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The discovery of an anomalous Hall effect (AHE) sensitive to the magnetic state of antiferromagnets can trigger a new era of spintronics, if materials that host a tunable and strong AHE are identified. Altermagnets are a new class of materials that can under certain conditions manifest a strong AHE, without having a net magnetization. But the ability to control their AHE is still lacking. In this study, we demonstrate that the AHE in altermagnetic {\alpha}-MnTe grown on GaAs(111) substrates can be "written on-demand" by cooling the material under an in-plane magnetic field. The magnetic field controls the strength and the coercivity of the AHE. Remarkably, this control is unique to {\alpha}-MnTe grown on GaAs and is absent in {\alpha}-MnTe grown on SrF2. The tunability that we reveal challenges our current understanding of the symmetry-allowed AHE in this material and opens new possibilities for the design of altermagnetic spintronic devices.

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries designed to capture universal place-specific attributes. Unlike existing methods that employ self-attention and generate the queries directly from the input features, BoQ employs distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, our technique provides an interpretable attention mechanism and integrates with both CNN and Vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, as a global retrieval technique (one-stage), BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https://github.com/amaralibey/Bag-of-Queries., Comment: Accepted at CVPR 2024

Let $X$ be a metric space. Recently in~[1] it was considered a new type of mappings $T\colon X\to X$ which can be characterized as mappings contracting perimeters of triangles. These mappings are defined by the condition based on the mapping of three points of the space instead of two, as it is adopted in many fixed-point theorems. In the present paper we consider so-called $(F,G)$-contracting mappings, which form a more general class of mappings than mappings contracting perimeters of triangles. The fixed-point theorem for these mappings is proved. We prove also a fixed-point theorem for mappings contracting perimeters of triangles in the sense of Edelstein., Comment: 14 pages, 1 figure. arXiv admin note: text overlap with arXiv:2308.01003

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.

Land use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called frontiers, defined here as places experiencing marked transformations due to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction, and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves, and actors anticipations as key mechanisms by which frontiers emerge. Processes of abnormal rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics, but can be inspirational for other frontier processes including for extractive resources, such as minerals.

With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the long-known Koopman operator theory to embed dynamical systems in latent spaces where their dynamics can be described linearly, enabling a number of appealing features. We introduce methods that enable to train such a model for long-term continuous reconstruction, even in difficult contexts where the data comes in irregularly-sampled time series. The potential for self-supervised learning is also demonstrated, as we show the promising use of trained dynamical models as priors for variational data assimilation techniques, with applications to e.g. time series interpolation and forecasting.

The synthesis of thin films of magnetic topological materials is necessary to achieve novel quantized Hall effects and electrodynamic responses. EuIn2As2 is a recently predicted topological axion insulator that has an antiferromagnetic ground state and an inverted band structure, but that has only been synthesized and studied as a single crystal. We report on the synthesis of c-axis oriented EuIn2As2 films on sapphire substrates by molecular beam epitaxy. By carefully tuning the substrate temperature during growth, we stabilize the Zintl phase of EuIn2As2 expected to be topologically non-trivial. The magnetic properties of these films reproduce those seen in single crystals, but their resistivity is enhanced when grown at lower temperatures. We additionally find that the magnetoresistance of EuIn2As2 is negative even up to fields as high as 31T. while it is highly anisotropic at low fields, it becomes nearly isotropic at high magnetic fields above 5T. Overall, the transport characteristics of EuIn2As2 appear similar to those of chalcogenide topological insulators, motivating the development of devices to gate tune the Fermi energy and reveal topological features in quantum transport., Comment: accepted Phys. Rev. Materials

Objective:Develop and validate an algorithm for analyzing the layout of PDF clinical documents to improve the performance of downstream natural language processing tasks. Materials and Methods: We designed an algorithm to process clinical PDF documents and extract only clinically relevant text. The algorithm consists of several steps: initial text extraction using a PDF parser, followed by classification into categories such as body text, left notes, and footers using a Transformer deep neural network architecture, and finally an aggregation step to compile the lines of a given label in the text. We evaluated the technical performance of the body text extraction algorithm by applying it to a random sample of documents that were annotated. Medical performance was evaluated by examining the extraction of medical concepts of interest from the text in their respective sections. Finally, we tested an end-to-end system on a medical use case of automatic detection of acute infection described in the hospital report. Results:Our algorithm achieved per-line precision, recall, and F1 score of 98.4, 97.0, and 97.7, respectively, for body line extraction. The precision, recall, and F1 score per document for the acute infection detection algorithm were 82.54 (95CI 72.86-91.60), 85.24 (95CI 76.61-93.70), 83.87 (95CI 76, 92-90.08) with exploitation of the results of the advanced body extraction algorithm, respectively. Conclusion:We have developed and validated a system for extracting body text from clinical documents in PDF format by identifying their layout. We were able to demonstrate that this preprocessing allowed us to obtain better performances for a common downstream task, i.e., the extraction of medical concepts in their respective sections, thus proving the interest of this method on a clinical use case., Comment: 22 pages, 5 figures

Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose, for example through the European Copernicus project. Simultaneously, the development of self-supervised learning (SSL) methods has sparked great interest in the remote sensing community, enabling to learn latent representations from unlabeled data to help treating downstream tasks for which there is few annotated examples, such as interpolation, forecasting or unmixing. Following this line, we train a deep learning model inspired from the Koopman operator theory to model long-term reflectance dynamics in an unsupervised way. We show that this trained model, being differentiable, can be used as a prior for data assimilation in a straightforward way. Our datasets, which are composed of Sentinel-2 multispectral image time series, are publicly released with several levels of treatment.

The objective of this study is to address the critical issue of de-identification of clinical reports in order to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse. We annotated a corpus of clinical documents according to 12 types of identifying entities, and built a hybrid system, merging the results of a deep learning model as well as manual rules. Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.

Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous driving as well as other computer vision tasks. It refers to the process of identifying a place depicted in a query image using only computer vision. At large scale, repetitive structures, weather and illumination changes pose a real challenge, as appearances can drastically change over time. Along with tackling these challenges, an efficient VPR technique must also be practical in real-world scenarios where latency matters. To address this, we introduce MixVPR, a new holistic feature aggregation technique that takes feature maps from pre-trained backbones as a set of global features. Then, it incorporates a global relationship between elements in each feature map in a cascade of feature mixing, eliminating the need for local or pyramidal aggregation as done in NetVLAD or TransVPR. We demonstrate the effectiveness of our technique through extensive experiments on multiple large-scale benchmarks. Our method outperforms all existing techniques by a large margin while having less than half the number of parameters compared to CosPlace and NetVLAD. We achieve a new all-time high recall@1 score of 94.6% on Pitts250k-test, 88.0% on MapillarySLS, and more importantly, 58.4% on Nordland. Finally, our method outperforms two-stage retrieval techniques such as Patch-NetVLAD, TransVPR and SuperGLUE all while being orders of magnitude faster. Our code and trained models are available at https://github.com/amaralibey/MixVPR., Comment: Accepted at WACV 2023

Learning deep representations for visual place recognition is commonly performed using pairwise or triple loss functions that highly depend on the hardness of the examples sampled at each training iteration. Existing techniques address this by using computationally and memory expensive offline hard mining, which consists of identifying, at each iteration, the hardest samples from the training set. In this paper we introduce a new technique that performs global hard mini-batch sampling based on proxies. To do so, we add a new end-to-end trainable branch to the network, which generates efficient place descriptors (one proxy for each place). These proxy representations are thus used to construct a global index that encompasses the similarities between all places in the dataset, allowing for highly informative mini-batch sampling at each training iteration. Our method can be used in combination with all existing pairwise and triplet loss functions with negligible additional memory and computation cost. We run extensive ablation studies and show that our technique brings new state-of-the-art performance on multiple large-scale benchmarks such as Pittsburgh, Mapillary-SLS and SPED. In particular, our method provides more than 100% relative improvement on the challenging Nordland dataset. Our code is available at https://github.com/amaralibey/GPM, Comment: Accepted at BMVC 2022

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render., Comment: Accepted at the Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) at WACV23

This paper aims to investigate representation learning for large scale visual place recognition, which consists of determining the location depicted in a query image by referring to a database of reference images. This is a challenging task due to the large-scale environmental changes that can occur over time (i.e., weather, illumination, season, traffic, occlusion). Progress is currently challenged by the lack of large databases with accurate ground truth. To address this challenge, we introduce GSV-Cities, a new image dataset providing the widest geographic coverage to date with highly accurate ground truth, covering more than 40 cities across all continents over a 14-year period. We subsequently explore the full potential of recent advances in deep metric learning to train networks specifically for place recognition, and evaluate how different loss functions influence performance. In addition, we show that performance of existing methods substantially improves when trained on GSV-Cities. Finally, we introduce a new fully convolutional aggregation layer that outperforms existing techniques, including GeM, NetVLAD and CosPlace, and establish a new state-of-the-art on large-scale benchmarks, such as Pittsburgh, Mapillary-SLS, SPED and Nordland. The dataset and code are available for research purposes at https://github.com/amaralibey/gsv-cities., Comment: Neurocomputing 2022

Background Clinical studies using real-world data may benefit from exploiting clinical reports, a particularly rich albeit unstructured medium. To that end, natural language processing can extract relevant information. Methods based on transfer learning using pre-trained language models have achieved state-of-the-art results in most NLP applications; however, publicly available models lack exposure to speciality-languages, especially in the medical field. Objective We aimed to evaluate the impact of adapting a language model to French clinical reports on downstream medical NLP tasks. Methods We leveraged a corpus of 21M clinical reports collected from August 2017 to July 2021 at the Greater Paris University Hospitals (APHP) to produce two CamemBERT architectures on speciality language: one retrained from scratch and the other using CamemBERT as its initialisation. We used two French annotated medical datasets to compare our language models to the original CamemBERT network, evaluating the statistical significance of improvement with the Wilcoxon test. Results Our models pretrained on clinical reports increased the average F1-score on APMed (an APHP-specific task) by 3 percentage points to 91%, a statistically significant improvement. They also achieved performance comparable to the original CamemBERT on QUAERO. These results hold true for the fine-tuned and from-scratch versions alike, starting from very few pre-training samples. Conclusions We confirm previous literature showing that adapting generalist pre-train language models such as CamenBERT on speciality corpora improves their performance for downstream clinical NLP tasks. Our results suggest that retraining from scratch does not induce a statistically significant performance gain compared to fine-tuning.

Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators. However, computations in memristors suffer from hardware non-idealities and are subject to different sources of noise that may negatively impact system performance. In this work, we theoretically analyze the mean squared error of DNNs that use memristor crossbars to compute MVM. We take into account both the quantization noise, due to the necessity of reducing the DNN model size, and the programming noise, stemming from the variability during the programming of the memristance value. Simulations on pre-trained DNN models showcase the accuracy of the analytical prediction. Furthermore the proposed method is almost two order of magnitude faster than Monte-Carlo simulation, thus making it possible to optimize the implementation parameters to achieve minimal error for a given power constraint., Comment: To be presented at AICAS 2022

The structure of nuclei around the neutron-rich nucleus 132Sn is of particular interest due to the vicinity of the Z = 50 and N = 82 shell closures and the r-process nucleosynthetic path. Four states in 131Sn with a strong single-particle-like component have previously been studied via the (d,p) reaction, with limited excitation energy resolution. The 130Sn(9Be,8Be)131Sn and 130Sn(13C,12C)131Sn single-neutron transfer reactions were performed in inverse kinematics at the Holifield Radioactive Ion Beam Facility using particle-gamma coincidence spectroscopy. The uncertainties in the energies of the single-particle-like states have been reduced by more than an order of magnitude using the energies of gamma rays. The previous tentative Jpi values have been confirmed. Decays from high-spin states in 131Sn have been observed following transfer on the isomeric component of the 130Sn beam. The improved energies and confirmed spin-parities of the p-wave states important to the r-process lead to direct-semidirect cross-sections for neutron capture on the ground state of 130Sn at 30 keV that are in agreement with previous analyses. A similar assessment of the impact of neutron-transfer on the isomer would require significant nuclear structure and reaction theory input. There are few measurements of transfer reaction on isomers, and this is the first on an isomer in the 132Sn region., Comment: 7 pages, 4 figures

National Oceanic and Atmospheric Administration's (NOAA's) Environmental Literacy Program (ELP) Community Resilience Education Theory of Change communicates the overarching philosophy guiding its grants program. It can also be used to inform project-level logic models, ensuring that a project's activities, outcomes, and goals are aligned with the ELP outcomes and goals articulated in this theory of change. Theories of change, much like logic models, are tools for planning, implementation, and evaluation of an initiative. They are broad in scope and are typically focused at the program level rather than on the individual project level. The ELP's aim in creating this theory of change is to outline the conceptual framework for the ways in which community resilience education can lead to increased community engagement and civic action, ultimately leading to a healthier, more resilient, and equitable society. This theory of change will serve a suite of purposes: (1) To provide a visual representation of the overarching philosophy that guides the current focus of the ELP grants program, informing program evaluation and future funding announcements. The theory of change is a tool to communicate the program's purpose, audiences, and activities, as well as the assumptions, intended outcomes, and ultimate end goal of ELP investments; (2) To offer current and future ELP grantees a resource to understand how their local efforts contribute to a broader, national effort to increase resilience to extreme weather, climate change, and other environmental hazards; (3) To aggregate effective approaches and outcomes identified by grantees; (4) To articulate the value of education in community, city, state, and national efforts to build community resilience to extreme weather, climate change, and other environmental hazards; and (5) To serve as a model for how environmental literacy contributes to resilience that others working in the field of community resilience might use.

Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise, which makes it very difficult to perform target recognition in SAR images. Inspired by great success of very deep convolutional neural networks (CNNs), this paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. First, YOLOv4 network is fine-tuned to detect the targets from the respective MF SAR target images. Second, a very deep CNN is trained from scratch on the moving and stationary target acquisition and recognition (MSTAR) database by using small filters throughout the whole net to reduce the speckle noise. Besides, using small-size convolution filters decreases the number of parameters in each layer and, therefore, reduces computation cost as the CNN goes deeper. The resulting CNN model is capable of extracting very deep features from the target images without performing any noise filtering or pre-processing techniques. Third, our approach proposes to use the multi-canonical correlation analysis (MCCA) to adaptively learn CNN features from different layers such that the resulting representations are highly linearly correlated and therefore can achieve better classification accuracy even if a simple linear support vector machine is used. Experimental results on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods., Comment: 20 pages. International Journal of Remote Sensing (2021)

To fulfill needs in oncological research a new Micromegas detector has been developed to follow radiolabelled drugs in living organisms at the single cell level. This article describes the proof-of-concept of such a detector and compares its ability to detect and assess sub-becquerel \tritium~activities with a commercial $\beta$-imager, Comment: 14 pages, to be submitted to NIMA

This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the computations, and develop an error propagation model that takes into account these two sources of errors. In addition to providing updated Kalman filter equations, the proposed error model accurately predicts the covariance of the estimation error and gives a relation between the performance of the filter and its energy consumption, depending on the noise level in the memories. Then, since memories are responsible for a large part of the energy consumption of embedded systems, optimization methods are introduced so as to minimize the memory energy consumption under a desired estimation performance of the filter. The first method computes the optimal energy levels allocated to each memory bank individually, and the second one optimizes the energy allocation per groups of memory banks. Simulations show a close match between the theoretical analysis and experimental results. Furthermore, they demonstrate an important reduction in energy consumption of more than 50%., Comment: 29 pages, 8 figures, Submitted to IEEE Transactions on Signal Processing

The steadily increasing luminosity of the LHC requires an upgrade with high-rate and high-resolution detector technology for the inner end cap of the ATLAS muon spectrometer: the New Small Wheels (NSW). In order to achieve the goal of precision tracking at a hit rate of about 15 kHz/cm$^2$ at the inner radius of the NSW, large area Micromegas quadruplets with 100\,\microns spatial resolution per plane have been produced. % IRFU, from the CEA research center of Saclay, is responsible for the production and validation of LM1 Micromegas modules. The construction, production, qualification and validation of the largest Micromegas detectors ever built are reported here. Performance results under cosmic muon characterisation will also be discussed., Comment: To be submitted to NIMA

In an experiment performed at the SPIRAL1 facility of GANIL, the beta decay of 32Ar has been studied by means of the "Silicon Cube" device associated with germanium clover detectors from the EXOGAM array. Beta-delayed protons and gamma rays have been observed and allowed the determination of all relevant decay branches. The Gamow-Teller strength distribution is compared to shell-model calculations and excellent agreement is found. The Fermi strength is inline with expectations. A quasi-complete decay scheme of 32Ar is established., Comment: accepted for publication in EPJA

Large-scale collections of electronic records constitute both an opportunity for the development of more accurate prediction models and a threat for privacy. To limit privacy exposure new privacy-enhancing techniques are emerging such as federated learning which enables large-scale data analysis while avoiding the centralization of records in a unique database that would represent a critical point of failure. Although promising regarding privacy protection, federated learning prevents using some data-cleaning algorithms thus inducing new biases. In this work we focus on the recurrent problem of duplicated records that, if not handled properly, may cause over-optimistic estimations of a model's performances. We introduce and discuss stratified cross-validation, a validation methodology that leverages stratification techniques to prevent data leakage in federated learning settings without relying on demanding deduplication algorithms., Comment: 13 pages, 5 figures

A novel mechanical approach is developed to explore by means of atom-scale simulation the concept of line tension at a solid-liquid-vapor contact line as well as its dependence on temperature, confinement, and solid/fluid interactions. More precisely, by estimating the stresses exerted along and normal to a straight contact line formed within a partially wet pore, the line tension can be estimated while avoiding the pitfalls inherent to the geometrical scaling methodology based on hemispherical drops. The line tension for Lennard-Jones fluids is found to follow a generic behavior with temperature and chemical potential effects that are all included in a simple contact angle parameterization. Former discrepancies between theoretical modeling and molecular simulation are resolved, and the line tension concept is shown to be robust down to molecular confinements. The same qualitative behavior is observed for water but the line tension at the wetting transition diverges or converges towards a finite value depending on the range of the solid/fluid interactions at play., Comment: 8 pages, 7 figures

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions., Comment: 4 pages, 3 figures

In this paper we propose a low-cost high-speed imaging line scan system. We replace an expensive industrial line scan camera and illumination with a custom-built set-up of cheap off-the-shelf components, yielding a measurement system with comparative quality while costing about 20 times less. We use a low-cost linear (1D) image sensor, cheap optics including a LED-based or LASER-based lighting and an embedded platform to process the images. A step-by-step method to design such a custom high speed imaging system and select proper components is proposed. Simulations allowing to predict the final image quality to be obtained by the set-up has been developed. Finally, we applied our method in a lab, closely representing the real-life cases. Our results shows that our simulations are very accurate and that our low-cost line scan set-up acquired image quality compared to the high-end commercial vision system, for a fraction of the price., Comment: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

Direct reaction techniques are powerful tools to study the single-particle nature of nuclei. Performing direct reactions on short-lived nuclei requires radioactive ion beams produced either via fragmentation or the Isotope Separation OnLine (ISOL) method. Some of the most interesting regions to study with direct reactions are close to the magic numbers where changes in shell structure can be tracked. These changes can impact the final abundances of explosive nucleosynthesis. The structure of the chain of tin isotopes is strongly influenced by the Z=50 proton shell closure, as well as the neutron shell closures lying in the neutron-rich, N=82, and neutron-deficient, N=50, regions. Here we present two examples of direct reactions on exotic tin isotopes. The first uses a one-neutron transfer reaction and a low-energy reaccelerated ISOL beam to study states in 131Sn from across the N=82 shell closure. The second example utilizes a one-neutron knockout reaction on fragmentation beams of neutron-deficient 106,108Sn. In both cases, measurements of gamma rays in coincidence with charged particles proved to be invaluable., Comment: 11 pages, 5 figures, Zakopane Conference on Nuclear Physics "Extremes of the Nuclear Landscape", Zakopane, Poland, August 31 - September 7, 2014

The extraction of detailed nuclear structure information from transfer reactions requires reliable, well-normalized data as well as optical potentials and a theoretical framework demonstrated to work well in the relevant mass and beam energy ranges. It is rare that the theoretical ingredients can be tested well for exotic nuclei owing to the paucity of data. The halo nucleus Be-11 has been examined through the 10Be(d,p) reaction in inverse kinematics at equivalent deuteron energies of 12,15,18, and 21.4 MeV. Elastic scattering of Be-10 on protons was used to select optical potentials for the analysis of the transfer data. Additionally, data from the elastic and inelastic scattering of Be-10 on deuterons was used to fit optical potentials at the four measured energies. Transfers to the two bound states and the first resonance in Be-11 were analyzed using the Finite Range ADiabatic Wave Approximation (FR-ADWA). Consistent values of the spectroscopic factor of both the ground and first excited states were extracted from the four measurements, with average values of 0.71(5) and 0.62(4) respectively. The calculations for transfer to the first resonance were found to be sensitive to the size of the energy bin used and therefore could not be used to extract a spectroscopic factor., Comment: 16 Pages, 10 figures

This paper provides descriptive data on differences in civic engagement between advantaged and disadvantaged groups. Because there is not always consensus on what measure best describes disadvantaged, this paper used multiple indicators (race, ethnicity, citizenship status, family income and educational attainment) across four measures of civic engagement: (1) Percentage of survey respondents who volunteer with a formal non-profit provides measure of formal volunteer rate by group; (2) Percentage of survey respondents who do not volunteer but attend community meetings or work on community problems indicates whether disadvantaged groups exhibit a preference for community-oriented work over what is generally accepted as volunteering with a formal organization; (3) Percentage of individuals who both volunteer and either attend community meetings or work to fix a problem in their neighborhoods to provide better understanding about how volunteers from some groups are also highly engaged community actors; and (4) Percentage of all respondents, volunteer and non-volunteer, who attend community meetings or work on community problems to provide another view of group differences in civic engagement. Using the 2005-2007 Current Population Survey's Annual Volunteer Supplement was used for source data, major findings include: (1) Family income and education predict both likelihood of civic engagement and the rates of attrition; (2) Higher levels of income and education predict higher civic participation and attrition rates; (3) Whites tend to have higher rates of civic engagement and lower attrition rates than blacks, Hispanics or Asians; similarly, native-born citizens have higher rates of civic engagement and lower attrition than immigrants; and (4) Findings for race, ethnicity and citizenship status hold even when family income and educational attainment are considered. A Technical Appendix includes description of the logistic regression used for this analysis. (Contains 4 footnotes, 2 figures and 7 tables.)

The best examples of halo nuclei, exotic systems with a diffuse nuclear cloud surrounding a tightly-bound core, are found in the light, neutron-rich region, where the halo neutrons experience only weak binding and a weak, or no, potential barrier. Modern direct reaction measurement techniques provide powerful probes of the structure of exotic nuclei. Despite more than four decades of these studies on the benchmark one-neutron halo nucleus Be-11, the spectroscopic factors for the two bound states remain poorly constrained. In the present work, the Be-10(d,p) reaction has been used in inverse kinematics at four beam energies to study the structure of Be-11. The spectroscopic factors extracted using the adiabatic model, were found to be consistent across the four measurements, and were largely insensitive to the optical potential used. The extracted spectroscopic factor for a neutron in a nlj = 2s1/2 state coupled to the ground state of Be-10 is 0.71(5). For the first excited state at 0.32 MeV, a spectroscopic factor of 0.62(4) is found for the halo neutron in a 1p1/2 state., Comment: 5 pages, 4 figures

In an effort to better understand the attitudes and behaviors of young people in America around volunteering, service-learning and other forms of community involvement, the Corporation for National and Community Service, in collaboration with the U.S. Census Bureau and Independent Sector, conducted the Youth Volunteering and Civic Engagement Survey (the Youth Volunteering Survey), a national survey of American youth. Between January and March of 2005, 3,178 Americans between the ages of 12 and 18 were asked about their volunteer activities and experiences with school-based service-learning projects, as well as their involvement with school, family, religious congregations, and community associations. This report, the first brief in the "Youth Helping America Series," highlights the state of youth volunteering and considers the relationship between youth volunteer behavior and three primary environments where youth form their social networks: family, religious organizations, and school. These social institutions play an essential role in connecting youth to volunteer opportunities and encouraging them to become engaged in service. Fostering environments that encourage volunteer activities are critical to creating a commitment to service and community involvement that will remain with them for their lifetime. This analysis looks to build on existing research that has demonstrated that connections to the community and volunteering form a positive feedback loop, whereby opportunities provided to youth to engage with others leads to a greater sense of reciprocity and trust that in turn leads youth to develop a personal ethic of community engagement. (Contains 21 endnotes and 9 charts.) [This report also sponsored by: The Center for Information & Research on Civic Learning and Engagement (CIRCLE); and the MetLife Foundation.]

Minkowski's second theorem on successive minima gives an upper bound on the volume of a convex body in terms of its successive minima. We study the problem to generalize Minkowski's bound by replacing the volume by the lattice point enumerator of a convex body. In this context we are interested in bounds on the coefficients of Ehrhart polynomials of lattice polytopes via the successive minima. Our results for lattice zonotopes and lattice-face polytopes imply, in particular, that for 0-symmetric lattice-face polytopes and lattice parallelepipeds the volume can be replaced by the lattice point enumerator., Comment: 16 pages, incorporated referee remarks, corrected proof of Theorem 1.2, added new co-author

We show that every (possibly unbounded) convex polygon $P$ in $R^2$ with $m$ edges can be represented by inequalities $p_1 \ge 0,...,p_n \ge 0,$ where the $p_i$'s are products of at most $k$ affine functions each vanishing on an edge of $P$ and $n=n(m,k)$ satisfies $s(m,k) \le n(m,k) \le (1+\epsilon_m) s(m,k)$ with $s(m,k):=\max \{m/k,\log_2 m\}$ and $\epsilon_m \to 0$ as $m \to \infty$. This choice of $n$ is asymptotically best possible. An analogous result on representing the interior of $P$ in the form $p_1 > 0,..., p_n > 0$ is also given. For $k \le m/\log_2 m$ these statements remain valid for representations with arbitrary polynomials of degree not exceeding $k$.

The proton-rich nucleus 33Ar has been studied by detailed proton and gamma-ray spectroscopy at the low-energy facility of SPIRAL at GANIL. Proton and gamma-ray singles and coincidence measurements allowed to establish a quasi complete decay scheme of this nucleus. By comparing the proton intensity to different daughter states, tentative spin assignments have been made for some of the states of 33Cl. The Gamow-Teller strength distribution is deduced and compared to shell-model calculations and a quenching factor is determined. States close to the isobaric analogue state are searched for with respect to isospin mixing., Comment: 14 pages, 9 figures, accepted for publication in PRC

Beta-decay branching ratios of 62Ga have been measured at the IGISOL facility of the Accelerator Laboratory of the University of Jyvaskyla. 62Ga is one of the heavier Tz = 0, 0+ -> 0+ beta-emitting nuclides used to determine the vector coupling constant of the weak interaction and the Vud quark-mixing matrix element. For part of the experimental studies presented here, the JYFLTRAP facility has been employed to prepare isotopically pure beams of 62Ga. The branching ratio obtained, BR= 99.893(24)%, for the super-allowed branch is in agreement with previous measurements and allows to determine the ft value and the universal Ft value for the super-allowed beta decay of 62Ga.

Structural barriers are systemic challenges embedded in culture, economy, and institutions that cause inequality. Produced by systems of power, they require large-scale investment and policy solutions to achieve meaningful change. In 2017, Center for Law and Social Policy (CLASP) conducted focus groups with young women of color to learn what structural barriers they encounter in daily life. This fact sheet includes insights from Hmong-American, Latina and Native young women about their experiences with educational equity. Uprooting educational equity and other structural barriers demands turning over the ground to reveal these root causes, partnering with young women of color to identify solutions, and seeding change in equitable ground. This fact sheet focuses on educational equity. The following topics are presented: (1) Disproportionate Income; (2) Teacher Quality; (3) Re-Engagement Opportunities; and (4) First-Generation College Students.

This paper explores the impact of emerging information technology on employment opportunities for non-college-educated and minority individuals during 1986-99, examining whether shifts in employment toward information technology related to upward shifts in skills that led to reduced entry-level and total employment opportunities for noncollege-educated and minority workers. The paper categorizes out-of-school, noninstitutionalized people as: nonhigh school graduates; high school graduates; some college graduates, including people with two-year degrees; and greater than or equal to college graduates. Employment is classified into core technology occupations, other-technology occupations, and basic occupations. Core technology occupations grew substantially faster than other occupation groups. There was a general upgrading of skills within occupations across industries. Minorities, except Latinos, experienced increases in relative education and skills. Employment per capita for the noncollege-educated workers group increased. Less-educated workers improved their employment during the latter 1990s in the core technology occupation group. Skills did not upgrade for entry-level workers in the basic occupation group, and education levels among these workers declined. Noncollege-educated, entry-level workers saw strong employment growth in basic occupations and modest growth in other technology-related occupations. (SM)

We determine lattice polytopes of smallest volume with a given number of interior lattice points. We show that the Ehrhart polynomials of those with one interior lattice point have largest roots with norm of order n^2, where n is the dimension. This improves on the previously best known bound n and complements a recent result of Braun where it is shown that the norm of a root of an Ehrhart polynomial is at most of order n^2. For the class of 0-symmetric lattice polytopes we present a conjecture on the smallest volume for a given number of interior lattice points and prove the conjecture for crosspolytopes. We further give a characterisation of the roots of the Ehrhart polyomials in the 3-dimensional case and we classify for n\leq 4 all lattice polytopes whose roots of their Ehrhart polynomials have all real part -1/2. These polytopes belong to the class of reflexive polytopes.

Masses of the radioactive isotopes 62Ga, 62Zn and 62Cu have been measured at the JYFLTRAP facility with a relative precision of better than 18 ppb. A Q_EC value of (9181.07 +- 0.54) keV for the superallowed decay of 62Ga is obtained from the measured cyclotron frequency ratios of 62Ga-62Zn, 62Ga-62Ni and 62Zn-62Ni ions. The resulting Ft-value supports the validity of the conserved vector current hypothesis (CVC). The mass excess values measured were (-51986.5 +-1.0) keV for 62Ga, (-61167.9 +- 0.9) keV for 62Zn and (-62787.2 +- 0.9) keV for 62Cu., Comment: 12 pages, 3 figures, 2 tables, submitted to Phys. Lett. B. v2: added acknowledgements

The nucleus 54Zn has been observed for the first time in an experiment at the SISSI/LISE3 facility of GANIL in the quasi-fragmentation of a 58Ni beam at 74.5 MeV/nucleon in a natNi target. The fragments were analysed by means of the ALPHA-LISE3 separator and implanted in a silicon-strip detector where correlations in space and time between implantation and subsequent decay events allowed us to generate almost background free decay spectra for about 25 different nuclei at the same time. Eight 54Zn implantation events were observed. From the correlated decay events, the half-life of 54Zn is determined to be 3.2 +1.8/-0.8 ms. Seven of the eight implantations are followed by two-proton emission with a decay energy of 1.48(2) MeV. The decay energy and the partial half-life are compared to model predictions and allow for a test of these two-proton decay models., Comment: 4 pages, 4 figures, accepted for publication in PRL

This paper is concerned with understanding how a scaffolding process is utilized in the natural setting of a middle-school mathematics class. Wood, Bruner, & Ross (1976) characterize scaffolding as a learning process of a novice which is assisted and dominated by the adult. Rogoff and Gardner (1984) also point out that "to make messages sufficiently redundant" is one way to provide scaffolding. This study examined classroom discourse when a new topic was introduced to the class. The teacher connected a new topic (combinations) to old content (permutations). He used abundant, similar, but slightly changed examples as referents to help the students attach meaning to symbols for permutations and then began to turn over the discourse to the students to support their development of that new content. (Author)