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Transition metal dichalcogenides and related layered materials in their monolayer and a few layers thicknesses regime provide a promising optoelectronic platform for exploring the excitonic- and many-body physics. Strain engineering has emerged as a potent technique for tuning the excitonic properties favorable for exciton-based devices. We have investigated the effects of nanoparticle-induced local strain on the optical properties of exciton, $X^0$, and trion, $X^\text{-}$, in monolayer WS$_2$. Biaxial tensile strain up to 2.0% was quantified and verified by monitoring the changes in three prominent Raman modes of WS$_2$: E${^1_{2g}}$($\Gamma$), A$_{1g}$, and 2LA(M). We obtained a remarkable increase of 34 meV in $X^\text{-}$ binding energy with an average tuning rate of 17.5 $\pm$ 2.5 meV/% strain across all the samples irrespective of the surrounding dielectric environment of monolayer WS$_2$ and the sample preparation conditions. At the highest tensile strain of $\approx$2%, we have achieved the largest binding energy $\approx$100 meV for $X^\text{-}$, leading to its enhanced emission intensity and thermal stability. By investigating strain-induced linewidth broadening and deformation potentials of both $X^0$ and $X^\text{-}$ emission, we elucidate that the increase in $X^\text{-}$ binding energy is due to strain-enhanced electron-phonon coupling. This work holds relevance for future $X^\text{-}$-based nano-opto-electro-mechanical systems and devices., Comment: 7 figures

Bilayers of transition-metal dichalcogenides show many exciting features, including long-lived interlayer excitons and wide bandgap tunability using strain. Not many investigations on experimental determinations of deformation potentials relating changes in optoelectronic properties of bilayer WSe$_2$ with the strain are present in the literature. Our experimental study focuses on three widely investigated high-symmetry points, K$_{c}$, K$_{v}$, and Q$_{c}$, where subscript c (v) refers to the conduction (valence) band, in the Brillouin zone of bilayer WSe$_2$. Using local biaxial strains produced by nanoparticle stressors, a theoretical model, and by performing the spatially- and spectrally-resolved photoluminescence measurements, we determine absolute deformation potential of -5.10 $\pm$ 0.24 eV for Q$_{c}$-K$_{v}$ indirect bandgap and -8.50 $\pm$ 0.92 eV for K$_{c}$-K$_{v}$ direct bandgap of bilayer WSe$_2$. We also show that $\approx$0.9% biaxial tensile strain is required to convert an indirect bandgap bilayer WSe$_2$ into a direct bandgap semiconductor. Moreover, we also show that a relatively small amount of localized strain $\approx$0.4% is required to make a bilayer WSe$_2$ as optically bright as an unstrained monolayer WSe$_2$. The bandgap deformation potentials measured here will drive advances in flexible electronics, sensors, and optoelectronic- and quantum photonic- devices through precise strain engineering.

Spot patterns, in which vegetation patches form a hexagonal lattice, are frequent in nature and could serve as an early-warning indicator of abrupt vegetation collapses. Consequently, they have been intensively studied using both individual-based models and density-based field equations. Yet, the relationship between these two approaches remains unclear, particularly in scenarios where vegetation dynamics exhibit strong long-range spatial correlations and traditional mean-field approximations fail. To solve this issue, we develop a new method that refines mean-field approximations by describing both the dynamics of the biomass density field and its spatial correlations. This new approach harnesses the strengths of both individual and density-based mdoels, treating spatial correlations explicitly and allowing for the identification of spatial instabilities resulting in periodic patterns. Our results indicate that this new approximation predicts the parameter regimes where regular periodic patterns emerge more accurately than mean-field models, suggesting that it could provide a more robust framework to perform further nonlinear analysis.

Recognising intent in collaborative human robot tasks can improve team performance and human perception of robots. Intent can differ from the observed outcome in the presence of mistakes which are likely in physically dynamic tasks. We created a dataset of 1227 throws of a ball at a target from 10 participants and observed that 47% of throws were mistakes with 16% completely missing the target. Our research leverages facial images capturing the person's reaction to the outcome of a throw to predict when the resulting throw is a mistake and then we determine the actual intent of the throw. The approach we propose for outcome prediction performs 38% better than the two-stream architecture used previously for this task on front-on videos. In addition, we propose a 1-D CNN model which is used in conjunction with priors learned from the frequency of mistakes to provide an end-to-end pipeline for outcome and intent recognition in this throwing task., Comment: Accepted October 2022 in Autonomous Robots. Published December 2022

Variational Autoencoders (VAE) are popular generative models used to sample from complex data distributions. Despite their empirical success in various machine learning tasks, significant gaps remain in understanding their theoretical properties, particularly regarding convergence guarantees. This paper aims to bridge that gap by providing non-asymptotic convergence guarantees for VAE trained using both Stochastic Gradient Descent and Adam algorithms.We derive a convergence rate of $\mathcal{O}(\log n / \sqrt{n})$, where $n$ is the number of iterations of the optimization algorithm, with explicit dependencies on the batch size, the number of variational samples, and other key hyperparameters. Our theoretical analysis applies to both Linear VAE and Deep Gaussian VAE, as well as several VAE variants, including $\beta$-VAE and IWAE. Additionally, we empirically illustrate the impact of hyperparameters on convergence, offering new insights into the theoretical understanding of VAE training.

Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG)., Comment: 10 pages, 7 figures

Chalcogenide perovskites such as BaZrS3 are promising candidates for next generation optoelectronics such as photodetectors and solar cells. Compared to widely studied polycrystalline thin films, single crystals of BaZrS3 with minimal extended and point defects, are ideal platform to study the material's intrinsic transport properties and to make first-generation optoelectronic devices. However, the surface dielectrics formed on BaZrS3 single crystals due to sulfating or oxidation have led to significant challenges to achieving high quality electrical contacts, and hence, realizing the high-performance optoelectronic devices. Here, we report the development of electrical contact fabrication processes on BaZrS3 single crystals, where various processes were employed to address the surface dielectric issue. Moreover, with optimized electrical contacts fabricated through dry etching, high-performance BaZrS3 photoconductive devices with a low dark current of 0.1 nA at 10 V bias and a fast transient photoresponse with rise and decay time of <0.2 s were demonstrated.

We present a general fabrication strategy for freestanding single-crystalline complex oxide device arrays via wet chemical etching-based microfabrication processes and epitaxial lift-off. Here, we used 0.5Ba(Zr$_{0.2}$Ti$_{0.8}$)O$_3$-0.5Ba(Zr$_{0.7}$Ti$_{0.3}$)O$_3$ (BCZT) as a model relaxor ferroelectric oxide system and La$_{0.7}$Sr$_{0.3}$MnO$_3$ as the sacrificial layer for demonstration. Arrays of SrRuO$_3$ (SRO) / BCZT / SRO ferroelectric capacitor mesas were first defined and isolated on the growth wafer, and then they were released using epitaxial lift-off with lithography-defined surrounding etching holes, after which the freestanding device arrays were integrated onto a glass substrate. Our proposed strategy sheds light on preparing various freestanding single-crystalline oxide devices and paves the way for their heterogeneous integration onto arbitrary substates.

Ferroelectricity is characterized by the presence of spontaneous and switchable macroscopic polarization. Scaling limits of ferroelectricity have been of both fundamental and technological importance, but the probes of ferroelectricity have often been indirect due to confounding factors such as leakage in the direct electrical measurements. Recent interest in low-voltage switching electronic devices squarely puts the focus on ultrathin limits of ferroelectricity in an electronic device form, specifically on the robustness of ferroelectric characteristics such as retention and endurance for practical applications. Here, we illustrate how manipulating the kinetic energy of the plasma plume during pulsed laser deposition can yield ultrathin ferroelectric capacitor heterostructures with high bulk and interface quality, significantly low leakage currents and a broad "growth window". These heterostructures venture into previously unexplored aspects of ferroelectric properties, showcasing ultralow switching voltages ($<$0.3 V), long retention times ($>$10$^{4}$s), and high endurance ($>$10$^{11}$cycles) in 20 nm films of the prototypical perovskite ferroelectric, BaTiO$_{3}$. Our work demonstrates that materials engineering can push the envelope of performance for ferroelectric materials and devices at the ultrathin limit and opens a direct, reliable and scalable pathway to practical applications of ferroelectrics in ultralow voltage switches for logic and memory technologies.

This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight continual pretraining by gradually increasing its RoPE base frequency with repository-level file packing and length-upsampled long-context data. Additionally, we also release instruction-tuned models with long-context support which are derived by further finetuning the long context base models on a mix of permissively licensed short and long-context instruction-response pairs. While comparing to the original short-context Granite code models, our long-context models achieve significant improvements on long-context tasks without any noticeable performance degradation on regular code completion benchmarks (e.g., HumanEval). We release all our long-context Granite code models under an Apache 2.0 license for both research and commercial use.

Low-weight, passive, thermal-adaptive radiation technologies are needed to maintain an operable temperature for spacecraft while they experience various energy fluxes. In this study, we used a thin-film coating with the Fabry-Perot (FP) effect to enhance emissivity contrast ({\Delta}{\epsilon}) between VO2 phase-change states. This coating utilizes a novel hybrid material architecture that combines VO2 with a mid- and long-wave infrared transparent chalcogenide, zinc sulfide (ZnS), as a cavity spacer layer. We simulated the design parameter space to obtain a theoretical maximum {\Delta}{\epsilon} of 0.63 and grew prototype devices. Using X-ray diffraction, Raman spectroscopy, and Fourier Transform Infrared (FTIR) Spectroscopy, we determined that an intermediate buffer layer of TiO2 is necessary to execute the crystalline growth of monoclinic VO2 on ZnS. Through temperature-dependent FTIR spectroscopy measurements, our fabricated devices demonstrated FP-cavity enhanced adaptive thermal emittance., Comment: 16 pages, 5 figures, full article

Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use., Comment: Corresponding Authors: Rameswar Panda, Ruchir Puri; Equal Contributors: Mayank Mishra, Matt Stallone, Gaoyuan Zhang

We study the dynamics of a three-dimensional generalization of Kitaev's honeycomb lattice spin model (defined on the hyperhoneycomb lattice) subjected to a harmonic driving of $J_z$, one of the three types of spin-couplings in the Hamiltonian. Using numerical solutions supported by analytical calculations based on a rotating wave approximation, we find that the system responds nonmonotonically to variations in the frequency $\omega$ (while keeping the driving amplitude $J$ fixed) and undergoes dynamical freezing, where at specific values of $\omega$, it gets almost completely locked in the initial state throughout the evolution. However, this freezing occurs only when a constant bias is present in the driving, i.e., when $J_z = J'+ J\cos{\omega t}$, with $J'\neq 0$. Consequently, the bias acts as a switch that triggers the freezing phenomenon. Dynamical freezing has been previously observed in other integrable systems, such as the one-dimensional transverse-field Ising model., Comment: 10 pages, 4 figures

BaTiS3, a quasi-1D complex chalcogenide, has gathered considerable scientific and technological interest due to its giant optical anisotropy and electronic phase transitions. However, the synthesis of high-quality BaTiS3 crystals, particularly those featuring crystal sizes of millimeters or larger, remains a challenge. Here, we investigate the growth of BaTiS3 crystals utilizing a molten salt flux of either potassium iodide, or a mixture of barium chloride and barium iodide. The crystals obtained through this method exhibit a substantial increase in volume compared to those synthesized via the chemical vapor transport method, while preserving their intrinsic optical and electronic properties. Our flux growth method provides a promising route towards the production of high-quality, large-scale single crystals of BaTiS3, which will greatly facilitate advanced characterizations of BaTiS3 and its practical applications that require large crystal dimensions. Additionally, our approach offers an alternative synthetic route for other emerging complex chalcogenides.

A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phase via a topotactic reaction with a strongly reducing agent such as CaH$_2$. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La$_{1-x}$X$_x$NiO$_2$ / SrTiO$_3$ (X = Ca and Sr) and Nd$_6$Ni$_5$O$_{12}$ / NdGaO$_3$ films, along with non-superconducting NdNiO$_2$ / SrTiO$_3$ and (Nd,Sr)NiO$_2$ / SrTiO$_3$. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates., Comment: 23 pages, 21 figures (including supplemental information)

Rare earth dopants are one of the most extensively studied optical emission centers for a broad range of applications such as laser optoelectronics, sensing, lighting, and quantum information technologies due to their narrow optical linewidth and exceptional coherence properties. Epitaxial doped oxide thin films can serve as a promising and controlled host to investigate rare-earth dopants suitable for scalable quantum memories, on-chip lasers and amplifiers. Here, we report high-quality epitaxial thin films of Tm-doped CaZrO$_3$ grown by pulsed laser deposition for infrared optoelectronic and quantum memory applications. We perform extensive structural and chemical characterization to probe the crystallinity of the films and the doping behavior. Low temperature photoluminescence measurements show sharp radiative transitions in the short-wave infrared range of 1.75 - 2 \mu m., Comment: 17 pages, 4 figures

Stochastic Gradient Descent (SGD) with adaptive steps is now widely used for training deep neural networks. Most theoretical results assume access to unbiased gradient estimators, which is not the case in several recent deep learning and reinforcement learning applications that use Monte Carlo methods. This paper provides a comprehensive non-asymptotic analysis of SGD with biased gradients and adaptive steps for convex and non-convex smooth functions. Our study incorporates time-dependent bias and emphasizes the importance of controlling the bias and Mean Squared Error (MSE) of the gradient estimator. In particular, we establish that Adagrad and RMSProp with biased gradients converge to critical points for smooth non-convex functions at a rate similar to existing results in the literature for the unbiased case. Finally, we provide experimental results using Variational Autoenconders (VAE) that illustrate our convergence results and show how the effect of bias can be reduced by appropriate hyperparameter tuning.

Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded single crystals in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline Ruddlesden-Popper Ban+1ZrnS3n+1, n = 2,3, which are derivatives of BaZrS3. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of mechanically stiff phonon glasses.