasses applications., Comment: Accepted by INTERSPEECH2024

In this paper, we extend the notion of Weyl modules for twisted toroidal Lie algebra $\mathcal{T}(\mu)$. We prove that the level one global Weyl modules of $\mathcal{T}(\mu)$ are isomorphic to the tensor product of the level one representation of twisted affine Lie algebras and certain lattice vertex algebras. As a byproduct, we calculate the graded character of the level one local Weyl modules of $\mathcal{T}(\mu)$.

In this paper, we derive the approximate controllability of fractional evolution hemivariational inequalities in reflexive Banach spaces involving Caputo fractional derivatives. We first show that the original problem is connected with a fractional differential inclusion problem involving the Clarke subdifferential operator, and then we prove the approximate controllability of the original problem via the approximate controllability of the connected fractional differential inclusion problem. In the meantime, we face the issue of convexity in the multivalued fixed point map due to the nonlinear nature of the duality map in reflexive Banach spaces involved in the expression of the control. We resolve this convexity issue and deduce main result. We also justify the abstract finding of this paper through an example.

Acoustic waves (AW) have recently emerged as an energy-efficient ice removal procedure compatible with functional and industrial-relevant substrates. However, critical aspects at fundamental and experimental levels have yet to be disclosed to optimize their operational conditions. Identifying the processes and mechanisms by which different types of AWs induce de-icing are some of these issues. Herein, using model LiNbO3 systems and two types of interdigitated transducers, we analyze the de-icing and anti-icing efficiencies and mechanisms driven by Rayleigh surface acoustic waves (R-SAW) and Lamb waves with 120 and 510 um wavelengths, respectively. Through the experimental analysis of de-icing and active anti-icing processes and the finite element simulation of the AW generation, propagation, and interaction with small ice aggregates, we disclose that Lamb waves are more favorable than R-SAWs to induce de-icing and/or prevent the freezing of droplets. Prospects for applications of this study are supported by proof of concept experiments, including de-icing in an ice wind tunnel, demonstrating that Lamb waves can efficiently remove ice layers covering large LN substrates. Results indicate that the de-icing mechanism may differ for Lamb waves or R-SAWs and that the wavelength must be considered as an important parameter for controlling the efficiency.

Cultural differences in common ground may result in pragmatic failure and misunderstandings during communication. We develop our method Rational Speech Acts for Cross-Cultural Communication (RSA+C3) to resolve cross-cultural differences in common ground. To measure the success of our method, we study RSA+C3 in the collaborative referential game of Codenames Duet and show that our method successfully improves collaboration between simulated players of different cultures. Our contributions are threefold: (1) creating Codenames players using contrastive learning of an embedding space and LLM prompting that are aligned with human patterns of play, (2) studying culturally induced differences in common ground reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction. Our code is publicly available at github.com/icwhite/codenames.

We implement the Fock-space perturbed relativistic coupled-cluster theory to compute the electric dipole polarizability of ground and low lying excited states, and nuclear spin-dependent parity violating transition amplitudes in Cs. Moreover, to check the accuracy of the wavefunctions used in the calculations, we compute the excitation energies, E1 transition amplitudes and magnetic dipole hyperfine constants for ground and low lying excited states. To improve the accuracy of the computed properties, we have incorporated the corrections from the relativistic and QED effects in our calculations. The contributions from triple excitations are accounted perturbatively. Our results on excitation energies, E1 transition amplitudes and hyperfine constants are in good agreement with the experimental results. Our polarizability results using FS-PRCC theory are in good agreement with the experimental data. Our values of parity-violating transition amplitudes are, in general, on the lower side of the previous values. From the analysis of the electron correlations, we find that the corrections from the Breit interaction and QED effects are important to get accurate results of nuclear spin-dependent parity-violating transition amplitudes in Cs. The largest cumulative contribution from Breit and QED corrections is found to be $\approx$ 3.2\% of the total value. The upper bound on the theoretical uncertainty in our calculated parity violating transition amplitudes is estimated to be about 1\%., Comment: 17 pages, 9 tables, 14 figures

Trust is a subjective yet fundamental component of human-computer interaction, and is a determining factor in shaping the efficacy of data visualizations. Prior research has identified five dimensions of trust assessment in visualizations (credibility, clarity, reliability, familiarity, and confidence), and observed that these dimensions tend to vary predictably along with certain features of the visualization being evaluated. This raises a further question: how do the design features driving viewers trust assessment vary with the characteristics of the viewers themselves? By reanalyzing data from these studies through the lens of individual differences, we build a more detailed map of the relationships between design features, individual characteristics, and trust behaviors. In particular, we model the distinct contributions of endogenous design features (such as visualization type, or the use of color) and exogenous user characteristics (such as visualization literacy), as well as the interactions between them. We then use these findings to make recommendations for individualized and adaptive visualization design.

We present the AstroSat UV Deep Field South (AUDFs), an imaging survey using the wide-field Ultraviolet Imaging Telescope on board AstroSat. AUDFs covers $\sim 236$ arcmin$^{2}$ of the sky area, including the Great Observatories Origins Deep Survey (GOODS) South field in F154W and N242W filters. The deep and shallow parts of AUDFs have exposure time $\sim 62000$ and $\sim31000$ sec respectively, in the F154W filter, while in the N242W filter, they are $\sim 64000$ and $\sim34000$ sec. These observations reached a $3\sigma$ depth of 27.2 and 27.7 AB mag with a $50\%$ completeness limit of 27 and 27.6 AB mag in the F154W and N242W filters, respectively. With the acquired depth, AUDFs is the deepest far and near-UV imaging data covering the largest area known to date at 1.2" - 1.6" spatial resolution. Two primary catalogs were constructed for the F154W and N242W filters, each containing 13495 and 19374 sources brighter than the 3$\sigma$ detection limit, respectively. Our galaxy counts power-law slope $\sim0.43$~dex~mag$^{-1}$ in the N242W filter matches well with HST/WFC3/UVIS observations. A wide range of extra-galactic science can be achieved with this unique data, such as providing a sample of galaxies emitting ionizing photons in the redshift range $z \sim 1 - 3$ and beyond; constraining the UV luminosity function, investigating the extended-UV (XUV) emission around star-forming galaxies and UV morphologies for $z < 1$. The UV catalog will enhance the legacy value of the existing optical/IR imaging and spectroscopic observations from ground and space-based telescopes on the GOODS South field., Comment: 26 pages, 19 figures, 7 tables, accepted for publication in the Astrophysical Journal Supplement Series, comments are welcome

Determining the He/H ratio in cool stars presents a fundamental astrophysical challenge. While this ratio is established for hot O and B stars, its extrapolation to cool stars remains uncertain due to the absence of helium lines in their observed spectra. We address this knowledge gap by focusing on the Sun as a representative cool star. We conduct spectroscopic analyses of the observed solar photospheric lines by utilizing a combination of MgH molecular lines and neutral Mg atomic lines including yet another combination of CH and C_2 molecular lines with neutral C atomic lines. Our spectroscopic analyses were further exploited by adopting solar model atmospheres constructed for distinct He/H ratios to determine the solar photospheric helium abundance. The helium abundance is determined by enforcing the fact that for an adopted model atmosphere with an appropriate He/H ratio, the derived Mg abundance from the neutral Mg atomic lines and that from the MgH molecular lines must be the same. Ditto holds for the C abundance derived from neutral C atomic lines and that from CH lines of the CH molecular band and C_2 lines from the C_2 Swan band. The estimated He/H ratio for the Sun is discussed based on the one-dimensional local thermodynamic equilibrium (1D LTE) model atmosphere. The helium abundance (He/H = 0.091(+0.019/+0.014)) obtained for the Sun serves as a critical reference point to characterize the He/H ratio of cool stars across the range in their effective temperature. Using this derived He/H ratio, the solar mass fractions are determined to be X_sun=0.7232(+0.0305/-0.0377), Y_sun=0.2633(+0.0384/-0.0311), and Z_sun=0.0135(+0.0006/-0.0007)., Comment: 13 Pages, 3 Figures and 2 Tables, Accepted for publication in the Astrophysical Journal (ApJ)

The natural language processing and multimedia field has seen a notable surge in interest in multimodal sentiment recognition. Hence, this study aims to employ Target-Dependent Multimodal Sentiment Analysis (TDMSA) to identify the level of sentiment associated with every target (aspect) stated within a multimodal post consisting of a visual-caption pair. Despite the recent advancements in multimodal sentiment recognition, there has been a lack of explicit incorporation of emotional clues from the visual modality, specifically those pertaining to facial expressions. The challenge at hand is to proficiently obtain visual and emotional clues and subsequently synchronise them with the textual content. In light of this fact, this study presents a novel approach called the Visual-to-Emotional-Caption Translation Network (VECTN) technique. The primary objective of this strategy is to effectively acquire visual sentiment clues by analysing facial expressions. Additionally, it effectively aligns and blends the obtained emotional clues with the target attribute of the caption mode. The experimental findings demonstrate that our methodology is capable of producing ground-breaking outcomes when applied to two publicly accessible multimodal Twitter datasets, namely, Twitter-2015 and Twitter-2017. The experimental results show that the suggested model achieves an accuracy of 81.23% and a macro-F1 of 80.61% on the Twitter-15 dataset, while 77.42% and 75.19% on the Twitter-17 dataset, respectively. The observed improvement in performance reveals that our model is better than others when it comes to collecting target-level sentiment in multimodal data using the expressions of the face.

Various linguistic and non-linguistic clues, such as excessive emphasis on a word, a shift in the tone of voice, or an awkward expression, frequently convey sarcasm. The computer vision problem of sarcasm recognition in conversation aims to identify hidden sarcastic, criticizing, and metaphorical information embedded in everyday dialogue. Prior, sarcasm recognition has focused mainly on text. Still, it is critical to consider all textual information, audio stream, facial expression, and body position for reliable sarcasm identification. Hence, we propose a novel approach that combines a lightweight depth attention module with a self-regulated ConvNet to concentrate on the most crucial features of visual data and an attentional tokenizer based strategy to extract the most critical context-specific information from the textual data. The following is a list of the key contributions that our experimentation has made in response to performing the task of Multi-modal Sarcasm Recognition: an attentional tokenizer branch to get beneficial features from the glossary content provided by the subtitles; a visual branch for acquiring the most prominent features from the video frames; an utterance-level feature extraction from acoustic content and a multi-headed attention based feature fusion branch to blend features obtained from multiple modalities. Extensive testing on one of the benchmark video datasets, MUSTaRD, yielded an accuracy of 79.86% for speaker dependent and 76.94% for speaker independent configuration demonstrating that our approach is superior to the existing methods. We have also conducted a cross-dataset analysis to test the adaptability of VyAnG-Net with unseen samples of another dataset MUStARD++.

We consider a Wireless Networked Control System (WNCS) where sensors provide observations to build a DT model of the underlying system dynamics. The focus is on control, scheduling, and resource allocation for sensory observation to ensure timely delivery to the DT model deployed in the cloud. \phuc{Timely and relevant information, as characterized by optimized data acquisition policy and low latency, are instrumental in ensuring that the DT model can accurately estimate and predict system states. However, optimizing closed-loop control with DT and acquiring data for efficient state estimation and control computing pose a non-trivial problem given the limited network resources, partial state vector information, and measurement errors encountered at distributed sensing agents.} To address this, we propose the \emph{Age-of-Loop REinforcement learning and Variational Extended Kalman filter with Robust Belief (AoL-REVERB)}, which leverages an uncertainty-control reinforcement learning solution combined with an algorithm based on Value of Information (VoI) for performing optimal control and selecting the most informative sensors to satisfy the prediction accuracy of DT. Numerical results demonstrate that the DT platform can offer satisfactory performance while halving the communication overhead., Comment: arXiv admin note: substantial text overlap with arXiv:2311.15985

Sarcasm is a type of irony, characterized by an inherent mismatch between the literal interpretation and the intended connotation. Though sarcasm detection in text has been extensively studied, there are situations in which textual input alone might be insufficient to perceive sarcasm. The inclusion of additional contextual cues, such as images, is essential to recognize sarcasm in social media data effectively. This study presents a novel framework for multimodal sarcasm detection that can process input triplets. Two components of these triplets comprise the input text and its associated image, as provided in the datasets. Additionally, a supplementary modality is introduced in the form of descriptive image captions. The motivation behind incorporating this visual semantic representation is to more accurately capture the discrepancies between the textual and visual content, which are fundamental to the sarcasm detection task. The primary contributions of this study are: (1) a robust textual feature extraction branch that utilizes a cross-lingual language model; (2) a visual feature extraction branch that incorporates a self-regulated residual ConvNet integrated with a lightweight spatially aware attention module; (3) an additional modality in the form of image captions generated using an encoder-decoder architecture capable of reading text embedded in images; (4) distinct attention modules to effectively identify the incongruities between the text and two levels of image representations; (5) multi-level cross-domain semantic incongruity representation achieved through feature fusion. Compared with cutting-edge baselines, the proposed model achieves the best accuracy of 92.89% and 64.48%, respectively, on the Twitter multimodal sarcasm and MultiBully datasets.

The emoticons are symbolic representations that generally accompany the textual content to visually enhance or summarize the true intention of a written message. Although widely utilized in the realm of social media, the core semantics of these emoticons have not been extensively explored based on multiple modalities. Incorporating textual and visual information within a single message develops an advanced way of conveying information. Hence, this research aims to analyze the relationship among sentences, visuals, and emoticons. For an orderly exposition, this paper initially provides a detailed examination of the various techniques for extracting multimodal features, emphasizing the pros and cons of each method. Through conducting a comprehensive examination of several multimodal algorithms, with specific emphasis on the fusion approaches, we have proposed a novel contrastive learning based multimodal architecture. The proposed model employs the joint training of dual-branch encoder along with the contrastive learning to accurately map text and images into a common latent space. Our key finding is that by integrating the principle of contrastive learning with that of the other two branches yields superior results. The experimental results demonstrate that our suggested methodology surpasses existing multimodal approaches in terms of accuracy and robustness. The proposed model attained an accuracy of 91% and an MCC-score of 90% while assessing emoticons using the Multimodal-Twitter Emoticon dataset acquired from Twitter. We provide evidence that deep features acquired by contrastive learning are more efficient, suggesting that the proposed fusion technique also possesses strong generalisation capabilities for recognising emoticons across several modes.

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

Microfluidic technology has emerged as a powerful tool for studying complex biological processes with enhanced precision and control. A microfluidic chip was designed to emulate human-like microvascular networks with precise control over channel geometry and flow conditions. By simulating blood flow dynamics during bleeding events, we successfully observed the real-time interactions of platelets and their aggregation induced by shear rate gradient at the wound site. Platelet dynamics is primarily influenced by physico-mechanical condition of blood vessels with pathophysiological condition of blood at close proximity of vascular injury site. This microfluidic platform facilitated the investigation of platelet adhesion, activation, and clot formation, providing a unique opportunity to study the spatiotemporal dynamics of platelet aggregation and blood clot. Our findings shed light on the intricate mechanisms underlying thrombus formation and platelet-mediated aggregation, offering a more accurate and dynamic representation of human haemostasis compared to traditional animal models. In the conventional approach, the human bleeding model is tried on mouse due to anatomy and pathological similarities between mouse and humans. This study will simplify and standardize the blood and vasculature conditions. The microfluidic-based replication of the bleeding model holds significant promise in advancing our understanding of clotting disorders and wound healing processes. Furthermore, it paves the way for targeted therapeutic interventions in managing bleeding disorders and enhancing clinical strategies for promoting efficient wound closure. Ultimately, this study demonstrates the potential of microfluidics to revolutionize haemostasis research and opens up new avenues for the development of personalized medicine approaches in the field of clotting disorders., Comment: 11 pages, 8 figures

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems. However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes is yet to be fully established. This study introduces NeuroSEM, a hybrid framework integrating PINNs with the high-fidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into Nektar++. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where data are available. We applied NeuroSEM to the Rayleigh-B\'enard convection system, including cases with missing thermal boundary conditions. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework's plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for an efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

Motivated by the recent experimental realization of a minimal Kitaev chain in quantum dot systems, we present our theoretical findings on the dynamics and fusion of MZMs at or near the $``$sweet spot" $t_h = \Delta$ (where the fermionic hopping $t_h$ and superconducting coupling $\Delta$ are equal). We investigated the dynamics and fusion of MZMs using time-dependent real-space local density-of-states methods. The movement of Majoranas and the detection of fusion channels are crucial for topological quantum computations. Additionally, we discuss our recent discovery of exotic $``$multi-site" MZMs in $Y$-shaped Kitaev wires, which is important for the potential braiding of Majoranas in $Y$-junctions formed from arrays of quantum dots. Finally, we present results on "non-trivial" fusion using canonical Kitaev wires at the sweet spot., Comment: 6 pages and 2 figures

Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently,"$\Delta$-machine learning" has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H$_3$O$^+$ to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0+MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test datasets, and then general fidelity tests such as energetics of stationary points, normal mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach.

We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. These dark-matter-dominated objects are intriguing due to potentially unusual formation channels that lead to their diffuse stellar component. Given the faintness of LSBGs, using standard observational techniques to characterize their total masses proves challenging. Weak gravitational lensing, which is less sensitive to the stellar component of galaxies, could be a promising avenue to estimate the masses of LSBGs. Our LSBG sample consists of 23,790 galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i< 0.60$, respectively. Combined with the DES Y3 shear catalog, we measure the tangential shear around these LSBGs and find signal-to-noise ratios of 6.67 for the red sample, 2.17 for the blue sample, and 5.30 for the full sample. We use the clustering redshifts method to obtain redshift distributions for the red and blue LSBG samples. Assuming all red LSBGs are satellites, we fit a simple model to the measurements and estimate the host halo mass of these LSBGs to be $\log(M_{\rm host}/M_{\odot}) = 12.98 ^{+0.10}_{-0.11}$. We place a 95% upper bound on the subhalo mass at $\log(M_{\rm sub}/M_{\odot})<11.51$. By contrast, we assume the blue LSBGs are centrals, and place a 95% upper bound on the halo mass at $\log(M_\mathrm{host}/M_\odot) < 11.84$. We find that the stellar-to-halo mass ratio of the LSBG samples is consistent with that of the general galaxy population. This work illustrates the viability of using weak gravitational lensing to constrain the halo masses of LSBGs., Comment: 16 pages, 15 figures

Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency., Comment: To be published in the 2nd Workshop on Causal Inference and Machine Learning in Practice, KDD 2024, August 25 to 29, 2024, Barcelona, Spain, 10 pages

Using high resolution Vlasov - Poisson simulations, evolution of driven ``cold" electron plasma wave (EPW) in the presence of stationary inhomogeneous background of ions is studied. Mode coupling dynamics between ``cold'' EPW with phase velocity $v_{\phi}$ greater than thermal velocity i.e $v_{\phi} \gg v_{thermal}$ and its inhomogeneity induced sidebands is illustrated as an initial value problem. In driven cases, formation of BGK like phase space structures corresponding to sideband modes due to energy exchange from primary mode to bulk particles via wave-wave and wave-particle interactions leading to particle trapping is demonstrated for inhomogeneous plasma. Qualitative comparison studies between initial value perturbation and driven problem is presented, which examines the relative difference in energy transfer time between the interacting modes. Effect of variation in background ion inhomogeneity amplitude as well as ion inhomogeneity scale length on the driven EPWs is reported.

We investigate how galaxy pairs are oriented in three dimensions within cosmic filaments using data from the EAGLE simulation. We identify filament spines using DisPerSE and isolate galaxies residing in filamentary environments. Employing a FoF algorithm, we delineate individual filaments and determine their axes by diagonalizing the moment of inertia tensor. The orientations of galaxy pairs relative to the axis of their host filament are analyzed. Our study covers diverse subsets of filaments identified through varying linking lengths, examining how galaxy pairs align with the filament axis across different spatial parameters such as pair separation and distance from the filament spine. We consistently observe a strong alignment pattern characterized by the probability distribution for the cosine of the orientation angle, which is nearly identical in each case. Furthermore, the study investigates the impact of redshift space distortions, confirming that the alignment signal persists in both real and redshift space. To validate our method, we employ Monte Carlo simulations with different theoretical probability distributions. Our results reaffirm previous findings with projected two-dimensional distributions and extend our understanding of how filaments impact the spatial organization and evolution of galaxies across cosmic scales., Comment: 15 pages, 7 figures, 2 tables. Comments and suggestions are welcome

Multilingual pretraining for transfer learning significantly boosts the robustness of low-resource monolingual ASR models. This study systematically investigates three main aspects: (a) the impact of transfer learning on model performance during initial training or fine-tuning, (b) the influence of transfer learning across dataset domains and languages, and (c) the effect on rare-word recognition compared to non-rare words. Our finding suggests that RNNT-loss pretraining, followed by monolingual fine-tuning with Minimum Word Error Rate (MinWER) loss, consistently reduces Word Error Rates (WER) across languages like Italian and French. WER Reductions (WERR) reach 36.2% and 42.8% compared to monolingual baselines for MLS and in-house datasets. Out-of-domain pretraining leads to 28% higher WERR than in-domain pretraining. Both rare and non-rare words benefit, with rare words showing greater improvements with out-of-domain pretraining, and non-rare words with in-domain pretraining.

Digital Twins (DTs) are set to become a key enabling technology in future wireless networks, with their use in network management increasing significantly. We developed a DT framework that leverages the heterogeneity of network access technologies as a resource for enhanced network performance and management, enabling smart data handling in the physical network. Tested in a Campus Area Network environment, our framework integrates diverse data sources to provide real-time, holistic insights into network performance and environmental sensing. We also envision that traditional analytics will evolve to rely on emerging AI models, such as Generative AI (GenAI), while leveraging current analytics capabilities. This capacity can simplify analytics processes through advanced ML models, enabling descriptive, diagnostic, predictive, and prescriptive analytics in a unified fashion. Finally, we present specific research opportunities concerning interoperability aspects and envision aligning advancements in DT technology with evolved AI integration., Comment: A shortened version of this paper is currently under review for publication in an IEEE magazine. If accepted, the copyright will be transferred to IEEE

In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse adapter. This high sparsity incurs no inference overhead, enables rapid switching directly in the fused mode, and significantly reduces concept-loss during multi-adapter fusion. Our extensive experiments on LVMs and LLMs demonstrate that finetuning merely 1-2% parameters in the base model is sufficient for many adapter tasks and significantly outperforms Low Rank Adaptation (LoRA). We also show that SHiRA is orthogonal to advanced LoRA methods such as DoRA and can be easily combined with existing techniques., Comment: Published at ICML 2024 Workshop on Foundation Models in the Wild. arXiv admin note: substantial text overlap with arXiv:2406.13175

We compare the performance of different methodologies for finding the ground state of the molecule BeH2. We implement adaptive, tetris-adaptive variational quantum eigensolver (VQE), and entanglement forging to reduce computational resource requirements. We run VQE experiments on IBM quantum processing units and use error mitigation, including twirled readout error extinction (TREX) and zero-noise extrapolation (ZNE) to reduce noise. Our results affirm the usefulness of VQE on noisy quantum hardware and pave the way for the usage of VQE related methods for large molecules., Comment: 6 pages, 4 figures

Turbulent convection in the interiors of the Sun and the Earth occurs at high Rayleigh numbers $Ra$, low Prandtl numbers $Pr$, and different levels of rotation rates. To understand the combined effects better, we study rotating turbulent convection for $Pr = 0.021$ (for which some laboratory data corresponding to liquid metals are available), and varying Rossby numbers $Ro$, using direct numerical simulations (DNS) in a slender cylinder of aspect ratio 0.1; this confinement allows us to attain high enough Rayleigh numbers. We are motivated by the earlier finding in the absence of rotation that heat transport at high enough $Ra$ is similar between confined and extended domains. We make comparisons with higher aspect ratio data where possible. We study the effects of rotation on the global transport of heat and momentum as well as flow structures (a) for increasing rotation at a few fixed values of $Ra$ and (b) for increasing $Ra$ (up to $10^{10}$) at the fixed, low Ekman number of $1.45 \times 10^{-6}$. We compare the results with those from unity $Pr$ simulations for the same range of $Ra$ and $Ro$, and with the non-rotating case over the same range of $Ra$ and low $Pr$. We find that the effects of rotation diminish with increasing $Ra$. These results and comparison studies suggest that, for high enough $Ra$, rotation alters convective flows in a similar manner for small and large aspect ratios, and so useful insights on the effects of high thermal forcing on convection can be obtained by considering slender domains., Comment: In Press, Journal of Fluid Mechanics

Based on an accurate determination of the potential energy surfaces of Rb$_3^+$ correlated to its first asymptotic limit Rb$^+$$+$Rb($5s$)$+$Rb($5s$), we identify the presence of intersections of a pair of singlet and triplet surfaces over all interparticle distances, leading to Jahn-Teller couplings. We elaborate scenarios for charge exchange between ultracold charged atom-dimer complex (Rb$+$Rb$_2^+$ or Rb$^+$$+$Rb$_2$), predicting a strong selectivity on the preparation of the initial state of the dimer. We also demonstrate that the JT couplings must drive the three-body recombination (TBR) of Rb$^+$, Rb, and Rb at ultracold energies. Using the current analysis, we provide a consistent picture of the TBR experiments performed in ion-atom hybrid Rb samples \cite{dieterle2020inelastic,harter2012single}. We also demonstrate the presence of JT coupling as a general phenomenon in the singly-charged homonuclear alkali triatomic systems., Comment: 13 pages, 16 Figures