Direct Mapping Research Articles

Neural Path Sampling for Rendering Pure Specular Light Transport

AbstractMulti‐bounce, pure specular light paths produce complex lighting effects, such as caustics and sparkle highlights, which are challenging to render due to their sparse and diverse nature. We introduce a learning‐based method for the efficient rendering of pure specular light transport. The key idea is training a neural network to model the distribution of all specular light paths between pairs of endpoints for one specular object. To achieve this, for each object, our method models the distribution of sparse and diverse specular light paths between two endpoints using smooth 2D maps of ray directions from one endpoint and represents these maps with a 2D convolutional network. We design a training scheme to efficiently sample specular light paths from the scene and train the network. Once trained, our method predicts specular light paths for a given pair of endpoints using the network and employs root‐finding‐based algorithms for rendering the specular light transport. Experimental results demonstrate that our method generates high‐quality results, supports dynamic lighting and moving objects within the scene, and significantly enhances the rendering speed of existing techniques.

RimSense

Smart eyewear's interaction mode has attracted significant research attention. While most commercial devices have adopted touch panels situated on the temple front of eyeglasses for interaction, this paper identifies a drawback stemming from the unparalleled plane between the touch panel and the display, which disrupts the direct mapping between gestures and the manipulated objects on display. Therefore, this paper proposes RimSense, a proof-of-concept design for smart eyewear, to introduce an alternative realm for interaction - touch gestures on eyewear rim. RimSense leverages piezoelectric (PZT) transducers to convert the eyeglass rim into a touch-sensitive surface. When users touch the rim, the alteration in the eyeglass's structural signal manifests its effect into a channel frequency response (CFR). This allows RimSense to recognize the executed touch gestures based on the collected CFR patterns. Technically, we employ a buffered chirp as the probe signal to fulfil the sensing granularity and noise resistance requirements. Additionally, we present a deep learning-based gesture recognition framework tailored for fine-grained time sequence prediction and further integrated with a Finite-State Machine (FSM) algorithm for event-level prediction to suit the interaction experience for gestures of varying durations. We implement a functional eyewear prototype with two commercial PZT transducers. RimSense can recognize eight touch gestures on the eyeglass rim and estimate gesture durations simultaneously, allowing gestures of varying lengths to serve as distinct inputs. We evaluate the performance of RimSense on 30 subjects and show that it can sense eight gestures and an additional negative class with an F1-score of 0.95 and a relative duration estimation error of 11%. We further make the system work in real-time and conduct a user study on 14 subjects to assess the practicability of RimSense through interactions with two demo applications. The user study demonstrates RimSense's good performance, high usability, learnability and enjoyability. Additionally, we conduct interviews with the subjects, and their comments provide valuable insight for future eyewear design.

Efficiency improvement tools for the roadmap of development of the high-tech direction “Hydrogen Energy”

Aim. Development of scientifically substantiated tools to increase the efficiency of the roadmap for the development of the high-tech direction “Hydrogen Energy”.Objectives. To evaluate the conformity of the standard passport of the road map of the high-tech direction to the tasks of the development of the direction “Hydrogen Energy”; to analyze the influence of the features of the direction “Hydrogen Energy” on the requirements to the road map; to propose tools to take into account the features of the direction “Hydrogen Energy” in the road map.Methods. The research was conducted on the basis of a set of general scientific methods (system analysis, method of dialectical cognition, etc.).Results. The need for additional functionality and tools for the roadmap “Hydrogen Energy” is shown, as the direction implies the development of an ecosystem of products based on several interrelated and competing technologies. By analyzing the features of the Hydrogen Energy roadmap, five tools are proposed to improve efficiency and reduce risks.Conclusions. The implementation of the proposed five tools will improve efficiency and reduce risks for the Hydrogen Energy roadmap by considering the structure of the direction, technology readiness level, and resource constraints.

Wavelet Approximation-Aware Residual Network for Single Image Deraining.

It has been made great progress on single image deraining based on deep convolutional neural networks (CNN). In most existing deep deraining methods, CNNs aim to learn a direct mapping from rainy images to clean rain-less images, and their architectures are becoming more and more complex. However, due to the limitation of mixing rain with object edges and background, it is difficult to separate rain and object/background, and the edge details of the image cannot be effectively recovered in the reconstruction process. To address this problem, we propose a novel wavelet approximation-aware residual network (WAAR), wherein rain is effectively removed from both low-frequency structures and high-frequency details at each level separately, especially in low-frequency sub-images at each level. After wavelet transform, we propose novel approximation aware (AAM) and approximation level blending (ALB) mechanisms to further aid the low-frequency networks at each level recover the structure and texture of low-frequency sub-images recursively, while the high-frequency network can effectively eliminate rain streaks through block connection and achieve different degrees of edge detail enhancement by adjusting hyperparameters. In addition, we also introduce block connection to enrich the high-frequency details in the high-frequency network, which is favorable for obtaining potential interdependencies between high- and low-frequency features. Experimental results indicate that the proposed WAAR exhibits strong performance in reconstructing clean and rain-free images, recovering real and undistorted texture structures, and enhancing image edges in comparison with the state-of-the-art approaches on synthetic and real image datasets. It shows the effectiveness of our method, especially on image edges and texture details.

Direct Mapping of Cytomechanical Homeostasis Destruction in Osteoarthritis Based on Silicon Nanopillar Array (Adv. Healthcare Mater. 32/2023)

Direct Mapping of Cytomechanical Homeostasis Destruction in Osteoarthritis Based on Silicon Nanopillar Array (Adv. Healthcare Mater. 32/2023)

Whole-Genome Sequencing of 5-Hydroxymethylcytosine at Base Resolution by Bisulfite-Free Single-Step Deamination with Engineered Cytosine Deaminase.

The epigenetic modification 5-hydroxymethylcytosine (5hmC) plays a crucial role in the regulation of gene expression. Although some methods have been developed to detect 5hmC, direct genome-wide mapping of 5hmC at base resolution is still highly desirable. Herein, we proposed a single-step deamination sequencing (SSD-seq) method, designed to precisely map 5hmC across the genome at single-base resolution. SSD-seq takes advantage of a screened engineered human apolipoprotein B mRNA-editing catalytic polypeptide-like 3A (A3A) protein, known as eA3A-v10, to selectively deaminate cytosine (C) and 5-methylcytosine (5mC) but not 5hmC. During sequencing, the deaminated C and 5mC are converted to uracil (U) and thymine (T), read as T in the sequencing data. However, 5hmC remains unaffected by eA3A-v10 and is read as C during sequencing. Consequently, the presence of C in the sequence reads indicates the original 5hmC. We applied SSD-seq to generate a base-resolution map of 5hmC in human lung tissue. Our findings revealed that 5hmC was predominantly localized to CpG dinucleotides. Furthermore, the base-resolution map of 5hmC generated by SSD-seq demonstrated a strong correlation with prior ACE-seq results. The advantages of SSD-seq are its single-step process, absence of bisulfite treatment or DNA glycosylation, cost effectiveness, and ability to detect and quantify 5hmC directly at single-base resolution.

Direct Mapping of Fluorine in Cation Disordered Rocksalt Cathodes

Direct Mapping of Fluorine in Cation Disordered Rocksalt Cathodes

Prediction model for the failure behavior of concrete under impact loading base on back propagation neural network

Predicting the failure behavior of concrete under impact loading is of great significance for the repair of buildings and improving the protection capabilities of modern defense systems. Conventional approaches such as experiments, theoretical analyses and numerical simulation methods have been widely used in the analysis of this problem. However, they are not always accessible in situations where high accuracy, fast computation and simple modeling are required at the same time. In this study, with projectile penetration as impact loading, an artificial neural network (ANN) model for predicting the failure behavior of concrete is proposed based on the back propagation (BP) algorithm. Feature selection is conducted when constructing the model and the direct mapping between penetration features and failure behavior parameters of concrete is established. The model outperforms commonly used empirical formulas in terms of prediction accuracy. Compared with theoretical analysis method, it does not rely on the failure mechanism which is not yet clearly under stood and does not require complicated parameters analysis. Furthermore, its calculation time is negligible compared with the numerical simulation as the output can be obtained in a second from the input penetration features. Moreover, tests are conducted to validate the feasibility and accuracy of the proposed model.

Direct Observation of Twin van der Waals Molecular Chains.

The van der Waals (vdW) assemblies are the most common structures of materials. However, direct mapping of intermolecular electron clouds of a vdW assembly has never been obtained, even though the intramolecular electron clouds were visualized by atomic-resolution techniques. In this report, we unprecedentedly mapped the intermolecular electron cloud of the assemblies of ethanol molecules via ethyl groups with high-resolution atomic force microscopy and scanning tunneling microscopy at 5 K, leading to the first visualization of vdW molecular chains, in which ethanol molecules assemble into twin vdW molecular chains in a reverse parallel configuration on the Ag(111) plane. Furthermore, spontaneous order-disorder transitions in the chain were dynamically observed, suggesting its unusual properties different from those of 2D vdW materials. These findings provide an "eye" to see the atomic world of vdW materials.

Utilizing of aquifer hydraulic parameters to assess the groundwater sustainability in the new reclamation area of Moghra Oasis: Western Desert—Egypt

The sustainability of groundwater aquifers requires evaluating several parameters, the most important of which are hydraulic parameters. Therefore, the essential aim of this research is to develop a management plan for the Moghra aquifer in order to prevent the expanding of water level decline and degradation of groundwater quality due to overexploitation and scarcity of recharge. To achieve this goal, all aquifer hydraulic parameters such as transmissivity, hydraulic conductivity, radius of interference, specific capacity, resulted drawdown, well loss, formation loss, well efficiency, and optimum safe yield had been measured for 40 groundwater wells drilled in the new reclaimed areas of Moghra Oasis. Based on geographic information system (GIS), hydrogeological cross sections and thematic maps for all parameters were created such as aquifer thickness, water table, groundwater flow direction, drawdown and groundwater salinity maps. The results revealed that clay and shale beds separated the three water-bearing formations of the Moghra aquifer. The aquifer-saturated thickness ranged from 30 and 102 m, and the groundwater level was below the mean sea level for all wells (ranges from − 72 to − 26.6 m). The calculated hydraulic parameters based on the analysis of long-duration pumping tests indicated that the studied aquifer has a wide variety of transmissivity (T) between 631 and 3768 m2/day, hydraulic conductivity (K) between 13.4 and 104.6 m/day, radius of influence from 126.3 to 581.3 m and specific capacity between 377.14 and 883.72 m2/day. On the other hand, the evaluation of existing drilled wells performance based on the results of step tests showed that well loss coefficient ranges between 0.0004749 and 0.0676 (h2/m5), formation loss coefficient varies from 3.34 × 10–8 to 4.80 × 10–6 (h/m2), well efficiency (γ) ranged from 50.53 to 98.08%, and optimum safe yield ranged from 40 to 98 (m3/h). Results of aquifer mapping and pumping tests can be more important for solving water scarcity issues, non-polluting water issues, health issues, and source of fresh water on the surface of the earth. The characterization of aquifer parameters in the study area, however, should be a significant component in the scientific planning and sustainability of groundwater.

Moving from ISAD(G) to a CIDOC CRM-based Linked Data Model in the Portuguese Archives

Archives are facing numerous challenges. On the one hand, archival assets are evolving to encompass digitized documents and increasing quantities of born-digital information in diverse formats. On the other hand, the audience is changing along with how it wishes to access archival material. Moreover, the interoperability requirements of cultural heritage repositories are growing. In this context, the Portuguese Archives started an ambitious program aiming to evolve its data model, migrate existing records, and build a new archival management system appropriate to both archival tasks and public access. The overall goal is to have a fine-grained and flexible description, more machine-actionable than the current one. This work describes ArchOnto, a linked open data model for archives, and rules for its automatic population from existing records. ArchOnto adopts a semantic web approach and encompasses the CIDOC Conceptual Reference Model and additional ontologies, envisioning interoperability with datasets curated by multiple communities of practice. Existing ISAD(G)-conforming descriptions are being migrated to the new model using the direct mappings provided here. We used a sample of 25 records associated with different description levels to validate the completeness and conformity of ArchOnto to existing data. This work is in progress and is original in several respects: (1) it is one of the first approaches to use CIDOC CRM in the context of archives, identifying problems and questions that emerged during the process and pinpointing possible solutions; (2) it addresses the balance in the model between the migration of existing records and the construction of new ones by archive professionals; and (3) it adopts an open world view on linking archival data to global information sources.

Temporal resolution of ultrafast compressive imaging using a single-chirped optical probe.

Ultrafast compressive imaging captures three-dimensional spatiotemporal information of transient events in a single shot. When a single-chirped optical probe is applied, the temporal information is obtained from the probe modulated in amplitude or phase using a direct frequency-time mapping method. Here, we extend the analysis of the temporal resolution of conventional one-dimensional ultrafast measurement techniques such as spectral interferometry to that in three-dimensional ultrafast compressive imaging. In this way, both the amplitude and phase of the probe are necessary for a full Fourier transform method, which obtains temporal information with an improved resolution determined by probe spectral bandwidth. The improved temporal resolution potentially enables ultrafast compressive imaging with an effective imaging speed at the quadrillion-frames-per-second level.

Prediction of the SF-6D utility score from Lung cancer FACT-L: a mapping study in China

ObjectiveTo develop a mapping algorithm for generating the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients.MethodsData were collected from 625 lung cancer patients in mainland China. The Spearman rank correlation coefficient and principal component analysis were used to evaluate the conceptual overlap between the FACT-L and SF-6D. Five model specifications and four statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit and beta-mixture regression models, which were used to directly estimate health utility, and ordered probit regression was used to predict the response level. The prediction performance was evaluated using the correlations between the root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the observed and predicted SF-6D scores. A five-fold cross-validation method was used to test the universality of each model and select the best model.ResultsThe average FACT-L score was 103.024. The average SF-6D score was 0.774. A strong correlation was found between FACT-L and SF-6D scores (ρ = 0.797). The ordered probit regression model with the total score of each dimension and its square term, as well as age and sex as covariates, was most suitable for mapping FACT-L to SF-6D scores (5-fold cross-validation: RMSE = 0.0854; MAE = 0.0655; CCC = 0.8197; AEs > 0.1 (%) = 53.44; AEs > 0.05 (%) = 21.76), followed by beta-mixture regression for direct mapping. The Bland‒Altman plots showed that the ordered probit regression M5 had the lowest proportion of prediction scores outside the 95% agreement limit (-0.166, 0.163) at 4.96%.ConclusionsThe algorithm reported in this paper enables lung cancer data from the FACT-L to be mapped to the utility of the SF-6D. The algorithm allows the calculation of quality-adjusted life years for cost-utility analyses of lung cancer.

Mapping the EORTC QLQ-C30 and QLQ H&N35 to the EQ-5D-5L and SF-6D for papillary thyroid carcinoma.

Empirical evidence for the EORTC QLQ C30 scale in thyroid cancer mapping algorithms has not been found in China, which limits the cost-utility analysis of patients with papillary thyroid carcinoma (PTC) population. We developed mapping algorithms that use the EORTC QLQ-C30 and QLQ H&N35 to predict EQ-5D-5L and SF-6D health utility scores for PTC patients. Data from 1050 Chinese PTC patients who completed the EORTC QLQ-C30, QLQ H&N35, EQ-5D-5L and SF-6D instruments were collected. Direct mapping (OLS, Tobit, Betamix) and indirect mapping functions (Order Probit) were used to estimate algorithms. The goodness-of-fit of mapping performance was assessed by MAE, RMSE, AIC, BIC, AE, and ICC. A fivefold cross-validation and random sample validation approach were used to test the stability of the models. The mean EQ-5D-5L and SF-6D utility scores were 0.8704 and 0.6368, respectively. We recommend the Betamix model for the EQ-5D-5L (MAE = 0.0363, RMSE = 0.0505, AIC = -3458.73, BIC = -3096.91, AE > 0.05(%) = 48.38, AE > 0.1(%) = 8.67, ICC = 0.8288 for the full sample dataset) and the Betamix model for the SF-6D (MAE = 0.0328, RMSE = 0.0417, AIC = -2788.91, BIC = -2605.51, AE > 0.05(%) = 42.76, AE > 0.1(%) = 3.62, ICC = 0.8657 for the full sample dataset), with EORTC QLQ-C30 all items, QLQ H&N35 all items, age and gender as the predicted variables showing the best performance. In the absence of preference-based quality of life tools, the mapping algorithms reported here are effective alternative for predicting the health utility of PTC patients, contributing to the cost-utility analysis studies.

Steady translating hollow vortex pair in weakly compressible flow

The weakly compressible translating hollow vortex pair is examined using the Imai–Lamla formula and a direct conformal mapping approach applied to a Rayleigh–Jansen expansion in the Mach number, M, taken to be small. The incompressible limit has been studied by Crowdy et al. (Eur. J. Mech. B Fluids 37 (2013), 180–186) who found explicit formulas for the solutions using conformal mapping. We improve upon their results by finding an explicit formula for the conformal map which had been left as an integral in Crowdy et al. (2013). The weakly compressible problem requires the solution of two boundary value problems in an annulus. This results in a linear parameter problem for the perturbed propagation velocity and speed along the vortex boundary. We find that two additional constraints are required to solve for the perturbed parameters. We require that the perturbation in vortex area and the perturbation in centroid separation to vanish. Three possible centroid definitions are given and the results worked out for each case. It is found that the correction to the propagation velocity is always negative, so that the vortex always slows down at first order due to compressible effects. Numerical results are consistent in the limit of small vortex size with the previous result by Leppington (J. Fluid Mech. 559 (2006), 45–55) that the propagation parameter is unchanged to O(M2).

Generating Angular-Varying Time Delays of THz Pulses via Direct Space-to-Time Mapping of Metasurface Structures.

We experimentally demonstrate the generation of double terahertz (THz) pulses with tailored angular-dependent time delays from a nonlinear metasurface excited by a near-infrared femtosecond pulse. The tailored temporal properties of the generated pulses emerge from a direct mapping of the nonlinear spatial response of the metasurface to the emitted THz temporal profile. We utilize the Pancharatnam-Berry phase to implement symmetric and antisymmetric metasurface configurations and show that the emitted patterns present spatiotemporal "X-shaped" profiles after collimation by a parabolic mirror, with angular-dependent pulse delays corresponding to the intended design. In addition, we show that the addition of polarization multiplexing presents the opportunity to achieve a full range of elliptical THz polarizations. Double pulse generation and spatiotemporal shaping of THz waves in general show potential for THz spectroscopy and molecular dynamics applications, particularly in pump-probe experiments.

Brugada Syndrome: A Comprehensive Review of Fundamental and Electrophysiological New Findings.

Brugada syndrome is characterized by pronounced J-ST segment elevation in the right precordial leads on surface electrocardiograms. The etiological underpinnings of these distinctive features have been the subject of extensive debate, encompassing various theories related to repolarization anomalies and conduction irregularities. Genetic investigations have unveiled SCN5A, the gene encoding NaV1.5, a critical sodium channel, as the most frequently implicated causative gene, with mutations typically manifesting as loss of function. Nonetheless, the detection rate of SCN5A mutations remains below 20%, underscoring the intricate genetic landscape of the syndrome. Histological analyses have divulged localized structural irregularities, primarily marked by fibrotic alterations, within the right ventricular outflow tract. Electrophysiological inquiries employing direct epicardial mapping techniques have uncovered localized conduction impediments concomitant with modifications in unipolar morphologies within the J-ST segment. Thus, the theory positing conduction abnormalities emerges as a compelling mechanism accounting for J-ST segment elevation. However, the precise mechanisms governing the onset of life-threatening tachyarrhythmias remain shrouded in uncertainty. Recent clinical case reports have proffered evidence supporting the notion that phase 2 reentry, arising from the marked heterogeneity in action potentials within the epicardial domain, may serve as the instigator of premature ventricular contractions, ultimately culminating in ventricular fibrillation. In light of these developments, it becomes increasingly evident that comprehending the mechanisms underlying the electrocardiographic manifestations and lethal arrhythmias in Brugada syndrome necessitates the consideration of a multifaceted perspective, transcending the binary discourse of repolarization versus depolarization anomalies.

Bulk-Switching Memristor-Based Compute-In-Memory Module for Deep Neural Network Training.

The constant drive to achieve higher performance in deep neural networks (DNNs) has led to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in place and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due to non-linear weight updates, device variations, and low-precision. In this work, a mixed-precision training scheme is experimentally implemented to mitigate these effects using a bulk-switching memristor-based CIM module. Low-precision CIM modules are used to accelerate the expensive VMM operations, with high-precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system-onchip of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and verifies that CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software-trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training.

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative information that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

Direct Mapping of Cytomechanical Homeostasis Destruction in Osteoarthritis Based on Silicon Nanopillar Array.

Osteoarthritis (OA) is the most prevalent joint degenerative disease characterized by chronic joint inflammation. The pathogenesis of OA has not been fully elucidated yet. Cartilage erosion is the most significant pathological feature in OA, which is considered the result of cytomechanical homeostasis destruction. The cytomechanical homeostasis is maintained by the dynamic interaction between cells and the extracellular matrix, which can be reflected by cell traction force (CTF). It is critical to assess the CTF to provide a deeper understanding of the cytomechanical homeostasis destruction and progression in OA. In this study, a silicon nanopillar array (Si-NP) with high spatial resolution and aspect ratio is fabricated to investigate the CTF in response to OA. It is discovered that the CTF is degraded in OA, which is attributed to the F-actin reorganization induced by the activation of RhoA/ROCK signaling pathway. Si-NP also shows promising potential as a mechanopharmacological assessment platform for OA drug screening and evaluation.