Alignment Technique Research Articles

Elucidating the impact of non-spherical morphology on kinetic behavior of graphite using single-particle microelectrode

The precise assessment of the kinetic properties of graphite is crucial for the design of the material and the development of electrochemical models for lithium-ion batteries. However, conventional evaluation methods using composite electrodes fall short in excluding the influence of its porous structure and evaluating the influence of particle shape on kinetic behavior. In this work, we elucidate the impact of non-spherical morphology on kinetic behavior of single artificial graphite particle, employing the single-particle microelectrode technique. Three single artificial graphite particles with different sizes are pre-cycled and exhibit outstanding rate capability, maintaining a maximum capacity retention of 78.1 % at 200 C. Further, exchange current density and diffusion coefficients are determined through Tafel plots and the potentiostatic intermittent titration technique (PITT), respectively. It is found that the particle with a higher exposure of edge planes present elevated exchange current density and diffusion coefficients. Considering that the graphite particles typically feature an ellipsoidal rather than spherical shape, we propose an approach based on an ellipsoidal diffusion model for extracting diffusion coefficients. This model takes into account the non-spherical morphology of graphite, addressing the limitations inherent in the geometric assumptions of previous methods. The average relative error between the diffusion coefficients derived from the ellipsoidal diffusion model and the previous method using spherical assumptions is 215 %, indicating that accurately depicting the shape of ellipsoidal graphite particles in the model is crucial for obtaining correct estimates of kinetic parameters. This study offers direct experimental evidence of superior kinetic behavior of edge planes over basal planes. To leverage this characteristic, it is recommended to employ an out-of-plane aligned architecture for composite electrodes using novel techniques, e.g., 3D printing or magnetic alignment techniques.

Assessment Accuracy of 2D vs. 3D Imaging for Custom-Made Acetabular Implants in Revision Hip Arthroplasty.

Revision total hip arthroplasty (rTHA) presents significant challenges, particularly in patients with severe acetabular bone defects. Traditional treatment options often fall short, leading to the emergence of custom-made 3D-printed acetabular implants. Accurate assessment of implant positioning is crucial for ensuring optimal postoperative outcomes and for providing feedback to the surgical team. This single-center, retrospective cohort study evaluates the accuracy of standard 2D radiographs versus 3D CT scans in assessing the positioning of these implants, aiming to determine if 2D imaging could serve as a viable alternative for the postoperative evaluation. We analyzed the implant positions of seven rTHA patients with severe acetabular defects (Paprosky ≥ Type IIIA) using an alignment technique that integrates postoperative 2D radiographs with preoperative CT plans. Two independent investigators, one inexperienced and one experienced, measured the positioning accuracy with both imaging modalities. Measurements included translational shifts from the preoperatively templated implant position in the craniocaudal (CC), lateromedial (LM), and ventrodorsal (VD) directions, as well as rotational differences in anteversion (AV) and inclination (INCL). The study demonstrated that 2D radiographs, when aligned with preoperative CT data, could accurately assess implant positions with precision nearly comparable to that of 3D CT scans. Observed deviations were 1.4 mm and 2.7 mm in CC and LM directions, respectively, and 3.6° in AV and 0.7° in INCL using 2D imaging, all within clinically acceptable ranges. For 3D CT assessments, mean interobserver variability was up to 0.9 mm for translational shifts and 1.4° for rotation, while for 2D alignment, observer differences were 1.4 mm and 3.2° for translation and rotation, respectively. Comparative analysis of mean results from both investigators, across all dimensions (CC, LM, AV, and INCL) for 2D and 3D matching, showed no significant differences. In conclusion, conventional anteroposterior 2D radiographs of the pelvis can sufficiently determine the positioning of custom-made acetabular implants in rTHA. This suggests that 2D radiography is a viable alternative to 3D CT scans, potentially enhancing the implementation and quality control of advanced implant technologies.

ScCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

Machine Learning Techniques for Blind Beam Alignment in mmWave Massive MIMO.

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at UE and BS, this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only 10% of the beams from the UE and BS codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at UE and BS vary from 128×128 to 1024×1024.

Fragment Aligned Molecular Orbital Analysis: An Innovative Tool for Analyzing Atypical Chemical Bonding.

In chemical research, it is a common practice to carry out quantum chemical calculations and analyze the canonical molecular orbitals (CMOs) obtained to study electronic structures of chemical systems. However, extensive orbital mixing of CMOs especially in molecular clusters significantly complicates our understanding of the electronic structures. In this paper, we have developed an innovative tool called fragment aligned molecular orbital (FAMO) analysis, which reconstructs our modular chemical picture by making use of the Procrustes analysis in statistical theory to align the occupied molecular orbitals of a molecular species against the occupied (molecular) orbitals of the constituting fragments of the cluster, and results in a set of chemically intuitive semilocalized orbitals. This alignment technique minimizes the extensive orbital mixing, thus allowing precise observation of bonding interactions in complex chemical systems. A few representative clusters have been selected as showcase examples to demonstrate the advantage of FAMO analysis in deciphering the distinct bonding interactions in cluster compounds.

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

In the early stages of aerospace design, reduced-order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the use of high-dimensional design spaces to capture detailed features and design variability accurately. However, these spaces introduce significant challenges, including the curse of dimensionality, which stems from both high-dimensional inputs and outputs necessitating substantial training data and computational effort. To address these complexities, this study introduces a novel multifidelity, parametric, and nonintrusive ROM framework designed for high-dimensional contexts. It integrates machine learning techniques for manifold alignment and dimension reduction—employing proper orthogonal decomposition and model-based active subspace—with multifidelity regression for ROM construction. Our approach is validated through two test cases: the 2D RAE 2822 airfoil and the 3D NASA CRM wing, assessing various fidelity levels, training data ratios, and sample sizes. Compared to the single-fidelity principal component–active subspace (PCAS) method, our multifidelity solution offers improved cost-accuracy benefits and achieves better predictive accuracy with reduced computational demands. Moreover, our methodology outperforms the manifold-aligned ROM method by 50% in handling scenarios with large input dimensions, underscoring its efficacy in addressing the complex challenges of aerospace design.

Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment

MotivationRetrosynthesis planning poses a formidable challenge in the organic chemical industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency.ResultsThis paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods.Scientific contributionWe present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.

Specific tibial landmarks to improve to accuracy of the tibial cut during total knee arthroplasty. A case control study.

More personalized alignment techniques in total knee arthroplasty (TKA) have recently been described particularly for the young and active patients. Performing the ideal tibial cut might be challenging with a conventional ancillary. Therefore the aims of this study were: (1) to describe specific tibial landmarks to optimize the tibial cut in TKA; (2) to compare the accuracy of the tibial cut with these landmarks compared to a conventional technique. This retrospective case-control study compared primary TKAs performed using a conventional technique with extramedullary guide associated with specific tibial landmarks. For each case, one control patient was matched based on body mass index (BMI), age, preoperative Hip Knee Ankle (HKA) angle, and Medial Proximal Tibial Angle (MPTA). All control patients were operated by the same surgeon and similar conventional technique but without landmarks. The MPTA target was to reproduce preoperative deformity with a 3° of varus limit. 34 TKA were included in each group. There was no preoperative difference between both groups. Mean age was 63years old ± 8. Mean BMI was 32kg/m2 ± 5. Mean HKA was 170.6° ± 2.5. Mean MPTA was 85.1° ± 2.3. The radiographic assessment was performed preoperatively and at 2months: HKA, mechanical Medial Distal Femoral Angle (mMDFA), MPTA, tibial slope, restoration of the joint line-height. The tibial landmarks corresponded to the line of insertion of the deep medial collateral ligament fibers extended to the capsular insertion above the Gerdy tubercle. The postoperative MPTA was significantly more varus (87.2° ± 1.6 in landmark group versus 88.3° ± 2.2; p = 0.027) and closer to preoperative bone deformity in landmark group (p = 0.002) with significantly less outliers than in the conventional group. There was no significant difference between both groups postoperatively for HKA (175.4° ± 2.3 versus 175.9° ± 2.5; p = 0.42); mMDFA (88.9° ± 2.3 versus 88.2° ± 2.1; p = 0.18); tibial slope (82.6° ± 1.9 versus 82.4° ± 2.6; p = 0.67), the restoration of the joint line-height (1.5mm ± 2 versus 1.8mm ± 2; p = 0.56). Specific tibial landmarks during TKA can be used to increase the accuracy of the tibial cut when using personalized alignment techniques in TKA. IV.

The Effect of Sportsmetrics Training on Landing Technique in Female Volleyball Players With Dynamic Knee Valgus

Background and Aims The most serious and severe injuries among female volleyball players is the knee injury, especially the anterior cruciate ligament (ACL) tear. Its key risk factors are dynamic knee valgus (DKV) and abnormal alignment during jump landing. One of the successful protocols regarding the prevention of knee injuries in female volleyball players is professional sportsmetrics volleyball training. In the present study, the effect of this protocol on DKV correction and landing error scoring system (LESS) was investigated. Methods The current research is quasi-experimental with a pre-test-post-test design. The statistical sample included 30 female volleyball players (Mean±SD age: 20.83±2.22 years, Mean±SD height: 168.16±6.47 cm, Mean±SD weight: 59.13±7.4 kg, Mean±SD body mass index (BMI): 20.83±1.52 kg/m2, and Mean±SD sports experience: 5.46±1.8 years) who were randomly divided into the training (15 people) and control (15 people) groups. Double leg squat and LESS tests were used to evaluate DKV and landing error. The exercise group participated in the present protocol for 6 weeks, three sessions per week. Each session was approximately 90 minutes, including warm-up exercises, sportsmetrics volleyball training, and cool-down. The analyses of covariance were used to evaluate the data at a significant level of P=0.05. Results The results of covariance analysis showed that performing these exercises significantly reduced (P=0.01) DKV (from 23.13 to 7.86) and landing error (from 17.46 to 7.6). In other words, the training group experienced a 66% decrease in DKV and a 56% decrease in landing error after performing six weeks of sportsmetrics volleyball training. Conclusion Sportsmetrics volleyball training has successfully improved performance and prevented ACL injury in female volleyball players. According to the present study results, performing these exercises leads to correct DKV and improves lower extremity alignment and landing technique. Therefore, performing sportsmetrics volleyball training is strongly recommended for female volleyball players with DKV.

Efficacy of Online Coital Alignment Technique in Female Orgasmic Disorder

Efficacy of Online Coital Alignment Technique in Female Orgasmic Disorder

Personalized total knee arthroplasty in patients with extra-articular deformities.

Over the years, with a better understanding of knee anatomy and biomechanics, superior implant designs, advanced surgical techniques, and the availability of precision tools such as robotics and navigation, a more personalized approach to total knee arthroplasty (TKA) has emerged. In the presence of extra-articular deformities, performing personalized TKA can be more challenging and specific considerations are required, since one has to deal with an acquired pathological anatomy. Performing personalized TKA surgery in patients with extra-articular deformities, the surgeon can: (1) resurface the joint, omitting the extra-articular deformity; (2) partially compensate the extra-articular deformity with intra-articular correction (hybrid technique), or (3) correct the extra-articular deformity combined with a joint resurfacing TKA (single stage or two-stage procedure). Omitting the acquired lower limb malalignment by resurfacing the knee has the advantages of respecting the joint surface anatomy and preserving soft tissue laxities. On the other hand, it maintains pathological joint load and lower limb kinematics with potentially detrimental outcomes. The hybrid technique can be performed in most cases. It circumvents complications associated with osteotomies and brings lower limb axes closer to native alignment. On the other hand, it creates some intra-articular imbalances, which may require soft tissue releases and/or constrained implants. Correcting the extra-articular deformity (through an osteotomy) in conjunction with joint resurfacing TKA represents the only true kinematic alignment technique, as it aims to reproduce native knee laxity and overall lower limb axis.

Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.

Philosophical Investigations into AI Alignment: A Wittgensteinian Framework

We argue that the later Wittgenstein’s philosophy of language and mathematics, substantially focused on rule-following, is relevant to understand and improve on the Artificial Intelligence (AI) alignment problem: his discussions on the categories that influence alignment between humans can inform about the categories that should be controlled to improve on the alignment problem when creating large data sets to be used by supervised and unsupervised learning algorithms, as well as when introducing hard coded guardrails for AI models. We cast these considerations in a model of human–human and human–machine alignment and sketch basic alignment strategies based on these categories and further reflections on rule-following like the notion of meaning as use. To sustain the validity of these considerations, we also show that successful techniques employed by AI safety researchers to better align new AI systems with our human goals are congruent with the stipulations that we derive from the later Wittgenstein’s philosophy. However, their application may benefit from the added specificities and stipulations of our framework: it extends on the current efforts and provides further, specific AI alignment techniques. Thus, we argue that the categories of the model and the core alignment strategies presented in this work can inform further AI alignment techniques.

BUILDING A SCALABLE DATASET FOR FRIDAY SERMONS OF AUDIO AND TEXT (SAT)

Context. Today, collecting and creating datasets in various sectors has become increasingly prevalent. Despite this widespread data production, a gap still exists in specialized domains, particularly in the Islamic Friday Sermons (IFS) domain. It is rich with theological, cultural, and linguistic studies that are relevant to Arab and Muslim countries, not just religious discourses. Objective. The goal of this research is to bridge this lack by introducing a comprehensive Sermon Audio and Text (SAT) dataset with its metadata. It seeks to provide an extensive resource for religion, linguistics, and sociology studies. Moreover, it aims to support advancements in Artificial Intelligence (AI), such as Natural Language Processing and Speech Recognition technologies. Method. The development of the SAT dataset was conducted through four distinct phases: planning, creation and processing, measurement, and deployment. The SAT dataset contains a collection of 21,253 audio and corresponding transcript files that were successfully created. Advanced audio processing techniques were used to enhance speech recognition and provide a dataset that is suitable for wide-range use. Results. The fine-tuned SAT dataset achieved a 5.13% Word Error Rate (WER), indicating a significant improvement in accuracy compared to the baseline model of Microsoft Azure Speech. This achievement indicates the dataset’s quality and the employed processing techniques’ effectiveness. In light of this, a novel Closest Matching Phrase (CMP) algorithm was developed to enhance the high confidence of equivalent speech-to-text by adjusting lower ratio phrases. Conclusions. This research contributes significant impact and insight into different studies, such as religion, linguistics, and sociology, providing invaluable insights and resources. In addition, it is demonstrating its potential in Artificial Intelligence (AI) and supporting its applications. In future research, we will focus on enriching this dataset expansion by adding a sign language video corpus, using advanced alignment techniques. It will support ongoing Machine Translation (MT) developments for a broader understanding of Islamic Friday Sermons across different linguistics and cultures.

Alignment assessment of anisotropic liquid crystals through an automated image processing algorithm

Liquid crystals (LCs) are recognised as an emerging type of responsive and functional materials that exhibit diverse technological applications. The spatial arrangement or alignment of LC molecules creates an optical anisotropy, which influences the propagation of light. This study presents an automated image processing algorithm designed to quantitatively assess LC alignment. The algorithm calculates the image structure tensor and computes a score ranging from 0 to 1 to represent the uniformity of LC alignment. The tool was verified on both artificially generated images of lines with pre-defined orientations and then tested on microscope images of LCs. For the latter, test samples of deformed helix ferroelectric LCs were constructed using three different alignment methods. Analysis of microscopic images of samples prepared utilising photoalignment, polymer rubbing, and oblique evaporation alignment techniques, determined average alignment scores (mean ± standard error of the mean) of 0.44 ± 0.03, 0.26 ± 0.1, and 0.17 ± 0.04, respectively, in comparison to commercially sourced control samples with a score of 0.64 ± 0.03 (n=5 each). This study paves the way for automated objective assessment of LC alignment, a critical aspect to ensure the optimal performance, reliability, and efficiency of various devices and sensors relying on LC technology.

The Trochlear Bisector as a New Landmark for Kinematic Alignment in Total Knee Arthroplasty: A Radiographic Study.

Background: In recent years, there has been considerable interest in prosthetic alignment techniques for total knee arthroplasty (TKA), particularly in the so-called kinematic alignment, which aims to restore the knee's native alignment. However, implementing this technique requires specialized instruments and procedural steps that can be laborious. This study introduces the bisector of the trochlear groove as a reliable landmark for performing the distal femoral cut while maintaining parallelism with the native femoral joint line. Methods: Three orthopedic specialists assessed 110 X-ray images of full-leg, weight-bearing lower limbs obtained from healthy individuals between January 2021 and December 2022. The bisector of the trochlear groove was identified on the X-ray images, and the angle between this bisector and the femoral joint line was measured. The consistency of these measurements across repeated assessments and different examiners was evaluated. Results: The bisector of the trochlear groove was found to be perpendicular to the femoral joint line, with a mean angle of 89.4°. The inter-rater reliability was 68% within ±1.3° from the mean, while the intra-rater reliability was 82% within ±1.5° from the mean. Conclusions: These results suggest that by performing a femoral cut perpendicular to the bisector of the trochlear groove, surgeons can inherently restore the femoral joint line of the native knee in patients where the native joint line is no longer identifiable due to the effect of osteoarthritis. This method may offer a viable and straightforward alternative to the standard surgical technique currently practiced for kinematic alignment in TKA.

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

Numerous deep learning models have been introduced for EEG-based Emotion recognition tasks. Nevertheless, the majority of these models are fully supervised, demanding substantial amounts of labeled EEG signals. The labeling process of EEG signals is both time-intensive and costly, involving numerous trials and meticulous analysis by experts. Recently, some advanced semi-supervised algorithms that can achieve a competitive performance with fully-supervised methods by using only a small set of labeled data have been presented. However, these algorithms are primarily developed for the image data type, and naïve adaptation of them for EEG applications results in unsatisfactory performance. To address this issue, we present a robust semi-supervised EEG-based method that exploits the best techniques from advanced semi-supervised algorithms in the computer vision domain enriched with novel regularization terms for unlabeled signals. The proposed regularization terms improve both the discriminability and diversity of the model’s predictions and effectively leverage prior knowledge about the class distributions, thereby achieving a superior performance compared to the distribution alignment techniques in state-of-the-art methods. We evaluate our method on the DEAP dataset for cross-subject valence/arousal emotion recognition tasks, and on the SEED in a cross-session setting. The results indicate that the proposed method consistently surpasses the peer methods at different numbers of labeled data by a large margin.

Simple visual focusing and alignment technology for digital lithography

A simple visual focusing and alignment technology for digital lithography, which requires only one imaging system to capture both focusing and alignment information, is proposed. Firstly, the Brenner function and the global search method were chosen among the various sharpness evaluation functions, achieving visual precision focusing. Then, the mutual mapping relationship between DMD plane and CCD plane was established by image alignment technique, realizing the visualized precision alignment. Finally, a digital lithography system based on the proposed method was built and experimentally verified, by which the preparation of a grating with a line width of 1.35 μm was achieved, reaching the limit of the system resolution, and the alignment accuracy of the method reached 0.2 μm. Moreover, the mapping exposure experiments showed that the pre-exposure patterns could be converted between DMD and CCD planes, and the visualization of microstructure preparation was truly realized. This method improves the convenience of microstructure preparation and can greatly meet the needs of material scientists regarding microstructure preparation.