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The Anomalous Viscous Fluid Dynamics (AVFD) framework is utilized to generate $^{197}_{79}Au+^{197}_{79}Au$, $^{96}_{44}Ru+^{96}_{44}Ru$, and $^{96}_{40}Zr+^{96}_{40}Zr$ collision events at $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV, aiming to investigate the Chiral Magnetic Effect (CME). The CME signal is modulated through the axial charge per entropy density ($n_5/s$), varied as 0.0, 0.1, and 0.2 to produce distinct data sets with varying CME signal strengths. Additionally, a 33$\%$ local charge conservation (LCC) is implemented in each event. These data sets are analyzed using CME-sensitive two- and three-particle correlators. Furthermore, the Sliding Dumbbell Method (SDM) is employed to identify potential CME-like events within each data set. The identified events exhibit characteristics consistent with CME. The CME fraction in these events is quantified while accounting for backgrounds derived from charge shuffle events and correlated background contributions. No enhancement in the CME signal is observed in $Ru+Ru$ collisions compared to $Zr+Zr$ collisions, despite the presence of four additional protons in $^{96}_{44}Ru$ nuclei compared to $^{96}_{40}Zr$ nuclei., Comment: 18 Pages, 10 Figures and 1 Table

Electrocardiogram (ECG) interpretation is a cornerstone of cardiac diagnostics. This paper explores a practical approach to enhance ECG image interpretation using the multimodal LLaMA 3.2 model. We used a parameter-efficient fine-tuning strategy, Low-Rank Adaptation (LoRA), specifically designed to boost the model's ability to understand ECG images and achieve better outcomes across a wide range of cardiac conditions. Our method is tailored for ECG analysis and leverages ECGInstruct, a large-scale instruction dataset with 1 Million samples. This dataset is a rich collection of synthesized ECG images, generated from raw ECG data from trusted open-source repositories like MIMIC-IV ECG and PTB-XL. Each ECG image in ECGInstruct comes with expert-written questions and detailed answers, covering diverse ECG interpretation scenarios, including complex cardiac conditions like Myocardial Infarction and Conduction Disturbances. Our fine-tuning approach efficiently adapts the LLaMA 3.2 model (built upon LLaMA 3) by integrating low-rank adaptation techniques, focusing on efficiency by updating only a small set of parameters, specifically ignoring the `lm_head` and `embed_tokens` layers. This paper details the model setup, our efficient fine-tuning method, and implementation specifics. We provide a thorough evaluation through extensive experiments, demonstrating the effectiveness of our method across various ECG interpretation tasks. The results convincingly show that our parameter-efficient LoRA fine-tuning achieves excellent performance in ECG image interpretation, significantly outperforming baseline models and reaching accuracy comparable to or exceeding traditional CNN-based methods in identifying a wide range of cardiac abnormalities, including over 70 conditions from the PTB-XL dataset.

We study the boundary behaviour of the Fefferman--Szeg\"o metric and several associated invariants in a $C^\infty$-smoothly bounded strictly pseudoconvex domain., Comment: 14 pages

Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constitutional Classifiers: safeguards trained on synthetic data, generated by prompting LLMs with natural language rules (i.e., a constitution) specifying permitted and restricted content. In over 3,000 estimated hours of red teaming, no red teamer found a universal jailbreak that could extract information from an early classifier-guarded LLM at a similar level of detail to an unguarded model across most target queries. On automated evaluations, enhanced classifiers demonstrated robust defense against held-out domain-specific jailbreaks. These classifiers also maintain deployment viability, with an absolute 0.38% increase in production-traffic refusals and a 23.7% inference overhead. Our work demonstrates that defending against universal jailbreaks while maintaining practical deployment viability is tractable.

The purpose of this article is to investigate the boundary behaviour of the Kobayashi--Fuks metric and several associated invariants on strictly pseudoconvex domains in the paradigm of scaling. This approach allows us to examine more invariants, such as the canonical invariant, holomorphic sectional curvature, and Ricci curvature of this metric, in a manner that extends and refines some existing analysis., Comment: 14 pages

Interactive agents capable of understanding and executing instructions in the physical world have long been a central goal in AI research. The Minecraft Collaborative Building Task (MCBT) provides one such setting to work towards this goal (Narayan-Chen, Jayannavar, and Hockenmaier 2019). It is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment. We focus on the challenging Builder Action Prediction (BAP) subtask of predicting correct action sequences in a given multimodal game context with limited training data (Jayannavar, Narayan-Chen, and Hockenmaier 2020). We take a closer look at evaluation and data for the BAP task, discovering key challenges and making significant improvements on both fronts to propose BAP v2, an upgraded version of the task. This will allow future work to make more efficient and meaningful progress on it. It comprises of: (1) an enhanced evaluation benchmark that includes a cleaner test set and fairer, more insightful metrics, and (2) additional synthetic training data generated from novel Minecraft dialogue and target structure simulators emulating the MCBT. We show that the synthetic data can be used to train more performant and robust neural models even with relatively simple training methods. Looking ahead, such data could also be crucial for training more sophisticated, data-hungry deep transformer models and training/fine-tuning increasingly large LLMs. Although modeling is not the primary focus of this work, we also illustrate the impact of our data and training methodologies on a simple LLM- and transformer-based model, thus validating the robustness of our approach, and setting the stage for more advanced architectures and LLMs going forward.

We explore the existence of closed geodesics and geodesic spirals for the Szeg\"o metric in a $C^{\infty}$-smoothly bounded strongly pseudoconvex domain $\Omega\subset\mathbb{C}^n$, which is not simply connected for $n \geq 2$., Comment: 10 pages

Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also ``think in space'' from videos? We present a novel video-based visual-spatial intelligence benchmark (VSI-Bench) of over 5,000 question-answer pairs, and find that MLLMs exhibit competitive - though subhuman - visual-spatial intelligence. We probe models to express how they think in space both linguistically and visually and find that while spatial reasoning capabilities remain the primary bottleneck for MLLMs to reach higher benchmark performance, local world models and spatial awareness do emerge within these models. Notably, prevailing linguistic reasoning techniques (e.g., chain-of-thought, self-consistency, tree-of-thoughts) fail to improve performance, whereas explicitly generating cognitive maps during question-answering enhances MLLMs' spatial distance ability., Comment: Project page: https://vision-x-nyu.github.io/thinking-in-space.github.io/

Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including Troubleshooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity of legacy sources and the pressures of strict time constraints. To enhance the efficiency of on-call engineers (OCEs) and streamline their daily workflows, we introduced DECO -- a comprehensive framework for developing, deploying, and managing enterprise-grade chatbots tailored to improve productivity in engineering routines. This paper details the design and implementation of the DECO framework, emphasizing its innovative NL2SearchQuery functionality and a hierarchical planner. These features support efficient and customized retrieval-augmented-generation (RAG) algorithms that not only extract relevant information from diverse sources but also select the most pertinent toolkits in response to user queries. This enables the addressing of complex technical questions and provides seamless, automated access to internal resources. Additionally, DECO incorporates a robust mechanism for converting unstructured incident logs into user-friendly, structured guides, effectively bridging the documentation gap. Feedback from users underscores DECO's pivotal role in simplifying complex engineering tasks, accelerating incident resolution, and bolstering organizational productivity. Since its launch in September 2023, DECO has demonstrated its effectiveness through extensive engagement, with tens of thousands of interactions from hundreds of active users across multiple organizations within the company.

Violence detection in surveillance videos is a critical task for ensuring public safety. As a result, there is increasing need for efficient and lightweight systems for automatic detection of violent behaviours. In this work, we propose an effective method which leverages human skeleton key-points to capture inherent properties of violence, such as rapid movement of specific joints and their close proximity. At the heart of our method is our novel Dynamic Interaction Feature Extraction Module (DIFEM) which captures features such as velocity, and joint intersections, effectively capturing the dynamics of violent behavior. With the features extracted by our DIFEM, we use various classification algorithms such as Random Forest, Decision tree, AdaBoost and k-Nearest Neighbor. Our approach has substantially lesser amount of parameter expense than the existing state-of-the-art (SOTA) methods employing deep learning techniques. We perform extensive experiments on three standard violence recognition datasets, showing promising performance in all three datasets. Our proposed method surpasses several SOTA violence recognition methods.

As command-line interfaces remain integral to high-performance computing environments, the risk of exploitation through stealthy and complex command-line abuse grows. Conventional security solutions struggle to detect these anomalies due to their context-specific nature, lack of labeled data, and the prevalence of sophisticated attacks like Living-off-the-Land (LOL). To address this gap, we introduce the Scalable Command-Line Anomaly Detection Engine (SCADE), a framework that combines global statistical models with local context-specific analysis for unsupervised anomaly detection. SCADE leverages novel statistical methods, including BM25 and Log Entropy, alongside dynamic thresholding to adaptively detect rare, malicious command-line patterns in low signal-to-noise ratio (SNR) environments. Experimental results show that SCADE achieves above 98% SNR in identifying anomalous behavior while minimizing false positives. Designed for scalability and precision, SCADE provides an innovative, metadata-enriched approach to anomaly detection, offering a robust solution for cybersecurity in high-computation environments. This work presents SCADE's architecture, detection methodology, and its potential for enhancing anomaly detection in enterprise systems. We argue that SCADE represents a significant advancement in unsupervised anomaly detection, offering a robust, adaptive framework for security analysts and researchers seeking to enhance detection accuracy in high-computation environments., Comment: Updated title and abstract for broader scope. Submitted to ACM CODASPY (The 15th ACM Conference on Data and Application Security and Privacy) Conference

Differential privacy (DP) has become the gold standard for privacy-preserving data analysis, but implementing it correctly has proven challenging. Prior work has focused on verifying DP at a high level, assuming the foundations are correct and a perfect source of randomness is available. However, the underlying theory of differential privacy can be very complex and subtle. Flaws in basic mechanisms and random number generation have been a critical source of vulnerabilities in real-world DP systems. In this paper, we present SampCert, the first comprehensive, mechanized foundation for differential privacy. SampCert is written in Lean with over 12,000 lines of proof. It offers a generic and extensible notion of DP, a framework for constructing and composing DP mechanisms, and formally verified implementations of Laplace and Gaussian sampling algorithms. SampCert provides (1) a mechanized foundation for developing the next generation of differentially private algorithms, and (2) mechanically verified primitives that can be deployed in production systems. Indeed, SampCert's verified algorithms power the DP offerings of Amazon Web Services (AWS), demonstrating its real-world impact. SampCert's key innovations include: (1) A generic DP foundation that can be instantiated for various DP definitions (e.g., pure, concentrated, R\'enyi DP); (2) formally verified discrete Laplace and Gaussian sampling algorithms that avoid the pitfalls of floating-point implementations; and (3) a simple probability monad and novel proof techniques that streamline the formalization. To enable proving complex correctness properties of DP and random number generation, SampCert makes heavy use of Lean's extensive Mathlib library, leveraging theorems in Fourier analysis, measure and probability theory, number theory, and topology.

Qubits that are intrinsically insensitive to depolarization and dephasing errors promise to significantly reduce the overhead of fault-tolerant quantum computing. At their optimal operating points, the logical states of these qubits exhibit both exponentially suppressed matrix elements and sweet spots in energy dispersion, rendering the qubits immune to depolarization and dephasing, respectively. We introduce a multimode qubit, the protomon, encoded in a fluxonium molecule circuit. Compared to the closely related $0$-$\pi$ qubit, the protomon offers several advantages in theory: resilience to circuit parameter disorder, minimal dephasing from intrinsic harmonic modes, and no dependence on static offset charge. As a proof of concept, we realize four protomon qubits. By tuning the qubits to various operating points identified with calibrated two-tone spectroscopy, we measure depolarization times ranging from 64 to 73 $\mu$s and dephasing times between 0.2 to 0.5 $\mu$s for one selected qubit. The discrepancy between the relatively short measured coherence times and theoretical predictions is not fully understood. This calls for future studies investigating the limiting noise factors, informing the direction for improving coherence times of the protomon qubit., Comment: 13 pages, 6 figures

Recent observations have revealed a super-virial temperature gas phase at log(T/K) $\sim7$ in the Milky Way, challenging existing galaxy-formation models. This hot gas phase was discovered toward extragalactic absorption sightlines and blank-sky emission fields, both at high galactic latitudes. The location of this hot component is unknown; is it in the extended circumgalactic medium (CGM) or in the interstellar medium (ISM) instead? We analyzed X-ray spectra from Chandra's High-Energy Transmission Grating (HETG) observations of 27 Galactic X-ray binaries (XRBs) to investigate whether the hot gas component is present in the ISM. We searched for absorption lines of SXVI K$\alpha$, SiXIV K$\alpha$, and NeX K$\alpha$, which are the tell-tale signatures of the hot gas and which have been detected toward extragalactic sightlines. Of the 27 targets, these lines were detected in the spectra of only 7, with two sources displaying broad line features likely intrinsic to the XRB systems. Additionally, most of the detected lines are time-variable, reinforcing their likely association with the XRBs. Our results suggest that the super-virial temperature gas is not a widespread component of the ISM but may instead be located in extraplanar regions or the extended CGM, in line with some recent simulation results., Comment: Accepted in the Astrophysical Journal (ApJ), 4 Figures and 4 tables

Edge detection has been one of the most difficult challenges in computer vision because of the difficulty in identifying the borders and edges from the real-world images including objects of varying kinds and sizes. Methods based on ensemble learning, which use a combination of backbones and attention modules, outperformed more conventional approaches, such as Sobel and Canny edge detection. Nevertheless, these algorithms are still challenged when faced with complicated scene photos. In addition, the identified edges utilizing the current methods are not refined and often include incorrect edges. In this work, we used a Cascaded Ensemble Canny operator to solve these problems and detect the object edges. The most difficult Fresh and Rotten and Berkeley datasets are used to test the suggested approach in Python. In terms of performance metrics and output picture quality, the acquired results outperform the specified edge detection networks, Comment: 2 Pages and 2 Figures

Earthquakes cause catastrophic damage to buildings and loss of human life. Civil engineers across the globe design earthquake-resistant buildings to minimize this damage. Conventionally, the structures are designed to resist the translational motions caused by an earthquake. However, with the increasing evidence of rotational ground motions in addition to the translational ground motions due to earthquakes, there is a crucial need to identify if these additional components have an impact on the existing structural design strategies. In this regard, the present study makes a novel attempt to obtain the dynamic properties of a large-scale prototype prestressed reinforced concrete bridge structure using six component (6C) ground motions. The structure is instrumented with conventional translational seismic sensors, rotational sensors and newly developed six-component sensors under operating and externally excited conditions. The recorded data is used to carry out Operational Modal Analysis and Experimental Modal Analysis of the bridge. Modal analysis using the rotational measurements shows that the expected location of maximum rotations on the bridge differs from the maximum translations. Therefore, further understanding the behavior of rotational motions is necessary for developing earthquake-resistant structural design strategies

We study several intrinsic properties of the Carath\'eodory and Szeg\"o metrics on finitely connected planar domains. Among them are the existence of closed geodesics and geodesic spirals, boundary behaviour of Gaussian curvatures, and $L^2$-cohomology. A formula for the Szeg\"o metric in terms of the Weierstrass $\wp$-function is obtained. Variations of these metrics and their Gaussian curvatures on planar annuli are also studied. Consequently, we obtain optimal universal upper bounds for their Gaussian curvatures and show that no universal lower bounds exist for their Gaussian curvatures. Moreover, it follows that there are domains where the Gaussian curvature of the Szeg\"o metric assumes both negative and positive values. Lastly, it is also observed that there is no universal upper bound for the ratio of the Szeg\"o and Carath\'eodory metrics., Comment: 28 pages, corrected typos

We consider the problem of developing interpretable and computationally efficient matrix decomposition methods for matrices whose entries have bounded support. Such matrices are found in large-scale DNA methylation studies and many other settings. Our approach decomposes the data matrix into a Tucker representation wherein the number of columns in the constituent factor matrices is not constrained. We derive a computationally efficient sampling algorithm to solve for the Tucker decomposition. We evaluate the performance of our method using three criteria: predictability, computability, and stability. Empirical results show that our method has similar performance as other state-of-the-art approaches in terms of held-out prediction and computational complexity, but has significantly better performance in terms of stability to changes in hyper-parameters. The improved stability results in higher confidence in the results in applications where the constituent factors are used to generate and test scientific hypotheses such as DNA methylation analysis of cancer samples., Comment: 33 pages, 18 figures

Despite recent advances in multimodal large language models (MLLMs), their development has predominantly focused on English- and western-centric datasets and tasks, leaving most of the world's languages and diverse cultural contexts underrepresented. This paper introduces Pangea, a multilingual multimodal LLM trained on PangeaIns, a diverse 6M instruction dataset spanning 39 languages. PangeaIns features: 1) high-quality English instructions, 2) carefully machine-translated instructions, and 3) culturally relevant multimodal tasks to ensure cross-cultural coverage. To rigorously assess models' capabilities, we introduce PangeaBench, a holistic evaluation suite encompassing 14 datasets covering 47 languages. Results show that Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts. Ablation studies further reveal the importance of English data proportions, language popularity, and the number of multimodal training samples on overall performance. We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs, promoting equity and accessibility across a broader linguistic and cultural spectrum., Comment: 54 pages, 27 figures

Gravitational waves from inspiralling neutron stars carry information about matter at extreme gravity and density. The binary neutron star (BNS) event GW170817 provided, for the first time, insight into dense matter through this window. Since then, another BNS (GW190425) and several neutron star-black hole events have been detected, although the tidal measurements were not expected to be well-constrained from them. Collective information regarding the behavior of nuclear matter at extreme densities can be done by performing a joint population inference for the masses, spins, and equation-of-state [1] to enable better understanding. This population inference, in turn, relies on accurate estimates of intrinsic parameters of individual events. In this study, we investigate how the differences in parameter inference of BNS events using different waveform models can affect the eventual inference of the nuclear equation-of-state. We use the state-of-the-art model TEOBResumS with IMRPhenomD NRTidalv2 as a comparison model., Comment: 13 pages, 10 figures

Prediction of failures in real-world robotic systems either requires accurate model information or extensive testing. Partial knowledge of the system model makes simulation-based failure prediction unreliable. Moreover, obtaining such demonstrations is expensive, and could potentially be risky for the robotic system to repeatedly fail during data collection. This work presents a novel three-step methodology for discovering failures that occur in the true system by using a combination of a limited number of demonstrations from the true system and the failure information processed through sampling-based testing of a model dynamical system. Given a limited budget $N$ of demonstrations from true system and a model dynamics (with potentially large modeling errors), the proposed methodology comprises of a) exhaustive simulations for discovering algorithmic failures using the model dynamics; b) design of initial $N_1$ demonstrations of the true system using Bayesian inference to learn a Gaussian process regression (GPR)-based failure predictor; and c) iterative $N - N_1$ demonstrations of the true system for updating the failure predictor. To illustrate the efficacy of the proposed methodology, we consider: a) the failure discovery for the task of pushing a T block to a fixed target region with UR3E collaborative robot arm using a diffusion policy; and b) the failure discovery for an F1-Tenth racing car tracking a given raceline under an LQR control policy., Comment: 8 pages, 7 figures

The high-precision measurements of exoplanet transit light curves that are now available contain information about the planet properties, their orbital parameters, and stellar limb darkening (LD). Recent 3D magneto-hydrodynamical (MHD) simulations of stellar atmospheres have shown that LD depends on the photospheric magnetic field, and hence its precise determination can be used to estimate the field strength. Among existing LD laws, the uses of the simplest ones may lead to biased inferences, whereas the uses of complex laws typically lead to a large degeneracy among the LD parameters. We have developed a novel approach in which we use a complex LD model but with second derivative regularisation during the fitting process. Regularisation controls the complexity of the model appropriately and reduces the degeneracy among LD parameters, thus resulting in precise inferences. The tests on simulated data suggest that our inferences are not only precise but also accurate. This technique is used to re-analyse 43 transit light curves measured by the NASA Kepler and TESS missions. Comparisons of our LD inferences with the corresponding literature values show good agreement, while the precisions of our measurements are better by up to a factor of 2. We find that 1D non-magnetic model atmospheres fail to reproduce the observations while 3D MHD simulations are qualitatively consistent. The LD measurements, together with MHD simulations, confirm that Kepler-17, WASP-18, and KELT-24 have relatively high magnetic fields ($>200$ G). This study paves the way for estimating the stellar surface magnetic field using the LD measurements., Comment: 14 pages, 12 figures (including 2 in the appendix), 2 tables, accepted for publication in MNRAS

We demonstrate that when people use large language models (LLMs) to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is. While personalized recommendations are often desired by users, it can be difficult in practice to distinguish cases of bias from cases of personalization: we find that models generate racially stereotypical recommendations regardless of whether the user revealed their identity intentionally through explicit indications or unintentionally through implicit cues. We argue that chatbots ought to transparently indicate when recommendations are influenced by a user's revealed identity characteristics, but observe that they currently fail to do so. Our experiments show that even though a user's revealed identity significantly influences model recommendations (p < 0.001), model responses obfuscate this fact in response to user queries. This bias and lack of transparency occurs consistently across multiple popular consumer LLMs (gpt-4o-mini, gpt-4-turbo, llama-3-70B, and claude-3.5) and for four American racial groups.

Understanding the evolution of radial sizes and instantaneous expansion speeds of coronal mass ejections (CMEs) is crucial for assessing their impact duration on Earth's environment. We introduce a non-conventional approach to derive the CME's radial sizes and expansion speeds at different instances during its passage over a single-point in situ spacecraft. We also estimate the CME's radial sizes and expansion speeds during its journey from the Sun to 1 AU using the 3D kinematics of different CME features, including the leading edge (LE), center, and trailing edge (TE). The continuous 3D kinematics of the CME is estimated by employing the GCS and SSSE reconstruction methods on multi-point observations from coronagraphs and heliospheric imagers combined with the drag-based model. We choose the 2010 April 3 CME as a suitable case for our study, promising a more accurate comparison of its remote and in situ observations. We show that the introduced non-conventional approach can provide better accuracy in estimating radial sizes and instantaneous expansion speeds of CMEs at different instances. We examine the aspect ratio of the CME, which influences its expansion behavior and shows the discrepancy between its value in the corona and interplanetary medium. Our study highlights significant inconsistencies in the arrival time, radial size, and expansion speed estimates obtained from remote and in situ observations. We advocate for future studies leveraging multi-spacecraft in situ observations and our non-conventional approach to analyze them to improve the comprehension of CME dynamics in the solar wind., Comment: 16 pages, 9 figures; accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

The Josephson diode effect (JDE), characterized by a unidirectional supercurrent across a Josephson junction, arises from non-reciprocal critical currents typically controlled by magnetic fields. In this study, we demonstrate JDE controlled by local electric fields, achieving complete polarity reversal through gate voltages on epitaxial Al-InAs Josephson junctions. Our observations reveal intricate effects of in-plane magnetic fields and gate voltages on the JDE. We develop a theoretical model that considers the coherent interplay between finite Cooper-pair momentum (fCPM) and anisotropic spin-orbit coupling (SOC), identifying that fCPM dominates JDE at low in-plane magnetic fields, while SOC governs its polarity reversal at high fields. This model aligns well with experimental data, illustrating precise electric control of JDE polarity through anisotropic SOC. These findings introduce a novel approach to controlling superconducting circuits, paving a route toward advanced applications in superconducting quantum devices., Comment: 42 pages, 19 figures, 1 table

Correspondence analysis, multiple correspondence analysis and their discriminant counterparts (i.e., discriminant simple correspondence analysis and discriminant multiple correspondence analysis) are methods of choice for analyzing multivariate categorical data. In these methods, variables are integrated into optimal components computed as linear combinations whose weights are obtained from a generalized singular value decomposition (GSVD) that integrates specific metric constraints on the rows and columns of the original data matrix. The weights of the linear combinations are, in turn, used to interpret the components, and this interpretation is facilitated when components are 1) pairwise orthogonal and 2) when the values of the weights are either large or small but not intermediate-a pattern called a simple or a sparse structure. To obtain such simple configurations, the optimization problem solved by the GSVD is extended to include new constraints that implement component orthogonality and sparse weights. Because multiple correspondence analysis represents qualitative variables by a set of binary variables, an additional group constraint is added to the optimization problem in order to sparsify the whole set representing one qualitative variable. This new algorithm-called group-sparse GSVD (gsGSVD)-integrates these constraints via an iterative projection scheme onto the intersection of subspaces where each subspace implements a specific constraint. In this paper, we expose this new algorithm and show how it can be adapted to the sparsification of simple and multiple correspondence analysis, and illustrate its applications with the analysis of four different data sets-each illustrating the sparsification of a particular CA-based analysis.

To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity into soft robots currently include appending rigid printed circuit boards (PCBs) to the robot, integrating soft logic gates, and exploiting material responses for material-embedded computation. Although promising, these approaches introduce limitations such as rigidity, tethers, or low logic gate density. The field of stretchable electronics has sought to solve these challenges, but a complete pipeline for direct integration of single-board computers, microcontrollers, and other complex circuitry into soft robots has remained elusive. We present a generalized method to translate any complex two-layer circuit into a soft, stretchable form. This enabled the creation of stretchable single-board microcontrollers (including Arduinos) and other commercial circuits (including Sparkfun circuits), without design simplifications. As demonstrations of the method's utility, we embed highly stretchable (>300% strain) Arduino Pro Minis into the bodies of multiple soft robots. This makes use of otherwise inert structural material, fulfilling the promise of the stretchable electronics field to integrate state-of-the-art computational power into robust, stretchable systems during active use., Comment: 45 pages, 19 figures total. Main text is 28 pages with 6 figures. The rest is supplementary

This paper concerns the propagation of high frequency wave-beams in highly turbulent atmospheres. Using a paraxial model of wave propagation, we show in the long-distance weak-coupling regime that the wavefields are approximately described by a complex Gaussian field whose scintillation index is unity. This provides a model of the speckle formation observed in many practical settings. The main step of the derivation consists in showing that closed-form moment equations in the It\^o-Schr\"odinger regime are still approximately satisfied in the paraxial regime. The rest of the proof is then an extension of results derived in [Bal, G. and Nair, A., arXiv:2402.17107.]

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement., Comment: Published in Journal of Field Robotics

This study presents particle-resolved direct numerical simulations using three-dimensional body-fitted hexahedral meshes to investigate the aerodynamic force and torque coefficients of non-spherical particles in compressible flows. The simulations focus on three particle shapes: a prolate spheroid, an oblate spheroid, and a rod-like particle, across a range of Mach numbers (0.3 to 2.0), angles of attack (0 degrees to 90 degrees), and particle Reynolds numbers (100 to 300). Results indicate that the particle shape significantly impacts the aerodynamic forces on a particle in a compressible flow, with oblate spheroids exhibiting the highest drag, lift, and torque values. Correlations for these aerodynamic coefficients of the particles in a compressible flow are developed and validated. These correlations advance multiphase flow modeling by improving the accuracy of point-particle simulations for non-spherical particles in compressible flows.

V. Matache (J. Operator Theory 73(1):243--264, 2015) raised an open problem about characterizing composition operators $C_{\phi}$ on the Hardy space $H^2$ and nonzero singular measures $\mu_1$, $\mu_2$ on the unit circle such that $C_{\phi}({S_{\mu_1}} H^2)\subseteq {S_{\mu_2}} H^2,$ where $S_{\mu_i}$ denotes the singular inner function corresponding to the measure $\mu_i,i=1,2$. In this article, we consider this problem in a more general setting. We characterize holomorphic self maps $\phi$ of the unit disk $\mathbb{D}$ and inner functions $\theta_1, \theta_2$ such that $C_{\phi}(\theta_1 H^p)\subseteq \theta_2 H^p,$ for $p>0$. Emphasis is given to Blaschke products and singular inner functions as a special case. We also give an another measure-theoretic characterization to above question when $\phi$ is an elliptic automorphism. For a given Blaschke product $\theta$, we discuss about finding all self maps $\phi$ such that $\theta H^p$ is invariant under $C_\phi$., Comment: 15 pages

Optical networks play a crucial role in todays digital topography, enabling the high-speed and reliable transmission of vast amounts of data over optical fibre for long distances. This paper provides an overview of optical networks, especially emphasising on their evolution with time.

Topology and superconductivity, two distinct phenomena offer unique insight into quantum properties and their applications in quantum technologies, spintronics, and sustainable energy technologies if system can be found where they coexist. Tin (Sn) plays a pivotal role here as an element due to its two structural phases, $\alpha$-Sn and $\beta$-Sn, exhibiting topological characteristics ($\alpha$-Sn) and superconductivity ($\beta$-Sn). In this study we show how precise control of $\alpha$ and $\beta$ phases of Sn thin films can be achieved by using molecular beam epitaxy grown buffer layers with systematic control over the lattice parameter. The resulting Sn films showed either $\beta$-Sn or $\alpha$-Sn phases as the lattice constant of the buffer layer was varied from 6.10 A to 6.48 A, covering the range between GaSb (closely matched to InAs) and InSb. The crystal structures of the $\alpha$- and $\beta$-Sn films were characterized by x-ray diffraction and confirmed by Raman spectroscopy and scanning transmission electron microscopy. The smooth and continuous surface morphology of the Sn films was validated using atomic force microscopy. The characteristics of $\alpha$- and $\beta$-Sn phases were further verified using electrical transport measurements by observing resistance drop near 3.7 K for superconductivity of the $\beta$-Sn phase and Shubnikov-de Haas oscillations for the $\alpha$-Sn phase. Density functional theory calculations showed that the stability of the Sn phases is highly dependent on lattice strain, with $\alpha$-Sn being more stable under tensile strain and $\beta$-Sn becoming favorable under compressive strain, which is in good agreement with experimental observations. Hence, this study sheds light on controlling Sn phases through lattice engineering, enabling innovative applications in quantum technologies and beyond.

Hot, \logT\ $\sim$ 7.5, gas was recently discovered in the Milky Way in extragalactic sightlines. In order to determine its location, here we present sightlines to Galactic X-ray binaries (XRBs) passing through the Interstellar Medium (ISM). In this pilot study we investigate absorption features of \SXVI, \SiXIV, and \NeX\ in the spectra of three XRBs, namely 4U 1735-44, 4U 1820-30, and Cyg X-2, using Chandra High Energy Transmission Grating archival observations. We do not detect any of these lines. {We determine the 2$\sigma$ upper limit for the equivalent widths of the undetected absorption lines and the column densities of the corresponding ions.} We note that the 2$\sigma$ upper limits for \SXVI\ \Ka\ and \SiXIV\ \Ka\ are an order of magnitude smaller than those previously detected in the extragalactic sightlines. Our finding suggests that if any gas at \logT\ $>7$ is present in the Galactic ISM, it is unlikely to be ubiquitous. This is an important result because it implies that \SXVI, \SiXIV\ {and \NeX\ }absorption detected in extragalactic sightlines is not from the ISM, but is likely from a hot gas phase in the extraplanar region beyond the ISM or in the extended CGM.

Our study focuses on characterizing the highly ionized gas within the Milky Way's (MW) Circumgalactic Medium (CGM) that gives rise to ionic transitions in the X-ray band 2 - 25 \AA. Utilizing stacked \Chandra/\ACISS\ \MEG\ and \LETG\ spectra toward QSO sightlines, we employ the self-consistent hybrid ionization code PHASE to model our data. The stacked spectra are optimally described by three distinct gas phase components: a \warm\ (\logT\ $\sim$ 5.5), \warmhot\ (\logT\ $\sim 6$), and \hot\ (\logT\ $\sim$ 7.5) components. These findings confirm the presence of the \hot\ component in the MW's CGM indicating its coexistence with a \warm\ and a \warmhot\ gas phases. We find this \hot\ component to be homogeneous in temperature but inhomogeneous in column density. The gas in the \hot\ component requires over-abundances relative to solar to be consistent with the Dispersion Measure (DM) from the Galactic halo reported in the literature. {For the hot phase we estimated a DM = $55.1^{+29.9}_{-23.7}$ pc cm$^{-3}$}. We conclude that this phase is either enriched in Oxygen, Silicon, and Sulfur, or has metallicity {over 6} times solar value, or a combination of both. We do not detect Fe L-shell absorption lines, implying O/Fe $\geq$ 4. The non-solar abundance ratios found in the super-virial gas component in the Galactic halo suggest that this phase arises from Galactic feedback.

In this paper, we present an inference method for determining neutron star parameters and constraining the nuclear equation of state (EOS) using the RIFT parameter inference engine. We incorporate externally-produced prior information about the EOS to improve the accuracy and efficiency of the inference process. We apply this method to the GW170817 event and assess its performance. Our results demonstrate the effectiveness of incorporating prior EOS information in the inference process, leading to sharper conclusions and more rapid inference on new detections. This approach has the potential to enhance our understanding of neutron stars and the nuclear EOS in future gravitational wave observations.

In recent years, the clandestine nature of darknet activities has presented an escalating challenge to cybersecurity efforts, necessitating sophisticated methods for the detection and classification of network traffic associated with these covert operations. The system addresses the significant challenge of class imbalance within Darknet traffic datasets, where malicious traffic constitutes a minority, hindering effective discrimination between normal and malicious behavior. By leveraging boosting algorithms like AdaBoost and Gradient Boosting coupled with decision trees, this study proposes a robust solution for network traffic classification. Boosting algorithms ensemble learning corrects errors iteratively and assigns higher weights to minority class instances, complemented by the hierarchical structure of decision trees. The additional Feature Selection which is a preprocessing method by utilizing Information Gain metrics, Fisher's Score, and Chi-Square test selection for features is employed. Rigorous experimentation with diverse Darknet traffic datasets validates the efficacy of the proposed multistage classifier, evaluated through various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive solution for accurate detection and classification of Darknet activities., Comment: 6 pages, 5 figures

Recent JWST observations of the sub-Neptune GJ 1214 b suggest that it hosts a high-metallicity (>100x solar), hazy atmosphere. Emission spectra of the planet show molecular absorption features, most likely due to atmospheric H2O. In light of this new information, we conduct a thorough reevaluation of the planet's internal structure. We consider interior models with mixed H/He/H2O envelopes of varying composition, informed by atmospheric constraints from the JWST phase curve, in order to determine possible bulk compositions and internal structures. Self-consistent atmospheric models consistent with the JWST observations are used to set boundary conditions for the interior. We find that a total envelope mass fraction of at least 8.1% is required to explain the planet's mass and radius. Regardless of H2O content, the maximum H/He mass fraction of the planet is 5.8%. We find that a 1:1 ice-to-rock ratio along with 3.4-4.8% H/He is also a permissible solution. In addition, we consider a pure H2O (steam) envelope and find that such a scenario is possible, albeit with a high ice-to-rock ratio of at least 3.76:1, which may be unrealistic from a planet formation standpoint. We discuss possible formation pathways for the different internal structures that are consistent with observations. Since our results depend strongly on the atmospheric composition and haze properties, more precise observations of the planet's atmosphere would allow for further constraints on its internal structure. This type of analysis can be applied to any sub-Neptune with atmospheric constraints to better understand its interior., Comment: Accepted for publication in ApJ Letters. 13 pages, 6 figures, 2 tables

The recent inference of sulphur dioxide (SO$_2$) in the atmosphere of the hot ($\sim$1100 K), Saturn-mass exoplanet WASP-39b from near-infrared JWST observations suggests that photochemistry is a key process in high temperature exoplanet atmospheres. This is due to the low ($<$1 ppb) abundance of SO$_2$ under thermochemical equilibrium, compared to that produced from the photochemistry of H$_2$O and H$_2$S (1-10 ppm). However, the SO$_2$ inference was made from a single, small molecular feature in the transmission spectrum of WASP-39b at 4.05 $\mu$m, and therefore the detection of other SO$_2$ absorption bands at different wavelengths is needed to better constrain the SO$_2$ abundance. Here we report the detection of SO$_2$ spectral features at 7.7 and 8.5 $\mu$m in the 5-12 $\mu$m transmission spectrum of WASP-39b measured by the JWST Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS). Our observations suggest an abundance of SO$_2$ of 0.5-25 ppm (1$\sigma$ range), consistent with previous findings. In addition to SO$_2$, we find broad water vapour absorption features, as well as an unexplained decrease in the transit depth at wavelengths longer than 10 $\mu$m. Fitting the spectrum with a grid of atmospheric forward models, we derive an atmospheric heavy element content (metallicity) for WASP-39b of $\sim$7.1-8.0 $\times$ solar and demonstrate that photochemistry shapes the spectra of WASP-39b across a broad wavelength range., Comment: Published in Nature

Large language models (LLMs) have gained significant attention due to their ability to mimic human language. Identifying texts generated by LLMs is crucial for understanding their capabilities and mitigating potential consequences. This paper analyzes datasets of varying text lengths: small, medium, and large. We compare the performance of machine learning algorithms on four datasets: (1) small (tweets from Election, FIFA, and Game of Thrones), (2) medium (Wikipedia introductions and PubMed abstracts), and (3) large (OpenAI web text dataset). Our results indicate that LLMs with very large parameters (such as the XL-1542 variant of GPT2 with 1542 million parameters) were harder (74%) to detect using traditional machine learning methods. However, detecting texts of varying lengths from LLMs with smaller parameters (762 million or less) can be done with high accuracy (96% and above). We examine the characteristics of human and machine-generated texts across multiple dimensions, including linguistics, personality, sentiment, bias, and morality. Our findings indicate that machine-generated texts generally have higher readability and closely mimic human moral judgments but differ in personality traits. SVM and Voting Classifier (VC) models consistently achieve high performance across most datasets, while Decision Tree (DT) models show the lowest performance. Model performance drops when dealing with rephrased texts, particularly shorter texts like tweets. This study underscores the challenges and importance of detecting LLM-generated texts and suggests directions for future research to improve detection methods and understand the nuanced capabilities of LLMs.

Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum resources. In this work, we will use eigenvector continuation (EC) to build a subspace from the low-lying states of a set of Hamiltonians. The basis vectors are prepared using truncated versions of standard state preparation methods such as imaginary time evolution (ITE), adiabatic state preparation (ASP), and variational quantum eigensolver (VQE). By using these truncated methods combined with eigenvector continuation, we can directly improve upon them, obtaining more accurate ground state energies at a reduced cost. We use several spin systems to demonstrate convergence even when methods like ITE and ASP fail, such as ASP in the presence of level crossings and ITE with vanishing energy gaps. We also showcase the noise resilience of this approach beyond the gains already made by having shallower quantum circuits. Our findings suggest that eigenvector continuation can be used to improve existing state preparation methods in the near term., Comment: 20 pages, 20 figures

The study of nonclassical properties of two-mode quantum states is particularly useful in quantum information theory because of the possibilities of obtaining entanglement and other two-mode quantum correlations in these states. Here we have investigated the possibilities of the existence of nonclassicality in a two-mode New generalized binomial state (TMNGBS). Specifically two-mode antibunching, Quadrature squeezing, sum \& difference squeezing, and various entanglement criteria e.g Shchukin-Vogel entanglement criterion, the uncertainty relation of SU(1,1) Algebra and EPR entanglement criterion are explored in two mode particular example of quantum state named as New generalized binomial state. Earlier we studied nonclassicality in single-mode NGBS, here we are extending our study toward the two-mode version of a quantum state. Here we provide the general expressions of moments for a two-mode quantum state (Fock basis) and explore the quantification in a particular example NGBS. It is found that antibunching, squeezing, and SV entanglement are possible with different limits of depending parameters but the entanglement criteria (EPR, SU (1,1) algebra and Cauchy - Schwarz inequality based)for NGBS are not possible. This study opens up the possibility of exploring the two-mode nonclassicality in other states too., Comment: 9 pages and 11 Figures