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Tungsten peaking is predicted in the core of a WEST plasma with total-f gyrokinetic simulations, including both collisional and turbulent transport. This prediction is validated with a synthetic diagnostic of the bolometry. Although nitrogen impurities are shown to reduce the neoclassical peaking of tungsten on-axis, the overall tungsten peaking increases when nitrogen impurities are present, as they reduce the turbulence screening off-axis. This finding is important for the plasma current ramp-up phase of ITER, where light impurities seeding will be desirable to achieve low temperatures at the plasma-facing components and reduce tungsten sputtering. It provides further argument for applying early ECRH heating to maintain margins on the core power balance. The neoclassical peaking factor is cross-verified between XGC and FACIT. The heat flux at separatrix and the heat load width are modeled by XGC and compared to WEST data., Comment: 15 pages, 17 figures

A $d$-subgroup of a lattice-ordered group ($\ell$-group) $G$ is a subgroup that contains the principal polars generated by each of its elements. We call $G$ a Martinez $\ell$-group if every convex $\ell$-subgroup of $G$ is a $d$-subgroup (equivalently: $G(a)=a^{\perp \perp}$ for every $a \in G$); known examples include all hyperarchimedean $\ell$-groups and all existentially closed abelian $\ell$-groups. This paper gives new characterizations of the Martinez $\ell$-groups, shows that the abelian Martinez $\ell$-groups form a radical class, investigates a related type of archimedean $\ell$-group called a Yosida $\ell$-group, and uses an analogue of a construction from ring theory, and other methods, to produce new examples of Martinez $\ell$-groups with special properties.

Despite the popularity of the two-tower model for unbiased learning to rank (ULTR) tasks, recent work suggests that it suffers from a major limitation that could lead to its collapse in industry applications: the problem of logging policy confounding. Several potential solutions have even been proposed; however, the evaluation of these methods was mostly conducted using semi-synthetic simulation experiments. This paper bridges the gap between theory and practice by investigating the confounding problem on the largest real-world dataset, Baidu-ULTR. Our main contributions are threefold: 1) we show that the conditions for the confounding problem are given on Baidu-ULTR, 2) the confounding problem bears no significant effect on the two-tower model, and 3) we point to a potential mismatch between expert annotations, the golden standard in ULTR, and user click behavior., Comment: Accepted at the CONSEQUENCES '24 workshop, co-located with ACM RecSys '24

With Nvidia's release of the Grace Superchip, all three big semiconductor companies in HPC (AMD, Intel, Nvidia) are currently competing in the race for the best CPU. In this work we analyze the performance of these state-of-the-art CPUs and create an accurate in-core performance model for their microarchitectures Zen 4, Golden Cove, and Neoverse V2, extending the Open Source Architecture Code Analyzer (OSACA) tool and comparing it with LLVM-MCA. Starting from the peculiarities and up- and downsides of a single core, we extend our comparison by a variety of microbenchmarks and the capabilities of a full node. The "write-allocate (WA) evasion" feature, which can automatically reduce the memory traffic caused by write misses, receives special attention; we show that the Grace Superchip has a next-to-optimal implementation of WA evasion, and that the only way to avoid write allocates on Zen 4 is the explicit use of non-temporal stores., Comment: 5 pages, 4 figures

The National Assessment of Educational Progress (NAEP) is an integral measure of academic progress over time. It is the largest nationally representative and continuing assessment of what our nation's students know and can do in various subjects such as civics, mathematics, reading, science, technology and engineering literacy, U.S. history, and writing. The program also provides valuable insights into students' educational experiences and opportunities to learn in and outside of the classroom. Elected officials, policymakers, and educators all use NAEP results to develop ways to improve education. The 2024 program will include: (1) assessments in mathematics and reading at grades 4, 8, and 12 and science at grade 8; (2) pilot testing in mathematics and reading at grades 4 and 8, to help improve future NAEP assessments and ensure that they continue to be a reliable measure of student achievement; (3) survey questionnaires for participating students, teachers, and principals, to provide a better understanding of factors that may be related to students' learning. This brief document highlights the NAEP 2024 assessment, teachers as essential partners in NAEP, and recent NAEP results. [For "NAEP 2023 Facts for Teachers. Grades 4, 8, and 12 Field Test," see ED627646.]

We analyze the low-energy dynamics of gap-between-jets cross sections at hadron colliders, for which phase factors in the hard amplitudes spoil collinear cancellations and lead to double (``super-leading'') logarithmic behavior. Based on a method-of-regions analysis, we identify three-loop contributions from perturbative active-active Glauber-gluon exchanges with the right structure to render the cross section consistent with PDF factorization below the gap veto scale. The Glauber contributions we identify are unambiguously defined without regulators beyond dimensional regularization., Comment: 7 pages, 3 figures

The concept of signatures and expected signatures is vital in data science, especially for sequential data analysis. The signature transform, a Cartan type development, translates paths into high-dimensional feature vectors, capturing their intrinsic characteristics. Under natural conditions, the expectation of the signature determines the law of the signature, providing a statistical summary of the data distribution. This property facilitates robust modeling and inference in machine learning and stochastic processes. Building on previous work by the present authors [Unified signature cumulants and generalized Magnus expansions, FoM Sigma '22] we here revisit the actual computation of expected signatures, in a general semimartingale setting. Several new formulae are given. A log-transform of (expected) signatures leads to log-signatures (signature cumulants), offering a significant reduction in complexity., Comment: arXiv admin note: text overlap with arXiv:2102.03345

Image-based reinforcement learning (RL) faces significant challenges in generalization when the visual environment undergoes substantial changes between training and deployment. Under such circumstances, learned policies may not perform well leading to degraded results. Previous approaches to this problem have largely focused on broadening the training observation distribution, employing techniques like data augmentation and domain randomization. However, given the sequential nature of the RL decision-making problem, it is often the case that residual errors are propagated by the learned policy model and accumulate throughout the trajectory, resulting in highly degraded performance. In this paper, we leverage the observation that predicted rewards under domain shift, even though imperfect, can still be a useful signal to guide fine-tuning. We exploit this property to fine-tune a policy using reward prediction in the target domain. We have found that, even under significant domain shift, the predicted reward can still provide meaningful signal and fine-tuning substantially improves the original policy. Our approach, termed Predicted Reward Fine-tuning (PRFT), improves performance across diverse tasks in both simulated benchmarks and real-world experiments. More information is available at project web page: https://sites.google.com/view/prft., Comment: L4DC 2024

Deploying machine learning algorithms for robot tasks in real-world applications presents a core challenge: overcoming the domain gap between the training and the deployment environment. This is particularly difficult for visuomotor policies that utilize high-dimensional images as input, particularly when those images are generated via simulation. A common method to tackle this issue is through domain randomization, which aims to broaden the span of the training distribution to cover the test-time distribution. However, this approach is only effective when the domain randomization encompasses the actual shifts in the test-time distribution. We take a different approach, where we make use of a single demonstration (a prompt) to learn policy that adapts to the testing target environment. Our proposed framework, PromptAdapt, leverages the Transformer architecture's capacity to model sequential data to learn demonstration-conditioned visual policies, allowing for in-context adaptation to a target domain that is distinct from training. Our experiments in both simulation and real-world settings show that PromptAdapt is a strong domain-adapting policy that outperforms baseline methods by a large margin under a range of domain shifts, including variations in lighting, color, texture, and camera pose. Videos and more information can be viewed at project webpage: https://sites.google.com/view/promptadapt., Comment: ICRA 2024

Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize their findings as text, have enormous potential to alleviate clinical workloads and increase patient access to high-quality medical care. While foundational models have stirred considerable interest in the medical community, it is unclear whether their general capabilities translate to real-world clinical utility. In this work, we show that foundation VLMs markedly underperform compared to practicing ophthalmologists on specialist tasks crucial to the care of patients with age-related macular degeneration (AMD). To address this, we initially identified the essential capabilities required for image-based clinical decision-making, and then developed a curriculum to selectively train VLMs in these skills. The resulting model, RetinaVLM, can be instructed to write reports that significantly outperform those written by leading foundation medical VLMs in disease staging (F1 score of 0.63 vs. 0.11) and patient referral (0.67 vs. 0.39), and approaches the diagnostic performance of junior ophthalmologists (who achieve 0.77 and 0.78 on the respective tasks). Furthermore, in a reader study involving two senior ophthalmologists with up to 32 years of experience, RetinaVLM's reports were found to be similarly correct (78.6% vs. 82.1%) and complete (both 78.6%) as reports written by junior ophthalmologists with up to 10 years of experience. These results demonstrate that our curriculum-based approach provides a blueprint for specializing generalist foundation medical VLMs to handle real-world clinical tasks., Comment: Submitted to Nature Medicine

Sterile neutrinos are a compelling candidate for generating neutrino masses and for elucidating the nature of dark matter. Astrophysical X-ray constraints on sterile neutrino dark matter decays, however, largely exclude the active-sterile mixing required to produce simultaneously the correct left-handed neutrino spectrum and keV-scale right-handed neutrino dark matter within a type-I seesaw framework. In this study, we demonstrate how these X-ray constraints can be circumvented through a time-dependent approach, thereby reviving a broad range of active-sterile mixing scenarios. Our minimal model incorporates two right-handed neutrinos, which form a two-component dark matter candidate, and an auxiliary scalar field that experiences a very late and still ongoing phase transition, leading to the spontaneous breaking of a global $ U(1)_N $ symmetry. Prior to this phase transition, only the right-handed neutrinos are massive, while the left-handed neutrinos remain massless because of the scalar field's vanishing expectation value. As the phase transition develops, the growing expectation value of the scalar field increases the active-sterile mixing, thereby opening dark matter decay channels and inducing neutrino masses. The time dependence allows the scenario to be consistent with X-ray constraints as well as current measurements of left-handed neutrino masses. The anticipated level of active-sterile mixing today is within the detection capabilities of the forthcoming TRISTAN (KATRIN) tritium-beta decay project. Additionally, cosmological surveys such as DESI or EUCLID and supernova neutrino observations can test the prediction of massless left-handed neutrinos prior to the phase transition., Comment: 16+4 pages, 3 figures

The Glauber series for non-global jet observables at hadron colliders simultaneously includes the super-leading logarithms alongside an arbitrary number of Glauber phases. Building on the formalism of arXiv:2405.05305, it is shown that the leading terms in this series for large $N_c$ can be resummed in closed form in renormalization-group improved perturbation theory. This remarkable observation suggests that large-$N_c$ methods might also be helpful to study other aspects of non-global logarithms at hadron colliders, and to combine our analytic results with amplitude-level parton showers., Comment: 21 pages, 2 figures

Despite the innovations in deep learning and generative AI, creating long term structure as well as the layers of repeated structure common in musical works remains an open challenge in music generation. We propose an attention layer that uses a novel approach applying user-supplied self-similarity matrices to previous time steps, and demonstrate it in our Similarity Incentivized Neural Generator (SING) system, a deep learning autonomous music generation system with two layers. The first is a vanilla Long Short Term Memory layer, and the second is the proposed attention layer. During generation, this attention mechanism imposes a suggested structure from a template piece on the generated music. We train SING on the MAESTRO dataset using a novel variable batching method, and compare its performance to the same model without the attention mechanism. The addition of our proposed attention mechanism significantly improves the network's ability to replicate specific structures, and it performs better on an unseen test set than a model without the attention mechanism.

This paper proposes to parameterize open loop controls in stochastic optimal control problems via suitable classes of functionals depending on the driver's path signature, a concept adopted from rough path integration theory. We rigorously prove that these controls are dense in the class of progressively measurable controls and use rough path methods to establish suitable conditions for stability of the controlled dynamics and target functional. These results pave the way for Monte Carlo methods to stochastic optimal control for generic target functionals and dynamics. We discuss the rather versatile numerical algorithms for computing approximately optimal controls and verify their accurateness in benchmark problems from Mathematical Finance.

Sparse matrix-vector products (SpMVs) are a bottleneck in many scientific codes. Due to the heavy strain on the main memory interface from loading the sparse matrix and the possibly irregular memory access pattern, SpMV typically exhibits low arithmetic intensity. Repeating these products multiple times with the same matrix is required in many algorithms. This so-called matrix power kernel (MPK) provides an opportunity for data reuse since the same matrix data is loaded from main memory multiple times, an opportunity that has only recently been exploited successfully with the Recursive Algebraic Coloring Engine (RACE). Using RACE, one considers a graph based formulation of the SpMV and employs s level-based implementation of SpMV for reuse of relevant matrix data. However, the underlying data dependencies have restricted the use of this concept to shared memory parallelization and thus to single compute nodes. Enabling cache blocking for distributed-memory parallelization of MPK is challenging due to the need for explicit communication and synchronization of data in neighboring levels. In this work, we propose and implement a flexible method that interleaves the cache-blocking capabilities of RACE with an MPI communication scheme that fulfills all data dependencies among processes. Compared to a "traditional" distributed memory parallel MPK, our new Distributed Level-Blocked MPK yields substantial speed-ups on modern Intel and AMD architectures across a wide range of sparse matrices from various scientific applications. Finally, we address a modern quantum physics problem to demonstrate the applicability of our method, achieving a speed-up of up to 4x on 832 cores of an Intel Sapphire Rapids cluster., Comment: 15 pages, 12 figures, 5 tables; added affiliation & extended acknowledgment

A new strategy is presented for systematically treating super-leading logarithmic contributions including higher-order Glauber exchanges for non-global LHC observables in renormalization-group (RG) improved perturbation theory. This represents an important improvement over previous approaches, as it allows for the consistent inclusion of the scale dependence of the strong coupling, thereby providing more reliable estimates of the scale uncertainties in theoretical predictions. The key idea is to rearrange the relevant RG evolution operator in such a way that all double-logarithmic corrections are exponentiated from the outset. This forms the starting point for the first resummation of super-leading logarithms at leading order in RG-improved perturbation theory for arbitrary $2\to M$ scattering processes. Moreover, the asymptotic scaling of subleading logarithmic corrections from higher-order Glauber exchanges is determined, demonstrating their parametric suppression., Comment: v2: 37 pages, 5 figures, matches published version

$\bf{W}^*$ is the category of the archimedean l-groups with distinguished strong order unit and unit-preserving l-group homomorphisms. For $G \in \bf{W}^*$, we have the canonical compact space $YG$, and Yosida representation $G \leq C(YG)$, thus, for $g \in G$, the cozero-set coz(g) in $YG$. The ideals at issue in $G$ include the principal ideals and polars, $G(g)$ and $g^{\perp \perp}$, respectively, and the $\bf{W}^*$-kernels of $\bf{W}^*$-morphisms from $G$. The ``coincidences of types" include these properties of $G$: (M) Each $G(g) = g^{\perp \perp}$; (Y) Each $G(g)$ is a $\bf{W}^*$-kernel; (CR) Each $g^{\perp \perp}$ is a $\bf{W}^*$-kernel (iff each coz(g) is regular open). For each of these, we give numerous ``rephrasings", and examples, and note that (M) = (Y) $\cap$ (CR). This paper is a companion to a paper in preparation by the present authors, which includes the present thrust in contexts less restrictive and more algebraic. Here, the focus on $\bf{W}^*$ brings topology to bear, and sharpens the view.

The property of a vector lattice of sufficiently many projections (SMP) is informed by restricting attention to archimedean $A$ with a distinguished weak order unit $u$ (the class, or category, $\bf{W}$), where the Yosida representation $A \leq D(Y(A,u))$ is available. Here, $A$ SMP is equivalent to $Y(A,u)$ having a $\pi$-base of clopen sets of a certain type called ``local". If the unit is strong, all clopen sets are local and $A$ is SMP if and only if $Y(A,u)$ has clopen $\pi$-base, a property we call $\pi$-zero-dimensional ($\pi$ZD). The paper is in two parts: the first explicates the similarities of SMP and $\pi$ZD; the second consists of examples, including $\pi$ZD but not SMP, and constructions of many SMP's which seem scarce in the literature.

Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data. While theoretically justified and extensively tested in simulation, ULTR techniques lack empirical validation, especially on modern search engines. The Baidu-ULTR dataset released for the WSDM Cup 2023, collected from Baidu's search engine, offers a rare opportunity to assess the real-world performance of prominent ULTR techniques. Despite multiple submissions during the WSDM Cup 2023 and the subsequent NTCIR ULTRE-2 task, it remains unclear whether the observed improvements stem from applying ULTR or other learning techniques. In this work, we revisit and extend the available experiments on the Baidu-ULTR dataset. We find that standard unbiased learning-to-rank techniques robustly improve click predictions but struggle to consistently improve ranking performance, especially considering the stark differences obtained by choice of ranking loss and query-document features. Our experiments reveal that gains in click prediction do not necessarily translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur., Comment: Tackling Climate Change with Machine Learning workshop at ICLR 2024

In this work, we introduce the Virtual In-Hand Eye Transformer (VIHE), a novel method designed to enhance 3D manipulation capabilities through action-aware view rendering. VIHE autoregressively refines actions in multiple stages by conditioning on rendered views posed from action predictions in the earlier stages. These virtual in-hand views provide a strong inductive bias for effectively recognizing the correct pose for the hand, especially for challenging high-precision tasks such as peg insertion. On 18 manipulation tasks in RLBench simulated environments, VIHE achieves a new state-of-the-art, with a 12% absolute improvement, increasing from 65% to 77% over the existing state-of-the-art model using 100 demonstrations per task. In real-world scenarios, VIHE can learn manipulation tasks with just a handful of demonstrations, highlighting its practical utility. Videos and code implementation can be found at our project site: https://vihe-3d.github.io.

We consider both $N$-player and mean-field games of optimal portfolio liquidation in which the players are not allowed to change the direction of trading. Players with an initially short position of stocks are only allowed to buy while players with an initially long position are only allowed to sell the stock. Under suitable conditions on the model parameters we show that the games are equivalent to games of timing where the players need to determine the optimal times of market entry and exit. We identify the equilibrium entry and exit times and prove that equilibrium mean-trading rates can be characterized in terms of the solutions to a highly non-linear higher-order integral equation with endogenous terminal condition. We prove the existence of a unique solution to the integral equation from which we obtain the existence of a unique equilibrium both in the mean-field and the $N$-player game.

Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis. Despite the importance of both short and long term analysis to clinical decision making, they remain understudied in medical deep learning. State of the art methods for spatiotemporal representation learning, developed for short natural videos, prioritize the detection of temporal constants rather than temporal developments. Moreover, they do not account for varying time intervals between acquisitions, which are essential for contextualizing observed changes. To address these issues, we propose two approaches. First, we combine clip-level contrastive learning with a novel temporal embedding to adapt to irregular time series. Second, we propose masking and predicting latent frame representations of the temporal sequence. Our two approaches outperform all prior methods on temporally-dependent tasks including cardiac output estimation and three prognostic AMD tasks. Overall, this enables the automated analysis of temporal patterns which are typically overlooked in applications of deep learning to medicine.

Tunisian Arabic (ISO 693-3: aeb) isa distinct variety native to Tunisia, derived from Arabic and enriched by various historical influences. This research introduces the "Normalized Orthography for Tunisian Arabic" (NOTA), an adaptation of CODA* guidelines for transcribing Tunisian Arabic using Arabic script. The aim is to enhance language resource development by ensuring user-friendliness and consistency. The updated standard addresses challenges in accurately representing Tunisian phonology and morphology, correcting issues from transcriptions based on Modern Standard Arabic., Comment: Final Report for the Derja Association. Camera-Ready for LPKM 2024

Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression. Methods: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known. We rely on comparisons of intraoperative monocular depth estimates and preoperative depth renders to identify modified regions. The new depths are integrated in these regions through volumetric fusion in a truncated signed distance function representation to generate an intraoperative 3D model that reflects tissue manipulation. Results: We quantitatively evaluate our approach by sequentially updating models for a five-step surgical progression in an ex vivo specimen. We compute the error between correspondences from the updated model and ground-truth intraoperative CT in the region of anatomical modification. The resulting models show a decrease in error during surgical progression as opposed to increasing when no update is employed. Conclusion: Our findings suggest that preoperative 3D anatomical models can be updated using intraoperative endoscopy video in navigated sinus surgery. Future work will investigate improvements to monocular depth estimation as well as removing the need for external navigation systems. The resulting ability to continuously update the patient model may provide surgeons with a more precise understanding of the current anatomical state and paves the way toward a digital twin paradigm for sinus surgery.

Prior work in style-controlled text generation has focused on tasks such as emulating the style of prolific literary authors, producing formal or informal text, and mitigating toxicity of generated text. Plentiful demonstrations of these styles are available, and as a result modern language models are often able to emulate them, either via prompting or discriminative control. However, in applications such as writing assistants, it is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample. For example, someone writing in a particular dialect may prefer writing suggestions that retain the same dialect. We find that instruction-tuned language models can struggle to reproduce author-specific style demonstrated in a prompt. Instead, we propose to guide a language model to generate text in a target style using contrastively-trained representations that capture stylometric features. Our approach (StyleMC) combines an author-adapted language model with sequence-level inference to improve stylistic consistency, and is found to be effective in a variety of conditions, including unconditional generation and style transfer. Additionally, we find that the proposed approach can serve as an effective anonymization method, by editing a document to mask authorship while preserving the original meaning

Motivated by situations in which the removal of a zero (a.k.a., an absorbing element) from a semigroup yields a subsemigroup with another zero, sets of quasi-zeros (a.k.a., quasi-absorbing elements) are introduced as well as primitive elements, minimal ideals and simple semigroups all with respect to a given ideal. An application to set optimization is discussed., Comment: 17 pages

Gyrokinetic tokamak plasmas can exhibit intrinsic toroidal rotation driven by the residual stress. While most studies have attributed the residual stress to the parallel-momentum flux from the turbulent $\boldsymbol{E}\times\boldsymbol{B}$ motion, the parallel-momentum flux from the drift-orbit motion (denoted $\Pi_\parallel^D$) and the $\boldsymbol{E}\times\boldsymbol{B}$-momentum flux from the $\boldsymbol{E}\times\boldsymbol{B}$ motion (denoted $\Pi_{E\times B}$) are often neglected. Here, we use the global total-$f$ gyrokinetic code XGC to study the residual stress in the core and the edge of a DIII-D H-mode plasma. Numerical results show that both $\Pi_\parallel^D$ and $\Pi_{E\times B}$ make up a significant portion of the residual stress. In particular, $\Pi_\parallel^D$ in the core is higher than the collisional neoclassical level in the presence of turbulence, while in the edge it represents an outflux of counter-current momentum even without turbulence. Using a recently developed ``orbit-flux'' formulation, we show that the higher-than-neoclassical-level $\Pi_\parallel^D$ in the core is driven by turbulence, while the outflux of counter-current momentum from the edge is mainly due to collisional ion orbit loss. These results suggest that $\Pi_\parallel^D$ and $\Pi_{E\times B}$ can be important for the study of intrinsic toroidal rotation.

The calculation of loop corrections to the correlation functions of quantum fields during inflation or in the de~Sitter background presents greater challenges than in flat space due to the more complicated form of the mode functions. While in flat space highly sophisticated approaches to Feynman integrals exist, similar tools still remain to be developed for cosmological correlators. However, usually only their late-time limit is of interest. We introduce the method-of-region expansion for cosmological correlators as a tool to extract the late-time limit, and illustrate it with several examples for the interacting, massless, minimally coupled scalar field in de~Sitter space. In particular, we consider the in-in correlator $\langle\phi^2(\eta,q)\phi(\eta,k_1)\phi(\eta,k_2)\rangle$, whose region structure is relevant to anomalous dimensions and matching coefficients in Soft de Sitter effective theory., Comment: 44 pages, 4 figures, LaTeX

The National Assessment of Educational Progress (NAEP) is an integral measure of academic progress over time. It is the largest nationally representative and continuing assessment of what our nation's students know and can do in various subjects such as mathematics, reading, and science. The program also provides valuable insights into students' educational experiences and opportunities to learn in and outside of the classroom. Elected officials, policymakers, and educators all use NAEP results to develop ways to improve education. The NAEP 2023 field test will explore an online assessment platform and transition to different devices, such as Chromebooks, that may be more familiar to students. By participating in the field test, schools will help ensure that upcoming NAEP digitally based assessments continue to be a reliable, meaningful, and efficient measure of student achievement. This brief document highlights the NAEP 2023 field test, teachers as essential partners in NAEP, and recent NAEP results. [For "NAEP 2022 Facts for Teachers," see ED627645.]