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Logical predictions about relationships between school aptitude and standardized achievement, aptitude and teachers' grades, and teachers' grades and standardized achievement can be made from the literature. These predictions are that (1) conventional school aptitude measures should predict standardized achievement test scores equally well for boys and girls but better for advantaged than disadvantaged children; (2) teachers' marks are more accurate for girls than for boys when judged against the sexes' standardized achievement test scores; are more accurate for middle-class than for disadvantaged children; and are least accurate for disadvantaged black males; and (3) teachers consistently give girls higher grades than boys but there are no important differences between boys' and girls' achievement when measured by standard achievement tests. The present study was conducted to test the strength of the relationships between pupil aptitude, standardized achievement and teachers' grades and to determine the percent of variance in grade point average accounted for by aptitude and standardized achievement in a representative sample of fifth- and ninth-grade Mexican-American, Black and Anglo students. (Author/RC)

Whole-school reforms have received widespread attention, but a critical limitation of the current literature is the lack of evidence around whether these extensive and costly interventions improve students' long-term outcomes after they leave reform schools. Leveraging Tennessee's statewide turnaround reforms, we use difference-in-differences models to estimate the effect of attending a turnaround middle school on student outcomes in high school, including test scores, attendance, chronic absenteeism, disciplinary actions, drop out, and high school graduation. We find little evidence to support improved long-run student outcomes -- mostly null effects that are nearly zero in magnitude. Our results contribute to a broad call for educational researchers to examine whether school reforms meaningfully affect student outcomes beyond short-term improvements in test scores.

A digital information explosion has transformed cities' residential and educational markets in ways that are still being uncovered. Although urban stratification scholars have increasingly scrutinized whether emerging digital platforms disrupt or reproduce longstanding segregation patterns, direct links between one theoretically important form of digital information--school quality data--and neighborhood and school segregation are rarely drawn. To clarify these dynamics, we leverage an exogenous digital information shock, in which the Los Angeles Times' website revealed measures of a particularly important school quality proxy--schools' value-added effectiveness--for nearly all elementary schools in the Los Angeles Unified School District. Results suggest that although the information shock had no detectable effects on residential sorting or neighborhood racial segregation, it did exert modest effects on school sorting--particularly for Latino and Asian students--albeit not in ways that materially diminished school racial segregation because the racial compositions of high and low value-added schools were broadly similar both before and after the information shock. We conclude that the urban stratification implications of digital information may be more nuanced than often appreciated, with effects shaped by racial heterogeneity in both constraints and preferences vis-à-vis specific types of information and operating through mechanisms beyond residential segregation.

Among the many inequalities associated with racial segregation in schools, one notable disparity is the unequal access to experienced teachers. Schools with high proportions of Black or Latinx students have a disproportionate share of inexperienced teachers, both throughout the nation and in California specifically (Clotfelter, Ladd, & Clifton, 2023; Darling-Hammond, 2004; Goldhaber, Lavery, & Theobald, 2015; Knight, 2019; Learning Policy Institute, 2023; Shields et al., 1999). State-level policies and analyses in California have addressed the racial distribution of teacher experience, including the State's release of three annual reports in 2015, 2016, and 2017 titled, "California State Plan to Ensure Equitable Access to Excellent Educators." This study analyzes seven years of the most recent publicly available data from California's public schools, focusing on the relationship between student race and teacher experience. The authors categorized teacher experience in various ways: identifying teachers as "novices" based on 1, 2, or 3 years of teaching experience, and calculating the average and median years of experience at each school. Segregation was assessed from multiple angles by grouping schools based on their racial composition in six different ways, including categories such as "majority non-white" and "90% or more underrepresented minorities" that measure varying degrees of racial concentration. It also presents a regression model to investigate whether the relationship between teacher experience and racial composition holds when taking into account other variables, such as poverty status. The multiple ways of assessing the relationship all point to a discouraging conclusion: California continues to maintain a system of segregated schools that are separate and unequal in terms of teacher experience. The remainder of this report details and provides context for these findings. It concludes with a discussion of several possible ways to address the inequitable distribution of teacher experience in California.

Parameterizing mathematical models of biological systems often requires fitting to stable periodic data. In cardiac electrophysiology this typically requires converging to a stable action potential through long simulations. We explore this problem through the theory of dynamical systems, bifurcation analysis and continuation methods; under which a converged action potential is a stable limit cycle. Various attempts have been made to improve the efficiency of identifying these limit cycles, with limited success. We demonstrate that continuation methods can more efficiently infer the converged action potential as proposed model parameter sets change during optimization or inference routines. In an example electrophysiology model this reduces parameter inference computation time by 70%. We also discuss theoretical considerations and limitations of continuation method use in place of time-consuming model simulations. The application of continuation methods allows more robust optimization by making extra runs from multiple starting locations computationally tractable, and facilitates the application of inference methods such as Markov Chain Monte Carlo to gain more information on the plausible parameter space.

In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned: 1. Understand what the system can do and where it is applied 2. You don't have to compute gradients to break an AI system 3. AI red teaming is not safety benchmarking 4. Automation can help cover more of the risk landscape 5. The human element of AI red teaming is crucial 6. Responsible AI harms are pervasive but difficult to measure 7. LLMs amplify existing security risks and introduce new ones 8. The work of securing AI systems will never be complete By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider.

Developing reliable healthcare AI models requires training with representative and diverse data. In imbalanced datasets, model performance tends to plateau on the more prevalent classes while remaining low on less common cases. To overcome this limitation, we propose DiffUltra, the first generative AI technique capable of synthesizing realistic Lung Ultrasound (LUS) images with extensive lesion variability. Specifically, we condition the generative AI by the introduced Lesion-anatomy Bank, which captures the lesion's structural and positional properties from real patient data to guide the image synthesis.We demonstrate that DiffUltra improves consolidation detection by 5.6% in AP compared to the models trained solely on real patient data. More importantly, DiffUltra increases data diversity and prevalence of rare cases, leading to a 25% AP improvement in detecting rare instances such as large lung consolidations, which make up only 10% of the dataset., Comment: Accepted by ISBI 2025

Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceeds a certain level. The accumulation essentially behaves as a scaling of the learning rate for the selected entries. Although this property prevents the slow-down of lateral movements in distributed gradient descent, it can deteriorate convergence in some settings. This work proposes a novel sparsification scheme that controls the learning rate scaling of error accumulation. The development of this scheme follows two major steps: first, gradient sparsification is formulated as an inverse probability (inference) problem, and the Bayesian optimal sparsification mask is derived as a maximum-a-posteriori estimator. Using the prior distribution inherited from Top-$k$, we derive a new sparsification algorithm which can be interpreted as a regularized form of Top-$k$. We call this algorithm regularized Top-$k$ (RegTop-$k$). It utilizes past aggregated gradients to evaluate posterior statistics of the next aggregation. It then prioritizes the local accumulated gradient entries based on these posterior statistics. We validate our derivation through numerical experiments. In distributed linear regression, it is observed that while Top-$k$ remains at a fixed distance from the global optimum, RegTop-$k$ converges to the global optimum at significantly higher compression ratios. We further demonstrate the generalization of this observation by employing RegTop-$k$ in distributed training of ResNet-18 on CIFAR-10, where it noticeably outperforms Top-$k$.

For 3-manifolds with torus boundary, the bordered Heegaard Floer invariants of Lipshitz--Ozsv\'ath--Thurston have a geometric interpretation as immersed multi-curves with local systems in the punctured torus according to the work of Hanselman--Rasmussen--Watson. We consider morphisms between these immersed curve invariants and show that they compute certain cobordism maps. More precisely, we relate composition in the Fukaya category of immersed curves in the punctured torus to composition of morphisms between the bordered Floer invariants, which have interpretations in terms of certain cobordism maps. We make use of this formalism to obstruct smooth equivalences between 4-manifolds with boundary, and between surfaces with boundary in the 4-ball.

This paper develops a mixed frequency vector autoregressive (MF-VAR) model to produce nowcasts and historical estimates of monthly real state-level GDP for the 50 U.S. states, plus Washington DC, from 1964 through the present day. The MF-VAR model incorporates state and U.S. data at the monthly, quarterly, and annual frequencies. Temporal and cross-sectional constraints are imposed to ensure that the monthly state-level estimates are consistent with official estimates of quarterly GDP at the U.S. and state-levels. We illustrate the utility of the historical estimates in better understanding state business cycles and cross-state dependencies. We show how the model produces accurate nowcasts of state GDP three months ahead of the BEA's quarterly estimates, after conditioning on the latest estimates of U.S. GDP.

For the 696 trans-Neptunian objects (TNOs) with absolute magnitudes $5.5 < H_r < 8.2$ detected in the Dark Energy Survey (DES), we characterize the relationships between their dynamical state and physical properties -- namely $H_r$, indicating size; colors, indicating surface composition; and flux variation semi-amplitude $A$, indicating asphericity and surface inhomogeneity. We seek ``birth'' physical distributions that can recreate these parameters in every dynamical class. We show that the observed colors of these TNOs are consistent with 2 Gaussian distributions in $griz$ space, ``near-IR bright'' (NIRB) and ``near-IR faint'' (NIRF), presumably an inner and outer birth population, respectively. We find a model in which both the NIRB and NIRF $H_r$ and $A$ distributions are independent of current dynamical states, supporting their assignment as birth populations. All objects are consistent with a common rolling $p(H_r)$, but NIRF objects are significantly more variable. Cold classicals (CCs) are purely NIRF, while hot classical (HC), scattered, and detached TNOs are consistent with $\approx70\%$ NIRB, and resonances' NIRB fractions show significant variation. The NIRB component of the HCs and of some resonances have broader inclination distributions than the NIRFs, i.e. their current dynamics retains information about birth location. We find evidence for radial stratification within the birth NIRB population, in that HC NIRBs are on average redder than detached or scattered NIRBs; a similar effect distinguishes CCs from other NIRFs. We estimate total object counts and masses of each class within our $H_r$ range. These results will strongly constrain models of the outer solar system., Comment: 52 pages, 13 figures, 4 appendices. Abstract abridged. Associated repository: https://github.com/bernardinelli/des_tno_likelihood

In this paper, we employ the concept of quasi-relative interior to analyze the method of Lagrange multipliers and establish strong Lagrangian duality for nonsmooth convex optimization problems in Hilbert spaces. Then, we generalize the classical support vector machine (SVM) model by incorporating a new geometric constraint or a regularizer on the separating hyperplane, serving as a regularization mechanism for the SVM. This new SVM model is examined using Lagrangian duality and other convex optimization techniques in both theoretical and numerical aspects via a new subgradient algorithm as well as a primal-dual method.

Massive stars play a pivotal role in shaping their galactic surroundings due to their high luminosity and intense ionizing radiation. However, the precise mechanisms governing the formation of massive stars remain elusive. Complex organic molecules (COMs) offer an avenue for studying star formation across the low- to high-mass spectrum because COMs are found in every young stellar object phase and offer insight into the structure and temperature. We aim to unveil evolutionary patterns of COM chemistry in 41 massive young stellar objects (MYSOs) sourced from diverse catalogues, using ALMA Band 6 spectra. Previous line analysis of these sources showed the presence of CH$_3$OH, CH$_3$CN, and CH$_3$CCH with diverse excitation temperatures and column densities, indicating a possible evolutionary path across sources. However, this analysis usually involves manual line extraction and rotational diagram fitting. Here, we improve upon this process by directly retrieving the physicochemical state of MYSOs from their dimensionally-reduced spectra. We use a Locally Linear Embedding to find a lower-dimensional projection for the physicochemical parameters obtained from individual line analysis. We identify clusters of similar MYSOs in this embedded space using a Gaussian Mixture Model. We find three groups of MYSOs with distinct physicochemical conditions: i) cold, COM-poor sources, ii) warm, medium-COM-abundance sources, and iii) hot, COM-rich sources. We then apply principal component analysis (PCA) to the spectral sample, finding further evidence for an evolutionary path across MYSO groups. Finally, we find that the physicochemical state of our sample can be derived directly from the spectra by training a simple random forest model on the first few PCA components. Our results highlight the effectiveness of dimensionality reduction in obtaining clear physical insights directly from MYSO spectra., Comment: 20 pages, 14 figures, 5 tables, accepted for publication in the A&A journal

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies., Comment: 23 pages, 10 figures, submitted to ApJ

Building upon [2308.02636], this article investigates the potential constraining power of persistent homology for cosmological parameters and primordial non-Gaussianity amplitudes in a likelihood-free inference pipeline. We evaluate the ability of persistence images (PIs) to infer parameters, compared to the combined Power Spectrum and Bispectrum (PS/BS), and we compare two types of models: neural-based, and tree-based. PIs consistently lead to better predictions compared to the combined PS/BS when the parameters can be constrained (i.e., for $\{\Omega_{\rm m}, \sigma_8, n_{\rm s}, f_{\rm NL}^{\rm loc}\}$). PIs perform particularly well for $f_{\rm NL}^{\rm loc}$, showing the promise of persistent homology in constraining primordial non-Gaussianity. Our results show that combining PIs with PS/BS provides only marginal gains, indicating that the PS/BS contains little extra or complementary information to the PIs. Finally, we provide a visualization of the most important topological features for $f_{\rm NL}^{\rm loc}$ and for $\Omega_{\rm m}$. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for $\Omega_{\rm m}$, while $f_{\rm NL}^{\rm loc}$ uses the filaments (1-cycles) in addition to the other two types of topological features., Comment: 28 pages, 8 figures, 4 tables

We investigate the difference between confidence in a belief distribution versus confidence over multiple priors using a lab experiment. Theory predicts that the average Bayesian posterior is affected by the former but is unaffected by the latter. We manipulate confidence over multiple priors by varying the time subjects view a black-and-white grid, of which the relative composition represents the prior. We find that when subjects view the grid for a longer duration, they have more confidence, under-update more, placing more weight on priors and less weight on signals when updating. Confidence within a belief distribution is varied by changing the prior beliefs; subjects are insensitive to this notion of confidence. Overall we find that confidence over multiple priors matters when it should not and confidence in prior beliefs does not matter when it should.

A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in medical practice, because poorly performing models may harm patients and lead to increased costs. We aim to assess the merits of classic and contemporary performance measures when validating predictive AI models for use in medical practice. We focus on models with a binary outcome. We discuss 32 performance measures covering five performance domains (discrimination, calibration, overall, classification, and clinical utility) along with accompanying graphical assessments. The first four domains cover statistical performance, the fifth domain covers decision-analytic performance. We explain why two key characteristics are important when selecting which performance measures to assess: (1) whether the measure's expected value is optimized when it is calculated using the correct probabilities (i.e., a "proper" measure), and (2) whether they reflect either purely statistical performance or decision-analytic performance by properly considering misclassification costs. Seventeen measures exhibit both characteristics, fourteen measures exhibited one characteristic, and one measure possessed neither characteristic (the F1 measure). All classification measures (such as classification accuracy and F1) are improper for clinically relevant decision thresholds other than 0.5 or the prevalence. We recommend the following measures and plots as essential to report: AUROC, calibration plot, a clinical utility measure such as net benefit with decision curve analysis, and a plot with probability distributions per outcome category., Comment: 60 pages, 8 tables, 11 figures, two supplementary appendices

Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by developing an extremely small and fast T2I model that generates high-resolution and high-quality images on mobile platforms. We propose several techniques to achieve this goal. First, we systematically examine the design choices of the network architecture to reduce model parameters and latency, while ensuring high-quality generation. Second, to further improve generation quality, we employ cross-architecture knowledge distillation from a much larger model, using a multi-level approach to guide the training of our model from scratch. Third, we enable a few-step generation by integrating adversarial guidance with knowledge distillation. For the first time, our model SnapGen, demonstrates the generation of 1024x1024 px images on a mobile device around 1.4 seconds. On ImageNet-1K, our model, with only 372M parameters, achieves an FID of 2.06 for 256x256 px generation. On T2I benchmarks (i.e., GenEval and DPG-Bench), our model with merely 379M parameters, surpasses large-scale models with billions of parameters at a significantly smaller size (e.g., 7x smaller than SDXL, 14x smaller than IF-XL).

Background: Multiple Sclerosis (MS), an autoimmune disease affecting millions worldwide, is characterized by its variable course, in which some patients will experience a more benign disease course and others a more active one, with the latter leading to permanent neural damage and disability. Methods: This study uses a Markov Chain model to demonstrate the probability of movement across different states on the Expanded Disability Status Scale (EDSS) and attempted to define worsening, improvement, cycling, and stability of these different pathways. Most importantly we were interested in assessing the lack of impermanence of confirmed disability worsening and if it could be estimated from the Markov model. Results: The study identified only 8.1% were considered worsening, 5.6% consistent improving and 86% cyclers and less than 1% consistently stable. More importantly we also found that many (approximately 30%) of participants with confirmed disability worsening (CDW) regressed to stages that were not considered worsening, on subsequent visits after CDW. Conclusions: These finding are similar to what has been reported previously as predictors of worsening, and also for a lack of durability of CDW, but our results suggest that clinical trial endpoints may need to be modified to more accurately capture differences between the treatment and control groups. Further, this suggests that the rate of worsening in trials that use time to CDW are overestimating the extent of CDW. The trials remain valid since the regressing applies to both treatment and control groups, but that the results may be underestimating the treatment benefit due to misclassification., Comment: 7 pages, 4 tables, 3 figues

The majority of the baryonic matter in the universe is in the form of astrophysical plasmas. The mass of the hot X-ray emitting gas in a cluster of galaxies has more mass than the galaxies in the cluster. With the launch of the XRISM microcalorimeter mission, space-based X-ray observations will achieve a record spectral resolving power of $R\equiv E/\Delta E \sim 1200$. With this resolving power, emission features associated with fine-structure energy levels of some species will be resolved, sometimes for the first time. The plasma code, CLOUDY, was not originally designed for high-resolution X-ray spectroscopy and throughout its history did not resolve fine-structure components of Lyman lines. Here we expand CLOUDY to resolve these fine-structure energy levels and obtain predicted X-ray spectra that match the resolution of new microcalorimeter observations. We show how the Lyman lines can be used as column density indicators and examine their sensitivity to external radiation fields and turbulence., Comment: 14 pages, 11 figures

With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention. However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits their use to citation-based domains and fails to capture the intricacies of facet constraints. In this paper, we propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets. We automatically decompose documents into facet units and generate (ir)relevant pairs by leveraging LLMs' intrinsic distinguishing capabilities; then, dynamically recomposing the units leads to facet-wise relevance-informed document pairs. Our modularization eliminates the need for pre-defined facet knowledge or labels. Further, to prove the FaBle's efficacy in a new domain beyond citation-based scientific paper retrieval, we release a benchmark dataset for educational exam item QBE. FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.

This study examines the impact of data snooping on neural networks for vulnerability detection in lifted code, building on previous research which used word2vec, and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained on datasets, including samples also used for neural network training and validation. The results show that introducing data snooping did not significantly alter model performance, suggesting that data snooping had a minimal impact or that samples randomly dropped as part of the methodology contained hidden features critical to achieving optimal performance. In addition, the findings reinforce the conclusions of previous research, which found that models trained with GPT-2 embeddings consistently outperformed neural networks trained with other embeddings. The fact that this holds even when data snooping is introduced into the embedding model indicates GPT-2's robustness in representing complex code features, even under less-than-ideal conditions., Comment: 7 pages, 2 figures

The heating of the solar corona and solar wind, through suprathermal particles and kinetic Alfv\'en waves within the 0 - 10 $R_{\rm Sun}$ range, has been a subject of great interest for many decades. This study investigates the acceleration and heating of charged particles and the role of KAWs in the solar corona. We investigate how KAWs transport energy and accelerate/heat the charged particles, focusing on the behavior of perturbed EM fields, Poynting flux vectors, net power transfer, resonant particle speed, group speed, and the damping length of KAWs. The study examines how these elements are influenced by suprathermal particles \kappa and the electron-to-ion temperature $T_e/T_i$. We use kinetic plasma theory coupled with the Vlasov-Maxwell model to investigate the dynamics of KAWs and particles. We assume a collisionless, homogeneous, and low-beta electron-ion plasma in which Alfv\'en waves travel in the kinetic limits. The results show the perturbed EM fields are significantly influenced by $\kappa$ and $T_e/T_i$. We evaluate both the parallel and perpendicular Poynting fluxes and find that the parallel Poynting flux dissipates gradually for lower \kappa values. The perpendicular flux dissipates quickly over shorter distances. Power deposition in solar flux tubes is significantly influenced by \kappa and Te/Ti. We find that particles can heat the solar corona over long distances in the parallel direction and short distances in the perpendicular direction. The group velocity of KAWs increases for lower \kappa values, and the damping length is enhanced under lower \kappa, suggesting longer energy transport distances. These findings offer a comprehensive understanding of particle-wave interactions in the solar corona and wind, with potential applications for missions such as the Parker Solar Probe (PSP), and can also apply to other environments., Comment: Accepted for publication in the Astronomy and Astrophysics (A&A) journal (2024)

Urban spatio-temporal (ST) forecasting is crucial for various urban applications such as intelligent scheduling and trip planning. Previous studies focus on modeling ST correlations among urban locations in offline settings, which often neglect the non-stationary nature of urban ST data, particularly, distribution shifts over time. This oversight can lead to degraded performance in real-world scenarios. In this paper, we first analyze the distribution shifts in urban ST data, and then introduce DOST, a novel online continual learning framework tailored for ST data characteristics. DOST employs an adaptive ST network equipped with a variable-independent adapter to address the unique distribution shifts at each urban location dynamically. Further, to accommodate the gradual nature of these shifts, we also develop an awake-hibernate learning strategy that intermittently fine-tunes the adapter during the online phase to reduce computational overhead. This strategy integrates a streaming memory update mechanism designed for urban ST sequential data, enabling effective network adaptation to new patterns while preventing catastrophic forgetting. Experimental results confirm DOST's superiority over state-of-the-art models on four real-world datasets, providing online forecasts within an average of 0.1 seconds and achieving a 12.89% reduction in forecast errors compared to baseline models.

We present a simple, yet powerful column density diagnostic for plasmas enabled by X-ray microcalorimeter observations. With the recent developments of the spectral simulation code Cloudy, inspired by the high spectral resolution of XRISM and Athena, we make predictions for the intensity ratio of the resolved fine-structure lines Ly$\alpha_1$ and Ly$\alpha_2$ of H-like ions. We show that this ratio can be observationally constrained and used as a plasma column density indicator. We demonstrate this with a XRISM observation of the high-mass X-ray binary Centaurus X-3. This diagnostic is useful for a wide range of X-ray emitting plasmas either collisionally or radiatively ionized., Comment: 3 pages, 4 figures

This paper presents a robot control algorithm suitable for safe reactive navigation tasks in cluttered environments. The proposed approach consists of transforming the robot workspace into the \emph{ball world}, an artificial representation where all obstacle regions are closed balls. Starting from a polyhedral representation of obstacles in the environment, obtained using exteroceptive sensor readings, a computationally efficient mapping to ball-shaped obstacles is constructed using quasi-conformal mappings and M\"obius transformations. The geometry of the ball world is amenable to provably safe navigation tasks achieved via control barrier functions employed to ensure collision-free robot motions with guarantees both on safety and on the absence of deadlocks. The performance of the proposed navigation algorithm is showcased and analyzed via extensive simulations and experiments performed using different types of robotic systems, including manipulators and mobile robots.

We scheme an efficient and reusable approach to quantum teleportation that allows cyclic teleportation of a two-qubit graph state around a quantum hamster wheel -- a ring of qubits entangled as a one-dimensional line prepared on the 20-qubit Quantinuum H1-1 ion-trap quantum processor. The qubits on the ring are periodically measured and reused to achieve a teleportation depth that exceeds the total number of available qubits in the quantum processor. Using the outcomes measured during teleportation, we calculate and apply byproduct operators through dynamic circuits to correct local transformations induced on the teleported state. We evaluate the quality of teleportation by tracing the preserved entanglement and fidelity of the teleported two-qubit graph state from its density matrix. In the real-machine experiments, we demonstrate that 58% of the entanglement of the teleported state is sustained with a measured two-qubit negativity of $0.291\pm0.018$ after three complete revolutions around the hamster wheel, or equivalently, after hopping across 56 qubits. By performing teleportation along a regenerating graph state, our work is a step forward in demonstrating the feasibility of measurement-based quantum computation., Comment: 6 pages, 3 figures

Recently, large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, controllable summarization with LLMs remains underexplored, limiting their ability to generate summaries that align with specific user preferences. In this paper, we first investigate the capability of LLMs to control diverse attributes, revealing that they encounter greater challenges with numerical attributes, such as length and extractiveness, compared to linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. Our GTE framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output. Based on this reflection, the model generates a well-adjusted summary. As a result, by allowing the model to reflect on its misalignment, we generate summaries that satisfy the desired attributes in surprisingly fewer iterations than other iterative methods solely using LLMs.

We show that human players' gameplay in the game of Wordle is influenced by the semantics, orthography, and morphology of the player's previous guesses. We demonstrate this influence by comparing actual human players' guesses to near-optimal guesses, showing that human players' guesses are biased to be similar to previous guesses semantically, orthographically, and morphologically.