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Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a 1H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists' evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment, extending its potential clinical applications., Comment: Accepted by Medical Image Analysis (MedIA)

Advancements in light modulator technology have been driving discoveries and progress across various fields. The problem of large-scale coherent optical control of atomic quantum systems-including cold atoms, ions, and solid-state color centers-presents among the most stringent requirements. This motivates a new generation of high-speed large-scale modulator technology with the following requirements: (R1) operation at a design wavelength of choice in the visible (VIS) to near-infrared (NIR) spectrum, (R2) a scalable technology with a high channel density (> 100mm-2 ), (R3) a high modulation speed (> 100MHz), and (R4) a high extinction ratio (> 20 dB). To fulfill these requirements, we introduce a modulator technology based on piezoelectrically actuated silicon nitride resonant waveguide gratings fabricated on 200mm diameter silicon wafers with CMOS compatible processes. We present a proof-of-concept device with 4 x 4 individually addressable 50 {mu}m x 50 {mu}m pixels or channels, each containing a resonant waveguide grating with a ~ 780 nm design wavelength, supporting > 100MHz modulation speeds, and a spectral response with > 20 dB extinction., Comment: 9 pages + 2 pages supplementary

We present archival HST and JWST ultraviolet through near infrared time series photometric observations of a massive minimal-contact binary candidate in the metal-poor galaxy WLM ($Z = 0.14 Z_{\odot}$). This discovery marks the lowest metallicity contact binary candidate observed to date. We determine the nature of the two stars in the binary by using the eclipsing binary modeling software (PHysics Of Eclipsing BinariEs; PHOEBE) to train a neural network to fit our observed panchromatic multi-epoch photometry. The best fit model consists of two hot MS stars ($T_1=29800^{+2300}_{-1700}$ K, $M_1=16^{+2}_{-3}~M_{\odot}$, and $T_2=18000^{+5000}_{-5000}$ K, $M_2=7^{+5}_{-3}~M_{\odot}$). We discuss plausible evolutionary paths for the system, and suggest the system is likely to be currently in a contact phase before ultimately ending in a merger. Future spectroscopy will help to further narrow down evolutionary pathways. This work showcases a novel use of data of JWST and HST imaging originally taken to characterize RR Lyrae. We expect time series imaging from LSST, BlackGEM, etc. to uncover similar types of objects in nearby galaxies., Comment: comments welcome

The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, potentially leading to distrust in human-machine interactions. To address this issue, we introduce a novel dataset of 1,015 tissue microarray core images, annotated by an international group of 54 pathologists. The annotations provide detailed localized pattern descriptions for Gleason grading in line with international guidelines. Utilizing this dataset, we develop an inherently explainable AI system based on a U-Net architecture that provides predictions leveraging pathologists' terminology. This approach circumvents post-hoc explainability methods while maintaining or exceeding the performance of methods trained directly for Gleason pattern segmentation (Dice score: 0.713 $\pm$ 0.003 trained on explanations vs. 0.691 $\pm$ 0.010 trained on Gleason patterns). By employing soft labels during training, we capture the intrinsic uncertainty in the data, yielding strong results in Gleason pattern segmentation even in the context of high interobserver variability. With the release of this dataset, we aim to encourage further research into segmentation in medical tasks with high levels of subjectivity and to advance the understanding of pathologists' reasoning processes., Comment: 58 pages, 15 figures (incl. supplementary)

The Type-II Seesaw Model provides an attractive scenario to account for Majorana-neutrino masses. Its extended Higgs sector, if sufficiently light, can have a rich and distinctive phenomenology at the LHC while yielding automatically an essentially Standard-Model-Higgs-like state. Several phenomenological studies have been devoted to the scalar sector of this model, as well as experimental searches focusing mostly on the (doubly-)charged states. In this paper we present an exhaustive study of the main production and decay channels of all the non-standard scalar states originating from the $SU(2)_L$ doublet and a complex triplet of the model. We stick to scenarios where lepton-number-violating decays are suppressed, for which present experimental limits are still weak, highlighting theoretical parameter sensitivities that were not previously emphasized in the literature and the uncertainties they can induce for the experimental searches at the LHC. A comprehensive classification of the various cascade decays and corresponding Standard Model particle multiplicities is provided. As an illustration, a detailed prospective search study at the LHC with an ATLAS-like detector is carried out on some benchmark points, for charged, doubly-charged, and, for the first time, neutral state productions, Comment: 58 pages, 13 figures

The proximity of the Large and Small Magellanic Clouds (LMC and SMC) provides the opportunity to study the impact of dwarf-dwarf interactions on their mass assembly with a unique level of detail. To this end, we analyze two-filter broadband imaging of 83 Hubble Space Telescope (HST) pointings covering 0.203 deg$^2$ towards the SMC, extending out to $\sim$3.5 kpc in projection from its optical center. Lifetime star formation histories (SFHs) fit to each pointing independently reveal an outside-in age gradient such that fields in the SMC outskirts are older on average. We measure radial gradients of the lookback time to form 90%, 75% and 50% of the cumulative stellar mass for the first time, finding $\delta$($\tau_{90}$, $\tau_{75}$, $\tau_{50}$)/$\delta$R = (0.61$^{+0.08}_{-0.07}$, 0.65$^{+0.09}_{-0.08}$, 0.82$^{+0.12}_{-0.16}$) Gyr/kpc assuming PARSEC evolutionary models and a commonly used elliptical geometry of the SMC, although our results are robust to these assumptions. The wing of the SMC deviates from this trend, forming 25\% of its cumulative mass over the most recent 3 Gyr due to a best-fit star formation rate that remains approximately constant. Our results are consistent with chemodynamical evidence of a tidally stripped SMC component in the foreground, and imply contributions to the observed SFH from multiple previous LMC-SMC interactions. We also compare our SMC SFH with results from a companion study of the LMC, finding that while the two galaxies present different internal, spatially resolved SFH trends, both the LMC and SMC have similar near-constant lifetime SFHs when viewed globally., Comment: ApJ in press. 40 pages, 18 figures, 5 tables including Appendices

The proximity of the Magellanic Clouds provides the opportunity to study interacting dwarf galaxies near a massive host, and spatial trends in their stellar population properties in particular, with a unique level of detail. The Scylla pure parallel program has obtained deep (80% complete to >1 mag below the ancient main sequence turnoff), homogeneous two-filter Hubble Space Telescope (HST) imaging sampling the inner star-forming disk of the Large Magellanic Cloud (LMC), the perfect complement to shallower, contiguous ground-based surveys. We harness this imaging together with extant archival data and fit lifetime star formation histories (SFHs) to resolved color-magnitude diagrams (CMDs) of 111 individual fields, using three different stellar evolutionary libraries. We validate per-field recovered distances and extinctions as well as the combined global LMC age-metallicity relation and SFH against independent estimates. We find that the present-day radial age gradient reverses from an inside-out gradient in the inner disk to an outside-in gradient beyond $\sim$2 disk scalelengths, supported by ground-based measurements. The gradients become relatively flatter at earlier lookback times, while the location of the inversion remains constant over an order of magnitude in lookback time, from $\sim$1$-$10 Gyr. This suggests at least one mechanism that predates the recent intense LMC-SMC interaction. We compare observed radial age trends to other late-type galaxies at fixed stellar mass and discuss similarities and differences in the context of potential drivers, implying strong radial migration in the LMC., Comment: ApJ in press. 45 pages, 17 figures, 9 tables including Appendices

Scylla is a deep Hubble Space Telescope survey of the stellar populations, interstellar medium and star formation in the LMC and SMC. As a pure-parallel complement to the Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) survey, Scylla obtained 342 orbits of ultraviolet (UV) through near-infrared (IR) imaging of the LMC and SMC with Wide Field Camera 3. In this paper, we describe the science objectives, observing strategy, data reduction procedure, and initial results from our photometric analysis of 96 observed fields. Although our observations were constrained by ULYSSES primary exposures, we imaged all fields in at least two filters (F475W and F814W), and 64% of fields in at least three and as many as seven WFC3 filters spanning the UV to IR. Overall, we reach average 50% completeness of $m_{\rm F225W}=26.0$, $m_{\rm F275W}=26.2$, $m_{\rm F336W}=26.9$, $m_{\rm F475W}=27.8$, $m_{\rm F814W}=25.5$, $m_{\rm F110W}=24.7$, and $m_{\rm F160W}=24.0$ Vega magnitudes in our photometric catalogs, which is faintward of the ancient main sequence turnoff in all filters. The primary science goals of Scylla include characterizing the structure and properties of dust in the MCs, as well as their spatially-resolved star formation and chemical enrichment histories. Our images and photometric catalogs, which represent the widest-area coverage of MCs with HST photometry to date, are available as a high-level science product at the Barbara A. Mikulski Archive for Space Telescopes., Comment: 25 pages, 16 figures, 8 tables. Accepted for publication in ApJS

Sodium MRI is an imaging technique used to visualize and quantify sodium concentrations in vivo, playing a role in many biological processes and potentially aiding in breast cancer characterization. Sodium MRI, however, suffers from inherently low signal-to-noise ratios (SNR) and spatial resolution, compared with conventional proton MRI. A deep-learning method, the Denoising Diffusion Probabilistic Models (DDPM), has demonstrated success across a wide range of denoising tasks, yet struggles with sodium MRI's unique noise profile, as DDPM primarily targets Gaussian noise. DDPM can distort features when applied to sodium MRI. This paper advances the DDPM by introducing the Rician Denoising Diffusion Probabilistic Models (RDDPM) for sodium MRI denoising. RDDPM converts Rician noise to Gaussian noise at each timestep during the denoising process. The model's performance is evaluated using three non-reference image quality assessment metrics, where RDDPM consistently outperforms DDPM and other CNN-based denoising methods., Comment: 3 figures

Recent advancements in diffusion models have significantly enhanced the quality of video generation. However, fine-grained control over camera pose remains a challenge. While U-Net-based models have shown promising results for camera control, transformer-based diffusion models (DiT)-the preferred architecture for large-scale video generation - suffer from severe degradation in camera motion accuracy. In this paper, we investigate the underlying causes of this issue and propose solutions tailored to DiT architectures. Our study reveals that camera control performance depends heavily on the choice of conditioning methods rather than camera pose representations that is commonly believed. To address the persistent motion degradation in DiT, we introduce Camera Motion Guidance (CMG), based on classifier-free guidance, which boosts camera control by over 400%. Additionally, we present a sparse camera control pipeline, significantly simplifying the process of specifying camera poses for long videos. Our method universally applies to both U-Net and DiT models, offering improved camera control for video generation tasks.

Cu$_2$OSeO$_3$ is a multiferroic insulating chiral magnet which exhibits various magnetic orderings at different conditions. The positions of these magnetic phase transitions are known to be sensitive to chemical substitution. Here, we present a universality class analysis of the Cu$_2$OSe$_{1-x}$Te$_x$O$_3$ with $(0\leq x \leq 0.1)$. Tellurium is a non-magnetic ion which, upon substitution into the selenium positions of the structure, applies a positive chemical pressure and expands the crystal lattice. Our SANS and magnetometry data indicate that Te inclusion lowers the paramagnetic to helical ordering temperature and the critical field required for the conical to field polarised phase transition. By using the Heat-map Modified Iteration Method that evaluates critical behaviour on both sides of the transition, we show that the Heisenberg to Ising universality class transition of the initial ordering is robust to internal chemical pressure. Additionally, we attribute the decreases with doping in both critical temperature and critical field to be due to decreases in the strength of the Dzyaloshinskii-Moriya and Symmetric Exchange Interactions., Comment: 21 pages and 9 figures (Manuscript)

Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing, and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE, which consistently outperformed standard cylinder modeling approaches. Our code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF.

Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol parsers, and language runtimes. Specifications for such systems are often standardized in natural language documents, like Instruction Set Architecture (ISA) specifications, Wasm specifications or IETF RFC's. Large Language Models (LLMs) have demonstrated potential in both generating tests and handling large volumes of natural language text, making them well-suited for utilizing artifacts like specification documents, bug reports, and code implementations. In this work, we leverage natural language and code artifacts to guide LLMs to generate targeted, meaningful tests that highlight meaningful behavioral differences between implementations, including those corresponding to bugs. We introduce DiffSpec, a framework for generating differential tests with LLMs using prompt chaining. We demonstrate the efficacy of DiffSpec on two different systems, namely, eBPF runtimes and Wasm validators. Using DiffSpec, we generated 359 differentiating tests, uncovering at least four distinct and confirmed bugs in eBPF, including a kernel memory leak, inconsistent behavior in jump instructions, and undefined behavior when using the stack pointer. We also found 279 differentiating tests in Wasm validators, that point to at least 2 confirmed and fixed bugs in Wizard Engine.

Nanophotonic resonators are central to numerous applications, from efficient spin-photon interfaces to laser oscillators and precision sensing. A leading approach consists of photonic crystal (PhC) cavities, which have been realized in a wide range of dielectric materials. However, translating proof-of-concept devices into a functional system entails a number of additional challenges, inspiring new approaches that combine: resonators with wavelength-scale confinement and high quality factors; scalable integration with integrated circuits and photonic circuits; electrical or mechanical cavity tuning; and, in many cases, a need for heterogeneous integration with functional materials such as III-V semiconductors or diamond color centers for spin-photon interfaces. Here we introduce a concept that generates a finely tunable PhC cavity at a select wavelength between two heterogeneous optical materials whose properties satisfy the above requirements. The cavity is formed by stamping a hard-to-process material with simple waveguide geometries on top of an easy-to-process material consisting of dielectric grating mirrors and active tuning capability. We simulate our concept for the particularly challenging design problem of multiplexed quantum repeaters based on arrays of cavity-coupled diamond color centers, achieving theoretically calculated unloaded quality factors of $10^6$, mode volumes as small as $1.2(\lambda/n_{eff})^3$, and maintaining >60 percent total on-chip collection efficiency of fluorescent photons. We further introduce a method of low-power piezoelectric tuning of these hybrid diamond cavities, simulating optical resonance shifts up to ~760 GHz and color center fluorescence tuning of 5 GHz independent of cavity tuning. These results will motivate integrated photonic cavities toward larger scale systems-compatible designs., Comment: 15 pages, 6 figures, 2 supplementary figures

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with inter-granular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These might include tungsten-based high-entropy alloys (W-HEAs) and the so-called tungsten-based "smart alloys". In this work, we present a computational approach that uses first-principles DFT electronic structure calculations to understand the effect of the chemical environment on tungsten-based alloys. In particular, we compared the Special Quasi-random Structure (SQS) and the DFT-coupled Monte Carlo (MC-DFT) methods when investigating the short-range order and elastic properties of two equimolar WCrTaTi and WVTaTi W-HEAs, and two WCrTi and WCrY W-SAs. We found that structures after MC-DFT calculation have lower cohesive energies than SQS structures, with shorter lattice constants, and large elastic properties values. Furthermore, distinct element segregation was studied by calculating the SRO parameter and radius distribution function. The total density of states suggested that the existence of SRO could improve the stability of the structure.

Ultrafast laser systems, those with a pulse duration on the order of picoseconds or less, have enabled advancements in a wide variety of fields. Of particular interest to this work, these laser systems are the key component to many High Energy Density (HED) physics experiments. Despite this, previous studies on the shape of the laser pulse within the HED community have focused primarily on pulse duration due to the relationship between pulse duration and peak intensity, while leaving the femtosecond scale structure of the pulse shape largely unstudied. To broaden the variety of potential pulses available for study, a method of reliably adjusting the pulse shape at the femtosecond scale using sub-nanometer resolution Direct Phase Control has been developed. This paper examines the capabilities of this new method compared to more commonplace dispersion-based pulse shaping methods. It also will detail the capabilities of the core algorithm driving this technique when used in conjunction with the WIZZLER and DAZZLER instruments that are common in high intensity laser labs. Finally, some discussion is given to possible applications on how the Direct Phase Control pulse shaping technique will be implemented in the future., Comment: 7 pages, 5 figures, 3 of these figures contain subfigures such that 11 image (png) files are used

Hundreds of exoplanets between 1-1.8 times the size of the Earth have been discovered on close in orbits. However, these planets show such a diversity in densities that some appear to be made entirely of iron, while others appear to host gaseous envelopes. To test this diversity in composition, we update the masses of 5 rocky exoplanets (HD 93963 A b, Kepler-10 b, Kepler-100 b, Kepler-407 b, and TOI-1444 b) and present the confirmation of a new planet (TOI-1011) using 187 high precision RVs from Gemini/MAROON-X and Keck/KPF. Our updated planet masses suggest compositions closer to that of the Earth than previous literature values for all planets in our sample. In particular, we report that two previously identified ``super-Mercuries'' (Kepler-100 b and HD 93963 A b) have lower masses that suggest less iron-rich compositions. We then compare the ratio of iron to rock-building species to the abundance ratios of those elements in their host stars. These updated planet compositions do not suggest a steep relationship between planet and host star compositions, contradictory to previous results, and suggest that planets and host stars have similar abundance ratios., Comment: Submitted to AJ 09/30/2024

Policies significantly shape computation's societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the ``border'' of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI's futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes., Comment: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24)

We present PDFed, a decentralized, aggregator-free, and asynchronous federated learning protocol for training image diffusion models using a public blockchain. In general, diffusion models are prone to memorization of training data, raising privacy and ethical concerns (e.g., regurgitation of private training data in generated images). Federated learning (FL) offers a partial solution via collaborative model training across distributed nodes that safeguard local data privacy. PDFed proposes a novel sample-based score that measures the novelty and quality of generated samples, incorporating these into a blockchain-based federated learning protocol that we show reduces private data memorization in the collaboratively trained model. In addition, PDFed enables asynchronous collaboration among participants with varying hardware capabilities, facilitating broader participation. The protocol records the provenance of AI models, improving transparency and auditability, while also considering automated incentive and reward mechanisms for participants. PDFed aims to empower artists and creators by protecting the privacy of creative works and enabling decentralized, peer-to-peer collaboration. The protocol positively impacts the creative economy by opening up novel revenue streams and fostering innovative ways for artists to benefit from their contributions to the AI space., Comment: Accepted to CM SIGGRAPH European Conference on Visual Media Production 2024

Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for pluralistic AI deliberation. Plurals consists of Agents (LLMs, optionally with personas) which deliberate within customizable Structures, with Moderators overseeing deliberation. Plurals is a generator of simulated social ensembles. Plurals integrates with government datasets to create nationally representative personas, includes deliberation templates inspired by democratic deliberation theory, and allows users to customize both information-sharing structures and deliberation behavior within Structures. Six case studies demonstrate fidelity to theoretical constructs and efficacy. Three randomized experiments show simulated focus groups produced output resonant with an online sample of the relevant audiences (chosen over zero-shot generation in 75% of trials). Plurals is both a paradigm and a concrete system for pluralistic AI. The Plurals library is available at https://github.com/josh-ashkinaze/plurals and will be continually updated.

Latency and energy consumption are key metrics in the performance of deep neural network (DNN) accelerators. A significant factor contributing to latency and energy is data transfers. One method to reduce transfers or data is reusing data when multiple operations use the same data. Fused-layer accelerators reuse data across operations in different layers by retaining intermediate data in on-chip buffers, which has been shown to reduce energy consumption and latency. Moreover, the intermediate data is often tiled (i.e., broken into chunks) to reduce the on-chip buffer capacity required to reuse the data. Because on-chip buffer capacity is frequently more limited than computation units, fused-layer dataflow accelerators may also recompute certain parts of the intermediate data instead of retaining them in a buffer. Achieving efficient trade-offs between on-chip buffer capacity, off-chip transfers, and recomputation requires systematic exploration of the fused-layer dataflow design space. However, prior work only explored a subset of the design space, and more efficient designs are left unexplored. In this work, we propose (1) a more extensive design space that has more choices in terms of tiling, data retention, recomputation and, importantly, allows us to explore them in combination, (2) a taxonomy to systematically specify designs, and (3) a model, LoopTree, to evaluate the latency, energy consumption, buffer capacity requirements, and off-chip transfers of designs in this design space. We validate our model against a representative set of prior architectures, achieving a worst-case 4% error. Finally, we present case studies that show how exploring this larger space results in more efficient designs (e.g., up to a 10$\times$ buffer capacity reduction to achieve the same off-chip transfers)., Comment: To be published in IEEE Transactions on Circuits and Systems for Artificial Intelligence

Non-consensual intimate media (NCIM) presents internet-scale harm to individuals who are depicted. One of the most powerful tools for requesting its removal is the Digital Millennium Copyright Act (DMCA). However, the DMCA was designed to protect copyright holders rather than to address the problem of NCIM. Using a dataset of more than 54,000 DMCA reports and over 85 million infringing URLs spanning over a decade, this paper evaluates the efficacy of the DMCA for NCIM takedown. Results show less than 50% of infringing URLs are removed from website hosts in 60 days, and Google Search takes a median of 11.7 days to deindex infringing content. Across web hosts, only 4% of URLs are removed within the first 48 hours. Additionally, the most frequently reported domains for non-commercial NCIM are smaller websites, not large platforms. We stress the need for new laws that ensure a shorter time to takedown that are enforceable across big and small platforms alike., Comment: under review

We investigate a human-like interpretable model of video understanding. Humans recognise complex activities in video by recognising critical spatio-temporal relations among explicitly recognised objects and parts, for example, an object entering the aperture of a container. To mimic this we build on a model which uses positions of objects and hands, and their motions, to recognise the activity taking place. To improve this model we focussed on three of the most confused classes (for this model) and identified that the lack of 3D information was the major problem. To address this we extended our basic model by adding 3D awareness in two ways: (1) A state-of-the-art object detection model was fine-tuned to determine the difference between "Container" and "NotContainer" in order to integrate object shape information into the existing object features. (2) A state-of-the-art depth estimation model was used to extract depth values for individual objects and calculate depth relations to expand the existing relations used our interpretable model. These 3D extensions to our basic model were evaluated on a subset of three superficially similar "Putting" actions from the Something-Something-v2 dataset. The results showed that the container detector did not improve performance, but the addition of depth relations made a significant improvement to performance., Comment: 5 pages, This manuscript has been accepted at the Human-inspired Computer Vision (HCV) ECCV 2024 Workshop. arXiv admin note: text overlap with arXiv:2107.05319

Non-consensual intimate media (NCIM) inflicts significant harm. Currently, victim-survivors can use two mechanisms to report NCIM - as a non-consensual nudity violation or as copyright infringement. We conducted an audit study of takedown speed of NCIM reported to X (formerly Twitter) of both mechanisms. We uploaded 50 AI-generated nude images and reported half under X's "non-consensual nudity" reporting mechanism and half under its "copyright infringement" mechanism. The copyright condition resulted in successful image removal within 25 hours for all images (100% removal rate), while non-consensual nudity reports resulted in no image removal for over three weeks (0% removal rate). We stress the need for targeted legislation to regulate NCIM removal online. We also discuss ethical considerations for auditing NCIM on social platforms., Comment: under review

The physical processes that produce X-ray Quasi-Periodic Eruptions (QPEs) recently discovered from the nuclei of several low-redshift galaxies are mysterious. Several pieces of observational evidence strongly suggest a link between QPEs and Tidal Disruption Events (TDE). Previous studies also reveal that the morphologies of TDE host galaxies are highly concentrated, with high Sersic indicies, bulge-to-total light (B/T) ratios, and stellar surface mass densities relative to the broader galaxy population. We use these distinctive properties to test the link between QPEs and TDEs, by comparing these parameters of QPE host galaxies to TDE host galaxies. We employ archival Legacy Survey images of a sample of 9 QPE host galaxies and a sample of 13 TDE host galaxies, and model their surface brightness profiles. We show that QPE host galaxies have high Sersic indices of ~3, high B/T ratios of ~0.5, and high surface mass densities of ~10^10 Msun kpc^-2. These properties are similar to TDE host galaxies, but are in strong contrast to a mass- and redshift-matched control sample of galaxies. We also find tentative evidence that the central black holes in both QPE and TDE host galaxies are undermassive relative to their stellar mass. The morphological similarities between QPE and TDE host galaxies at the population level add to the mounting evidence of a physical link between these phenomena, and favor QPE models that also invoke TDEs., Comment: 12 pages, 4 figures, submitted to ApJ

Adversarial multiplayer games are an important object of study in multiagent learning. In particular, polymatrix zero-sum games are a multiplayer setting where Nash equilibria are known to be efficiently computable. Towards understanding the limits of tractability in polymatrix games, we study the computation of Nash equilibria in such games where each pair of players plays either a zero-sum or a coordination game. We are particularly interested in the setting where players can be grouped into a small number of teams of identical interest. While the three-team version of the problem is known to be PPAD-complete, the complexity for two teams has remained open. Our main contribution is to prove that the two-team version remains hard, namely it is CLS-hard. Furthermore, we show that this lower bound is tight for the setting where one of the teams consists of multiple independent adversaries. On the way to obtaining our main result, we prove hardness of finding any stationary point in the simplest type of non-convex-concave min-max constrained optimization problem, namely for a class of bilinear polynomial objective functions.

Compositing an object into an image involves multiple non-trivial sub-tasks such as object placement and scaling, color/lighting harmonization, viewpoint/geometry adjustment, and shadow/reflection generation. Recent generative image compositing methods leverage diffusion models to handle multiple sub-tasks at once. However, existing models face limitations due to their reliance on masking the original object during training, which constrains their generation to the input mask. Furthermore, obtaining an accurate input mask specifying the location and scale of the object in a new image can be highly challenging. To overcome such limitations, we define a novel problem of unconstrained generative object compositing, i.e., the generation is not bounded by the mask, and train a diffusion-based model on a synthesized paired dataset. Our first-of-its-kind model is able to generate object effects such as shadows and reflections that go beyond the mask, enhancing image realism. Additionally, if an empty mask is provided, our model automatically places the object in diverse natural locations and scales, accelerating the compositing workflow. Our model outperforms existing object placement and compositing models in various quality metrics and user studies.

Several domains of society will be disrupted once millions of high-quality qubits can be brought together to perform fault-tolerant quantum computing (FTQC). All quantum computing hardware available today is many orders of magnitude removed from the requirements for FTQC. The intimidating challenges associated with integrating such complex systems have already been addressed by the semiconductor industry -hence many qubit makers have retrofitted their technology to be CMOS-compatible. This compatibility, however, can have varying degrees ranging from the mere ability to fabricate qubits using a silicon wafer as a substrate, all the way to the co-integration of qubits with high-yield, low-power advanced electronics to control these qubits. Extrapolating the evolution of quantum processors to future systems, semiconductor spin qubits have unique advantages in this respect, making them one of the most serious contenders for large-scale FTQC. In this review, we focus on the overlap between state-of-the-art semiconductor spin qubit systems and CMOS industry Very Large-Scale Integration (VLSI) principles. We identify the main differences in spin qubit operation, material, and system requirements compared to well-established CMOS industry practices. As key players in the field are looking to collaborate with CMOS industry partners, this review serves to accelerate R&D towards the industrial scale production of FTQC processors., Comment: 36 pages, 5 figures

This paper addresses the situation in which treatment effects are reported using educational or psychological outcome measures comprised of multiple questions or "items." A distinction is made between a treatment effect on the construct being measured, which is referred to as impact, and item-specific treatment effects that are not due to impact, which are referred to as differential item functioning (DIF). By definition, impact generalizes to other measures of the same construct (i.e., measures that use different items), while DIF is dependent upon the specific items that make up the outcome measure. To distinguish these two cases, two estimators of impact are compared: an estimator that naively aggregates over items, and a less efficient one that is highly robust to DIF. The null hypothesis that both are consistent estimators of the true treatment impact leads to a Hausman-like specification test of whether the naive estimate is affected by item-level variation that would not be expected to generalize beyond the specific outcome measure used. The performance of the test is illustrated with simulation studies and a re-analysis of 34 item-level datasets from 22 randomized evaluations of educational interventions. In the empirical example, the dependence of reported effect sizes on the type of outcome measure (researcher-developed or independently developed) was substantially reduced after accounting for DIF. Implications for the ongoing debate about the role of researcher-developed assessments in education sciences are discussed., Comment: 32 pages, 6 figures

How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government's extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.

Building trees to represent or to fit distances is a critical component of phylogenetic analysis, metric embeddings, approximation algorithms, geometric graph neural nets, and the analysis of hierarchical data. Much of the previous algorithmic work, however, has focused on generic metric spaces (i.e., those with no a priori constraints). Leveraging several ideas from the mathematical analysis of hyperbolic geometry and geometric group theory, we study the tree fitting problem as finding the relation between the hyperbolicity (ultrametricity) vector and the error of tree (ultrametric) embedding. That is, we define a vector of hyperbolicity (ultrametric) values over all triples of points and compare the $\ell_p$ norms of this vector with the $\ell_q$ norm of the distortion of the best tree fit to the distances. This formulation allows us to define the average hyperbolicity (ultrametricity) in terms of a normalized $\ell_1$ norm of the hyperbolicity vector. Furthermore, we can interpret the classical tree fitting result of Gromov as a $p = q = \infty$ result. We present an algorithm HCCRootedTreeFit such that the $\ell_1$ error of the output embedding is analytically bounded in terms of the $\ell_1$ norm of the hyperbolicity vector (i.e., $p = q = 1$) and that this result is tight. Furthermore, this algorithm has significantly different theoretical and empirical performance as compared to Gromov's result and related algorithms. Finally, we show using HCCRootedTreeFit and related tree fitting algorithms, that supposedly standard data sets for hierarchical data analysis and geometric graph neural networks have radically different tree fits than those of synthetic, truly tree-like data sets, suggesting that a much more refined analysis of these standard data sets is called for., Comment: 12 pages, 2 figures, 14 pages supplementary. 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

New multinuclear MRI techniques, such as sodium MRI, generally suffer from low image quality due to an inherently low signal. Postprocessing methods, such as image denoising, have been developed for image enhancement. However, the assessment of these enhanced images is challenging especially considering when there is a lack of high resolution and high signal images as reference, such as in sodium MRI. No-reference Image Quality Assessment (NR-IQA) metrics are approaches to solve this problem. Existing learning-based NR-IQA metrics rely on labels derived from subjective human opinions or metrics like Signal-to-Noise Ratio (SNR), which are either time-consuming or lack accurate ground truths, resulting in unreliable assessment. We note that deep learning (DL) models have a unique characteristic in that they are specialized to a characteristic training set, meaning that deviations between the input testing data from the training data will reduce prediction accuracy. Therefore, we propose a novel DL-based NR-IQA metric, the Model Specialization Metric (MSM), which does not depend on ground-truth images or labels. MSM measures the difference between the input image and the model's prediction for evaluating the quality of the input image. Experiments conducted on both simulated distorted proton T1-weighted MR images and denoised sodium MR images demonstrate that MSM exhibits a superior evaluation performance on various simulated noises and distortions. MSM also has a substantial agreement with the expert evaluations, achieving an averaged Cohen's Kappa coefficient of 0.6528, outperforming the existing NR-IQA metrics., Comment: 13 pages, 3 figures

Detecting actions in videos, particularly within cluttered scenes, poses significant challenges due to the limitations of 2D frame analysis from a camera perspective. Unlike human vision, which benefits from 3D understanding, recognizing actions in such environments can be difficult. This research introduces a novel approach integrating 3D features and depth maps alongside RGB features to enhance action recognition accuracy. Our method involves processing estimated depth maps through a separate branch from the RGB feature encoder and fusing the features to understand the scene and actions comprehensively. Using the Side4Video framework and VideoMamba, which employ CLIP and VisionMamba for spatial feature extraction, our approach outperformed our implementation of the Side4Video network on the Something-Something V2 dataset. Our code is available at: https://github.com/SadeghRahmaniB/DEAR, Comment: 5 pages, 1 figure, 1 table, accepted at Human-inspired Computer Vision, ECCV

In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a "Retakes" round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of "human-like"). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play., Comment: The project website is at https://davidbdurst.com/mlmove/

Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness. We explore a solution to this conundrum based on batch parallelism, an approach for designing concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones. We present OBatcher-an OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. It solves the first challenge (how to use) by suggesting a new lightweight implicit batching design that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into efficient batch-parallel ones. We showcase OBatcher with a diverse set of benchmarks. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations and that (b) their throughput scales reasonably with the number of processors., Comment: Extended version of the OOPSLA'24 paper

Long-term Action Quality Assessment (AQA) evaluates the execution of activities in videos. However, the length presents challenges in fine-grained interpretability, with current AQA methods typically producing a single score by averaging clip features, lacking detailed semantic meanings of individual clips. Long-term videos pose additional difficulty due to the complexity and diversity of actions, exacerbating interpretability challenges. While query-based transformer networks offer promising long-term modeling capabilities, their interpretability in AQA remains unsatisfactory due to a phenomenon we term Temporal Skipping, where the model skips self-attention layers to prevent output degradation. To address this, we propose an attention loss function and a query initialization method to enhance performance and interpretability. Additionally, we introduce a weight-score regression module designed to approximate the scoring patterns observed in human judgments and replace conventional single-score regression, improving the rationality of interpretability. Our approach achieves state-of-the-art results on three real-world, long-term AQA benchmarks. Our code is available at: https://github.com/dx199771/Interpretability-AQA, Comment: Accepted to British Machine Vision Conference (BMVC) 2024

The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of truncation and validity in interpretation of results with an eye towards practicality; "reconstruction-level results" visits the question of how best to design analyses directly targeting inference of EFT parameters; "logistics of combining likelihoods" addresses the challenges of bringing a diverse array of measurements into a cohesive whole; "unfolded results" tackles the question of designing fiducial measurements for later use in EFT interpretations, and the benefits and limitations of unfolding; and "building a sample library" addresses how best to generate simulation samples for use in data analysis. This document serves as a summary of presentations, subsequent discussions, and actionable items identified over the course of the workshop.

Quantum computers must achieve large-scale, fault-tolerant operation to deliver on their promise of transformational processing power [1-4]. This will require thousands or millions of high-fidelity quantum gates and similar numbers of qubits [5]. Demonstrations using neutral-atom qubits trapped and manipulated by lasers have shown that this modality can provide high two-qubit gate (CZ) fidelities and scalable operation [6-10]. However, the gates in these demonstrations are driven by lasers that do not resolve individual qubits, with universal computation enabled by physical mid-circuit shuttling of the qubits. This relatively slow operation will greatly extend runtimes for useful, large-scale computation. Here we demonstrate a universal neutral-atom quantum computer with gate rates limited by optical switching times, rather than shuttling, by individually addressing tightly focused laser beams at an array of single atoms. We achieve CZ fidelity of 99.35(4)% and local single qubit RZ gate fidelity of 99.902(8)%. Moreover, we demonstrate non-destructive readout of alkali-atom qubits with sub-percent loss, which boosts operational speed. This technique also enables us to measure 99.73(3)% CZ fidelity with atom-loss events excluded, which is a record among long lived neutral-atom qubits and highlights the path to higher fidelity and error correction. Our results represent a critical step towards large-scale, fault-tolerant neutral-atom quantum computers that can execute computations on practical timescales.

Stellar flares from K and M dwarfs release panchromatic radiation characterized by a significantly higher brightness temperature ($\sim$9-20 kK) than the star. The increased frequency of magnetic activity on young low-mass stars results in the energy released during flaring events becoming a notable contributor to the radiation environment. This study focuses on the $\beta$ Pictoris moving group (24 $\pm$ 3 Myr) for the analysis of young, low-mass star flaring rates within the framework of larger flare studies. The calibration of long-term optical flare statistics is crucial to updating flare activity-age relations and the interpretation of exoplanet atmosphere observations. Using the $\beta$ Pictoris moving group, we develop a modular flare extraction pipeline sensitive to low-mass stellar flares in observations from the Transiting Exoplanet Survey Satellite. This pipeline is built to characterize flare properties of these stars such as total energy and cumulative flare rate. Consistent with previous studies, this sample (N=49) shows higher cumulative flare rates than early type and old field stars by at least an order of magnitude. Fitted flare frequency distributions for both early and late type M dwarfs show an average slope of $1.58 \pm 0.23$ with earlier stars flaring with lower or similar rates to late types. A typical member in this sample has daily ($\mathrm{\sim 1 \, d^{-1}}$ ) flares with TESS band energies of $10^{32} - 10^{33}$ ergs. The optical flare rates and energies for this group provide essential context into the co-evolution of host stars and associated planets., Comment: 13 pages, 7 figures, Accepted for publication in the Astrophysical Journal

We present the validation of two TESS super-Earth candidates transiting the mid-M dwarfs TOI-6002 and TOI-5713 every 10.90 and 10.44 days, respectively. The first star (TOI-6002) is located $32.038\pm0.019$ pc away, with a radius of $0.2409^{+0.0066}_{-0.0065}$ \rsun, a mass of $0.2105^{+0.0049}_{-0.0048}$ \msun, and an effective temperature of $3229^{+77}_{-57}$ K. The second star (TOI-5713) is located $40.946\pm0.032$ pc away, with a radius of $0.2985^{+0.0073}_{-0.0072}$ \rsun, a mass of $0.2653\pm0.0061$ \msun, and an effective temperature of $3225^{+41}_{-40}$ K. We validated the planets using TESS data, ground-based multi-wavelength photometry from many ground-based facilities, as well as high-resolution AO observations from Keck/NIRC2. TOI-6002 b has a radius of $1.65^{+0.22}_{-0.19}$ \re\ and receives $1.77^{+0.16}_{-0.11} S_\oplus$. TOI-5713 b has a radius of $1.77_{-0.11}^{+0.13} \re$ and receives $2.42\pm{0.11} S_\oplus$. Both planets are located near the radius valley and near the inner edge of the habitable zone of their host stars, which makes them intriguing targets for future studies to understand the formation and evolution of small planets around M-dwarf stars.