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In order to assist higher education institutions and their students during the pandemic, the federal government established the Higher Education Emergency Relief (HEER) Fund, which directed over $75 billion to institutions of higher education--including nearly $25 billion to community colleges--over a three-year period. The U.S. Department of Education worked on a rapid timeline to distribute these funds to institutions, which they could use to provide direct aid to students facing financial challenges and cover institutional costs related to the pandemic. Drawing on a survey of community colleges in six states--California, Michigan, New York (State University of New York [SUNY] colleges), Ohio, Tennessee, and Texas--this report provides insight into the specific pandemic recovery activities colleges implemented, colleges' perceptions of how successful HEER funds were in addressing student and institutional needs during the pandemic, and colleges' views of unmet needs. The institutional survey was completed by 170 out of a total of 265 community colleges in the six states. Key findings from the report: (1) Colleges spent nearly all the HEER funds they received. Given the large amount of HEER funding and the fact that colleges did not need to submit a proposal and budget for how they would use the funds, it should not be assumed that colleges would have spent all the money they received. Yet colleges spent nearly all the funds they received by the time the HEER program ended in June 2023; (2) HEER funds met a variety of student and institutional needs during the pandemic. Colleges had relatively few problems using the funds and felt that the aid was successful in mitigating student and institutional hardships; (3) Colleges focused on retaining existing students; they employed a variety of methods to support students in need. Colleges used HEER funds to support and retain existing (pre-pandemic) students rather than to recruit new students. They focused on supporting students with financial exigencies, including those experiencing food and housing insecurity. They used institutional aid to forgive debt owed to the college and to provide food, housing, and childcare assistance; (4) Spending patterns suggest that colleges experienced similar challenges during the pandemic and often prioritized the same objectives. Despite differences in state contexts and institutional settings, colleges tended to allocate funds in similar ways. For example, most colleges used aid for campus safety and technology hardware. Expenditure patterns also shifted over time in similar ways, indicating that colleges were responsive to evolving needs; (5) Expenditures related to campus safety and technology remained strong but decreased in frequency over time; expenditures to support students' mental health increased in frequency. Mental health services was the only expenditure category that increased in frequency in each of the three years of funding, likely reflecting the toll the pandemic took on students' mental health; (6) Comparing pre- and post-pandemic spending, HEER funds had the most impact on increasing support for technology hardware, high-speed internet, and housing assistance. Colleges used HEER funds both to fund existing services and to begin offering new ones. Fewer than a third of colleges had services in place to provide technology hardware, high-speed internet, and housing assistance before the pandemic; many more did so afterward; (7) Concerns about the end of HEER funding and priorities for future funding expose a need for continued flexible resources to address students' financial needs. Colleges' main concern about the end of HEER funding was that it would limit their ability to support students during an emergency. Their top priority for using future funding was additional student aid; and (8) Rural and vocational/technical colleges (as defined by the Carnegie Classification) may have had fewer resources prior to the pandemic and may be in greater need of additional support. Colleges in towns and rural areas and colleges focused on technical training were less likely to offer a number of supports both pre- and post-pandemic. Rural colleges were also less likely to report having received additional funding for pandemic recovery from sources other than HEER funds. Overall, while the survey findings suggest that HEER largely met the goals for which it was intended, they also point to the importance of addressing systemic challenges facing community college students and the institutions that serve them. Now that the immediate crisis of the pandemic has passed and HEER funding has ended, there is an opportunity to think strategically about the investments that are needed to promote student success over the long term, particularly for underserved and financially vulnerable students who are the most at risk of stopping out or not enrolling in the first place.

The Higher Education Emergency Relief (HEER) Fund had two main purposes: (1) to ensure that colleges could continue to provide education to students in the wake of the pandemic and (2) to provide emergency financial assistance through colleges directly to students. Four years after the onset of the pandemic, this ARCC Network brief uses college financial data--including data recently released for fiscal year 2022--to look at what happened to college finances and to assess the importance of HEER funding for the financial solvency of community colleges over the course of the pandemic. The authors find that during the peak years of the pandemic (2020-2022), community colleges lost huge numbers of students: On average, colleges lost 580 full-time-equivalent (FTE) students or 15% of pre-pandemic year-on-year enrollment, whereas college enrollments typically fluctuate by +/-2% each year. The authors also find that federal HEER funding saved community colleges from massive losses in tuition revenue during the pandemic. Instead of losing revenue, HEER funding increased total revenue per college from $81 million to $84 million, covering lost tuition and offsetting new costs associated with the pandemic. HEER funding also provided $4 million in student aid per college, on average, making up for lost resources students experienced during the pandemic. The authors--who also wrote an associated blog post that includes an interactive dashboard on this topic--contend that community colleges are confronting new fiscal challenges post-pandemic. As HEER funding has ended, many community colleges are faced with a difficult fiscal outlook, with increased costs due to inflation and decreased overall enrollments relative to pre-pandemic levels. Navigating these new fiscal realities will be extremely challenging for community colleges, raising new concerns about the viability of many colleges in the post-pandemic era. [This report was written with the Accelerating Recovery in Community Colleges (ARCC) Network.]

CCRC recently conducted two major studies to shed light on the feasibility of implementing guided pathways reforms at scale and on the association between implementation and improvements in student outcomes. The first is an evaluation of the AACC Pathways Project, which involved 30 colleges from around the country that were committed to making guided pathways reforms, and the second is an evaluation of guided pathways implementation in three states--Ohio, Tennessee, and Washington State--that launched initiatives to assist colleges across their systems--70 institutions in total--to adopt reforms. This brief summarizes the results of these studies. One of the main findings is that whole-college reform is feasible but takes time--at least five years--to carry out. The authors also find that there is a positive relationship between the scaled implementation of complementary sets of guided pathways practices and some measures of early student momentum in AACC Pathways colleges that made the most progress in implementing guided pathways reforms and in one state (Tennessee) that got an early start on reforms and made good progress; in places that made less progress, the association is weak or not evident. A third major finding is that while students from all backgrounds may benefit from guided pathways reforms, these reforms are not sufficient to close equity gaps between racial and ethnic groups. The authors conclude with five recommendations for community colleges undertaking guided pathways reforms: (1) Offer ongoing case-managed advising by field, predictable schedules, and other supports to help students complete their plans; (2) Remove the obstacle to student success created by prerequisite remediation, particularly in math, and integrate support for students to master college-level courses in their field of study; (3) Take steps to strengthen teaching and learning in program gateway courses, not just math and English composition; (4) Extend tailored guided pathways supports for students from underserved groups; and (5) Build on-ramps to career-path college degree programs for underserved K-12 students after high school.

We present a new algorithm for identifying superbubbles in HI column density maps of both observed and simulated galaxies that has only a single adjustable parameter. The algorithm includes an automated galaxy-background separation step to focus the analysis on the galactic disk. To test the algorithm, we compare the superbubbles it finds in a simulated galactic disk with the ones it finds in 21cm observations of a similar galactic disk. The sizes and radial distribution of those superbubbles are indeed qualitatively similar. However, superbubbles in the simulated galactic disk have lower central HI column densities. The HI superbubbles in the simulated disk are spatially associated with pockets of hot gas. We conclude that the algorithm is a promising method for systematically identifying and characterizing superbubbles using only HI column density maps that will enable standardized tests of stellar feedback models used in galaxy simulations., Comment: 10 pages, 11 figures. Submitted to MNRAS

Transiting giant exoplanets around M-dwarf stars (GEMS) are rare, owing to the low-mass host stars. However, the all-sky coverage of TESS has enabled the detection of an increasingly large number of them to enable statistical surveys like the \textit{Searching for GEMS} survey. As part of this endeavour, we describe the observations of six transiting giant planets, which includes precise mass measurements for two GEMS (K2-419Ab, TOI-6034b) and statistical validation for four systems, which includes validation and mass upper limits for three of them (TOI-5218b, TOI-5616b, TOI-5634Ab), while the fourth one -- TOI-5414b is classified as a `likely planet'. Our observations include radial velocities from the Habitable-zone Planet Finder on the Hobby-Eberly Telescope, and MAROON-X on Gemini-North, along with photometry and high-contrast imaging from multiple ground-based facilities. In addition to TESS photometry, K2-419Ab was also observed and statistically validated as part of the K2 mission in Campaigns 5 and 18, which provides precise orbital and planetary constraints despite the faint host star and long orbital period of $\sim 20.4$ days. With an equilibrium temperature of only 380 K, K2-419Ab is one of the coolest known well-characterized transiting planets. TOI-6034 has a late F-type companion about 40\arcsec~away, making it the first GEMS host star to have an earlier main-sequence binary companion. These confirmations add to the existing small sample of confirmed transiting GEMS., Comment: Accepted in AJ

Robustness, the ability of a system to maintain performance under significant and unanticipated environmental changes, is a critical property for robotic systems. While biological systems naturally exhibit robustness, there is no comprehensive understanding of how to achieve similar robustness in robotic systems. In this work, we draw inspirations from biological systems and propose a design principle that advocates active interconnections among system components to enhance robustness to environmental variations. We evaluate this design principle in a challenging long-horizon manipulation task: solving lockboxes. Our extensive simulated and real-world experiments demonstrate that we could enhance robustness against environmental changes by establishing active interconnections among system components without substantial changes in individual components. Our findings suggest that a systematic investigation of design principles in system building is necessary. It also advocates for interdisciplinary collaborations to explore and evaluate additional principles of biological robustness to advance the development of intelligent and adaptable robotic systems.

Research continues to accumulate evidence that Goffin's cockatoos (Cacatua goffiniana) can solve wide sets of mechanical problems, such as tool use, tool manufacture, and solving mechanical puzzles. However, the proximate mechanisms underlying this adaptive behavior are largely unknown. In this study, we analyze how three Goffin's cockatoos learn to solve a specific mechanical puzzle, a lockbox. The observed behavior results from the interaction between a complex environment (the lockbox) and different processes that jointly govern the animals' behavior. We thus jointly analyze the parrots' (1) engagement, (2) sensorimotor skill learning, and (3) action selection. We find that neither of these aspects could solely explain the animals' behavioral adaptation and that a plausible model of proximate mechanisms (including adaptation) should thus also jointly address these aspects. We accompany this analysis with a discussion of methods that may be used to identify such mechanisms. A major point we want to make is, that it is implausible to reliably identify a detailed model from the limited data of one or a few studies. Instead, we advocate for a more coarse approach that first establishes constraints on proximate mechanisms before specific, detailed models are formulated. We exercise this idea on the data we present in this study., Comment: Accepted for publication at journal Adaptive Behavior with SAGE publishing

Annotations are a critical component of visualizations, helping viewers interpret the visual representation and highlighting critical data insights. Despite their significant role, we lack an understanding of how annotations can be incorporated into other data representations, such as physicalizations and sonifications. Given the emergent nature of these representations, sonifications, and physicalizations lack formalized conventions (e.g., design space, vocabulary) that can introduce challenges for audiences to interpret the intended data encoding. To address this challenge, this work focuses on how annotations can be more tightly integrated into the design process of creating sonifications and physicalizations. In an exploratory study with 13 designers, we explore how visualization annotation techniques can be adapted to sonic and physical modalities. Our work highlights how annotations for sonification and physicalizations are inseparable from their data encodings., Comment: 4 pages, 3 figures, to be published in Proceedings of IEEE VIS 2024

Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at inference is critical to warn the clinicians that the model likely failed. This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted magnetic resonance imaging and computed tomography. By reducing the dimensions of the bottleneck features with either principal component analysis or uniform manifold approximation and projection, images the models failed on were detected with high performance and minimal computational load. In addition, this work explored a non-parametric alternative to the MD, a k-th nearest neighbors distance (KNN). KNN drastically improved scalability and performance over MD when both were applied to raw and average-pooled bottleneck features., Comment: Expansion of "Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation" arXiv:2308.03723. Submitted to the Journal for Machine Learning in Biomedical Imaging. Code available at https://github.com/mckellwoodland/dimen_reduce_mahal

How we perceive objects around us depends on what we actively attend to, yet our eye movements depend on the perceived objects. Still, object segmentation and gaze behavior are typically treated as two independent processes. Drawing on an information processing pattern from robotics, we present a mechanistic model that simulates these processes for dynamic real-world scenes. Our image-computable model uses the current scene segmentation for object-based saccadic decision-making while using the foveated object to refine its scene segmentation recursively. To model this refinement, we use a Bayesian filter, which also provides an uncertainty estimate for the segmentation that we use to guide active scene exploration. We demonstrate that this model closely resembles observers' free viewing behavior, measured by scanpath statistics, including foveation duration and saccade amplitude distributions used for parameter fitting and higher-level statistics not used for fitting. These include how object detections, inspections, and returns are balanced and a delay of returning saccades without an explicit implementation of such temporal inhibition of return. Extensive simulations and ablation studies show that uncertainty promotes balanced exploration and that semantic object cues are crucial to form the perceptual units used in object-based attention. Moreover, we show how our model's modular design allows for extensions, such as incorporating saccadic momentum or pre-saccadic attention, to further align its output with human scanpaths., Comment: 35+16 pages, 8+4 figures

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Due to some significantly contradicting research results, we reconsider the problem of the online exploration of a simple grid cell environment. In this model an agent attains local information about the direct four-neigbourship of a current grid cell and can also successively build a map of all detected cells. Beginning from a starting cell at the boundary of the environment, the agent has to visit any cell of the grid environment and finally has to return to its starting position. The performance of an online strategy is given by competitive analysis. We compare the number of overall cell visits (number of steps) of an online strategy to the number of such visits in the optimal offline solution under full information of the environment in advance. The corresponding worst-case ratio gives the competitive ratio. The aforementioned contradiction among two publications turns out to be as follows: There is a journal publication that claims to present an optimal competitive strategy with ratio 7/6 and a former conference paper that presents a lower bound of 20/17. In this note we extract the flaw in the upper bound and also present a new slightly improved and (as we think) simplified general lower bound of 13/11.

Understanding the noise characteristics of high quantum efficiency silicon-based ultraviolet detectors, developed by the Microdevices Lab at the Jet Propulsion Laboratory, is critical for current and proposed UV missions using these devices. In this paper, we provide an overview of our detector noise characterization test bench that uses delta-doped, photon counting, Electron-multiplying CCDs (EMCCDs) to understand the fundamental noise properties relevant to all silicon CCDs and CMOS arrays. This work attempts to identify the source of the dark current plateau that has been previously measured with photon-counting EMCCDs and is known to be prevalent in other silicon-based arrays. It is suspected that the plateau could be due to a combination of detectable photons in the tail of blackbody radiation of the ambient instrument, low-level light leaks, and a non-temperature-dependent component that varies with substrate voltage. Our innovative test setup delineates the effect of the ambient environment during dark measurements by independently controlling the temperature of the detector and surrounding environment. We present the design of the test setup and preliminary results., Comment: Submitted for Proceedings of SPIE Astronomical Telescopes+Instrumentation 2024, Paper number: 13093-26

Bosonic codes offer a hardware-efficient strategy for quantum error correction by redundantly encoding quantum information in the large Hilbert space of a harmonic oscillator. However, experimental realizations of these codes are often limited by ancilla errors propagating to the encoded logical qubit during syndrome measurements. The Kerr-cat qubit has been proposed as an ancilla for these codes due to its theoretically-exponential noise bias, which would enable fault-tolerant error syndrome measurements, but the coupling required to perform these syndrome measurements has not yet been demonstrated. In this work, we experimentally realize driven parametric coupling of a Kerr-cat qubit to a high-quality-factor microwave cavity and demonstrate a gate set enabling universal quantum control of the cavity. We measure the decoherence of the cavity in the presence of the Kerr-cat and discover excess dephasing due to heating of the Kerr-cat to excited states. By engineering frequency-selective dissipation to counteract this heating, we are able to eliminate this dephasing, thereby demonstrating a high on-off ratio of control. Our results pave the way toward using the Kerr-cat to fault-tolerantly measure error syndromes of bosonic codes.

Although community colleges contribute to STEM education and the workforce in a variety of ways, it is impossible to determine how much they contribute--or to assess the effectiveness of policies and programs designed to strengthen STEM education in community colleges--without agreement on what constitutes STEM. In this report, the authors review the conceptual challenges of defining STEM and propose a definition that recognizes STEM-oriented contributions from the dual missions of community colleges: preparing students for transfer to a four-year institution, which the authors refer to as STEM-Transfer, and training students for technical jobs--largely unaccounted for in popular definitions of STEM education--which they call STEM-Tech. STEM-Transfer relates directly to programs of study that teach explicitly STEM subject matter (biological sciences; chemistry; earth, atmospheric, and ocean sciences; engineering; mathematics; physics; and mechanic/production technologies), while STEM-Tech encompasses a range of technical programs of study--including those in computer and information sciences and the health professions--that community colleges offer. The authors then provide an accounting of STEM within the community college sector and develop a catalog of STEM programs and awards. Broadly, community colleges appear to provide significant amounts of STEM coursework, and there are more than three times as many community college students enrolled in STEM-Tech programs than in STEM-Transfer programs; similarly, community colleges award many more associate degrees in STEM-Tech than in STEM-Transfer. The authors conclude with an analysis of how many community college graduates work in STEM and of their relative collective earnings in the workforce. Based on their findings, they argue that a broader definition of STEM is more appropriate and should be adopted by federal agencies and researchers.

Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for simulating physico-chemical processes of complex systems, including reactive systems, and for training machine learning models and force fields. Speed and stability issues on traditional hardware preclude routine AIMD simulations for larger systems and longer timescales. We postulate that any practically useful AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on a moderate cloud resource. In this work, we implement a computing workflow that enables routine calculations at this throughput and demonstrate results for several non-trivial atomistic dynamical systems. In particular, we have employed the GPU implementation of the Quantum ESPRESSO code which we will show increases AIMD productivity compared to the CPU version. In order to take advantage of transient servers (which are more cost and energy effective compared to the stable servers), we have implemented automatic restart/continuation of the AIMD runs within the Schr\"odinger Materials Science Suite. Finally, to reduce simulation size and thus reduce compute time when modeling surfaces, we have implemented a wall potential constraint. Our benchmarks using several reactive systems (lithium anode surface/solvent interface, hydrogen diffusion in an iron grain boundary) show a significant speed up when running on a GPU-enabled transient server using our updated implementation.

Object Pose Estimation is a crucial component in robotic grasping and augmented reality. Learning based approaches typically require training data from a highly accurate CAD model or labeled training data acquired using a complex setup. We address this by learning to estimate pose from weakly labeled data without a known CAD model. We propose to use a NeRF to learn object shape implicitly which is later used to learn view-invariant features in conjunction with CNN using a contrastive loss. While NeRF helps in learning features that are view-consistent, CNN ensures that the learned features respect symmetry. During inference, CNN is used to predict view-invariant features which can be used to establish correspondences with the implicit 3d model in NeRF. The correspondences are then used to estimate the pose in the reference frame of NeRF. Our approach can also handle symmetric objects unlike other approaches using a similar training setup. Specifically, we learn viewpoint invariant, discriminative features using NeRF which are later used for pose estimation. We evaluated our approach on LM, LM-Occlusion, and T-Less dataset and achieved benchmark accuracy despite using weakly labeled data., Comment: 3DV 2024

In this article we investigate a model for the temperature within a Lithium-Ion battery. The model takes the form of a parabolic PDE for the temperature coupled with two elliptic PDE's for the electric potential within the solid and electrolyte phases. The primary difficulty comes from the coupling term, which is given by the Butler-Volmer equation. It features an exponential nonlinearity of both the electric potentials and the reciprocal of the temperature. Another difficulty arising in the temperature equation are the gradients of the electric potentials squared showing up on the right-hand side. Due to the nonlinearity, meaningful estimates for the temperature are currently not known. In spite of this, our investigation reveals the local existence of continuous temperature for the Lithium-Ion Battery.

The azimuthal correlation angle, $\Delta\phi$, between the scattered lepton and the leading jet in deep inelastic $e^{\pm}p$ scattering at HERA has been studied using data collected with the ZEUS detector at a centre-of-mass energy of $\sqrt{s} = 318 \;\mathrm{GeV}$, corresponding to an integrated luminosity of $326 \;\mathrm{pb}^{-1}$. A measurement of jet cross sections in the laboratory frame was made in a fiducial region corresponding to photon virtuality $10 \;\mathrm{GeV}^2 < Q^2 < 350 \;\mathrm{GeV}^2$, inelasticity $0.04 < y < 0.7$, outgoing lepton energy $E_e > 10 \;\mathrm{GeV}$, lepton polar angle $140^\circ < \theta_e < 180^\circ$, jet transverse momentum $2.5 \;\mathrm{GeV} < p_\mathrm{T,jet} < 30 \;\mathrm{GeV}$, and jet pseudorapidity $-1.5 < \eta_\mathrm{jet} < 1.8$. Jets were reconstructed using the $k_\mathrm{T}$ algorithm with the radius parameter $R = 1$. The leading jet in an event is defined as the jet that carries the highest $p_\mathrm{T,jet}$. Differential cross sections, $d\sigma/d\Delta\phi$, were measured as a function of the azimuthal correlation angle in various ranges of leading-jet transverse momentum, photon virtuality and jet multiplicity. Perturbative calculations at $\mathcal{O}(\alpha_{s}^2)$ accuracy successfully describe the data within the fiducial region, although a lower level of agreement is observed near $\Delta\phi \rightarrow \pi$ for events with high jet multiplicity, due to limitations of the perturbative approach in describing soft phenomena in QCD. The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.

Data structures and algorithms are essential building blocks for programs, and \emph{distributed data structures}, which automatically partition data across multiple memory locales, are essential to writing high-level parallel programs. While many projects have designed and implemented C++ distributed data structures and algorithms, there has not been widespread adoption of an interoperable model allowing algorithms and data structures from different libraries to work together. This paper introduces distributed ranges, which is a model for building generic data structures, views, and algorithms. A distributed range extends a C++ range, which is an iterable sequence of values, with a concept of segmentation, thus exposing how the distributed range is partitioned over multiple memory locales. Distributed data structures provide this distributed range interface, which allows them to be used with a collection of generic algorithms implemented using the distributed range interface. The modular nature of the model allows for the straightforward implementation of \textit{distributed views}, which are lightweight objects that provide a lazily evaluated view of another range. Views can be composed together recursively and combined with algorithms to implement computational kernels using efficient, flexible, and high-level standard C++ primitives. We evaluate the distributed ranges model by implementing a set of standard concepts and views as well as two execution runtimes, a multi-node, MPI-based runtime and a single-process, multi-GPU runtime. We demonstrate that high-level algorithms implemented using generic, high-level distributed ranges can achieve performance competitive with highly-tuned, expert-written code., Comment: To appear in ACM International Conference on Supercomputing (ICS) 2024

This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence., Comment: 30 pages, 0 figures, submitted for review to a journal

In this paper, we study the Bohr inequality with lacunary series to the single valued (resp. vector-valued) holomorphic function defined in unit ball of finite dimensional Banach sequence space. Also, we extend the Bohr inequality with an alternating series to the higher-dimensional space.

The LHCb detector has undergone a major upgrade for LHC Run 3. This Upgrade I detector facilitates operation at higher luminosity and utilises full-detector information at the LHC collision rate, critically including the use of vertex information. A new vertex locator system, the VELO Upgrade, has been constructed. The core element of the new VELO are the double-sided pixelated hybrid silicon detector modules which operate in vacuum close to the LHC beam in a high radiation environment. The construction and quality assurance tests of these modules are described in this paper. The modules incorporate 200 \mum thick, n-on-p silicon sensors bump-bonded to 130 \nm technology ASICs. These are attached with high precision to a silicon microchannel substrate that uses evaporative CO$_2$ cooling. The ASICs are controlled and read out with flexible printed circuits that are glued to the substrate and wire-bonded to the chips. The mechanical support of the module is given by a carbon fibre plate, two carbon fibre rods and an aluminium plate. The sensor attachment was achieved with an average precision of 21 $\mathrm{\mu m}$, more than 99.5\% of all pixels are fully functional, and a thermal figure of merit of 3 \mathrm{Kcm^{2}W^{-1}}$ was achieved. The production of the modules was successfully completed in 2021, with the final assembly and installation completed in time for data taking in 2022.

The chiral phase boundary of nuclear matter is expected to have a critical point where the rapid crossover of lattice methods at zero chemical potential becomes a first-order phase transition. Phenomenological models based on the AdS/CFT correspondence, known as AdS/ QCD, have succeeded in capturing many features of nuclear matter, with recent progress in producing the expected critical point. We study a model that produces a critical point in the chiral phase diagram by introducing a coupling between the scalar chiral field and the dilaton. We examine the effect of the scalar-dilaton coupling on the critical point. We also study the zero-temperature chiral dynamics, which must allow for spontaneous chiral symmetry breaking in the limit of zero quark mass. We find that when the scalar-dilaton coupling is large enough to ensure correct zero-temperature chiral dynamics, a critical point is present only if the quark mass is greater than 12.8 MeV., Comment: 18 pages, 9 figures

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.

The dynamical response of magnetic domain walls to external magnetic fields in ultra-thin multilayer magnetic films is determined not only by the composition and thickness of the layers but also by the growth conditions. Growth conditions can induce significant structural changes inside the layers and at the interfaces between them, affecting in particular the dynamics of domain walls, their mobility, elastic tension, and the pinning forces acting on them. In this work, we focus specifically on the effect of Ir layer growth pressure in Ta/Pt/Co/Ir/Ta ultra-thin multilayers films. Measurements of the DC magnetic properties, domain wall velocity and domain morphology in the creep regime for both constant and alternating field pulses, were performed for a batch of samples where the Ir layer was grown at different pressures. We find that the saturation magnetization, the effective anisotropy constant and the domain wall surface tension grow with increasing pressure and saturate at a threshold pressure, while the Dzyaloshinskii-Moriya field and the strength of the disorder remain practically unaltered over the range of pressures considered., Comment: 13 pages, 9 figures

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device., Comment: to be published in OOPSLA'24

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Dose-finding trials are a key component of the drug development process and rely on a statistical design to help inform dosing decisions. Triallists wishing to choose a design require knowledge of operating characteristics of competing methods. This is often assessed using a large-scale simulation study with multiple designs and configurations investigated, which can be time-consuming and therefore limits the scope of the simulation. We introduce a new approach to the design of simulation studies of dose-finding trials. The approach simulates all potential outcomes that individuals could experience at each dose level in the trial. Datasets are simulated in advance and then the same datasets are applied to each of the competing methods to enable a more efficient head-to-head comparison. In two case-studies we show sizeable reductions in Monte Carlo error for comparing a performance metric between two competing designs. Efficiency gains depend on the similarity of the designs. Comparing two Phase I/II design variants, with high correlation of recommending the same optimal biologic dose, we show that the new approach requires a simulation study that is approximately 30 times smaller than the conventional approach. Furthermore, advance-simulated trial datasets can be reused to assess the performance of designs across multiple configurations. We recommend researchers consider this more efficient simulation approach in their dose-finding studies and we have updated the R package escalation to help facilitate implementation., Comment: 27 pages, 4 figures, 1 table

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents' prior information and of centrality in the network, i.e., the number of immediate neighbors. To study these heterogeneities, we not only consider them in our model, proposing a novel network generator with heterogeneous centrality, but also introduce uncertainty as an additional dimension. By quantifying the uncertainty of each agent, we provide a mechanism for agents to adaptively weigh their individual against social information. As uncertainties develop according to the interactions between agents, they capture information on heterogeneities. Therefore, uncertainty is a relevant additional observable in the study of complex collective opinion dynamics that we use to show the bidirectional relationship of heterogeneous centrality and information. Furthermore, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially under heterogeneity, and provide guidelines for avoiding performance-decreasing errors in uncertainty modeling. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems., Comment: 15 pages, 7 figures

High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are derived from an increasing number of species, in an increasing number of brain regions, and with an increasing number of techniques. Each of these large-scale datasets, often surpassing petascale levels, is typically accompanied by a unique and varied set of metadata. These datasets can be used to derive connectomes, or neuron-synapse level connectivity diagrams, to investigate the fundamental organization of neural circuitry, neuronal development, and neurodegenerative disease. Standardization is essential to facilitate comparative connectomics analysis and enhance data utilization. Although the neuroinformatics community has successfully established and adopted data standards for many modalities, this effort has not yet encompassed EM and XRM/ XHN connectomics data. This lack of standardization isolates these datasets, hindering their integration and comparison with other research performed in the field. Towards this end, our team formed a working group consisting of community stakeholders to develop Image and Experimental Metadata Standards for EM and XRM/XHN data to ensure the scientific impact and further motivate the generation and sharing of these data. This document addresses version 1.1 of these standards, aiming to support metadata services and future software designs for community collaboration. Standards for derived annotations are described in a companion document. Standards definitions are available on a community github page. We hope these standards will enable comparative analysis, improve interoperability between connectomics software tools, and continue to be refined and improved by the neuroinformatics community., Comment: 15 Pages, 3 figures, 2 tables