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Identifying individual tissues, so-called tissue segmentation, in diabetic foot ulcer (DFU) images is a challenging task and little work has been published, largely due to the limited availability of a clinical image dataset. To address this gap, we have created a DFUTissue dataset for the research community to evaluate wound tissue segmentation algorithms. The dataset contains 110 images with tissues labeled by wound experts and 600 unlabeled images. Additionally, we conducted a pilot study on segmenting wound characteristics including fibrin, granulation, and callus using deep learning. Due to the limited amount of annotated data, our framework consists of both supervised learning (SL) and semi-supervised learning (SSL) phases. In the SL phase, we propose a hybrid model featuring a Mix Transformer (MiT-b3) in the encoder and a CNN in the decoder, enhanced by the integration of a parallel spatial and channel squeeze-and-excitation (P-scSE) module known for its efficacy in improving boundary accuracy. The SSL phase employs a pseudo-labeling-based approach, iteratively identifying and incorporating valuable unlabeled images to enhance overall segmentation performance. Comparative evaluations with state-of-the-art methods are conducted for both SL and SSL phases. The SL achieves a Dice Similarity Coefficient (DSC) of 84.89%, which has been improved to 87.64% in the SSL phase. Furthermore, the results are benchmarked against two widely used SSL approaches: Generative Adversarial Networks and Cross-Consistency Training. Additionally, our hybrid model outperforms the state-of-the-art methods with a 92.99% DSC in performing binary segmentation of DFU wound areas when tested on the Chronic Wound dataset. Codes and data are available at https://github.com/uwm-bigdata/DFUTissueSegNet.

Incorporating scientific knowledge into deep learning (DL) models for materials-based simulations can constrain the network's predictions to be within the boundaries of the material system. Altering loss functions or adding physics-based regularization (PBR) terms to reflect material properties informs a network about the physical constraints the simulation should obey. The training and tuning process of a DL network greatly affects the quality of the model, but how this process differs when using physics-based loss functions or regularization terms is not commonly discussed. In this manuscript, several PBR methods are implemented to enforce stress equilibrium on a network predicting the stress fields of a high elastic contrast composite. Models with PBR enforced the equilibrium constraint more accurately than a model without PBR, and the stress equilibrium converged more quickly. More importantly, it was observed that independently fine-tuning each implementation resulted in more accurate models. More specifically, each loss formulation and dataset required different learning rates and loss weights for the best performance. This result has important implications on assessing the relative effectiveness of different DL models and highlights important considerations when making a comparison between DL methods.

Spin-squeezing in systems with single-particle control is a well-established resource of modern quantum technology. Applied in an optical lattice clock can reduce the statistical uncertainty of spectroscopic measurements. Here, we consider dynamic generation of spin-squeezing with ultra-cold bosonic atoms with two internal states loaded into an optical lattice in the strongly interacting regime as realized with state-of-the-art experiments using a quantum gas microscope. We show that anisotropic interactions and inhomogeneous magnetic fields generate scalable spin-squeezing if their magnitudes are sufficiently small, but not negligible. The effect of non-uniform filling caused by hole doping, non-zero temperature and external confinement is studied at a microscopic level demonstrating their limiting role in the dynamics and scaling of spin squeezing., Comment: 17 pages, 10 figures

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79% to 100% for Region of Interest (ROI) without location classifications, 73.98% to 100% for ROI with location classifications, and 78.10% to 100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Autonomous experimentation systems have been used to greatly advance the integrated computational materials engineering (ICME) paradigm. This paper outlines a framework that enables the design and selection of data collection workflows for autonomous experimentation systems. The framework first searches for data collection workflows that generate high-quality information and then selects the workflow that generates the \emph{best, highest-value} information as per a user-defined objective. We employ this framework to select the \emph{user-defined best} high-throughput workflow for material characterization on an additively manufactured Ti-6Al-4V sample for the purposes of outlining a basic materials characterization scenario, reducing the collection time of backscattered electron scanning electron scanning electron microscopy images by a factor of 5 times as compared to the benchmark workflow for the case study presented, and by a factor of 85 times as compared to the workflow used in the previously published study., Comment: Corrected author emails

The classification of wound severity is a critical step in wound diagnosis. An effective classifier can help wound professionals categorize wound conditions more quickly and affordably, allowing them to choose the best treatment option. This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red. The green class denotes wounds still in the early stages of healing and are most likely to recover with adequate care. Wounds in the yellow category require more attention and treatment than those in the green category. Finally, the red class denotes the most severe wounds that require prompt attention and treatment. A dataset containing different types of wound images is designed with the help of wound specialists. Nine deep learning models are used with applying the concept of transfer learning. Several stacked models are also developed by concatenating these transfer learning models. The maximum accuracy achieved on multi-class classification is 68.49%. In addition, we achieved 78.79%, 81.40%, and 77.57% accuracies on green vs. yellow, green vs. red, and yellow vs. red classifications for binary classifications., Comment: 19 pages, 5 figures, 5 tables

In this work, we study temperature sensing with finite-sized strongly correlated systems exhibiting quantum phase transitions. We use the quantum Fisher information (QFI) approach to quantify the sensitivity in the temperature estimation, and apply a finite-size scaling framework to link this sensitivity to critical exponents of the system around critical points. We numerically calculate the QFI around the critical points for two experimentally-realizable systems: the spin-1 Bose-Einstein condensate and the spin-chain Heisenberg XX model in the presence of an external magnetic field. Our results confirm finite-size scaling properties of the QFI. Furthermore, we discuss experimentally-accessible observables that (nearly) saturate the QFI at the critical points for these two systems.

Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electronic Medical Records (EMR) of patients is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using very limited information collected during routine care in a specialized wound care facility. The generated prognosis variables are used in developing a predictive model for chronic wound healing trajectory. Our novel medical GAN can produce both continuous and categorical features from EMR. Moreover, we applied temporal information to our model by considering data collected from the weekly follow-ups of patients. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing. The ability of the proposed model to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application. Using the generated samples in training predictive models improved the classification accuracy by 6.66-10.01% compared to the previous EMR-GAN. Additionally, the suggested prognosis classifier has achieved the area under the curve (AUC) of 0.975, 0.968, and 0.849 when training the network using data from the first three visits, first two visits, and first visit, respectively. These results indicate a significant improvement in wound healing prediction compared to the previous prognosis models., Comment: 20 pages, 5 figures, 5 tables

Acute and chronic wounds with varying etiologies burden the healthcare systems economically. The advanced wound care market is estimated to reach $22 billion by 2024. Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise. Recently automatic wound segmentation methods based on deep learning have shown promising performance but require large datasets for training and it is unclear which methods perform better. To address these issues, we propose the Foot Ulcer Segmentation challenge (FUSeg) organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). We built a wound image dataset containing 1,210 foot ulcer images collected over 2 years from 889 patients. It is pixel-wise annotated by wound care experts and split into a training set with 1010 images and a testing set with 200 images for evaluation. Teams around the world developed automated methods to predict wound segmentations on the testing set of which annotations were kept private. The predictions were evaluated and ranked based on the average Dice coefficient. The FUSeg challenge remains an open challenge as a benchmark for wound segmentation after the conference.

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map is also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information are designed with the help of wound specialists. The multi-modal network is developed by concatenating the image-based and location-based classifier's outputs with some other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 77.33% to 100% on different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95% to 98.08% on different experiments. The proposed multi-modal network also shows a significant improvement in results from the previous works of literature., Comment: 30 pages, 10 figures, 15 tables

Millions of people are affected by acute and chronic wounds yearly across the world. Continuous wound monitoring is important for wound specialists to allow more accurate diagnosis and optimization of management protocols. Machine Learning-based classification approaches provide optimal care strategies resulting in more reliable outcomes, cost savings, healing time reduction, and improved patient satisfaction. In this study, we use a deep learning-based method to classify burn wound images into two or three different categories based on the wound conditions. A pre-trained deep convolutional neural network, AlexNet, is fine-tuned using a burn wound image dataset and utilized as the classifier. The classifier's performance is evaluated using classification metrics such as accuracy, precision, and recall as well as confusion matrix. A comparison with previous works that used the same dataset showed that our designed classifier improved the classification accuracy by more than 8%.

Polycrystalline materials, such as metals, are comprised of heterogeneously oriented crystals. Observed crystal orientations are modelled as a sample from an orientation distribution function (ODF), which determines a variety of material properties and is therefore of great interest to practitioners. Observations consist of quaternions, 4-dimensional unit vectors reflecting both orientation and rotation of a single crystal. Thus, an ODF must account for known crystal symmetries as well as satisfy the unit length constraint. A popular method for estimating ODFs non-parametrically is symmetrized kernel density estimation. However, disadvantages of this approach include difficulty in interpreting results quantitatively, as well as in quantifying uncertainty in the ODF. We propose to use a mixture of symmetric Bingham distributions as a flexible parametric ODF model, inferring the number of mixture components, the mixture weights, and scale and location parameters based on crystal orientation data. Furthermore, our Bayesian approach allows for structured uncertainty quantification of the parameters of interest. We discuss details of the sampling methodology and conclude with analyses of various orientation datasets, interpretations of parameters of interest, and comparison with kernel density estimation methods.

We demonstrate that a many-body nonlocality is a resource for ultra-precise metrology. This result is achieved by linking the sensitivity of a quantum sensor with a combination of many-body correlation functions that witness the nonlocality. We illustrate our findings with some prominent examples---a collection of spins forming an Ising chain and a gas of ultra-cold atoms in any two-mode configuration., Comment: 7 pages, 3 figures

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists the specialists in the field to classify the wounds with less financial and time costs. Different machine learning and deep learning-based wound classification methods have been proposed in the literature. In this study, we have developed an ensemble Deep Convolutional Neural Network-based classifier to classify wound images including surgical, diabetic, and venous ulcers, into multi-classes. The output classification scores of two classifiers (patch-wise and image-wise) are fed into a Multi-Layer Perceptron to provide a superior classification performance. A 5-fold cross-validation approach is used to evaluate the proposed method. We obtained maximum and average classification accuracy values of 96.4% and 94.28% for binary and 91.9\% and 87.7\% for 3-class classification problems. The results show that our proposed method can be used effectively as a decision support system in classification of wound images or other related clinical applications.

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. Particularly, MobileNetV2 stands out among others due to its lightweight architecture and uncompromised performance. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. We build an annotated wound image dataset consisting of 1,109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks.

Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assessment. Specifically, we provide an extensive review of research methods on wound measurement (segmentation) and wound diagnosis (classification). We also reviewed recent work on wound assessment systems (including hardware, software, and mobile apps). More than 250 articles were retrieved from various publication databases and online resources, and 115 of them were carefully selected to cover the breadth and depth of most recent and relevant work to convey the current review to its fulfillment., Comment: 18 pages, 9 tables, 1 figure

We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system. The wound localizer has been developed by using YOLOv3 model, which is then turned into an iOS mobile application. The developed localizer can detect the wound and its surrounding tissues and isolate the localized wounded region from images, which would be very helpful for future processing such as wound segmentation and classification due to the removal of unnecessary regions from wound images. For Mobile App development with video processing, a lighter version of YOLOv3 named tiny-YOLOv3 has been used. The model is trained and tested on our own image dataset in collaboration with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is compared with SSD model, showing that YOLOv3 gives a mAP value of 93.9%, which is much better than the SSD model (86.4%). The robustness and reliability of these models are also tested on a publicly available dataset named Medetec and shows a very good performance as well., Comment: 8 pages, 5 figures, 1 table

A scheme for dynamical stabilization of entanglement quantified by the quantum Fisher information is analyzed numerically and analytically for bimodal and spin-1 Bose-Einstein condensates in the context of atomic interferometry. The scheme consists of twisting dynamics followed by a single rotation of a state which limits further evolution around stable center fixed points in the mean-field phase space. The resulting level of entanglement is of the order or larger than at the moment of rotation. It is demonstrated that the readout measurement of parity quantifies the level of entanglement during entire evolution., Comment: 16 pages, 10 figures, 1 video file

We consider a hybrid system of matter and light as a sensing device and quantify the role of cooperative effects. The latter generically enhance the precision with which modifications of the effective light-matter coupling constant can be measured. In particular, considering a fundamental model of $N$ qubits coupled to a single electromagnetic mode, we show that the ultimate bound for the precision shows double-Heisenberg scaling: $\Delta\theta\propto1/(Nn)$, with $N$ and $n$ being the number of qubits and photons, respectively. Moreover, even using classical states and measuring only one subsystem, a Heisenberg-times-shot-noise scaling, i.e. $1/(N\sqrt{n})$ or $1/(n\sqrt{N})$, is reached. As an application, we show that a Bose-Einstein condensate trapped in a double-well potential within an optical cavity can detect the gravitational acceleration $g$ with the relative precision of $\Delta g/g\simeq10^{-9}\text{Hz}^{-1/2}$. The analytical approach presented in this study takes into account the leakage of photons through the cavity mirrors, and allows to determine the sensitivity when $g$ is inferred via measurements on atoms or photons., Comment: 13 pages, 3 figures

The origin of cube recrystallization texture in medium to high stacking-fault energy fcc metals has been debated for almost 70 years. Despite numerous experimental and simulation studies, many issues regarding the nucleation and growth of cube grains remain unresolved. Here we apply a full field crystal plasticity model utilizing a dislocation density based constitutive theory to study the deformation and texture evolution in copper (Cu) under plane strain compression. Additionally, we use the phase field method, along with a stochastic nucleation model, for static recrystallization simulations. Simulation results show that the volume fraction of the cube component during deformation decreases with increasing strain. Although cube grains are not stable during plane strain compression, some of the non-cube grains rotate towards cube and develop narrow cube bands near the grain boundary region. With increasing deformation, the cube component accumulates dislocation density faster than other texture components. High stored energy in the cube regions leads to preferential nucleation of cube grains during static recrystallization. These cube nuclei originate from the intergranular cube bands. Although the cube component has a clear nucleation advantage, none of the texture component appears to have a growth advantage. Instead, simulation results show that heterogeneous distribution of nuclei has a profound influence on the resulting grain size distribution. During recrystallization, a significant increase in cube volume fraction is observed mainly due to high nucleation frequency of cube grains.

The growth in the demand for precisely crafted many-body systems of spin-$1/2$ particles/qubits is due to their top-notch versatility in application-oriented quantum-enhanced protocols and the fundamental tests of quantum theory. Here we address the question: how quantum is a chain of spins? We demonstrate that a single element of the density matrix carries the answer. Properly analyzed it brings information about the extent of the many-body entanglement and the non-locality. This method can be used to tailor and witness highly non-classical effects in many-body systems with possible applications to quantum computing, ultra-precise metrology or large-scale tests of quantum mechanics. As a proof of principle, we investigate the extend of non-locality and entanglement in ground states and thermal states of experimentally accessible spin chains., Comment: 9 pages, 6 figures

We show that matter-wave interference fringes formed by two overlapping atomic clouds can yield information about the non-local Bell correlations. To this end, we consider a simple atomic interferometer, where the clouds are released from the double-well potential and the relative phase is estimated from the density fit to this interference pattern. The Bell correlations can be deduced from the sensitivity of the phase obtained in this way. We examine the relation between these two quantities for a wide range of ground states of the double-well, scanning through the attractive and the repulsive interactions. The presented analysis includes the effects of finite temperature, when excited states are thermally occupied. We also consider the impact of the spatial resolution of the single-atom detectors and the fluctuations of the energy mismatch between the wells. These results establish a link between the fundamental (non-locality) and application-oriented (quantum metrology) aspects of entanglement., Comment: 5 pages, 2 figures

Implementation of the quantum interferometry concept to spin-1 atomic Bose-Einstein condensates is analyzed by employing a polar state evolved in time. In order to identify the best interferometric configurations, the quantum Fisher information is maximized. Three optimal configurations are identified, among which one was not reported in the literature yet, although it gives the highest value of the quantum Fisher information in experimentally achievable short time dynamics. Details of the most optimal configurations are investigated based on the error-propagation formula which includes the interaction-based readout protocol to reduce the destructive effect of detection noise. In order to obtain Heisenberg scaling accessible by present day experimental techniques, an efficient measurement and a method for the inversion of dynamics were developed, as necessary for the protocol's implementation., Comment: 26 pages, 10 figures

The forward transformation from face centered cubic austenite to body centered cubic/tetragonal martensite in ferrous alloys can significantly in- fluence the microstructure and mechanical properties of the material. Inferring possible high temperature crystal orientations from observations of ambient temperature transformation microstructures is hindered by parent austenite- twin interactions and scatter in the orientation relationship. This creates a major limitation for studying variant selection phenomena and characteriz- ing microstructural response to high temperature thermomechanical process- ing conditions. In this work, composition tables are developed that detail the product variant boundary misorientation relationships for parent-parent, parent-twin and twin-twin boundary intersections for the Kurdjumov-Sachs (KS), Nishiyama-Wassermann (NW), and an experimentally determined irra- tional orientation relationship. The frequently referenced KS and NW ori- entation relationships produce significantly different results from experimental observations. Furthermore, the introduction of a twin into the parent austenite introduces a substantially larger number of misorientation relationships when the orientation relationship is irrational. The effects of crystal symmetry on misorientation results are determined by considering both body centered cu- bic and body centered tetragonal martensite structures. Lastly, it is observed that some shared variants are found between twins and parents when assuming cubic symmetry but not tetragonal symmetry. The results and relationships may be useful towards accurate and consistent reconstructions of the parent austenite microstructure from observations of martensite., Comment: Preprint submitted to Metallurgical and Materials Transactions A

Under high pressure, Zr undergoes a transformation from its ambient equilibrium hexagonal close packed $\alpha$ phase to a simple hexagonal $\omega$ phase. Subsequent unloading to ambient conditions does not see a full reversal to the $\alpha$ phase, but rather a retainment of significant $\omega$. Previously, the thermal stability of the $\omega$ phase was investigated via in-situ synchrotron X-ray diffraction analysis of the isothermal annealing of Zr samples shocked to 8 and 10.5 GPa at temperatures 443, 463, 483, and 503 K. The phase volume fractions were tracked quantitatively and the dislocation densities were tracked semi-quantitatively. Trends included a rapid initial (transient) transformation rate from $\omega\to\alpha$ followed by a plateau to a new metastable state with lesser retained $\omega$ (asymptotic). A significant reduction in dislocation densities in the $\omega$ phase was observed prior to initiation of an earnest reverse transformation, leading to the hypothesis that the $\omega\to\alpha$ transformation from is being hindered by defects in the $\omega$ phase. As a continuation of this work, we present a temperature dependent model that couples the removal of dislocations in the $\omega$ phase and the reverse transformation via a barrier energy that is associated with the free energy of remaining dislocations. The reduction of dislocations in the $\omega$ phase occur as a sum of glide and climb controlled processes, both of which dictate the transient and asymptotic behavior of the annealing process respectively., Comment: 26 pages, 11 figures

We consider general three-mode interferometers using a spin-1 atomic Bose-Einstein condensate with macroscopic magnetization. We show that these interferometers, combined with the measurement of the number of particles in each output port, provide an ultra-high phase sensitivity. We construct effective two-mode interferometers which involve two Zeeman modes showing that they also provide an ultra-high phase sensitivity but of a bit reduced factor in the corresponding Fisher information. A special case of zero magnetization is shown to persist the efficiency of the two-mode interferometry., Comment: 13 pages, 6 figures

The quaternion Bingham distribution has been used to model preferred crystallographic orientation, or crystallographic texture, in polycrystalline materials in the materials science and geological communities. A primary difficulty in applying the Bingham distribution has been the lack of an efficient method for fitting the distribution parameters with respect to the materials underlying crystallographic symmetry or any statistical sample symmetry due to processing. In this paper we present a symmetrized distribution, based on the quaternion Bingham, which can account for any general combination of crystallographic or sample symmetries. We also introduce a numerical scheme for estimating the parameters of the symmetrized distribution based on the well known expectation maximization algorithm., Comment: Accepted 4/2016 as Short Communication to Journal of Applied Crystallography

We study the thermally activated, slow conversion of the hysteretically retained $\omega$ phase into stable $\alpha$ phase in recovered samples of shocked zirconium. The $\omega$-phase decays in time following an algebraic law, unlike the predictions of the nucleation-growth framework for first order transitions, and residual volume fractions of phases and dislocation densities are related by a power law. We propose an explanation for the annealing mechanism through coupled dynamics of dislocations and phase change. We find that the long-time behavior is controlled by the interplay of dislocations, shear fluctuations, and remnant volume fractions of phases, which lead to an algebraic decay in time. For late time, thermally activated quantities such as the dislocation mobility and nucleation rate set the timescale and control the algebraic behavior, respectively. At high enough temperatures this behavior is effectively indistinguishable from standard Avrami kinetics., Comment: 6 pages, 3 figures

Many time-dependent deformation processes at elevated temperatures produce significant concurrent microstructure changes that can alter the mechanical properties in a profound manner. Such microstructure evolution is usually absent in mesoscale deformation models and simulations. Here we present an integrated full-field modeling scheme that couples the mechanical response with the underlying microstructure evolution. As a first demonstration, we integrate a fast Fourier transform-based elasto-viscoplastic (FFT-EVP) model with a phase-field (PF) recrystallization model, and carry out three-dimensional (3D) simulations of dynamic recrystallization (DRX) in polycrystalline copper. A physics-based coupling between FFT-EVP and PF is achieved by (1) adopting a dislocation-based constitutive model in FFT-EVP, which allows the predicted dislocation density distribution to be converted to a stored energy distribution and passed to PF, and (2) implementing a stochastic nucleation model for DRX. Calibrated with the experimental DRX stress-strain curves, the integrated model is able to deliver full-field mechanical and microstructural information, from which quantitative description and analysis of DRX can be achieved. It is suggested that the initiation of DRX occurs significantly earlier than previous predictions, due to heterogeneous deformation. DRX grains are revealed to form at both grain boundaries and junctions (e.g., quadruple junctions) and tend to maintain a triple line with old grains during the growth. The resulted stress redistribution is found to have a profound influence on the subsequent dislocation evolution and the softening., Comment: 24 pages, 12 figures Revised 12/2/2015

We investigate near-field energy transfer between chemically synthesized quantum dots (QDs) and two-dimensional semiconductors. We fabricate devices in which electrostatically gated semiconducting monolayer molybdenum disulfide (MoS2) is placed atop a homogenous self-assembled layer of core-shell CdSSe QDs. We demonstrate efficient non-radiative F\"orster resonant energy transfer (FRET) from QDs into MoS2 and prove that modest gate-induced variation in the excitonic absorption of MoS2 lead to large (~500%) changes in the FRET rate. This, in turn, allows for up to ~75% electrical modulation of QD photoluminescence intensity. The hybrid QD/MoS2 devices operate within a small voltage range, allow for continuous modification of the QD photoluminescence intensity, and can be used for selective tuning of QDs emitting in the visible-IR range., Comment: 19 Pages, 11 figures. Main text and supporting information. Nano Letters, 2015, Article ASAP