Class Of Processes Research Articles

Architectural technology and labour organisation at the late Neolithic Liangzhu City, Yangtze Delta region, China

Building the UNESCO World Cultural Heritage site of Liangzhu City and its hinterlands was an enormous undertaking that required an unprecedented level of architectural innovations, clever logistic planning, and sophisticated labour organisation. We draw on environmental, archaeological and experimental data on the preparation and construction of grass-wrapped clay blocks at the Liangzhu City and investigate the importance of technological innovations to understand architectural energetics and labour organisation behind the unprecedented urbanisation at Liangzhu and beyond in prehistoric Yangtze Delta region. We estimate that each clay block took only around 5 min to prepare by 2–4 workers. Considering digging and other tasks, a small group of 3–4 workers would have been the most efficient arrangement in the preparation of grass-wrapped clay blocks. We reproduced different types of clay blocks, classified their sizes, and identified the standard sized (45x15 × 15cm) clay blocks that match with archaeological finds. Our results also show that standard-sized clay blocks had a more optimal grass-earth ratio which increased the drainage efficiency of the built clay-blocked structures and their resistance to weathering in wet conditions. The different architectural technologies applied by the Liangzhu builders constituted the Liangzhu builders’ earth-building toolkit. To maximize the efficiency, labour was divided and organised according to different construction tasks. The clay-blocked structures and stone beddings were mostly built by smaller groups, who were responsible for the preparation, transportation and construction tasks. Mobilization and division of labour during these construction activities might not follow the classical top-down process. Instead, the need to apply different architectural technologies, each restrained by availability of resources, convenience of transportation, and/or intrinsic engineering properties, at construction sites in a predominantly aquatic environment, prompted a creative form of labour organisation. Our study therefore provides fresh insights into understanding labour organisation, social structure and power relations at the Liangzhu Civilisation.

Improved clinical data imputation via classical and quantum determinantal point processes

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.

Improved financial forecasting via quantum machine learning

Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.

The quantum cartpole: A benchmark environment for non-linear reinforcement learning

Feedback-based control is the de-facto standard when it comes to controlling classical stochastic systems and processes. However, standard feedback-based control methods are challenged by quantum systems due to measurement induced backaction and partial observability. Here we remedy this by using weak quantum measurements and model-free reinforcement learning agents to perform quantum control. By comparing control algorithms with and without state estimators to stabilize a quantum particle in an unstable state near a local potential energy maximum, we show how a trade-off between state estimation and controllability arises. For the scenario where the classical analogue is highly nonlinear, the reinforcement learned controller has an advantage over the standard controller. Additionally, we demonstrate the feasibility of using transfer learning to develop a quantum control agent trained via reinforcement learning on a classical surrogate of the quantum control problem. Finally, we present results showing how the reinforcement learning control strategy differs from the classical controller in the non-linear scenarios.

Giant electron-mediated phononic nonlinearity in semiconductor-piezoelectric heterostructures.

Efficient and deterministic nonlinear phononic interactions could revolutionize classical and quantum information processing at radio frequencies in much the same way that nonlinear photonic interactions have at optical frequencies. Here we show that in the important class of phononic materials that are piezoelectric, deterministic nonlinear phononic interactions can be enhanced by orders of magnitude via the heterogeneous integration of high-mobility semiconductor materials. To this end, a lithium niobate and indium gallium arsenide heterostructure is utilized to produce the most efficient three- and four-wave phononic mixing to date, to the best of our knowledge. We then show that the conversion efficiency can be further enhanced by applying semiconductor bias fields that amplify the phonons. We present a theoretical model that accurately predicts the three-wave mixing efficiencies in this work and extrapolate that these nonlinearities can be enhanced far beyond what is demonstrated here by confining phonons to smaller dimensions in waveguides and optimizing the semiconductor material properties.

BBD: a new hybrid method for geospatial building boundary detection from huge size satellite imagery

AbstractBuildings that are constructed without the necessary permits and building inspections affect many areas, including safety, health, the environment, social order, and the economy. For this reason, it is essential to determine the number of buildings and their boundaries. Determining the boundaries of a building based solely on its location in the world is a challenging task. In the context of this research, a new approach, BBD, is proposed to detect architectural objects from large-scale satellite imagery, which is an application of remote sensing, together with the geolocations of buildings and their boundaries on the Earth. In the proposed BBD method, open-source GeoServer and TileCache software process huge volumes of satellite imagery that cannot be analyzed with classical data processing techniques using deep learning models. In the proposed BBD method, YOLOv5, DETR, and YOLO-NAS models were used for building detection. SAM was used for the segmentation process in the BBD technique. In addition, the performance of the RefineNet model was investigated, as it performs direct building segmentation, unlike the aforementioned methods. The YOLOV5, DETR and YOLO-NAS models in BBD for building detection obtained an f1 score of 0.744, 0.615, and 0.869 respectively on the images generated by the classic TileCache. However, the RefineNet model, which uses the data generated by the classic TileCache, achieved an f1 score of 0.826 in the building segmentation process. Since the images produced by the classic TileCache are divided into too many parts, the buildings cannot be found as a whole in the images. To overcome these problems, a fine-tuning based optimization was performed. Thanks to the proposed fine-tuning, the modified YOLOv5, DETR, YOLO-NAS, and RefineNet models achieved F1 scores of 0.883, 0.772, 0.975 and 0.932, respectively. In the proposed BBD approach, the modified YOLO-NAS approach was the approach that detected the highest number of objects with an F1 score of 0.975. The YOLO-NAS-SAM approach detected the boundaries of the buildings with high performance by obtaining an IoU value of 0.912.

Structure-Function Analysis of the Essential Mycobacterium tuberculosis P450 Drug Target, CYP121A1.

Cytochrome P450 CYP121A1 is a well-known drug target against Mycobacterium tuberculosis, the human pathogen that causes the deadly disease tuberculosis (TB). CYP121A1 is a unique P450 enzyme because it uses classical and non-classical P450 catalytic processes and has distinct structural features among P450s. However, a detailed investigation of CYP121A1 protein structures in terms of active site cavity dynamics and key amino acids interacting with bound ligands has yet to be undertaken. To address this research knowledge gap, 53 CYP121A1 crystal structures were investigated in this study. Critical amino acids required for CYP121A1's overall activity were identified and highlighted this enzyme's rigid architecture and substrate selectivity. The CYP121A1-fluconazole crystal structure revealed a novel azole drug-P450 binding mode in which azole heme coordination was facilitated by a water molecule. Fragment-based inhibitor approaches revealed that CYP121A1 can be inhibited by molecules that block the substrate channel or by directly interacting with the P450 heme. This study serves as a reference for the precise understanding of CYP121A1 interactions with different ligands and the structure-function analysis of P450 enzymes in general. Our findings provide critical information for the synthesis of more specific CYP121A1 inhibitors and their development as novel anti-TB drugs.

Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring

The conservation of hydrological resources involves continuously monitoring their contamination. A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q‐learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus‐based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1–3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state‐of‐the‐art approaches.

Epidemiology and management of tinea capitis in France: a 6-year nationwide retrospective survey.

Tinea capitis (TC) is still a frequent dermatophytosis in France, both autochthonous and imported. A nationwide retrospective survey was performed and a total of 4,395 TC cases were recorded within 36 French mycology laboratories during a 6-year period. TC is a disease that occurs in childhood with 85% of the cases occurring before 10 years old and 94% before the age of 15. Anthropophilic origin was predominant with 779 cases of Trichophyton tonsurans (32.6%), 738 cases of Trichophyton soudanense/T. violaceum (31%), and 445 cases of Microsporum audouinii (19.2%). Of note, T. tonsurans represents more than 80% of the cases in the French West Indies (Martinique and Guadeloupe). By contrast, zoophilic species were less prevalent with mainly M. canis (10.3%) confirming the shift from zoophilic to anthropophilic species observed in many centers during the last decades. During this survey, diagnosis methods were also collected. Most labs had a classical process for the diagnosis: microscopic direct examination associated to cultures on Sabouraud and Sabouraud-cycloheximide media (incubated between 25±5°C for 2 to 3 weeks) in all laboratories. Identification of the causal dermatophyte was performed by microscopic and macroscopic examination of the cultures in 100% of the labs, with various specific culture media available when fructification was insufficient (mainly malt or potato-dextrose agar, or Borelli medium). New techniques were also implemented with the introduction of MALDI-TOF mass spectrometry identification in more than two third of the labs, and molecular identification available if necessary in half of the labs.

Valley Pseudospin Polarized Evanescent Coupling between Microwave Ring Resonator and Waveguide in Phononic Topological Insulators.

A coupled ring-waveguide structure is at the core of bosonic wave-based information processing systems, enabling advanced wave manipulations such as filtering, routing, and multiplexing. However, its miniaturization is challenging due to momentum conservation issues in rings with larger curvature that induce significant backscattering and radiation leakage and hampering stable operation. Here, we address it by taking an alternative approach of using topological technology in wavelength-scale and microwave ring-waveguide coupled systems built in nanoengineered phononic crystals. Our approach, which leverages pseudospin conservation in valley topological systems, eliminates phonon backscattering and achieves directional evanescent coupling. The resultant hypersonic waves in the tiny ring exhibit robust transport and resonant circulation. Furthermore, the ring-waveguide hybridization enables critical coupling, where valley-dependent ring-waveguide interference blocks the transmission. Our findings reveal the capability of topological phenomena for managing ultrahigh-frequency phonons in nano/microscale structures and pave the way for advanced phononic circuits in classical and quantum signal processing applications.

Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database

In this study, we critically evaluate the performance of various interatomic potentials/force fields against a benchmark ab initio database for bulk amorphous alumina. The interatomic potentials tested in this work include all major fixed charge and variable charge models developed to date for alumina. Additionally, we introduce a novel machine learning interatomic potential constructed using the NequIP framework based on graph neural networks. Our findings reveal that the fixed-charge potential developed by Matsui and coworkers offers the most optimal balance between computational efficiency and agreement with ab initio data for stoichiometric alumina. Such balance cannot be provided by machine learning potentials when comparing performance with Matsui potential on the same computing infrastructure using a single Graphical Processing Unit. For non-stoichiometric alumina, the variable charge potentials, in particular ReaxFF, exhibit an impressive concordance with density functional theory calculations. However, our NequIP potentials trained on a small fraction of the ab initio database easily surpass ReaxFF in terms of both accuracy and computational performance. This is achieved without large overhead in terms of potential fitting and fine-tuning, often associated with the classical potential development process as well as training of standard deep neural network potentials, thus advocating for the use of data-efficient machine learning potentials like NequIP for complex cases of non-stoichiometric amorphous oxides.

Combined use of radiomics and artificial neural networks for the three‐dimensional automatic segmentation of glioblastoma multiforme

AbstractGlioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumour that has the worst prognosis in adults. Currently, the automatic segmentation of this kind of tumour is being intensively studied. Here, the automatic three‐dimensional segmentation of the GBM is achieved with its related subzones (active tumour, inner necrosis, and peripheral oedema). Preliminary segmentations were first defined based on the four basic magnetic resonance imaging modalities and classic image processing methods (multithreshold Otsu, Chan–Vese active contours, and morphological erosion). After an automatic gap‐filling post processing step, these preliminary segmentations were combined and corrected by a supervised artificial neural network of multilayer perceptron type with a hidden layer of 80 neurons, fed by 30 selected radiomic features of gray intensity and texture. Network classification has an overall accuracy of 83.9%, while the complete combined algorithm achieves average Dice similarity coefficients of 89.3%, 80.7%, 79.7%, and 66.4% for the entire region of interest, active tumour, oedema, and necrosis segmentations, respectively. These values are in the range of the best reported in the present bibliography, but even with better Hausdorff distances and lower computational costs. Results presented here evidence that it is possible to achieve the automatic segmentation of this kind of tumour by traditional radiomics. This has relevant clinical potential at the time of diagnosis, precision radiotherapy planning, or post‐treatment response evaluation.

Synthesis of ZSM-5 Zeolite Nanosheets with Tunable Silanol Nest Contents across an Ultra-wide pH Range and Their Catalytic Validation.

Zeolite synthesis under acidic conditions has always presented a challenge. In this study, we successfully prepared series of ZSM-5 zeolite nanosheets (Z-5-SCA-X) over a broad pH range (4 to 13) without the need for additional supplements. This achievement was realized through aggregation crystallization of ZSM-5 zeolite subcrystal (Z-5-SC) with highly short-range ordering and ultrasmall size extracted from the synthetic system of ZSM-5 zeolite. Furthermore, the crystallization behavior of Z-5-SC was investigated, revealing its non-classical crystallization process under mildly alkaline and acidic conditions (pH < 10), and the combination of classical and non-classical processes under strongly alkaline conditions (pH ≥ 10). What's particularly intriguing is that, the silanol nest content in the resultant Z-5-SCA-X samples appears to be dependent on the pH values during the Z-5-SC crystallization process rather than its crystallinity. Finally, the results of the furfuryl alcohol etherification reaction demonstrate that reducing the concentration of silanol nests significantly enhances the catalytic performance of the Z-5-SCA-X zeolite. The ability to synthesize zeolite in neutral and acidic environments without the additional mineralizing agents not only broadens the current view of traditional zeolite synthesis but also provides a new approach to control the silanol nest content of zeolite catalysts.

Designing Complex Tapestries with Photography-Inspired Manipulation of Self-Organized Thin-Films.

Thin-films patterned with complex motifs are of fundamental interest because of their advanced optical, mechanical and electronic properties, but fabrication of these materials remains challenging. Self-organization strategies, such as immersion controlled reaction-diffusion patterning, have shown great potential for production of patterned thin-films. However, the autonomous nature of such processes limits controllable pattern customizability and complexity. Here, it is demonstrated that photography inspired manipulation processes can overcome this limitation to create highly-complex tapestries of micropatterned films (MPF's). Inspired by classical photographic processes, MPF's are developed, bleached, exposed, fixed, and contoured into user-defined shapes and photographic toning reactions are used to convert the chemical composition MPF's, while preserving the original stripe patterns. By applying principles of composite photography, highly complex tapestries composed of multiple MPF layers are designed, where each layer can be individually manipulated into a specific shape and composition. By overcoming fundamental limitations, this synergistic approach broadens the design possibilities of reaction-diffusion processes, furthering the potential of self-organization strategies for the development of complexmaterials.

A resource-efficient quantum convolutional neural network

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the classical Convolutional Neural Network (CNN) model, the QCNN model requires excellent numerical performance or efficient computational resources to showcase its potential quantum advantages, particularly in classical data processing tasks. In this paper, we propose a computationally resource-efficient QCNN model referred to as RE-QCNN. Specifically, through a comprehensive analysis of the complexity associated with the forward and backward propagation processes in the quantum convolutional layer, our results demonstrate a significant reduction in computational resources required for this layer compared to the classical CNN model. Furthermore, our model is numerically benchmarked on recognizing images from the MNIST and Fashion-MNIST datasets, achieving high accuracy in these multi-class classification tasks.

Cryogenic lithium-niobate-on-insulator optical filter.

Photonic integrated circuits have garnered significant attention and experienced rapid development in recent years. To provide fundamental building blocks for scalable optical classical and quantum information processing, one important direction is to develop cryogenic compatible photonic integrated devices. Here, we prepare one optical filter on a lithium-niobate-on-insulator (LNOI) platform based on a multimode waveguide grating and verify its availability at temperature from 295 to 7 K. We find that the integrated optical filter still shows good quality under cryogenic conditions, and the shift of the working wavelength at different temperatures is well explained by the index variation of the material. These results advance LNOI integrated optical devices in applications under cryogenic conditions.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

AbstractFlow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Enhancing quantum annealing in digital–analog quantum computing

Digital–analog quantum computing (DAQC) offers a promising approach to addressing the challenges of building a practical quantum computer. By efficiently allocating resources between digital and analog quantum circuits, DAQC paves the way for achieving optimal performance. We propose an algorithm designed to enhance the performance of quantum annealing. This method employs a quantum gate to estimate the goodness of the final annealing state and find the ground state of combinatorial optimization problems. We explore two strategies for integrating the quantum annealing circuit into the DAQC framework: (1) state preparation, and (2) embedding within the quantum gate. While the former strategy does not yield performance improvements, we discover that the latter enhances performance within a specific range of annealing time. Algorithms demonstrating enhanced performance utilize the imaginary part of the inner product of two states from different quantum annealing settings. This measure reflects not only the energy of the classical cost function but also the trajectory of the quantum dynamics. This study provides an example of how processing quantum data using a quantum circuit can outperform classical data processing, which discards quantum information.

Electrochemical Hydrogenation of Aliphatic Aldehydes and Acids using Pentlandite Catalysts

AbstractElectrochemical hydrogenations are considered a sustainable alternative to classical thermocatalytic processes prevalent in industrial conversions. Using a base metal sulfide of the pentlandite class, the hydrogenation of glutaraldehyde and propionic acid was investigated. While propionic acid could not be converted, glutaraldehyde was conveniently transformed to the semi‐ and fully hydrogenated products 5‐hydroxypentanal and 1,5‐pentanediol with a partial current density of up to 34 mA cm−2 and a Faraday efficiency of 34 %. Crucial factors for a stable and efficient reaction were found to be the use of an appropriate buffer, avoidance of low pH and the used membrane type. The reaction was implemented into a zero‐gap cell reaching a single pass conversion of up to 22 %, underlining the potential for future application.

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation maximization to reconstruct the image and split Bregman algorithm to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.