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Acquiring and annotating surgical data is often resource-intensive, ethical constraining, and requiring significant expert involvement. While generative AI models like text-to-image can alleviate data scarcity, incorporating spatial annotations, such as segmentation masks, is crucial for precision-driven surgical applications, simulation, and education. This study introduces both a novel task and method, SimGen, for Simultaneous Image and Mask Generation. SimGen is a diffusion model based on the DDPM framework and Residual U-Net, designed to jointly generate high-fidelity surgical images and their corresponding segmentation masks. The model leverages cross-correlation priors to capture dependencies between continuous image and discrete mask distributions. Additionally, a Canonical Fibonacci Lattice (CFL) is employed to enhance class separability and uniformity in the RGB space of the masks. SimGen delivers high-fidelity images and accurate segmentation masks, outperforming baselines across six public datasets assessed on image and semantic inception distance metrics. Ablation study shows that the CFL improves mask quality and spatial separation. Downstream experiments suggest generated image-mask pairs are usable if regulations limit human data release for research. This work offers a cost-effective solution for generating paired surgical images and complex labels, advancing surgical AI development by reducing the need for expensive manual annotations., Comment: 12 pages, 17 figures, 4 tables, project page at https://camma-public.github.io/endogen/

Target Speech Extraction (TSE) traditionally relies on explicit clues about the speaker's identity like enrollment audio, face images, or videos, which may not always be available. In this paper, we propose a text-guided TSE model StyleTSE that uses natural language descriptions of speaking style in addition to the audio clue to extract the desired speech from a given mixture. Our model integrates a speech separation network adapted from SepFormer with a bi-modality clue network that flexibly processes both audio and text clues. To train and evaluate our model, we introduce a new dataset TextrolMix with speech mixtures and natural language descriptions. Experimental results demonstrate that our method effectively separates speech based not only on who is speaking, but also on how they are speaking, enhancing TSE in scenarios where traditional audio clues are absent. Demos are at: https://mingyue66.github.io/TextrolMix/demo/, Comment: Accepted by ICASSP 2025

Long-period radio transients are a novel class of astronomical objects characterised by prolonged periods ranging from 18 minutes to 54 minutes. They exhibit highly polarised, coherent, beamed radio emission lasting only 10--100 seconds. The intrinsic nature of these objects is subject to speculation, with highly magnetised white dwarfs and neutron stars being the prevailing candidates. Here we present ASKAP J183950.5-075635.0 (hereafter, ASKAP J1839-0756), boasting the longest known period of this class at 6.45 hours. It exhibits emission characteristics of an ordered dipolar magnetic field, with pulsar-like bright main pulses and weaker interpulses offset by about half a period are indicative of an oblique or orthogonal rotator. This phenomenon, observed for the first time in a long-period radio transient, confirms that the radio emission originates from both magnetic poles and that the observed period corresponds to the rotation period. The spectroscopic and polarimetric properties of ASKAP J1839-0756 are consistent with a neutron star origin, and this object is a crucial piece of evidence in our understanding of long-period radio sources and their links to neutron stars., Comment: 44 pages, 14 figures, 2 tables This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Astronomy, and is available online at https://doi.org/10.1038/s41550-024-02452-z

Extended Reality (XR) enables a plethora of novel interactive shared experiences. Ideally, users are allowed to roam around freely, while audiovisual content is delivered wirelessly to their Head-Mounted Displays (HMDs). Therefore, truly immersive experiences will require massive amounts of data, in the range of tens of gigabits per second, to be delivered reliably at extremely low latencies. We identify Millimeter-Wave (mmWave) communications, at frequencies between 24 and 300 GHz, as a key enabler for such experiences. In this article, we show how the mmWave state of the art does not yet achieve sufficient performance, and identify several key active research directions expected to eventually pave the way for extremely-high-quality mmWave-enabled interactive multi-user XR., Comment: Published in IEEE Communications Magazine

We present the strongest limits to date on the mixing angle, $\theta$, with which a new scalar particle, $S$, mixes with the Higgs field in the mass range $100$ $MeV

While current high-resolution depth estimation methods achieve strong results, they often suffer from computational inefficiencies due to reliance on heavyweight models and multiple inference steps, increasing inference time. To address this, we introduce PatchRefiner V2 (PRV2), which replaces heavy refiner models with lightweight encoders. This reduces model size and inference time but introduces noisy features. To overcome this, we propose a Coarse-to-Fine (C2F) module with a Guided Denoising Unit for refining and denoising the refiner features and a Noisy Pretraining strategy to pretrain the refiner branch to fully exploit the potential of the lightweight refiner branch. Additionally, we introduce a Scale-and-Shift Invariant Gradient Matching (SSIGM) loss to enhance synthetic-to-real domain transfer. PRV2 outperforms state-of-the-art depth estimation methods on UnrealStereo4K in both accuracy and speed, using fewer parameters and faster inference. It also shows improved depth boundary delineation on real-world datasets like CityScape, ScanNet++, and KITTI, demonstrating its versatility across domains.

Forecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performance begs the question: how can we benchmark and evaluate these forecasters instantaneously? Following the consistency check framework, we measure the performance of forecasters in terms of the consistency of their predictions on different logically-related questions. We propose a new, general consistency metric based on arbitrage: for example, if a forecasting AI illogically predicts that both the Democratic and Republican parties have 60% probability of winning the 2024 US presidential election, an arbitrageur can trade against the forecaster's predictions and make a profit. We build an automated evaluation system that generates a set of base questions, instantiates consistency checks from these questions, elicits the predictions of the forecaster, and measures the consistency of the predictions. We then build a standard, proper-scoring-rule forecasting benchmark, and show that our (instantaneous) consistency metrics correlate with LLM forecasters' ground truth Brier scores (which are only known in the future). We also release a consistency benchmark that resolves in 2028, providing a long-term evaluation tool for forecasting., Comment: 55 pages, 25 figures. Submitted to ICLR 2025

We study the homeomorphism groups of ordinals equipped with their order topology, focusing on successor ordinals whose limit capacity is also a successor. This is a rich family of groups that has connections to both permutation groups and homeomorphism groups of manifolds. For ordinals of Cantor--Bendixson degree one, we prove that the homeomorphism group is strongly distorted and uniformly perfect, and we classify its normal generators. As a corollary, we recover and provide a new proof of the classical result that the subgroup of finite permutations in the symmetric group on a countably infinite set is the maximal proper normal subgroup. For ordinals of higher Cantor--Bendixson degree, we establish a semi-direct product decomposition of the (pure) homeomorphism group. When the limit capacity is one, we further compute the abelianizations and determine normal generating sets of minimal cardinality for these groups., Comment: 28 pages

Learning-based solutions for long-tailed recognition face difficulties in generalizing on balanced test datasets. Due to imbalanced data prior, the learned \textit{a posteriori} distribution is biased toward the most frequent (head) classes, leading to an inferior performance on the least frequent (tail) classes. In general, the performance can be improved by removing such a bias by eliminating the effect of imbalanced prior modeled using the number of class samples (frequencies). We first observe that the \textit{effective prior} on the classes, learned by the model at the end of the training, can differ from the empirical prior obtained using class frequencies. Thus, we propose a novel approach to accurately model the effective prior of a trained model using \textit{a posteriori} probabilities. We propose to correct the imbalanced prior by adjusting the predicted \textit{a posteriori} probabilities (Prior2Posterior: P2P) using the calculated prior in a post-hoc manner after the training, and show that it can result in improved model performance. We present theoretical analysis showing the optimality of our approach for models trained with naive cross-entropy loss as well as logit adjusted loss. Our experiments show that the proposed approach achieves new state-of-the-art (SOTA) on several benchmark datasets from the long-tail literature in the category of logit adjustment methods. Further, the proposed approach can be used to inspect any existing method to capture the \textit{effective prior} and remove any residual bias to improve its performance, post-hoc, without model retraining. We also show that by using the proposed post-hoc approach, the performance of many existing methods can be improved further., Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of $1.11\times 10^{21}$ protons on target, a $70\%$ increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $> 99\%$ CL$_\mathrm{s}$ in all kinematic variables., Comment: 8 pages, 5 figures, 1 table

Knowledge graphs (KGs) are valuable for representing structured, interconnected information across domains, enabling tasks like semantic search, recommendation systems and inference. A pertinent challenge with KGs, however, is that many entities (i.e., heads, tails) or relationships are unknown. Knowledge Graph Completion (KGC) addresses this by predicting these missing nodes or links, enhancing the graph's informational depth and utility. Traditional methods like TransE and ComplEx predict tail entities but struggle with unseen entities. Textual-based models leverage additional semantics but come with high computational costs, semantic inconsistencies, and data imbalance issues. Recent LLM-based models show improvement but overlook contextual information and rely heavily on entity descriptions. In this study, we introduce a contextualized BERT model for KGC that overcomes these limitations by utilizing the contextual information from neighbouring entities and relationships to predict tail entities. Our model eliminates the need for entity descriptions and negative triplet sampling, reducing computational demands while improving performance. Our model outperforms state-of-the-art methods on standard datasets, improving Hit@1 by 5.3% and 4.88% on FB15k-237 and WN18RR respectively, setting a new benchmark in KGC., Comment: MuslML Workshop, 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

For many, it can be intimidating or even impossible to seek professional medical help if they have symptoms of an illness. As such, some people approach platforms like Reddit or Quora for a community-based conversation in an attempt to diagnose themselves. In this paper, we unearth what motivates people to share personal health information on these platforms. From an online survey and in-depth interviews, we present who this population of users are, and what, where, and why they are posting. Our evaluation finds that tech-savvy young adults are more likely to post on online platforms about potentially sensitive or highly specific topics for convenience, fast response, and a sense of community. Most importantly, we found that anonymity, distrust of physicians, and prior experience with platforms were key factors that affected behavior., Comment: 5 pages, 2 figures, Spring 2021 HCI Seminar, Columbia University, NY

Magnetic reconnection, a fundamental plasma process, is pivotal in understanding energy conversion and particle acceleration in astrophysical systems. While extensively studied in two-dimensional (2D) configurations, the dynamics of reconnection in three-dimensional (3D) systems remain under-explored. In this work, we extend the classical tearing mode instability to 3D by introducing a modulation along the otherwise uniform direction in a 2D equilibrium, given by $g(y)$, mimicking a flux tube-like configuration. We perform linear stability analysis (both analytically and numerically) and direct numerical simulations to investigate the effects of three-dimensionality. Our findings reveal that the 3D tearing instability exhibits reduced growth rates compared to 2D by a factor of $\int g(y)^{1/2} dy~/\int dy$, with the dispersion relation maintaining similar scaling characteristics. We show that the modulation introduces spatially varying resistive layer properties, which influence the reconnection dynamics. Remarkably, we find that Sweet-Parker scaling for the reconnection rate persists even in the absence of a guide field., Comment: 18 pages, 11 figures, comments are welcome

Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multimedia content, which provides additional narrations at suitable intervals to describe visual elements, catering specifically to the needs of visually impaired audiences. In this paper, we introduce $\mathrm{CA^3D}$, the pioneering unified Context-Aware Automatic Audio Description system that provides AD event scripts with precise locations in the long cinematic content. Specifically, $\mathrm{CA^3D}$ system consists of: 1) a Temporal Feature Enhancement Module to efficiently capture longer term dependencies, 2) an anchor-based AD event detector with feature suppression module that localizes the AD events and extracts discriminative feature for AD generation, and 3) a self-refinement module that leverages the generated output to tweak AD event boundaries from coarse to fine. Unlike conventional methods which rely on metadata and ground truth AD timestamp for AD detection and generation tasks, the proposed $\mathrm{CA^3D}$ is the first end-to-end trainable system that only uses visual cue. Extensive experiments demonstrate that the proposed $\mathrm{CA^3D}$ improves existing architectures for both AD event detection and script generation metrics, establishing the new state-of-the-art performances in the AD automation., Comment: 10 pages

We study an inflation model with nonminimal derivative coupling that features a coupling between the derivative of the inflaton field and the Einstein tensor. This model naturally amplifies curvature perturbations at small scales via gravitationally enhanced friction, a mechanism critical for the formation of primordial black holes and the associated production of potentially detectable scalar-induced gravitational waves. We derive analytical expressions for the primordial power spectrum, enabling efficient exploration of the model parameter space without requiring computationally intensive numerical solutions of the Mukhanov-Sasaki equation. Using the third data release of the Parkes Pulsar Timing Array (PPTA DR3), we constrain the model parameters characterizing the coupling function: $\phi_c = 3.7^{+0.3}_{-0.5} M_\mathrm{P}$, $\log_{10} \omega_L = 7.1^{+0.6}_{-0.3}$, and $\log_{10} \sigma = -8.3^{+0.3}_{-0.6}$ at 90\% confidence level. Our results demonstrate the growing capability of pulsar timing arrays to probe early Universe physics, complementing traditional cosmic microwave background observations by providing unique constraints on inflationary dynamics at small scales., Comment: 14 pages, 5figures

High-dimensional quantum entanglement in photons offers notable technological advancements over traditional qubit-based systems, including increased information density and enhanced security. However, such high-dimensional states are vulnerable to disruption by complex disordered media, presenting significant challenges in practical applications. Spatially-entangled photons are conventionally generated using a nonlinear crystal via spontaneous parametric down conversion (SPDC). While the effect of disorder on spatially entangled photons in the near field of the crystal is well understood, the impact of disorder in the far field is more complex. In this work, we present a systematic study of the randomization of two-photon correlations caused by arbitrary phase distortions in the far field by breaking it down into odd and even parity components. First, we theoretically show that the two-photon field is only sensitive to the even-parity part of the phase distortion. In follow-up experiments, we employ a deformable mirror to implement random phase distortions, separating the contributions of odd and even parity phases using Zernike polynomials. The experimental results are in agreements with the theoretical predictions. Subsequently, we perform numerical simulations to show that these results extend to stronger degrees of disorder. Our key finding is that, since two-photon correlations are only affected by the even-parity component of phase modulations, the number of independent adaptive optics elements required for optimizing the correlation can be effectively halved, offering a significant practical advantage in managing disorder in quantum systems., Comment: 11 pages, 9 figures

Traditional evaluation metrics like BLEU and ROUGE fall short when capturing the nuanced qualities of generated text, particularly when there is no single ground truth. In this paper, we explore the potential of Large Language Models (LLMs), specifically Google Gemini 1, to serve as automatic evaluators for non-standardized metrics in summarization and dialog-based tasks. We conduct experiments across multiple prompting strategies to examine how LLMs fare as quality evaluators when compared with human judgments on the SummEval and USR datasets, asking the model to generate both a score as well as a justification for the score. Furthermore, we explore the robustness of the LLM evaluator by using perturbed inputs. Our findings suggest that while LLMs show promise, their alignment with human evaluators is limited, they are not robust against perturbations and significant improvements are required for their standalone use as reliable evaluators for subjective metrics., Comment: Accepted at ICON 2024

Appearing in the modular transformation formula for the classical theta function $\theta_3(z)$ is the sum $S(h,k):=\sum_{j=1}^{k-1}(-1)^{j+1+[hj/k]}$, which is an analogue of the classical Dedekind sum $s(h,k).$ We establish several properties for $S(h,k)$ and $S(k) := \sum_{h=1}^{k-1}S(h,k).$ Several conjectures about the values of $S(k)$ are given.

In various video-language learning tasks, the challenge of achieving cross-modality alignment with multi-grained data persists. We propose a method to tackle this challenge from two crucial perspectives: data and modeling. Given the absence of a multi-grained video-text pretraining dataset, we introduce a Granularity EXpansion (GEX) method with Integration and Compression operations to expand the granularity of a single-grained dataset. To better model multi-grained data, we introduce an Iterative Approximation Module (IAM), which embeds multi-grained videos and texts into a unified, low-dimensional semantic space while preserving essential information for cross-modal alignment. Furthermore, GEXIA is highly scalable with no restrictions on the number of video-text granularities for alignment. We evaluate our work on three categories of video tasks across seven benchmark datasets, showcasing state-of-the-art or comparable performance. Remarkably, our model excels in tasks involving long-form video understanding, even though the pretraining dataset only contains short video clips.

We have conducted a GPU accelerated reprocessing of $\sim 87\%$ of the archival data from the High Time Resolution Universe South Low Latitude (HTRU-S LowLat) pulsar survey by implementing a pulsar search pipeline that was previously used to reprocess the Parkes Multibeam pulsar survey (PMPS). We coherently searched the full 72-min observations of the survey with an acceleration search range up to $|50|\, \rm m\,s^{-2}$, which is most sensitive to binary pulsars experiencing nearly constant acceleration during 72 minutes of their orbital period. Here we report the discovery of 71 pulsars, including 6 millisecond pulsars (MSPs) of which five are in binary systems, and seven pulsars with very high dispersion measures (DM $>800 \, \rm pc \, cm^{-3}$). These pulsar discoveries largely arose by folding candidates to a much lower spectral signal-to-noise ratio than previous surveys, and exploiting the coherence of folding over the incoherent summing of the Fourier components to discover new pulsars as well as candidate classification techniques. We show that these pulsars could be fainter and on average more distant as compared to both the previously reported 100 HTRU-S LowLat pulsars and background pulsar population in the survey region. We have assessed the effectiveness of our search method and the overall pulsar yield of the survey. We show that through this reprocessing we have achieved the expected survey goals including the predicted number of pulsars in the survey region and discuss the major causes as to why these pulsars were missed in previous processings of the survey., Comment: 18 Pages, 12 figures, 9 tables. Accepted for publication in MNRAS

Here, we present a new thermomechanical geodynamic, numerical implementation that incorporates Maxwell viscoelastic rheology accounting for temperature-dependent power-law dislocation creep and pressure-sensitive, non-associated Drucker-Prager brittle failure, as well as for volumetric stresses and strains during viscoplastic flow, a departure from the traditional incompressible assumptions. In solving for energy conservation, we incorporate the heat source term resulting from irreversible mechanical deformations, which embodies viscoelastic and viscoplastic work, and by considering the total stress tensor and total inelastic strain rate tensors, including dilatant plasticity effects for lithospheric-scale applications, instead of only the shear terms as is usually assumed for incompressible materials. This form of the work term thus allows to consider, volumetric deformation and to couple the energy equation to the constitutive description, and hence the stress balance, via the evolving temperature field. Code design enables us to switch individual features of this general rheology ``on or off'' and thus to benchmark this implementation with published numerical experiments of crustal-scale shortening experiments. We investigate whether ``brittle-plastic'' compressibility can promote or inhibit localization of deformation and thermal evolution during compression for crustal, and upper mantle rheology. For both crustal-scale and lithospheric-scale experiments, we establish that the feedback from volumetric dissipation, while contributing to temperature increase along with shear dissipation, can potentially slow down heat production per unit time, depending on the choice of boundary conditions. Our new implementation can be used to address buckling problems and collision tectonics.

The proliferation of several streaming services in recent years has now made it possible for a diverse audience across the world to view the same media content, such as movies or TV shows. While translation and dubbing services are being added to make content accessible to the local audience, the support for making content accessible to people with different abilities, such as the Deaf and Hard of Hearing (DHH) community, is still lagging. Our goal is to make media content more accessible to the DHH community by generating sign language videos with synthetic signers that are realistic and expressive. Using the same signer for a given media content that is viewed globally may have limited appeal. Hence, our approach combines parametric modeling and generative modeling to generate realistic-looking synthetic signers and customize their appearance based on user preferences. We first retarget human sign language poses to 3D sign language avatars by optimizing a parametric model. The high-fidelity poses from the rendered avatars are then used to condition the poses of synthetic signers generated using a diffusion-based generative model. The appearance of the synthetic signer is controlled by an image prompt supplied through a visual adapter. Our results show that the sign language videos generated using our approach have better temporal consistency and realism than signing videos generated by a diffusion model conditioned only on text prompts. We also support multimodal prompts to allow users to further customize the appearance of the signer to accommodate diversity (e.g. skin tone, gender). Our approach is also useful for signer anonymization., Comment: Published in Proceedings of ECCV, Workshop on Assistive Computer Vision and Robotics, 2024

Consider the problem of determining the optimal applied electric field to drive a molecule from an initial state to a desired target state. For even moderately sized molecules, solving this problem directly using the exact equations of motion -- the time-dependent Schr\"odinger equation (TDSE) -- is numerically intractable. We present a solution of this problem within time-dependent Hartree-Fock (TDHF) theory, a mean field approximation of the TDSE. Optimality is defined in terms of minimizing the total control effort while maximizing the overlap between desired and achieved target states. We frame this problem as an optimization problem constrained by the nonlinear TDHF equations; we solve it using trust region optimization with gradients computed via a custom-built adjoint state method. For three molecular systems, we show that with very small neural network parametrizations of the control, our method yields solutions that achieve desired targets within acceptable constraints and tolerances., Comment: 8 pages, 2 figures

Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive the geometry of occluded regions based on visible cues. Prior methods primarily rely on synthetic datasets and focus on metric depth estimation, limiting their generalization to real-world settings due to domain shifts and scalability challenges. In this paper, we propose a novel formulation of amodal depth estimation in the wild, focusing on relative depth prediction to improve model generalization across diverse natural images. We introduce a new large-scale dataset, Amodal Depth In the Wild (ADIW), created using a scalable pipeline that leverages segmentation datasets and compositing techniques. Depth maps are generated using large pre-trained depth models, and a scale-and-shift alignment strategy is employed to refine and blend depth predictions, ensuring consistency in ground-truth annotations. To tackle the amodal depth task, we present two complementary frameworks: Amodal-DAV2, a deterministic model based on Depth Anything V2, and Amodal-DepthFM, a generative model that integrates conditional flow matching principles. Our proposed frameworks effectively leverage the capabilities of large pre-trained models with minimal modifications to achieve high-quality amodal depth predictions. Experiments validate our design choices, demonstrating the flexibility of our models in generating diverse, plausible depth structures for occluded regions. Our method achieves a 69.5% improvement in accuracy over the previous SoTA on the ADIW dataset.

We conduct the first-ever Pulsar Polarization Array (PPA) analysis to detect the ultralight Axion-Like Dark Matter (ALDM) using the polarization data of 22 millisecond pulsars from the third data release of Parkes Pulsar Timing Array. As one of the major dark matter candidates, the ultralight ALDM exhibits a pronounced wave nature on astronomical scales and offers a promising solution to small-scale structure issues within local galaxies. While the linearly polarized pulsar light travels through the ALDM galactic halo, its position angle (PA) can be subject to an oscillation induced by the ALDM Chern-Simons coupling with electromagnetic field. The PPA is thus especially suited for detecting the ultralight ALDM by correlating polarization data across the arrayed pulsars. To accomplish this task, we develop an advanced Bayesian analysis framework that allows us to construct pulsar PA residual time series, model noise contributions properly and search for pulsar cross-correlations. We find that for an ALDM density of $\rho_0=0.4\,\textrm{GeV}/\textrm{cm}^3$, the Parkes PPA offers the best global limits on the ALDM Chern-Simons coupling, namely $\lesssim 10^{-13.5}-10^{-12.2}~{\rm GeV}^{-1}$, for the mass range of $10^{-22} - 10^{-21}~{\rm eV}$. The crucial role of pulsar cross-correlation in recognizing the nature of the derived limits is also highlighted., Comment: 6+15 pages, 10 figures, 2 tables, submitted to the journal

Open-universe 3D layout generation arranges unlabeled 3D assets conditioned on language instruction. Large language models (LLMs) struggle with generating physically plausible 3D scenes and adherence to input instructions, particularly in cluttered scenes. We introduce LayoutVLM, a framework and scene layout representation that exploits the semantic knowledge of Vision-Language Models (VLMs) and supports differentiable optimization to ensure physical plausibility. LayoutVLM employs VLMs to generate two mutually reinforcing representations from visually marked images, and a self-consistent decoding process to improve VLMs spatial planning. Our experiments show that LayoutVLM addresses the limitations of existing LLM and constraint-based approaches, producing physically plausible 3D layouts better aligned with the semantic intent of input language instructions. We also demonstrate that fine-tuning VLMs with the proposed scene layout representation extracted from existing scene datasets can improve performance., Comment: project website: https://ai.stanford.edu/~sunfanyun/layoutvlm/

Let $P(z)$ be a polynomial of degree $n,$ then it is known that for $\alpha\in\mathbb{C}$ with $|\alpha|\leq \frac{n}{2},$ \begin{align*} \underset{|z|=1}{\max}|\left|zP^{\prime}(z)-\alpha P(z)\right|\leq \left|n-\alpha\right|\underset{|z|=1}{\max}|P(z)|. \end{align*} This inequality includes Bernstein's inequality, concerning the estimate for $|P^\prime(z)|$ over $|z|\leq 1,$ as a special case. In this paper, we extend this inequality to $L_p$ norm which among other things shows that the condition on $\alpha$ can be relaxed. We also prove similar inequalities for polynomials with restricted zeros., Comment: L^{p}$-inequalities, Bernstein's inequality, polynomials

In this review paper, we explore operator aspects in extremal properties of Bernstein-type polynomial inequalities. We shall also see that a linear operator which send polynomials to polynomials and have zero-preserving property naturally preserve Bernstein's inequality., Comment: Polynomials, Inequalities in complex domain, Bernstein's Inequality, Gauss-Lucas theorem

Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA); however, they often struggle with fundamental engineering problems, and there is a scarcity of specialized datasets for training on topics like digital electronics. To address this gap, we propose a benchmark dataset called ElectroVizQA specifically designed to evaluate MLLMs' performance on digital electronic circuit problems commonly found in undergraduate curricula. This dataset, the first of its kind tailored for the VQA task in digital electronics, comprises approximately 626 visual questions, offering a comprehensive overview of digital electronics topics. This paper rigorously assesses the extent to which MLLMs can understand and solve digital electronic circuit questions, providing insights into their capabilities and limitations within this specialized domain. By introducing this benchmark dataset, we aim to motivate further research and development in the application of MLLMs to engineering education, ultimately bridging the performance gap and enhancing the efficacy of these models in technical fields.

Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by such models are still reasonable in quality when evaluated based on traditional metrics such as FID score, precision, and recall. However, these metrics fail to capture the medical/biological plausibility of the generated images. Human expert feedback has been used to get biological plausibility which demonstrates that these generated images have very low plausibility. Recently, the research community has further integrated this human feedback through Reinforcement Learning from Human Feedback(RLHF), which generates more medically plausible images. However, incorporating human feedback is a costly and slow process. In this work, we propose a novel approach to improve the medical plausibility of generated images without the need for human feedback. We introduce IMPROVE:Improving Medical Plausibility without Reliance on Human Validation - An Enhanced Prototype-Guided Diffusion Framework, a prototype-guided diffusion process for medical image generation and show that it substantially enhances the biological plausibility of the generated medical images without the need for any human feedback. We perform experiments on Bone Marrow and HAM10000 datasets and show that medical accuracy can be substantially increased without human feedback.

Recently, a class of long-period radio transients (LPTs) has been discovered, exhibiting emission on timescales thousands of times longer than radio pulsars. Several models had been proposed implicating either a strong magnetic field neutron star, isolated white dwarf pulsar, or a white dwarf binary system with a low-mass companion. While several models for LPTs also predict X-ray emission, no LPTs have been detected in X-rays despite extensive searches. Here we report the discovery of an extremely bright LPT (10-20 Jy in radio), ASKAP J1832-0911, which has coincident radio and X-ray emission, both with a 44.2-minute period. The X-ray and radio luminosities are correlated and vary by several orders of magnitude. These properties are unique amongst known Galactic objects and require a new explanation. We consider a $\gtrsim0.5$ Myr old magnetar with a $\gtrsim 10^{13}$ G crustal field, or an extremely magnetised white dwarf in a binary system with a dwarf companion, to be plausible explanations for ASKAP J1832-0911, although both explanations pose significant challenges to formation and emission theories. The X-ray detection also establishes a new class of hour-scale periodic X-ray transients of luminosity $\sim10^{33}$ erg/s associated with exceptionally bright coherent radio emission., Comment: 52 pages, 17 figures, 3 tables

Masala-CHAI is the first fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a long-standing challenge in automating netlist generation for analog circuits within circuit design automation. Automating this workflow could accelerate the creation of finetuned LLMs for analog circuit design and verification. We identify key challenges in this automation and evaluate the multi-modal capabilities of state-of-the-art LLMs, particularly GPT-4, to address these issues. We propose a three-step workflow to overcome current limitations: labeling analog circuits, prompt tuning, and netlist verification. This approach aims to create an end-to-end SPICE netlist generator from circuit schematic images, tackling the long-standing hurdle of accurate netlist generation. Our framework demonstrates significant performance improvements, tested on approximately 2,100 schematics of varying complexity. We open-source this solution for community-driven development.

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first bootstrap a model based on simulation with access to the full statevector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.

The majority of atomic nuclei have deformed shapes and nearly all these shapes are symmetric with respect to reflection. There are only a few reflection asymmetric pear-shaped nuclei that have been found in actinide and lanthanide regions, which have static octupole deformation. These nuclei possess an intrinsic electric dipole moment due to the shift between the center of charge and the center of mass. This manifests in the enhancement of the electric dipole transition rates. In this article, we report on the measurement of the lifetimes of the high spin levels of the two alternate parity bands in $^{100}$Ru through the Doppler Shift Attenuation Method. The estimated electric dipole transition rates have been compared with the calculated transition rates using the triaxial projected shell model without octupole deformation, and are found to be an order of magnitude enhanced. Thus, the observation of seven inter-leaved electric dipole transitions with enhanced rates establish $^{100}$Ru as possibly the first octupole deformed nucleus reported in the A $\approx$ 100 mass region.

We focus on a sample of 42 sources in the vicinity of the bow-shock source IRS 1W (N-sources), located at the distance of $6.05''$ north-east of the supermassive black hole (SMBH) Sagittarius A* (Sgr A*), within the radius of $1.35''$. We present the first proper motion measurements of N-sources and find that a larger subset of N-sources (28 sources) exhibit a north-westward flying angle. These sources can be bound by an intermediate mass black hole (IMBH) or the concentration that we observe is due to a disk-like distribution projection along the line of sight. We detect the N-sources in $H$, $K_s$, and $L$' bands. The north-westward flying sources could be a bound collection of stars. We discuss a tentative existence of an IMBH or an inclined disk distribution to explain a significant overdensity of stars. The first scenario of having an IMBH implies the lower limit of $\sim 10^4~M_\odot$ for the putative IMBH. Our measurements for the first time reveal that the dense association of stars containing IRS 1W is a co-moving group of massive, young stars. This stellar association might be the remnant core of a massive stellar cluster that is currently being tidally stripped as it inspirals towards Sgr A*. The second scenario suggests that the appearance of the N-sources might be influenced by the projection of a disk-like distribution of younger He-stars and/or dust-enshrouded stars., Comment: 21 pages, 17 figures; published in the Astrophysical Journal

Let $D$ be a digraph of order $n$ with adjacency matrix $A(D)$. For $\alpha\in[0,1)$, the $A_{\alpha}$ matrix of $D$ is defined as $A_{\alpha}(D)=\alpha {\Delta}^{+}(D)+(1-\alpha)A(D)$, where ${\Delta}^{+}(D)=\mbox{diag}~(d_1^{+},d_2^{+},\dots,d_n^{+})$ is the diagonal matrix of vertex outdegrees of $D$. Let $\sigma_{1\alpha}(D),\sigma_{2\alpha}(D),\dots,\sigma_{n\alpha}(D)$ be the singular values of $A_{\alpha}(D)$. Then the trace norm of $A_{\alpha}(D)$, which we call $\alpha$ trace norm of $D$, is defined as $\|A_{\alpha}(D)\|_*=\sum_{i=1}^{n}\sigma_{i\alpha}(D)$. In this paper, we study the variation in $\alpha$ trace norm of a digraph when a vertex or an arc is deleted. As an application of these results, we characterize oriented trees and unicyclic digraphs with maximum $\alpha$ trace norm.

We explore the consequences of varying the pump beam waist that illuminates a nonlinear crystal, realizing spontaneous parametric down-conversion (SPDC). The coherence is transferred from the marginal one-photon wavefunction to the two-photon wavefunction where it manifests into entanglement in the form of spatial correlation. We interpret this as a consequence of the number of independent emitters, called the biphoton birth zones, targeted by the pump beam on the crystal. The birth zone number $N$ characterises the number of such birth zones that fit along a diameter of the region illuminated by the pump waist. To experimentally observe the duality between the one- and two-photon interference, we employ a double slit and analyse their visibilities $V_m$ and $V_\text{12}$ respectively. We demonstrate the conservation of the quantity $V_m^2+V_\text{12}^2$. Finally, we identify three regimes of entanglement of the down-converted photons based on $N$. We show that changing the pump waist lets us actively control the degree of entanglement letting us access these regimes. We provide implications of each regime, and mention experimental use cases thereof., Comment: 11 pages, 5 figures

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Ferroelectric field-effect transistors integrated on 2D semiconducting platforms are extremely relevant for low power electronics. Here, we propose and demonstrate a novel phase-change ferroelectric field effect transistor (PCFE-FET) for steep switching applications. Our gate stack is engineered as a ferroelectric Lanthanum doped hafnium oxide (LHO) proximity coupled with Mott insulator Ti$_x$O$_{2x-1}$(N$_y$) and is integrated onto a 2D MoS$_2$ channel. The interplay of partial polarization switching in the ferroelectric LHO layer and reversible field-tunable metal-insulator transition (MIT) in Ti$_x$O$_{2x-1}$(N$_y$) layer concomitantly triggers polar to non-polar phase transition in the LHO layer between 200 and 220 K. This results in distinctive step-like features in the channel current during DC measurements, and random current fluctuations in high-speed measurements with slim anticlockwise hysteresis. Our devices show subthreshold slopes as steep as 25 mV/dec at 210 K, breaking the Boltzmann limit. Our gate stack is also potentially tunable for operation at temperatures of interest, presenting innovative gate stack engineering approaches for low-power computing solutions.

The Vela pulsar (J0835-4510) is known to exhibit variations in Faraday rotation and dispersion on multi-decade timescales due to the changing sightline through the surrounding Vela supernova remnant and the Gum Nebula. Until now, variations in Faraday rotation towards Vela have not been studied on timescales less than around a decade. We present the results of a high-cadence observing campaign carried out with the Aperture Array Verification System 2 (AAVS2), a prototype SKA-Low station, which received a significant bandwidth upgrade in 2022. We collected observations of the Vela pulsar and PSR J0630-2834 (a nearby pulsar located outside the Gum Nebula), spanning $\sim 1\,\mathrm{yr}$ and $\sim 0.3\,\mathrm{yr}$ respectively, and searched for linear trends in the rotation measure (RM) as a function of time. We do not detect any significant trends on this timescale ($\sim$months) for either pulsar, but the constraints could be greatly improved with more accurate ionospheric models. For the Vela pulsar, the combination of our data and historical data from the published literature have enabled us to model long-term correlated trends in RM and dispersion measure (DM) over the past two decades. We detect a change in DM of $\sim 0.3\,\mathrm{cm}^{-3}\,\mathrm{pc}$ which corresponds to a change in electron density of $\sim 10^5\,\mathrm{cm}^{-3}$ on a transverse length scale of $\sim$1-2 au. The apparent magnetic field strength in the time-varying region changes from $240^{+30}_{-20}\,\mu\mathrm{G}$ to $-6.2^{+0.7}_{-0.9}\,\mu\mathrm{G}$ over the time span of the data set. As well as providing an important validation of polarimetry, this work highlights the pulsar monitoring capabilities of SKA-Low stations, and the niche science opportunities they offer for high-precision polarimetry and probing the microstructure of the magneto-ionic interstellar medium., Comment: 16 pages, 9 figures, 2 tables, published in PASA

We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g., humans or real-time systems) often face challenges in processing all possible information at hand (e.g., due to cognitive biases or resource constraints), which can degrade decision efficacy. DISS addresses these challenges through policies that dynamically select the most effective features and options to forward to the black-box decision-maker for prediction. We develop a scalable frequentist data acquisition strategy and a decision-maker mimicking technique for enhanced budget efficiency. We explore several impactful applications of DISS, including biased decision-maker support, expert assignment optimization, large language model decision support, and interpretability. Empirical validation of our proposed DISS methodology shows superior performance to state-of-the-art methods across various applications.

Nonlinear superconducting circuits can be used as amplifiers, transducers, and qubits. Only a handful of superconducting circuits have been analyzed or built, so many high-performing configurations likely remain undiscovered. We seek to catalog this design space by enumerating all superconducting circuits -- up to five nodes in size -- built of capacitors, inductors, and Josephson junctions. Using graph isomorphism, we remove redundant configurations to construct a set of unique circuits. We define the concept of a ``Hamiltonian class'' and sort the resulting circuit Hamiltonians based on the types of variables present and the structure of their coupling. Finally, we search for novel superconducting qubits by explicitly considering all three node circuits, showing how the results of our enumeration can be used as a starting point for circuit design tasks., Comment: 16 pages, 11 figures, 11 tables

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license