Search

Whole-body CT is used for multi-trauma patients in the search of any and all injuries. Since an initial assessment needs to be rapid and the search for lesions is done for the whole body, very little time can be allocated for the inspection of a specific anatomy. In particular, intracranial hemorrhages are still missed, especially by clinical students. In this work, we present a Deep Learning approach for highlighting such lesions to improve the diagnostic accuracy. While most works on intracranial hemorrhages perform segmentation, detection only requires bounding boxes for the localization of the bleeding. In this paper, we propose a novel Voxel-Complete IoU (VC-IoU) loss that encourages the network to learn the 3D aspect ratios of bounding boxes and leads to more precise detections. We extensively experiment on brain bleeding detection using a publicly available dataset, and validate it on a private cohort, where we achieve 0.877 AR30, 0.728 AP30, and 0.653 AR30, 0.514 AP30 respectively. These results constitute a relative +5% improvement in Average Recall for both datasets compared to other loss functions. Finally, as there is little data currently publicly available for 3D object detection and as annotation resources are limited in the clinical setting, we evaluate the cost of different annotation methods, as well as the impact of imprecise bounding boxes in the training data on the detection performance.

We present a novel way to probe inelastic dark matter using cosmic-ray (CR) cooling in active galactic nuclei (AGNs). Dark matter (DM) in the vicinity of supermassive black holes may scatter off CRs, resulting in the rapid cooling of CRs for sufficiently large cross sections. This in turn can alter the high-energy neutrino and gamma-ray fluxes detected from these sources. We show that AGN cooling bounds obtained through the multimessenger data of NGC 1068 and TXS 0506+056 allow to reach unprecedently large mass splittings for inelastic DM ($\gtrsim$ TeV), orders of magnitude larger than those probed by direct detection experiments and DM capture in neutron stars. Furthermore, we demonstrate that cooling bounds from AGNs can probe thermal light DM with small mass splittings. This provides novel and complementary constraints in parts of a parameter space accessible solely by colliders and beam dump experiments., Comment: 6 pages, 4 figures. Supplementary Material: 6 pages, 5 figures

Liquid droplets on fiber are often observed both in nature and in different engineering applications, like a fog harvesting mesh. Knowledge about drop-on-fiber morphology and its shedding under the influence of gravity can allow for the design of better separation technology. Mutual interaction of surface tension forces arising out of the surface energies of the liquid and the fiber solid, and the weight of the liquid droplet gives rise to different morphologies of the droplet, which may occur in a stable or meta-stable configuration. Predicting the droplet shape on a fiber of specified dimension and surface wettability accurately for a given volume of liquid is challenging since the curvature of both the droplet and the cylinder influence the phenomenon. We have numerically investigated the droplet shape and transition criterion for various volumes at different contact angles under the effect of varying Bond numbers using an open-source surface evolver code. It is observed that depending upon the relative dimensions of the liquid droplet, the fiber diameter, wettability, and gravity, the liquid exists on the fiber either in 'barrel' or in 'clamshell' shape. A relation between shedding volume and the Bond number is deduced, and the detachment volumes are calculated., Comment: Presented at the 8th International and 47th National Conference on Fluid Mechanics and Fluid Power (FMFP) December 09-11, 2020, IIT Guwahati, Guwahati-781039, Assam, India. Received Professor M. G. Deshpande best paper award 2020 at FMFP

We report relaxation oscillations in a one-dimensional array of Josephson junctions. The oscillations are circuit-dual to those ordinarily observed in single junctions. The dual circuit quantitatively accounts for temporal dynamics of the array, including the dependence on biasing conditions. Injection locking the oscillations results in well-developed current plateaux. A thermal model explains the relaxation step of the oscillations., Comment: 7 pages, 8 figures

Gubser flow is an evolution with cylindrical and boost symmetries, which can be best studied by mapping the future wedge of Minkowski space (R$^{3,1}$) to dS$_3$ $\times$ $\mathbb{R}$ in a conformal relativistic theory. Here, we sharpen our previous analytic results and validate them via the first numerical exploration of the Gubser flow in a holographic conformal field theory. Remarkably, the leading generic behavior at large de Sitter time is free-streaming in transverse directions and the sub-leading behavior is that of a color glass condensate. We also show that Gubser flow can be smoothly glued to the vacuum outside the future Minkowski wedge generically given that the energy density vanishes faster than any power when extrapolated to early proper time or to large distances from the central axis. We find that at intermediate times the ratio of both the transverse and longitudinal pressures to the energy density converge approximately to a fixed point which is hydrodynamic only for large initial energy densities. We argue that our results suggest that the Gubser flow is better applied to collective behavior in jets rather than the full medium in the phenomenology of heavy ion collisions and can reveal new clues to the mechanism of confinement., Comment: 59 pages, 16 figures

We develop a set of algorithms to solve a broad class of Design of Experiment (DoE) problems efficiently. Specifically, we consider problems in which one must choose a subset of polymers to test in experiments such that the learning of the polymeric design rules is optimal. This subset must be selected from a larger set of polymers permissible under arbitrary experimental design constraints. We demonstrate the performance of our algorithms by solving several pragmatic nucleic acid therapeutics engineering scenarios, where limitations in synthesis of chemically diverse nucleic acids or feasibility of measurements in experimental setups appear as constraints. Our approach focuses on identifying optimal experimental designs from a given set of experiments, which is in contrast to traditional, generative DoE methods like BIBD. Finally, we discuss how these algorithms are broadly applicable to well-established optimal DoE criteria like D-optimality.

Patients with Intracranial Hemorrhage (ICH) face a potentially life-threatening condition, and patient-centered individualized treatment remains challenging due to possible clinical complications. Deep-Learning-based methods can efficiently analyze the routinely acquired head CTs to support the clinical decision-making. The majority of early work focuses on the detection and segmentation of ICH, but do not model the complex relations between ICH and adjacent brain structures. In this work, we design a tailored object detection method for ICH, which we unite with segmentation-grounded Scene Graph Generation (SGG) methods to learn a holistic representation of the clinical cerebral scene. To the best of our knowledge, this is the first application of SGG for 3D voxel images. We evaluate our method on two head-CT datasets and demonstrate that our model can recall up to 74% of clinically relevant relations. This work lays the foundation towards SGG for 3D voxel data. The generated Scene Graphs can already provide insights for the clinician, but are also valuable for all downstream tasks as a compact and interpretable representation.

Medical image distributions shift constantly due to changes in patient population and discrepancies in image acquisition. These distribution changes result in performance deterioration; deterioration that continual learning aims to alleviate. However, only adaptation with data rehearsal strategies yields practically desirable performance for medical image segmentation. Such rehearsal violates patient privacy and, as most continual learning approaches, overlooks unexpected changes from out-of-distribution instances. To transcend both of these challenges, we introduce a distribution-aware replay strategy that mitigates forgetting through auto-encoding of features, while simultaneously leveraging the learned distribution of features to detect model failure. We provide empirical corroboration on hippocampus and prostate MRI segmentation.

The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly in remote areas. Our research addresses this issue by implementing Neural Cellular Automata (NCA) training directly on smartphones for accessible X-ray lung segmentation. We confirm the practicality and feasibility of deploying and training these advanced models on five Android devices, improving medical diagnostics accessibility and bridging the tech divide to extend machine learning benefits in medical imaging to low- and middle-income countries (LMICs). We further enhance this approach with an unsupervised adaptation method using the novel Variance-Weighted Segmentation Loss (VWSL), which efficiently learns from unlabeled data by minimizing the variance from multiple NCA predictions. This strategy notably improves model adaptability and performance across diverse medical imaging contexts without the need for extensive computational resources or labeled datasets, effectively lowering the participation threshold. Our methodology, tested on three multisite X-ray datasets -- Padchest, ChestX-ray8, and MIMIC-III -- demonstrates improvements in segmentation Dice accuracy by 0.7 to 2.8%, compared to the classic Med-NCA. Additionally, in extreme cases where no digital copy is available and images must be captured by a phone from an X-ray lightbox or monitor, VWSL enhances Dice accuracy by 5-20%, demonstrating the method's robustness even with suboptimal image sources.

We provide practical simulation methods for scalar field theories on a quantum computer that yield improved asymptotics as well as concrete gate estimates for the simulation and physical qubit estimates using the surface code. We achieve these improvements through two optimizations. First, we consider a different approach for estimating the elements of the S-matrix. This approach is appropriate in general for 1+1D and for certain low-energy elastic collisions in higher dimensions. Second, we implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians formulated both in the field occupation basis and field amplitude basis. Our algorithms are based on either second-order Trotterization or qubitization. The cost of Trotterization in occupation basis scales as $\widetilde{O}(\lambda N^7 |\Omega|^3/(M^{5/2} \epsilon^{3/2})$ where $\lambda$ is the coupling strength, $N$ is the occupation cutoff $|\Omega|$ is the volume of the spatial lattice, $M$ is the mass of the particles and $\epsilon$ is the uncertainty in the energy calculation used for the $S$-matrix determination. Qubitization in the field basis scales as $\widetilde{O}(|\Omega|^2 (k^2 \Lambda +kM^2)/\epsilon)$ where $k$ is the cutoff in the field and $\Lambda$ is a scaled coupling constant. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of $4\times 10^6$ physical qubits and $10^{12}$ $T$-gates which corresponds to roughly one day on a superconducting quantum computer with surface code and a cycle time of 100 ns, placing simulation of scalar field theory within striking distance of the gate counts for the best available chemistry simulation results., Comment: main text, 50 pages, supplementary 64 pages

Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for this study, encompassing diverse question categories and chart formats. We investigate two key aspects: 1) the models' ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. Our analysis reveals significant performance variations based on question and chart types, highlighting both strengths and weaknesses of current models. Additionally, we identify areas for improvement and propose future research directions to build more robust and reliable CQA systems. This study sheds light on the limitations of current models and paves the way for future advancements in the field., Comment: 22 pages, 7 Tables, 3 Figures, 25 examples

Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of MCTS in transportation routing services, where the algorithm is integrated to develop optimized route plans. These plans are required to meet a range of constraints and requirements simultaneously, further complicating the task of explaining the algorithm's operation in real-world contexts. To address this critical research gap, we introduce a novel computation tree logic-based explainer for MCTS. Our framework begins by taking user-defined requirements and translating them into rigorous logic specifications through the use of language templates. Then, our explainer incorporates a logic verification and quantitative evaluation module that validates the states and actions traversed by the MCTS algorithm. The outcomes of this analysis are then rendered into human-readable descriptive text using a second set of language templates. The user satisfaction of our approach was assessed through a survey with 82 participants. The results indicated that our explanatory approach significantly outperforms other baselines in user preference., Comment: Accepted by the Proceedings of the 27th European Conference on Artificial Intelligence (ECAI)

Thin Si-doped Al-rich (Al>0.85) regrown Al(Ga)N layers were deposited on AlN on Sapphire template using metal-organic chemical vapor deposition (MOCVD) techniques. The optimization of the deposition conditions such as temperature, V/III ratio, deposition rate, and Si concentration resulted in a high charge carrier concentration (>$10^{15}/cm^{3}$) in the Si-doped Al-rich Al(Ga)N films. A pulsed deposition condition was employed to achieve a controllable Al composition greater than 95% and to prevent unintended Ga incorporation in the AlGaN material deposited using the close-coupled showerhead reactor. Also, the effect of unintentional Si incorporation on free charge carrier concentration at the regrowth interface was observed by varying the thickness of the regrown Al(Ga)N layer. A maximum charge carrier concentration of $4.8\times 10^{16}/cm^3$ and $7.5\times 10^{15}/cm^3$ were achieved for Al0.97Ga0.03N and AlN films with thickness <300 nm compared to previously reported n-Al(Ga)N films with thickness $\ge$400 nm deposited using MOCVD technique., Comment: 13 pages, 5 figures

Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves together three groundbreaking studies that revolutionize SNN performance through the introduction of heterogeneity in neuron and synapse dynamics. We explore the transformative impact of Heterogeneous Recurrent Spiking Neural Networks (HRSNNs), supported by rigorous analytical frameworks and novel pruning methods like Lyapunov Noise Pruning (LNP). Our findings reveal how heterogeneity not only enhances classification performance but also reduces spiking activity, leading to more efficient and robust networks. By bridging theoretical insights with practical applications, this comprehensive summary highlights the potential of SNNs to outperform traditional neural networks while maintaining lower computational costs. Join us on a journey through the cutting-edge advancements that pave the way for the future of intelligent, energy-efficient neural computing., Comment: 20 pages, 12 figures, 5 tables. arXiv admin note: text overlap with arXiv:2211.04297, arXiv:2302.11618, arXiv:2403.03409

Binary neutron-star (BNS) mergers are accompanied by multi-messenger emissions, including gravitational wave (GW), neutrino, and electromagnetic signals. Some fraction of BNS mergers may result in a rapidly spinning magnetar as a remnant, which can enhance both the EM and neutrino emissions. In this study, we model the neutrino emissions from such systems and discuss the prospects for detecting the neutrinos coincident with GW signatures. We consider a scenario where a magnetar remnant drives a pulsar wind using its spin energy. The wind interacts with the surrounding kilonova ejecta, forming a nebula filled with non-thermal photons. Ions and nuclei extracted from the magnetar's surface can be accelerated in the polar-cap and the termination-shock regions. We investigate the neutrino fluences resulting from photomeson interactions, where accelerated CR protons interact with the photons in the nebula. Our findings indicate that the peak neutrino fluence is $\sim 10^{-2}\rm GeV~cm^{-2}$ for a source at $40$ Mpc, which is reached approximately $\mathcal{O}( 1-10\ {\rm days})$ post merger. Finally, we examine the potential for GW-triggered stacking searches with IceCube-Gen2 using next-generation GW detectors such as the Cosmic Explorer (CE) and the Einstein Telescope (ET). We conclude that, assuming an optimistic neutrino emission model, a combination of CE+ET would offer a high probability of neutrino detection from these sources within an operational timescale of $\sim 20$ years. In case of non-detection, $2 \sigma$ level constraints on model parameters can be established within similar joint operation timescales., Comment: 21 pages, 8 figures

Binary neutron star (BNS) mergers can be sources of ultrahigh-energy (UHE) cosmic rays and potential emitters of UHE neutrinos. The upcoming and current radio neutrino detectors like the Giant Radio Array for Neutrino Detection (GRAND), IceCube-Gen2 Radio, and the Radio Neutrino Observatory in Greenland (RNO-G) are projected to reach the required sensitivities to search for these neutrinos. In particular, in conjunction with the next-generation of gravitational wave (GW) detectors like Cosmic Explorer (CE) and Einstein Telescope (ET), GW-triggered stacking searches can be performed with the UHE neutrino detectors. In this work, we explore the prospects of such searches by implementing in our analysis an upper distance limit based on the sky-localization capabilities of the GW detectors from which meaningful triggers can be collected. We find that if each GW burst is associated with a total isotropic-equivalent energy of $\sim 10^{50} - 10^{51}$ erg emitted in UHE neutrinos, along with a corresponding beaming fraction of $1$\%, GRAND and IceCube-Gen2 Radio have a large probability ($\sim 99$\%) to detect a coincident neutrino event using the joint combination of CE+ET in a timescale of less than 15 years of operation for our fiducial choice of parameters. In case of nondetections, the parameter spaces can be constrained at $3\sigma$ level in similar timescales of operation. We also highlight and discuss the prospects of such joint radio neutrino detector network, their importance, and their role in facilitating synergic GW and neutrino observations in the next era of multimessenger astrophysics., Comment: 18 pages, 6 figures

The explosion in Machine Learning (ML) over the past ten years has led to a dramatic increase in demand for GPUs to train ML models. Because it is prohibitively expensive for most users to build and maintain a large GPU cluster, large cloud providers (Microsoft Azure, Amazon AWS, Google Cloud) have seen explosive growth in demand for renting cloud-based GPUs. In this cloud-computing paradigm, a user must specify their demand for GPUs at every moment in time, and will pay for every GPU-hour they use. ML training jobs are known to be parallelizable to different degrees. Given a stream of ML training jobs, a user typically wants to minimize the mean response time across all jobs. Here, the response time of a job denotes the time from when a job arrives until it is complete. Additionally, the user is constrained by some operating budget. Specifically, in this paper the user is constrained to use no more than $b$ GPUs per hour, over a long-run time average. The question is how to minimize mean response time while meeting the budget constraint. Because training jobs receive a diminishing marginal benefit from running on additional GPUs, allocating too many GPUs to a single training job can dramatically increase the overall cost paid by the user. Hence, an optimal rental policy must balance a tradeoff between training cost and mean response time. This paper derives the optimal rental policy for a stream of training jobs where the jobs have different levels of parallelizability (specified by a speedup function) and different job sizes (amounts of inherent work). We make almost no assumptions about the arrival process and about the job size distribution. Our optimal policy specifies how many GPUs to rent at every moment in time and how to allocate these GPUs.

Received signal strength (RSS)--based cooperative localization has gained significant attention due to its straightforward system architectures and cost-effectiveness. In this paper, we propose Cooperative Localization Techniques (with Unknown Parameters), referred to as CTUP(s), which consider uncertainty in anchor nodes' locations and assume the transmit power and \textcolor{blue}{path loss exponent (PLE)} to be unknown. Unlike prior studies, CTUP(s) address unknowns by estimating these parameters, along with the location of target nodes. The non-convex and non-linear nature of the maximum likelihood (ML) estimator of the problem is addressed through relaxation techniques, employing Taylor series expansion, semidefinite relaxation (SDR), and the epigraph method. The resulting problem is solved using semidefinite second-order cone programming (SDP-SOCP), leveraging the precision of SDP and the simplicity of SOCP. We deployed an extensive network comprising 50 BLE nodes covering an area of 640~m $\times$ 180~m to gather RSS data. The precise location of the nodes is obtained using real-time kinematics global positioning system (RTK-GPS), which is treated as the ground truth. Furthermore, to replicate real-world scenarios, we recorded the positions of the anchor nodes using a standard GPS, thereby introducing uncertainty into the anchor node locations. Extensive simulation and hardware experimentation demonstrate the superior performance of CTUP compared to existing techniques.

The determination of Lande's g-factor and the damping constant is central to extracting crucial information about the spin-spin and spin-orbit interactions in magnetically ordered systems. In insulating compounds based on 3d elements, the spin-orbit interaction can be effectively probed by investigating the ground state of the corresponding free 3d ion within the crystalline environment. For metallic systems, the problem is non-trivial because of additional band structure effects. Here, we investigate the anisotropic magnetization dynamics in bulk single crystalline Fe5GeTe2, a van der Waals 2D itinerant ferromagnet at room temperature, using broadband ferromagnetic resonance spectroscopy. Strikingly, contrary to the results of ab-initio calculations, we do not observe any intrinsic anisotropy of magnetization damping close to room temperature, suggesting diminished role of spin-orbit interaction. However, there is a sizable anisotropy in the Lande's g factor near room temperature which is attributed to anisotropic critical spin fluctuations.

Domain wall (DW) networks have a large impact on cosmology and present interesting dynamics that can be controlled by various scaling regimes. In the first stage after spontaneous breaking of the discrete symmetry, the network is seeded with `DW precursors', the zeros of a tachyonic field. At sufficiently weak coupling, this stage can be quite long. The network is then driven to a non-relativistic scaling regime: in flat spacetime the correlation length grows like $L\sim t^{\kappa}$ with $\kappa=1/2$. We focus on the precursor regime in cosmology, assuming a power-law scale factor $a\propto t^\alpha$. We obtain the scaling exponent as a function of the external parameter, $\kappa(\alpha)$, by explicit computation in $1+1$ and $2+1$ dimensions, and find a smooth transition from nonrelativistic scaling with $\kappa\simeq 1/2$ for $\alpha\lesssim1/2$ to DW gas regime $\kappa \simeq \alpha$ for $\alpha\gtrsim1/2$, confirming previous arguments. The precise form of the transition $\kappa(\alpha)$ is surprisingly independent of dimension, suggesting that similar results should also be valid in $3+1$ dimensions., Comment: 13 pages, 6 figures

A near memory hardware accelerator, based on a novel direct path computational model, for real-time emulation of radio frequency systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASIC) and field programmable gate arrays (FPGA) methodologies: 1). The ASIC testchip implementation, using TSMC 28nm CMOS, leverages distributed autonomous control to extract concurrency in compute as well as low latency. It achieves a $518$ MHz per channel bandwidth in a prototype $4$-node system. The maximum emulation range supported in this paradigm is $9.5$ km with $0.24$ $\mu$s of per-sample emulation latency. 2). The FPGA-based implementation, evaluated on a Xilinx ZCU104 board, demonstrates a $9$-node test case (two Transmitters, one Receiver, and $6$ passive reflectors) with an emulation range of $1.13$ km to $27.3$ km at $215$ MHz bandwidth.

In this paper we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of $M$ objects all interacting with one another, the tapped delay line model's computational requirements scale as $O(M^3)$ per sample: there are $O(M^2)$ channels, each with $O(M)$ complexity. In this paper, we develop a new ``direct path" model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an $O(M^2)$ per sample scaling of the computational requirements. The impact of this is drastic, a $200$ object scenario sees about a $100\times$ reduction in the number of per sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle level C++ simulator of a hardware implementation of the model.

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 534 authors from 83 institutions, 12 pages, 7 figures. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Transient execution attacks have been one of the widely explored microarchitectural side channels since the discovery of Spectre and Meltdown. However, much of the research has been driven by manual discovery of new transient paths through well-known speculative events. Although a few attempts exist in literature on automating transient leakage discovery, such tools focus on finding variants of known transient attacks and explore a small subset of instruction set. Further, they take a random fuzzing approach that does not scale as the complexity of search space increases. In this work, we identify that the search space of bad speculation is disjointedly fragmented into equivalence classes, and then use this observation to develop a framework named Shesha, inspired by Particle Swarm Optimization, which exhibits faster convergence rates than state-of-the-art fuzzing techniques for automatic discovery of transient execution attacks. We then use Shesha to explore the vast search space of extensions to the x86 Instruction Set Architecture (ISAs), thereby focusing on previously unexplored avenues of bad speculation. As such, we report five previously unreported transient execution paths in Instruction Set Extensions (ISEs) on new generation of Intel processors. We then perform extensive reverse engineering of each of the transient execution paths and provide root-cause analysis. Using the discovered transient execution paths, we develop attack building blocks to exhibit exploitable transient windows. Finally, we demonstrate data leakage from Fused Multiply-Add instructions through SIMD buffer and extract victim data from various cryptographic implementations., Comment: USENIX Security Symposium, 2024

The following question arises naturally in the study of graph streaming algorithms: "Is there any graph problem which is "not too hard", in that it can be solved efficiently with total communication (nearly) linear in the number $n$ of vertices, and for which, nonetheless, any streaming algorithm with $\tilde{O}(n)$ space (i.e., a semi-streaming algorithm) needs a polynomial $n^{\Omega(1)}$ number of passes?" Assadi, Chen, and Khanna [STOC 2019] were the first to prove that this is indeed the case. However, the lower bounds that they obtained are for rather non-standard graph problems. Our first main contribution is to present the first polynomial-pass lower bounds for natural "not too hard" graph problems studied previously in the streaming model: $k$-cores and degeneracy. We devise a novel communication protocol for both problems with near-linear communication, thus showing that $k$-cores and degeneracy are natural examples of "not too hard" problems. Indeed, previous work have developed single-pass semi-streaming algorithms for approximating these problems. In contrast, we prove that any semi-streaming algorithm for exactly solving these problems requires (almost) $\Omega(n^{1/3})$ passes. Our second main contribution is improved round-communication lower bounds for the underlying communication problems at the basis of these reductions: * We improve the previous lower bound of Assadi, Chen, and Khanna for hidden pointer chasing (HPC) to achieve optimal bounds. * We observe that all current reductions from HPC can also work with a generalized version of this problem that we call MultiHPC, and prove an even stronger and optimal lower bound for this generalization. These two results collectively allow us to improve the resulting pass lower bounds for semi-streaming algorithms by a polynomial factor, namely, from $n^{1/5}$ to $n^{1/3}$ passes., Comment: Accepted at CCC 2024

An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated requests. Optimal repositioning is computationally challenging due to the exponential number of ways to allocate responders between locations and the uncertainty in future requests. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search, which may require minutes of computation for each decision in a domain where seconds can save lives. We address the issue of long decision times by introducing a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning. To address the computational challenges posed by large, variable-dimensional, and discrete state and action spaces, we propose: (1) actor-critic based agents that incorporate transformers to handle variable-dimensional states and actions, (2) projections to fixed-dimensional observations to handle complex states, and (3) combinatorial techniques to map continuous actions to discrete allocations. We evaluate our approach using real-world data from two U.S. cities, Nashville, TN and Seattle, WA. Our experiments show that compared to the state of the art, our approach reduces computation time per decision by three orders of magnitude, while also slightly reducing average ambulance response time by 5 seconds.

Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI., Comment: Extension of arXiv:2404.17047

An efficient third-order algebraic diagrammatic construction (ADC) theory has been implemented to calculate ionisation potential, electron attachment and excitation energy (IP/EA/EE-ADC(3)) in a four-component relativistic framework. We have used polarisation propagator formulation for third-order perturbation theory to access the excitation energies (EE), and for IP/EA, a single-particle propagator has been used based on a non-Dyson formulation. The benchmarking calculations have been performed on various types of systems to test the accuracy of the four component ADC(3) scheme for the computation of IP, EA and EE. We have applied our IP-ADC(3) to demonstrate the computation of splitting in the IP states for halogen monoxides (XO, X = Cl, Br, I ) due to spin-orbital coupling in the 2^{\Pi} ground state and compared it with experimental results. Next, we have studied the effect of relativity and the size of the basis set on the electron attachment calculations of halogen atoms (F, Cl, Br, I and At) using EA-ADC(3). As our next step, we have shown the efficiency of four component ADC(3) in computing excitation energies of triiodide ion and compared with relativistic equation of motion coupled cluster with singles and doubles (EOM-CCSD), intermediate Hamiltonian Fock space coupled cluster (IHFS-CC) and other EOM-CCSD schemes in which spin-orbit coupling is incorporated with different degrees of approximation. Finally, we have also investigated the excitation energies and transition dipole moments for the four excited states of Xe atom and compared them with our recent four-component EOM-CCSD implementation and relativistic finite field Fock space coupled cluster results, along with the experimental estimates.

In this work, we have reconstructed the extended $f(\mathcal{P})$ cubic gravity and symmetric $f(\mathcal{Q})$ teleparallel gravity from $(m,n)$-type Barrow Holographic Dark Energy (BHDE) and find the unknown functions $f(\mathcal{P})$ and $f(\mathcal{Q})$ in terms of $\mathcal{P}$ and $\mathcal{Q}$ by taking the universe to be flat, homogeneous and isotropic. We then analyzed the behavior and stability of each model for the entire stages of the evolution of the universe by studying several important parameters such as the deceleration parameter, equation of state (EoS) parameter $\omega_{DE}$, square of the speed of sound $v_s^2$. Apart from this, we have studied the cosmographic behavior by plotting the jerk parameter, snap parameter, and lerk parameter against the redshift. We have also examined the $\omega'_{DE}-\omega_{DE}$ phase plane and $(r,s^*)$, $(r,q)$ statefinder parameters that provide valuable insights into the dynamics of the universe and the distinctive features of the dark energy. All these analyses pointed out that our model can produce a universe going through an accelerated expansion with the quintessence type dark energy.

Step Chemical Reaction Networks (step CRNs) are an augmentation of the Chemical Reaction Network (CRN) model where additional species may be introduced to the system in a sequence of ``steps.'' We study step CRN systems using a weak subset of reaction rules, \emph{void} rules, in which molecular species can only be deleted. We demonstrate that step CRNs with only void rules of size (2,0) can simulate threshold formulas (TFs) under linear resources. These limited systems can also simulate threshold \emph{circuits} (TCs) by modifying the volume of the system to be exponential. We then prove a matching exponential lower bound on the required volume for simulating threshold circuits in a step CRN with (2,0)-size rules under a restricted \emph{gate-wise} simulation, thus showing our construction is optimal for simulating circuits in this way., Comment: arXiv admin note: text overlap with arXiv:2402.08220

The ever-increasing need for power consumption in electronic devices, coupled with the requirement for thinner size, calls for the development of efficient heat spreading components. Vapor chambers (VCs), because of their ability to effectively spread heat over a large area by two-phase heat transfer, seem ideal for such applications. However, creating thin and efficient vapor chambers that work over a wide range of power inputs is a persisting challenge. VCs that use wicks for circulating the phase changing media, suffer from capillary restrictions, dry-out, clogging, increase in size and weight, and can often be costly. Recent developments in wick-free wettability patterned vapor chambers replace traditional wicks with laser-fabricated wickless components. An experimental setup allows for fast testing and experimental evaluation of water-charged VCs with liquid-assisted cooling. The sealed chamber can maintain vacuum for long durations, and can be used for testing of very thin wick-free VCs. This work extends our previous study by decreasing overall thickness of the wick-free VC down to 3 mm and evaluates its performance. Furthermore, the impact of wettability patterns on VC performance is investigated, by carrying out experiments both in non-patterned and patterned VCs. Experiments are first carried out on a wick-free VC with no wettability patterns and comprising of an entirely superhydrophilic evaporator coupled with a hydrophobic condenser. Thereafter, wettability patterns that aid the rapid return of water to the heated site on the evaporator and improve condensation on the condenser of the vapor chamber are implemented. The thermal characteristics show that the patterned VCs outperform the non-patterned VCs under all scenarios. The patterned VCs exhibit low thermal resistance independent of fluid charging ratio withstanding higher power inputs without thermal dry-outs., Comment: Presented at IEEE ITherm (Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems) 2023. Orlando, FL, US. Corresponding: cmm@uic.edu

Efficient thermal management in high-power electronics cooling can be achieved using phase-change heat transfer devices, such as vapor chambers. Traditional vapor chambers use wicks to transport condensate for efficient thermal exchange and to prevent "dry-out" of the evaporator. However, wicks in vapor chambers present significant design challenges arising out of large pressure drops across the wicking material, which slows down condensate transport rates and increases the chances for dry-out. Thicker wicks add to overall thermal resistance, while deterring the development of thinner devices by limiting the total thickness of the vapor chamber. Wickless vapor chambers eliminate the use of metal wicks entirely, by incorporating complementary wettability-patterned flat plates on both the evaporator and the condenser side. Such surface modifications enhance fluid transport on the evaporator side, while allowing the chambers to be virtually as thin as imaginable, thereby permitting design of thermally efficient thin electronic cooling devices. While wick-free vapor chambers have been studied and efficient design strategies have been suggested, we delve into real-life applications of wick-free vapor chambers in forced air cooling of high-power electronics. An experimental setup is developed wherein two Si-based MOSFETs of TO-247-3 packaging having high conduction resistance, are connected in parallel and switched at 100 kHz, to emulate high frequency power electronics operations. A rectangular copper wick-free vapor chamber spreads heat laterally over a surface 13 times larger than the heating area. This chamber is cooled externally by a fan that circulates air at room temperature. The present experimental setup extends our previous work on wick-free vapor chambers, while demonstrating the effectiveness of low-cost air cooling in vapor-chamber enhanced high-power electronics applications., Comment: Presented at IEEE ITherm (Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems) 2023, Orlando FL. Corresponding author: cmm@uic.edu

We postprocess a three-dimensional general relativistic, full transport neutrino radiation magnetohydrodynamics simulation of the black hole--accretion disk--wind system thought to be a potential outcome of the GW170817 merger to investigate the presence of electron lepton number (ELN-XLN) crossings in the neutrino angular distribution. Neutrinos are evolved with an explicit Monte Carlo method and can interact with matter via emission, absorption, or scattering. Within the postprocessing framework, we find ubiquitous occurrence of ELN-XLN crossings at early times ($\sim$ 11ms) but this does not hold for later times in the simulation. At postmerger times of $ \sim$ 60 ms and beyond, ELN-XLN crossings are only present near the equator. We provide a detailed analysis of the neutrino radiation field to investigate the origin and time evolution of these crossings. Previous reports have suggested ubiquitous flavor crossings persisting throughout the simulation lifetime, albeit for different sets of conditions for the merger remnant, the treatment of hydrodynamics and neutrino transport. Even though we do not perform a direct comparison with other published works, we qualitatively assess the reasons for the difference with our results. The geometric structure and evolution of the ELN-XLN crossings found in our analysis, and by extension, fast flavor instabilities have important implications for heavy element nucleosynthesis in neutron star mergers.

The Active Matter Evaluation Package (AMEP) is a Python library for analyzing simulation data of particle-based and continuum simulations. It provides a powerful and simple interface for handling large data sets and for calculating and visualizing a broad variety of observables that are relevant to active matter systems. Examples range from the mean-square displacement and the structure factor to cluster-size distributions, binder cumulants, and growth exponents. AMEP is written in pure Python and is based on powerful libraries such as NumPy, SciPy, Matplotlib, and scikit-image. Computationally expensive methods are parallelized and optimized to run efficiently on workstations, laptops, and high-performance computing architectures, and an HDF5-based data format is used in the backend to store and handle simulation data as well as analysis results. AMEP provides the first comprehensive framework for analyzing simulation results of both particle-based and continuum simulations (as well as experimental data) of active matter systems. In particular, AMEP also allows it to analyze simulations that combine particle-based and continuum techniques such as used to study the motion of bacteria in chemical fields or for modeling particle motion in a flow field. AMEP is available at https://amepproject.de and can be installed via conda and pip., Comment: See https://github.com/amepproject/amep for the source code and https://amepproject.de/ for the documentation

In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source Generative AI. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact., Comment: Accepted to ICML'24 as a position paper

The use of graphene in spintronic devices is contingent on its ability to convert a spin current into a charge current. We have systematically investigated the spin pumping induced spin-to-charge current conversion at the Graphene/FM interface and the effect of interface modification through high spin orbit coupling (SOC) material (Pt) as an interlayer (IL) of varying thicknesses by using broadband FMR spectroscopy. The spin mixing conductance is enhanced from $1.66 \times 10^{18}$ m$^{-2}$ to $2.72 \times 10^{18}$ m$^{-2}$ whereas the spin current density is enhanced from 0.135$\pm $0.003 to 0.242$\pm$0.004 MA/m$^{2}$ at the Graphene/FM interface due to the interface modification using high SOC material Pt as an interlayer. The spin current to charge current conversion efficiency turns out to be $\approx 0.003$ nm for the Graphene/FM interface. These findings support the idea that Graphene in combination with high SOC material (Pt) could be a potential candidate for spintronic applications, specifically for spin-torque-based memory applications., Comment: 8 Pages, 6 figures

Recently, radio emission from tidal disruption events (TDEs) has been observed from months to years after the optical discovery. Some of the TDEs including ASASSN-14ae, ASASSN-15oi, AT 2018hyz, and AT 2019dsg are accompanied by the late-time rebrightening phase characterized by a rapid increase in the radio flux. We show that it can be explained by the off-axis two-component jet model, in which the late-time rebrightening arises from the off-axis view of a decelerating narrower jet with an initial Lorentz factor of ~10 and a jet opening angle of ~0.1 rad, while the early-time radio emission is attributed to the off-axis view of a wider jet component. We also argue that the rate density of jetted TDEs inferred from these events is consistent with the observations., Comment: 8+2 pages, 2+2 figures, 1 table, accepted for publication in PRDL

The momentum space Josephson effect describes the supercurrent flow between weakly coupled Bose-Einstein condensates (BECs) at two discrete momentum states. Here, we experimentally observe this exotic phenomenon using a BEC with Raman-induced spin-orbit coupling, where the tunneling between two local band minima is implemented by the momentum kick of an additional optical lattice. A sudden quench of the Raman detuning induces coherent spin-momentum oscillations of the BEC, which is analogous to the a.c. Josephson effect. We observe both plasma and regular Josephson oscillations in different parameter regimes. The experimental results agree well with the theoretical model and numerical simulation, and showcase the important role of nonlinear interactions. We also show that the measurement of the Josephson plasma frequency gives the Bogoliubov zero quasimomentum gap, which determines the mass of the corresponding pseudo-Goldstone mode, a long-sought phenomenon in particle physics. The observation of momentum space Josephson physics offers an exciting platform for quantum simulation and sensing utilizing momentum states as a synthetic degree., Comment: 12 pages, 11 figures

This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach. We address the mislabeling issue in real-time HMDs, where benign segments in malware time-series incorrectly inherit malware labels, leading to increased false positives. Utilizing the proposed Malicious Discriminative Score within the MIL framework, RT-HMD effectively identifies localized malware behaviors, thereby improving the predictive accuracy. Empirical analysis, using a hardware telemetry dataset collected from a mobile platform across 723 benign and 1033 malware samples, shows a 5% precision boost while maintaining recall, outperforming baselines affected by mislabeled benign segments., Comment: Under peer review

Let $G$ be a semisimple complex algebraic group with a simple Lie algebra $\mathfrak{g}$, and let $\mathcal{M}^0_{G}$ denote the moduli stack of topologically trivial stable $G$-bundles on a smooth projective curve $C$. Fix a theta characteristic $\kappa$ on $C$ which is even in case $\dim{\mathfrak{g}}$ is odd. We show that there is a nonempty Zariski open substack ${\mathcal U}_\kappa$ of $\mathcal{M}^0_{G}$ such that $H^i(C,\, \text{ad}(E_G)\otimes\kappa) \,=\, 0$, $i\,=\, 1,\, 2$, for all $E_G\,\in\, {\mathcal U}_\kappa$. It is shown that any such $E_G$ has a canonical connection. It is also shown that the tangent bundle $T{U}_\kappa$ has a natural splitting, where $U_{\kappa}$ is the restriction of $\mathcal{U}_{\kappa}$ to the semi-stable locus. We also produce an isomorphism between two naturally occurring $\Omega^1_{{M}^{rs}_{G}}$--torsors on the moduli space of regularly stable ${M}^{rs}_{G}$., Comment: 16 Pages, Final version, to appear in International Journal of Mathematics

We view the mind-body problem in terms of the two interconnected problems of phenomenal consciousness and mental causation, namely, how subjective conscious experience can arise from physical neurological processes and how conscious mental states can causally act upon the physical world. In order to address these problems, I develop here a non-physicalist framework that combines two apparently antithetical views: the materialist view of the mind as a product of the brain and the metaphysical view of consciousness rooted in an underlying hidden reality. I discuss how this framework resolves the problem of mental causation while being simultaneously consistent with fundamental physical principles. I will elucidate how the framework ties in to the perspective of meaning that acts as the bridge between physical neurological processes and the conscious mind. Moreover, we will see how both our awareness of the self and our representation of the external world are connected to this perspective.

A large proportion of jobs submitted to modern computing clusters and data centers are parallelizable and capable of running on a flexible number of computing cores or servers. Although allocating more servers to such a job results in a higher speed-up in the job's execution, it reduces the number of servers available to other jobs, which in the worst case, can result in an incoming job not finding any available server to run immediately upon arrival. Hence, a key question to address is: how to optimally allocate servers to jobs such that (i) the average execution time across jobs is minimized and (ii) almost all jobs find at least one server immediately upon arrival. To address this question, we consider a system with $n$ servers, where jobs are parallelizable up to $d^{(n)}$ servers and the speed-up function of jobs is concave and increasing. Jobs not finding any available servers upon entry are blocked and lost. We propose a simple server allocation scheme that achieves the minimum average execution time of accepted jobs while ensuring that the blocking probability of jobs vanishes as the system becomes large ($n \to \infty$). This result is established for various traffic conditions as well as for heterogeneous workloads. To prove our result, we employ Stein's method which also yields non-asymptotic bounds on the blocking probability and the mean execution time. Furthermore, our simulations show that the performance of the scheme is insensitive to the distribution of job execution times.

Let $k$ be an arbitrary field. We construct examples of regular local $k$-algebras $R$ (of positive dimension) for which the ring of differential operators $D_k(R)$ is trivial in the sense that it contains {\it no} operators of positive order. The examples are excellent in characteristic zero but not in positive characteristic. These rings can be viewed as being non-singular but they are not simple as $D$-modules, laying to rest speculation that $D$-simplicity might characterize a nice class of singularities in general. In prime characteristic, the construction also provides examples of {\it regular} local rings $R$ (with fraction field a function field) whose Frobenius push-forward $F_*^eR$ is {\it indecomposable} as an $R$-module for all $e\in \mathbb N$. Along the way, we investigate hypotheses on a local ring $(R, m)$ under which $D$-simplicity for $R$ is equivalent to $D$-simplicity for its $m$-adic completion, and give examples of rings for which the differential operators do not behave well under completion. We also generalize a characterization of $D$-simplicity due to Jeffries in the $\mathbb N$-graded case: for a Noetherian local $k$-algebra $(R, m, k)$, $D$-simplicity of $R$ is equivalent to surjectivity of the natural map $D_k(R)\to D_k(R, k)$., Comment: Comments welcome

Let $X=X_1\sqcup X_2\sqcup\ldots\sqcup X_k$ be a partitioned set of variables such that the variables in each part $X_i$ are noncommuting but for any $i\neq j$, the variables $x\in X_i$ commute with the variables $x'\in X_j$. Given as input a square matrix $T$ whose entries are linear forms over $\mathbb{Q}\langle{X}\rangle$, we consider the problem of checking if $T$ is invertible or not over the universal skew field of fractions of the partially commutative polynomial ring $\mathbb{Q}\langle{X}\rangle$ [Klep-Vinnikov-Volcic (2020)]. In this paper, we design a deterministic polynomial-time algorithm for this problem for constant $k$. The special case $k=1$ is the noncommutative Edmonds' problem (NSINGULAR) which has a deterministic polynomial-time algorithm by recent results [Garg-Gurvits-Oliveira-Wigderson (2016), Ivanyos-Qiao-Subrahmanyam (2018), Hamada-Hirai (2021)]. En-route, we obtain the first deterministic polynomial-time algorithm for the equivalence testing problem of $k$-tape \emph{weighted} automata (for constant $k$) resolving a long-standing open problem [Harju and Karhum"{a}ki(1991), Worrell (2013)]. Algebraically, the equivalence problem reduces to testing whether a partially commutative rational series over the partitioned set $X$ is zero or not [Worrell (2013)]. Decidability of this problem was established by Harju and Karhum\"{a}ki (1991). Prior to this work, a \emph{randomized} polynomial-time algorithm for this problem was given by Worrell (2013) and, subsequently, a deterministic quasipolynomial-time algorithm was also developed [Arvind et al. (2021)].

Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements. Their temporal evolution is often driven by transitions between a finite number of discrete states. Despite significant advancements in predictive modeling through deep learning, such interactions among many elements have rarely explored as a specific domain for predictive modeling. We present Attentive Recurrent Neural Cellular Automata (AR-NCA), to effectively discover unknown local state transition rules by associating the temporal information between neighboring cells in a permutation-invariant manner. AR-NCA exhibits the superior generalizability across various system configurations (i.e., spatial distribution of states), data efficiency and robustness in extremely data-limited scenarios even in the presence of stochastic interactions, and scalability through spatial dimension-independent prediction., Comment: Accepted in Learning for Dynamics and Control Conference (L4DC) 2024

We have presented the theory, implementation, and benchmark results for the one-electronic variant of spin-free exact two-component (SFX2C1e) linear response coupled cluster (LRCCSD) theory for static and dynamic polarizabilities of atoms and molecules in the spin-adapted formulation. The resolution of identity (RI) approximation for two-electron integrals has been used to reduce the computational cost of the calculation and has been shown to have a negligible effect on accuracy. The calculated static and dynamic polarizability values agree very well with the more expensive X2C-LRCCSD and experimental results. Our calculated results show that accurate predictions of polarizabilities of atoms and molecules containing heavy atoms require the use of a large basis set containing an adequate number of diffuse functions, in addition to accounting for electron correlation and relativistic effects.

We demonstrate that the solutions of three-dimensional gravity obtained by gluing two copies of a spacetime across a junction constituted of a tensile string are in one-to-one correspondence with the solutions of the Nambu-Goto equation in the same spacetime up to a finite number of rigid deformations related to worldsheet and spacetime isometries. The non-linear Nambu-Goto equation satisfied by the average of the embedding coordinates of the junction emerges directly from the junction conditions along with the rigid deformations and corrections due to the tension. Therefore, the equivalence principle generalizes non-trivially to the string. Our results are valid both in three-dimensional flat and AdS spacetimes. In the context of AdS$_3$/CFT$_2$ correspondence, our setup could be used to describe a class of interfaces in the conformal field theory featuring relative time reparametrization at the interface which encodes the solution of the Nambu-Goto equation corresponding to the bulk junction., Comment: 31 pages, 1 figure; minor clarifications added, version to appear in JHEP

Infrastructure as Code (IaC) is a revolutionary approach which has gained significant prominence in the Industry. IaC manages and provisions IT infrastructure using machine-readable code by enabling automation, consistency across the environments, reproducibility, version control, error reduction and enhancement in scalability. However, IaC orchestration is often a painstaking effort which requires specialised skills as well as a lot of manual effort. Automation of IaC is a necessity in the present conditions of the Industry and in this survey, we study the feasibility of applying Large Language Models (LLM) to address this problem. LLMs are large neural network-based models which have demonstrated significant language processing abilities and shown to be capable of following a range of instructions within a broad scope. Recently, they have also been adapted for code understanding and generation tasks successfully, which makes them a promising choice for the automatic generation of IaC configurations. In this survey, we delve into the details of IaC, usage of IaC in different platforms, their challenges, LLMs in terms of code-generation aspects and the importance of LLMs in IaC along with our own experiments. Finally, we conclude by presenting the challenges in this area and highlighting the scope for future research., Comment: Accepted in ICON2023