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The neighborhood, whether reestablished in the city or in a new town, at its best avoids not only racial and income segregation, but also age segregation. Since the three-generation family within a single dwelling no longer is likely, the neighborhood should be restored to accommodate all generations. This study explores how the concept of neighborhood can be reestablished and how it can become a three-generation neighborhood. (Author/MLF)

Math fact fluency--the ability to quickly and accurately recall basic addition, subtraction, multiplication, and division facts--is a foundational mathematical skill. "Zaner-Bloser Building Fact Fluency (BFF)" was designed to build students' conceptual understanding of arithmetic operations and increase procedural fluency among elementary school students, with the goal of strengthening math learning. LXD Research conducted a study to examine Grade 3 students' math proficiency levels on the standardized Iowa Statewide Assessment of Student Progress (ISASP) across five "Building Fact Fluency" participating schools and 20 similar Iowa comparison schools from spring 2021-spring 2023. One "Building Fact Fluency" pilot school began the program in fall 2021, and four additional schools began the program in fall of 2022. The pilot "Building Fact Fluency" school, Year 2-only "Building Fact Fluency" schools, and comparison schools had a similar percentage of students on grade level at baseline. However, Grade 3 students in "Building Fact Fluency" schools showed consistently greater improvements in the percentage of Grade 3 students on grade level in math.

The Equity Playbook Initiative Implementation in Kentucky underwent an efficacy study conducted by LXD Research during the 2022-2023 school year. The study aimed to assess the initiative's impact on student academic achievement and school climate. A total of 55 public schools participating in the Equity Playbook Initiative were compared with 240 statistically similar non-participating schools. The demographic breakdown of the sample included 65% economically disadvantaged students, with a significant representation of 30% African American students and 35% non-white students. The findings indicated that Equity Playbook schools significantly improved key academic outcomes, particularly among economically disadvantaged students. Notably, students attending Equity Playbook schools, especially those with a high percentage of economically disadvantaged students, showed significant improvement in testing on grade level in Science and Math compared to their counterparts in non-participating schools. Additionally, the study revealed a reduction in the Kentucky Summative Assessment (KSA) achievement gap for economically disadvantaged students among participating schools. Furthermore, the completion rates of advanced courses for African American and non-white students in Equity Playbook schools showed marked improvement compared to the schools that did not participate in the initiative. Qualitative interviews with state, district, and school-level educators and staff also provided positive feedback regarding the effects of the Equity Playbook Initiative. The findings suggest that the Equity Playbook Initiative holds promise in promoting student academic achievement and enhancing school climate. This study encourages further evaluation and broader adoption of the Equity Playbook Initiative in various districts and schools across Kentucky to leverage its potential benefits. [This report was prepared for engage2learn by LXD Research.]

LXD Research conducted a study to evaluate the impact of the Zaner-Bloser Kickstart: Number Sense program, a math intervention designed to enhance foundational math skills among first and second-grade students in an Oregon school district over a three-year period from spring 2019 to Spring 2023. The program was implemented in two schools, engaging 130 first graders and 163 second graders. The student population included 15% Hispanic/Latino students, 8% English Language Learners, 15% students with diagnosed disabilities, and approximately 70% of students qualifying for Free/Reduced Price lunch. The district represents at least 19 different language backgrounds, reflecting a diverse and multicultural student body. The intervention comprised daily 20-minute group lessons focused on developing critical math skills, conducted by instructors without prior math teaching experience. The program's effectiveness was evaluated by comparing third-grade math proficiency rates between Kickstart students and their peers from comparison schools of similar size across the state, utilizing publicly available proficiency scores from the Oregon Statewide Assessments (OSAS) as the benchmark. Key findings demonstrated that Zaner-Bloser Kickstart: Number Sense students consistently outperformed their counterparts in comparison schools over multiple academic years. Specifically, in the 2021-2022 academic year, 44% of all Kickstart students achieved math proficiency, compared to 26% in comparison schools. In the 2022-2023 academic year, the proficiency rate for all Kickstart students was 39%, compared to 26% in comparison schools. Additionally, first graders' proficiency increased from 43% in 2021-2022 to 56% in 2022-2023, while second graders' proficiency rose from 35% to 47% over the same period. These results suggest that the Zaner-Bloser Kickstart: Number Sense program is highly effective in significantly improving early math outcomes, fostering a solid foundation in math for young learners, and potentially bridging educational gaps. The study's rigorous methodology and findings support Zaner-Bloser Kickstart: Number Sense as a comprehensive program that meets the criteria for ESSA Level 2. [This report was produced by Zaner-Bloser.]

LXD Research conducted a study to examine the relationship between investment in Zaner-Bloser Handwriting program and Grade 3 student proficiency in handwriting in Alabama. Data from 4,759 students across 54 schools were analyzed, with schools categorized into Low Zaner-Bloser Handwriting (>$1 and [less than or equal to]$5 per student per year), Moderate Zaner-Bloser Handwriting (>$5 and [less than or equal to]$10 per student per year), and High Zaner-Bloser Handwriting (>$10 per student per year) investment groups. The demographic profile of the sample included a diverse range of students, notably with a significant representation of low-income and minority groups. The study revealed a substantial positive correlation between the level of ZB-HW investment and handwriting proficiency. Notably, 91% of students in the High ZB-HW investment group achieved proficiency, compared to 82% in the Moderate group and 69% in the Low investment group. Additionally, each $1 increase in per-student investment was associated with an 18% increase in the likelihood of achieving proficiency. These findings highlight the effectiveness of Zaner-Bloser Handwriting in enhancing handwriting skills and underscore the importance of targeted investment in educational resources to support student outcomes. The findings and rigorous study design support Zaner-Bloser Handwriting as a comprehensive program that meets the criteria for ESSA Level 3. [This report was produced by Zaner-Bloser.]

For decades, assessing income has served as the tried-and-true method for creating financial aid packages--scholarships, grants, and loans--for the nation's college students. Each year, students and families living with low and moderate incomes submit income documentation to colleges, states, and the federal government in hopes of qualifying for financial aid to defray college costs. But what if a student's wealth status revealed more about their ability to cover college costs than their income did? Income is an important measure, but it provides only a snapshot of the full resources available to students and their families. Wealth, too, impacts a student's access and ability to pay for college as well as their chances of graduating and turning career aspirations into a well-paying job and fulfilling life. Examining income alone masks deep and persistent inequities in wealth by race or ethnicity. This gap, which is rooted in generations of discrimination and systemic barriers to opportunity, affects the college dreams of countless people, particularly those from historically marginalized communities. Limitations in available data mean researchers and policymakers know relatively little about how wealth impacts higher education, with far more attention focused on understanding and addressing the effects of income. This paper seeks to address that missing piece by exploring the impact of wealth on students' experiences in higher education and how these experiences, in turn, influence wealth-building.

Through qualitative methodology, the researcher examined the challenges that Black male college students at predominantly White institutions face when seeking to locate or establish relationships with mentors at their institutions. Semi-structured interviews were conducted to get first-hand knowledge of ten senior undergraduate Black males pursuing postgraduate studies at predominantly White institutions. In addition, the researcher conducted the interviews through the Zoom application. Moreover, the researcher organized and transcribed the interviews, spreadsheets, transcripts, notes, and findings for the themes through the Sonix application using coding. The purpose of this action research study was to examine the impact mentoring has on Black male college students pursuing postgraduate education at predominantly White institutions. In this study, the researcher examined how Black male college students can establish mentoring relationships at predominately White institutions. Those students cannot take advantage of the impact and influence mentors provide as they pursue postgraduate education. The findings, which are of utmost importance, suggest that mentoring played an essential role in assisting Black male college students by giving them a voice in how predominantly White institutions can help them increase academic performance, self-esteem, and social skills.

This study aimed to assess the effectiveness of the Heggerty Bridge to Reading program for first-grade students compared to a business-as-usual reading program by employing a mixed-methods approach encompassing a matched quasi-experimental design, teacher surveys, and interviews. In light of the learning disruptions caused by the COVID-19 pandemic, the study underscores the necessity of explicit reading instruction, with a focus on phonemic awareness and systematic phonics. Results indicate that the Bridge to Reading program, integrating phonemic awareness lessons with daily explicit phonics instruction, significantly improves student achievement on MAP Growth and MAP Fluency formative assessments, leading to higher RIT scores and growth compared to the control group. Analysis of student subgroups reveals significant progress among lower-achieving students, indicating effective support in bridging foundational reading skill gaps from kindergarten. Moreover, the program surpasses the comparison group in meeting projected growth targets, with a greater proportion of students reaching their mid-year growth target. Feedback from educators in the treatment group underscores positive perceptions, with teachers reporting enhanced understanding of reading methods and alignment with literacy development. The study also examines demographic factors and baseline scores, revealing significant impacts in areas such as phonological awareness, phonics, and word recognition. Additionally, the study outlines future steps, including qualitative data collection and end-of-year quantitative analysis, to further elucidate the program's efficacy. These preliminary findings suggest that the Bridge to Reading program holds promise in bolstering student achievement in foundational reading skills, particularly amidst the challenges posed by the COVID-19 pandemic. [This report was published with Heggerty.]

Quality of health is greatly impacted by the quality of measurement, hence active monitoring of patient's vitals status greatly affects recovery and improves prognosis. In this paper, we proposed an IoT health based monitoring system which comprises of a wearable wristband mounted on the patient wrist to eliminate the concept of patient's being static. The wearable wristband composed of a digital temperature sensor and a pulse rate sensor can remotely monitor health status and utilizing Thingspeak result viewed via a web app or delivered on a smartphone to a specialist who is in a remote location. In order to achieve high accuracy in readings, the signal measured from the pulse rate sensor was passed through a two stage Hp-Lp circuit to eliminate DC offset and noise. In addition, a scaling factor of 10.41909 was formulated and used to convert the digital output from the pulse rate sensor into a more accurate representation of the patient heat rate readings. The results displayed by the proposed system proved the system to be well suited and reliable for healthcare monitoring., Comment: Online: ISSN 2645-2960

This study explored the impact of the 95 Phonics Core Program® (95 PCP) on student literacy achievement across third graders who started the school year at different ability levels. The primary focus of this report was to investigate the relationship between changes in phonics scores with the beginning and end-of-year i-Ready® assessments and outcomes on State of Texas Assessments of Academic Readiness (STAAR®). To this end, i-Ready scores were collected from 4 elementary schools in the Southside Independent School District in San Antonio, Texas, during the 2022-2023 academic school year. Third graders from all schools were aggregated by beginning-of-year (BOY) placement level in terms of grade-level skills (kindergarten, first grade, second grade, third grade). Outcomes were expressed in terms of gains on scale scores and performance levels in STAAR. The analysis compared EOY performance levels for students who grew different amounts in their phonics skills during the school year. The analysis progressed through the following questions, each adding a piece to the overall picture.

This study explores the implementation of the engage2Learn program among English as a Second Language (ESL) educators in the Providence Public School District and its impact on student outcomes over three years (2021-2023). Utilizing Grade 1-5 WIDA ACCESS scores, this study examines cohort differences in change in student proficiency levels before and after e2L coaching. The results indicated positive associations between e2L coaching and ELL student ELA proficiency as indexed by year-over-year growth in WIDA Access scores. In addition to the overall effect of e2L coaching, ELL student growth was greatest among students of teachers who earned two or more e2L best practice badges. Qualitative data in the form of teacher surveys supported the quantitative findings with positive feedback from participating educators. These educators also noted that they had actively integrated e2L coaching strategies into their daily teaching practices. The study suggests a significant benefit when ELL teachers build capacity in educational best practices aligning with high-quality ESL program characteristics. Despite some limitations including non-random assignment of teachers, the study provides insights for educator professional development, ESL policymakers, and educators.

Multispectral imaging sensors typically have wavelength-dependent resolution, which reduces the ability to distinguish small features in some spectral bands. Existing super-resolution methods upsample a multispectral image (MSI) to achieve a common resolution across all bands but are typically sensor-specific, computationally expensive, and may assume invariant image statistics across multiple length scales. In this paper, we introduce ResSR, an efficient and modular residual-based method for super-resolving the lower-resolution bands of a multispectral image. ResSR uses singular value decomposition (SVD) to identify correlations across spectral bands and then applies a residual correction process that corrects only the high-spatial frequency components of the upsampled bands. The SVD formulation improves the conditioning and simplifies the super-resolution problem, and the residual method retains accurate low-spatial frequencies from the measured data while incorporating high-spatial frequency detail from the SVD solution. While ResSR is formulated as the solution to an optimization problem, we derive an approximate closed-form solution that is fast and accurate. We formulate ResSR for any number of distinct resolutions, enabling easy application to any MSI. In a series of experiments on simulated and measured Sentinel-2 MSIs, ResSR is shown to produce image quality comparable to or better than alternative algorithms. However, it is computationally faster and can run on larger images, making it useful for processing large data sets., Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing

A random set $S$ is $p$-spread if, for all sets $T$, $$\mathbb{P}(S \supseteq T) \leq p^{|T|}.$$ There is a constant $C>1$ large enough that for every graph $G$ with maximum degree $D$, there is a $C/D$-spread distribution on $(D+1)$-colorings of $G$. Making use of a connection between thresholds and spread distributions due to Frankston, Kahn, Narayanan, and Park, a palette sparsification theorem of Assadi, Chen, and Khanna follows., Comment: 18 pages

A graphon is a limiting object used to describe the behaviour of large networks through a function that captures the probability of edge formation between nodes. Although the merits of graphons to describe large and unlabelled networks are clear, they traditionally are used for describing only binary edge information, which limits their utility for more complex relational data. Decorated graphons were introduced to extend the graphon framework by incorporating richer relationships, such as edge weights and types. This specificity in modelling connections provides more granular insight into network dynamics. Yet, there are no existing inference techniques for decorated graphons. We develop such an estimation method, extending existing techniques from traditional graphon estimation to accommodate these richer interactions. We derive the rate of convergence for our method and show that it is consistent with traditional non-parametric theory when the decoration space is finite. Simulations confirm that these theoretical rates are achieved in practice. Our method, tested on synthetic and empirical data, effectively captures additional edge information, resulting in improved network models. This advancement extends the scope of graphon estimation to encompass more complex networks, such as multiplex networks and attributed graphs, thereby increasing our understanding of their underlying structures.

Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers. This genomic complexity is known to be associated with poorer outcomes for patients but describing its contribution in detail has been difficult. Copy number alterations can affect large regions spanning whole chromosomes or the entire genome itself but can also be localised to only small segments of the genome and no methods exist that allow this multi-scale nature to be quantified. In this paper, we address this using Wave-LSTM, a signal decomposition approach designed to capture the multi-scale structure of complex whole genome copy number profiles. Using wavelet-based source separation in combination with deep learning-based attention mechanisms. We show that Wave-LSTM can be used to derive multi-scale representations from copy number profiles which can be used to decipher sub-clonal structures from single-cell copy number data and to improve survival prediction performance from patient tumour profiles.

Modern applications often leverage multiple views of a subject of study. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental principles about the characteristics of each representation and about the system. However, existing methods to characterize such relationships either lack the expressivity required to capture complex nonlinearities, describe only sources of variance that are shared between views, or discard geometric information that is crucial to interpreting the data. Here, we develop a nonlinear neural network-based method that, given paired samples of high-dimensional views, disentangles low-dimensional shared and private latent variables underlying these views while preserving intrinsic data geometry. Across multiple simulated and real datasets, we demonstrate that our method outperforms competing methods. Using simulated populations of lateral geniculate nucleus (LGN) and V1 neurons we demonstrate our model's ability to discover interpretable shared and private structure across different noise conditions. On a dataset of unrotated and corresponding but randomly rotated MNIST digits, we recover private latents for the rotated view that encode rotation angle regardless of digit class, and places the angle representation on a 1-d manifold, while shared latents encode digit class but not rotation angle. Applying our method to simultaneous Neuropixels recordings of hippocampus and prefrontal cortex while mice run on a linear track, we discover a low-dimensional shared latent space that encodes the animal's position. We propose our approach as a general-purpose method for finding succinct and interpretable descriptions of paired data sets in terms of disentangled shared and private latent variables.

We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, called TS-SpGEMM, has important applications in multi-source breadth-first search, influence maximization, sparse graph embedding, and algebraic multigrid solvers. Unfortunately, popular distributed algorithms like sparse SUMMA deliver suboptimal performance for TS-SpGEMM. To address this limitation, we develop a novel distributed-memory algorithm tailored for TS-SpGEMM. Our approach employs customized 1D partitioning for all matrices involved and leverages sparsity-aware tiling for efficient data transfers. In addition, it minimizes communication overhead by incorporating both local and remote computations. On average, our TS-SpGEMM algorithm attains 5x performance gains over 2D and 3D SUMMA. Furthermore, we use our algorithm to implement multi-source breadth-first search and sparse graph embedding algorithms and demonstrate their scalability up to 512 Nodes (or 65,536 cores) on NERSC Perlmutter.

Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fidelity CFD methods, represents a barrier to their development. To address this, a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics has been generated, based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. Mesh generation and scale-resolving CFD was executed using consistent and validated automatic workflows representative of the industrial state-of-the-art. Geometries and rich aerodynamic data are published in open-source formats. To our knowledge, this is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.

JWST provides new opportunities to cross-check the HST Cepheid/SNeIa distance ladder, which yields the most precise local measure of H0. We analyze early JWST subsamples (~1/4 of the HST sample) from the SH0ES and CCHP groups, calibrated by a single anchor (N4258). We find HST Cepheid distances agree well (~1 sigma) with all 8 combinations of methods, samples, and telescopes: JWST Cepheids, TRGB, and JAGB by either group, plus HST TRGB and Miras. The comparisons explicitly include the measurement uncertainty of each method in N4258, an oft-neglected but dominant term. Mean differences are ~0.03 mag, far smaller than the 0.18 mag "Hubble tension." Combining all measures produces the strongest constraint yet on the linearity of HST Cepheid distances, 0.994+-0.010, ruling out distance-dependent bias or offset as the source of the tension at ~7 sigma. Yet, measurements of H0 from current JWST subsamples produce large sampling differences whose size and direction we can directly estimate from the full HST set. We show that Delta(H0)~2.5 km/s/Mpc between the CCHP JWST program and the full HST sample is entirely consistent with differences in sample selection. Combining all JWST samples produces a new, distance-limited set of 16 SNeIa at D<25 Mpc and more closely resembles the full sample thanks to "reversion to the mean" of larger samples. Using JWST Cepheids, JAGB, and TRGB, we find 73.4+-2.1, 72.2+-2.2, and 72.1+-2.2 km/s/Mpc, respectively. Explicitly accounting for SNe in common, the combined-sample three-method result from JWST is H0=72.6+-2.0, similar to H0=72.8 expected from HST Cepheids in the same galaxies. The small JWST sample trivially lowers the Hubble tension significance due to small-sample statistics and is not yet competitive with the HST set (42 SNeIa and 4 anchors), which yields 73.2+-0.9. Still, the joint JWST sample provides important crosschecks which the HST data passes., Comment: submitted to ApJ, comments welcome and appreciated

Formulating order metrics that sensitively quantify the degree of order/disorder in many-particle systems in $d$-dimensional Euclidean space $\mathbb{R}^d$ across length scales is an outstanding challenge in physics, chemistry, and materials science. Since an infinite set of $n$-particle correlation functions is required to fully characterize a system, one must settle for a reduced set of structural information, in practice. We initiate a program to use the number variance $\sigma_N^2(R)$ associated with a spherical sampling window of radius $R$ (which encodes pair correlations) and an integral measure derived from it $\Sigma_N(R_i,R_j)$ that depends on two specified radial distances $R_i$ and $R_j$. Across the first three space dimensions ($d = 1,2,3$), we find these metrics can sensitively describe and categorize the degree of order/disorder of 41 different models of antihyperuniform, nonhyperuniform, disordered hyperuniform, and ordered hyperuniform many-particle systems at a specified length scale $R$. Using our local variance metrics, we demonstrate the importance of assessing order/disorder with respect to a specific value of $R$. These order metrics could also aid in the inverse design of structures with prescribed scale-specific degrees of order/disorder that yield desired physical properties. In future work, it would be fruitful to explore the use of higher-order moments [S. Torquato et al., Phys. Rev. X, 11, 021028 (2021)] to devise even more sensitive order metrics., Comment: 19 pages, 8 figures

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi, an extension of the CasADi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training., Comment: RAL 2024 submission

In this paper we provide a computer assisted proof that about two thousand surgeries far away from the ideal point in the hyperbolic Dehn filling space of the figure-eight knot complement are infinitesimally projectively rigid. We also prove that for projective deformations of the figure-eight knot complement sufficiently close to the complete hyperbolic structure, the induced map on the first cohomology of the longitude of the boundary torus is non-zero. This paper provides a complementary piece to the results of Heusener and Porti who showed that for each k in Z, there is a sufficiently large Nk for which every k/n-Dehn filling on the figure-eight knot complement for n larger than Nk is infinitesimally projectively rigid. In the process of the proof, we provide explicit representations of the figure-eight knot complement in PSO(3,1) which are rational in the real and imaginary parts of the shapes of the ideal tetrahedra used to glue the knot complement together.

We study the visual quality judgments of human subjects on digital human avatars (sometimes referred to as "holograms" in the parlance of virtual reality [VR] and augmented reality [AR] systems) that have been subjected to distortions. We also study the ability of video quality models to predict human judgments. As streaming human avatar videos in VR or AR become increasingly common, the need for more advanced human avatar video compression protocols will be required to address the tradeoffs between faithfully transmitting high-quality visual representations while adjusting to changeable bandwidth scenarios. During transmission over the internet, the perceived quality of compressed human avatar videos can be severely impaired by visual artifacts. To optimize trade-offs between perceptual quality and data volume in practical workflows, video quality assessment (VQA) models are essential tools. However, there are very few VQA algorithms developed specifically to analyze human body avatar videos, due, at least in part, to the dearth of appropriate and comprehensive datasets of adequate size. Towards filling this gap, we introduce the LIVE-Meta Rendered Human Avatar VQA Database, which contains 720 human avatar videos processed using 20 different combinations of encoding parameters, labeled by corresponding human perceptual quality judgments that were collected in six degrees of freedom VR headsets. To demonstrate the usefulness of this new and unique video resource, we use it to study and compare the performances of a variety of state-of-the-art Full Reference and No Reference video quality prediction models, including a new model called HoloQA. As a service to the research community, we will be publicly releasing the metadata of the new database at https://live.ece.utexas.edu/research/LIVE-Meta-rendered-human-avatar/index.html., Comment: Data will be made available after the paper is accepted. This paper is a preprint of a work currently under a second round of peer review in IEEE TIP

Advances in materials and fabrication of superconducting devices allows the exploration of novel quantum effects in synthetic superconducting systems beyond conventional Josephson junction arrays. As an example, we introduce a new circuit element, the Y-splitter, a superconducting loop with three leads and three Josephson junctions, smaller or comparable in size to the superconducting coherence length of the material. By tuning magnetic flux through an array of Y-splitters, Cooper-pair transport can be made to interfere destructively, while spatially separated split Cooper pairs propagate coherently. We consider an array of Y-splitters connected in a two-dimensional star [Archimedean (3,$12^2$)] geometry, deformable into the kagome lattice, and find a rich phase diagram that includes topological superconducting phases with Chern numbers $\pm 2$. Experimental realization appears feasible., Comment: 15 pages, 11 figures

Several recent techniques for modifying topological codes with single-qubit Clifford operators have shown high resilience against pure Pauli noise. Paramount to these findings has been the demonstration that several variants exhibit error thresholds often attaining or exceeding the zero-rate hashing bound, a known benchmark for code-capacity noise channels, for biased noise. Additionally, direct comparison with the hashing bound has shown that several topological codes outperform the hashing bound at points of finite Pauli noise biases. Motivated by these observations, we study zero-rate holographic quantum error correction codes, discovering very high threshold values under diverse and finitely-biased noise channels using a tensor-network decoding approach. Our results establish that all codes tested achieve or surpass the hashing bound at various points, ranging from pure 2-Pauli noise ($\eta = 0$) to pure 1-Pauli noise ($\eta = +\infty$), thereby demonstrating that holographic codes exhibit excellent error tolerance in the code-capacity picture. Such findings imply the existence of a structured and systematic method for constructing high-threshold codes suitable for realistically motivated noise channels. To our knowledge, this work is also the first instance of such remarkable threshold behavior in stabilizer quantum codes for the pure 2-Pauli noise regime, as well as for finitely-biased noise channels., Comment: 8 pages, 5 figures, 2 tables

We compare and contrast the kinematics of various classes of stars, both normal (with M-K spectral types) and peculiar. Numerous plots show the differences in spatial and velocity distributions as well as distributions in kinetic energy vs. angular momentum (KE vs. LZ) space. In the latter plots, young, thin disk stars on nearly circular orbits cling to the left edge of a so-called solar parabola, while older objects on elliptical orbits fill the central parabolic region. Some of the young Wolf-Rayet stars violate this trend due to smaller semi-major axes than the Sun or orbital eccentricities. Deviation of the vertex of the velocity ellipsoid is discussed as an indication of population and youth, with an emphasis on Ap, Bp, Am, Fm, Herbig AeBe, and lambda Boo stars. Both the vertex deviation and phase space distribution provide useful insights., Comment: 11 pages, 7 figures

The fractional AC Josephson effect is a discerning property of topological superconductivity in hybrid Josephson junctions. Recent experimental observations of missing odd Shapiro steps and half Josephson frequency emission in various materials have sparked significant debate regarding their potential origin in the effect. In this study, we present microwave emission measurements on a resistively shunted Josephson junction based on a HgTe quantum well. We demonstrate that, with significant spurious inductance in the shunt wiring, the experiment operates in a nonlinear dynamic regime characterized by period-doubling. This leads to additional microwave emission peaks at half of the Josephson frequency, $f_J/2$, which can mimic the $4\pi$-periodicity of topological Andreev states. The observed current-voltage characteristics and emission spectra are well-described by a simple RCLSJ model. Furthermore, we show that the nonlinear dynamics of the junction can be controlled using gate voltage, magnetic field, and temperature, with our model accurately reproducing these effects without incorporating any topological attributes. Our observations urge caution in interpreting emission at $f_J/2$ as evidence for gapless Andreev bound states in topological junctions and suggest the appropriate parameter range for future experiments., Comment: 13 pages, 8 figures

We introduce an $E(n)$-equivariant Transformer architecture for spatio-temporal graph data. By imposing rotation, translation, and permutation equivariance inductive biases in both space and time, we show that the Spacetime $E(n)$-Transformer (SET) outperforms purely spatial and temporal models without symmetry-preserving properties. We benchmark SET against said models on the charged $N$-body problem, a simple physical system with complex dynamics. While existing spatio-temporal graph neural networks focus on sequential modeling, we empirically demonstrate that leveraging underlying domain symmetries yields considerable improvements for modeling dynamical systems on graphs.

Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, such as gender or racial biases, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates independently of prior bias-type knowledge and protected attribute labels. Our approach proactively identifies biases during model training by utilizing auxiliary models, which are trained concurrently by predicting the performance of the main model without relying on task labels. Additionally, we implement these auxiliary models at various levels of the feature maps of the main model, enabling the detection of a broader and more nuanced range of bias features. Through experiments on racial and gender biases in sentiment and occupation classification tasks, our method effectively reduces social biases without the need for demographic annotations. Moreover, our approach not only matches but often surpasses the efficacy of methods that require detailed demographic insights, marking a significant advancement in bias mitigation techniques.

The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation and tuning of a search system is largely based on relevance labels, which indicate whether a document is useful for a specific search and user. However, collecting relevance judgments on a large scale is costly and resource-intensive. Consequently, typical experiments rely on third-party labelers who may not always produce accurate annotations. The LLMJudge challenge aims to explore an alternative approach by using LLMs to generate relevance judgments. Recent studies have shown that LLMs can generate reliable relevance judgments for search systems. However, it remains unclear which LLMs can match the accuracy of human labelers, which prompts are most effective, how fine-tuned open-source LLMs compare to closed-source LLMs like GPT-4, whether there are biases in synthetically generated data, and if data leakage affects the quality of generated labels. This challenge will investigate these questions, and the collected data will be released as a package to support automatic relevance judgment research in information retrieval and search., Comment: LLMJudge Challenge Overview, 3 pages

The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retrieval researchers together around the topic of LLMs for evaluation in information retrieval that gathered attention with the advancement of large language models and generative AI. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers., Comment: LLM4Eval Workshop Report

Here we discuss spectral properties of the burnt pancake graph $\mathbf{BP}_n$. More precisely, we prove that the adjacency spectrum of $\mathbf{BP}_n$ contains all integer values in the set $\{0, 1, \ldots, n\}\setminus\{\left\lfloor n/2 \right\rfloor\}$, Comment: 8 pages, 1 figure

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

The decays of the $\chi_{b1}(1P)$, $\chi_{b2}(1P)$, $\chi_{b1}(2P)$ and $\chi_{b2}(2P)$~mesons into the~$\Upsilon(1S)\mu^+\mu^-$ final state are observed with a high significance using proton-proton collision data collected with the LHCb detector and corresponding to an integrated luminosity of 9fb$^{-1}$. The newly observed decays together with the $\Upsilon(2S)\rightarrow \Upsilon(1S)\pi^+\pi^-$ and $\Upsilon(3S)\rightarrow \Upsilon(2S)\pi^+\pi^-$ decay modes are used for precision measurements of the mass and mass splittings for the hidden-beauty states., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-025.html

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care., Comment: 5 Figures, 3 Tables, 1 Supplementary Table

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and the difficult task of malware detection.

We study a percolation model with restrictions on the opening of sites on the square lattice. In this model, each site $s \in \mathbb{Z}^{2}$ starts closed and an attempt to open it occurs at time $t=t_s$, where $(t_s)_{s \in \mathbb{Z}^2}$ is a sequence of independent random variables uniformly distributed on the interval $[0,1]$. The site will open if the volume difference between the two largest clusters adjacent to it is greater than or equal to a constant $r$ or if it has at most one adjacent cluster. Through numerical analysis, we determine the critical threshold $t_c(r)$ for various values of $r$, verifying that $t_c(r)$ is non-decreasing in $r$ and that there exists a critical value $r_c=5$ beyond which percolation does not occur. Additionally, when $t=1$ and $1 \leq r \leq 9$, we estimate the averages of the density of open sites, the number of distinct cluster volumes, and the volume of the largest cluster.

First-principles calculations are performed to investigate the electro-optic (EO) and elasto-optic effects of the three-dimensional (bulk) and two-dimensional (monolayer) ferroelectric NbOI$_{2}$. Remarkably large linear EO and elasto-optic coefficients are discovered in both systems, when under stress-free conditions. We further found that the EO responses of bulk and monolayer NbOI$_{2}$ can be further enhanced with epitaxial strain, because of a strain-driven ferroelectric-to-paraelectric transition that originates from the softening of some polar optical modes. Our findings thus point out that NbOI$_{2}$, as well as other niobium oxide dihalides are highly promising for paving the way for potentially efficient nonlinear optical device applications., Comment: 6 pages, 3 figures

The ability of Large Language Models (LLMs) to generate structured outputs, such as JSON, is crucial for their use in Compound AI Systems. However, evaluating and improving this capability remains challenging. In this work, we introduce StructuredRAG, a benchmark of six tasks designed to assess LLMs' proficiency in following response format instructions. We evaluate two state-of-the-art LLMs, Gemini 1.5 Pro and Llama 3 8B-instruct with 4-bit quantization using two distinct prompting strategies. We introduce these prompting strategies as f-String and Follow the Format (FF) prompting. Across 24 experiments, we find an average success rate of 82.55%. We further find a high variance in performance across tasks, models, and prompting strategies with success rates ranging from 0 to 100%. We find that Llama 3 8B-instruct often performs competitively with Gemini 1.5 Pro. We observe that task complexity significantly influences performance, with tasks involving lists or composite object outputs proving more challenging. Our findings highlight the need for further research into improving the reliability and consistency of structured output generation in LLMs. We have open-sourced our experimental code and results at github.com/weaviate/structured-rag., Comment: Preprint. 10 pages, 6 figures

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a proposed space observatory which will use arrays of thousands of kinetic inductance detectors (KIDs) to perform low- and moderate-resolution spectroscopy throughout the far-infrared. The detectors must have noise equivalent powers (NEPs) at or below 0.1 aW/sqrt(Hz) to be subdominant to noise from sky backgrounds and thermal noise from PRIMA's cryogenically cooled primary mirror. Using a Radio Frequency System on a Chip for multitone readout, we measure the NEPs of detectors on a flight-like array designed to observe at a wavelength of 210 microns. We find that 92% of the KIDs measured have an NEP below 0.1 aW/sqrt(Hz) at a noise frequency of 10 Hz., Comment: 7 pages, 7 figures

Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to support the majority of domains outside of deep learning. Furthermore, a compiler that can easily be updated to reflect changes at both application and hardware levels would enable more agile development and design space exploration of accelerators, allowing hardware designers to realize closer-to-optimal performance. In this work, we discuss how large language models (LLMs) could be leveraged to build such a compiler. Specifically, we demonstrate the ability of GPT-4 to achieve high pass rates in translating code to the Gemmini accelerator, and prototype a technique for decomposing translation into smaller, more LLM-friendly steps. Additionally, we propose a 2-phase workflow for utilizing LLMs to generate hardware-optimized code., Comment: 4 page workshop paper

Large Language Models (LLMs) have shown remarkable performance in Natural Language Processing tasks, including Machine Translation (MT). In this work, we propose a novel MT pipeline that integrates emotion information extracted from a Speech Emotion Recognition (SER) model into LLMs to enhance translation quality. We first fine-tune five existing LLMs on the Libri-trans dataset and select the most performant model. Subsequently, we augment LLM prompts with different dimensional emotions and train the selected LLM under these different configurations. Our experiments reveal that integrating emotion information, especially arousal, into LLM prompts leads to notable improvements in translation quality., Comment: 6 pages, In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT), Bangkok, Thailand, 2024