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This research uses the implementation of a school suspension ban in Maryland to test whether a top-down state-initiated ban on suspensions in early primary grades can influence school behavior regarding school discipline. Beginning in the fall of 2017, the State of Maryland banned the use of out-of-school suspensions for grades PK-2, unless a student posed an "imminent threat" to staff or students. This research investigates (1) what was the effect of the ban on discipline outcomes for students in both treated grades and upper elementary grades not subject to the ban? (2) did schools bypass the ban by coding more events as threatening or increasing the use of in-school suspensions? and (3) were there differential effects for students in groups that are historically suspended more often? Using a comparative interrupted time series strategy, we find that the ban is associated with a substantial reduction in, but not a total elimination of, out-of-school suspensions for targeted grades without substitution of in-school suspensions. Disproportionalities by race and other characteristics remain after the ban. Grades not subject to the ban experienced few effects, suggesting the ban did not trigger a schoolwide response that reduced exclusionary discipline.

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditionally, these analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a 1mm T1-weighted scan. This excludes most clinical MRI scans, which are often anisotropic and lack the necessary T1 contrast. To enable large-scale neuroimaging studies using vast clinical data, we introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs) and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans. The method consistently produced precise cortical reconstructions and high parcellation accuracy across varied MRI contrasts and resolutions. Cortical thickness estimates are precise enough to capture aging effects independently of MRI contrast, although accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce., Comment: 16 pages in the manuscript with 11 page supplementary material

In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely used data sets, ranging from minor errors to severe mislabeling, can negatively influence learning and distort performance metrics. We perform an in-depth analysis of popular HPE data sets to show the extent and nature of label inaccuracies. Our findings suggest that accounting for the impact of faulty labels will facilitate the development of more robust and accurate HPE models for a variety of real-world applications. We show improved performance with cleansed data., Comment: 15 pages, 7 figures, 5 tables

The sub-Jovian desert is a region in the mass-period and radius-period parameter space, typically encompassing short-period ranges between super-Earths and hot Jupiters, that exhibits an intrinsic dearth of planets. This scarcity is likely shaped by photoevaporation caused by the stellar irradiation received by giant planets that have migrated inward. We report the detection and characterization of TOI-3568 b, a transiting super-Neptune with a mass of $26.4\pm1.0$ M$_\oplus$, a radius of $5.30\pm0.27$ R$_\oplus$, a bulk density of $0.98\pm0.15$ g cm$^{-3}$, and an orbital period of 4.417965(5) d situated in the vicinity of the sub-Jovian desert. This planet orbiting a K dwarf star with solar metallicity, was identified photometrically by TESS. It was characterized as a planet by our high-precision radial velocity monitoring program using MAROON-X at Gemini North, supplemented by additional observations from the SPICE large program with SPIRou at CFHT. We performed a Bayesian MCMC joint analysis of the TESS and ground-based photometry, MAROON-X and SPIRou radial velocities, to measure the orbit, radius, and mass of the planet, as well as a detailed analysis of the high-resolution flux and polarimetric spectra to determine the physical parameters and elemental abundances of the host star. Our results reveal TOI-3568 b as a hot super-Neptune, rich in hydrogen and helium with a core of heavier elements with a mass between 10 and 25 M$_\oplus$. We analyzed the photoevaporation status of TOI-3568 b and found that it experiences one of the highest EUV luminosities among planets with a mass M$_{\rm p}$ $<2$ M$_{\rm Nep}$, yet it has an evaporation lifetime exceeding 5 Gyr. Positioned in the transition between two significant populations of exoplanets on the mass-period and energy diagrams, this planet presents an opportunity to test theories concerning the origin of the sub-Jovian desert., Comment: Accepted for publication in A&A on September 4, 2024

We present a benchmarking protocol that combines the characterization of boron nitride (BN) crystals and films with the evaluation of the electronic properties of graphene on these substrates. Our study includes hBN crystals grown under different conditions and scalable BN films deposited by either chemical or physical vapor deposition (CVD or PVD). We explore the complete process from boron nitride growth, over its optical characterization by time-resolved cathodoluminescence (TRCL), to the optical and electronic characterization of graphene by Raman spectroscopy after encapsulation and Hall bar processing. Within our benchmarking protocol we achieve a homogeneous electronic performance within each Hall bar device through a fast and reproducible processing routine. We find that a free exciton lifetime of 1 ns measured on as-grown hBN crystals by TRCL is sufficient to achieve high graphene room temperature charge carrier mobilities of 80,000 cm$^2$/(Vs) at a carrier density of |n| = 10$^{12}$ cm$^{-2}$, while respective exciton lifetimes around 100 ps yield mobilities up to 30,000 cm$^2$/(Vs). For scalable PVD-grown BN films, we measure carrier mobilities exceeding 10,000 cm$^2$/(Vs) which correlates with a graphene Raman 2D peak linewidth of 22 cm$^{-1}$. Our work highlights the importance of the Raman 2D linewidth of graphene as a critical metric that effectively assesses the interface quality (i.e. surface roughness) to the BN substrate, which directly affects the charge carrier mobility of graphene. Graphene 2D linewidth analysis is suitable for all BN substrates and is particularly advantageous when TRCL or BN Raman spectroscopy cannot be applied to specific BN materials such as amorphous or thin films. This underlines the superior role of spatially-resolved spectroscopy in the evaluation of BN crystals and films for the use of high-mobility graphene devices., Comment: 18 pages, 11 figures

Combining high peak and high average power has long been a key challenge of ultrafast laser technology, crucial for applications such as laser-plasma acceleration and strong-field physics. A promising solution lies in post-compressed ytterbium lasers, but scaling these to high pulse energies presents a major bottleneck. Post-compression techniques, particularly Herriott-type multi-pass cells (MPCs), have enabled large peak power boosts at high average powers but their pulse energy acceptance reaches practical limits defined by setup size and coating damage threshold. In this work, we address this challenge and demonstrate a novel type of compact, energy-scalable MPC (CMPC). By employing a novel MPC configuration and folding the beam path, the CMPC introduces a new degree of freedom for downsizing the setup length, enabling compact setups even for large pulse energies. We experimentally and numerically verify the CMPC approach, demonstrating post-compression of 8 mJ pulses from 1 ps down to 51 fs in atmospheric air using a cell roughly 45 cm in length at low fluence values. Additionally, we discuss the potential for energy scaling up to 200 mJ with a setup size reaching 2.5 m. Our work presents a new approach to high-energy post-compression, with up-scaling potential far beyond the demonstrated parameters. This opens new routes for achieving the high peak and average powers necessary for demanding applications of ultrafast lasers., Comment: 18 pages, 13 figures

We present a numerical method for extrapolating polarizability measurements to dc as done in the assessment of blackbody radiation shifts for ion-based clocks. The method explicitly accounts for the frequency dependence of relevant atomic transitions without introducing an ad hoc modelling function. It incorporates \emph{a priori} atomic structure calculations, which allows measurements to be augmented by calculations if there is insufficient data to make a purely measurement based estimate. The method also provides indicators of inconsistencies between theory and experiment or inadequacies of the data for making an extrapolation. We use results from Al$^+$, Lu$^+$, and Yb$^+$ to illustrate features of the method., Comment: 8 pages, 1 figure

When photographers and other editors of image material produce an image, they make a statement about what matters by situating some objects in the foreground and others in the background. While this prominence of objects is a key analytical category to qualitative scholars, recent quantitative approaches to automated image analysis have not yet made this important distinction but treat all areas of an image similarly. We suggest carefully considering objects' prominence as an essential step in analyzing images as data. Its modeling requires defining an object and operationalizing and measuring how much attention a human eye would pay. Our approach combines qualitative analyses with the scalability of quantitative approaches. Exemplifying object prominence with different implementations -- object size and centeredness, the pixels' image depth, and salient image regions -- we showcase the usefulness of our approach with two applications. First, we scale the ideology of eight US newspapers based on images. Second, we analyze the prominence of women in the campaign videos of the U.S. presidential races in 2016 and 2020. We hope that our article helps all keen to study image data in a conceptually meaningful way at scale., Comment: Working Paper

Earth's magnetotail, a night-side region characterized by stretched magnetic field lines and strong plasma currents, is the primary site for the release of magnetic field energy and its transformation into plasma heating and kinetic energy plus charged particle acceleration during magnetic reconnection. In this study, we demonstrate that the efficiency of this acceleration can be sufficiently high to produce populations of relativistic and ultra-relativistic electrons, with energies up to several MeV, which exceeds all previous theoretical and simulation estimates. Using data from the low altitude ELFIN and CIRBE CubeSats, we show multiple events of relativistic electron bursts within the magnetotail, far poleward of the outer radiation belt. These bursts are characterized by power-law energy spectra and can be detected during even moderate substorms.

With advanced synthetic techniques, a wide variety of well-defined graphene nano-ribbons (GNRs) can be produced with atomic precision. Hence, finding the relation between their structures and properties becomes important for the rational design of GNRs. In this work, we explore the complete chemical space of gulf-edged zigzag graphene nanoribbons (ZGNR-Gs), a subclass of zigzag GNRs in which the zigzag edges miss carbon atoms in a regular sequence. We demonstrate that the electronic properties of ZGNR-Gs depend on four structural parameters: ribbon width, gulf edge size, unit length, and gulf offset. Using tight-binding calculations and the Hubbard model, we find that all ZGNR-Gs are semiconductors with varying band gaps; there are no metals in this class of materials. Notably, when spin polarization is considered, most ZGNR-Gs exhibit antiferromagnetic behavior, with the spin moments and spin-induced band gap opening being stabilized by longer zigzag segments at the edges. Furthermore, we provide simple empirical rules that describe the Z2 topological invariant based on the aforementioned structural parameters. By analyzing the full chemical space of ZGNR-Gs, we offer insights into the design of GNRs with desired electronic, magnetic, and topological properties for nanoelectronic applications.

The discovery of the superconductivity in the infinite-layer nickelates, as topotactically reduced from their respective perovskite percussors via co-annealing with CaH2, extends the understanding in superconductivity. Nevertheless, whether the incorporated 1H composition is critical to the infinite-layer superconductivity recently arouses considerable debates, while the central challenge lies in the quantification of 1H that is easily interfered by the conventional electron or orbital associated processes. Herein, we demonstrate the irrelevance between the superconductivity in the infinite-layer nickelates and their incorporated 1H composition, assisted by nuclear reaction analysis (NRA) and heavy ion energy recoil detection analysis (HIERDA) based on the nuclear interactions at MeV energy scale. These approaches completely overwhelm the conventional interferes, such as ionization, activation and chemical bonds, and achieves the 1H quantification within superconducting La0.8Sr0.2NiO2 (or Nd0.8Sr0.2NiO2). A large diversity of 1H composition far beyond the previously expected critical dome was observed, while their TC were not changed significantly. Furthermore, the superconductivity was demonstrated to be achievable for La0.8Sr0.2NiO2 reduced by Al without any hydrogen associated process, while the superconducting properties for the CaH2 reduced La0.8Sr0.2NiO2 is rather stable after long term exposure in air, despite the high volatility of 1H within oxides. All these results indicate that the 1H incorporation composition is not critical to the superconductivity of the infinite-layer nickelates.

Many bacteria live in natural and clinical environments with abundant macromolecular polymers. Macromolecular fluids commonly display viscoelasticity and non-Newtonian rheological behavior; it is unclear how these complex-fluid properties affect bacterial transport in flows. Here we combine high-resolution microscopy and numerical simulations to study bacterial response to shear flows of various macromolecular fluids. In stark contrast to the case in Newtonian shear flows, we found that flagellated bacteria in macromolecular flows display a giant capacity of upstream swimming (a behavior resembling fish swimming against current) near solid surfaces: The cells can counteract flow washing at shear rates up to ~65 $s^{-1}$, one order of magnitude higher than the limit for cells swimming in Newtonian flows. The significant enhancement of upstream swimming depends on two characteristic complex-fluid properties, namely viscoelasticity and shear-thinning viscosity; meanwhile, increasing the viscosity with a Newtonian polymer can prevent upstream motion. By visualizing flagellar bundles and modeling bacterial swimming in complex fluids, we explain the phenomenon as primarily arising from the augmentation of a "weathervane effect" in macromolecular flows due to the presence of a viscoelastic lift force and a shear-thinning induced azimuthal torque promoting the alignment of bacteria against the flow direction. Our findings shed light on bacterial transport and surface colonization in macromolecular environments, and may inform the design of artificial helical microswimmers for biomedical applications in physiological conditions.

Bacteria often exhibit upstream swimming, which can cause the contamination of biomedical devices and the infection of organs including the urethra or lungs. This process, called rheotaxis, has been studied extensively in Newtonian fluids. However, most microorganisms thrive in non-Newtonian fluids that contain suspended polymers such as mucus and biofilms. Here, we investigate the rheotatic behavior of E. coli near walls in non-Newtonian fluids. Our experiments demonstrate that bacterial upstream swimming is enhanced by an order of magnitude in shear-thinning polymeric fluids relative to Newtonian fluids. This result is explained by direct numerical simulations, revealing a torque that promotes the alignment of bacteria against the flow. From this analysis, we develop a theoretical model that accurately describes experimental rheotatic data in both Newtonian and shear-thinning fluids.

There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring. This raises two fundamental questions: what is causing the fault and how can we fix it? Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy". The suggestions are not always actionable which may raise the interest in asking "what if we do this instead?" In this work, we provide a proof of concept for utilizing counterfactual explanations as what-if analysis. We perform this on the PRONOSTIA dataset with a temporal convolutional network as the anomaly detector. Our method presents the counterfactuals in the form of a what-if analysis for this base problem to inspire future work for more complex systems and scenarios., Comment: 8 pages, 6 figures, 3 tables

Inside a medium, showers originating from a very high energy particle develop via medium-induced splitting processes such as bremsstrahlung and pair production. During shower development, two consecutive splittings sometimes overlap quantum mechanically, so that they cannot be treated independently. Some of these effects can be absorbed into an effective value of a medium parameter known as $\hat q$. Previous calculations (with certain simplifying assumptions) have found that, after adjusting the value of $\hat q$, the leftover effect of overlapping splittings is quite small for purely gluonic large-$N_c$ showers but is very much larger for large-$N_f$ QED showers, at comparable values of $N\alpha$. Here, by investigating the same problem for QCD with *quarks* in the large-$N_f$ limit of many quark flavors, we make a first study of whether the small effect in purely gluonic showers (i) was merely an accident or (ii) is more broadly characteristic of such overlap effects in QCD. We also offer a qualitative explanation for the large size of the effect in QED vs. QCD., Comment: 73 pages, 25 figures

We propose a fitness measure quantifying multi-scale complexity for cellular automaton states, using compressibility as a proxy for complexity. The use of compressibility is grounded in the concept of Kolmogorov complexity, which defines the complexity of an object by the size of its smallest representation. With this fitness function, we explore the complexity range accessible to the well-known Flow Lenia cellular automaton, using image compression algorithms to assess state compressibility. Using a Genetic Algorithm to evolve Flow Lenia patterns, we conduct experiments with two primary objectives: 1) generating patterns of specific complexity levels, and 2) exploring the extrema of Flow Lenia's complexity domain. Evolved patterns reflect the complexity targets, with higher complexity targets yielding more intricate patterns, consistent with human perceptions of complexity. This demonstrates that our fitness function can effectively evolve patterns that match specific complexity objectives within the bounds of the complexity range accessible to Flow Lenia under a given hyperparameter configuration., Comment: Presented as Late-Breaking Abstract poster at The 2024 Conference on Artificial Life (ALIFE2024). 3 Pages, 2 Figures

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

Spurious solar-wind effects are a potential noise source in the measurements of the future Laser Interferometer Space Antenna (LISA). Comparative models are used to predict the possible impact of this noise factor and estimate spurious solar-wind effects on acceleration noise in LISA Pathfinder (LPF). Data from NASA's Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, served as a reliable source of solar-wind data. The data sets were compared over the 114-day time period from March 1, 2016 to June 23, 2016. To evaluate these effects, the data from both satellites were formatted, gap-filled, and adapted for comparison, and a coherence plot comparing the results of the Fast Fourier Transformations. The coherence plot suggested that solar-wind had a minuscule effect on the LPF, and higher frequency coherence (LISA's main observing band) can be attributed to random chance correlation. This result indicates that measurable correlation due to solar-wind noise over 3-month timescales can be ruled out as a noise source. This is encouraging, although another source of noise from the sun, solar irradiance pressure, is estimated to have a more significant effect and has yet to be analyzed., Comment: 14 pages, 9 figures, will be submitted to Classical and Quantum Gravity

We show that a large family of tensorial metasurfaces can be found that perform an identical wave transformation, showing that even when the conditions of reciprocity and passivity are imposed, there still remain many solutions to the design problem. As an example, we explore the case of a metasurface that rotates a single input polarization, showing we can parameterize the set of equivalent reciprocal metasurfaces in terms of a single complex parameter. Through allowing dissipation and gain within the response, the surface can have many different functionalities in the orthogonal polarization, opening up a new route for the design of multiplexed metasurfaces.

Activities such as the movement of passengers and goods, the transfer of physical or digital assets, web navigation and even successive passes in football, result in timestamped paths through a physical or virtual network. The need to analyse such paths has produced a new modelling paradigm in the form of higher-order networks which are able to capture temporal and topological characteristics of sequential data. This has been complemented by sequence mining approaches, a key example being sequential motifs measuring the prevalence of recurrent subsequences. Previous work on higher-order networks has focused on how to identify the optimal order for a path dataset, where the order can be thought of as the number of steps of memory encoded in the model. In this paper, we build on these approaches to consider which orders are necessary to reproduce different path characteristics, from path lengths to counts of sequential motifs, viewing paths generated from different higher-order models as null models which capture features of the data up to a certain order, and randomised otherwise. Furthermore, we provide an important extension to motif counting, whereby cases with self-loops, starting nodes, and ending nodes of paths are taken into consideration. Conducting a thorough analysis using path lengths and sequential motifs on a diverse range of path datasets, we show that our approach can shed light on precisely where models of different order overperform or underperform, and what this may imply about the original path data., Comment: 23 pages, 8 figures, submitted to the Journal of Complex Networks

During recent years interest has been rising for applications of vector light beams towards magnetic field sensing. In particular, a series of experiments were performed to extract information about properties of static magnetic fields from absorption profiles of light passing through an atomic gas target. In the present work, we propose an extension to this method for oscillating magnetic fields. To investigate this scenario, we carried out theoretical analysis based on the time-dependent density matrix theory. We found that absorption profiles, even when averaged over typical observation times, are indeed sensitive to both strength and frequency of the time-dependent field, thus opening the prospect for a powerful diagnostic technique. To illustrate this sensitivity, we performed detailed calculations for the $5s \;\, {}^2S_{1/2}$ ($F=1$) $-$ $5p \;\, {}^2 P_{3/2}$ ($F=0$) transition in rubidium atoms, subject to a superposition of an oscillating (test) and a static (reference) magnetic field., Comment: 10 pages, 9 figures

Ternary transition metal nitrides (TMNs) promise to significantly expand the material design space by opening new functionality and enhancing existing properties. However, most systems have only been investigated computationally and limited understanding of their stabilizing mechanisms restricts translation to experimental synthesis. To better elucidate key factors in designing ternary TMNs, we experimentally fabricate and analyze the physical properties of the ternary Zr-Pt-N system. Structural analysis and DFT modeling demonstrate that Pt substitutes nitrogen on the non-metallic sublattice, which destabilizes the rock-salt structure and forms a complex cubic phase. We also show insolubility of Pt in the Zr-Pt-N at 45 at % with the formation of a secondary Pt-rich phase. The measured reduced plasma frequency, decrease in resistivity, and decrease in hardness reflect a dominance of metallic behavior in bonding. Contrary to previous computational predictions, Zr-Pt-N films are shown to be metastable systems where even low Pt concentrations (1%) facilitate a solid reaction with the Si-substrate, that is inaccessible in ZrN films., Comment: 6 pages, 3 figures

In this letter we report the first scientific result based on antineutrinos emitted from the BR2 reactor at SCK CEN. The SoLid experiment uses a novel type of highly granular detector whose basic detection unit combines two scintillators, PVT and 6LiF:ZnS(Ag), to measure antineutrinos via their inverse-beta-decay products. An advantage of PVT is its highly linear response as a function of deposited particle energy. The full-scale detector comprises 12800 voxels and operates over a very short 6.3--8.9 m baseline from the reactor core. The detector segmentation and its 3D imaging capabilities facilitate the extraction of the positron energy from the rest of the visible energy, allowing the latter to be utilised for signal-background discrimination. We present a result based on 280 reactor-on days (55 MW mean power) and 172 reactor-off days, respectively, of live data-taking. A total of 29479 $\pm$ 603 (stat.) antineutrino candidates have been selected, corresponding to an average rate of 105 events per day and a signal-to-background ratio of 0.27. A search for disappearance of antineutrinos to a sterile state has been conducted using complementary model-dependent frequentist and Bayesian fits, providing constraints on the allowed region of the Reactor Antineutrino Anomaly., Comment: 8 pages, 6 figures

A $(\beta,\delta,\Delta)$-padded decomposition of an edge-weighted graph $G = (V,E,w)$ is a stochastic decomposition into clusters of diameter at most $\Delta$ such that for every vertex $v\in V$, the probability that $\rm{ball}_G(v,\gamma\Delta)$ is entirely contained in the cluster containing $v$ is at least $e^{-\beta\gamma}$ for every $\gamma \in [0,\delta]$. Padded decompositions have been studied for decades and have found numerous applications, including metric embedding, multicommodity flow-cut gap, muticut, and zero extension problems, to name a few. In these applications, parameter $\beta$, called the padding parameter, is the most important parameter since it decides either the distortion or the approximation ratios. For general graphs with $n$ vertices, $\beta = \Theta(\log n)$. Klein, Plotkin, and Rao showed that $K_r$-minor-free graphs have padding parameter $\beta = O(r^3)$, which is a significant improvement over general graphs when $r$ is a constant. A long-standing conjecture is to construct a padded decomposition for $K_r$-minor-free graphs with padding parameter $\beta = O(\log r)$. Despite decades of research, the best-known result is $\beta = O(r)$, even for graphs with treewidth at most $r$. In this work, we make significant progress toward the aforementioned conjecture by showing that graphs with treewidth $\rm{tw}$ admit a padded decomposition with padding parameter $O(\log \rm{tw})$, which is tight. As corollaries, we obtain an exponential improvement in dependency on treewidth in a host of algorithmic applications: $O(\sqrt{ \log n \cdot \log(\rm{tw})})$ flow-cut gap, max flow-min multicut ratio of $O(\log(\rm{tw}))$, an $O(\log(\rm{tw}))$ approximation for the 0-extension problem, an $\ell^{O(\log n)}_\infty$ embedding with distortion $O(\log \rm{tw})$, and an $O(\log \rm{tw})$ bound for integrality gap for the uniform sparsest cut.

The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed individual source studies and statistical studies of large samples of X-ray sources detected in ACIS and HRC-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides carefully-curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial, photometric, spectral, and temporal source properties, as well as science-ready X-ray data products. The latter includes multiple types of source- and field-based FITS format products that can be used as a basis for further research, significantly simplifying followup analysis of scientifically meaningful source samples. We discuss in detail the algorithms used for the CSC Release 2 Series, including CSC 2.0, which includes 317,167 unique X-ray sources on the sky identified in observations released publicly through the end of 2014, and CSC 2.1, which adds Chandra data released through the end of 2021 and expands the catalog to 407,806 sources. Besides adding more recent observations, the CSC Release 2 Series includes multiple algorithmic enhancements that provide significant improvements over earlier releases. The compact source sensitivity limit for most observations is ~5 photons over most of the field of view, which is ~2x fainter than Release 1, achieved by co-adding observations and using an optimized source detection approach. A Bayesian X-ray aperture photometry code produces robust fluxes even in crowded fields and for low count sources. The current release, CSC 2.1, is tied to the Gaia-CRF3 astrometric reference frame for the best sky positions for catalog sources., Comment: 66 pages, 17 figures, 16 tables, accepted for publication in The Astrophysical Journal Supplement Series

Transmission spectroscopy has been a workhorse technique over the past two decades to constrain the physical and chemical properties of exoplanet atmospheres. One of its classical key assumptions is that the portion of the atmosphere it probes -- the terminator region -- is homogeneous. Several works in the past decade, however, have put this into question for highly irradiated, hot ($T_{eq}\gtrsim 1000$ K) gas giant exoplanets both empirically and via 3-dimensional modelling. While models predict clear differences between the evening (day-to-night) and morning (night-to-day) terminators, direct morning/evening transmission spectra in a wide wavelength range has not been reported for an exoplanet to date. Under the assumption of precise and accurate orbital parameters on WASP-39 b, here we report the detection of inhomogeneous terminators on the exoplanet WASP-39 b, which allows us to retrieve its morning and evening transmission spectra in the near-infrared ($2-5\ \mu$m) using JWST. We observe larger transit depths in the evening which are, on average, $405 \pm 88$ ppm larger than the morning ones, also having qualitatively larger features than the morning spectrum. The spectra are best explained by models in which the evening terminator is hotter than the morning terminator by $177^{+65}_{-57}$ K with both terminators having C/O ratios consistent with solar. General circulation models (GCMs) predict temperature differences broadly consistent with the above value and point towards a cloudy morning terminator and a clearer evening terminator., Comment: Published in Nature at https://www.nature.com/articles/s41586-024-07768-4. All code to produce plots (with data) can be found at https://github.com/nespinoza/wasp39-terminators

This work explores power handling solutions for high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept \cite{rutherford_manta_2024}. The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only $\sim$50 cm ($\sim$40\% of the minor radius). The UEDGE code package is used for the boundary plasma solution, to track plasma temperatures and fluxes to the divertor targets. It is found that for $P_\mathrm{SOL}$ = 25 MW and $n_\mathrm{sep} = 0.96 \times 10^{20}$ m$^{-3}$, conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For external impurity injection of just 0.13\% Ne, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW/m$^{2}$, while the electron temperature $T_\mathrm{e}$ remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario, $P_\mathrm{SOL}$ can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1\% neon (Ne) impurity content, the divertor still experiences less than 10 MW/m$^{2}$ at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550\degree C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01\% Ne content.

Purpose: We aimed to develop a data-driven multiomics approach integrating radiomics, dosiomics, and delta features to predict treatment response at an earlier stage (intra-treatment) for brain metastases (BMs) patients treated with PULSAR. Methods: We conducted a retrospective study of 39 patients with 69 BMs treated with PULSAR. Radiomics, dosiomics, and delta features were extracted from pretreatment and intra-treatment MRI scans and dose distributions. Six individual models and an ensemble feature selection (EFS) model were constructed using SVM and evaluated via stratified 5-fold cross-validation. The classification task distinguished lesions with >20% volume reduction at follow-up. We assessed performance metrics including sensitivity, specificity, accuracy, precision, F1 score, and AUC. Various feature extraction and ensemble selection scenarios were explored to enhance model robustness and reduce overfitting. Results: The EFS model, integrating features from pre-treatment radiomics and dosiomics, intra-treatment radiomics, and delta-radiomics, outperformed six individual models. It achieved an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top 9 features, six were derived from post-wavelet transformation, and three from original images. The discrete wavelet transform decomposes volumetric images into multi-resolution components, offering a more comprehensive characterization of underlying structures. Conclusion: Our study demonstrated the feasibility of employing a data-driven multiomics approach to predict tumor volume changes in BMs patients with PULSAR. The EFS model demonstrates enhanced performance compared with six individual models, emphasizing the importance of integrating both pretreatment and intra-treatment data. This application holds promise for optimizing BMs management, potentially mitigating risks associated with under- or over-treatment.

We here report on the progress of the HHH Workshop, that took place in Dubrovnik in July 2023. After the discovery of a particle that complies with the properties of the Higgs boson of the Standard Model, all SM parameters are in principle determined. However, in order to verify or falsify the model, the full form of the potential has to be determined. This includes the measurement of the triple and quartic scalar couplings. We here report on ongoing progress of measurements for multi scalar final states, with an emphasis on three SM-like scalar bosons at 125 GeV, but also mentioning other options. We discuss both experimental progress and challenges as well as theoretical studies and models that can enhance such rates with respect to the SM predictions., Comment: 117 pages, 56 figures; Whitepaper resulting from HHH Workshop in Dubrovnik 2023, https://indico.cern.ch/event/1232581/; v2: small typos corrected

A finite element method is introduced to track interface evolution governed by the level set equation. The method solves for the level set indicator function in a narrow band around the interface. An extension procedure, which is essential for a narrow band level set method, is introduced based on a finite element $L^2$- or $H^1$-projection combined with the ghost-penalty method. This procedure is formulated as a linear variational problem in a narrow band around the surface, making it computationally efficient and suitable for rigorous error analysis. The extension method is combined with a discontinuous Galerkin space discretization and a BDF time-stepping scheme. The paper analyzes the stability and accuracy of the extension procedure and evaluates the performance of the resulting narrow band finite element method for the level set equation through numerical experiments.

This report exam­ines how demographics and outcomes changed when schools that had enrolled a dis­pro­por­tion­ate share of the sys­tem's white stu­dents entered the city's cen­tral­ized enroll­ment sys­tem in New Orleans. It finds that the schools enter­ing that sys­tem (OneApp/?NCAP) led to increased access to those schools for Black and oth­er non­white stu­dents. While schools have been reluc­tant to give up con­trol over enroll­ment, these schools did not see declines in white enroll­ment or aca­d­e­m­ic per­for­mance after join­ing the enroll­ment system.

Unified enrollment (UE) systems were designed to improve efficiency, equity, and transparency in school choice processes, but research has focused on efficiency gains. This study examines whether moving from decentralized enrollment processes to UE mitigates or exacerbates racial segregation that often occurs in choice systems. Specifically, we examine a subset of charter schools in New Orleans that had enrolled disproportionately high numbers of white students prior to entering UE. We find that UE entry was associated with increased enrollment of nonwhite students in these schools without offsetting declines in white enrollment, facilitated by schools also increasing total enrollment after entering UE. We find no meaningful impacts of UE on school accountability measures, student or teacher mobility, or student discipline.

The American Enterprise Institute, EducationCounsel, and The Century Foundation undertook a joint process to examine--across ideological lines--the nature of the existing system that finances graduate education, including its benefits and downsides, and to design a framework that should guide the improvement of federal policy regarding graduate student financing. This report aims to outline policy options that address the burden on students, particularly the disproportionate burden facing students of color, and the growing cost of the graduate loan programs for taxpayers. The shared goal of this process was improved outcomes for students and better value for taxpayers. Analysis and discussion of different policy options led to the conclusion that any politically viable and effective approach must address five different dimensions of the problem through the following set of policy levers: (1) set reasonable loan limits; (2) award grant aid to students and institutions to address equity and social good considerations; (3) ensure sufficient value and return on investment for students and taxpayers; (4) enhance the regulatory structure and consumer protections for private lending; and (5) improve data disclosure and transparency.

In 2022, after increasing pressures from the COVID pandemic and a shifting political climate, the nation's largest teachers' union warned that over half of teachers were considering exiting the teaching profession. More recent national and local work suggests that such evidence in the media is often not supported by administrative data and that reports of a teacher exodus are often exaggerated or incorrect. There is no public data on teacher vacancies in Maryland, so in this study the supply of teachers in Baltimore City Public Schools (City Schools) was examined, with a particular focus on sources of potential teacher shortages that emerged during the COVID-19 era. Data for this study was provided by the Maryland Longitudinal Data System Center (MLDSC), an independent state agency that supports research on education and workforce outcomes in Maryland. Through descriptive evidence on teacher entry and exit from 2016 to 2022, it was shown that teacher retention rates in City Schools actually improved over pre-COVID levels. However, the post-COVID period also saw an increase in the number of new teachers hired with temporary teaching certificates. This suggests that City Schools faces new challenges to recruit and develop the next generation of teachers for Baltimore students.