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Semiconductor membranes find their widespread use in various research fields targeting medical, biological, environmental, and optical applications. Often such membranes derive their functionality from an inherent nanopatterning, which renders the determination of their, e.g., optical, electronic, mechanical, and thermal properties a challenging task. In this work we demonstrate the non-invasive, all-optical thermal characterization of around 800-nm-thick and 150-$\mu$m-wide membranes that consist of wurtzite GaN and a stack of In$_{0.15}$Ga$_{0.85}$N quantum wells as a built-in light source. Due to their application in photonics such membranes are bright light emitters, which challenges their non-invasive thermal characterization by only optical means. As a solution, we combine two-laser Raman thermometry with (time-resolved) photoluminescence measurements to extract the in-plane (i.e., $c$-plane) thermal conductivity $\kappa_{\text{in-plane}}$ of our membranes. Based on this approach, we can disentangle the entire laser-induced power balance during our thermal analysis, meaning that all fractions of reflected, scattered, transmitted, and reemitted light are considered. As a result of our thermal imaging via Raman spectroscopy, we obtain $\kappa_{\text{in-plane}}\,=\,165^{+16}_{-14}\,$Wm$^{-1}$K$^{-1}$ for our best membrane, which compares well to our simulations yielding $\kappa_{\text{in-plane}}\,=\,177\,$Wm$^{-1}$K$^{-1}$ based on an ab initio solution of the linearized phonon Boltzmann transport equation. Our work presents a promising pathway towards thermal imaging at cryogenic temperatures, e.g., when aiming to elucidate experimentally different phonon transport regimes via the recording of non-Fourier temperature distributions., Comment: Main text (4 figures and 15 pages) and Supplemental Material (3 supplemental figures and 4 pages)

This article builds upon the protocol for digital transfers described by Goodell, Toliver, and Nakib, which combines privacy by design for consumers with strong compliance enforcement for recipients of payments and self-validating assets that carry their own verifiable provenance information. We extend the protocol to allow for the verification that reissued assets were created in accordance with rules prohibiting the creation of new assets by anyone but the issuer, without exposing information about the circumstances in which the assets were created that could be used to identify the payer. The modified protocol combines an audit log with zero-knowledge proofs, so that a consumer spending an asset can demonstrate that there exists a valid entry on the audit log that is associated with the asset, without specifying which entry it is. This property is important as a means to allow money to be reissued within the system without the involvement of system operators within the zone of control of the original issuer. Additionally, we identify a key property of privacy-respecting electronic payments, wherein the payer is not required to retain secrets arising from one transaction until the following transaction, and argue that this property is essential to framing security requirements for storage of digital assets and the risk of blackmail or coercion as a way to exfiltrate information about payment history. We claim that the design of our protocol strongly protects the anonymity of payers with respect to their payment transactions, while preventing the creation of assets by any party other than the original issuer without destroying assets of equal value., Comment: 28 pages, 2 figures

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities., Comment: 13 pages, 8 figures

Nations around the world are conducting research into the design of central bank digital currency (CBDC), a new, digital form of money that would be issued by central banks alongside cash and central bank reserves. Retail CBDC would be used by individuals and businesses as form of money suitable for routine commerce. An important motivating factor in the development of retail CBDC is the decline of the popularity of central bank money for retail purchases and the increasing use of digital money created by the private sector for such purposes. The debate about how retail CBDC would be designed and implemented has led to many proposals, which have sparked considerable debate about business models, regulatory frameworks, and the socio-technical role of money in general. Here, we present a critical analysis of the existing proposals. We examine their motivations and themes, as well as their underlying assumptions. We also offer a reflection of the opportunity that retail CBDC represents and suggest a way forward in furtherance of the public interest., Comment: 31 pages, 1 figure

The Tamura model is a particular type of 1$^{\text{st}}$ Born approximation of the phonon-isotope scattering problem. The expression for the mode-resolved phonon-isotope scattering rates in this model, derived in 1983, is still widely used in ab initio transport calculations. While the original work emphasized its applicability to low-energy acoustic phonons only, it has, nevertheless, been also applied to optical phonons in the field of phonon transport. The model has the salient feature of being a perturbation theory on top of a virtual crystal background. As such, this approach does not correspond to the proper methodology for solving the phonon-substitution defect problem in the respective limit. Here, we explore three avenues to go beyond the Tamura model and carry out calculations on a set of common materials to compare the different approaches. This work allows a systematic improvement of the treatment of phonon-isotope scattering in ab initio phonon transport, while unifying it with the general phonon-substitution scattering problem.

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means., Comment: 7 pages, 5 figures

We present the simultaneous optical and thermal analysis of a freestanding photonic semiconductor membrane made from wurtzite III-nitride material. By linking micro-photoluminescence ($\mu$PL) spectroscopy with Raman thermometry, we demonstrate how a robust value for the thermal conductivity $\kappa$ 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. Further complication is given by the fact that this membrane is made from direct bandgap semiconductors, designed to emit light based on an In$_{x}$Ga$_{1-x}$N ($x=0.15$) quantum well embedded in GaN. To meet these challenges, we designed a novel experimental setup that enables the necessary optical and thermal characterizations in parallel. We perform micro-Raman thermometry, either based on a heating laser that 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 obtain temperature maps over tens of micrometers with a spatial resolution less than $1\,\mu\text{m}$, yielding $\kappa\,=\,95^{+11}_{-7}\,\frac{\text{W}}{\text{m}\cdot \text{K}}$ for the $\textit{c}$-plane of our $\approx\,250\text{-nm}$-thick membrane at around room temperature, which compares well to our $\textit{ab initio}$ calculations applied to a simplified structure. Based on these calculations, we explain the particular relevance of the temperature probe volume, as quasi-ballistic transport of heat-carrying phonons occurs on length scales beyond the penetration depths of the heating laser and even its focus spot radius. The present work represents a significant step towards non-invasive, highly spatially resolved, and still quantitative thermometry performed on a photonic membrane., Comment: 28 pages, 14 figures, and Supplemental Material

Since the appearance of Covid-19 in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 3nd COV19D competition for Covid-19 Severity Prediction. In order to deal with the big gap between the validation and test results that were shown in the previous version of this competition, we proposed to combine the prediction of 2D and 3D CNN predictions. For the 2D CNN approach, we propose 2B-InceptResnet architecture which consists of two paths for segmented lungs and infection of all slices of the input CT-scan, respectively. Each path consists of ConvLayer and Inception-ResNet pretrained model on ImageNet. For the 3D CNN approach, we propose hybrid-DeCoVNet architecture which consists of four blocks: Stem, four 3D-ResNet layers, Classification Head and Decision layer. Our proposed approaches outperformed the baseline approach in the validation data of the 3nd COV19D competition for Covid-19 Severity Prediction by 36%., Comment: arXiv admin note: substantial text overlap with arXiv:2206.15431

This paper analyzes the performance of the Fractal Decomposition Algorithm (FDA) metaheuristic applied to low-dimensional continuous optimization problems. This algorithm was originally developed specifically to deal efficiently with high-dimensional continuous optimization problems by building a fractal-based search tree with a branching factor linearly proportional to the number of dimensions. Here, we aim to answer the question of whether FDA could be equally effective for low-dimensional problems. For this purpose, we evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB) for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that overall the FDA in its current form does not perform well enough. Among different function groups, FDA shows its best performance on Misc. moderate and Weak structure functions.

In this work, we tackle the Dynamic Optimization Problem (DOP) of IA in a real-world application using a Dynamic Optimization Algorithm (DOA) called Fractal Decomposition Algorithm (FDA), introduced by recently. We used FDA to perform IA on CCTV camera feed from a tunnel. As the camera viewpoint can change by multiple reasons such as wind, maintenance, etc. the alignment is required to guarantee the correct functioning of video-based traffic security system.

We present a new efficient OpenCL-based Accelerator for large scale Convolutional Neural Networks called Fast Inference on FPGAs for Convolution Neural Network (FFCNN). FFCNN is based on a deeply pipelined OpenCL kernels architecture. As pointed out before, high-level synthesis tools such as the OpenCL framework can easily port codes originally designed for CPUs and GPUs to FPGAs, but it is still difficult to make OpenCL codes run efficiently on FPGAs. This work aims to propose an efficient FPGA implementation of OpenCL High-Performance Computing Applications. To do so, a Data reuse and task mapping techniques are also presented to improve design efficiency. In addition, the following motivations were taken into account when developing FFCNN: 1) FFCNN has been designed to be easily implemented on Intel OpenCL SDK based FPGA design flow. 2) In FFFCN, different techniques have been integrated to improve the memory band with and throughput. A performance analysis is conducted on two deep CNN for Large-Scale Images classification. The obtained results, and the comparison with other works designed to accelerate the same types of architectures, show the efficiency and the competitiveness of the proposed accelerator design by significantly improved performance and resource utilization.

Diamond is one of the most studied materials because of its unique combination of remarkable electrical, mechanical, thermal and optical properties. Using a fully self-consistent ab initio theory of coupled electron-phonon transport, we reveal another striking behavior: a huge drag enhancement of the thermopower of lightly doped diamond. Thermopower values of around 100,000 microvolts per Kelvin are found at 100 K, significantly exceeding the highest previously measured value in the correlated metal FeSb2, and occurring at much higher temperatures. The enormous thermopower in diamond arises primarily from exceptionally weak anharmonic phonon decay around and below 100 K that facilitates efficient momentum exchange between charge carriers and phonons through electron-phonon interactions. Exceedingly large thermoelectric power factors are also identified. This work gives insights into the physics of the coupled electron-phonon system in solids and advances our understanding of thermoelectric transport in the regime of strong drag.

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

We present a scalable architecture for electronic retail payments via central bank digital currency and offer a solution to the perceived conflict between robust regulatory oversight and consumer affordances such as privacy and control. Our architecture combines existing work in payment systems and digital currency with a new approach to digital asset design for managing unforgeable, stateful, and oblivious assets without relying on either a central authority or a monolithic consensus system. Regulated financial institutions have a role in every transaction, and the consumer affordances are achieved through the use of non-custodial wallets that unlink the sender from the recipient in the transaction channel. This approach is fully compatible with the existing two-tiered banking system and can complement and extend the roles of existing money services businesses and asset custodians., Comment: 24 pages, 7 figures, 2 tables

By employing ab initio Migdal-Eliashberg calculations, we predict a 4-fold enhancement of the superconducting critical temperature of MgB$_{2}$ when proximity-coupled to the topological crystal Bi$_{2}$Se$_{3}$. We support this result with calculations using the general Leavens scaling method. We show that this effect is a result of dynamic resonant anti-shielding of Cooper pairs by plasmon polarons of Dirac electrons in the topological crystal. Our calculations show that such superconductivity enhancement varies strongly with Coulomb coupling between plasmon polarons and Cooper pairs, with a pronounced maximum of $\textit{T}_{c}$ at a critical value of the coupling parameter. This feature is universal, and so can occur in other superconductor-topological crystal combinations, including with non-phonon mediated superconductors. We discuss methods to experimentally optimize the key coupling parameter.

elphbolt is a modern Fortran (2018 standard) code for efficiently solving the coupled electron-phonon Boltzmann transport equations from first principles. Using results from density functional and density functional perturbation theory as inputs, it can calculate the effect of the non-equilibrium phonons on the electronic transport (phonon drag) and non-equilibrium electrons on the phononic transport (electron drag) in a fully self-consistent manner and obeying the constraints mandated by thermodynamics. It can calculate the lattice, charge, and thermoelectric transport coefficients for the temperature gradient and electric fields, and the effect of the mutual electron-phonon drag on these transport properties. The code fully exploits the symmetries of the crystal and the transport-active window to allow the sampling of extremely fine electron and phonon wave vector meshes required for accurately capturing the drag phenomena. The coarray feature of modern Fortran, which offers native and convenient support for parallelization, is utilized. The code is compact, readable, well-documented, and extensible by design.

The People's Bank of China (PBOC) has launched an ambitious project to develop a digital currency for use in domestic, retail transactions, and is, by far, the most advanced globally in this regard. In addition to involving a diverse set of stakeholders, the PBOC established a set of fundamental principles, including privacy, inclusiveness, and conservatism, and has articulated its progress in a public document translated into English. We maintain that although both its first principles and its conclusions drawn from the research conducted by the PBOC from 2014 to date are broadly reasonable and appropriate, the PBOC has also missed some important considerations and entertained some questionable assumptions, which many central banks around the world have also done. In this analysis, we consider the strengths and weaknesses of the digital currency proposition articulated by the PBOC as it exists today, and we propose one fundamental and specific change for the PBOC and other central banks around the world: The architecture must accommodate privacy-preserving, non-custodial wallets. With this change and a related set of minor adjustments, China has an opportunity to lead the world in the implementation of a central bank digital currency (CBDC) solution that protects the authority of the central bank to implement monetary policy, preserves the role of public-sector and private-sector banking institutions, promotes the efficiency of retail transactions and businesses, satisfies regulatory objectives, and safeguards the human rights of retail consumers, including their privacy and their right to participate in the economy. We hope that the PBOC, and other central banks around the world, will have the resolve and strength of purpose to implement our proposed change and carry on with implementing a CBDC architecture that serves the interests of its users., Comment: 14 pages