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We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

In this paper, we introduce flubbles, a new definition of "bubbles" corresponding to variants in a (pan)genome graph $G$. We then show a characterization for flubbles in terms of equivalence classes regarding cycles in an intermediate data structure we built from the spanning tree of the $G$, which leads us to a linear time and space solution for finding all flubbles. Furthermore, we show how a related characterization also allows us to efficiently detect what we define as hairpin inversions: a cycle preceded and followed by the same path in the graph; being the latter necessarily traversed both ways, this structure corresponds to inversions. Finally, Inspired by the concept of Program Structure Tree introduced fifty years ago to represent the hierarchy of the control structure of a program, we define a tree representing the structure of G in terms of flubbles, the flubble tree, which we also find in linear time. The hierarchy of variants introduced by the flubble tree paves the way for new investigations of (pan)genomic structures and their decomposition for practical analyses. We have implemented our methods into a prototype tool named povu which we tested on human and yeast data. We show that povu can find flubbles and also output the flubble tree while being as fast (or faster than) well established tools that find bubbles, such as vg and BubbleGun. Moreover, we show how, within the same time, povu can find hairpin inversions that, to the best of our knowledge, no other tool is able to find. Our tool is freely available at https://github.com/urbanslug/povu/ under the MIT License.

Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio data, we investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM, a multimodal foundation model built on the largest wearable-signals dataset with the most extensive range of sensor modalities to date. Our results establish the scaling laws of LSM for tasks such as imputation, interpolation and extrapolation, both across time and sensor modalities. Moreover, we highlight how LSM enables sample-efficient downstream learning for tasks like exercise and activity recognition.

Haptic devices have shown to be valuable in supplementing surgical training, especially when providing haptic feedback based on user performance metrics such as wrench applied by the user on the tool. However, current 6-axis force/torque sensors are prohibitively expensive. This paper presents the design and calibration of a low-cost, six-axis force/torque sensor specially designed for laparoscopic haptic training applications. The proposed design uses Hall-effect sensors to measure the change in the position of magnets embedded in a silicone layer that results from an applied wrench to the device. Preliminary experimental validation demonstrates that these sensors can achieve an accuracy of 0.45 N and 0.014 Nm, and a theoretical XY range of +/-50N, Z range of +/-20N, and torque range of +/-0.2Nm. This study indicates that the proposed low-cost 6-axis force/torque sensor can accurately measure user force and provide useful feedback during laparoscopic training on a haptic device.

The development of tools and techniques to analyze and extract organizations data habits from privacy policies are critical for scalable regulatory compliance audits. Unfortunately, these tools are becoming increasingly limited in their ability to identify compliance issues and fixes. After all, most were developed using regulation-agnostic datasets of annotated privacy policies obtained from a time before the introduction of landmark privacy regulations such as EUs GDPR and Californias CCPA. In this paper, we describe the first open regulation-aware dataset of expert-annotated privacy policies, C3PA (CCPA Privacy Policy Provision Annotations), aimed to address this challenge. C3PA contains over 48K expert-labeled privacy policy text segments associated with responses to CCPA-specific disclosure mandates from 411 unique organizations. We demonstrate that the C3PA dataset is uniquely suited for aiding automated audits of compliance with CCPA-related disclosure mandates., Comment: 9 pages, EMNLP 2024

Summary: With the rapid development of long-read sequencing technologies, the era of individual complete genomes is approaching. We have developed wgatools, a cross-platform, ultrafast toolkit that supports a range of whole genome alignment (WGA) formats, offering practical tools for conversion, processing, statistical evaluation, and visualization of alignments, thereby facilitating population-level genome analysis and advancing functional and evolutionary genomics. Availability and Implementation: wgatools supports diverse formats and can process, filter, and statistically evaluate alignments, perform alignment-based variant calling, and visualize alignments both locally and genome-wide. Built with Rust for efficiency and safe memory usage, it ensures fast performance and can handle large datasets consisting of hundreds of genomes. wgatools is published as free software under the MIT open-source license, and its source code is freely available at https://github.com/wjwei-handsome/wgatools. Contact: weiwenjie@westlake.edu.cn (W.W.) or liuhaijun@yzwlab.cn (H.-J.L.).

We present nifty-ls, a software package for fast and accurate evaluation of the Lomb-Scargle periodogram. nifty-ls leverages the fact that Lomb-Scargle can be computed using a non-uniform FFT (NUFFT), which we evaluate with the Flatiron Institute NUFFT package (finufft). This approach achieves a many-fold speedup over the Press & Rybicki (1989) method as implemented in Astropy and is simultaneously many orders of magnitude more accurate. nifty-ls also supports fast evaluation on GPUs via CUDA and integrates with the Astropy Lomb-Scargle interface. nifty-ls is publicly available as open-source software., Comment: 4 pages, 1 figure, submitted to RNAAS. Software available at https://github.com/flatironinstitute/nifty-ls/

Computational Pangenomics is an emerging field that studies genetic variation using a graph structure encompassing multiple genomes. Visualizing pangenome graphs is vital for understanding genome diversity. Yet, handling large graphs can be challenging due to the high computational demands of the graph layout process. In this work, we conduct a thorough performance characterization of a state-of-the-art pangenome graph layout algorithm, revealing significant data-level parallelism, which makes GPUs a promising option for compute acceleration. However, irregular data access and the algorithm's memory-bound nature present significant hurdles. To overcome these challenges, we develop a solution implementing three key optimizations: a cache-friendly data layout, coalesced random states, and warp merging. Additionally, we propose a quantitative metric for scalable evaluation of pangenome layout quality. Evaluated on 24 human whole-chromosome pangenomes, our GPU-based solution achieves a 57.3x speedup over the state-of-the-art multithreaded CPU baseline without layout quality loss, reducing execution time from hours to minutes., Comment: SC 2024

We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors., Comment: 11 pages, 8 figures, 1 table, accepted to IEEE VIS 2024 Conference

In many classification tasks designed for AI or human to solve, gold labels are typically included within the label space by default, often posed as "which of the following is correct?" This standard setup has traditionally highlighted the strong performance of advanced AI, particularly top-performing Large Language Models (LLMs), in routine classification tasks. However, when the gold label is intentionally excluded from the label space, it becomes evident that LLMs still attempt to select from the available label candidates, even when none are correct. This raises a pivotal question: Do LLMs truly demonstrate their intelligence in understanding the essence of classification tasks? In this study, we evaluate both closed-source and open-source LLMs across representative classification tasks, arguing that the perceived performance of LLMs is overstated due to their inability to exhibit the expected comprehension of the task. This paper makes a threefold contribution: i) To our knowledge, this is the first work to identify the limitations of LLMs in classification tasks when gold labels are absent. We define this task as Classify-w/o-Gold and propose it as a new testbed for LLMs. ii) We introduce a benchmark, Know-No, comprising two existing classification tasks and one new task, to evaluate Classify-w/o-Gold. iii) This work defines and advocates for a new evaluation metric, OmniAccuracy, which assesses LLMs' performance in classification tasks both when gold labels are present and absent.

Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions. In this paper, we focus on evaluating the probabilistic reasoning capabilities of LMs using idealized and real-world statistical distributions. We perform a systematic evaluation of state-of-the-art LMs on three tasks: estimating percentiles, drawing samples, and calculating probabilities. We evaluate three ways to provide context to LMs 1) anchoring examples from within a distribution or family of distributions, 2) real-world context, 3) summary statistics on which to base a Normal approximation. Models can make inferences about distributions, and can be further aided by the incorporation of real-world context, example shots and simplified assumptions, even if these assumptions are incorrect or misspecified. To conduct this work, we developed a comprehensive benchmark distribution dataset with associated question-answer pairs that we have released publicly., Comment: EMNLP 2024 (Main), 21 pages, 9 figures, 2 tables

We a present and analyze rpCholesky-QR, a randomized preconditioned Cholesky-QR algorithm for computing the thin QR factorization of real mxn matrices with rank n. rpCholesky-QR has a low orthogonalization error, a residual on the order of machine precision, and does not break down for highly singular matrices. We derive rigorous and interpretable two-norm perturbation bounds for rpCholesky-QR that require a minimum of assumptions. Numerical experiments corroborate the accuracy of rpCholesky-QR for preconditioners sampled from as few as 3n rows, and illustrate that the two-norm deviation from orthonormality increases with only the condition number of the preconditioned matrix, rather than its square -- even if the original matrix is numerically singular.

We measure the clustering of Lyman Alpha Emitting galaxies (LAEs) selected from the One-hundred-square-degree DECam Imaging in Narrowbands (ODIN) survey, with spectroscopic follow-up from Dark Energy Spectroscopic Instrument (DESI). We use DESI spectroscopy to optimize our selection and to constrain the interloper fraction and redshift distribution of our narrow-band selected sources. We select samples of 4000 LAEs at z=2.45 and 3.1 in 9 sq.deg. centered on the COSMOS field with median LyA fluxes of 10^{-16}erg/s/cm2. Covariances and cosmological inferences are obtained from a series of mock catalogs built upon high-resolution N-body simulations that match the footprint, number density, redshift distribution and observed clustering of the sample. We find that both samples have a correlation length of r_0=(3.0\pm 0.2)Mpc/h. Within our fiducial cosmology these correspond to 3D number densities of 10^{-3} h^3/Mpc^3 and, from our mock catalogs, biases of 1.7 and 2.0 at z=2.45 and 3.1, respectively. We discuss the implications of these measurements for the use of LAEs as large-scale structure tracers for high-redshift cosmology., Comment: 26 pages, 13 figures; minor revisions to match version accepted by JCAP

In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard, representing state-of-the-art methods extensively employed for fully training or pre-training networks across various vision tasks. The exploration of fine-tuning approaches has emerged as a current focal point, addressing the need for efficient model tuning with reduced GPU memory usage and time costs while enhancing overall performance, as exemplified by methodologies like low-rank adaptation (LoRA). Key questions arise: which pre-training technique yields optimal results - adversarial, contrastive, reconstruction, or diffusion denoising? How does the performance of these approaches vary as the complexity of fine-tuning is adjusted? This study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks in independent identical distribution (IID) cohorts. We underscore the significance of fine-tuning by examining various cases, including full tuning, decoder tuning, top-level tuning, and fine-tuning of linear parameters using LoRA. Systematic summaries of model performance and efficiency are presented, leveraging metrics such as accuracy, time cost, and memory efficiency. To empirically demonstrate our findings, we focus on a multi-task segmentation-classification challenge involving the paracingulate sulcus (PCS) using different 3D Convolutional Neural Network (CNN) architectures by using the TOP-OSLO cohort comprising 596 subjects.

Using a scale-free $N$-body simulation generated with the ABACUS $N$-body code, we test the robustness of halo mass accretion histories via their convergence to self-similarity. We compare two halo finders, ROCKSTAR and COMPASO. We find superior self-similarity in halo mass accretion histories determined using ROCKSTAR, with convergence to 5% or better between $\sim10^2$ to $10^5$ particles. For COMPASO we find weaker convergence over a similar region, with at least 10% between $\sim10^2$ to $10^4$ particles. Furthermore, we find the convergence to self-similarity improves as the simulation evolves, with the largest and deepest regions of convergence appearing after the scale factor quadrupled from the time at which non-linear structures begin to form. With sufficient time evolution, halo mass accretion histories are converged to self-similarity within 5% with as few as $\sim70$ particles for COMPASO and within 2% for as few as $\sim30$ particles for ROCKSTAR., Comment: 19 pages, 12 figures

We present and analyze PackIt!, a turn-based game consisting of packing rectangles on an $n \times n$ grid. PackIt! can be easily played on paper, either as a competitive two-player game or in \emph{solitaire} fashion. On the $t$-th turn, a rectangle of area $t$ or $t+1$ must be placed in the grid. In the two-player format of PackIt! whichever player places a rectangle last wins, whereas the goal in the solitaire variant is to perfectly pack the $n \times n$ grid. We analyze conditions for the existence of a perfect packing over $n \times n$, then present an automated reasoning approach that allows finding perfect games of PackIt! up to $n = 50$ which includes a novel SAT-encoding technique of independent interest, and conclude by proving an NP-hardness result., Comment: Accepted at FUN with Algorithms 2024

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community. The existing deep learning systems for health acoustics are often narrowly trained and evaluated on a single task, which is limited by data and may hinder generalization to other tasks. To mitigate these gaps, we develop HeAR, a scalable self-supervised learning-based deep learning system using masked autoencoders trained on a large dataset of 313 million two-second long audio clips. Through linear probes, we establish HeAR as a state-of-the-art health audio embedding model on a benchmark of 33 health acoustic tasks across 6 datasets. By introducing this work, we hope to enable and accelerate further health acoustics research., Comment: 4 tables, 4 figures, 6 supplementary tables, 3 supplementary figures

A measurement of a primordial non-Gaussianity (PNG) signal through late- or early-Universe probes has the potential to transform our understanding of the physics of the primordial Universe. While large-scale structure observables in principle contain vital information, interpreting these measurements is challenging due to poorly understood astrophysical effects. Luckily, $N$-body simulations, such as the public \textsc{AbacusPNG} set presented in this study, consisting of 9 boxes, each of size $L_{\rm box} = 2~{\rm Gpc}/h$ and particle mass of $1.01 \times 10^{10} \ M_\odot/h$, provide a viable path forward. As validation, we find good agreement between the simulations and our expectations from one-loop perturbation theory and the `separate universe' method for the matter bispectrum, matter power spectrum and the halo bias parameter associated with PNG, $b_\phi$. As a science application, we investigate the link between halo assembly bias and $b_\phi$ for halo properties known to play a vital role in accurately predicting galaxy clustering: concentration, shear (environment), and accretion rate. We find a strong response for all three parameters, suggesting that the connection between $b_\phi$ and the assembly history of halos needs to be taken into account by future PNG analyses. We further perform the first study of the $b_\phi$ parameter from fits to early DESI data of the luminous red galaxy (LRG) and quasi-stellar object (QSO) samples and comment on the effect on $f_{\rm NL}$ constraints for the allowed galaxy-halo models (note that $\sigma [f_{\rm NL}] \propto \frac{\sigma [b_\phi]}{b_\phi}$). We find that the error on $f_{\rm NL}$ is 21, 6, 22 for the LRGs at $z = 0.5$ and $z = 0.8$ and QSOs at $z = 1.4$, respectively, suggesting that a thorough understanding of galaxy assembly bias is warranted so as to perform robust high-precision analysis of local-type PNG with future surveys., Comment: 21 pages, 7 figures, 2 tables

With changing attitudes around knowledge, medicine, art, and technology, the human body has become a source of information and, ultimately, shareable and analyzable data. Centuries of illustrations and visualizations of the body occur within particular historical, social, and political contexts. These contexts are enmeshed in different so-called data cultures: ways that data, knowledge, and information are conceptualized and collected, structured and shared. In this work, we explore how information about the body was collected as well as the circulation, impact, and persuasive force of the resulting images. We show how mindfulness of data cultural influences remain crucial for today's designers, researchers, and consumers of visualizations. We conclude with a call for the field to reflect on how visualizations are not timeless and contextless mirrors on objective data, but as much a product of our time and place as the visualizations of the past.