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We study the line defect half-indices of 3d $\mathcal{N}=2$ supersymmetric Chern-Simons (CS) theories with (special)unitary, symplectic, orthogonal and exceptional gauge groups. We find that they have several beautiful infinite product $q$-series expressions in terms of Ramanujan's general theta function. For the theories with fundamental chiral multiplets, the pairs of the Neumann half-indices and one-point functions of the fundamental Wilson lines form a basis for the line defect indices in terms of the Rogers-Ramanujan type functions. Furthermore, the theories with an adjoint chiral admit the expressions as the eta-products. In particular, for the $SU(N)_{-2N}$ CS theory, there is a one-to-one correspondence between the BPS boundary local operators and the $N$-core partitions., Comment: 68 pages

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

Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6x of an ordered lattice of equivalent density.

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-causing flow microstate is reached. However, factors that lead to clogging remain elusive. Here we experimentally observe over 50,000 clogging events for a tridisperse mixture of quasi-2D circular grains, and utilize a variety of machine learning (ML) methods to search for predictive signatures of clogging microstates. This approach fares just modestly better than chance. Nevertheless, our analysis using linear Support Vector Machines (SVMs) highlights the position of potential arch cornerstones as a key factor in clogging likelihood. We verify this experimentally by varying the position of a fixed (cornerstone) grain, and show that such a grain dictates the size of feasible flow-ending arches, and thus the time and mass of each flow. Positioning this grain correctly can even increase the ejected mass by over 50%. Our findings demonstrate that interpretable ML algorithms like SVMs can uncover meaningful physics even when their predictive power is below the standards of conventional ML practice., Comment: 16 pages, 11 figures

Future X-ray astrophysics missions will survey large areas of the sky with unparalleled sensitivity, enabled by lightweight, high-resolution optics. These optics inherently produce curved focal surfaces with radii as small as 2 m, requiring a large area detector system that closely conforms to the curved focal surface. We have embarked on a project using a curved charge-coupled device (CCD) detector technology developed at MIT Lincoln Laboratory to provide large-format, curved detectors for such missions, improving performance and simplifying design. We present the current status of this work, which aims to curve back-illuminated, large-format (5 cm x 4 cm) CCDs to 2.5-m radius and confirm X-ray performance. We detail the design of fixtures and the curving process, and present intial results on curving bare silicon samples and monitor devices and characterizing the surface geometric accuracy. The tests meet our accuracy requirement of <5 $\mu$m RMS surface non-conformance for samples of similar thickness to the functional detectors. We finally show X-ray performance measurements of planar CCDs that will serve as a baseline to evaluate the curved detectors. The detectors exhibit low noise, good charge-transfer efficiency, and excellent, uniform spectroscopic performance, including in the important soft X-ray band., Comment: 18 pages, 13 figures, submitted to the Proceedings of SPIE, Astronomical Telescopes + Instrumentation 2024

Landforms such as sand dunes act as roughness elements to Atmospheric Boundary Layer (ABL) flows, triggering the development of new scales of turbulent motions. These turbulent motions, in turn, energize and kick-up sand particles, influencing sediment transport and ultimately the formation and migration of dunes -- with knock on consequences for dust emission. While feedbacks between flow and form have been studied at the scale of dunes, research has not examined how the development of an Internal Boundary Layer (IBL) over the entire dune field influences sediment-transporting turbulence. Here, we deploy large-eddy simulation of an ABL encountering a natural roughness transition: the sand dunes at White Sands National Park, New Mexico. We analyze turbulence producing motions and how they change as the IBL grows over the dune field. Frequency spectrum and Reynolds shear stress profiles show that IBL thickness determines the largest scales of turbulence. More, the developing IBL enhances the frequency, magnitude and duration of sweep and ejection events -- turbulence producing motions whose peaks systematically migrate away from the wall as the IBL thickens. Because sweep and ejection events are known to drive sediment transport, our findings provide a mechanism for coupling the co-evolution of the landscape and the ABL flow over it. More broadly, our results have implications for how roughness transitions influence the transport of pollutants, particulates, heat, and moisture., Comment: 31 pages, 15 figures, 2 tables

The Arcus Probe mission concept has been submitted as an Astrophysics Probe Explorer candidate. It features two co-aligned high-resolution grating spectrometers: one for the soft x-ray band and one for the far UV. Together, these instruments can provide unprecedented performance to address important key questions about the structure and dynamics of our universe across a large range of length scales. The X-ray Spectrometer (XRS) consists of four parallel optical channels, each featuring an x-ray telescope with a fixed array of 216 lightweight, high-efficiency blazed transmission gratings, and two CCD readout arrays. Average spectral resolving power $\lambda/\Delta \lambda > 2,500$ ($\sim 3500$ expected) across the 12-50 \AA \ band and combined effective area $> 350$ cm$^2$ ($> 470$ cm$^2$ expected) near OVII wavelengths are predicted, based on the measured x-ray performance of spectrometer prototypes and detailed ray trace modeling. We describe the optical and structural design of the grating arrays, from the macroscopic grating petals to the nanoscale gratings bars, grating fabrication, alignment, and x-ray testing. Recent x-ray diffraction efficiency results from chemically thinned grating bars are presented and show performance above mission assumptions., Comment: 30 pages, 11 figures, submitted to JATIS

It is shown on the examples of Moore and Gosper curves that two spatially shifted or twisted, pre-asymptotic space-filling curves can produce large-scale superstructures akin to moir\'e patterns. To study physical phenomena emerging from these patterns, a geometrical coupling coefficient based on the Neumann integral is introduced. It is found that moir\'e patterns appear most defined at the peaks of those coefficients. A physical interpretation of these coefficients as a measure for inductive coupling between radiofrequency resonators leads to a design principle for strongly overlapping resonators with vanishing mutual inductance, which might be interesting for applications in MRI. These findings are demonstrated in graphical, numerical, and physical experiments., Comment: 10 pages, 6 figures

Over the past several years, the misidentification of SpaceX Starlink satellites as Unidentified Aerial Phenomena (UAP) by pilots and laypersons has generated unnecessary aviation risk and confusion. The many deployment and orbital evolution strategies, coupled with changing sun specular reflection angles, contribute to this gap in space situational awareness. In this paper we present a case analysis of an incident that generated multiple, corroborating reports of a UAP from five pilots on two commercial airline flights over the Pacific Ocean on August 10th, 2022. This incident included two cell phone photos and a video of an unrecognizable and possibly anomalous phenomenon. We then use supplemental two-line elements (TLEs) for the Starlink train of satellites launched that same day and Automatic Dependent Surveillance Broadcast (ADS-B) data from the flight with the photographs to reconstruct a view of these satellites from the cockpit at the time and place of the sighting. The success of this work demonstrates an approach that could, in principle, warn aviators about satellites that could be visible in unusual or novel illumination configurations, thus increasing space situational awareness and supporting aviation safety. We conclude with recommendations for governments and satellite operators to provide better a-priori data that can be used to create advisories to aviators and the public. The automated simulation of known specular reflection off constellations of satellites could also support researchers investigating sightings of unfamiliar aerial or aerospace objects as likely being from normal versus novel space events., Comment: 35 pages, 10 figures, submitted to the 4th IAA Conference on Space Situational Awareness (ICSSA), May 8-10 2024, Daytona Beach, FL, USA; Abstract number AH-92328

We study the Wilson line defect half-indices of 3d $\mathcal{N}=2$ supersymmetric $SU(N)$ Chern-Simons theories of level $k\le -N$ with Neumann boundary conditions for the gauge fields, together with 2d Fermi multiplets and fundamental 3d chiral multiplets to cancel the gauge anomaly. We derive some exact results and also make some conjectures based on expansions of the $q$-series. We find several interesting connections with special functions known in the literature, including Rogers-Ramanujan functions for which we conjecture integral representations, and the appearance of Appell-Lerch sums for certain Wilson line half-index grand canonical ensembles which reveal an unexpected appearance of mock modular functions. We also find intriguing $q$-difference equations relating half-indices to Wilson line half-indices. Some of these results also have a description in terms of a dual theory with Dirichlet boundary conditions for the vector multiplet in the dual theory., Comment: 51 pages, v3: published version in JHEP

The time-dependent forcing and work per cycle required to drive sinusoidal spinning of a taut-line buzzer is analytically derived, both on and off resonance, from the nonlinear equation of motion. To test predictions, a model experimental system is constructed and characterized in terms of contraction versus twist angle and damped oscillations. The predicted force profile is then approximately implemented by hand. Nearly sinusoidal motion is observed, and the energy injection per cycle needed to maintain steady state oscillations is found to agree with theory. Additional force profiles are implemented, one to maximize non-sinusoidal response and one to maximize the response per operator effort. With the latter, an Aluminum disk of radius 5~cm and height 0.95~cm was spun at a peak speed of over 11,000~RMP for fifteen minutes. The corresponding hand-powered centrifuge system would required $1.5\times$ more force, twice the power, and triple the time in order to run $90\times$ more samples than prior state-of-art.

Equations of motion are developed for the oscillatory rotation of a disk suspended between twisted strings kept under tension by a hanging mass, to which additional forces may be applied. In the absence of forcing, damped harmonic oscillations are observed to decay with an exponential time envelope for two different string types. This is consistent with damping caused by string viscosity, rather than air turbulence, and may be quantified in terms of a quality factor. To test the proposed equation of motion and model for viscous damping within the string, we measure both the natural oscillation frequency and the quality factor for widely varied values of string length, string radius, disk moment of inertia, and hanging mass. The data are found to scale in good accord with predictions. A variation where rotational kinetic energy is converted back and forth to spring potential energy is also discussed., Comment: 7 pages, 3 figures, 1 table

Creep is a generic descriptor of slow motions -- in the context of materials, it describes quasi-static deformation of a solid when subjected to stresses below the global yield, at which all rigidity collapses and the material flows. Here, we experimentally investigate creep, flow, and the transition between the two states in a granular heap flow. Within the surface flowing layer the dimensionless strain rate diminishes with depth, there is an absence of spatial correlations, and there is no aging dynamics. Beneath this layer, the bulk creeps via localized avalanches of plasticity, and there is significant aging. The transition between fast surface flow and slow bulk creep and aging is observed to be in the vicinity of a critical inertial number of $I = 10^{-5}$. Surprisingly, at the cessation of surface flow and the `quenching' of the pile, creep persists in the absence of the flowing layer; albeit with significant differences for a pile that experiences a long duration of surface flow (strongly annealed) and one where flow during preparation does not last long (weakly annealed). Our results contribute to an emerging view of athermal granular creep, showing similarities across dry and submerged systems. Quenched quiescent heaps that creep indefinitely, however, present a challenge to granular rheology, and open new possibilities for interpreting and casting creep and deformation of soils in nature.

We show that cube-connected cycles graphs $CCC_n$ are distance-balanced, and nicely distance-balanced if and only if $n$ is even., Comment: 10 pages, 3 figures

The Kov\'ats Retention index (RI) is a quantity measured using gas chromatography and commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6.

When wet soil becomes fully saturated by intense rainfall, or is shaken by an earthquake, it may fluidize catastrophically. Sand-rich slurries are treated as granular suspensions, where the failure is related to an unjamming transition. Mud flows are modeled as gels, where yielding and shear-thinning behaviors arise from inter-particle attraction and clustering. Here we show that the full range of complex flow behaviors previously reported for natural debris flows can be reproduced with three ingredients: water, silica sand, and kaolin clay. Going from sand-rich to clay-rich suspensions, we observe continuous transition from brittle to ductile yielding. We propose a general constitutive relation for soil suspensions, with a particle rearrangement time that is controlled by yield stress and jamming distance. Our experimental results are supported by models for amorphous solids, suggesting that the paradigm of non-equilibrium phase transitions can help us understand and predict the complex behaviors of Soft Earth suspensions.

Teleoperation of mobile manipulators within a home environment can significantly enhance the independence of individuals with severe motor impairments, allowing them to regain the ability to perform self-care and household tasks. There is a critical need for novel teleoperation interfaces to offer effective alternatives for individuals with impairments who may encounter challenges in using existing interfaces due to physical limitations. In this work, we iterate on one such interface, HAT (Head-Worn Assistive Teleoperation), an inertial-based wearable integrated into any head-worn garment. We evaluate HAT through a 7-day in-home study with Henry Evans, a non-speaking individual with quadriplegia who has participated extensively in assistive robotics studies. We additionally evaluate HAT with a proposed shared control method for mobile manipulators termed Driver Assistance and demonstrate how the interface generalizes to other physical devices and contexts. Our results show that HAT is a strong teleoperation interface across key metrics including efficiency, errors, learning curve, and workload. Code and videos are located on our project website.

Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards -- including erosion and the undermining of urban infrastructure -- consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments., Comment: Published to IEEE OCEANS 2022

This multi-disciplinary case study details how a public web application combines information and game design to visualize effects of user-defined policies intended to reduce plastic waste. Contextualizing this open source software within a broader lineage of digital media research, this user experience exploration outlines potential directions for facilitating conversation between artificial intelligence, scientists, and decision makers during an iterative policy building process. Furthermore, this system dissection reveals how this interactive science effort considers the practicalities of a treaty's shifting priorities and proposals in its designs. Specifically, this historically situated investigation of the tool's approach highlights options for centering human decision making where artificial intelligence helps reason about interventions but does not prescribe them. Finally, analysis summarizes this application's specific game design-inspired mechanics and their efforts to: enable users' agency to explore solution possibilities freely, invite deep engagement with scientific findings, and simultaneously serve multiple audiences with divergent objectives and expertise., Comment: 29 pages of which 8 are citations, 4 figures, latex generated from markdown via Pandoc (https://pandoc.org/) for Arxiv

In X-ray observations of hard state black hole X-ray binaries, rapid variations in accretion disc and coronal power-law emission are correlated and show Fourier-frequency-dependent time lags. On short (~0.1 s) time-scales, these lags are thought to be due to reverberation and therefore may depend strongly on the geometry of the corona. Low-frequency quasi-periodic oscillations (QPOs) are variations in X-ray flux that have been suggested to arise because of geometric changes in the corona, possibly due to General Relativistic Lense-Thirring precession. Therefore one might expect the short-term time lags to vary on the QPO time-scale. We performed novel spectral-timing analyses on NICER observations of the black hole X-ray binary MAXI J1820+070 during the hard state of its outburst in 2018 to investigate how the short-term time lags between a disc-dominated and a coronal power-law-dominated energy band vary on different time-scales. Our method can distinguish between variability due to the QPO and broadband noise, and we find a linear correlation between the power-law flux and lag amplitude that is strongest at the QPO frequency. We also introduce a new method to resolve the QPO signal and determine the QPO-phase-dependence of the flux and lag variations, finding that both are very similar. Our results are consistent with a geometric origin of QPOs, but also provide evidence for a dynamic corona with a geometry varying in a similar way over a broad range of time-scales, not just the QPO time-scale., Comment: 19 pages, 14 figures, 3 tables, published in MNRAS, includes link to reproduction package

Momentum-indirect excitons composed of electrons and holes in different valleys define optoelectronic properties of many semiconductors, but are challenging to detect due to their weak coupling to light. The identification of an excitons' valley character is further limited by complexities associated with momentum-selective probes. Here, we study the photoluminescence of indirect excitons in controllably strained prototypical 2D semiconductors (WSe$_2$, WS$_2$) at cryogenic temperatures. We find that these excitons i) exhibit valley-specific energy shifts, enabling their valley fingerprinting, and ii) hybridize with bright excitons, becoming directly accessible to optical spectroscopy methods. This approach allows us to identify multiple previously inaccessible excitons with wavefunctions residing in K, $\Gamma$, or Q valleys in the momentum space as well as various types of defect-related excitons. Overall, our approach is well-suited to unravel and tune intervalley excitons in various semiconductors., Comment: 9 pages, 4 figures, 1 table

Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) that last for an extended period before flow spontaneously restarts, permanent clogs that last indefinitely, and non-Gaussian, non-monotonic flow-rate statistics. These aspects have been extensively studied independently, but a model of hopper flow that includes all three has not been formulated. Here, we introduce such a phenomenological model that provides a unifying dynamical explanation of all three behaviors: a coupling between the flow rate and a hidden configurational mode that controls the stability of clogs. In the theory, flow rate evolves according to Langevin dynamics with multiplicative noise and an absorbing state at zero flow, conditional on the hidden mode. The model fully reproduces the statistics of pause and clog events taken from a large ($>40,000$ flows) experimental dataset, including non-exponentially distributed clogging times and non-Gaussian flow rate distribution, and explains the stretched-exponential growth of the average clogging time with outlet size. Further, we highlight several microscopic configurational features of this hidden mode, including size and smoothness of the static arch structure formed during pauses and clogs. Our work provides a unifying framework for several poorly understood granular phenomena, and suggests numerous new paths toward further understanding of this complex system., Comment: 7 pages, 4 figures; SM included as an ancillary PDF file

The structure and intensity of turbulence in the atmospheric boundary layer (ABL) drives fluxes of sediment, contaminants, heat, moisture and CO$_2$ at the Earth's surface. Where ABL flows encounter changes in roughness -- such as cities, wind farms, forest canopies and landforms -- a new mesoscopic flow scale is introduced: the internal boundary layer (IBL), which represents a near-bed region of transient flow adjustment that develops over kilometers. This important scale lies within a gap in present observational capabilities of ABL flows, and simplified models fail to capture the sensitive dependence of turbulence on roughness geometry. Here we use large-eddy simulations, run over high-resolution topographic data and validated against field observations, to examine the structure of the ABL across a natural roughness transition: the emergent sand dunes at White Sands National Park. We observe that development of the IBL is triggered by the abrupt transition from smooth playa surface to dunes; however, continuous changes in the size and spacing of dunes over several kilometers influence the downwind patterns of boundary stress and near-bed turbulence. Coherent flow structures grow and merge over the entire $\sim$10-km distance of the dune field, and modulate the influence of large-scale atmospheric turbulence on the bed. Simulated boundary stresses in the developing IBL explain the observed downwind decrease in dune migration, demonstrating a mesoscale coupling between flow and form that governs landscape dynamics. More broadly, our findings demonstrate the importance of resolving both turbulence and realistic roughness for understanding fluid-boundary interactions in environmental flows., Comment: 11 Pages, 4 Figures, 76 References

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here we introduce a nonlinear learning metamaterial -- an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR and nonlinear regression, without a computer. We find our nonlinear learning metamaterial reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning., Comment: 11 pages 8 figures

This paper investigates the impact of assignment to the Accelerating Literacy for Adolescents (ALFA) Lab, an additional semester-long class taken during an elective period, on ninth grade student reading achievement, motivation, and frequency. We conducted a regression discontinuity (RD) study where 1,378 students from diverse high schools in four states over three years were assigned either to the treatment (ALFA, n=540) if their score on a pre-assignment reading measure fell below a threshold cutoff or to business as usual (BAU, n=838) if they scored at or above the cutoff. The study found positive but non-significant effects on reading achievement, motivation, and frequency. Bayesian sensitivity analyses suggest positive effects are more likely than not. We discuss several issues that may have contributed to the failure to detect significant impacts: Student absences, student attrition, and limited instructional time due to the COVID-19 pandemic. [This is the in press version of an article published in "Journal of Research on Educational Effectiveness."]

Motivating today's adolescents to read is a challenge. This article discusses the kinds of literacy instructional practices that leverage what we know about adolescent motivation. It also reviews several evidence-based literacy interventions designed to engage and motivate adolescent readers that can potentially lead to improvements in student reading motivation and academic outcomes. [This paper will be published in "Phi Delta Kappan."]

Coupled learning is a contrastive scheme for tuning the properties of individual elements within a network in order to achieve desired functionality of the system. It takes advantage of physics both to learn using local rules and to "compute" the output response to input data, thus enabling the system to perform decentralized computation without the need for a processor or external memory. We demonstrate a proof-of-concept mechanical network that can learn simple tasks such as self-symmetrizing via iterative tuning of individual spring rest lengths. These mechanical networks could feasibly be scaled and automated to solve increasingly complex tasks, hinting at a new class of smart metamaterials., Comment: 14 pages, 7 figures

As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmentally unsustainable. Neuromorphic computing, or the implementation of ML in hardware, has the potential to reduce this cost. In particular, recent laboratory prototypes of self-learning electronic circuits, examples of ``physical learning machines," open the door to analog hardware that directly employs physics to learn desired functions from examples. In this work, we show that this hardware platform allows for even further reduction of energy consumption by using good initial conditions as well as a new learning algorithm. Using analytical calculations, simulation and experiment, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution--greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving power efficiency given a specific tolerance to error., Comment: 15 pages, 7 figures

The conversion of raw images into quantifiable data can be a major hurdle in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a portion of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg., Comment: 6 Pages 3 Figures

We present the first joint NuSTAR and NICER observations of the ultra-compact X-ray binary (UCXB) 4U 0614+091. This source shows quasi-periodic flux variations on the timescale of ~days. We use reflection modeling techniques to study various components of the accretion system as the flux varies. We find that the flux of the reflected emission and the thermal components representing the disk and the compact object trend closely with the overall flux. However, the flux of the power-law component representing the illuminating X-ray corona scales in the opposite direction, increasing as the total flux decreases. During the lowest flux observation, we see evidence of accretion disk truncation from roughly 6 gravitational radii to 11.5 gravitational radii. This is potentially analogous to the truncation seen in black hole low-mass X-ray binaries, which tends to occur during the low/hard state at sufficiently low Eddington ratios., Comment: 14 Pages, 6 Figures, 6 Tables, Accepted for publication in ApJ