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Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static experiments, it has never been possible to reconstruct a full 3d tomography during a dynamic experiment which cannot be interrupted. In this work, we propose that neural rendering tools can be used to drive the paradigm shift to enable 3d reconstruction during dynamic events. First, we derive theoretical results to support the selection of projections angles. Via a combination of synthetic and experimental data, we demonstrate that neural radiance fields can reconstruct data modalities of interest more efficiently than conventional reconstruction methods. Finally, we develop a spatio-temporal model with spline-based deformation field and demonstrate that such model can reconstruct the spatio-temporal deformation of lattice samples in real-world experiments., Comment: 24 pages, 14 figures. Submitted to NeurIPS 2024 ML4PS. For associated visualizations, see https://neural-xray.github.io/nerfxray

Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related information contained in the shared model remains a challenge. To address this problem, federated unlearning has emerged as a critical research direction, seeking to remove information from globally trained models without harming the model performance on the remaining data. Most modern federated unlearning methods use costly approaches such as the use of remaining clients data to retrain the global model or methods that would require heavy computation on client or server side. We introduce Contribution Dampening (ConDa), a framework that performs efficient unlearning by tracking down the parameters which affect the global model for each client and performs synaptic dampening on the parameters of the global model that have privacy infringing contributions from the forgetting client. Our technique does not require clients data or any kind of retraining and it does not put any computational overhead on either the client or server side. We perform experiments on multiple datasets and demonstrate that ConDa is effective to forget a client's data. In experiments conducted on the MNIST, CIFAR10, and CIFAR100 datasets, ConDa proves to be the fastest federated unlearning method, outperforming the nearest state of the art approach by at least 100x. Our emphasis is on the non-IID Federated Learning setting, which presents the greatest challenge for unlearning. Additionally, we validate ConDa's robustness through backdoor and membership inference attacks. We envision this work as a crucial component for FL in adhering to legal and ethical requirements.

This paper investigates the frequency of transiting planetary systems around metal-polluted white dwarfs using high-cadence photometry from ULTRACAM and ULTRASPEC on the ground, and space-based observations with TESS. Within a sample of 313 metal-polluted white dwarfs with available TESS light curves, two systems known to have irregular transits are blindly recovered by box-least-squares and Lomb-Scargle analyses, with no new detections, yielding a transit fraction of 0.8 (-0.4, +0.6) per cent. Planet detection sensitivities are determined using simulated transit injection and recovery for all light curves, producing upper limit occurrences over radii from dwarf to Kronian planets, with periods from 1 h to 27 d. The dearth of short-period, transiting planets orbiting polluted white dwarfs is consistent with engulfment during the giant phases of stellar evolution, and modestly constrains dynamical re-injection of planets to the shortest orbital periods. Based on simple predictions of transit probability, where (R + Rp)/a ~ 0.01, the findings here are nominally consistent with a model where 100 per cent of polluted white dwarfs have circumstellar debris near the Roche limit; however, the small sample size precludes statistical confidence in this result. Single transits are also ruled out in all light curves using a search for correlated outliers, providing weak constraints on the role of Oort-like comet clouds in white dwarf pollution., Comment: 10 pages, 5 figures and 1 table. Accepted for publication in MNRAS

The companion to PSR J1622-0315, one of the most compact known redback millisecond pulsars, shows extremely low irradiation despite its short orbital period. We model this system to determine the binary parameters, combining optical observations from NTT in 2017 and NOT in 2022 with the binary modeling code ICARUS. We find a best-fit neutron star mass of $2.3 \pm 0.4\,\text{M}_\odot $, and a companion mass of $0.15 \pm 0.02\,\text{M}_\odot$. We detect for the first time low-level irradiation from asymmetry in the minima as well as a change in the asymmetry of the maxima of its light curves over five years. Using star spot models, we find better fits than those from symmetric direct heating models, with consistent orbital parameters. We discuss an alternative scenario where the changing asymmetry is produced by a variable intrabinary shock. In summary, we find that PSR J1622-0315 combines low irradiation with variable light curve asymmetry, and a relatively high neutron star mass., Comment: 18 pages, 11 figures

The nature of the minute-to-hour long Fast X-ray Transients (FXTs) localised by telescopes such as Chandra, Swift, and XMM-Newton remains mysterious, with numerous models suggested for the events. Here, we report multi-wavelength observations of EP240315a, a 1600 s long transient detected by the Einstein Probe, showing it to have a redshift of z=4.859. We measure a low column density of neutral hydrogen, indicating that the event is embedded in a low-density environment, further supported by direct detection of leaking ionising Lyman-continuum. The observed properties are consistent with EP240315a being a long-duration gamma-ray burst, and these observations support an interpretation in which a significant fraction of the FXT population are lower-luminosity examples of similar events. Such transients are detectable at high redshifts by the Einstein Probe and, in the (near) future, out to even larger distances by SVOM, THESEUS, and Athena, providing samples of events into the epoch of reionisation., Comment: 41 pages, 7 figures, submitted

Combining archival photometric observations from multiple large-area surveys spanning the past 17 years, we detect long-term variability in the light curves of ZTFJ032833.52-121945.27 (ZTFJ0328-1219), ZTFJ092311.41+423634.16 (ZTFJ0923+4236) and WD1145+017, all known to exhibit transits from planetary debris. ZTFJ0328-1219 showed an overall fading in brightness from 2011 through to 2015, with a maximum dimming of ~0.3 mag, and still remains ~0.1 mag fainter compared to 2006. We complement the analysis of the long-term behaviour of these systems with high-speed photometry. In the case of ZTFJ0923+4236 and WD1145+017, the time-series photometry exhibits vast variations in the level of transit activity, both in terms of numbers of transits, as well as their shapes and depths, and these variations correlate with the overall brightness of the systems. Inspecting the current known sample of white dwarfs with transiting debris, we estimate that similar photometric signatures may be detectable in one in a few hundred of all white dwarfs. Accounting for the highly aligned geometry required to detect transits, our estimates imply that a substantial fraction of all white dwarfs exhibiting photospheric metal pollution from accreted debris host close-in planetesimals that are currently undergoing disintegration., Comment: MNRAS in press

Additive manufacturing (AM) enables the development of high-performance architected cellular materials, emphasizing the growing importance of establishing programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) AM process on the energy absorption and failure characteristics of thermoplastic lattice materials through multiscale experiments and predictive modelling. Lattices with four distinct unit cell topologies and three varying relative densities are manufactured, and their in-plane mechanical response under quasi-static compression is measured. Macroscale testing and micro-CT imaging reveal relative density-dependent damage mechanisms and failure modes, prompting the development of a robust predictive modelling framework to capture process-induced performance variation and damage. For lower relative density lattices, an FE model based on the extended Drucker-Prager material model, incorporating Bridgman correction with crazing failure criteria, accurately captures the crushing response. As lattice density increases, interfacial damage along bead-bead interfaces becomes predominant, necessitating the enrichment of the model with a microscale cohesive zone model to capture interfacial debonding. All proposed models are validated, highlighting inter-bead damage as the primary factor limiting energy absorption performance in FFF-printed lattices. Finally, the predictive modelling introduces an enhancement factor, providing a straightforward approach to assess the influence of the AM process on energy absorption performance, facilitating the inverse design of FFF-printed lattices. This approach enables a critical evaluation of how FFF processes can be improved to achieve the highest attainable performance and mitigate failures in architected cellular materials.

Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as a \textit{production function}, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at \underline{https://github.com/respai-lab/ecoval}., Comment: KDD-2024

Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural networks (GNNs) as a faster surrogate model compared to traditional methods such as finite element modelling. In this work, we generate a big dataset of structure-property relationships for strut-based lattices. The dataset is made available to the community which can fuel the development of methods anchored in physical principles for the fitting of fourth-order tensors. In addition, we present a higher-order GNN model trained on this dataset. The key features of the model are (i) SE(3) equivariance, and (ii) consistency with the thermodynamic law of conservation of energy. We compare the model to non-equivariant models based on a number of error metrics and demonstrate its benefits in terms of predictive performance and reduced training requirements. Finally, we demonstrate an example application of the model to an architected material design task. The methods which we developed are applicable to fourth-order tensors beyond elasticity such as piezo-optical tensor etc., Comment: International Conference on Learning Representations 2024

The trapped-ion quantum charge-coupled device (QCCD) architecture is a leading candidate for advanced quantum information processing. In current QCCD implementations, imperfect ion transport and anomalous heating can excite ion motion during a calculation. To counteract this, intermediate cooling is necessary to maintain high-fidelity gate performance. Cooling the computational ions sympathetically with ions of another species, a commonly employed strategy, creates a significant runtime bottleneck. Here, we demonstrate a different approach we call exchange cooling. Unlike sympathetic cooling, exchange cooling does not require trapping two different atomic species. The protocol introduces a bank of "coolant" ions which are repeatedly laser cooled. A computational ion can then be cooled by transporting a coolant ion into its proximity. We test this concept experimentally with two $^{40}\mathrm{Ca}^{+}$ ions, executing the necessary transport in 107 $\mathrm{\mu s}$, an order of magnitude faster than typical sympathetic cooling durations. We remove over 96%, and as many as 102(5) quanta, of axial motional energy from the computational ion. We verify that re-cooling the coolant ion does not decohere the computational ion. This approach validates the feasibility of a single-species QCCD processor, capable of fast quantum simulation and computation., Comment: 22 pages, 7 figures; matching publication

White dwarfs, the extremely dense remnants left behind by most stars after their death, are characterised by a mass comparable to that of the Sun compressed into the size of an Earth-like planet. In the resulting strong gravity, heavy elements sink toward the centre and the upper layer of the atmosphere contains only the lightest element present, usually hydrogen or helium. Several mechanisms compete with gravitational settling to change a white dwarf's surface composition as it cools, and the fraction of white dwarfs with helium atmospheres is known to increase by a factor ~2.5 below a temperature of about 30,000 K; therefore, some white dwarfs that appear to have hydrogen-dominated atmospheres above 30,000 K are bound to transition to be helium-dominated as they cool below it. Here we report observations of ZTF J203349.8+322901.1, a transitioning white dwarf with two faces: one side of its atmosphere is dominated by hydrogen and the other one by helium. This peculiar nature is likely caused by the presence of a small magnetic field, which creates an inhomogeneity in temperature, pressure or mixing strength over the surface. ZTF J203349.8+322901.1 might be the most extreme member of a class of magnetic, transitioning white dwarfs -- together with GD 323, a white dwarf that shows similar but much more subtle variations. This new class could help shed light on the physical mechanisms behind white dwarf spectral evolution., Comment: 45 pages, 11 figures

With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models. In this work, we explore unlearning for the regression problem, particularly in deep learning models. Unlearning in classification and simple linear regression has been considerably investigated. However, unlearning in deep regression models largely remains an untouched problem till now. In this work, we introduce deep regression unlearning methods that generalize well and are robust to privacy attacks. We propose the Blindspot unlearning method which uses a novel weight optimization process. A randomly initialized model, partially exposed to the retain samples and a copy of the original model are used together to selectively imprint knowledge about the data that we wish to keep and scrub off the information of the data we wish to forget. We also propose a Gaussian fine tuning method for regression unlearning. The existing unlearning metrics for classification are not directly applicable to regression unlearning. Therefore, we adapt these metrics for the regression setting. We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications. Our methods show excellent performance for all these datasets across all the metrics. Source code: https://github.com/ayu987/deep-regression-unlearning, Comment: Accepted in ICML 2023

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, generative adversarial networks (GANs), and variational auto-encoder (VAEs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. The proposed metric assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for ``high quality'' synthetic data. Being a single score metric and having an implicit bound, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models. We also give a comparative analysis between TabSynDex and existing synthetic tabular data evaluation metrics. This shows the effectiveness and universality of our metric over the existing metrics. Source Code: \url{https://github.com/vikram2000b/tabsyndex}, Comment: IEEE Transactions on Artificial Intelligence

Machine unlearning has become an important area of research due to an increasing need for machine learning (ML) applications to comply with the emerging data privacy regulations. It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch. Recently, several efforts have been put in to make unlearning to be effective and efficient. We propose a novel machine unlearning method by exploring the utility of competent and incompetent teachers in a student-teacher framework to induce forgetfulness. The knowledge from the competent and incompetent teachers is selectively transferred to the student to obtain a model that doesn't contain any information about the forget data. We experimentally show that this method generalizes well, is fast and effective. Furthermore, we introduce the zero retrain forgetting (ZRF) metric to evaluate any unlearning method. Unlike the existing unlearning metrics, the ZRF score does not depend on the availability of the expensive retrained model. This makes it useful for analysis of the unlearned model after deployment as well. We present results of experiments conducted for random subset forgetting and class forgetting on various deep networks and across different application domains.~Source code is at: https://github.com/vikram2000b/bad-teaching-unlearning, Comment: Accepted in AAAI 2023

The present paradigm in design and modelling of lattice architected mechanical metamaterials is mostly limited to traditional numerical methods like finite element analysis. Recently, the use of machine learning and artificial intelligence techniques have become popular and here we extend these ideas to architected metamaterials. We show that truss based lattices have a natural resemblance to computational graphs which serve as an input for the rapidly emerging field of graph neural networks (GNNs). A dataset comprising thousands of such extremely complex lattices is trained using a GNN to predict the underlying dominant deformation mechanism viz. stretching and bending. The trained GNN achieves > 90% accuracy on a previously unseen complex lattice dataset. Such graph-based learning of metamaterials has the capability to predict a range of properties, from elastic moduli to fracture toughness and promises AI driven discovery of emergent metamaterials possessing superlative properties., Comment: 7 pages, 6 figures

Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models. Due to an increasing need for regulatory compliance required for ML applications, machine unlearning is becoming an emerging research problem. The right to be forgotten requests come in the form of removal of a certain set or class of data from the already trained ML model. Practical considerations preclude retraining of the model from scratch after discarding the deleted data. The few existing studies use either the whole training data, or a subset of training data, or some metadata stored during training to update the model weights for unlearning. However, in many cases, no data related to the training process or training samples may be accessible for the unlearning purpose. We therefore ask the question: is it possible to achieve unlearning with zero training samples? In this paper, we introduce the novel problem of zero-shot machine unlearning that caters for the extreme but practical scenario where zero original data samples are available for use. We then propose two novel solutions for zero-shot machine unlearning based on (a) error minimizing-maximizing noise and (b) gated knowledge transfer. These methods remove the information of the forget data from the model while maintaining the model efficacy on the retain data. The zero-shot approach offers good protection against the model inversion attacks and membership inference attacks. We introduce a new evaluation metric, Anamnesis Index (AIN) to effectively measure the quality of the unlearning method. The experiments show promising results for unlearning in deep learning models on benchmark vision data-sets. The source code is available here: https://github.com/ayu987/zero-shot-unlearning, Comment: Accepted in IEEE Transactions on Information Forensics and Security (TIFS)

Dynamic program slicing can significantly reduce the code developers need to inspect by narrowing it down to only a subset of relevant program statements. However, despite an extensive body of research showing its usefulness, dynamic slicing is still short from production-level use due to the high cost of runtime instrumentation. As an alternative, we propose statistical program slicing, a novel hybrid dynamic-static slicing technique that explores the trade-off between accuracy and runtime cost. Our approach relies on modern hardware support for control flow monitoring and a novel, cooperative heap memory tracing mechanism combined with static program analysis for data flow tracking. We evaluate statistical slicing for debugging on 21 failures from 6 widely deployed applications and show it recovers 94% of the program statements on a dynamic slice with only 5% overhead.

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a single or multiple class(es) of data from a ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for a single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, does not put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. Source code: https://github.com/vikram2000b/Fast-Machine-Unlearning, Comment: Accepted in IEEE Transactions on Neural Networks and Learning Systems

A promising progenitor scenario for Type Ia supernovae (SNeIa) is the thermonuclear detonation of a white dwarf in a close binary system with another white dwarf. After the primary star explodes, the surviving donor can be spontaneously released as a hypervelocity runaway. One such runaway donor candidate is LP 398-9, whose orbital trajectory traces back $\approx 10^5$ years to a known supernova remnant. Here we report the discovery of carbon-rich circumstellar material around LP 398-9, revealed by a strong infrared excess and analyzed with follow-up spectroscopy. The circumstellar material is most plausibly composed of inflated layers from the star itself, mechanically and radioactively heated by the past companion's supernova. We also detect a 15.4 hr periodic signal in the UV and optical light curves of LP 398-9, which we interpret as surface rotation. The rotation rate is consistent with theoretical predictions from this supernova mechanism, and the brightness variations could originate from surface inhomogeneity deposited by the supernova itself. Our observations strengthen the case for this double-degenerate SNIa progenitor channel, and motivate the search for more runaway SNIa donors., Comment: Accepted to MNRAS

Fabricating high-performance and/or high-density flexible electronics on plastic substrates is often limited by the poor dimensional stability of polymer substrates. This can be mitigated by using glass carriers during fabrication, but removing the plastic substrate from a large-area carrier without damaging the electronics remains challenging. Here we present a large-area photonic lift-off (PLO) process to rapidly separate polymer films from rigid carriers. PLO uses a 150 microsecond pulse of broadband light from flashlamps to lift off functional thin films from a glass carrier substrate coated with a light-absorber layer (LAL). A 3D finite element model indicates that the polymer/LAL interface reaches 865 degrees C during PLO, but the top surface of the PI reaches only 118 degrees C. To demonstrate the feasibility of this process in the production of flexible electronics, an array of indium zinc oxide (IZO) thin-film transistors (TFTs) was fabricated on a polyimide substrate and then photonically lifted off from the glass carrier. The TFT mobility was 3.15 cm2V-1s-1 before and after PLO, indicating no significant change during PLO. The flexible TFTs were mechanically robust, with no reduction in mobility while bent. The PLO process can offer unmatched high-throughput solutions in large-area flexible electronics production.

Rapid progress in additive manufacturing methods has created a new class of ultralight and strong architected metamaterials that resemble periodic truss structures. The mechanical performance of these metamaterials with a very large number of unit cells is ultimately limited by their tolerance to damage and defects, but an understanding of this sensitivity has remained elusive. Using a stretching-dominated micro-architecture and metamaterial specimens comprising millions of unit-cells we show that not only is the stress intensity factor, as used in conventional elastic fracture mechanics, insufficient to characterize fracture but also that conventional fracture testing protocols are inadequate. Via a combination of numerical calculations and asymptotic analyses, we extend the ideas of fracture mechanics and develop a general test and design protocol for the failure of metamaterials., Comment: 58 pages, 5 figures, Supplementary Information, 4 Supplementary Movies

In part I (arXiv:2006.12393) (accepted in JPP), we simulated individual dynamics of a rising bubble and a falling droplet in an incompressible limit of a strongly coupled dusty plasma (SCDP) using a generalized hydrodynamic viscoelastic fluid model. In interest to understand hetero- (bubble-drop) interactions between them, we extend this study to their combined evolution through two arrangements: First, both are placed side-by-side in a row at the same height. We observe that the overall dynamic governs by the competition between the rotational motion induces due to the pairing between two co-rotating inner vorticity lobes and net vertical motion of two dipolar-vorticities (upward for bubble and downward for droplet) induces due to the gravity. In second arrangement where the vertically aligned bubble (below) and droplet (above) after collision exchange their blobs/lobes and subsequently start to move horizontally in opposite directions away from each other. This horizontal movement gets slower with increasing coupling strength. For the these arrangement, we consider the varying distance between the fixed-size bubble and droplet, and varying coupling strength which represents the nature of our medium. A series of 2D numerical simulations have been conducted to visualize these interactions

Natural hard composites like human bone possess a combination of strength and toughness that exceeds that of their constituents and of many engineered composites. This augmentation is attributed to their complex hierarchical structure, spanning multiple length scales; in bone, characteristic dimensions range from nanoscale fibrils to microscale lamellae to mesoscale osteons and macroscale organs. The mechanical properties of bone have been studied, with the understanding that the isolated microstructure at micro- and nano-scales gives rise to superior strength compared to that of whole tissue, and the tissue possesses an amplified toughness relative to that of its nanoscale constituents. Nanoscale toughening mechanisms of bone are not adequately understood at sample dimensions that allow for isolating salient microstructural features, because of the challenge of performing fracture experiments on small-sized samples. We developed an in-situ three-point bend experimental methodology that probes site-specific fracture behavior of micron-sized specimens of hard material. Using this, we quantify crack initiation and growth toughness of human trabecular bone with sharp fatigue pre-cracks and blunt notches. Our findings indicate that bone with fatigue cracks is two times tougher than that with blunt cracks. In-situ data-correlated electron microscopy videos reveal this behavior arises from crack-bridging by nanoscale fibril structure. The results reveal a transition between fibril-bridging (~1 $\mu$m) and crack deflection/twist (~500 $\mu$m) as a function of length-scale, and quantitatively demonstrate hierarchy-induced toughening in a complex material. This versatile approach enables quantifying the relationship between toughness and microstructure in various complex material systems and provides direct insight for designing biomimetic composites.

Rayleigh-Taylor and Buoyancy-driven instabilities are very common instabilities for an inhomogeneous medium. We examine here how these instabilities grow for incompressible viscoelastic fluids like a strongly coupled dusty plasma by using incompressible generalized hydrodynamic (i-GHD) fluid model. Since the dust particles are pretty massive, the gravitational attraction of earth has a significant role in its dynamics. In this paper the acceleration due to gravity g and the role of strong coupling in the context of gravitationally stratified dusty plasma fluid have been considered. We find that the appearance of elasticity speed up the growth of viscoelastic RT instability by reducing the effect of viscosity at timescales shorter than the Maxwell relaxation time ${\tau_m}$. The buoyancy driven situation with spatially localized (in both dimensions) of low/high density regions placed in a background medium has also been evolved in the presence of gravity. Our studies show that both the instabilities get suppressed with increasing coupling strength of the medium. This suppression has been illustrated analytically as well as by carrying out a two-dimensional fully nonlinear simulation.

Dusty plasma experiments can be performed quite easily in strong coupling regime. In our previous work [Phys. Plasmas 21, 073705 (2014)], we numerically explored such plasmas with constant density and observed the transverse shear (TS) waves from a rotating vortex. Laboratory dusty plasmas are good examples of homogeneous plasmas however heterogeneity (e.g. density, temperature, charge) may be due to the existence of voids, different domains with different orientations, presence of external forces like magnetic and/or electric, size/charge imbalance, etc. Here, we examine how the density heterogeneity in dusty plasmas responds to the circularly rotating vortex monopoles, namely smooth and sharp cut-off. For this purpose we have carried out a series of two-dimensional viscoelastic fluid simulations in the framework of generalized hydrodynamics (GHD) fluid model. The rotating vortices are placed at the interface of two incompressible fluids with different densities. The smooth rotating vortex causes two things: First, the densities are stretched to form the spiral density waves; secondly, the TS waves propagate radially into the surrounding media according to the shear wave speed. We notice that the spiral density arms are distinguishable in the early time while later get smeared out. The sharp rotating vortex creates sharp shear flows which in turn favor the Kelvin-Helmholtz (KH) instability across the interfaces. In such flows for the GHD system, the interplay between the emitted TS waves and the vortices of KH instability distorts the formation of the regular spiral density arms around the rotor

White dwarfs are the burnt out cores of Sun-like stars and are the final fate of 97% of all stars in our Galaxy. The internal structure and composition of white dwarfs are hidden by their high gravities, which causes all elements, apart from the lightest ones, to settle out of their atmospheres. The most direct method to probe the inner structure of stars and white dwarfs in detail is via asteroseismology. Here we present the first known pulsating white dwarf in an eclipsing binary system, enabling us to place extremely precise constraints on the mass and radius of the white dwarf from the light curve, independent of the pulsations. This 0.325M$_\odot$ white dwarf --- one member of SDSS J115219.99+024814.4 --- will serve as a powerful benchmark to constrain empirically the core composition of low-mass stellar remnants and investigate the effects of close binary evolution on the internal structure of white dwarfs., Comment: Accepted for publication in Nature Astronomy

We develop a numerical methodology for the calculation of mode-I R-curves of brittle and elastoplastic lattice materials, and unveil the impact of lattice topology, relative density and constituent material behavior on the toughening response of 2D isotropic lattices. The approach is based on finite element calculations of the J-integral on a single-edge-notch-bend (SENB) specimen, with individual bars modeled as beams having a linear elastic or a power-law elasto-plastic constitutive behavior and a maximum strain-based damage model. Results for three 2D isotropic lattice topologies (triangular, hexagonal and kagome) and two constituent materials (representative of a brittle ceramic (silicon carbide) and a strain hardening elasto-plastic metal (titanium alloy)) are presented. We extract initial fracture toughness and R-curves for all lattices and show that (i) elastic brittle triangular lattices exhibit toughening (rising R-curve), and (ii) elasto-plastic triangular lattices display significant toughening, while elasto-plastic hexagonal lattices fail in a brittle manner. We show that the difference in such failure behavior can be explained by the size of the plastic zone that grows upon crack propagation, and conclude that the nature of crack propagation in lattices (brittle vs ductile) depends both on the constituent material and the lattice architecture. While results are presented for 2D truss-lattices, the proposed approach can be easily applied to 3D truss and shell-lattices, as long as the crack tip lies within the empty space of a unit cell., Comment: 40 pages, 14 figures

Cool subdwarfs are metal-poor low-mass stars that formed during the early stages of the evolution of our Galaxy. Because they are relatively rare in the vicinity of the Sun, we know of few cool subdwarfs in the solar neighbourhood, and none with both the mass and the radius accurately determined. This hampers our understanding of stars at the low-mass end of the main-sequence. Here we report the discovery of SDSSJ235524.29+044855.7 as an eclipsing binary containing a cool subdwarf star, with a white dwarf companion. From the light-curve and the radial-velocity curve of the binary we determine the mass and the radius of the cool subdwarf and we derive its effective temperature and luminosity by analysing its spectral energy distribution. Our results validate the theoretical mass-radius-effective temperature-luminosity relations for low-mass low-metallicity stars., Comment: Accepted

Crack advance from short or long pre-cracks is predicted by the progressive failure of a cohesive zone in a strain gradient, elasto-plastic solid. The presence of strain gradients leads to the existence of an elastic zone at the tip of a stationary crack, for both the long crack and the short crack cases. This is in sharp contrast with previous asymptotic analyses of gradient solids, where elastic strains were neglected. The presence of an elastic singularity at the crack tip generates stresses which are sufficiently high to activate quasi-cleavage. For the long crack case, crack growth resistance curves are predicted for a wide range of ratios of cohesive zone strength to yield strength. Remarkably, this feature of an elastic singularity is preserved for short cracks, leading to a severe reduction in tensile ductility. In qualitative terms, these predictions resemble those of discrete dislocation calculations, including the concept of a dislocation-free zone at the crack tip.

We present 45 ground-based photometric observations of the K2-22 system collected between December 2016 and May 2017, which we use to investigate the evolution of the transit of the disintegrating planet K2-22b. Last observed in early 2015, in these new observations we recover the transit at multiple epochs and measure a typical depth of <1.5%. We find that the distribution of our measured transit depths is comparable to the range of depths measured in observations from 2014 and 2015. These new observations also support ongoing variability in the K2-22b transit shape and time, although the overall shallowness of the transit makes a detailed analysis of these transit parameters difficult. We find no strong evidence of wavelength-dependent transit depths for epochs where we have simultaneous coverage at multiple wavelengths, although our stacked Las Cumbres Observatory data collected over days-to-months timescales are suggestive of a deeper transit at blue wavelengths. We encourage continued high-precision photometric and spectroscopic monitoring of this system in order to further constrain the evolution timescale and to aid comparative studies with the other few known disintegrating planets., Comment: Accepted for publication in the Astronomical Journal

Brown dwarfs and giant planets orbiting close to a host star are subjected to significant irradiation that can modify the properties of their atmospheres. In order to test the atmospheric models that are used to describe these systems, it is necessary to obtain accurate observational estimates of their physical properties (masses, radii, temperatures, albedos). Interacting compact binary systems provide a natural laboratory for studying strongly irradiated sub-stellar objects. As the mass-losing secondary in these systems makes a critical, but poorly understood transition from the stellar to the sub-stellar regime, it is also strongly irradiated by the compact accretor. In fact, the internal and external energy fluxes are both expected to be comparable in these objects, providing access to an unexplored irradiation regime. However, the atmospheric properties of such donors have so far remained largely unknown. Here, we report the direct spectroscopic detection and characterisation of an irradiated sub-stellar donor in an accreting white dwarf binary system. Our near-infrared observations allow us to determine a model-independent mass estimate for the donor of $M_2=0.055\pm0.008M_{\odot}$ and an average spectral type of ${\rm L1}\pm{\rm1}$, supporting both theoretical predictions and model-dependent observational constraints. Our time-resolved data also allow us to estimate the average irradiation-induced temperature difference between the day and night sides on the sub-stellar donor, $\Delta{\rm T}\simeq57$~K, and the maximum difference between the hottest and coolest parts of its surface, of $\Delta{\rm T}_{max}\simeq200$~K. The observations are well described by a simple geometric reprocessing model with a bolometric (Bond) albedo of $A_B<0.54$ at 2-$\sigma$ confidence level, consistent with high reprocessing efficiency, but poor lateral heat redistribution in the donor's atmosphere., Comment: 19 pages, 4 figures, published in Nature 19 May

Multiplex networks (a system of multiple networks that have different types of links but share a common set of nodes) arise naturally in a wide spectrum of fields. Theoretical studies show that in such multiplex networks, correlated edge dynamics between the layers can have a profound effect on dynamical processes. However, how to extract the correlations from real-world systems is an outstanding challenge. Here we provide a null model based on Markov chains to quantify correlations in edge dynamics found in longitudinal data of multiplex networks. We use this approach on two different data sets: the network of trade and alliances between nation states, and the email and co-commit networks between developers of open source software. We establish the existence of "dynamical spillover" showing the correlated formation (or deletion) of edges of different types as the system evolves. The details of the dynamics over time provide insight into potential causal pathways.

We present simultaneous multi-color optical photometry using ULTRACAM of the transiting exoplanet KIC 12557548 b (also known as KIC 1255 b). This reveals, for the first time, the color dependence of the transit depth. Our g and z transits are similar in shape to the average Kepler short-cadence profile, and constitute the highest-quality extant coverage of individual transits. Our Night 1 transit depths are 0.85 +/- 0.04% in z; 1.00 +/- 0.03% in g; and 1.1 +/- 0.3% in u. We employ a residual-permutation method to assess the impact of correlated noise on the depth difference between the z and g bands and calculate the significance of the color dependence at 3.2{\sigma}. The Night 1 depths are consistent with dust extinction as observed in the ISM, but require grain sizes comparable to the largest found in the ISM: 0.25-1{\mu}m. This provides direct evidence in favor of this object being a disrupting low-mass rocky planet, feeding a transiting dust cloud. On the remaining four nights of observations the object was in a rare shallow-transit phase. If the grain size in the transiting dust cloud changes as the transit depth changes, the extinction efficiency is expected to change in a wavelength- and composition-dependent way. Observing a change in the wavelength-dependent transit depth would offer an unprecedented opportunity to determine the composition of the disintegrating rocky body KIC 12557548 b. We detected four out-of-transit u band events consistent with stellar flares., Comment: 6 pages, 4 figures, accepted for publication in ApJL

Zero- to ultra-low-field nuclear magnetic resonance (ZULF NMR) provides a new regime for the measurement of nuclear spin-spin interactions free from effects of large magnetic fields, such as truncation of terms that do not commute with the Zeeman Hamiltonian. One such interaction, the magnetic dipole-dipole coupling, is a valuable source of spatial information in NMR, though many terms are unobservable in high-field NMR, and the coupling averages to zero under isotropic molecular tumbling. Under partial alignment, this information is retained in the form of so-called residual dipolar couplings. We report zero- to ultra-low-field NMR measurements of residual dipolar couplings in acetonitrile-2-$^{13}$C aligned in stretched polyvinyl acetate gels. This represents the first investigation of dipolar couplings as a perturbation on the indirect spin-spin $J$-coupling in the absence of an applied magnetic field. As a consequence of working at zero magnetic field, we observe terms of the dipole-dipole coupling Hamiltonian that are invisible in conventional high-field NMR. This technique expands the capabilities of zero- to ultra-low-field NMR and has potential applications in precision measurement of subtle physical interactions, chemical analysis, and characterization of local mesoscale structure in materials., Comment: 6 pages, 3 figures

We report sensitive detection of the nuclear quadrupolar interaction of the 14N nuclear spin of the nitrogen-vacancy (NV) center using the electron spin echo envelope modulation technique. We applied a weak transverse magnetic field to the spin system so that certain forbidden transitions became weakly allowed due to second-order effects involving the nonsecular terms of the hyperfine interaction. The weak transitions cause modulation of the electron spin-echo signal, and a theoretical analysis suggests that the modulation frequency is primarily determined by the nuclear quadrupolar frequency; numerical simulations confirm the analytical results and show excellent quantitative agreement with experiments. This is an experimentally simple method of detecting quadrupolar interactions, and it can be used to study spin systems with an energy structure similar to that of the nitrogen vacancy center., Comment: 18 pages, 4 figures

Optically hyperpolarized $^{129}$Xe gas has become a powerful contrast agent in nuclear magnetic resonance (NMR) spectroscopy and imaging, with applications ranging from studies of the human lung to the targeted detection of biomolecules. Equally attractive is its potential use to enhance the sensitivity of microfluidic NMR experiments, in which small sample volumes yield poor sensitivity. Unfortunately, most $^{129}$Xe polarization systems are large and non-portable. Here we present a microfabricated chip that optically polarizes $^{129}$Xe gas. We have achieved $^{129}$Xe polarizations greater than 0.5$\%$ at flow rates of several microliters per second, compatible with typical microfluidic applications. We employ in situ optical magnetometry to sensitively detect and characterize the $^{129}$Xe polarization at magnetic fields of 1 $\mu$T. We construct the device using standard microfabrication techniques, which will facilitate its integration with existing microfluidic platforms. This device may enable the implementation of highly sensitive $^{129}$Xe NMR in compact, low-cost, portable devices.

The application of magnetic resonance (MR) spectroscopy at progressively smaller length scales may eventually permit "chemical imaging" of spins at the surfaces of materials and biological complexes. In particular, the negatively charged nitrogen-vacancy (NV-) centre in diamond has been exploited as an optical transducer for nanoscale nuclear magnetic resonance. However, the spectra of detected spins are generally broadened by their interaction with proximate paramagnetic NV- centres through coherent and incoherent mechanisms. Here we demonstrate a detection technique that can resolve the spectra of electron spins coupled to NV- centres, namely substitutional nitrogen (NS) and neutral nitrogen-vacancy (NV0) centres in diamond, through optically detected cross-relaxation. The hyperfine spectra of these spins are a unique chemical identifier, suggesting the possibility, in combination with recent results in diamonds harbouring shallow NV- implants, that the spectra of spins external to the diamond can be similarly detected., Comment: 29 pages, 7 figures

We demonstrate that the spin decoherence of nitrogen vacancy (NV) centers in diamond can be suppressed by a transverse magnetic field if the electron spin bath is the primary decoherence source. The NV spin coherence, created in "a decoherence-free subspace" is protected by the transverse component of the zero-field splitting, increasing the spin-coherence time about twofold. The decoherence due to the electron spin bath is also suppressed at magnetic fields stronger than ~25 gauss when applied parallel to the NV symmetry axis. Our method can be used to extend the spin-coherence time of similar spin systems for applications in quantum computing, field sensing, and other metrologies., Comment: 20 pages, 4 figures

Low mass white dwarfs are the remnants of disrupted red giant stars in binary millisecond pulsars and other exotic binary star systems. Some low mass white dwarfs cool rapidly, while others stay bright for millions of years due to stable fusion in thick surface hydrogen layers. This dichotomy is not well understood so their potential use as independent clocks to test the spin-down ages of pulsars or as probes of the extreme environments in which low mass white dwarfs form cannot be fully exploited. Here we present precise mass and radius measurements for the precursor to a low mass white dwarf. We find that only models in which this star has a thick hydrogen envelope can match the strong constraints provided by our new observations. Very cool low mass white dwarfs must therefore have lost their thick hydrogen envelopes by irradiation from pulsar companions or by episodes of unstable hydrogen fusion (shell flashes). We also find that this low mass white dwarf precursor is a new type of pulsating star. The observed pulsation frequencies are sensitive to internal processes that determine whether this star will undergo shell flashes., Comment: 15 pages, 3 figures. Published in Nature

Dynamic nuclear polarisation, which transfers the spin polarisation of electrons to nuclei, is routinely applied to enhance the sensitivity of nuclear magnetic resonance; it is also critical in spintronics, particularly when spin hyperpolarisation can be produced and controlled optically or electrically. Here we show the complete polarisation of nuclei located near the optically-polarised nitrogen-vacancy (NV) centre in diamond. When approaching the ground-state level anti-crossing condition of the NV electron spins, 13C nuclei in the first-shell are polarised in a pattern that depends sensitively and sharply upon the magnetic field. Based on the anisotropy of the hyperfine coupling and of the optical polarisation mechanism, we predict and observe a complete reversal of the nuclear spin polarisation with a few-mT change in the magnetic field. The demonstrated sensitive magnetic control of nuclear polarisation at room temperature will be useful for sensitivity-enhanced NMR, nuclear-based spintronics, and quantum computation in diamond.

We operate a nitrogen vacancy (NV-) diamond magnetometer at ambient temperatures and study the dependence of its bandwidth on experimental parameters including optical and microwave excitation powers. We introduce an analytical theory that yields an explicit formula for the response of an ensemble of NV- spins to an oscillating magnetic field, such as in NMR applications. We measure a detection bandwidth of 1.6 MHz and a sensitivity of 4.6 nT/Hz^(1/2), unprecedented in a detector with this active volume and close to the photon shot noise limit of our experiment., Comment: 4 pages; 4 figures; supporting information

Entanglement within qubits are studied for the subspace of definite particle states or definite number of up spins. A transition from an algebraic decay of entanglement within two qubits with the total number $N$ of qubits, to an exponential one when the number of particles is increased from two to three is studied in detail. In particular the probability that the concurrence is non-zero is calculated using statistical methods and shown to agree with numerical simulations. Further entanglement within a block of $m$ qubits is studied using the log-negativity measure which indicates that a transition from algebraic to exponential decay occurs when the number of particles exceeds $m$. Several algebraic exponents for the decay of the log-negativity are analytically calculated. The transition is shown to be possibly connected with the changes in the density of states of the reduced density matrix, which has a divergence at the zero eigenvalue when the entanglement decays algebraically., Comment: Substantially added content (now 24 pages, 5 figures) with a discussion of the possible mechanism for the transition. One additional author in this version that is accepted for publication in Phys. Rev. A

Collective phenomena with universal properties have been observed in many complex systems with a large number of components. Here we present a microscopic model of the emergence of scaling behavior in such systems, where the interaction dynamics between individual components is mediated by a global variable making the mean-field description exact. Using the example of financial markets, we show that asset price can be such a global variable with the critical role of coordinating the actions of agents who are otherwise independent. The resulting model accurately reproduces empirical properties such as the universal scaling of the price fluctuation and volume distributions, long-range correlations in volatility and multiscaling., Comment: 5 pages, 3 figures

Results from spectroscopic observations of the Intermediate Polar (IP) EX Hya in quiescence during 1991 and 2001 are presented. Spin-modulated radial velocities consistent with an outer disc origin were detected for the first time in an IP. The spin pulsation was modulated with velocities near ~500-600 km/s. These velocities are consistent with those of material circulating at the outer edge of the accretion disc, suggesting corotation of the accretion curtain with material near the Roche lobe radius. Furthermore, spin Doppler tomograms have revealed evidence of the accretion curtain emission extending from velocities of ~500 km/s to ~1000 km/s. These findings have confirmed the theoretical model predictions of King & Wynn (1999), Belle et al. (2002) and Norton et al. (2004) for EX Hya, which predict large accretion curtains that extend to a distance close to the Roche lobe radius in this system. Evidence for overflow stream of material falling onto the magnetosphere was observed, confirming the result of Belle et al. (2005) that disc overflow in EX Hya is present during quiescence as well as outburst. It appears that the hbeta and hgamma spin radial velocities originated from the rotation of the funnel at the outer disc edge, while those of halpha were produced due to the flow of material along the field lines far from the white dwarf (narrow component) and close to the white dwarf (broad-base component), in agreement with the accretion curtain model., Comment: 10 pages, 11 figures (figures 3, 4, 7 and 8 at reduced resolution, originals available on request). Accepted for publication in Monthly Notices of the Royal Astronomical Society