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In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-reviewed invited paper at ISPD 2023 questioned its performance claims, despite failing to run our method as described in Nature. For example, it did not pre-train the RL method (removing its ability to learn from prior experience), used substantially fewer compute resources (20x fewer RL experience collectors and half as many GPUs), did not train to convergence (standard practice in machine learning), and evaluated on test cases that are not representative of modern chips. Recently, Igor Markov published a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it). Although AlphaChip has already achieved widespread adoption and impact, we publish this response to ensure that no one is wrongly discouraged from innovating in this impactful area.

(abstract shortened due to space constraints) Existing constructions of seeded extractors with short seed length and large output length run in time $\Omega(n \log(1/\varepsilon))$ and often slower, where $n$ is the input source length and $\varepsilon$ is the error of the extractor. Since cryptographic applications of extractors require $\varepsilon$ to be small, the resulting runtime makes these extractors unusable in practice. Motivated by this, we explore constructions of strong seeded extractors with short seeds computable in nearly-linear time $O(n \log^c n)$, for any error $\varepsilon$. We show that an appropriate combination of modern condensers and classical approaches for constructing seeded extractors for high min-entropy sources yields strong extractors for $n$-bit sources with any min-entropy $k$ and any target error $\varepsilon$ with seed length $d=O(\log(n/\varepsilon))$ and output length $m=(1-\eta)k$ for an arbitrarily small constant $\eta>0$, running in nearly-linear time, after a reasonable one-time preprocessing step (finding a primitive element of $\mathbb{F}_q$ with $q=poly(n/\varepsilon)$ a power of $2$) that is only required when $k<2^{C\log^* n}\cdot\log^2(n/\varepsilon)$, for a constant $C>0$ and $\log^*$ the iterated logarithm, and which can be implemented in time $polylog(n/\varepsilon)$ under mild conditions on $q$. As a second contribution, we give an instantiation of Trevisan's extractor that can be evaluated in truly linear time in the RAM model, as long as the number of output bits is at most $\frac{n}{\log(1/\varepsilon)polylog(n)}$. Previous fast implementations of Trevisan's extractor ran in $\widetilde{O}(n)$ time in this setting. In particular, these extractors directly yield privacy amplification protocols with the same time complexity and output length, and communication complexity equal to their seed length., Comment: 40 pages

Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. While machine learning (ML) models have demonstrated radiologist-level accuracy in detecting prostate cancer from these two sequences, this study investigates the potential of ML-enabled methods using only the T2w sequence as input during inference time. We first discuss the technical feasibility of such a T2-only approach, and then propose a novel ML formulation, where DWI sequences - readily available for training purposes - are only used to train a meta-learning model, which subsequently only uses T2w sequences at inference. Using multiple datasets from more than 3,000 prostate cancer patients, we report superior or comparable performance in localising radiologist-identified prostate cancer using our proposed T2-only models, compared with alternative models using T2-only or both sequences as input. Real patient cases are presented and discussed to demonstrate, for the first time, the exclusively true-positive cases from models with different input sequences., Comment: Code: https://github.com/wxyi057/MetaT2

The current system of scholarly publishing is often criticized for being slow, expensive, and not transparent. The rise of open access publishing as part of open science tenets, promoting transparency and collaboration, together with calls for research assesment reforms are the results of these criticisms. The emergence of new open access publishers presents a unique opportunity to empirically test how universities and countries respond to shifts in the academic publishing landscape. These new actors challenge traditional publishing models, offering faster review times and broader accessibility, which could influence strategic publishing decisions. Our findings reveal a clear division in European publishing practices, with countries clustering into two groups distinguished by the ratio of publications in new open access journals with accelerated review times versus legacy journals. This divide underscores a broader shift in academic culture, highlighting new open access publishing venues as a strategic factor influencing national and institutional publishing practices, with significant implications for research accessibility and collaboration across Europe.

PDS 453 is a rare highly inclined disk where the stellar photosphere is seen at grazing incidence on the disk surface. Our goal is take advantage of this geometry to constrain the structure and composition of this disk, in particular the fact that it shows a 3.1 $\mu$m water ice band in absorption that can be related uniquely to the disk. We observed the system in polarized intensity with the VLT/SPHERE instrument, as well as in polarized light and total intensity using the HST/NICMOS camera. Infrared archival photometry and a spectrum showing the water ice band are used to model the spectral energy distribution under Mie scattering theory. Based on these data, we fit a model using the radiative transfer code MCFOST to retrieve the geometry and dust and ice content of the disk. PDS 453 has the typical morphology of a highly inclined system with two reflection nebulae where the disk partially attenuates the stellar light. The upper nebula is brighter than the lower nebula and shows a curved surface brightness profile in polarized intensity, indicating a ring-like structure. With an inclination of 80{\deg} estimated from models, the line-of-sight crosses the disk surface and a combination of absorption and scattering by ice-rich dust grains produces the water ice band. PDS 453 is seen highly inclined and is composed of a mixture of silicate dust and water ice. The radial structure of the disk includes a significant jump in density and scale height at a radius of 70 au in order to produce a ring-like image. The depth of the 3.1 $\mu$m water ice band depends on the amount of water ice, until it saturates when the optical thickness along the line-of-sight becomes too large. Therefore, quantifying the exact amount of water from absorption bands in edge-on disks requires a detailed analysis of the disk structure and tailored radiative transfer modeling., Comment: 11 pages, 11 figures

We provide bounds on the tail probabilities for simple procedures that generate random samples _without replacement_, when the probabilities of being selected need not be equal.

We present a thorough analysis of the use of CXL-based heterogeneous systems. We built a cluster of server systems that combines different vendor's CPUs and various types of CXL devices. We further developed a heterogeneous memory benchmark suite, Heimdall, to profile the performance of such heterogeneous systems. By leveraging Heimdall, we unveiled the detailed architecture design in these systems, drew observations on optimizing performance for workloads, and pointed out directions for future development of CXL-based heterogeneous systems.

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy image crops. Compared to a previous published ViT-L/8 MAE, our new model achieves a 60% improvement in linear separability of genetic perturbations and obtains the best overall performance on whole-genome biological relationship recall and replicate consistency benchmarks. Beyond scaling, we developed two key methods that improve performance: (1) training on a curated and diverse dataset; and, (2) using biologically motivated linear probing tasks to search across each transformer block for the best candidate representation of whole-genome screens. We find that many self-supervised vision transformers, pretrained on either natural or microscopy images, yield significantly more biologically meaningful representations of microscopy images in their intermediate blocks than in their typically used final blocks. More broadly, our approach and results provide insights toward a general strategy for successfully building foundation models for large-scale biological data., Comment: NeurIPS 2024 Foundation Models for Science Workshop (38th Conference on Neural Information Processing Systems). 18 pages, 7 figures

Background: Cone-beam computed tomography (CBCT) plays a crucial role in image-guided radiotherapy, but artifacts and noise make them unsuitable for accurate dose calculation. Artificial intelligence methods have shown promise in enhancing CBCT quality to produce synthetic CT (sCT) images. However, existing methods either produce images of suboptimal quality or incur excessive time costs, failing to satisfy clinical practice standards. Methods and materials: We propose a novel hybrid conditional latent diffusion model for efficient and accurate CBCT-to-CT synthesis, named HC$^3$L-Diff. We employ the Unified Feature Encoder (UFE) to compress images into a low-dimensional latent space, thereby optimizing computational efficiency. Beyond the use of CBCT images, we propose integrating its high-frequency knowledge as a hybrid condition to guide the diffusion model in generating sCT images with preserved structural details. This high-frequency information is captured using our designed High-Frequency Extractor (HFE). During inference, we utilize denoising diffusion implicit model to facilitate rapid sampling. We construct a new in-house prostate dataset with paired CBCT and CT to validate the effectiveness of our method. Result: Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of sCT quality and generation efficiency. Moreover, our medical physicist conducts the dosimetric evaluations to validate the benefit of our method in practical dose calculation, achieving a remarkable 93.8% gamma passing rate with a 2%/2mm criterion, superior to other methods. Conclusion: The proposed HC$^3$L-Diff can efficiently achieve high-quality CBCT-to-CT synthesis in only over 2 mins per patient. Its promising performance in dose calculation shows great potential for enhancing real-world adaptive radiotherapy., Comment: 13 pages, 5 figures

We present a comparison of different quantum state preparation algorithms and their overall efficiency for the Schwinger model with a theta term. While adiabatic state preparation (ASP) is proved to be effective, in practice it leads to large CNOT gate counts to prepare the ground state. The quantum approximate optimization algorithm (QAOA) provides excellent results while keeping the CNOT counts small by design, at the cost of an expensive classical minimization process. We introduce a ``blocked'' modification of the Schwinger Hamiltonian to be used in the QAOA that further decreases the length of the algorithms as the size of the problem is increased. The rodeo algorithm (RA) provides a powerful tool to efficiently prepare any eigenstate of the Hamiltonian, as long as its overlap with the initial guess is large enough. We obtain the best results when combining the blocked QAOA ansatz and the RA, as this provides an excellent initial state with a relatively short algorithm without the need to perform any classical steps for large problem sizes., Comment: 11 pages, 9 figures, 4 tables

Many promising explosion models for the elusive origin of Type Ia supernovae (SNe Ia) ultimately fail to completely reproduce a number of observed properties of these events. One limiting factor for many of these models is the use of the local thermodynamic equilibrium (LTE) assumption in the calculation of their synthetic observables, which has been shown to prevent the accurate prediction of a number of fundamental features of SNe Ia. The inclusion of high-accuracy non-LTE physics, however, increases computational cost and complexity such that multidimensional non-LTE calculations are often unfeasible, which can be problematic for models that are inherently multidimensional. In this work, we conduct radiative transfer calculations using 1D profiles that each correspond with a line of sight from an asymmetric, 2D SN Ia model. We find, in LTE, that the synthetic observables from these calculations efficiently reproduce those from the 2D calculation when an equivalence of bolometric luminosities between the 1D and 2D treatments is enforced. This allows for the accurate calculation of synthetic observables in 1D while still preserving multidimensional effects associated with the model. We leverage this to produce high accuracy observables from 1D non-LTE calculations, showing significantly improved agreement with observation, including a roughly 50% reduction of $B$-band decline rate into congruence with the observed Phillips relation. Additionally, our non-LTE observables show Si II $\lambda$5972 pEWs that are much more similar to observation, while spanning multiple Branch classes, suggesting that some spectral classifications of SNe Ia arise from line of sight effects., Comment: 18 pages, 14 figures, submitted to ApJ

Training large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning the compromise between time and compute, marks the threshold beyond which greater data parallelism leads to diminishing returns. To operationalize it, we propose a measure of CBS and pre-train a series of auto-regressive language models, ranging from 85 million to 1.2 billion parameters, on the C4 dataset. Through extensive hyper-parameter sweeps and careful control of factors such as batch size, momentum, and learning rate along with its scheduling, we systematically investigate the impact of scale on CBS. Then we fit scaling laws with respect to model and data sizes to decouple their effects. Overall, our results demonstrate that CBS scales primarily with data size rather than model size, a finding we justify theoretically through the analysis of infinite-width limits of neural networks and infinite-dimensional least squares regression. Of independent interest, we highlight the importance of common hyper-parameter choices and strategies for studying large-scale pre-training beyond fixed training durations.

A twist between two systems offers the possibility to drastically change the underlying physical properties. To that end, we study the bandstructure of twisted moir\'e potentials in detail. At sets of commensurate twisting angles, the low energy single-particle spectrum of a twisted moir\'e potential will form into distinct bands and gaps. To a first approximation, energy bands can be qualitatively modelled by harmonic states, localised in different potential minima. The bands are intrinsically linked to the number of distinct minima and size of the moir\'e unit cell, with smaller cells producing larger gaps and vice versa. For shallower potential depths, degeneracies between harmonic states are lifted by virtue of anharmonic confinement and coupling between states. Depending on the exact geometry of potential minima, bands can then be classified by $4$ unique forms of tight-binding models. We find excellent agreement between the continuous spectrum and fitting to our tight-binding models, allowing for accurate tunnelling rates and onsite energies to be extracted. Our results are directly relevant to the bosonic, many-body problem, and thus provide further understanding on the relative stability of quantum phases both in theory and experiments. In particular, the prominence of gaps can be mapped to strongly correlated insulating phases. Furthermore, tunnelling rates of different bands serve as thresholds on temperature in which a phase can be either a normal fluid or superfluid., Comment: 19 pages, 14 figures, comments welcome

Calcium signals in pancreatic beta-cells collectives show a sharp transition from uncorrelated to correlated state resembling a phase transition as the slowly increasing glucose concentration crosses the tipping point. However, the exact nature or the order of this phase transition is not well understood. Using confocal microscopy to record the collective calcium activation of beta-cells in an intact islet under changing glucose concentration in increasing and then decreasing way, we first show that in addition to the sharp transition, the coordinated calcium response exhibits a hysteresis indicating a critical, first order transition. A network model of beta-cells combining link selection and coordination mechanisms capture the observed hysteresis loop and the critical nature of the transition. Our results point towards the understanding the role of islets as tipping elements in the pancreas that interconnected by perfusion, diffusion and innervation cause the tipping dynamics and abrupt insulin release.

Non-classical and entangled light states are of fundamental interest in quantum mechanics and they are a powerful tool for the emergence of new quantum technologies. The development of methods that can lead to the generation of such light states is therefore of high importance. Recently, we have demonstrated that intense laser-matter interactions can serve towards this direction. Specifically, we have shown how the use of fully quantized approaches in intense laser-matter interactions and the process of high harmonic generation, can lead to the generation high photon-number non-classical (optical Schr\"{o}dinger's "cat" or squeezed) and entangled states from the far-infrared (IR) to the extreme-ultraviolet (XUV). Here, after a brief introduction on the fundamentals, we summarize the operation principles of these approaches and we discuss the future directions of non-classical light engineering using strong laser fields, and the potential applications in ultrafast and quantum information science. Our findings open the way to a novel quantum nonlinear spectroscopy method, based on the interplay between the quantum properties of light with that of quantum matter., Comment: Invited Topical Review submitted to J. Phys. B

Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START) AI/ML Working Group. Collectively, our group mission statement is "Where is the Human-in-the-loop as Gen AI systems become more powerful and autonomous?" with an emphasis on the ethical applications of Gen AI, guided by using these systems to remove drudgery from human work while simultaneously increasing opportunities for humans to experience more collective creativity and innovation. The HWO mission stands to benefit dramatically from generative models for different data types including text, time series/spectra, and image data. These cover a wide range of applications in science and engineering for HWO, including: mission development acceleration, data analysis and interpretation, enhancing imaging capabilities, anomaly detection, predictive modeling and simulation, data augmentation for machine learning, instrument calibration and optimization, public engagement and education, and assisting in mission planning. As an example, through sensitivity analysis of simulated exoplanet population science data sets of various generative model complexity, we can reverse engineer the measurement uncertainty requirements for HWO instruments to produce data that can constrain population models and thus inform HWO design requirements. This approach to HWO design is one example of a strategy that can ensure that HWO remains AI-ready. Through presenting herein a combination of visionary ideas balanced with grounded validated use case examples, we aim to support the development of a long-term strategy to keep HWO AI-ready as it moves forward., Comment: Lack of guidelines for submitting work that came out of the HWO START TAG working groups.

Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open Knowledge Network program aim to create an ecosystem of nation-scale, cross-disciplinary GeoKGs that provide AI-ready geospatial data aligned with FAIR principles. However, building this infrastructure presents key challenges, including 1) managing large volumes of data, 2) the computational complexity of discovering topological relations via SPARQL, and 3) conflating multi-scale raster and vector data. Discrete Global Grid Systems (DGGS) help tackle these issues by offering efficient data integration and representation strategies. The KnowWhereGraph utilizes Google's S2 Geometry -- a DGGS framework -- to enable efficient multi-source data processing, qualitative spatial querying, and cross-graph integration. This paper outlines the implementation of S2 within KnowWhereGraph, emphasizing its role in topologically enriching and semantically compressing data. Ultimately, this work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs.

This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based on sampled displacements and spatial transformation functions. These benefits include a clear connection between learning-based image registration and segmentation, which in turn motivates two cases of image registration approaches using (pre-)trained segmentation networks. Based on the segment anything model (SAM), a vision foundation model for segmentation, we develop a new registration algorithm SAMReg, which does not require any training (or training data), gradient-based fine-tuning or prompt engineering. The proposed SAMReg models are evaluated across five real-world applications, including intra-subject registration tasks with cardiac MR and lung CT, challenging inter-subject registration scenarios with prostate MR and retinal imaging, and an additional evaluation with a non-clinical example with aerial image registration. The proposed methods outperform both intensity-based iterative algorithms and DDF-predicting learning-based networks across tested metrics including Dice and target registration errors on anatomical structures, and further demonstrates competitive performance compared to weakly-supervised registration approaches that rely on fully-segmented training data. Open source code and examples are available at: https://github.com/sqhuang0103/SAMReg.git.

Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological data. Deep learning has shown promise in exploring these relationships using various data types. However, transcriptomics, which provides detailed insights into cellular states, is still underused due to its high noise levels and limited data availability. Recent advancements in transcriptomics sequencing provide new opportunities to uncover valuable insights, especially with the rise of many new foundation models for transcriptomics, yet no benchmark has been made to robustly evaluate the effectiveness of these rising models for perturbation analysis. This article presents a novel biologically motivated evaluation framework and a hierarchy of perturbation analysis tasks for comparing the performance of pretrained foundation models to each other and to more classical techniques of learning from transcriptomics data. We compile diverse public datasets from different sequencing techniques and cell lines to assess models performance. Our approach identifies scVI and PCA to be far better suited models for understanding biological perturbations in comparison to existing foundation models, especially in their application in real-world scenarios., Comment: Neurips 2024 AIDrugX Workshop

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema for KnowWhereGraph. This broad overview provides insight into the requirements and design specifications for the graph and its schema, including the development methodology (modular ontology modeling) and the resources utilized to implement, materialize, and deploy KnowWhereGraph with its end-user interfaces and public query SPARQL endpoint.

Understanding quantum materials -- solids in which quantum-mechanical interactions among constituent electrons yield a great variety of novel emergent phenomena -- is a forefront challenge in modern condensed matter physics. This goal has driven the invention and refinement of several experimental methods, which can spectroscopically determine the elementary excitations and correlation functions that determine material properties. This Perspectives article focuses on the future experimental and theoretical trends of resonant inelastic x-ray scattering (RIXS), which is a remarkably versatile and rapidly growing technique for probing different charge, lattice, spin, and orbital excitations in quantum materials. We provide a forward-looking introduction to RIXS and outline how this technique is poised to deepen our insight into the nature of quantum materials and their emergent electronic phenomena., Comment: Review of RIXS to appear in Physical Review X as a Perspective

In this article we first prove existence of minimizers of the Landau-de Gennes energy for liquid crystals with homogeneous external magnetic field and strong uniaxial planar anchoring. Next we consider the asymptotics of solutions to the joint minimization of the energy w.r.t. the function and its boundary condition. This constitutes a generalization to arbitrary regular particle shapes of the results obtained in [BLS2024] for $\lambda=\infty$. Moreover we show the absence of line singularities in some asymptotic parameter regimes. Finally we characterize the optimal orientation of particles vis-\`a-vis the magnetic field direction and compute it explicitly for different particle shapes., Comment: 43 pages, 9 figures

The Jones-Wenzl projectors are particular elements of the Temperley-Lieb algebra essential to the construction of quantum 3-manifold invariants. As a first step toward categorifying quantum 3-manifold invariants, Cooper and Krushkal categorified these projectors. In another direction, Naisse and Putyra gave a categorification of the Temperley-Lieb algebra compatible with odd Khovanov homology, introducing new machinery called grading categories. We provide a generalization of Naisse and Putyra's work in the spirit of Bar-Natan's canopolies or Jones's planar algebras, replacing grading categories with grading multicategories. We use our setup to prove the existence and uniqueness of categorified Jones-Wenzl projectors in odd Khovanov homology. This result quickly implies the existence of a new, "odd" categorification of the colored Jones polynomial., Comment: 113 pages, many figures. Comments welcomed!

Viral signal in wastewater offers a promising opportunity to assess and predict the burden of infectious diseases. That has driven the widespread adoption and development of wastewater monitoring tools by public health organizations. Recent research highlights a strong correlation between COVID-19 hospitalizations and wastewater viral signals, and validates that increases in wastewater measurements may offer early warnings of an increase in hospital admissions. Previous studies (e.g. Peng et al. 2023) utilize distributed lag models to explore associations of COVID-19 hospitalizations with lagged SARS-CoV-2 wastewater viral signals. However, the conventional distributed lag models assume the duration time of the lag to be fixed, which is not always plausible. This paper presents Markov-modulated models with distributed lasting time, treating the duration of the lag as a random variable defined by a hidden Markov chain. We evaluate exposure effects over the duration time and estimate the distribution of the lasting time using the wastewater data and COVID-19 hospitalization records from Ottawa, Canada during June 2020 to November 2022. The different COVID-19 waves are accommodated in the statistical learning. In particular, two strategies for comparing the associations over different time intervals are exemplified using the Ottawa data. Of note, the proposed Markov modulated models, an extension of distributed lag models, are potentially applicable to many different problems where the lag time is not fixed.

This white paper is the result of a collaboration by those that attended a workshop at the Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on Theoretical Justifications and Motivations for Early High-Profile FRIB Experiments. It covers a wide range of topics related to the science that will be explored at FRIB. After a brief introduction, the sections address: (II) Overview of theoretical methods, (III) Experimental capabilities, (IV) Structure, (V) Near-threshold Physics, (VI) Reaction mechanisms, (VII) Nuclear equations of state, (VIII) Nuclear astrophysics, (IX) Fundamental symmetries, and (X) Experimental design and uncertainty quantification., Comment: 227 pages, 24 figures

The quasidiffusion (QD) method, also known as the Variable Eddington Factor (VEF) method in the astrophysical community, is an established iterative method for accelerating source iterations in SN calculations. A great advantage of the QD method is that the diffusion equation that accelerates the SN source iterations can be discretized in any valid discretization without concern for consistency with the transport discretization. QD has comparable effectiveness with diffusion synthetic acceleration (DSA), but the converged scalar flux of the diffusion equation will differ from the transport solution by the spatial truncation errors. Larsen et al. introduced a new consistent QD method (CQD), which includes a straightforwardly defined transport consistency factor closely related to the well-known coarse mesh finite difference (CMFD) and DSA methods. The CQD method preserves the discretized scalar flux solution of the SN equations, and it is stable for problems with optically thin spatial cells, but just like nonlinear diffusion acceleration (NDA), it degrades in performance and eventually becomes unstable when the spatial cells become greater than about one mean free path thick. In this paper, we performed a formal Fourier analysis of the CQD method to show that its theoretical spectral radius is essentially the same as that of the NDA method. To improve the stability of CQD, we introduce the lpCQD method, which adopts the idea of the linear prolongation CMFD (lpCMFD) method.

Distributed Asynchronous Object Store (DAOS) is a novel software-defined object store leveraging Non-Volatile Memory (NVM) devices, designed for high performance. It provides a number of interfaces for applications to undertake I/O, ranging from a native object storage API to a DAOS FUSE module for seamless compatibility with existing applications using POSIX file system APIs. In this paper we discuss these interfaces and the options they provide, exercise DAOS through them with various I/O benchmarks, and analyse the observed performance. We also briefly compare the performance with a distributed file system and another object storage system deployed on the same hardware, and showcase DAOS' potential and increased flexibility to support high-performance I/O., Comment: 9 pages, 38 figures, PDSW'24

This paper addresses the capacitated periodic review inventory control problem, focusing on a retailer managing multiple products with limited shared resources, such as storage or inbound labor at a facility. Specifically, this paper is motivated by the questions of (1) what does it mean to backtest a capacity control mechanism, (2) can we devise and backtest a capacity control mechanism that is compatible with recent advances in deep reinforcement learning for inventory management? First, because we only have a single historic sample path of Amazon's capacity limits, we propose a method that samples from a distribution of possible constraint paths covering a space of real-world scenarios. This novel approach allows for more robust and realistic testing of inventory management strategies. Second, we extend the exo-IDP (Exogenous Decision Process) formulation of Madeka et al. 2022 to capacitated periodic review inventory control problems and show that certain capacitated control problems are no harder than supervised learning. Third, we introduce a `neural coordinator', designed to produce forecasts of capacity prices, guiding the system to adhere to target constraints in place of a traditional model predictive controller. Finally, we apply a modified DirectBackprop algorithm for learning a deep RL buying policy and a training the neural coordinator. Our methodology is evaluated through large-scale backtests, demonstrating RL buying policies with a neural coordinator outperforms classic baselines both in terms of cumulative discounted reward and capacity adherence (we see improvements of up to 50% in some cases).

We consider the problem of learning a realization of a partially observed dynamical system with linear state transitions and bilinear observations. Under very mild assumptions on the process and measurement noises, we provide a finite time analysis for learning the unknown dynamics matrices (up to a similarity transform). Our analysis involves a regression problem with heavy-tailed and dependent data. Moreover, each row of our design matrix contains a Kronecker product of current input with a history of inputs, making it difficult to guarantee persistence of excitation. We overcome these challenges, first providing a data-dependent high probability error bound for arbitrary but fixed inputs. Then, we derive a data-independent error bound for inputs chosen according to a simple random design. Our main results provide an upper bound on the statistical error rates and sample complexity of learning the unknown dynamics matrices from a single finite trajectory of bilinear observations., Comment: 35 pages, 3 figures

Modern Mixed-Criticality Systems (MCSs) rely on hardware heterogeneity to satisfy ever-increasing computational demands. However, most of the heterogeneous co-processors are designed to achieve high throughput, with their micro-architectures executing the workloads in a streaming manner. This streaming execution is often non-preemptive or limited-preemptive, preventing tasks' prioritisation based on their importance and resulting in frequent occurrences of algorithmic priority and/or criticality inversions. Such problems present a significant barrier to guaranteeing the systems' real-time predictability, especially when co-processors dominate the execution of the workloads (e.g., DNNs and transformers). In contrast to existing works that typically enable coarse-grained context switch by splitting the workloads/algorithms, we demonstrate a method that provides fine-grained context switch on a widely used open-source DNN accelerator by enabling instruction-level preemption without any workloads/algorithms modifications. As a systematic solution, we build a real system, i.e., Make Each Switch Count (MESC), from the SoC and ISA to the OS kernel. A theoretical model and analysis are also provided for timing guarantees. Experimental results reveal that, compared to conventional MCSs using non-preemptive DNN accelerators, MESC achieved a 250x and 300x speedup in resolving algorithmic priority and criticality inversions, with less than 5\% overhead. To our knowledge, this is the first work investigating algorithmic priority and criticality inversions for MCSs at the instruction level., Comment: Accepted at the 2024 IEEE Real-Time Systems Symposium (RTSS)

High-harmonic generation (HHG) involves the up-conversion of a high-intensity driving field into its harmonic orders. This process is intrinsically non-classical, requiring from quantum mechanics for a complete explanation as, under suitable conditions, involves phenomena such as particle tunneling through a potential barrier. When exposed to a high-intensity, low-frequency laser field, bound electrons ionize via tunneling, accelerate under the driving field, and recombine with the parent ion, emitting high-harmonic radiation. However, electrons can follow two distinct pathways -- short and long trajectories -- during these steps. In this work, we evaluate the signatures left by these trajectories on the quantum optical state after HHG, and observe that they lead to entanglement between the driving field and the generated harmonics. By leveraging these correlations, we use harmonic generation to herald the creation of optical Schr\"odinger cat-like states in the driving field. Additionally, using an ab-initio approach, we examine how propagation effects, which spatially separate the harmonic contributions from short and long trajectories, influence the non-classical characteristics of the emitted light., Comment: 23 pages (13 main text + 10 appendix). 12 figures (8 main text + 4 appendix). Comments are welcome

Music has been called the temporal art par excellence. Yet, as this paper explains, it is also the atemporal art par excellence. The contradiction is, however, only apparent, and a result of viewing music from two possible perspectives. That it has these two perspectives is the focus of this paper. In particular, the way in which these two aspects of music allow it to function as a kind of conduit between transcendent and immanent; immaterial and material. This can help explain the power of music to touch places deep in the soul (the part of us that transcends matter and time), that other forms of art struggle to reach. A somewhat similar debate occurs in looking at mathematics from an ontological point of view. In particular the treatment of the real numbers. There are curious properties of real numbers that seem to put them, like music, in the realm of the transcendent: in terms of the amount of information to specify them, one requires infinite computer time since there is no repeating pattern to their decimal expansions. One must simply evolve the sequence, working through it, despite the fact that it might have a perfectly situated home in Platonia. In other words, bringing them into this world demands a temporal element. We explore these and other links to a variety of issues in physics, ultimately arguing for dual-aspect monism., Comment: To appear in K. Cunio, K. Parry, and D. Rickles (eds.), Harmony of the Spheres: New Essays. ANU Press

In Canada's northern regions, linear disturbances such as roads, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population (Rangifer tarandus). To address the critical need for management of these disturbances, there is a strong emphasis on developing mapping approaches that accurately identify forest habitat fragmentation. The traditional approach is manually generating maps, which is time-consuming and lacks the capability for frequent updates. Instead, applying deep learning methods to multispectral satellite imagery offers a cost-effective solution for automated and regularly updated map production. Deep learning models have shown promise in extracting paved roads in urban environments when paired with high-resolution (<0.5m) imagery, but their effectiveness for general linear feature extraction in forested areas from lower resolution imagery remains underexplored. This research employs a deep convolutional neural network model based on the VGGNet16 architecture for semantic segmentation of lower resolution (10m) Sentinel-2 satellite imagery, creating precise multi-class linear disturbance maps. The model is trained using ground-truth label maps sourced from the freely available Alberta Institute of Biodiversity Monitoring Human Footprint dataset, specifically targeting the Boreal and Taiga Plains ecozones in Alberta, Canada. Despite challenges in segmenting lower resolution imagery, particularly for thin linear disturbances like seismic exploration lines that can exhibit a width of 1-3 pixels in Sentinel-2 imagery, our results demonstrate the effectiveness of the VGGNet model for accurate linear disturbance retrieval. By leveraging the freely available Sentinel-2 imagery, this work advances cost-effective automated mapping techniques for identifying and monitoring linear disturbance fragmentation.

Two-dimensional van der Waals semiconductors are promising for future nanoelectronics. However, integrating high-k gate dielectrics for device applications is challenging as the inert van der Waals material surfaces hinder uniform dielectric growth. Here, we report a liquid metal oxide-assisted approach to integrate ultrathin, high-k HfO2 dielectric on 2D semiconductors with atomically smooth interfaces. Using this approach, we fabricated 2D WS2 top-gated transistors with subthreshold swings down to 74.5 mV/dec, gate leakage current density below 10-6 A/cm2, and negligible hysteresis. We further demonstrate a one-step van der Waals integration of contacts and dielectrics on graphene. This can offer a scalable approach toward integrating entire prefabricated device stack arrays with 2D materials. Our work provides a scalable solution to address the crucial dielectric engineering challenge for 2D semiconductors, paving the way for high-performance 2D electronics.

It has been shown in \href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.170402}{\textit{Phys. Rev. Lett.}, \textbf{108} 170402 (2012)}, that quantum tunneling is instantaneous using a time-of-arrival (TOA) operator constructed by Weyl quantization of the classical TOA. However, there are infinitely many possible quantum images of the classical TOA, leaving it unclear if one is uniquely preferred over the others. This raises the question on whether instantaneous tunneling time is simply an artifact of the chosen ordering rule. Here, we demonstrate that tunneling time vanishes for all possible quantum images of the classical arrival time, irrespective of the ordering rule between the position and momentum observables. The result still holds for TOA-operators that are constructed independent of canonical quantization, while still imposing the correct algebra defined by the time-energy canonical commutation relation.

The growth of deep learning (DL) relies heavily on huge amounts of labelled data for tasks such as natural language processing and computer vision. Specifically, in image-to-text or image-to-image pipelines, opinion (sentiment) may be inadvertently learned by a model from human-generated image captions. Additionally, learning may be affected by the variety and diversity of the provided captions. While labelling large datasets has largely relied on crowd-sourcing or data-worker pools, evaluating the quality of such training data is crucial. This study proposes an evaluation method focused on sentiment and semantic richness. That method was applied to the COCO-MS dataset, comprising approximately 150K images with segmented objects and corresponding crowd-sourced captions. We employed pre-trained models (Twitter-RoBERTa-base and BERT-base) to extract sentiment scores and variability of semantic embeddings from captions. The relation of the sentiment score and semantic variability with object categories was examined using multiple linear regression. Results indicate that while most captions were neutral, about 6% of the captions exhibited strong sentiment influenced by specific object categories. Semantic variability of within-image captions remained low and uncorrelated with object categories. Model-generated captions showed less than 1.5% of strong sentiment which was not influenced by object categories and did not correlate with the sentiment of the respective human-generated captions. This research demonstrates an approach to assess the quality of crowd- or worker-sourced captions informed by image content.

Noncentrosymmetric 2D superconductors with large spin-orbit coupling offer an opportunity to explore superconducting behaviors far beyond the Pauli limit. One such superconductor, few-layer T$_d$-MoTe$_2$, has large upper critical fields that can exceed the Pauli limit by up to 600%. However, the mechanisms governing this enhancement are still under debate, with theory pointing towards either spin-orbit parity coupling or tilted Ising spin-orbit coupling. Moreover, ferroelectricity concomitant with superconductivity has been recently observed in the bilayer, where strong changes to superconductivity can be observed throughout the ferroelectric transition pathway. Here, we report the superconducting behavior of bilayer T$_d$-MoTe$ _2$ under an in-plane magnetic field, while systematically varying magnetic field angle and out-of-plane electric field strength. We find that superconductivity in bilayer MoTe$_2$ exhibits a two-fold symmetry with an upper critical field maxima occurring along the b-axis and minima along the a-axis. The two-fold rotational symmetry remains robust throughout the entire superconducting region and ferroelectric hysteresis loop. Our experimental observations of the spin-orbit coupling strength (up to 16.4 meV) agree with the spin texture and spin splitting from first-principles calculations, indicating that tilted Ising spin-orbit coupling is the dominant underlying mechanism.

Future electronics require aggressive scaling of channel material thickness while maintaining device performance. Two-dimensional (2D) semiconductors are promising candidates, but despite over two decades of research, experimental performance still lags theoretical expectations. Here, we develop an oxygen-free approach to push the electrical transport of 2D field-effect transistors toward the theoretical phonon-limited intrinsic mobility. We achieve record carrier mobilities of 91 (132) cm2V-1s-1 for mono- (bi-) layer MoS2 transistors on SiO2 substrate. Statistics from over 60 devices confirm that oxygen-free fabrication enhances key figures of merit by more than an order of magnitude. While previous studies suggest that 2D transition metal dichalcogenides such as MoS2 and WS2 are stable in air, we show that short-term ambient exposure can degrade their device performance through irreversible oxygen chemisorption. This study emphasizes the criticality of avoiding oxygen exposure, offering guidance for device manufacturing for fundamental research and practical applications of 2D materials.

Photonic lanterns are waveguide devices enabling high throughput single mode spectroscopy and high angular resolution. We aim to present the first on-sky demonstration of a photonic lantern (PL) operating in visible light, to measure its throughput and assess its potential for high-resolution spectroscopy of compact objects. We used the SCExAO instrument (a double stage extreme AO system installed at the Subaru telescope) and FIRST mid-resolution spectrograph (R 3000) to test the visible capabilities of the PL on internal source and on-sky observations. The best averaged coupling efficiency over the PL field of view was measured at 51% +/- 10% with a peak at 80%. We also investigate the relationship between coupling efficiency and the Strehl ratio for a PL, comparing them with those of a single-mode fiber (SMF). Findings show that in the AO regime, a PL offers better coupling efficiency performance than a SMF, especially in the presence of low spatial frequency aberrations. We observed Ikiiki (alpha Leo - mR = 1.37) and `Aua (alpha Ori - mR = -1.17) at a frame rate of 200 Hz. Under median seeing conditions (about 1 arcsec measured in H band) and large tip/tilt residuals (over 20 mas), we estimated an average light coupling efficiency of 14.5% +/- 7.4%, with a maximum of 42.8% at 680 nm. We were able to reconstruct both star's spectra, containing various absorption lines. The successful demonstration of this device opens new possibilities in terms of high throughput single-mode fiber-fed spectroscopy in the Visible. The demonstrated on-sky coupling efficiency performance would not have been achievable with a single SMF injection setup under similar conditions, partly because the residual tip/tilt alone exceeded the field of view of a visible SMF (18 mas at 700 nm). Thus emphasizing the enhanced resilience of PL technology to such atmospheric disturbances. The additional, Comment: Accepted in Astronomy and Astrophysics journal on 9/11/2024

Organ transplantation serves as the primary therapeutic strategy for end-stage organ failures. However, allograft rejection is a common complication of organ transplantation. Histological assessment is essential for the timely detection and diagnosis of transplant rejection and remains the gold standard. Nevertheless, the traditional histochemical staining process is time-consuming, costly, and labor-intensive. Here, we present a panel of virtual staining neural networks for lung and heart transplant biopsies, which digitally convert autofluorescence microscopic images of label-free tissue sections into their brightfield histologically stained counterparts, bypassing the traditional histochemical staining process. Specifically, we virtually generated Hematoxylin and Eosin (H&E), Masson's Trichrome (MT), and Elastic Verhoeff-Van Gieson (EVG) stains for label-free transplant lung tissue, along with H&E and MT stains for label-free transplant heart tissue. Subsequent blind evaluations conducted by three board-certified pathologists have confirmed that the virtual staining networks consistently produce high-quality histology images with high color uniformity, closely resembling their well-stained histochemical counterparts across various tissue features. The use of virtually stained images for the evaluation of transplant biopsies achieved comparable diagnostic outcomes to those obtained via traditional histochemical staining, with a concordance rate of 82.4% for lung samples and 91.7% for heart samples. Moreover, virtual staining models create multiple stains from the same autofluorescence input, eliminating structural mismatches observed between adjacent sections stained in the traditional workflow, while also saving tissue, expert time, and staining costs., Comment: 21 Pages, 5 Figures

The petaling effect, induced by pupil fragmentation from the telescope spider, drastically affects the performance of high contrast instruments by inducing core splitting on the PSF. Differential piston/tip/tilt aberrations within each optically separated fragment of the pupil are poorly measured by commonly used Adaptive Optics (AO) systems. We here pursue a design of dedicated low-order wavefront sensor -- or petalometers -- to complement the main AO. Interferometric devices sense differential aberrations between fragments with optimal sensitivity; their weakness though is their limitation to wrapped phase measurements. We show that by combining multiple spectral channels, we increase the capture range for petaling aberrations beyond several microns, enough to disambiguate one-wave wrapping errors made by the main AO system. We propose here to implement a petalometer from the multi-wavelength imaging mode of the VAMPIRES visible-light instrument, deployed on SCExAO at the Subaru Telescope. The interferometric measurements obtained in four spectral channels through a 7 hole non-redundant mask allow us to effiiently reconstruct diffierential piston between pupil petals., Comment: Paper 13097-89 from SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Quantum state engineering of light is of great interest for quantum technologies, particularly generating non-classical states of light, and is often studied through quantum conditioning approaches. Recently, we demonstrated that such approaches can be applied in intense laser-atom interactions to generate optical "cat" states by using intensity measurements and classical post-selection of the measurement data. Post-processing of the sampled data set allows to select specific events corresponding to measurement statistics as if there would be non-classical states of light leading to these measurement outcomes. However, to fully realize the potential of this method for quantum state engineering, it is crucial to thoroughly investigate the role of the involved measurements and the specifications of the post-selection scheme. We illustrate this by analyzing post-selection schemes recently developed for the process of high harmonic generation, which enables generating optical cat states bright enough to induce non-linear phenomena. These findings provide significant guidance for quantum light engineering and the generation of high-quality, intense optical cat states for applications in non-linear optics and quantum information science., Comment: 14 pages, 12 figures (11 main text + 1 appendix)

Document spanners have been proposed as a formal framework for declarative Information Extraction (IE) from text, following IE products from the industry and academia. Over the past decade, the framework has been studied thoroughly in terms of expressive power, complexity, and the ability to naturally combine text analysis with relational querying. This demonstration presents SpannerLib a library for embedding document spanners in Python code. SpannerLib facilitates the development of IE programs by providing an implementation of Spannerlog (Datalog-based documentspanners) that interacts with the Python code in two directions: rules can be embedded inside Python, and they can invoke custom Python code (e.g., calls to ML-based NLP models) via user-defined functions. The demonstration scenarios showcase IE programs, with increasing levels of complexity, within Jupyter Notebook., Comment: 4 pages

Typical simulation approaches for evaluating the performance of statistical methods on populations embedded in social networks may fail to capture important features of real-world networks. It can therefore be unclear whether inference methods for causal effects due to interference that have been shown to perform well in such synthetic networks are applicable to social networks which arise in the real world. Plasmode simulation studies use a real dataset created from natural processes, but with part of the data-generation mechanism known. However, given the sensitivity of relational data, many network data are protected from unauthorized access or disclosure. In such case, plasmode simulations cannot use released versions of real datasets which often omit the network links, and instead can only rely on parameters estimated from them. A statistical framework for creating replicated simulation datasets from private social network data is developed and validated. The approach consists of simulating from a parametric exponential family random graph model fitted to the network data and resampling from the observed exposure and covariate distributions to preserve the associations among these variables.