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High-entropy alloys (HEAs) are potential candidates for applications as refractory materials. While dual-phase refractory HEAs containing an ordered phase exhibit high hardness, there is high interest in developing intermetallic-free and single-phase refractory HEAs with high hardness. In this study, a new equiatomic HEA ZrHfNbTaW with an ultrahigh hardness of 860 Hv is developed. The alloy is first synthesized with a dual-phase structure via arc melting and further homogenized to a single body-centered cubic (BCC) structure by phase transformation via high-pressure torsion (HPT), using the concept of ultra-severe plastic deformation process. The ultrahigh hardness of the alloy, which is higher than those reported for refractory alloys and single-phase HEAs, is attributed to (i) solution hardening by severe lattice distortion, (ii) Hall-Petch grain boundary hardening by the formation of nanograins with 12 nm average size, and (iii) dislocation hardening confirmed by high-resolution transmission electron microscopy.

The persistent existence of plastic waste causes serious problems for the environment, directly and indirectly affecting the health of organisms and humans. Photoreforming is a nature-friendly method that only uses solar energy to convert plastic waste into green hydrogen (H2) and valuable organic products. This study shows that a high-entropy oxynitride (HEON) photocatalyst, synthesized by the addition of nitrogen to a Ti-Zr-Hf-Nb-Ta-containing high-entropy oxide (HEO), exhibits a higher potential for the production of H2, formic acid and acetic acid from polyethylene terephthalate (PET) photoreforming compared to the relevant HEO. Examination of X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) by synchrotron light shows that, in addition to hybridization of 2p orbitals from oxygen and nitrogen, nitrogen atoms distort the structure and completely change the neighborhood of niobium and titanium (a main contributor to the conduction band), expands the atomic bonds of zirconium and tantalum, contracts the atomic bonds of hafnium and decreases the binding energy of titanium, niobium and tantalum. These electronic structure changes lead to a narrower bandgap and diminished electron-hole recombination, enhancing the photoreforming performance. This study introduces HEONs with distorted atomic bond structures as efficient low-bandgap and stable catalysts for transforming plastics into high-value organic chemicals and H2 by photocatalysis.

Early detection of cancer is critical in improving treatment outcomes and increasing survival rates, particularly for common cancers such as lung, breast, and prostate which collectively contribute to a significant global mortality burden. With advancements in imaging technologies and data processing, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for analyzing and classifying medical images, enabling more precise cancer detection. This paper provides a comprehensive review of recent studies leveraging CNN models for detecting ten different types of cancer. Each study employs distinct CNN architectures to identify patterns associated with these cancers, utilizing diverse datasets. Key differences and strengths of these architectures are meticulously compared and analyzed, highlighting their efficacy in improving early detection. Beyond reviewing the performance and limitations of CNN-based cancer detection methods, this study explores the feasibility of integrating CNNs into clinical settings as an early detection tool, potentially complementing or replacing traditional methods. Despite significant progress, challenges remain, including data diversity, result interpretation, and ethical considerations. By identifying the best-performing CNN architectures and providing a comparative analysis, this study aims to contribute a comprehensive perspective on the application of CNNs in cancer detection and their role in advancing diagnostic capabilities in healthcare.

Tantalum (Ta) has recently received considerable attention in manufacturing robust superconducting quantum circuits. Ta offers low microwave loss, high kinetic inductance compared to aluminium (Al) and niobium (Nb), and good compatibility with complementary metal-oxide-semiconductor (CMOS) technology, which is essential for quantum computing applications. Here, we demonstrate the fabrication engineering of thickness-dependent high quality factor (high-Q_i) Ta superconducting microwave coplanar waveguide resonators. All films are deposited on high-resistivity silicon substrates at room temperature without additional substrate heating. Before Ta deposition, a niobium (Nb) seed layer is used to ensure a body-centred cubic lattice ({\alpha}-Ta) formation. We further engineer the kinetic inductance (L_K) resonators by varying Ta film thicknesses. High L_K is a key advantage for applications because it facilitates the realisation of high-impedance, compact quantum circuits with enhanced coupling to qubits. The maximum internal quality factor Q_i of ~ 3.6 * 10^6 is achieved at the high power regime for 100 nm Ta, while the highest kinetic inductance is obtained to be 0.6 pH/sq for the thinnest film, which is 40 nm. This combination of high Q_i and high L_K highlights the potential of Ta microwave circuits for high-fidelity operations of compact quantum circuits., Comment: 18 pages, 7 figures

Sequential recommendation systems aim to provide personalized recommendations for users based on their interaction history. To achieve this, they often incorporate auxiliary information, such as textual descriptions of items and auxiliary tasks, like predicting user preferences and intent. Despite numerous efforts to enhance these models, they still suffer from limited personalization. To address this issue, we propose a new paradigm, which we term preference discerning. In preference dscerning, we explicitly condition a generative sequential recommendation system on user preferences within its context. To this end, we generate user preferences using Large Language Models (LLMs) based on user reviews and item-specific data. To evaluate preference discerning capabilities of sequential recommendation systems, we introduce a novel benchmark that provides a holistic evaluation across various scenarios, including preference steering and sentiment following. We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences. Therefore, we propose a new method named Mender ($\textbf{M}$ultimodal Prefer$\textbf{en}$ce $\textbf{d}$iscern$\textbf{er}$), which improves upon existing methods and achieves state-of-the-art performance on our benchmark. Our results show that Mender can be effectively guided by human preferences even though they have not been observed during training, paving the way toward more personalized sequential recommendation systems. We will open-source the code and benchmarks upon publication., Comment: 11 pages + references and appendix

Despite significant advancements in wireless smart implants over the last two decades, current implantable devices still operate passively and require additional electronic modules for wireless transmission of the stored biological data. To address these challenges, we propose an innovative wireless force sensing paradigm for implantable systems through the integration of mechanical metamaterials and nano energy harvesting technologies. We demonstrate composite mechanical metamaterial implants capable of serving as all-in-one wireless force sensing units, incorporating functions for power generation, sensing and transmission with ultra-low power requirements. In this alternative communication approach, the electrical signals harvested by the implants from mechanical stimuli are utilized directly for the wireless transmission of the sensed data. We conduct experimental and theoretical studies to demonstrate the wireless detection of the generated strain-induced polarization electric field using electrodes. The feasibility of the proposed wireless force sensing approach is evaluated through a proof-of-concept orthopedic implant in the form of a total knee replacement. The findings indicate that the created wireless, electronic-free metamaterial implants with a power output as low as 0.1 picowatts enable direct, self-powered wireless communication during force sensing across air, simulated body fluid and animal tissue. We validate the functionality of the proposed implants through a series of experiments conducted on an ex vivo human cadaver knee specimen. Furthermore, the effect of electrode size and placement on the strength of the received signals is examined. Finally, we highlight the potential of our approach to create a diverse array of mechanically-tunable wireless force sensing implants without relying on any external power sources.

Photoreforming is a sustainable photocatalytic process that degrades plastic waste while simultaneously producing hydrogen (H2) from water. However, this process has received limited attention due to the scarcity of effective catalysts capable of both plastic degradation and H2 production, such as titanium dioxide (TiO2). In this study, an active catalyst is developed by stabilizing the high-pressure orthorhombic phase of TiO2, known as columbite, using a high pressure torsion (HPT) method. This material effectively degrades polyethylene terephthalate (PET) plastic under light, converting it into valuable organic compounds such as formic acid, terephthalate, glycolic acid, and acetic acid. Additionally, it produces a significant amount of H2. The findings show that the high-pressure orthorhombic phase, especially in the presence of oxygen vacancies, enhances catalytic H2 production and microplastic degradation by increasing light absorption, reducing electron-hole recombination, and generating hydroxyl radicals. These results highlight the substantial potential of modified high-pressure TiO2 photocatalysts in simultaneously addressing the plastic waste crisis and the demand for H2 fuel.

Bismuth (III) oxide (Bi2O3) has been highly studied as a photocatalyst for green hydrogen production due to its low band gap, yet its efficiency requires enhancement. This study synthesizes a defective and strained black Bi2O3 by severe straining under high pressure, via a high-pressure torsion method, to improve its photocatalytic hydrogen production. The material rich in oxygen vacancies exhibits a ten-fold improvement in water splitting with excellent cycling stability. Such improvement is due to improved light absorption, narrowing band gap and reduced irradiative electron-hole recombination. Moreover, the valence band bottom energy positively increases by straining leading to a high overpotential for hydrogen production. This research highlights the potential of vacancies and lattice strain in developing dopant-free active catalysts for water splitting.

Simultaneous catalytic hydrogen (H2) production and plastic waste degradation under light, known as photoreforming, is a novel approach to green fuel production and efficient waste management. Here, we use a high-entropy oxide (HEO), a new family of catalysts with five or more principal cations in their structure, for plastic degradation and simultaneous H2 production. The HEO shows higher activity than that of P25 TiO2, a benchmark photocatalyst, for the degradation of polyethylene terephthalate (PET) plastics in water. Several valuable products are produced by photoreforming of PET bottles and microplastics including H2, terephthalate, ethylene glycol and formic acid. The high activity is attributed to the diverse existence of several cations in the HEO lattice, lattice defects, and appropriate charge carrier lifetime. These findings suggest that HEOs possess high potential as new catalysts for concurrent plastic waste conversion and clean H2 production.

Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representations. However, this approach requires storing a unique representation for each item, resulting in significant memory requirements as the number of items grow. In contrast, the recently proposed generative retrieval paradigm offers a promising alternative by directly predicting item indices using a generative model trained on semantic IDs that encapsulate items' semantic information. Despite its potential for large-scale applications, a comprehensive comparison between generative retrieval and sequential dense retrieval under fair conditions is still lacking, leaving open questions regarding performance, and computation trade-offs. To address this, we compare these two approaches under controlled conditions on academic benchmarks and propose LIGER (LeveragIng dense retrieval for GEnerative Retrieval), a hybrid model that combines the strengths of these two widely used methods. LIGER integrates sequential dense retrieval into generative retrieval, mitigating performance differences and enhancing cold-start item recommendation in the datasets evaluated. This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.

Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performance of a system and its runtime activities. This process typically examines various aspects of a system's runtime behavior, such as the execution frequency of functions or methods, to forecast performance metrics like program execution time. By using performance models, developers can predict expected performance and thereby effectively identify and address unexpected performance regressions when actual performance deviates from the model's predictions. One common and precise method for capturing performance behavior is software tracing, which involves instrumenting the execution of a program, either at the kernel level (e.g., system calls) or application level (e.g., function calls). However, due to the nature of tracing, it can be highly resource-intensive, making it impractical for production environments where resources are limited. In this work, we propose statistical approaches to reduce tracing overhead by identifying and excluding performance-insensitive code regions, particularly application-level functions, from tracing while still building accurate performance models that can capture performance degradations. By selecting an optimal set of functions to be traced, we can construct optimized performance models that achieve an R-2 score of up to 99% and, sometimes, outperform full tracing models (models using non-optimized tracing data), while significantly reducing the tracing overhead by more than 80% in most cases. Our optimized performance models can also capture performance regressions in our studied programs effectively, demonstrating their usefulness in real-world scenarios. Our approach is fully automated, making it ready to be used in production environments with minimal human effort.

Investigating the microscopic details of the proximity effect is crucial for both key experimental applications and fundamental inquiries into nanoscale devices featuring superconducting elements. In this work, we develop a framework motivated by experiments to study induced superconducting correlations in hybrid nanoscale devices featuring layered superconductor-normal heterostructures using the Keldysh non-equilibrium Green's functions. Following a detailed method for analyzing the induced pair amplitude in a prototypical one-dimensional hybrid, we provide insights into the proximity effect within and outside the Andreev approximation. Our analysis also uncovers a disorder-induced crossover in the correlation patterns of the system. By elucidating the spectral distribution of the induced pair amplitude, we investigate the pair correlations established in a recent experiment [Phys.Rev.Lett.128,127701], providing a theoretical basis for the enhanced Cooper pair injection demonstrated through the lens of the induced pair correlations, thereby establishing the promise of our methods in guiding new experiments in hybrid quantum devices., Comment: 10 pages, 6 figures. comments welcome

Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency (RF) chain, giving rise to the concept of fluid antenna multiple access (FAMA). While FAMA is originally designed to deal with interference mainly by position change and treat interference as noise, this is not rate optimal, especially when suffering from a strong interference channel (IC) where all positions have strong interference. To tackle this, this paper considers a two-user strong IC where FAMA is used in conjunction with simultaneous nonunique decoding (SND). Specifically, we analyze the key statistics for the signal-to-noise ratio (SNR) and interference-to-noise ratio (INR) for a canonical two-user IC setup, and subsequently derive the delay outage rate (DOR), outage probability (OP) and ergodic capacity (EC) of the FAMA-IC. Our numerical results illustrate huge benefits of FAMA with SND over traditional fixed-position antenna systems (TAS) with SND in the fading IC.

The Martian isotopic record displays a dichotomy in volatile compositions. Interior volatiles from the mantle record a chondritic heritage (e.g., H, N, Kr, Xe) whereas the atmospheric reservoir of Kr and Xe - which do not currently experience escape - record heritage from a solar-like source. Motivated by disparate inferences on the source of Martian atmospheric volatiles (outgassed versus nebular captured), we consider hybrid-source accretionary atmospheres in which a high molecular weight (e.g., CO2-rich) outgassed component is mixed in with the low molecular weight H2/He-rich nebular atmosphere. We conduct calculations of nebular capture with and without a mixed-in high molecular weight outgassed component during the lifetime of the solar nebula. Mixing in an outgassed component enhances the captured nebular inventory by 1-3 orders of magnitude - depending on the outgassed inventory - relative to "pure" nebular capture. These observations and calculations suggest that the Martian atmosphere arose as a hybrid mixture of outgassed and nebular-derived components and that - irrespective of the precise composition of the outgassed component - was mainly composed of molecular hydrogen. The consequences for Martian atmospheric history are discussed., Comment: 23 Pages, 4 Figures

Transformers serve as the backbone architectures of Foundational Models, where a domain-specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have recently emerged as a leading model in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks. However, the development of tokenizers for graphs has lagged behind other modalities, with existing approaches relying on heuristics or GNNs co-trained with Transformers. To address this, we introduce GQT (\textbf{G}raph \textbf{Q}uantized \textbf{T}okenizer), which decouples tokenizer training from Transformer training by leveraging multi-task graph self-supervised learning, yielding robust and generalizable graph tokens. Furthermore, the GQT utilizes Residual Vector Quantization (RVQ) to learn hierarchical discrete tokens, resulting in significantly reduced memory requirements and improved generalization capabilities. By combining the GQT with token modulation, a Transformer encoder achieves state-of-the-art performance on 16 out of 18 benchmarks, including large-scale homophilic and heterophilic datasets. The code is available at: https://github.com/limei0307/graph-tokenizer

Large Language Models (LLMs), despite achieving state-of-the-art results in a number of evaluation tasks, struggle to maintain their performance when logical reasoning is strictly required to correctly infer a prediction. In this work, we propose Argument Generation as a method of forcing models to utilize their reasoning capabilities when other approaches such as chain-of-thought reasoning prove insufficient. Our method involves the generation of arguments for each possible inference result, and asking the end model to rank the generated arguments. We show that Argument Generation can serve as an appropriate substitute for zero-shot prompting techniques without the requirement to add layers of complexity. Furthermore, we argue that knowledge-probing techniques such as chain-of-thought reasoning and Argument Generation are only useful when further reasoning is required to infer a prediction, making them auxiliary to more common zero-shot approaches. Finally, we demonstrate that our approach forces larger gains in smaller language models, showcasing a complex relationship between model size and prompting methods in foundation models., Comment: Accepted to Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual at EMNLP 2024

Language Models (LMs) are increasingly challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks. Following this trend, we propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks, without requiring any architectural modification. By preserving the LM's original architecture, our approach retains a key benefit of LM instruction tuning: the ability to jointly train on diverse datasets, fostering greater flexibility and efficiency. To achieve this, we introduce two key augmentation strategies: (1) Enriching LMs' input using topological and semantic retrieval methods, which provide richer contextual information, and (2) guiding the LMs' classification process through a lightweight GNN classifier that effectively prunes class candidates. Our experiments on real-world datasets show that backbone Flan-T5 models equipped with these augmentation strategies outperform state-of-the-art text-output node classifiers and are comparable to top-performing vector-output node classifiers. By bridging the gap between specialized task-specific node classifiers and general LMs, this work paves the way for more versatile and widely applicable graph learning models. We will open-source the code upon publication.

Backscatter communication (BC) emerges as a pivotal technology for ultra-low-power energy harvesting applications, but its practical deployment is often hampered by notable security vulnerabilities. Physical layer authentication (PLA) offers a promising solution for securing BC by leveraging the unique characteristics of the communication medium. However, existing PLA approaches often fall short due to limited signal strength in practical BC scenarios and performance deterioration with increasing distance between the tag and the reader. Moreover, achieving mutual authentication has been largely neglected in current PLA schemes, given the passive nature of tags and their limited computational and energy resources. This paper introduces a reconfigurable intelligent surfaces (RIS)-aided PLA scheme based on the physical features of received signals at legitimate endpoints through cascade links in monostatic BC (MBC) systems. By considering a RIS operating in its near-optimal conditions between a tag and a reader, the proposed PLA leverages the RIS-enhanced power delivery detected by the tag's energy detector and the optimized received signal strength (RSS) at the reader's signal processing unit, addressing the conventional challenges of mutual authentication, low PLA performance, and limited secure coverage area inherent in BC systems. Through theoretical analysis and extensive simulations, we show that as long as RIS is controlled by a trusted party in the network, it can boost the authentication performance across different system settings and strengthen the security features. Additionally, we analyze to explore the potential security threats when the RIS is compromised by an adversary, assessing its impact on the system's PLA performance and secrecy capacity, providing a comprehensive understanding of the security implications for RIS-aided MBC under such circumstances.

Backscatter communication (BC) has emerged as a pivotal wireless communication paradigm owing to its low-power and cost-effective characteristics. However, BC faces various challenges from its low signal detection rate to its security vulnerabilities. Recently, reconfigurable intelligent surfaces (RIS) have surfaced as a transformative technology addressing power and communication performance issues in BC. However, the potential of RIS in addressing the security challenges of BC remains uncharted. This paper investigates the secrecy performance of RIS-aided BC, where all channels are distributed according to the Fisher-Snedecor $\mathcal{F}$ distribution. Specifically, we consider a RIS with $N$ reflecting elements to help a backscatter device (BD) establish a smart environment and enhance the secrecy performance in BC. Due to the nature of BC systems, our analysis considers two possible scenarios (i) in the absence of direct links and (ii) in the presence of direct links. In both cases, we first derive compact analytical expressions of the probability density function (PDF) and cumulative distribution function (CDF) for the received signal-to-noise ratio (SNR) at both a legitimate receiver and an eavesdropper. Then, to analyze the secrecy performance, we further derive analytical expressions of the average secrecy capacity (ASC) and secrecy outage probability (SOP) for both mentioned scenarios. In addition, regarding the importance of system behavior in a high SNR regime, we provide an asymptotic analysis of the SOP and ASC. Eventually, the Monte-Carlo simulation is used to validate the analytical results, revealing that utilizing RIS can greatly improve the secrecy performance of the BC system relative to traditional BC setups that do not incorporate RIS.

Cryptography misuses are prevalent in the wild. Crypto APIs are hard to use for developers, and static analysis tools do not detect every misuse. We developed SafEncrypt, an API that streamlines encryption tasks for Java developers. It is built on top of the native Java Cryptography Architecture, and it shields developers from crypto complexities and erroneous low-level details. Experiments showed that SafEncrypt is suitable for developers with varying levels of experience., Comment: ESEM 2024

Early prediction of seizures and timely interventions are vital for improving patients' quality of life. While seizure prediction has been shown in software-based implementations, to enable timely warnings of upcoming seizures, prediction must be done on an edge device to reduce latency. Ideally, such devices must also be low-power and track long-term drifts to minimize maintenance from the user. This work presents SPIRIT: Stochastic-gradient-descent-based Predictor with Integrated Retraining and In situ accuracy Tuning. SPIRIT is a complete system-on-a-chip (SoC) integrating an unsupervised online-learning seizure prediction classifier with eight 14.4 uW, 0.057 mm2, 90.5 dB dynamic range, Zoom Analog Frontends. SPIRIT achieves, on average, 97.5%/96.2% sensitivity/specificity respectively, predicting seizures an average of 8.4 minutes before they occur. Through its online learning algorithm, prediction accuracy improves by up to 15%, and prediction times extend by up to 7x, without any external intervention. Its classifier consumes 17.2 uW and occupies 0.14 mm2, the lowest reported for a prediction classifier by >134x in power and >5x in area. SPIRIT is also at least 5.6x more energy efficient than the state-of-the-art., Comment: Lacking critical data to prove an end-to-end verification of the system

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at https://github.com/FZJ-JSC/jubench., Comment: To be published in Proceedings of The International Conference for High Performance Computing Networking, Storage, and Analysis (SC '24) (2024)

Backscattering tag-to-tag networks (BTTNs) represent a passive radio frequency identification (RFID) system that enables direct communication between tags within an external radio frequency (RF) field. However, low spectral efficiency and short-range communication capabilities, along with the ultra-low power nature of the tags, create significant challenges for reliable and practical applications of BTTNs. To address these challenges, this paper introduces integrating an indoor reconfigurable intelligent surface (RIS) into BTTN and studying RIS's impact on the system's performance. To that end, we first derive compact analytical expressions of the probability density function (PDF) and cumulative distribution function (CDF) for the received signal-to-noise ratio (SNR) at the receiver tag by exploiting the moment matching technique. Then, based on the derived PDF and CDF, we further derive analytical expressions of outage probability (OP), bit error rate (BER), and average capacity (AC) rate. Eventually, the Monte Carlo simulation is used to validate the accuracy of the analytical results, revealing that utilizing RIS can greatly improve the performance of BTTNs in terms of AC, BER, OP, and coverage region relative to traditional BTTNs setups that do not incorporate RIS.

This paper examines the impact of emerging fluid antenna systems (FAS) on reconfigurable intelligent surface (RIS)-aided secure communications. Specifically, we consider a classic wiretap channel, where a fixed-antenna transmitter sends confidential information to an FAS-equipped legitimate user with the help of an RIS, while an FAS-equipped eavesdropper attempts to decode the message. To evaluate the proposed wireless scenario, we first introduce the cumulative distribution function (CDF) and probability density function (PDF) of the signal-to-noise ratio (SNR) at each node, using the central limit theorem and the Gaussian copula function. We then derive a compact analytical expression for the secrecy outage probability (SOP). Our numerical results reveal how the incorporation of FAS and RIS can significantly enhance the performance of secure communications.

Astronomical impacts by small solar system bodies (meteoroids, asteroids, comets, and transitional objects) are considered a mechanism for delivering amino acids and their polymerization to proteins in early Earth conditions. High-pressure torsion (HPT) is a new methodology to simulate such impacts and clarify the behavior of biomolecules. In this study, two amino acids, crystalline L-serine and L-glutamic acid that were detected in meteorites, are processed by HPT and examined by ex situ X-ray diffraction, Raman spectroscopy, nuclear magnetic resonance, Fourier transform infrared spectroscopy, and in situ mechanical shear testing. No polymerization, chemical reactions, or phase transformations are detected after HPT, indicating that the stability and presence of these two amino acids in meteorites are quite reasonable. However, some microstructural and mechanical changes like crystal size reduction to the nanometer level, crystal defect formation, lattice expansion by vacancy formation, and shear strength enhancement to the steady state are found which are similar to the behaviors reported in metals and ceramics after HPT processing.

Plastic waste entering the environment through landfilling or improper disposal poses substantial risks to ecosystems and human health. Photoreforming is emerging as a clean photocatalytic technology that degrades plastic waste to organic compounds while simultaneously producing hydrogen fuel. This study introduces high-pressure torsion (HPT), a severe plastic deformation (SPD) method, as an innovative technique to enhance the photoreforming of polypropylene (PP) plastic mixed with a brookite TiO2 photocatalyst. Hydrogen production systematically increases with the number of HPT turns, accompanied by the formation of valuable small organic molecules. The enhancement in photocatalytic activity is attributed to strain-induced defect formation in both catalysts and plastics, as well as the creation of catalyst/plastic interphases that enhance charge carrier transport between inorganic and organic phases. These findings reveal a new functional application for SPD in energy conversion and sustainability.

Photoreforming is a clean photocatalytic technology for simultaneous plastic waste degradation and hydrogen fuel production, but there are still limited active and stable catalysts for this process. This work introduces the brookite polymorph of TiO2 as an active photocatalyst for photoreforming with an activity higher than anatase and rutile polymorphs for both hydrogen production and plastic degradation. Commercial brookite successfully converts polyethylene terephthalate (PET) plastic to acetic acid under light. The high activity of brookite is attributed to good charge separation, slow decay and moderate electron trap energy, which lead to a higher generation of hydrogen and hydroxyl radicals and accordingly enhanced photo-oxidation of PET plastic. These results introduce brookite as a stable and active catalyst for the photoconversion of water contaminated with microplastics to value-added organic compounds and hydrogen.

Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We explore a different device form factor, where dry electrodes are placed around the neck instead. 11-word, multi-speaker voiced EMG classifiers trained on data recorded with this device achieve 92.7% accuracy. Ablation studies reveal the importance of having more than two electrodes on the neck, and phonological analyses reveal similar classification confusions between neck-only and neck-and-face form factors. Finally, speech-EMG correlation experiments demonstrate a linear relationship between many EMG spectrogram frequency bins and self-supervised speech representation dimensions.

For finite dimensional algebras over algebraically closed fields, we study the sets of pairwise Hom-orthogonal modules and obtain new results on some open conjectures on the behaviour of bricks and several related problems, which we generally refer to as brick-Brauer-Thrall (bBT) conjectures. Using some algebraic and geometric tools, and in terms of the notion of Hom-orthogonality, we find necessary and sufficient conditions for the existence of infinite families of bricks of the same dimension. This sheds new lights on the bBT conjectures and we prove many of them for some new families of algebras. Our results imply some interesting algebraic and geometric characterizations of brick-finite algebras as conceptual generalizations of local algebras. We also verify the bBT conjectures for any algebra whose Auslander-Reiten quiver has a generalized standard component. On the one hand, our work relates and develops some recent studies of the bBT conjectures which are conducted in several directions. On the other hand, it extends some earlier results of Chindris-Kinser-Weyman, where the algebras with preprojective components were treated., Comment: 23 pages

Convolutional Neural Network (CNN) models often lack the ability to incorporate human input, which can be addressed by Augmented Reality (AR) headsets. However, current AR headsets face limitations in processing power, which has prevented researchers from performing real-time, complex image recognition tasks using CNNs in AR headsets. This paper presents a method to deploy CNN models on AR headsets by training them on computers and transferring the optimized weight matrices to the headset. The approach transforms the image data and CNN layers into a one-dimensional format suitable for the AR platform. We demonstrate this method by training the LeNet-5 CNN model on the MNIST dataset using PyTorch and deploying it on a HoloLens AR headset. The results show that the model maintains an accuracy of approximately 98%, similar to its performance on a computer. This integration of CNN and AR enables real-time image processing on AR headsets, allowing for the incorporation of human input into AI models., Comment: 10 pages 8 figures 4300 words

The fields of machine learning and artificial intelligence drive researchers to explore energy-efficient, brain-inspired new hardware. Reservoir computing encompasses recurrent neural networks for sequential data processing and matches the performance of other recurrent networks with less training and lower costs. However, traditional software-based neural networks suffer from high energy consumption due to computational demands and massive data transfer needs. Photonic reservoir computing overcomes this challenge with energy-efficient neuromorphic photonic integrated circuits or NeuroPICs. Here, we introduce a reservoir NeuroPIC used for modulation format identification in C-band telecommunication network monitoring. It is built on a silicon-on-insulator platform with a 4-port reservoir architecture consisting of a set of physical nodes connected via delay lines. We comprehensively describe the NeuroPIC design and fabrication, experimentally demonstrate its performance, and compare it with simulations. The NeuroPIC incorporates non-linearity through a simple digital readout and achieves close to 100% accuracy in identifying several configurations of quadrature amplitude modulation formats transmitted over 20 km of optical fiber at 32 GBaud symbol rate. The NeuroPIC performance is robust against fabrication imperfections like waveguide propagation loss, phase randomization, etc. and delay line length variations. Furthermore, the experimental results exceeded numerical simulations, which we attribute to enhanced signal interference in the experimental NeuroPIC output. Our energy-efficient photonic approach has the potential for high-speed temporal data processing in a variety of applications., Comment: 32 pages, including supporting information

We give a combinatorial classification of torus equivariant vector bundles on a (normal) projective T-variety of complexity-one. This extends the classification of equivariant line bundles on complexity-one T-varieties by Petersen-S\"uss on one hand, and Klyachko's classification of equivariant vector bundles on toric varieties on the other hand. A main ingredient in our classification is the classification of torus equivariant vector bundles on toric schemes over a DVR in terms of piecewise affine maps to the (extended) Bruhat-Tits building of the general linear group., Comment: 29 pages, 1 figure. arXiv admin note: text overlap with arXiv:2402.18712

Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundant manipulator. Utilizing Rapidly Random Tree Star (RRT*) and Spherical Linear Interpolation (SLERP) algorithms, the interface achieves end-effector s progression through collision-free path with smooth rotation. Next, Sequential Quadratic Programming (SQP) achieve robot s configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a seven Degree-OF-Freedom (DOF) manipulator, indicating its potential to advance current practices in robot programming. The validation of this paper include two implementations demonstrating the value of human-in-the-loop and context awareness in robotics., Comment: 10 pages, 13 figures

High-pressure torsion (HPT) can facilitate phase transformations in titanium dioxide (TiO2) and stabilize its high-pressure columbite phase, as an active photocatalyst, by shear straining under high pressure. This study aims to understand the mechanism underlying the acceleration of the anatase-to-columbite phase transformation by shear strain. A mechanism by considering sheared crystal structures as intermediate phases was proposed and examined using quantum mechanics in the framework of density functional theory (DFT) and HPT experiments. DFT energy and phonon calculations demonstrated the viability of the sheared structures as intermediate phases. Furthermore, the sheared structures were observed experimentally as new metastable phases using high-resolution transmission electron microscopy. These findings can explain the significant effect of shear strain on pressure-induced phase transitions, reported during severe plastic deformation of various metals and ceramics.

High-entropy oxides (HEOs), containing at least five principal cations, have recently emerged as promising photocatalysts for hydrogen production via water splitting. Despite their high potential, the impact of the cation mixtures on photocatalytic activity remains poorly understood. This study investigates the high-entropy photocatalyst TiZrHfNbTaO11 using first-principles calculations combined with experimental methods to elucidate the effects of various elements on electronic structure and water splitting performance. The results indicate that the HEO exhibits a bandgap comparable to TiO2 polymorphs rutile, brookite and anatase. Cations with lower electronegativity, such as hafnium and zirconium, provide the strongest water adsorption energy, serving as active sites for water adsorption. Additionally, the co-presence of highly electronegative cations like niobium and tantalum adjacent to hafnium and zirconium enhances charge transfer to water molecules, improving splitting efficiency. These findings suggest novel strategies for designing high-entropy photocatalysts by synergistic incorporating cations with different electronegativities.

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been shown to reversibly store hydrogen at room temperature, but the design of HEAs with appropriate thermodynamics is still challenging. To explore HEAs with appropriate hydride formation enthalpy, this study employs machine learning (ML), in particular, Gaussian process regression (GPR) using four different kernels by training with 420 datum points collected from literature and curated here. The developed ML models are used to predict the formation enthalpy of hydrides for the TixZr2-xCrMnFeNi (x = 0.5, 1.0 and 1.5) system, which is not in the training set. The predicted values by ML are consistent with data from experiments and density functional theory (DFT). The present study thus introduces ML as a rapid and reliable approach for the design of HEAs with hydride formation enthalpies of -25 to -39 kJ/mol for hydrogen storage at room temperature.

Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce the first-of-its-kind algorithm that can be combined with any existing DP mechanisms to generate synthetic relational databases. Our algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors in terms of low-order marginal distributions while maintaining referential integrity. Finally, we provide both DP and theoretical utility guarantees for our algorithm.

This paper studies the performance of a wireless powered communication network (WPCN) under the non-orthogonal multiple access (NOMA) scheme, where users take advantage of an emerging fluid antenna system (FAS). More precisely, we consider a scenario where a transmitter is powered by a remote power beacon (PB) to send information to the planar NOMA FAS-equipped users through Rayleigh fading channels. After introducing the distribution of the equivalent channel coefficients to the users, we derive compact analytical expressions for the outage probability (OP) in order to evaluate the system performance. Additionally, we present asymptotic OP in the high signal-to-noise ratio (SNR) regime. Eventually, results reveal that deploying the FAS with only one activated port in NOMA users can significantly enhance the WPCN performance compared with using traditional antenna systems (TAS)., Comment: This manuscript has been submitted to the 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

In this paper we propose an $\ell_1$-regularized GLS estimator for high-dimensional regressions with potentially autocorrelated errors. We establish non-asymptotic oracle inequalities for estimation accuracy in a framework that allows for highly persistent autoregressive errors. In practice, the Whitening matrix required to implement the GLS is unkown, we present a feasible estimator for this matrix, derive consistency results and ultimately show how our proposed feasible GLS can recover closely the optimal performance (as if the errors were a white noise) of the LASSO. A simulation study verifies the performance of the proposed method, demonstrating that the penalized (feasible) GLS-LASSO estimator performs on par with the LASSO in the case of white noise errors, whilst outperforming it in terms of sign-recovery and estimation error when the errors exhibit significant correlation., Comment: 13 pages, 6 figures

High-entropy alloys (HEAs) and their corresponding high-entropy hydrides are new potential candidates for negative electrode materials of nickel-metal hydride (Ni-MH) batteries. This study investigates the cyclic electrochemical hydrogen storage performance of two AB-type HEAs (A: hydride-forming elements, B: non-hydride-forming elements) in Ni-MH batteries. TiV2ZrCrMnFeNi with a dual-phase structure shows a fast activation and a low charge transfer impedance with a discharge capacity of 150 mAhg-1, while TiV1.5Zr1.5CrMnFeNi with a single phase shows a slow activation and a capacity of only 60 mAhg-1. The better electrochemical performance of TiV2ZrCrMnFeNi was attributed to its higher vanadium/zirconium ratio and abundant interphase boundaries, which act as hydrogen paths and heterogeneous hydride nucleation sites. These results suggest the high potential of dual-phase HEAs as new active electrode materials for Ni-MH batteries.

We introduce a notion of tropical vector bundle on a tropical toric variety which is a tropical analogue of a torus equivariant vector bundle on a toric variety. Alternatively it can be called a toric matroid bundle. We define equivariant $K$-theory and characteristic classes of these bundles. As a particular case, we show that any matroid comes with tautological tropical toric vector bundles over the permutahedral toric variety and the corresponding equivariant $K$-classes and Chern classes recover the tautological classes of matroids constructed in the recent work of Berger-Eur-Spink-Tseng. In analogy with toric vector bundles, we define sheaf of sections and Euler characteristic as well as positivity notions such as global generation, ampleness and nefness for tropical toric vector bundles. Moreover, we prove a vanishing of higher cohomologies result. Finally, we study the splitting of our tropical toric vector bundles and, in particular, an analogue of Grothendieck's theorem on splitting of vector bundles on projective line., Comment: Title changed. We now call the main combinatorial objects introduced in the paper "tropical toric vector bundles" (previously we called them "toric matroid bundles"). New material added, in particular a theorem about vanishing of higher cohomologies (after tensoring with a high power of an ample line bundle) was added. 38 pages, 3 figures

Background: Aortic stenosis (AS) is the most common acquired valvar disease and is associated with increased risk for frailty. Frailty as a geriatric syndrome is associated with muscle weakness and a compromised autonomic nervous system (ANS) performance in older adults. The purpose of the current work was to assess differences in both motor and ANS performance, and interaction between them, as symptoms of frailty in community dwelling older adults with and without AS. Results: Eighty-six participants were recruited, including 30 with (age=72$\pm$11, 10 non-frail and 20 pre-frail/frail) and 56 without AS (age=80$\pm$8, 12 non-frail and 44 pre-frail/frail). There was a significant difference in UEF motor score between older adults with and without AS (p<0.01, mean values of 0.57$\pm$0.25 and 0.48$\pm$0.23, respectively). Differences in UEF motor score was also observed between the frailty groups (p=0.02, mean values of 0.55$\pm$0.24 and 0.40$\pm$0.20 for pre-frail/frail and non-frail, respectively). CCM parameters showed significant differences between the frailty groups (p=0.02, mean CCM of 0.69$\pm$0.05 for non-frail and 0.54$\pm$0.03 for pre-frail/frail), but not between the AS groups (p>0.70). No significant interaction was observed between frailty and AS condition (p>0.08). Conclusion: Current findings suggest that ANS measures may be highly associated with frailty regardless of AS condition. Combining motor and HR dynamics parameters in a multimodal model may provide a promising tool for frailty assessment, Comment: arXiv admin note: substantial text overlap with arXiv:2303.13591

The mass production and the practical number of cryogenic quantum devices producible in a single chip are limited to the number of electrical contact pads and wiring of the cryostat or dilution refrigerator. It is, therefore, beneficial to contrast the measurements of hundreds of devices fabricated in a single chip in one cooldown process to promote the scalability, integrability, reliability, and reproducibility of quantum devices and to save evaluation time, cost and energy. Here, we use a cryogenic on-chip multiplexer architecture and investigate the statistics of the 0.7 anomaly observed on the first three plateaus of the quantized conductance of semiconductor quantum point contact (QPC) transistors. Our single chips contain 256 split gate field effect QPC transistors (QFET) each, with two 16-branch multiplexed source-drain and gate pads, allowing individual transistors to be selected, addressed and controlled through an electrostatic gate voltage process. A total of 1280 quantum transistors with nano-scale dimensions are patterned in 5 different chips of GaAs heterostructures. From the measurements of 571 functioning QPCs taken at temperatures T= 1.4 K and T= 40 mK, it is found that the spontaneous polarisation model and Kondo effect do not fit our results. Furthermore, some of the features in our data largely agreed with van Hove model with short-range interactions. Our approach provides further insight into the quantum mechanical properties and microscopic origin of the 0.7 anomaly in QPCs, paving the way for the development of semiconducting quantum circuits and integrated cryogenic electronics, for scalable quantum logic control, readout, synthesis, and processing applications.

Mode Division Multiplexing (MDM) is a technique used over the past decade in Silicon Photonics (SiPh) to incorporate more data into communication links by employing higher-order transverse electric or transverse magnetic modes. MDM was primarily used in optical communication; however, in recent years, there have been several applications of MDM in optical computing, including both classical and quantum computing. Although MDM has shown great promise for increasing the throughput of optical communication and the accuracy and fidelity of optical computation, there are a few challenges towards expanding its applications. One major challenge is the lack of process design kits (PDKs) and building block libraries compatible with standard SiPh foundries. Here, we present a comprehensive library of MDM components developed using classical and inverse design, compatible with standard 220 nm SiPh foundries. The library includes thermo-optic phase shifters, mode multiplexers and demultiplexers, mode converters, mode exchangers, and multi-mode interference couplers. We also discuss our recent achievements in MDM for datacom, classical and quantum optical computing, including a mode-selective switch for mode-reconfigurable optical add-drop multiplexer (ROADM), multimode multiply-accumulate operation, and multimode photonic quantum processors.

We prove two versions of a global implicit function theorem, which involve no loss of derivative, for Keller's $ C_c^1 $-mappings between arbitrary Fr\'{e}chet spaces. Subsequently, within this framework, we apply these theorems to establish the global existence and uniqueness of solutions to initial value problems that involve the loss of one derivative. Moreover, we prove a Lagrange multiplier theorem by employing indirect applications of the global implicit function theorems through submersions and transversality.

This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

Let $G$ be a connected reductive algebraic group over $\mathbb{C}$ with a maximal compact subgroup $K$. Let $G/H$ be a (quasi-affine) spherical homogeneous space. In the first part of the paper, following Akhiezer's definition of spherical functions, we introduce a $K$-invariant map $sLog_{\Gamma, t}: G/H \to \mathbb{R}^s$ which depends on a choice of a finite set $\Gamma$ of dominant weights and $s = |\Gamma|$. We call $sLog_{\Gamma, t}$ a spherical logarithm map. We show that when $\Gamma$ generates the highest weight monoid of $G/H$, the image of the spherical logarithm map parametrizes $K$-orbits in $G/H$. This idea of using the spherical functions to understand the geometry of the space $K \backslash G/H$ of $K$-orbits in $G/H$ can be viewed as a generalization of the classical Cartan decomposition. In the second part of the paper, we define the spherical amoeba (depending on $\Gamma$ and $t$) of a subvariety $Y$ of $G/H$ as $sLog_{\Gamma, t}(Y)$, and we ask for conditions under which the image of a subvariety $Y \subset G/H$ under $sLog_{\Gamma, t}$ converges, as $t \to 0$, in the sense of Kuratowski to its spherical tropicalization as defined by Tevelev and Vogiannou. We prove a partial result toward answering this question, which shows in particular that the valuation cone is always contained in the Kuratowski limit of the spherical amoebae of $G/H$. We also show that the limit of the spherical amoebae of $G/H$ is equal to its valuation cone in a number of interesting examples, including when $G/H$ is horospherical, and in the case when $G/H$ is the space of hyperbolic triangles., Comment: 29 pages, 2 figures