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In-person tutoring has been shown to improve academic achievement. Though less well-researched, virtual tutoring has also shown a positive effect on achievement but has only been studied in grade five or above. We present findings from the first randomized controlled trial of virtual tutoring for young children (grades K-2). Students were assigned to 1:1 tutoring, 2:1 tutoring, or a control group. Assignment to any virtual tutoring increased early literacy skills by 0.05-0.08 SD with the largest effects for 1:1 tutoring (0.07-0.12 SD). Students initially scoring well below benchmark and first graders experienced the largest gains from 1:1 tutoring (0.15 and 0.20 SD, respectively). Effects are smaller than typically seen from in-person early literacy tutoring programs but still positive and statistically significant, suggesting promise particularly in communities with in-person staffing challenges. [On Your Mark and Uplift Education were partners in this research and additional support was provided by the National Student Support Accelerator Team.]

The Peach-Koehler force between disclination lines was originally formulated in the study of crystalline solids, and has since been adopted to provide a notion of interactions between disclination lines in nematic liquid crystals. Here, we argue that the standard formulation of this interaction force seemingly fails for materials where there is a symmetry-broken ground state, and suggest that this is due to the interaction between disclination lines and merons: non-singular yet non-trivial topological solitons. We examine this in the context of chiral nematic (cholesteric) liquid crystals, which provide a natural setting for studying these interactions due to their energetic preference for meron tubes in the form of double-twist cylinders. Through a combination of theory and simulation we demonstrate that, for sufficiently strong chirality, defects of $+1/2$ winding will change their winding through the emission of a meron line, and that interactions between the merons and defects dominate over defect-defect interactions. Instead of Peach-Koehler framework, we employ a method based on contact topology - the Gray stability theorem - to directly calculate the velocity field of the material. We apply our framework to point defects as well as disclination lines. Our results have implications not just for chiral materials, but also for other phases with modulated ground states, such as the twist-bend and splay-bend nematics., Comment: 16 pages, 5 figures

Twisted 2D material heterostructures provide an exciting platform for investigating new fundamental physical phenomena. Many of the most interesting behaviours emerge at small twist angles, where the materials reconstruct to form areas of perfectly stacked crystal separated by partial dislocations. However, understanding the properties of these systems is often impossible without correlative imaging of their local reconstructed domain architecture, which exhibits random variations due to disorder and contamination. Here we demonstrate a simple and widely accessible route to visualise domains in as-produced twisted transition metal dichalcogenide (TMD) heterostructures using electron channelling contrast imaging (ECCI) in the scanning electron microscope (SEM). This non-destructive approach is compatible with conventional substrates and allows domains to be visualised even when sealed beneath an encapsulation layer. Complementary theoretical calculations reveal how a combination of elastic and inelastic scattering leads to contrast inversions at specified detector scattering angles and sample tilts. We demonstrate that optimal domain contrast is therefore achieved by maximising signal collection while avoiding contrast inversion conditions.

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered., Comment: 14 pages, 7 figures

In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners. A particular challenge for establishing common ground arises in remote dialogue (occurring in disaster relief or search-and-rescue tasks), where a human and robot are engaged in a joint navigation and exploration task of an unfamiliar environment, but where the robot cannot immediately share high quality visual information due to limited communication constraints. Engaging in a dialogue provides an effective way to communicate, while on-demand or lower-quality visual information can be supplemented for establishing common ground. Within this paradigm, we capture propositional semantics and the illocutionary force of a single utterance within the dialogue through our Dialogue-AMR annotation, an augmentation of Abstract Meaning Representation. We then capture patterns in how different utterances within and across speaker floors relate to one another in our development of a multi-floor Dialogue Structure annotation schema. Finally, we begin to annotate and analyze the ways in which the visual modalities provide contextual information to the dialogue for overcoming disparities in the collaborators' understanding of the environment. We conclude by discussing the use-cases, architectures, and systems we have implemented from our annotations that enable physical robots to autonomously engage with humans in bi-directional dialogue and navigation., Comment: 52 pages, 14 figures

In this paper, we present a novel method for pile-up removal of pp interactions using variational inference with diffusion models, called Vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal. We evaluate the performance of Vipr in a sample of jets from simulated $t\bar{t}$ events overlain with pile-up contamination. Vipr outperforms SoftDrop in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios., Comment: 19 pages, 13 figures

To wield an object means to hold and move it in a way that exploits its functions. When we wield tools -- such as writing with a pen or cutting with scissors -- our hands would reach very specific poses, often drastically different from how we pick up the same objects just to transport them. In this work, we investigate the design of tool-wielding multi-finger robotic hands based on a hypothesis: the poses that a tool and a hand reach during tool-wielding -- what we call "foundational poses" (FPs) -- can be used as a selection mechanism in the design process. We interpret FPs as snapshots that capture the workings of underlying mechanisms formed by the tool and the hand, and one hand can form multiple mechanisms with the same tool. We tested our hypothesis in a hand design experiment, where we developed a sampling-based design optimization framework that uses FPs to computationally generate many different hand designs and evaluate them in multiple metrics. The results show that more than $99\%$ of the $10,785$ generated hand designs successfully wielded tools in simulation, supporting our hypothesis. Meanwhile, our methods provide insights into the non-convex, multi-objective hand design optimization problem that could be hard to unveil otherwise, such as clustering and the Pareto front. Lastly, we demonstrate our methods' real-world feasibility and potential with a hardware prototype equipped with rigid endoskeleton and soft skin.

2D materials research has reached significant scientific milestones, accompanied by a rapidly growing industrial sector in the two decades since the field's inception. Such rapid progress requires pushing past the boundary of what is technically and scientifically feasible and carries the risk of disseminating irreproducible research results. This Expert Recommendation addresses the need for enhanced reproducibility in 2D materials science and physics. Through a comprehensive examination of the factors that affect reproducibility the authors present a set of concrete guidelines designed to improve the reliability of research results. The introduction of a Standardised Template for Experimental Procedures (STEP) offers a novel approach to documenting experimental details that are crucial for replication and troubleshooting. We emphasise the importance of involving stakeholders from research, industry, publishing, funding agencies, and policymaking to foster a culture of transparency, reliability, and trust without blind angles and critical oversights. By addressing systemic issues that hinder reproducibility and presenting actionable steps for improvement, we aim to pave the way for more robust research practices in 2D materials science, contributing to the field's scientific maturation and the subsequent development of beneficial technologies., Comment: 24 pages, 3 textboxes, 1 figure, 2 tables

The topological understanding of nematic liquid crystals is traditionally centered on singularities, or defects, and their classification via homotopy theory. However, this approach has ultimately proved insufficient to properly capture a range of complex behaviours that have been reported in three dimensions. To address this, we argue that a finer understanding of topology is required, in which non-singular but non-trivial topological solitons - so-called merons - play a central role in mediating interactions between disclination lines. We present a comprehensive framework for capturing such behaviour that draws heavily on aspects of Morse theory; the key notion being that merons appear singular under projection onto a two-dimensional surface. This permits the use of singularity theory and dividing curves to characterise nematic textures via tomography, as well as an understanding of topological transitions via surgery theory. We use our ideas to understand and classify complex three-dimensional behaviours, such as the linking, rewiring and crossing of disclination lines, as well as to provide a new perspective on defect charge., Comment: 31 pages, 11 figures

Practice-based teacher education has increasingly been adopted as an alternative to more traditional, conceptually-focused pedagogies, yet the field lacks causal evidence regarding the relative efficacy of these approaches. To address this issue, we randomly assigned 185 college students to one of three experimental conditions reflective of common conceptually-focused and practice-based teacher preparation pedagogies. We find significant and large positive effects of practice-based pedagogies on participants' skills in eliciting and responding to student thinking as demonstrated through a written assessment and a short teaching episode. Our findings contribute to a developing evidence base that can assist policymakers and teacher educators in designing effective teacher preparation at scale.

Morphodynamic descriptions of fluid deformable surfaces are relevant for a range of biological and soft matter phenomena, spanning materials that can be passive or active, as well as ordered or topological. However, a principled, geometric formulation of the correct hydrodynamic equations has remained opaque, with objective rates proving a central, contentious issue. We argue that this is due to a conflation of several important notions that must be disambiguated when describing fluid deformable surfaces. These are the Eulerian and Lagrangian perspectives on fluid motion, and three different types of gauge freedom: in the ambient space; in the parameterisation of the surface, and; in the choice of frame field on the surface. We clarify these ideas, and show that objective rates in fluid deformable surfaces are time derivatives that are invariant under the first of these gauge freedoms, and which also preserve the structure of the ambient metric. The latter condition reduces a potentially infinite number of possible objective rates to only two: the material derivative and the Jaumann rate. The material derivative is invariant under the Galilean group, and therefore applies to velocities, whose rate captures the conservation of momentum. The Jaumann derivative is invariant under all time-dependent isometries, and therefore applies to local order parameters, or symmetry-broken variables, such as the nematic $Q$-tensor. We provide examples of material and Jaumann rates in two different frame fields that are pertinent to the current applications of the fluid mechanics of deformable surfaces., Comment: 27 pages, 4 figures

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward data retrieval and provide deep, contextual insights into patient care. However, they often suffer from discrepancies due to unintuitive EHR system designs and human errors, posing serious risks to patient safety. To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs. EHRCon was crafted in collaboration with healthcare professionals using the MIMIC-III EHR dataset, and includes manual annotations of 4,101 entities across 105 clinical notes checked against database entries for consistency. EHRCon has two versions, one using the original MIMIC-III schema, and another using the OMOP CDM schema, in order to increase its applicability and generalizability. Furthermore, leveraging the capabilities of large language models, we introduce CheckEHR, a novel framework for verifying the consistency between clinical notes and database tables. CheckEHR utilizes an eight-stage process and shows promising results in both few-shot and zero-shot settings. The code is available at https://github.com/dustn1259/EHRCon.

The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide efficient heat extraction. By adopting tools from inventory control, we characterize the structure of these optimal control policies, exploring the impact of different energy cost-rate structures such as time-of-use (TOU) pricing and peak pricing. While classical threshold policies are optimal under TOU costs, introducing peak pricing challenges their optimality, emphasizing the need for carefully designed control strategies in the presence of significant peak costs. We provide theoretical findings and simulation studies on this phenomenon, offering insights for more efficient industrial refrigeration management.

We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimates of the magnet errors. The technique is demonstrated using experimental data from CLARA, an accelerator test facility at Daresbury Laboratory., Comment: 31 pages, 18 figures

"The Appalachian Region: A Data Overview from the 2017-2021 American Community Survey," also known as "The Chartbook," draws from the most recent American Community Survey and comparable Census Population Estimates. The report contains over 300,000 data points about Appalachia's economy, income, employment, education, and other important indicators--all presented at regional, subregional, state, and county levels. Though that data was collected before, and during the initial ten months of, the COVID-19 pandemic, they provide a critical benchmark for comparison when more pandemic and post-pandemic information becomes available. [For the 2016-2020 Chartbook, see ED625962.]

Integer winding disclinations are unstable in a nematic and are removed by an `escape into the third dimension', resulting in a non-singular texture. This process is frustrated in a cholesteric material due to the requirement of maintaining a uniform handedness and instead results in the formation of strings of point defects, as well as complex three-dimensional solitons such as heliknotons that consist of linked dislocations. We give a complete description of this frustration using methods of contact topology. Furthermore, we describe how this frustration can be exploited to stabilise regions of the material where the handedness differs from the preferred handedness. These `twist solitons' are stable in numerical simulation and are a new form of topological defect in cholesteric materials that have not previously been studied., Comment: 18 pages, 9 figures

We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous manipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nearly 400 hand designs, testing grasp quality under external force disturbances. We validate the optimized designs in the real world through teleoperation of pickup and reorient manipulation tasks. Our real world evaluation, from over 900 teleoperated tasks, shows that the trend in design performance in simulation resembles that of the real world. Furthermore, we show that optimized hand designs from our approach outperform existing soft robot hands from prior work in the real world. The results highlight the usefulness of simulation in guiding parameter choices for anthropomorphic soft robotic hand systems, and the effectiveness of our automated design iteration approach, despite the sim-to-real gap.

The ubiquity and energy needs of industrial refrigeration has prompted several research studies investigating various control opportunities for reducing energy demand. This work focuses on one such opportunity, termed compressor sequencing, which entails intelligently selecting the operational state of the compressors to service the required refrigeration load with the least possible work. We first study the static compressor sequencing problem and observe that deriving the optimal compressor operational state is computationally challenging and can vary dramatically based on the refrigeration load. Thus we introduce load shifting in conjunction with compressor sequencing, which entails strategically precooling the facility to allow for more efficient compressor operation. Interestingly, we show that load shifting not only provides benefits in computing the optimal compressor operational state, but also can lead to significant energy savings. Our results are based on and compared to real-world sensor data from an operating industrial refrigeration site of Butterball LLC located in Huntsville, AR, which demonstrated that without load shifting, even optimal compressor operation results in compressors often running at intermediate capacity levels, which can lead to inefficiencies. Through collected data, we demonstrate that a load shifting approach for compressor sequencing has the potential to reduce energy use of the compressors up to 20% compared to optimal sequencing without load shifting.

Hand motion capture data is now relatively easy to obtain, even for complicated grasps; however this data is of limited use without the ability to retarget it onto the hands of a specific character or robot. The target hand may differ dramatically in geometry, number of degrees of freedom (DOFs), or number of fingers. We present a simple, but effective framework capable of kinematically retargeting multiple human hand-object manipulations from a publicly available dataset to a wide assortment of kinematically and morphologically diverse target hands through the exploitation of contact areas. We do so by formulating the retarget operation as a non-isometric shape matching problem and use a combination of both surface contact and marker data to progressively estimate, refine, and fit the final target hand trajectory using inverse kinematics (IK). Foundational to our framework is the introduction of a novel shape matching process, which we show enables predictable and robust transfer of contact data over full manipulations while providing an intuitive means for artists to specify correspondences with relatively few inputs. We validate our framework through thirty demonstrations across five different hand shapes and six motions of different objects. We additionally compare our method against existing hand retargeting approaches. Finally, we demonstrate our method enabling novel capabilities such as object substitution and the ability to visualize the impact of design choices over full trajectories.