Miracle-dz/UniFAR-FTUs huggingface dataset:
Joined optical and thermal characterization of a III-nitride semiconductor membrane by micro-photoluminescence spectroscopy and Raman thermometry

We present the simultaneous optical and thermal analysis of a freestanding photonic semiconductor membrane made from wurtzite III-nitride material. By linking micro-photoluminescence spectroscopy with Raman thermometry and other spectroscopic techniques, we demonstrate how a robust value for the thermal conductivity κ can be obtained using only optical, non-invasive means. For this, we consider the balance of different contributions to thermal transport given by, e.g., excitons, charge carriers, and heat carrying phonons. In principal, all these contributions can be of relevance in a photonic membrane on different lengthscales. Further complication is given by the fact that this membrane is made from direct bandgap semiconductors, designed to emit light based on an InxGa1−xN (x = 0.15) quantum well embedded in GaN. Thus, III-nitride membranes similar to the one in the focus of this study, have already successfully been used for laser diode structures facing thermal limitations. To meet these intricate challenges, we designed a novel experimental setup that enables the necessary optical and thermal characterizations in parallel. After the optical characterization by micro-photoluminescence, we follow a careful step-by-step approach to quantify the thermal properties of our photonic membrane. Therefore, we perform micro-Raman thermometry, either based on a heating laser that also acts as a probe laser (1-laser Raman thermometry), or based on two lasers, providing the heating and the temperature probe separately (2-laser Raman thermometry). For the latter technique, we can obtain temperature maps over several tens of micrometers with a spatial resolution less than 1 μm. As a result, the temperature probe volume using the 2-laser Raman thermometry technique can be increased by a factor exceeding 100 compared to the conventional 1-laser Raman thermometry technique, which impacts the derivation of the thermal conductivity κ . Only based on our largest temperature probe volume we derive κ = 95+11 −7 W m·K for the c-plane of our ≈ 250-nm-thick photonic membrane at around room temperature, which compares well to our ab initio calculations, applied to a simplified structure, yielding κ = 136 W m·K . Based on these calculations, we explain the particular relevance of the temperature probe volume, as quasi-ballistic transport of heat-carrying phonons with high relevance for κ occurs on length scales beyond the penetration depths of the heating laser and even its focus spot radius. The 1-laser Raman thermometry technique, therefore, fails to determine realistic κ values, while with the 2-laser Raman thermometry technique one can probe temperatures in sufficiently large volumes. The present work represents a significant step towards non-invasive, highly spatially resolved, and still quantitative thermometry performed on a photonic membrane made of a direct bandgap semiconductor, which is of particular relevance for photonic applications.
Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-2: Data Collection Procedure

This paper is the second part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between uncertainties and the performance of a battery management system. In this paper, clearly defined approaches for low-rate OCV data collection are defined and described in detail. The data collection is designed with consideration to several parameters that affect the experimental time. Firstly, a more suitable method to fully charge the battery at different C-Rates is defined. Secondly, the OCV characterization following the full charge is described for various performance comparisons. Finally, optimal and efficient resistance estimation profiles are discussed. From the voltage, current and time data recorded using the procedure described in this paper, the OCV-SOC relationship is characterized and its uncertainties are modeled in the third part of this series of papers.
Honesty is the Best Policy: On the Accuracy of Apple Privacy Labels Compared to Apps' Privacy Policies

Apple introduced \textit{privacy labels} in Dec. 2020 as a way for developers to report the privacy behaviors of their apps. While Apple does not validate labels, they do also require developers to provide a privacy policy, which offers an important comparison point. In this paper, we applied the NLP framework of Polisis to extract features of the privacy policy for 515,920 apps on the iOS App Store comparing the output to the privacy labels. We identify discrepancies between the policies and the labels, particularly as it relates to data collected that is linked to users. We find that 287$\pm196$K apps' privacy policies may indicate data collection that is linked to users than what is reported in the privacy labels. More alarming, a large number of (97$\pm30$\%) of the apps that have {\em Data Not Collected} privacy label have a privacy policy that indicates otherwise. We provide insights into potential sources for discrepancies, including the use of templates and confusion around Apple's definitions and requirements. These results suggest that there is still significant work to be done to help developers more accurately labeling their apps. Incorporating a Polisis-like system as a first-order check can help improve the current state and better inform developers when there are possible misapplication of privacy labels.
Probabilistic Linguistic Knowledge and Token-level Text Augmentation

This paper investigates the effectiveness of token-level text augmentation and the role of probabilistic linguistic knowledge within a linguistically-motivated evaluation context. Two text augmentation programs, REDA and REDA$_{NG}$, were developed, both implementing five token-level text editing operations: Synonym Replacement (SR), Random Swap (RS), Random Insertion (RI), Random Deletion (RD), and Random Mix (RM). REDA$_{NG}$ leverages pretrained $n$-gram language models to select the most likely augmented texts from REDA's output. Comprehensive and fine-grained experiments were conducted on a binary question matching classification task in both Chinese and English. The results strongly refute the general effectiveness of the five token-level text augmentation techniques under investigation, whether applied together or separately, and irrespective of various common classification model types used, including transformers. Furthermore, the role of probabilistic linguistic knowledge is found to be minimal.
Determining the Velocity Fine Structure by a Laser Anemometer in VAD operation

The theoretical basis for determining the dissipation e, by measuring the velocity structure function with a CW-laser anemometer has been derived in the case of calm wind conditions. If there is a well defined mean wind speed the structure function can be obtained by having the laser beam pointing in one direction and measure a time series of the Doppler wind velocity component along the beam. Applying Taylor’s hypothesis the structure function can be calculated. This technique was discussed by Kristensen et al. (2011). Taylor’s hypothesis cannot, however, be used if there is no mean wind. Then it is necessary to “create” a mean wind by turning the laser beam. Since the instrument is not moved the beam will describe a cone which could be a VAD-scanning. In any case the measured velocity components will not be parallel and this implies that the measured structure function will contain a term which is proportional to the total variance. The theoretical expression for the line-filtered structure function is derived in two equivalent ways, one in physical space and one in wave-number space, of which the last can be reliably evaluated by numerical integration. Also a practical approximate equation, derived in the physical space, is presented. ISBN 978–87–92896–14–5
Improved Convergence Bounds For Operator Splitting Algorithms With Rare Extreme Errors

In this paper, we improve upon our previous work[24,22] and establish convergence bounds on the objective function values of approximate proximal-gradient descent (AxPGD), approximate accelerated proximal-gradient descent (AxAPGD) and approximate proximal ADMM (AxWLM-ADMM) schemes. We consider approximation errors that manifest rare extreme events and we propagate their effects through iterations. We establish probabilistic asymptotic and non-asymptotic convergence bounds as functions of the range (upper/lower bounds) and variance of approximation errors. We use the derived bound to assess AxPGD in a sparse model predictive control of a spacecraft system and compare its accuracy with previously derived bounds.
Robust Policy Optimization in Continuous-time Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Stochastic Control

Following the recent resurgence in establishing linear control theoretic benchmarks for reinforcement leaning (RL)-based policy optimization (PO) for complex dynamical systems with continuous state and action spaces, an optimal control problem for a continuous-time infinite-dimensional linear stochastic system possessing additive Brownian motion is optimized on a cost that is an exponent of the quadratic form of the state, input, and disturbance terms. We lay out a model-based and model-free algorithm for RL-based stochastic PO. For the model-based algorithm, we establish rigorous convergence guarantees. For the sampling-based algorithm, over trajectory arcs that emanate from the phase space, we find that the Hamilton-Jacobi Bellman equation parameterizes trajectory costs -- resulting in a discrete-time (input and state-based) sampling scheme accompanied by unknown nonlinear dynamics with continuous-time policy iterates. The need for known dynamics operators is circumvented and we arrive at a reinforced PO algorithm (via policy iteration) where an upper bound on the $\mathcal{H}_2$ norm is minimized (to guarantee stability) and a robustness metric is enforced by maximizing the cost with respect to a controller that includes the level of noise attenuation specified by the system's $H_\infty$ norm. Rigorous robustness analyses is prescribed in an input-to-state stability formalism. Our analyses and contributions are distinguished by many natural systems characterized by additive Wiener process, amenable to \^Ito's stochastic differential calculus in dynamic game settings.
Efficient Sampling of Noisy Shallow Circuits Via Monitored Unraveling

We introduce a classical algorithm for sampling the output of shallow, noisy random circuits on two-dimensional qubit arrays. The algorithm builds on the recently-proposed"space-evolving block decimation"(SEBD) and extends it to the case of noisy circuits. SEBD is based on a mapping of 2D unitary circuits to 1D {\it monitored} ones, which feature measurements alongside unitary gates; it exploits the presence of a measurement-induced entanglement phase transition to achieve efficient (approximate) sampling below a finite critical depth $T_c$. Our noisy-SEBD algorithm unravels the action of noise into measurements, further lowering entanglement and enabling efficient classical sampling up to larger circuit depths. We analyze a class of physically-relevant noise models (unital qubit channels) within a two-replica statistical mechanics treatment, finding weak measurements to be the optimal (i.e. most disentangling) unraveling. We then locate the noisy-SEBD complexity transition as a function of circuit depth and noise strength in realistic circuit models. As an illustrative example, we show that circuits on heavy-hexagon qubit arrays with noise rates of $\approx 2\%$ per CNOT, based on IBM Quantum processors, can be efficiently sampled up to a depth of 5 iSWAP (or 10 CNOT) gate layers. Our results help sharpen the requirements for practical hardness of simulation of noisy hardware.
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visual inputs, collecting responses for image-based instructions. However, visual instruction-tuned models cannot comprehend textual details within images well. This work enhances the current visual instruction tuning pipeline with text-rich images (e.g., movie posters, book covers, etc.). Specifically, we first use publicly available OCR tools to collect results on 422K text-rich images from the LAION dataset. Moreover, we prompt text-only GPT-4 with recognized texts and image captions to generate 16K conversations, each containing question-answer pairs for text-rich images. By combining our collected data with previous multi-modal instruction-following data, our model, LLaVAR, substantially improves the LLaVA model's capability on text-based VQA datasets (up to 20% accuracy improvement) while achieving an accuracy of 91.42% on ScienceQA. The GPT-4-based instruction-following evaluation also demonstrates the improvement of our model on both natural images and text-rich images. Through qualitative analysis, LLaVAR shows promising interaction (e.g., reasoning, writing, and elaboration) skills with humans based on the latest real-world online content that combines text and images. We make our code/data/models publicly available at https://llavar.github. io/.
Gauge and time-reparametrization invariant spin-half fields

We present a fermion model characterized by an anticommuting-parameter shift symmetry. The Hamiltonian formulation exhibits a combination of first-class and second-class constraints. We derive the well-known Dirac equation by fixing the gauge in a covariant manner, enabling the fields to propagate accordingly. Notably, the model inherently possesses invariance under reparametrizations of time. Consequently, the Hamiltonian vanishes, setting it apart from the conventional framework of Dirac's theory. Furthermore, we establish a correspondence between these particles and the zero-modes of the massless Rarita-Schwinger system, bringing forth the intriguing implication that they may describe a supergravity ground state.
Sparse Grouping and Invariant Representations for Estimation and Recognition

Cette these developpe plusieurs contributions pour le traitement du signal et des images ainsi que pour la vision par ordinateur. La premiere partie inclut un nouvel algorithme de debruitage des sons et un algorithme de super-resolution pour l'agrandissement des images. Ces algorithmes sont bases sur de nouvelles representations parcimonieuses par blocs. Une procedure de seuillage par bloc temps-frequence est introduite pour le debruitage audio, qui permet de reduire le bruit sans introduire d'artefacts, avec des resultats perceptuels et numeriques nettement superieurs a l'etat de l'art. Cette premiere partie introduit aussi un nouveau cadre mathematique et algorithmique pour des problemes inverses, avec une regularisation non-lineaire sur des dictionnaires de blocs geometriques dans une representation parcimonieuse. Une approche generale pour la super-resolution d'images est introduite et des resultats numeriques ameliorant l'etat de l'art sont obtenus. La deuxieme partie de la these introduit un algorithme (ASIFT) de mise en correspondance d'images, qui est invariant relativement a des transformations affines. Il est demontre que cet algorithme satisfait les contraintes d'invariance et qu'il peut effectuer des correspondances entre des objets observes sous des angles arbitraires. Sa complexite numerique est du meme ordre que les algorithmes les plus efficaces, avec une robustesse bien superieure grâce a son invariance affine. La troisieme partie de la these introduit une implementation biologiquement plausible de groupements visuels. Inspire par les mecanismes de synchronisation neuronale en groupement perceptuel, un algorithme general base sur des oscillateurs neuronaux est propose pour effectuer des groupements visuels. Cet algorithme permet d'obtenir des resultats prometteurs sur plusieurs problemes, dont le groupement de points, l'integration de contours, et la segmentation d'images.
X-ray shielding properties of bismuth-borate glass doped with rare earth ions

Abstract In this study, the X-ray shielding competence of the 15Bi2O3 + 75H3BO2 + 10Sm2O3, 15Bi2O3 + 75H3BO2 + 10Nd2O3, and 15Bi2O3 + 75H3BO2 + 10CeO2 glasses was investigated using the Phy-X/PSD simulation software and validated using the XCOM simulation software. The Sm3+-doped bismuth-borate glass gave the highest linear attenuation coefficients and effective atomic number, and the lowest half-value layer, tenth-value layer, and mean free path. Thus, it is the most effective radiation shielding material compared to the Nd3+- and Ce4+-doped bismuth-borate glasses. It was also observed that the Sm3+-doped bismuth-borate glass also has better radiation-shielding competence than various glass systems that have been recently investigated in the literature. Graphic abstract X-rays are an ionizing radiation that is used in various industries such as the food industry, nuclear research facilities, nuclear power plants, radiography, dentistry, and gas supply pipeline maintenance. Exposure to this radiation poses health risks to humans. As a result, it is vital to shield them as much as possible. This can be performed using numerous materials that have high density and high X-ray attenuation coefficients. Metallic lead and concrete are the conventional materials that have been used for shielding X-rays in hospitals and nuclear facilities. However, lead is very toxic to humans, and it is no longer preferred in radiation shielding. The disadvantage of concrete is that it is not transparent. As a result, different researchers have been developing heavy-element based glasses and testing their radiation-shielding competence so that they can be used as an alternative to lead and concrete. A few years ago, new bismuth-borate glasses, which are doped with Sm3+, Nd3+, and Ce4+, were developed. Their X-ray shielding properties were simulated, in this work, using the Phy-X/PSD software and verified using the XCOM software. The results show that the Sm3+-doped bismuth-borate glass is, by far, the most effective radiation-shielding glass compared to the Nd3+- and Ce4+-doped glasses. This is illustrated with the figure above, which depicts the linear attenuation coefficient of the three glasses in the photon energy range of 30–300 keV. In this figure, SSm, SNd, and SCe refer to the Sm3+-, Nd3+-, and Ce4+-doped bismuth-borate glasses, respectively. Furthermore, it is interesting to note that the SSm glass is also more effective than 15Bi2O3 + 10Li2O + 10ZnO + 10GeO2 + 55TeO2 glass, which was found to be the most effective sample in one of the recent studies in the literature.
Community-engaged renewable energy deployment: a strategic niche management perspective

ABSTRACT This article explores emergent practice in renewable energy adoption supporting the pursuit of UN Sustainable Development Goal 7, to ‘Ensure access to affordable, reliable, sustainable and modern energy for all. ’ A Strategic Niche Management (SNM) model is adapted to interpret a complex web of activities being undertaken in seven Australian longitudinal case studies. The SNM model was developed to better reflect the evolutionary and strong collaborative nature of the observed entrepreneurial practice. A combination of an interactive SNM model and PESTLE analysis helps to provide an explanation in different settings incorporating a theoretical feedback loop. We conclude that community engagement is helping to deliver targeted renewable energy outcomes in the pursuit of UN SDG7 outcomes through collaborative actions being taken at an enterprise project level. Advice is included for practitioners following the renewable energy deployment path articulated.
On the Syntax of Existential Sentences in Standard Arabic

Existential sentences are noncanonical sentences that express the (non)existence of an entity. They have not received considerable attention in Standard Arabic (SA). This paper, thus, aims to propose a syntactic analysis of SA existential sentences. We first discuss the key grammatical properties of the four syntactic items, namely the copular verb, the expletive hunaaka, the pivot, and the coda phrase, that comprise the SA existential sentence. The expletive and the pivot are obligatory items in the structure, whereas the copular verb and the coda are optional. In our proposed analysis, we assume that the expletive is inserted in the Spec-TP to satisfy the EPP feature on T. We argue for the presence of a predicate phrase headed by Pred which hosts the pivot in its specifier and takes a null existential predicate as its complement. The fact that the expletive must precede the pivot and the locality requirement of the predication relation support locating the pivot in the Spec-PredP. We also assume that the coda is an adjunct, which is supported by its optionality and its inability to function as an NP. The conclusions reached may contribute to the ongoing discussion of existential constructions by presenting the perspective of SA.
Patience and subjective well-being

ABSTRACT We show that patience is a key determinant of subjective well-being differences between countries. To address potential endogeneity bias, we apply an instrumental variable approach using the share of Protestants in the country, which prior literature has associated with patience and used as an instrument. While tentative, our findings are consistent with a sizable causal effect of patience on subjective well-being. They are robust across several specifications and hold for three different measures of subjective well-being: life satisfaction, positive affect, and negative affect.
Corporate responses to climate change risks: evidence from Australia

ABSTRACT This study aims to (1) explore the extent to which Australian companies respond to regulatory, physical and market risks associated with climate change, and (2) examine the impact of stakeholder pressure and corporate governance structure on the extent of corporate response to these risks. We collected survey data from 120 top risk managers of Australian companies. Our analysis shows that companies respond to regulatory risks to a greater extent than to physical and market risks. With respect to the impact of stakeholder pressure, the results show that overall, pressure from government, non-governmental organisations, competitors, and the media are positively and significantly associated with companies’ climate change risk responses. Disaggregated analyses show differences in how particular stakeholder groups’ pressure affects the corporate response to physical risks. With respect to corporate governance structure, the results demonstrate that female representation on the board of directors and existence of a climate change risk committee facilitate companies’ increased climate change risk response.