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We study the semi-classical dynamics of a scalar field in the background of a black hole in an asymptotically AdS (AAdS) spacetime, in the framework of the Hamiltonian formulation of General Relativity. The small diffeomorphism (gauge) symmetries generated by the Hamiltonian and momentum constraints are completely fixed by the maximal slicing and spatial harmonic/Dirac gauge conditions after which the residual phase space degrees of freedom are gauge invariant. While many of our results are valid for AAdS$_{d+1}$ spacetimes, we mainly discuss the $d=2$ case of the static BTZ solution. We present the explicit solution for the smooth maximal slicing of the fully extended BTZ solution where the spatial slices cut across the horizons, asymptote to the usual Schwarzschild slices, do not include the past and future singularities, and for which the lapse remains non-zero at the bifurcate point. We also derive unique large diffeomorphisms that asymptote to time translations on both boundaries in the maximal slicing gauge. We present the solution of the scalar field wave equation in this gauge in terms of its boundary values which correspond to boundary CFT operators by the AdS/CFT dictionary. We explicitly construct the finite, time-dependent Hamiltonian in terms of a discrete set of mode functions of the scalar field that are smooth and differentiable across the horizons of the fully extended BTZ black hole. These modes mix the boundary operators from the two sides and are appropriate linear combinations of the Hartle-Hawking-Unruh modes. This Hamiltonian is an operator in the product of the two CFTs associated to the two boundaries and describes the time evolution of CFT operators. Our results are valid for evolution times smaller than the scrambling time during which the fully extended BTZ solution continues to be a valid saddle point of the quantum gravity path integral., Comment: 44 pages + 39 pages of appendices

Semantic maps are fundamental for robotics tasks such as navigation and manipulation. They also enable yield prediction and phenotyping in agricultural settings. In this paper, we introduce an efficient and scalable approach for active semantic mapping in horticultural environments, employing a mobile robot manipulator equipped with an RGB-D camera. Our method leverages probabilistic semantic maps to detect semantic targets, generate candidate viewpoints, and compute corresponding information gain. We present an efficient ray-casting strategy and a novel information utility function that accounts for both semantics and occlusions. The proposed approach reduces total runtime by 8% compared to previous baselines. Furthermore, our information metric surpasses other metrics in reducing multi-class entropy and improving surface coverage, particularly in the presence of segmentation noise. Real-world experiments validate our method's effectiveness but also reveal challenges such as depth sensor noise and varying environmental conditions, requiring further research., Comment: Preprint (under review)

This paper presents a comprehensive chatbot system designed to handle a wide range of audio-related queries by integrating multiple specialized audio processing models. The proposed system uses an intent classifier, trained on a diverse audio query dataset, to route queries about audio content to expert models such as Automatic Speech Recognition (ASR), Speaker Diarization, Music Identification, and Text-to-Audio generation. A 3.8 B LLM model then takes inputs from an Audio Context Detection (ACD) module extracting audio event information from the audio and post processes text domain outputs from the expert models to compute the final response to the user. We evaluated the system on custom audio tasks and MMAU sound set benchmarks. The custom datasets were motivated by target use cases not covered in industry benchmarks and included ACD-timestamp-QA (Question Answering) as well as ACD-temporal-QA datasets to evaluate timestamp and temporal reasoning questions, respectively. First we determined that a BERT based Intent Classifier outperforms LLM-fewshot intent classifier in routing queries. Experiments further show that our approach significantly improves accuracy on some custom tasks compared to state-of-the-art Large Audio Language Models and outperforms models in the 7B parameter size range on the sound testset of the MMAU benchmark, thereby offering an attractive option for on device deployment.

Sub-seasonal wind speed forecasts provide valuable guidance for wind power system planning and operations, yet the forecasting skills of surface winds decrease sharply after two weeks. However, large-scale variables exhibit greater predictability on this time scale. This study explores the potential of leveraging non-linear relationships between 500 hPa geopotential height (Z500) and surface wind speed to improve subs-seasonal wind speed forecasting skills in Europe. Our proposed framework uses a Multiple Linear Regression (MLR) or a Convolutional Neural Network (CNN) to regress surface wind speed from Z500. Evaluations on ERA5 reanalysis indicate that the CNN performs better due to their non-linearity. Applying these models to sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts, various verification metrics demonstrate the advantages of non-linearity. Yet, this is partly explained by the fact that these statistical models are under-dispersive since they explain only a fraction of the target variable variance. Introducing stochastic perturbations to represent the stochasticity of the unexplained part from the signal helps compensate for this issue. Results show that the perturbed CNN performs better than the perturbed MLR only in the first weeks, while the perturbed MLR's performance converges towards that of the perturbed CNN after two weeks. The study finds that introducing stochastic perturbations can address the issue of insufficient spread in these statistical models, with improvements from the non-linearity varying with the lead time of the forecasts.

We address the challenge of conducting inference for a categorical treatment effect related to a binary outcome variable while taking into account high-dimensional baseline covariates. The conventional technique used to establish orthogonality for the treatment effect from nuisance variables in continuous cases is inapplicable in the context of binary treatment. To overcome this obstacle, an orthogonal score tailored specifically to this scenario is formulated which is based on a variance-weighted projection. Additionally, a novel Bayesian framework is proposed to facilitate valid inference for the desired low-dimensional parameter within the complex framework of high-dimensional logistic regression. We provide uniform convergence results, affirming the validity of credible intervals derived from the posterior distribution. The effectiveness of the proposed method is demonstrated through comprehensive simulation studies and real data analysis.

Autonomous under-canopy navigation faces additional challenges compared to over-canopy settings - for example the tight spacing between the crop rows, degraded GPS accuracy and excessive clutter. Keypoint-based visual navigation has been shown to perform well in these conditions, however the differences between agricultural environments in terms of lighting, season, soil and crop type mean that a domain shift will likely be encountered at some point of the robot deployment. In this paper, we explore the use of Meta-Learning to overcome this domain shift using a minimal amount of data. We train a base-learner that can quickly adapt to new conditions, enabling more robust navigation in low-data regimes.

We introduce a novel unsteady shear protocol, which we name Rotary Shear (RS), where the flow and vorticity directions are continuously rotated around the velocity gradient direction by imposing two out-of-phase oscillatory shear (OS) in orthogonal directions. We perform numerical simulations of dense suspensions of rigid non-Brownian spherical particles at volume fractions ($\phi$) between 0.40 and 0.55 subject to this new RS protocol and compare to the classical OS protocol. We find that the suspension viscosity displays a similar non-monotonic response as the strain amplitude ($\gamma_0$) is increased: a minimum viscosity is found at an intermediate, volume-fraction dependent strain amplitude. However, the suspension dynamics is different in the new protocol. Unlike the OS protocol, suspensions under RS do not show self-adsorbing states at any $\gamma_0$ and do not undergo the reversible-irreversible transition: the stroboscropic particle dynamics are always diffusive, which we attribute to the fact that the RS protocol is irreversible. To validate this hypothesis, we introduce a reversible-RS (RRS) protocol, a combination of RS and OS, where we rotate the shear direction (as in RS) until it is instantaneously reversed (as in OS), and find the resulting rheology and dynamics to be closer to OS. Detailed microstructure analysis shows that both the OS and RRS protocols result in a contact-free, isotropic to an in-contact, anisotropic microstructure at the dynamically reversible-to-irreversible transition. The RS protocol does not render such a transition, and the dynamics remain diffusive with an in-contact, anisotropic microstructure for all strain amplitudes.

This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a commodity Android mobile CPU. Notably, our experiments show that Llama Guard 3-1B-INT4 attains comparable or superior safety moderation scores to its larger counterpart, Llama Guard 3-1B, despite being approximately 7 times smaller in size (440MB).

Blockchain networks are facing increasingly heterogeneous computational demands, and in response, protocol designers have started building specialized infrastructure to supply that demand. This paper introduces Resonance: a new kind of transaction fee mechanism for the general two-sided market setting (with users on one side and nodes on the other), where both sides of the market exhibit a high degree of heterogeneity. We allow users submitting transactions to have arbitrary valuations for inclusion, nodes responsible for executing transactions to incur arbitrary costs for running any bundle of transactions, and further allow for arbitrary additional constraints on what allocations are valid. These constraints can, for example, be used to prevent state conflicts by requiring transactions that utilize the same part of the network's state to not be executed in parallel. They also enable support for new transaction types, such as transactions that require multiple nodes for execution (e.g. to run multi-party computation for better transaction privacy). Resonance's design utilizes competition among sophisticated brokers to find individualized prices for each transaction and node. We show that at pure Nash equilibria, Resonance finds an efficient outcome and minimizes the need for strategization by users and nodes. It is also budget-balanced, individually rational for all parties, and computationally tractable.

Autonomous flapping-wing micro-aerial vehicles (FWMAV) have a host of potential applications such as environmental monitoring, artificial pollination, and search and rescue operations. One of the challenges for achieving these applications is the implementation of an onboard sensor suite due to the small size and limited payload capacity of FWMAVs. The current solution for accurate state estimation is the use of offboard motion capture cameras, thus restricting vehicle operation to a special flight arena. In addition, the small payload capacity and highly non-linear oscillating dynamics of FWMAVs makes state estimation using onboard sensors challenging due to limited compute power and sensor noise. In this paper, we develop a novel hardware-in-the-loop (HWIL) testing pipeline that recreates flight trajectories of the Harvard RoboBee, a 100mg FWMAV. We apply this testing pipeline to evaluate a potential suite of sensors for robust altitude and attitude estimation by implementing and characterizing a Complimentary Extended Kalman Filter. The HWIL system includes a mechanical noise generator, such that both trajectories and oscillatinos can be emulated and evaluated. Our onboard sensing package works towards the future goal of enabling fully autonomous control for micro-aerial vehicles., Comment: 12 pages, 7 figures, submitted to IJRR

We derive the equation for pressure within a neutron star, taking into account a non-zero cosmological constant ($\Lambda$). We then examine the stability of the neutron star's equilibrium state in the presence of cosmological constant. Our analysis shows that the theorem used to assess the stability of stellar structures at equilibrium remains applicable to neutron stars even when a cosmological constant is considered. We further numerically solve the stellar structure equations and determine the mass of neutron star using different equations of state (EOS). Moreover, we observe that the value of the cosmological constant ($\Lambda \geq 10^{-11} \rm m^{-2}$) causes a significant change in the mass-radius relationship of neutron stars., Comment: 12 pages, 4 figures

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical Engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M University, each answered by approximately 225 students. Both the LLM and TAs followed the same instructor-provided rubric to ensure grading consistency. We evaluated performance using Spearman's rank correlation coefficient and Root Mean Square Error (RMSE) to assess the alignment between rankings and the accuracy of scores assigned by GPT-4o and TAs under zero- and few-shot grading settings. In the zero-shot setting, GPT-4o demonstrated a strong correlation with TA grading, with Spearman's rank correlation coefficient exceeding 0.6 in seven out of ten datasets and reaching a high of 0.9387. Our analysis reveals that GPT-4o performs well when grading criteria are straightforward but struggles with nuanced answers, particularly those involving synonyms not present in the rubric. The model also tends to grade more stringently in ambiguous cases compared to human TAs. Overall, ChatGPT shows promise as a tool for grading conceptual questions, offering scalability and consistency., Comment: 21 pages, 21 figures

The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-ray emission from NGC 1275 during 2022 December and 2023 January. The second outburst, observed on 2023 January 10, was the more intense of the two, with flux reaching 58$\%$ of the Crab Nebula flux above 80 GeV. The differential energy spectrum measured between 80 GeV and 1.5 TeV can be described by a power law with a spectral index of $\Gamma = - 2.90 \pm 0.16_{stat}$ for both flaring events. The broadband spectral energy distribution derived from these flares, along with quasisimultaneous low-energy counterparts, suggests that the observed gamma-ray emission can be explained using a homogeneous single-zone synchrotron self-Compton model. The physical parameters derived from this model for both flaring states are similar. The intermediate state observed between two flaring episodes is explained by a lower Doppler factor or magnetic field, which subsequently returned to its previous value during the high-activity state observed on 2023 January 10., Comment: 7 Pages, 5 Figures, and 1 Table

While frontier large language models (LLMs) are capable tool-using agents, current AI systems still operate in a strict turn-based fashion, oblivious to passage of time. This synchronous design forces user queries and tool-use to occur sequentially, preventing the systems from multitasking and reducing interactivity. To address this limitation, we introduce asynchronous AI agents capable of parallel processing and real-time tool-use. Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting, integrated with automatic speech recognition and text-to-speech. Drawing inspiration from the concepts originally developed for real-time operating systems, this work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.

The functional linear regression model has been widely studied and utilized for dealing with functional predictors. In this paper, we study the Nystr\"om subsampling method, a strategy used to tackle the computational complexities inherent in big data analytics, especially within the domain of functional linear regression model in the framework of reproducing kernel Hilbert space. By adopting a Nystr\"om subsampling strategy, our aim is to mitigate the computational overhead associated with kernel methods, which often struggle to scale gracefully with dataset size. Specifically, we investigate a regularization-based approach combined with Nystr\"om subsampling for functional linear regression model, effectively reducing the computational complexity from $O(n^3)$ to $O(m^2 n)$, where $n$ represents the size of the observed empirical dataset and $m$ is the size of subsampled dataset. Notably, we establish that these methodologies will achieve optimal convergence rates, provided that the subsampling level is appropriately selected. We have also demonstrated a numerical example of Nystr\"om subsampling in the RKHS framework for the functional linear regression model., Comment: 16 pages, 1 figure

Digital payment systems have revolutionized financial transactions, offering unparalleled convenience and accessibility to users worldwide. However, the increasing popularity of these platforms has also attracted malicious actors seeking to exploit their vulnerabilities for financial gain. To address this challenge, robust and adaptable scam detection mechanisms are crucial for maintaining the trust and safety of digital payment ecosystems. This paper presents a comprehensive approach to scam detection, focusing on the Unified Payments Interface (UPI) in India, Google Pay (GPay) as a specific use case. The approach leverages Large Language Models (LLMs) to enhance scam classification accuracy and designs a digital assistant to aid human reviewers in identifying and mitigating fraudulent activities. The results demonstrate the potential of LLMs in augmenting existing machine learning models and improving the efficiency, accuracy, quality, and consistency of scam reviews, ultimately contributing to a safer and more secure digital payment landscape. Our evaluation of the Gemini Ultra model on curated transaction data showed a 93.33% accuracy in scam classification. Furthermore, the model demonstrated 89% accuracy in generating reasoning for these classifications. A promising fact, the model identified 32% new accurate reasons for suspected scams that human reviewers had not included in the review notes., Comment: 10 pages, 7 figures

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements., Comment: 5 pages, 4 figures

In this paper, we extend a result of Schwick concerning normality and sharing values in one complex variable for families of holomorphic curves taking values in $\mathbb{P}^n$. We consider wandering moving hyperplanes (i.e., depending on the respective holomorphic curve in the family under consideration), and establish a sufficient condition of normality concerning shared hyperplanes.

In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest in leveraging recent advances in deep learning to learn the mapping between velocity maps and seismic waveform images directly from data. Despite the variety of architectures explored in previous works, several open questions still remain unanswered such as the effect of latent space sizes, the importance of manifold learning, the complexity of translation models, and the value of jointly solving forward and inverse problems. We propose a unified framework to systematically characterize prior research in this area termed the Generalized Forward-Inverse (GFI) framework, building on the assumption of manifolds and latent space translations. We show that GFI encompasses previous works in deep learning for subsurface imaging, which can be viewed as specific instantiations of GFI. We also propose two new model architectures within the framework of GFI: Latent U-Net and Invertible X-Net, leveraging the power of U-Nets for domain translation and the ability of IU-Nets to simultaneously learn forward and inverse translations, respectively. We show that our proposed models achieve state-of-the-art (SOTA) performance for forward and inverse problems on a wide range of synthetic datasets, and also investigate their zero-shot effectiveness on two real-world-like datasets.

Randomized controlled trials (RCTs) can be used to generate guarantees on treatment effects. However, RCTs often spend unnecessary resources exploring sub-optimal treatments, which can reduce the power of treatment guarantees. To address these concerns, we develop a two-stage RCT where, first on a data-driven screening stage, we prune low-impact treatments, while in the second stage, we develop high probability lower bounds on the treatment effect. Unlike existing adaptive RCT frameworks, our method is simple enough to be implemented in scenarios with limited adaptivity. We derive optimal designs for two-stage RCTs and demonstrate how we can implement such designs through sample splitting. Empirically, we demonstrate that two-stage designs improve upon single-stage approaches, especially in scenarios where domain knowledge is available in the form of a prior. Our work is thus, a simple, yet effective, method to estimate high probablility certificates for high performant treatment effects on a RCT.

Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50\% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some applications of manifold learning and dimensionality reduction techniques, specifically the Self-Organizing Map (SOM), on the optical+NIR SED space of galaxies, with a focus on sample comparison, selection biases, and predictive power using a small subset. To this end, we utilize a large photometric sample from the five CANDELS fields and a subset with spectroscopic measurements from the KECK MOSDEF survey in two redshift bins at $z\sim1.5$ and $z\sim2.2$. We trained SOM with the photometric data and mapped the spectroscopic data onto it as our study case. We found that MOSDEF targets do not cover all SED shapes existing in the SOM. Our findings reveal that Active Galactic Nuclei (AGN) within the MOSDEF sample are mapped onto the more massive regions of the SOM, confirming previous studies and known selection biases towards higher-mass, less dusty galaxies. Furthermore, SOM were utilized to map measured spectroscopic features, examining the relationship between metallicity variations and galaxy mass. Our analysis confirmed that more massive galaxies exhibit lower [OIII]/H$\beta$ and [OIII]/[OII] ratios and higher H$\alpha$/H$\beta$ ratios, consistent with the known mass-metallicity relation. These findings highlight the effectiveness of SOM in analyzing and visualizing complex, multi-dimensional datasets, emphasizing their potential in data-driven astronomical studies.

We present the properties of a massive, large, dusty, metal-rich, star-forming galaxy at z_spec=6.73. GOODSN-100182 was observed with JWST/NIRSpec as part of the AURORA survey, and is also covered by public multi-wavelength HST and JWST imaging. While the large mass of GOODSN-100182 (~10^10 M_sun) was indicated prior to JWST, NIRCam rest-optical imaging now reveals the presence of an extended disk (r_eff~1.5 kpc). In addition, the NIRSpec R~1000 spectrum of GOODSN-100182 includes the detection of a large suite of rest-optical nebular emission lines ranging in wavelength from [OII]3727 up to [NII]6583. The ratios of Balmer lines suggest significant dust attenuation (E(B-V)_gas=0.40+0.10/-0.09), consistent with the red rest-UV slope inferred for GOODSN-100182 (beta=-0.50+/-0.09). The star-formation rate based on dust-corrected H-alpha emission is log(SFR(H-alpha)/ M_sun/yr)=2.02+0.13/-0.14, well above the z~7 star-forming main sequence in terms of specific SFR. Strikingly, the ratio of [NII]6583/H-alpha emission suggests almost solar metallicity, as does the ratio ([OIII]5007/H-beta)/([NII]6583/H-alpha) and the detection of the faint [FeII]4360 emission feature, whereas the [OIII]5007/[OII]3727 ratio suggests roughly 50% solar metallicity. Overall, the excitation and ionization properties of GOODSN-100182 more closely resemble those of typical star-forming galaxies at z~2-3 rather than z~7. Based on public spectroscopy of the GOODS-N field, we find that GOODSN-100182 resides within a significant galaxy overdensity, and is accompanied by a spectroscopically-confirmed neighbor galaxy. GOODSN-100182 demonstrates the existence of mature, chemically-enriched galaxies within the first billion years of cosmic time, whose properties must be explained by galaxy formation models., Comment: 16 pages, 13 figures, submitted to ApJ

We experimentally show that the Inelastic Cooper-Pair Tunneling Amplifier (ICTA), implementing a DC-powered parametric amplification scheme, can achieve gain and noise performance similar to that of Josephson parametric amplifiers. Using experimental data and simulations, we show that the ICTA has near-quantum-limited noise as long as the integral voltage bias noise divided by the superconducting flux quantum is below the amplification bandwidth. We observe a gain of 20 dB with noise below 1.7 times the quantum limit when the full width at half maximum of the integral voltage noise, expressed as frequency, is 5.6 MHz., Comment: 14 pages, 5 figures

We examine star-formation and dust properties for a sample of 660 galaxies at $1.37\leq z\leq 2.61$ in the MOSDEF survey by dividing them into groups with similarly-shaped spectral energy distributions (SEDs). For each group, we combine the galaxy photometry into a finely-sampled composite SED, and stack their spectra. This method enables the study of more complete galaxy samples, including galaxies with very faint emission lines. We fit these composite SEDs with Prospector to measure the stellar attenuation and SED-based star-formation rates (SFRs). We also derive emission-line properties from the spectral stacks, including Balmer decrements, dust-corrected SFRs, and metallicities. We find that stellar attenuation correlates most strongly with mass, while nebular attenuation correlates strongly with both mass and SFR. Furthermore, the excess of nebular compared to stellar attenuation correlates most strongly with SFR. The highest SFR group has 2 mag of excess nebular attenuation. Our results are consistent with a model in which star-forming regions become more dusty as galaxy mass increases. To explain the increasing excess nebular attenuation, we require a progressively larger fraction of star formation to occur in highly-obscured regions with increasing SFR. This highly-obscured star formation could occur in dusty clumps or central starbursts. Additionally, as each galaxy group represents a different evolutionary stage, we study their locations on the UVJ and SFR-mass diagrams. As mass increases, metallicity and dust attenuation increase, while sSFR decreases. However, the most massive group moves towards the quiescent region of the UVJ diagram, while showing less obscuration, potentially indicating removal of dust., Comment: 21 pages, 7 figures, accepted for publication in ApJ

The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.73 obtained based purely on spectroscopic data, robustly tracing galaxy star formation rates (SFRs) beyond the peak of the cosmic star formation history. We compare our results with theoretical predictions from three different simulations and find good agreement at z~4-6. The UV LFs of this spectroscopically-confirmed sample are in good agreement with pre-JWST measurements obtained with photometrically-selected objects. Finally, we derive SFR functions and integrate these to compute the evolution of the cosmic star-formation rate densities across z~4-6, finding values in good agreement with recent UV estimates from Lyman-break galaxies, which imply a continuous decrease in SFR density by a factor of 3x over z~4 to z~6. Our work shows the power of NIRCam grism observations to efficiently provide new tests for early galaxy formation models based on emission line statistics., Comment: 17 pages, 14 figures

We introduce a novel Bayesian approach for both covariate selection and sparse precision matrix estimation in the context of high-dimensional Gaussian graphical models involving multiple responses. Our approach provides a sparse estimation of the three distinct sparsity structures: the regression coefficient matrix, the conditional dependency structure among responses, and between responses and covariates. This contrasts with existing methods, which typically focus on any two of these structures but seldom achieve simultaneous sparse estimation for all three. A key aspect of our method is that it leverages the structural sparsity information gained from the presence of irrelevant covariates in the dataset to introduce covariate-level sparsity in the precision and regression coefficient matrices. This is achieved through a Bayesian conditional random field model using a hierarchical spike and slab prior setup. Despite the non-convex nature of the problem, we establish statistical accuracy for points in the high posterior density region, including the maximum-a-posteriori (MAP) estimator. We also present an efficient Expectation-Maximization (EM) algorithm for computing the estimators. Through simulation experiments, we demonstrate the competitive performance of our method, particularly in scenarios with weak signal strength in the precision matrices. Finally, we apply our method to a bike-share dataset, showcasing its predictive performance., Comment: 27 pages in main paper, 33 pages in Supplementary, 4 figures

The robot learning community has made great strides in recent years, proposing new architectures and showcasing impressive new capabilities; however, the dominant metric used in the literature, especially for physical experiments, is "success rate", i.e. the percentage of runs that were successful. Furthermore, it is common for papers to report this number with little to no information regarding the number of runs, the initial conditions, and the success criteria, little to no narrative description of the behaviors and failures observed, and little to no statistical analysis of the findings. In this paper we argue that to move the field forward, researchers should provide a nuanced evaluation of their methods, especially when evaluating and comparing learned policies on physical robots. To do so, we propose best practices for future evaluations: explicitly reporting the experimental conditions, evaluating several metrics designed to complement success rate, conducting statistical analysis, and adding a qualitative description of failures modes. We illustrate these through an evaluation on physical robots of several learned policies for manipulation tasks.

We present the theory of the longitudinal spin Seebeck effect between a Heisenberg spin-1/2 chain and a conductor. The effect consists of the generation of a spin current across the spin chain-conductor interface in response to the temperature difference between the two systems. In this setup, the current is given by the convolution of the local spin susceptibilities of the spin chain and the conductor. We find the spin current to be fully controlled, both in the magnitude and the sign, by the backscattering interaction between spinons, fractionalized spin excitations of the Heisenberg chain. In particular, it vanishes when the spinons form a non-interacting spinon gas. Our analytical results for the local spin susceptibility at the open end of the spin chain are in excellent agreement with numerical DMRG simulations.

Two-dimensional (2D) semiconductors have emerged as leading candidates for the development of low-power and multifunctional computing applications, thanks to their qualities such as layer-dependent band gap tunability, high carrier mobility, and excellent electrostatic control. Here, we explore a pair of 2D semiconductors with broken-gap (Type III) band alignment and demonstrate a highly gate-tunable p-MoTe$_{2}$/n-SnS$_{2}$ heterojunction tunnel field-effect transistor with multifunctional behavior. Employing a dual-gated asymmetric device geometry, we unveil its functionality as both a forward and backward rectifying device. Consequently, we observe a highly gate-tunable negative differential resistance (NDR), with a gate-coupling efficiency of $\eta \simeq 0.5$ and a peak-to-valley ratio of $\sim$ 3 down to 150K. By employing density functional theory and exploring the density of states, we determine that interband tunneling within the valence bands is the cause of the observed NDR characteristics. The combination of band-to-band tunneling and gate controllability of NDR signal open the pathway for realizing gate-tunable 2D material-based neuromorphic and energy-efficient electronics., Comment: 22 pages, 5 figures

We present the long-term photometric and spectroscopic analysis of a transitioning SN~IIn/Ibn from $-$10.8 d to 150.7 d post $V$-band maximum. SN~2021foa shows prominent He {\sc i} lines comparable in strength to the H$\alpha$ line around peak, placing SN~2021foa between the SN~IIn and SN~Ibn populations. The spectral comparison shows that it resembles the SN~IIn population at pre-maximum, becomes intermediate between SNe~IIn/Ibn and at post-maximum matches with SN~IIn 1996al. The photometric evolution shows a precursor at $-$50 d and a light curve shoulder around 17d. The peak luminosity and color evolution of SN 2021foa are consistent with most SNe~IIn and Ibn in our comparison sample. SN~2021foa shows the unique case of a SN~IIn where the narrow P-Cygni in H$\alpha$ appear at later stages. The H$\alpha$ profile consists of a narrow (500 -- 1200 km s$^{-1}$) component, intermediate width (3000 -- 8000 km s$^{-1}$) and broad component in absorption. Temporal evolution of the H$\alpha$ profile favours a disk-like CSM geometry. Hydrodynamical modelling of the lightcurve well reproduces a two-component CSM structure with different densities ($\rho$ $\propto$ r$^{-2}$ -- $\rho$ $\propto$ r$^{-5}$), mass-loss rates (10$^{-3}$ -- 10$^{-1}$ M$_{\odot}$ yr$^{-1}$) assuming a wind velocity of 1000 km s$^{-1}$ and having a CSM mass of 0.18 M$_{\odot}$. The overall evolution indicates that SN~2021foa most likely originated from a LBV star transitioning to a WR star with the mass-loss rate increasing in the period from 5 to 0.5 years before the explosion or it could be due to a binary interaction., Comment: Submitted to MNRAS; 20 pages, 16 figures, 4 tables

In many domains, it is difficult to obtain the race data that is required to estimate racial disparity. To address this problem, practitioners have adopted the use of proxy methods which predict race using non-protected covariates. However, these proxies often yield biased estimates, especially for minority groups, limiting their real-world utility. In this paper, we introduce two new contextual proxy models that advance existing methods by incorporating contextual features in order to improve race estimates. We show that these algorithms demonstrate significant performance improvements in estimating disparities on real-world home loan and voter data. We establish that achieving unbiased disparity estimates with contextual proxies relies on mean-consistency, a calibration-like condition.

We introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user's environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback., Comment: UIST 2024

Deep learning-based methods that jointly perform the task of acoustic echo and noise reduction (AENR) often require high memory and computational resources, making them unsuitable for real-time deployment on low-resource platforms such as embedded devices. We propose a low-complexity hybrid approach for joint AENR by employing a single model to suppress both residual echo and noise components. Specifically, we integrate the state-of-the-art (SOTA) ULCNet model, which was originally proposed to achieve ultra-low complexity noise suppression, in a hybrid system and train it for joint AENR. We show that the proposed approach achieves better echo reduction and comparable noise reduction performance with much lower computational complexity and memory requirements than all considered SOTA methods, at the cost of slight degradation in speech quality., Comment: 5 pages, 2 figures

This article focuses on the study of zero-sum invariants of finite non-abelian groups. We address two main problems: the first centers on the ordered Davenport constant and the second on Gao's constant. We establish a connection between the ordered Davenport constant and the small Davenport constant for a finite non-abelian group of even order, which in turn gives a relation with the Noether number. Additionally, we confirm a conjecture of Gao and Li for a non-abelian group of order $2p^{\alpha}$, where $p$ is a prime. Furthermore, we prove a conjecture that connects the ordered Davenport constant to the Loewy length for certain classes of finite $2$-groups., Comment: 15 pages

Let $Q$ be the matrix $\displaystyle \begin{pmatrix} a & b \\ 1 & 0 \end{pmatrix}$ in $GL_2(\mathbb{F}_q)$ where $\mathbb{F}_q$ is a finite field, and let $G$ be the finite cyclic group generated by $Q$. We consider the action of $G$ on the set $\mathbb{F}_q \times \mathbb{F}_q$. In particular, we study certain relationships between the lengths of the non-trivial orbits of $G$, and their frequency of occurrence. This is done in part by investigating the order of elements of a product in an abelian group when the product has prime power order. For $q$ a prime and $b=1$, the orbits correspond to Fibonacci type linear recurrences modulo $q$ for different initial conditions. We also derive certain conditions under which the roots of the characteristic polynomial of $Q$ are generators of $\mathbb{F}_q^\times$. Examples are included to illustrate the theory., Comment: 23 pages

This paper addresses the challenge of achieving stable adaptive teleoperation and improving the convergence rate in the presence of high communication time delays. We employ a passivity-based formalism to establish stability using wave variables and wave scattering techniques, and we enhance the convergence rate by combining it with predictor-based approaches. The elevated time delay within the teleoperated communication layer is known to induce an oscillatory behavior, which reduces the convergence rate and increases the settling time in the convergence of power variables. This issue is addressed in this paper by utilizing a Smith predictor on the operator end and Minimum Jerk (MJ) predictor on the remote end. We present experimental and simulation results to demonstrate the improvements, ensuring stable teleoperation under high communication time delays., Comment: accepted for publication and presented at ACC 2024

In this work, we systematically study the electronic structure and stability of spin states of the [Fe-(tpy-ph)$_2$]$^{2+}$ molecule in both gas phase and on a Au(111) substrate using density functional theory +U (DFT+U) calculations. We find that the stability of the Fe$^{2+}$ ion's spin states is significantly influenced by the Hubbard U parameter. In the gas phase, the low-spin (LS, S=0) state is found to be energetically favorable for U(Fe) $\leq$ 3 eV, whereas the high-spin (HS, S=2) state is stabilized for U(Fe) > 3 eV. Interaction with the Au(111) substrate is found to elevate the critical U for the spin-state transition to 3.5 eV. Additionally, we perform L-edge X-ray absorption spectroscopy (XAS) calculations based on time-dependent DFT (TD-DFT) for both HS and LS states. The calculated XAS suggests that the HS state more closely aligns with the experimental observations, indicating the potential coexistence of the HS state as the initial state during the X-ray excitation process. These findings enrich our understanding of spin-state dynamics in [Fe-(tpy-ph)$_2$]$^{2+}$., Comment: 21 pages, 5 figures, 3 tables

We use JWST/NIRSpec observations from the Assembly of Ultradeep Rest-optical Observations Revealing Astrophysics (AURORA) survey to constrain the shape of the nebular attenuation curve of a star-forming galaxy at z=4.41, GOODSN-17940. We utilize 11 unblended HI recombination lines to derive the attenuation curve spanning optical to near-infrared wavelengths (3751-9550 \r{A}). We then leverage a high-S/N spectroscopic detection of the rest-frame ultraviolet continuum in combination with rest-UV photometric measurements to constrain the shape of the curve at ultraviolet wavelengths. While this UV constraint is predominantly based on stellar emission, the large measured equivalent widths of H$\alpha$ and H$\beta$ indicate that GOODSN-17940 is dominated by an extremely young stellar population <10 Myr in age such that the UV stellar continuum experiences the same attenuation as the nebular emission. The resulting combined nebular attenuation curve spans 1400-9550 \r{A} and has a shape that deviates significantly from commonly assumed dust curves in high-redshift studies. Relative to the Milky Way, SMC, and Calzetti curves, the new curve has a steeper slope at long wavelengths ($\lambda>5000$ \r{A}) while displaying a similar slope across blue-optical wavelengths ($\lambda=3750-5000$ \r{A}). In the ultraviolet, the new curve is shallower than the SMC and Calzetti curves and displays no significant 2175 \r{A} bump. This work demonstrates that the most commonly assumed dust curves are not appropriate for all high-redshift galaxies. These results highlight the ability to derive nebular attenuation curves for individual high-redshift sources with deep JWST/NIRSpec spectroscopy, thereby improving the accuracy of physical properties inferred from nebular emission lines., Comment: 21 pages, 6 figures, 1 table, submitted to ApJ

In the dynamic metric $k$-median problem, we wish to maintain a set of $k$ centers $S \subseteq V$ in an input metric space $(V, d)$ that gets updated via point insertions/deletions, so as to minimize the objective $\sum_{x \in V} \min_{y \in S} d(x, y)$. The quality of a dynamic algorithm is measured in terms of its approximation ratio, "recourse" (the number of changes in $S$ per update) and "update time" (the time it takes to handle an update). The ultimate goal in this line of research is to obtain a dynamic $O(1)$ approximation algorithm with $\tilde{O}(1)$ recourse and $\tilde{O}(k)$ update time. Dynamic $k$-median is a canonical example of a class of problems known as dynamic $k$-clustering, that has received significant attention in recent years. To the best of our knowledge, however, previous papers either attempt to minimize the algorithm's recourse while ignoring its update time, or minimize the algorithm's update time while ignoring its recourse. For dynamic $k$-median, we come arbitrarily close to resolving the main open question on this topic, with the following results. (I) We develop a new framework of randomized local search that is suitable for adaptation in a dynamic setting. For every $\epsilon > 0$, this gives us a dynamic $k$-median algorithm with $O(1/\epsilon)$ approximation ratio, $\tilde{O}(k^{\epsilon})$ recourse and $\tilde{O}(k^{1+\epsilon})$ update time. This framework also generalizes to dynamic $k$-clustering with $\ell^p$-norm objectives, giving similar bounds for the dynamic $k$-means and a new trade-off for dynamic $k$-center. (II) If it suffices to maintain only an estimate of the value of the optimal $k$-median objective, then we obtain a $O(1)$ approximation algorithm with $\tilde{O}(k)$ update time. We achieve this result via adapting the Lagrangian Relaxation framework to the dynamic setting., Comment: Accepted at FOCS 2024

We study the risks of validator reuse across multiple services in a restaking protocol. We characterize the robust security of a restaking network as a function of the buffer between the costs and profits from attacks. For example, our results imply that if attack costs always exceed attack profits by 10\%, then a sudden loss of .1\% of the overall stake (e.g., due to a software error) cannot result in the ultimate loss of more than 1.1\% of the overall stake. We also provide local analogs of these overcollateralization conditions and robust security guarantees that apply specifically for a target service or coalition of services. All of our bounds on worst-case stake loss are the best possible. Finally, we bound the maximum-possible length of a cascade of attacks. Our results suggest measures of robustness that could be exposed to the participants in a restaking protocol. We also suggest polynomial-time computable sufficient conditions that can proxy for these measures.

Tracking plant features is crucial for various agricultural tasks like phenotyping, pruning, or harvesting, but the unstructured, cluttered, and deformable nature of plant environments makes it a challenging task. In this context, the recent advancements in foundational models show promise in addressing this challenge. In our work, we propose PlantTrack where we utilize DINOv2 which provides high-dimensional features, and train a keypoint heatmap predictor network to identify the locations of semantic features such as fruits and leaves which are then used as prompts for point tracking across video frames using TAPIR. We show that with as few as 20 synthetic images for training the keypoint predictor, we achieve zero-shot Sim2Real transfer, enabling effective tracking of plant features in real environments.

Recent studies have made significant progress in addressing dexterous manipulation problems, particularly in in-hand object reorientation. However, there are few existing works that explore the potential utilization of developed dexterous manipulation controllers for downstream tasks. In this study, we focus on constrained dexterous manipulation for food peeling. Food peeling presents various constraints on the reorientation controller, such as the requirement for the hand to securely hold the object after reorientation for peeling. We propose a simple system for learning a reorientation controller that facilitates the subsequent peeling task. Videos are available at: https://taochenshh.github.io/projects/veg-peeling.

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer., Comment: 6 pages, 7 figures

We have used a combined sample of JADES and CEERS objects in order to constrain ionizing photon production efficiency ($\xi_{\rm ion}$) from JWST/NIRSpec and JWST/NIRCam data. We examine 163 objects at 1.06 < z < 6.71 with significant (3$\sigma$) spectroscopic detections of H$\alpha$ and H$\beta$ in order to constrain intrinsic H$\alpha$ luminosities corrected from nebular dust attenuation via Balmer decrements. We constrain dust-corrected UV luminosities from best-fit spectral-energy distribution modeling. We find a sample median log$_{10}$($\xi{\rm ion,0}$/erg Hz$^{-1}$) = $25.29^{+0.29}_{-0.37}$, assuming f$_{\rm esc}$=0 for the escape fraction of Lyman continuum emission. We find significant correlation between $\xi_{\rm ion,0}$ and z, with 17 objects at z > 4.64 having median log$_{10}$($\xi_{\rm ion,0}$/erg Hz$^{-1}$) = $25.38^{+0.38}){-0.38}$, with those below having log$_{10}$($\xi_{\rm ion,0}$/erg Hz$^{-1}$) = $25.24^{+0.30}_{-0.33}$. We also find significant, positive correlations between $\xi_{\rm ion,0}$ and LUV; W{\lambda}([O iii]); [O iii]{\lambda}5007/[O ii]{\lambda}{\lambda}3726, 3729; and inverse correlations with metallicity. In contrast with some previous results, we find no trends between $\xi_{\rm ion,0}$ and stellar mass, stellar dust attenuation, or UV slope. Applying a multivariate fit to $\xi_{\rm ion,0}$, z, and MUV to an empirically-motivated model of reionization, and folding in f$_{\rm esc}$ estimates from direct observations of the Lyman continuum at z ~ 3 from the Keck Lyman Continuum Spectroscopic survey, we find that the number of ionizing photons entering the IGM causes reionization to end at z ~ 5 - 7., Comment: 17 pages, 8 figures, submitted to ApJ

For a finite group $G,$ $D(G)$ is defined as the least positive integer $k$ such that for every sequence $S=g_1 g_2\cdots g_k$ of length $k$ over $G$, there exist $1 \le i_1 < i_2 <\cdots < i_m \le k $ such that $\prod_{j=1}^{m} g_{i_{\sigma(j)}}=1$ holds for $\sigma = id,$ identity element of $S_m.$ For a finite abelian group, this group invariant, known as the Davenport constant, is crucial in the theory of non-unique factorization domains. The precise value of this invariant, even for a finite abelian group of rank greater than $2$, is not known yet. In 1977, Olson and White first worked with this invariant for finite non-abelian groups. After that in 2004, Dimitrov dealt with it, where he proved that $D(G)\leq L(G)$ for a finite $p$-group $G$, where $p$ is a prime and $L(G)$ is the Loewy length of $\mathbb{F}_pG.$ He conjectured that equality holds for all finite $p$-groups. In this article, we compute $D(G)$ for a certain subclass of $2$-generated finite $p$-groups of nilpotency class two and show that the conjecture is true by determining the precise value of the Loewy length of $\mathbb{F}_pG.$ We also evaluate $D(G)$ for finite dicyclic, semi-dihedral and some other groups.

We consider a nonclassical state generated by an atom-cavity field interaction in presence of a driven field. In the scheme, the two-level atom is moved through the cavity and driven by a classical field. The atom interacts dispersively with the cavity field, which results in a photon-number-dependent Stark shift. Assuming that the atom enters the cavity in the excited state $|{a}\rangle$, the obtained output cavity field is taken into account. The state vector $|\psi(t)\rangle$ describes the entire atom-field system but in our work we deal with the statistical aspects of the cavity field only. The quantum state that corresponds to the output cavity field is obtained by tracing out the atom part from $|{\psi(t)}\rangle\langle{\psi(t)}|$. Different quantum phase properties such as quantum phase distribution, angular $Q$ phase function, phase dispersion are evaluated for the obtained radiation field. The second-order correlation function $g^2(0)$, an indirect phase characteristic is also considered., Comment: 9 pages, 6 figures

A number of companies recently worked together to release the new Open Compute Project MX standard for low-precision computation, aimed at efficient neural network implementation. In this paper, we describe and evaluate the first open-source FPGA implementation of the arithmetic defined in the standard. Our designs fully support all the standard's concrete formats for conversion into and out of MX formats and for the standard-defined arithmetic operations, as well as arbitrary fixed-point and floating-point formats. Certain elements of the standard are left as implementation-defined, and we present the first concrete FPGA-inspired choices for these elements, which we outline in the paper. Our library of optimized hardware components is available open source, and can be used to build larger systems. For this purpose, we also describe and release an open-source Pytorch library for quantization into the new standard, integrated with the Brevitas library so that the community can develop novel neural network designs quantized with MX formats in mind. We demonstrate the usability and efficacy of our libraries via the implementation of example neural networks such as ResNet-18 on the ImageNet ILSVRC12 dataset. Our testing shows that MX is very effective for formats such as INT5 or FP6 which are not natively supported on GPUs. This gives FPGAs an advantage as they have the flexibility to implement a custom datapath and take advantage of the smaller area footprints offered by these formats., Comment: 8 pages, 4 figures