Search

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments., Comment: 6 Pages, 10 Figures

We study, the interplay between topology and electron-electron interactions in the moir\'{e} MoTe$_2$/WSe$_2$ heterobilayer. In our analysis we apply an effective two-band model with complex hoppings that incorporate the Ising-type spin-orbit coupling and lead to a non-trivial topology after the application of perpendicular electric field (displacement field). The model is supplemented by on-site and inter-site Coulomb repulsion terms and treated by both Hartree-Fock and Gutzwiller methods. According to our analysis, for the case of one hole per moir\'{e} unit cell, the system undergoes two phase transitions with increasing displacement field. The first one is from an in-plane 120$^\circ$ antiferromagnetic charge transfer insulator to a topological insulator. At the second transition, the system becomes topologically trivial and an out-of-plane ferromagnetic metallic phase becomes stable. In the topological region a spontaneous spin-polarization appears and the holes are distributed in both layers. Moreover, the hybridization of states from different layers and different valleys is allowed near the Fermi level. Those aspects are in qualitative agreement with the available experimental data. Additionally, we analyze the influence of the intersite Coulomb repulsion terms on the appearance of the topological phase as well as on the formation of the charge density wave state.

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside the three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters ($\log g$, $T_{\text{eff}}$, $A_V$, $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope., Comment: 32 pages, 24 figures, 5 tables, submitted to MNRAS

A complete understanding of the initial conditions of high-mass star formation and what processes determine multiplicity require the study of the magnetic field (B-field) in young, massive cores. Using ALMA 250 GHz polarization (0.3" = 1000 au) and ALMA 220 GHz high-angular resolution observations (0.05" = 160 au), we have performed a full energy analysis including the B-field at core scales and have assessed what influences the multiplicity inside a massive core previously believed to be in the prestellar phase. With 31 Msun, the G11.92 MM2 core has a young CS outflow with a dynamical time scale of a few thousand years. At high-resolution, the MM2 core fragments into a binary system with a projected separation of 505 au and a binary mass ratio of 1.14. Using the DCF method with an ADF analysis, we estimate in this core a B-field strength of 6.2 mG and a mass-to-flux ratio of 18. The MM2 core is strongly subvirialized with a virial parameter of 0.064, including the B-field. The high mass-to-flux ratio and low virial parameter indicate that this massive core is very likely undergoing runaway collapse, which is in direct contradiction with the core-accretion model. The MM2 core is embedded in a filament that has a velocity gradient consistent with infall. In line with clump-fed scenarios, the core can grow in mass at a rate of 1.9--5.6 x 10^-4 Msun/yr. In spite of the B-field having only a minor contribution to the total energy budget at core scales, it likely plays a more important role at smaller scales by setting the binary properties. Considering energy ratios and a fragmentation criterion at the core scale, the binary could have been formed by core fragmentation. The binary properties (separation and mass ratio), however, are also consistent with radiation-magnetohydrodynamic simulations with super-Alfvenic, supersonic (or sonic) turbulence that form binaries by disk fragmentation., Comment: Accepted for publications in ApJ (9 pages, 3 figures, Appendix)

In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.

This work proposes a dephasing mechanism for generating symmetrically located long-range entangled pairs in a lattice. We consider a one-dimensional fermionic lattice with nearest-neighbor hopping subjected to a local dephasing at the central site. The dephasing-induced dynamics is strongly relaxing under strong symmetries and give rise to unique steady states in different symmetry sectors. Through explicit analytical calculations, we identify the symmetry sectors that support these strongly correlated robust pairs over arbitrarily long distances and provide analytical expressions of the steady-state correlation matrices. This experimentally relevant work promises applications in emerging quantum technologies.

In recent times, Transformer-based language models are making quite an impact in the field of natural language processing. As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for various applications in bioinformatics. In this regard, this paper introduces the major developments of Transformer-based models in the recent past in the context of nucleotide sequences. We have reviewed and analysed a large number of application-based papers on this subject, giving evidence of the main characterizing features and to different approaches that may be adopted to customize such powerful computational machines. We have also provided a structured description of the functioning of Transformers, that may enable even first time users to grab the essence of such complex architectures. We believe this review will help the scientific community in understanding the various applications of Transformer-based language models to nucleotide sequences. This work will motivate the readers to build on these methodologies to tackle also various other problems in the field of bioinformatics.

In this work, we analyze dead layer comprising non-polar monoclinic (m) phase in $Hf_xZr_{1-x}O_2$ (HZO)-based ferroelectric (FE) material using first principles analysis. We show that with widely used tungsten (W) metal electrode, the spatial distribution of the oxygen vacancy across the cross-section plays a key role in dictating the favorability of m- phase formation at the metal-HfO2 interface. The energetics are also impacted by the polarization direction as well as the depth of oxygen vacancy, i.e., position along the thickness. At the metal - $HfO_2$ interface, when polarization points towards the metal and vacancy forms at trigonally bonded O atomic site, both interfacial relaxation and m- phase formation can lead to dead layers. For vacancies at other oxygen atomic sites and polarization direction, dead layer is formed due to sole interfacial relaxation with polar phase. We also establish the relative favorability of the m-phase dead layer for different Zr concentrations (x=1 and x = 0.5) and metal electrodes. According to our analysis, 50% Zr doped $HfO_2$ exhibits less probability of m-phase dead layer formation compared to pure $HfO_2$. Moreover, with electrodes consisting of noble metal (Pt, Pd, Os, Ru, Rh), m-phase dead layer formation is less likely. Therefore, for these metals, dead layer forms mainly due to the interfacial relaxation with polar phase., Comment: 15 pages and 7 figures in main article, 3 pages of supplementary information

Superconductivity in doped SrTiO$_3$ has remained an enduring mystery for over 50 years. The material's status as a ``quantum" ferroelectric metal, characterized by a soft polar mode, suggests that quantum criticality could play a pivotal role in the emergence of its superconducting state. We show that the system is amenable to a strong coupling (Eliashberg) pairing analysis, with the dominant coupling to the soft mode being a ``dynamical'' Rashba coupling. We compute the expected $T_c$ for the entire phase diagram, all the way to the quantum critical point and beyond. We demonstrate that the linear coupling is sufficient to obtain a rough approximation of the experimentally measured phase diagram, but that nonlinear coupling terms are crucial in reproducing the finer features in the ordered phase. The primary role of nonlinear terms at the peak of the superconducting dome is to enhance the effective linear coupling induced by the broken order, shifting the dome's maximum into the ordered phase. Our theory quantitatively reproduces the three-dimensional experimental phase diagram in the space of carrier density, distance from the quantum critical point and temperature, and allows us to estimate microscopic parameters from the experimental data., Comment: 24 pages, 12 figures

Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs for translating code between different programming languages. Since LLMs are more effective with natural language, using natural language as an intermediate representation in code translation tasks presents a promising approach. In this work, we investigate using NL-specification as an intermediate representation for code translation. We evaluate our method using three datasets, five popular programming languages, and 29 language pair permutations. Our results show that using NL-specification alone does not lead to performance improvements. However, when combined with source code, it provides a slight improvement over the baseline in certain language pairs. Besides analyzing the performance of code translation, we also investigate the quality of the translated code and provide insights into the issues present in the translated code.

Graphical User Interfaces (GUIs) are critical to human-computer interaction, yet automating GUI tasks remains challenging due to the complexity and variability of visual environments. Existing approaches often rely on textual representations of GUIs, which introduce limitations in generalization, efficiency, and scalability. In this paper, we introduce Aguvis, a unified pure vision-based framework for autonomous GUI agents that operates across various platforms. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements, and employs a consistent action space to ensure cross-platform generalization. To address the limitations of previous work, we integrate explicit planning and reasoning within the model, enhancing its ability to autonomously navigate and interact with complex digital environments. We construct a large-scale dataset of GUI agent trajectories, incorporating multimodal reasoning and grounding, and employ a two-stage training pipeline that first focuses on general GUI grounding, followed by planning and reasoning. Through comprehensive experiments, we demonstrate that Aguvis surpasses previous state-of-the-art methods in both offline and real-world online scenarios, achieving, to our knowledge, the first fully autonomous pure vision GUI agent capable of performing tasks independently without collaboration with external closed-source models. We open-sourced all datasets, models, and training recipes to facilitate future research at https://aguvis-project.github.io/., Comment: https://aguvis-project.github.io/

Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While FL preserves data privacy by eliminating centralized storage, it encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model due to the heterogeneity in client data distributions. Among the various forms of data heterogeneity, label skew emerges as a particularly formidable and prevalent issue, especially in domains such as image classification. To address these challenges, we begin with comprehensive experiments to pinpoint the underlying issues in the FL training process. Based on our findings, we then introduce an innovative dual-strategy approach designed to effectively resolve these issues. First, we introduce an adaptive loss function for client-side training, meticulously crafted to preserve previously acquired knowledge while maintaining an optimal equilibrium between local optimization and global model coherence. Secondly, we develop a dynamic aggregation strategy for aggregating client models at the server. This approach adapts to each client's unique learning patterns, effectively addressing the challenges of diverse data across the network. Our comprehensive evaluation, conducted across three diverse real-world datasets, coupled with theoretical convergence guarantees, demonstrates the superior efficacy of our method compared to several established state-of-the-art approaches.

In application-specific designs, owing to the trade-off between power consumption and speed, optimization of various circuit parameters has become a challenging task. Several of the performance metrics, viz. energy efficiency, gain, performance, and noise immunity, are interrelated and difficult to tune. Such efforts may result in a great deal of manual iterations which in turn increase the computational overhead. Thus, it is important to develop a methodology that not only explores large design space but also reduces the computational time. In this work, we investigate the viability of using a SPICE and Python IDE (PIDE) interface to optimize integrated circuits. The SPICE simulations are carried out using 22 nm technology node with a nominal supply voltage of 0.8 V. The SPICE-PIDE optimizer, as delineated in this work, is able to provide the best solution sets considering various performance metrics and design complexities for 5 transistor level converters., Comment: Table: 4, Figure: 4, and References: 18

The accurate computational study of wavepacket nuclear dynamics is considered to be a classically intractable problem, particularly with increasing dimensions. Here we present two algorithms that, in conjunction with other methods developed by us, will form the basis for performing quantum nuclear dynamics in arbitrary dimensions. For one algorithm, we present a direct map between the Born-Oppenheimer Hamiltonian describing the wavepacket time-evolution and the control parameters of a spin-lattice Hamiltonian that describes the dynamics of qubit states in an ion-trap quantum computer. This map is exact for three qubits, and when implemented, the dynamics of the spin states emulate those of the nuclear wavepacket. However, this map becomes approximate as the number of qubits grow. In a second algorithm we present a general quantum circuit decomposition formalism for such problems using a method called the Quantum Shannon Decomposition. This algorithm is more robust and is exact for any number of qubits, at the cost of increased circuit complexity. The resultant circuit is implemented on IBM's quantum simulator (QASM) for 3-7 qubits. In both cases the wavepacket dynamics is found to be in good agreement with the classical result and the corresponding vibrational frequencies obtained from the wavepacket density time-evolution, are in agreement to within a few tenths of a wavenumbers.

Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate system) are studied and the distance between the ego vehicle and the object in the context of autonomous driving is measured. To classify the object, faster Region-based Convolution Neural Network (R-CNN) with inception v2 is utilized. First, a network is trained with customized dataset to estimate the reference position of objects as well as the distance from the vehicle. From camera calibration to computing the distance, cutting-edge technologies of computer vision algorithms in a series of processes are applied to generate a 3D reference point of the region of interest. The foremost step in this process is generating a disparity map using the concept of stereo vision.

Ferroelectric materials underlie key optical technologies in optical communications, integrated optics and quantum computing. Yet, there is a lack of a consistent thermodynamic framework to predict the optical properties of ferroelectrics and the mutual connections among ferroelectric polarization, optical properties, and optical dispersion. For example, there is no existing thermodynamic model for establishing the relationship between the ferroelectric polarization and the optical properties in the visible spectrum. Here we present a thermodynamic theory of the linear optical and electro-optic properties of ferroelectrics by separating the lattice and electronic contributions to the total polarization. We introduce a biquadratic coupling between the lattice and electronic contributions validated by both first-principles calculations and experimental measurements. As an example, we derive the temperature and wavelength-dependent anisotropic optical properties of BaTiO3, including the full linear optical dielectric tensor and the linear electro-optic (Pockels) effect through multiple ferroelectric phase transitions, which are in excellent agreement with existing experimental data and first principles calculations. This general framework incorporates essentially all optical properties of materials, including coupling between the ionic and electronic order parameters, as well as their dispersion and temperature dependence, and thus offers a powerful theoretical tool for analyzing light-matter interactions in ferroelectrics-based optical devices., Comment: 29 pages, 8 figures

Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, are believed to arise from SGD. This article explores the causality aspect of gradient descent. Specifically, we show that the gradient descent procedure has an implicit granger-causal relationship between the reduction in loss and a change in parameters. By suitable modifications, we make this causal relationship explicit. A causal approach to gradient descent has many significant applications which allow greater control. In this article, we illustrate the significance of the causal approach using the application of Pruning. The causal approach to pruning has several interesting properties - (i) We observe a phase shift as the percentage of pruned parameters increase. Such phase shift is indicative of an optimal pruning strategy. (ii) After pruning, we see that minima becomes "flatter", explaining the increase in accuracy after pruning weights.

In this article, we deduce test properties, in the sense of finitely many vanishing of Ext or Tor, of CM (Cohen-Macaulay) modules whose multiplicity and number of generators (resp., type) are related by certain inequalities. We apply these test behaviour, along with other results, to characterize various kinds of local rings, including hypersurface rings of multiplicity at most two, via finite vanishing of Ext or Tor involving such CM modules. As further applications, we verify the long-standing (Generalized) Auslander-Reiten Conjecture for every CM module of minimal multiplicity., Comment: comments are welcome!

Two strings are considered to have parameterized matching when there exists a bijection of the parameterized alphabet onto itself such that it transforms one string to another. Parameterized matching has application in software duplication detection, image processing, and computational biology. We consider the problem for which a pattern $p$, a text $t$ and a mismatch tolerance limit $k$ is given and the goal is to find all positions in text $t$, for which pattern $p$, parameterized matches with $|p|$ length substrings of $t$ with at most $k$ mismatches. Our main result is an algorithm for this problem with $O(\alpha^2 n\log n + n \alpha^2 \sqrt{\alpha} \log \left( n \alpha \right))$ time complexity, where $n = |t|$ and $\alpha = |\Sigma|$ which is improving for $k=\tilde{\Omega}(|\Sigma|^{5/3})$ the algorithm by Hazay, Lewenstein and Sokol. We also present a hashing based probabilistic algorithm for this problem when $k = 1$ with $O \left( n \log n \right)$ time complexity, which we believe is algorithmically beautiful., Comment: 17 pages, 2 figures

The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission toward 146 massive star-forming regions in the ATOMS survey. We detected SiO emission in 136 regions and distinguished broad and narrow components across the extension of 118 sources (including 58 UC $H_{II}$ regions) with an average angular resolution of 2.5$^{\prime}$$^{\prime}$. The derived SiO luminosity ($L_{SiO}$) across the whole sample shows that the majority of $L_{SiO}$ (above 66$\%$) can be attributed to broad SiO, indicating its association with strong outflows. The comparison of the ALMA SiO images with the filamentary skeletons identified from H$^{13}$CO$^{+}$ and in the infrared data (at 4.5, 8, and 24 $mu$m), further confirms that most SiO emission originates from outflows. However, note that for nine sources in our sample, the observed SiO emission may be generated by expanding UC $H_{II}$ regions. There is a moderate positive correlation between the bolometric luminosity ($L_{bol}$) and $L_{SiO}$ for both components (narrow and broad). The UC $H_{II}$ sources show a weaker positive correlation between $L_{bol}$ and $L_{SiO}$ and higher $L_{SiO}$ compared to the sources without UC $H_{II}$ regions. These results imply that the SiO emission from UC $H_{II}$ sources might be affected by UV-photochemistry induced by UC $H_{II}$ regions., Comment: 23 pages, 14 figures

Inhomogeneity-governed carrier localization has been investigated in three sets of InGaN/GaN multi-quantum-disk light-emitting diode (LED) structures grown by plasma-assisted molecular beam epitaxy (PAMBE) under different process conditions. A temperature-dependent study of the luminescence peak positions reveals that samples prepared under certain process conditions exhibit a thermal distribution of carriers from the localized states that show the typical S-shaped dependence in luminescence characteristics. The absence of an S-shaped nature in the other sample prepared with relatively higher In-flux infers a superior homogeneity in alloy composition. Further investigation manifested superior optical properties for the samples where the S-shape nature is found to be absent.

We compute the contribution of the gluonic component of the energy-momentum tensor (EMT) to the angular momentum density in various decompositions. We use the light-front Hamiltonian technique, and a two-component formalism in light-front gauge, where the constrained degrees of freedom are eliminated. Instead of a nucleon, we consider a simple composite spin-$1/2$ state, namely a quark dressed with a gluon. We present two dimensional light-front distributions in transverse impact parameter space, and compare the different angular momentum decompositions at the density level. Incorporating also the contribution coming from the quark part of the EMT, we verify the spin sum rule for such a state., Comment: 26 pages, 3 figures

Analyzing large datasets and summarizing it into useful information is the heart of the data mining process. In healthcare, information can be converted into knowledge about patient historical patterns and possible future trends. During the COVID-19 pandemic, data mining COVID-19 patient information poses an opportunity to discover patterns that may signal that the patient is at high risk for death. COVID-19 patients die from sepsis, a complex disease process involving multiple organ systems. We extracted the variables physicians are most concerned about regarding viral septic infections. With the aim of distinguishing COVID-19 patients who survive their hospital stay and those COVID-19 who do not, the authors of this study utilize the Support Vector Machine (SVM) and the Random Forest (RF) classification techniques to classify patients according to their demographics, laboratory test results, and preexisting health conditions. After conducting a 10-fold validation procedure, we assessed the performance of the classification through a Receiver Operating Characteristic (ROC) curve, and a Confusion Matrix was used to determine the accuracy of the classifiers. We also performed a cluster analysis on the binary factors, such as if the patient had a preexisting condition and if sepsis was identified, and the numeric values from patient demographics and laboratory test results as predictors.

By tuning superconductivity with an applied magnetic field, we use neutrons to compare the magnetic ordered phases in superconducting and normal states of CeCo$_{0.5}$Rh$_{0.5}$In$_{5}$. At zero field, CeCo$_{0.5}$Rh$_{0.5}$In$_{5}$ displays both superconductivity ($T_{c}$=1.3 K) and spatially long-ranged commensurate $\uparrow\downarrow\uparrow\downarrow$ antiferromagnetism ($T_{N}$=2.5 K, $\vec{Q}_{0}=({1\over 2}, {1\over 2}, {1\over 2})$). Neutron spectroscopy fails to measure propagating magnetic excitations with only temporally overdamped fluctuations observable. On applying a magnetic field we find anisotropic behavior in the static magnetism. When the field is along the crystallographic $c$-axis, no change in the static magnetic response is observable. However when the field is oriented within the $a-b$ plane, an increase in $T_{N}$ and change in the critical response are measured. At low temperatures in the superconducting phase, the elastic magnetic intensity increases linearly ($\propto |H|$) with small $a-b$ oriented fields. However, this trend is interrupted at intermediate fields where commensurate block $\uparrow\uparrow\downarrow\downarrow$ magnetism with propagation vector $\vec{Q}=({1\over 2}, {1\over 2}, {1\over 4})$ forms. For large applied fields in the [1 $\overline{1}$ 0] direction which completely suppresses superconductivity, weakly incommensurate magnetic order along $L$ is observed to replace the commensurate response present in the superconducting and vortex phases. We suggest field-induced incommensurate static magnetism, present in the normal state of superconducting and antiferromagnetic CeCo$_{0.5}$Rh$_{0.5}$In$_{5}$ for $a-b$ plane oriented magnetic fields. We speculate that these field dependent properties are tied to the field induced anisotropy associated with the local Ce$^{3+}$ crystal field environment of the tetragonal `115' structure., Comment: (13 pages, 7 figures, to be published in Phys. Rev. B)

We perform a detailed investigation of interacting phantom cosmology, by applying the powerful method of dynamical system analysis. We consider two well-studied interaction forms, namely one global and one local one, while the novel ingredient of our work is the examination of new potentials for the phantom field. Our analysis shows the existence of saddle matter-dominated points, stable dark-energy dominated points, and scaling accelerating solutions, that can attract the Universe at late times. As we show, some of the stable accelerating scaling attractors, in which dark matter and dark energy can co-exist, alleviating the cosmic coincidence problem, are totally new, even for the previously studied interaction rates, and arise purely from the novel potential forms., Comment: 17 pages including bibliography; 10 tables, 4 figures; comments are welcome

Quantifying forest carbon is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent ML methods and RS combinations, especially with the consideration of forest characteristics. This study systematically analyzed 25 papers meeting strict inclusion criteria from over 80 related studies, identifying 28 ML methods and key combinations of RS data. Random Forest had the most frequent appearance (88\% of studies), while Extreme Gradient Boosting showed superior performance in 75\% of the studies in which it was compared with other methods. Sentinel-1 emerged as the most utilized remote sensing source, with multi-sensor approaches (e.g., Sentinel-1, Sentinel-2, and LiDAR) proving especially effective. Our findings provide grounds for recommending best practices in integrating machine learning and remote sensing for accurate and scalable forest carbon stock estimation., Comment: First author and corresponding author: Autumn Nguyen

The complexity and curvature of a module, introduced by Avramov, measure the growth of Betti and Bass numbers of a module, and distinguish the modules of infinite homological dimension. The notion of complexity was extended by Avramov-Buchweitz to pairs of modules that measure the growth of Ext modules. The related notion of Tor complexity was first studied by Dao. Inspired by these notions, we define Ext and Tor curvature of pairs of modules. The aim of this article is to study (Ext and Tor) complexity and curvature of pairs of certain CM (Cohen-Macaulay) modules, and establish lower bounds of complexity and curvature of pairs of modules in terms of that of a single module. It is well known that the complexity and curvature of the residue field characterize complete intersection local rings. As applications of our results, we provide some upper bounds of the curvature of the residue field in terms of curvature and multiplicity of any nonzero CM module. As a final upshot, these allow us to characterize complete intersection local rings (including hypersurfaces and regular rings) in terms of complexity and curvature of pairs of certain CM modules. In particular, under some additional hypotheses, we characterize complete intersection or regular local rings via injective curvature of the ring or that of the module of K\"{a}hler differentials., Comment: 29 pages, Comments are welcome

Estimating the true background in an astronomical image is fundamental to detecting faint sources. In a typical low-photon count astronomical image, such as in the far and near-ultraviolet wavelength range, conventional methods relying on the 3-sigma clipping and median or mode estimation often fail to capture the true background level accurately. As a consequence, differentiating true sources from noise peaks remains a challenging task. Additionally, in such images, effectively identifying and excluding faint sources during the background estimation process remains crucial, as undetected faint sources could contaminate the background. This results in overestimating the true background and obscuring the detection of very faint sources. To tackle this problem, we introduce a geometric approach based on the method of steepest descent to identify local minima in an astronomical image. The proposed algorithm based on the minima statistics effectively reduces the confusion between sources and background in the image; thereby ensuring a better background estimation and enhancing the reliability of faint source detection. Our algorithm performs well compared to conventional methods in estimating the background even in crowded field images. In low-photon count, less crowded images, our algorithm recovers the background within 10\%, while traditional methods drastically underestimate it by a few orders of magnitude. In crowded fields, the conventional methods overestimates the background by $\sim 200\%$ whereas our algorithm recovers the true background within $\sim 14\%$. We provide a simple prescription to create a background map using our algorithm and discuss its application in large astronomical surveys., Comment: Accepted for publication in ApJS, 12 figures, 1 table

In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. Protecting individual privacy is crucial, yet many existing machine learning and data publishing algorithms struggle with high-dimensional data, facing challenges related to computational efficiency and privacy preservation. To address these challenges, we introduce an effective data publishing algorithm \emph{DP-CDA}. Our proposed algorithm generates synthetic datasets by randomly mixing data in a class-specific manner, and inducing carefully-tuned randomness to ensure formal privacy guarantees. Our comprehensive privacy accounting shows that DP-CDA provides a stronger privacy guarantee compared to existing methods, allowing for better utility while maintaining strict level of privacy. To evaluate the effectiveness of DP-CDA, we examine the accuracy of predictive models trained on the synthetic data, which serves as a measure of dataset utility. Importantly, we identify an optimal order of mixing that balances privacy guarantee with predictive accuracy. Our results indicate that synthetic datasets produced using the DP-CDA can achieve superior utility compared to those generated by traditional data publishing algorithms, even when subject to the same privacy requirements., Comment: Under review in Elsevier Array

We derive a completely positive post-Markovian master equation (PMME) from a microscopic Markovian collisional model framework, incorporating bath memory effects via a probabilistic single-shot measurement approach. This phenomenological master equation is both analytically solvable and numerically tractable. Depending on the choice of the memory kernel function, the PMME can be reduced to the exact Nakajima-Zwanzig equation or the Markovian master equation, enabling a broad spectrum of dynamical behaviors. We also investigate thermalization using the derived equation, revealing that the post-Markovian dynamics accelerates the thermalization process, exceeding rates observed within the Markovian framework. Our approach solidifies the assertion that "collisional models can simulate any open quantum dynamics", underscoring the versatility of the models in realizing open quantum systems., Comment: 18 pages, 2 figures, Comments are welcome

Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as personally identifiable information (PII). Consequently, in recent times research in the area of Machine Unlearning (MUL) has become active, the main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering from performance loss on regular tasks. In this work, we examine the robustness of the existing MUL techniques for their ability to enable leakage-proof forgetting in LLMs. In particular, we examine the effect of data transformation on forgetting, i.e., is an unlearned LLM able to recall forgotten information if there is a change in the format of the input? Our findings on the TOFU dataset highlight the necessity of using diverse data formats to quantify unlearning in LLMs more reliably., Comment: Accepted at EMNLP 2024 Findings; 21 pages (5 page main content + references + appendix)

The dynamics of capillary rise under different geometric and fluid conditions have the common signatures of rapid rise followed by an equilibrium state that describe the underlying competing forces. We present a new interpretation of capillary dynamics using a linear damped system where modulation of damping and forcing characteristics are achieved using axisymmetric channels with sinusoidal variation in radius. The complete axisymmetric design space ranging from hydrophilic channels that enable spontaneous imbibition to hydrophobic channels, that required external pressure mechanisms is modeled and the force dynamics is split into simultaneous damping and forcing characteristics. We introduce the product of damping and forcing terms as the new parameter that effectively characterizes rise dynamics across various geometric and flow conditions, encompassing both flow-enhancing and flow-inhibiting scenarios. The monotonic nature of this parameter enables the development of a stochastic optimization method that can determine optimal channel geometries for controlled capillary rise.

This letter reports on Solidly-Mounted Bidimensional Mode Resonators (S2MRs) based on 30% Scandium-doped Aluminum Nitride (ScAlN) on Silicon Carbide (SiC), operating near 16 GHz. Experimental results demonstrate mechanical quality factors (Qm) as high as 380, electromechanical coupling coefficients (kt2) of 4.5%, an overall Figure of Merit (FOM = Qmkt2) exceeding 17, and power handling greater than 20 dBm for devices closely matched to 50 ohm. To the best of the authors' knowledge, S2MRs exhibit the highest Key Performance Indicators (KPIs) among solidly mounted resonators in the Ku band, paving the way for the integration of nanoacoustic devices on fast substrates with high-power electronics, tailored for military and harsh environment applications., Comment: Submitted to IEEE EDL

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors \textit{viz.}, client-specific layer importance score that selects the most important VLM layers for fine-tuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F$^3$OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method.

In this study, we investigate gender bias in Bangla pretrained language models, a largely under explored area in low-resource languages. To assess this bias, we applied gender-name swapping techniques to existing datasets, creating four manually annotated, task-specific datasets for sentiment analysis, toxicity detection, hate speech detection, and sarcasm detection. By altering names and gender-specific terms, we ensured these datasets were suitable for detecting and mitigating gender bias. We then proposed a joint loss optimization technique to mitigate gender bias across task-specific pretrained models. Our approach was evaluated against existing bias mitigation methods, with results showing that our technique not only effectively reduces bias but also maintains competitive accuracy compared to other baseline approaches. To promote further research, we have made both our implementation and datasets publicly available https://github.com/sajib-kumar/Gender-Bias-Mitigation-From-Bangla-PLM

Phenotypic plasticity is a key factor in driving the evolution of species in the predator-prey interaction. The natural environment is replete with phenotypic plasticity, which is the source of inducible defences against predators, including concealment, cave-dwelling, mimicry, evasion, and revenge. In this work, a predator-prey model is proposed where the prey species shows inducible defence against their predators. The dynamics produce a wide range of non-trivial and impactful results, including the stabilizing effect of the defence mechanism. The model is also analyzed in the presence of spatio-temporal diffusion in a bounded domain. It is found in the numerical simulation that the Turing domain shrinks with the increase of defence level. The work is extended further by introducing a nonlocal term in the intra-specific competition of the prey species. The Turing instability condition has been studied for the local model around the coexisting steady state, followed by the Turing and non-Turing patterns in the presence of the nonlocal interaction term. The work reveals how an increase in inducible defence reduces the Turing domain in the local interaction model but expands it when the range of nonlocal interactions is extended, suggesting a higher likelihood of species colonization., Comment: arXiv admin note: text overlap with arXiv:2310.01392

Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with given degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Given these findings, we discuss several implications of AI-personalized translation tools in facilitating communication in collaborative contexts., Comment: 23 pages, 5 figures, 1 table

Self-assembly of nanoscale synthetic subunits is a promising bottom-up strategy for fabrication of functional materials. Here, we introduce a design principle for DNA origami nanoparticles of 50-nm size, exploiting modularity, to make a family of versatile subunits that can target an abundant variety of self-assembled structures. The subunits are based on a core module that remains constant among all the subunits. Variable bond modules and angle modules are added to the exterior of the core to control interaction specificity, strength and structural geometry. A series of subunits with designed bond/angle modules are demonstrated to self-assemble into a rich variety of structures with different Gaussian curvatures, exemplified by sheets, spherical shells, and tubes. The design features flexible joints implemented using single-stranded angle modules between adjacent subunits whose mechanical properties, such as bending elastic moduli, are inferred from cryo-EM. Our findings suggest that incorporating a judicious amount of flexibility in the bond provides error tolerances in design and fabrication while still guaranteeing target fidelity. Lastly, while increasing flexibility could introduce greater variability and potential errors in assembly, these effects can be counterbalanced by increasing the number of distinct bonds, thereby allowing for precise targeting of specific structural binding angles within a broad range of configurations., Comment: 43 pages, 4 main figures, 9 supplementary figures

Fainter standard stars are essential for the calibration of larger telescopes. This work adds to the CALSPEC (calibration spectra) database 19 faint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5 and 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space Telescope) STIS (Space Telescope Imaging Spectrometer) spectrophotometry between 1150 and 3000~\AA\ with a resolution of $\sim$500. Pure hydrogen WD models are fit to these UV spectra and to six-band HST/WFC3 (Wide Field Camera 3) photometry at 0.28 to 1.6~\micron\ to construct predicted model SEDs (spectral energy distributions) covering wavelengths from 900~\AA\ to the JWST (James Webb Space Telescope) limit of 30~\micron\ using well-established CALSPEC procedures for producing flux standards with the goal of 1\% accuracy.

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

The concentration-virial mass (c-M) relation is a fundamental scaling relation within the standard cold dark matter ($\Lambda$CDM) framework well established in numerical simulations. However, observational constraints of this relation are hampered by the difficulty of characterising the properties of dark matter haloes. Recent comparisons between simulations and observations have suggested a systematic difference of the c-M relation, with higher concentrations in the latter. In this work, we undertake detailed comparisons between simulated galaxies and observations of a sample of strong-lensing galaxies. We explore several factors of the comparison with strong gravitational lensing constraints, including the choice of the generic dark matter density profile, the effect of radial resolution, the reconstruction limits of observed versus simulated mass profiles, and the role of the initial mass function in the derivation of the dark matter parameters. Furthermore, we show the dependence of the c-M relation on reconstruction and model errors through a detailed comparison of real and simulated gravitational lensing systems. An effective reconciliation of simulated and observed c-M relations can be achieved if one considers less strict assumptions on the dark matter profile, for example, by changing the slope of a generic NFW profile or focusing on rather extreme combinations of stellar-to-dark matter distributions. A minor effect is inherent to the applied method: fits to the NFW profile on a less well-constrained inner mass profile yield slightly higher concentrations and lower virial masses., Comment: 9 pages, 9 figures, 2 tables. Accepted for publication in A&A

We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge requirements of Large Language Models (LLMs). Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting parametric knowledge from LLMs, followed by using a specialized query optimizer for refining these queries. This process ensures the retrieval of only the most pertinent information essential for generating accurate responses. Moreover, to enhance flexibility and reduce computational costs, we propose a trainable scheme for our pipeline that utilizes a smaller, tunable model as the query optimizer, which is refined through knowledge distillation from a larger teacher model. Our evaluations on various question-answering (QA) datasets and with different retrieval systems show that ERRR consistently outperforms existing baselines, proving to be a versatile and cost-effective module for improving the utility and accuracy of RAG systems.

As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation

We explore some of the existing research in HCI around technology for older adults and examine the role of LLMs in enhancing it. We also discuss the digital divide and emphasize the need for inclusive technology design. At the same time, we also surface concerns regarding privacy, security, and the accuracy of information provided by LLMs, alongside the importance of user-centered design to make technology accessible and effective for the elderly. We show the transformative possibilities of LLM-supported interactions at the intersection of aging, technology, and human-computer interaction, advocating for further research and development in this area., Comment: This short paper was accepted at CHI 2024 Workshop on HCI and Aging: New Directions, New Principles

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

In the quest to unravel the mysteries of the strong force and the underlying properties of the quark-gluon plasma, the ALICE collaboration at CERN has carried out a comprehensive study focusing on the correlations between net-conserved quantities such as net-baryon, net-charge and net-strangeness. These correlations play a crucial role in the study of QCD phase structure as they are closely related to the ratios of thermodynamic susceptibilities in lattice QCD calculations. This work mainly focuses on the correlations between net-kaon and net-proton, and net-kaon and net-charge in Pb$-$Pb collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV using data recorded during LHC Run 2. The net-proton and net-kaon serve as proxies for net-baryon and net-strangeness, respectively, with measurements analyzed as a function of collision centrality. Theoretical predictions from the Thermal-FIST model are compared with experimental results, providing insights into the effects of resonance decays and charge conservation laws on the correlations., Comment: 4 pages, 2 figures; contribution to the 21st International Conference on Strangeness in Quark Matter (SQM-2024). arXiv admin note: text overlap with arXiv:2409.10939

The tree edit distance (TED) between two rooted ordered trees with $n$ nodes labeled from an alphabet $\Sigma$ is the minimum cost of transforming one tree into the other by a sequence of valid operations consisting of insertions, deletions and relabeling of nodes. The tree edit distance is a well-known generalization of string edit distance and has been studied since the 1970s. Years of steady improvements have led to an $O(n^3)$ algorithm [DMRW 2010]. Fine-grained complexity casts light onto the hardness of TED showing that a truly subcubic time algorithm for TED implies a truly subcubic time algorithm for All-Pairs Shortest Paths (APSP) [BGMW 2020]. Therefore, under the popular APSP hypothesis, a truly subcubic time algorithm for TED cannot exist. However, unlike many problems in fine-grained complexity for which conditional hardness based on APSP also comes with equivalence to APSP, whether TED can be reduced to APSP has remained unknown. In this paper, we resolve this. Not only we show that TED is fine-grained equivalent to APSP, our reduction is tight enough, so that combined with the fastest APSP algorithm to-date [Williams 2018] it gives the first ever subcubic time algorithm for TED running in $n^3/2^{\Omega(\sqrt{\log{n}})}$ time. We also consider the unweighted tree edit distance problem in which the cost of each edit is one. For unweighted TED, a truly subcubic algorithm is known due to Mao [Mao 2022], later improved slightly by D\"{u}rr [D\"{u}rr 2023] to run in $O(n^{2.9148})$. Their algorithm uses bounded monotone min-plus product as a crucial subroutine, and the best running time for this product is $\tilde{O}(n^{\frac{3+\omega}{2}})\leq O(n^{2.6857})$ (where $\omega$ is the exponent of fast matrix multiplication). In this work, we close this gap and give an algorithm for unweighted TED that runs in $\tilde{O}(n^{\frac{3+\omega}{2}})$ time., Comment: Abstract shortened to meet arXiv requirements

Recently a one-dimensional Schwarzian type theory was proposed as an effective dual theory of pure gravity in (2+1) dimensional asymptotically flat spacetimes \cite{Bhattacharjee:2023sfd}. This codimension-two `celestial' dual captures the Bekenstein-Hawking entropy of bulk flat cosmologies in semiclassical limit. In this paper, we extend this analysis beyond semiclassical approximation and evaluate the one-loop exact partition function of this celestial dual theory. Our analysis results in novel nontrivial logarithmic corrections to the area term of entropy, appearing from the one-loop path integral., Comment: 19 pages, minor changes and references added