Simple Pattern Research Articles

Subcortical life, evolution of flattened body, and constrained mating posture in the earwig Platylabia major (Insecta: Dermaptera: "Anisolabididae").

Many animals take advantage of the shaded, humid, and protected environments in subcortical spaces, i.e., thin spaces under the loosened bark of dead trees. Permanent inhabitants of subcortical spaces often show specialized morphologies, such as a miniaturized or dorsoventrally flattened body. However, the evolutionary consequences of these specialized morphologies on behavioral, ecological, and life-history traits have been little studied. We studied the mating biology and anatomy of Platylabia major (usually placed in the family Anisolabididae), which is an obligate inhabitant of subcortical spaces with a paper-like flattened body, and compared them with those of two thicker, spongiphorid earwigs, Nesogaster amoenus and Paralabellula curvicauda. Mating trials in various settings showed that Pl. major requires thin spaces sandwiched by two planes to accomplish genital coupling and insemination. In contrast, the thicker species, although also frequently found in subcortical spaces, could mate on a single horizontal plane due to the ability of the male to twist its abdomen through approximately 180°. Examination by micro-computed tomography and a reagent-based clearing technique revealed no substantive differences in the configuration of mid-abdominal musculature between the species. The dorsal and lateral muscles of Pl. major, which are almost parallel to the antero-posterior body axis for accommodation within the thin abdomen, seemed incapable of producing the power to twist the abdomen. The abdominal musculature conforms to a simple pattern in both male and female earwigs, which is repeated in each of the pregenital segments. We conclude that small differences in the range of motion of each abdominal segment can result in large differences in possible mating postures and positions. Surgical experiments also demonstrated that both right and left penises of Pl. major are competent and used for insemination with no lateral bias, as in most other earwigs with twin penises studied to date.

Efficient Super-Resolution System With Block-Wise Hybridization and Quantized Winograd on FPGA

Super-resolution (SR) techniques aim to restore a high-resolution image from low-resolution images, which are often used to assist the enhancement of image/video quality under the rapid development of high-resolution and high-framerate media. Recently, neural network (NN)-based methods perform much better image reconstruction quality than classical approaches. However, the unacceptable computation complexity as well as the huge memory footprints of NNs limit the throughputs and scalability of these SR systems. In this work, we analyze several key issues in the design of NN-based SR systems first. Then, we propose a three-level systematic optimization methodology for SR systems to reduce computation overhead and keep image quality. At the algorithm level, we introduce image blocking to SR tasks and develop a block-wise SR algorithm based on the hybrid of NN and interpolation with a consistent image block evaluation metric. The configurable hybrid parameters help the SR algorithm to achieve a flexible trade-off between the computation overhead and image quality. At the operator level, we focus on the transpose convolution operators commonly used for upsampling in SR neural networks. We propose an efficient Winograd-based transposed convolution acceleration method. Through the efficient sub-convolutions conversion and the Winograd specialization, this methods enables unified Winograd transformations and simplified data access patterns. At the data level, we propose a novel quantization method for Winograd-aware SR neural networks to get better quantized accuracy. Comprehensive evaluations demonstrate the effectiveness of these optimizations. Our SR system reduces a large number of multiplications with great scalability and supports 4K@120fps and 8K@30fps outputs with acceptable image quality degradation.

Lateralized Learning to Solve Complex Boolean Problems.

Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ontologies. Biological nervous systems have the ability to abstract knowledge from simple and small-scale problems in order to then apply it to resolve more complex problems in similar and related domains. It is thought that lateral asymmetry of biological brains allows modular learning to occur at different levels of abstraction, which can then be transferred between tasks. This work develops a novel evolutionary machine-learning (EML) system that incorporates lateralization and modular learning at different levels of abstraction. The results of analyzable Boolean tasks show that the lateralized system has the ability to encapsulate underlying knowledge patterns in the form of building blocks of knowledge (BBK). Lateralized abstraction transforms complex problems into simple ones by reusing general patterns (e.g., any parity problem becomes a sequence of the 2-bit parity problem). By enabling abstraction in evolutionary computation, the lateralized system is able to identify complex patterns (e.g., in hierarchical multiplexer (HMux) problems) better than existing systems.

Electrical synapses for a pooling layer of the convolutional neural network in retinas.

We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry.

Detection of regularities in auditory sequences before and at term-age in human neonates

During the last trimester of gestation, fetuses and preterm neonates begin to respond to sensory stimulation and to discover the structure of their environment. Yet, neuronal migration is still ongoing. This late migration notably concerns the supra-granular layers neurons, which are believed to play a critical role in encoding predictions and detecting regularities. In order to gain a deeper understanding of how the brain processes and perceives regularities during this stage of development, we conducted a study in which we recorded event-related potentials (ERP) in 31-wGA preterm and full-term neonates exposed to alternating auditory sequences (e.g. “ba ga ba ga ba”), when the regularity of these sequences was violated by a repetition (e.g., ``ba ga ba ga ga''). We compared the ERPs in this case to those obtained when violating a simple repetition pattern (“ga ga ga ga ga” vs. “ga ga ga ga ba”). Our results indicated that both preterm and full-term neonates were able to detect violations of regularity in both types of sequences, indicating that as early as 31 weeks gestational age, human neonates are sensitive to the conditional statistics between successive auditory elements. Full-term neonates showed an early and similar mismatch response (MMR) in the repetition and alternating sequences. In contrast, 31-wGA neonates exhibited a two-component MMR. The first component which was only observed for simple sequences with repetition, corresponded to sensory adaptation. It was followed much later by a deviance-detection component that was observed for both alternation and repetition sequences. This pattern confirms that MMRs detected at the scalp may correspond to a dual cortical process and shows that deviance detection computed by higher-level regions accelerates dramatically with brain maturation during the last weeks of gestation to become indistinguishable from bottom-up sensory adaptation at term.

Dynamic Patterns and Modeling of Early COVID-19 Transmission by Dynamic Mode Decomposition.

Understanding the transmission patterns and dynamics of COVID-19 is critical to effective monitoring, intervention, and control for future pandemics. The aim of this study was to investigate the spatial and temporal characteristics of COVID-19 transmission during the early stage of the outbreak in the US, with the goal of informing future responses to similar outbreaks. We used dynamic mode decomposition (DMD) and national data on COVID-19 cases (April 6, 2020-October 9, 2020) to model the spread of COVID-19 in the US as a dynamic system. DMD can decompose the complex evolution of disease cases into linear combinations of simple spatial patterns or structures (modes) with time-dependent mode amplitudes (coefficients). The modes reveal the hidden dynamic behaviors of the data. We identified geographic patterns of COVID-19 spread and quantified time-dependent changes in COVID-19 cases during the study period. The magnitude analysis from the dominant mode in DMD showed that California, Louisiana, Kansas, Georgia, and Texas had higher numbers of COVID-19 cases than other areas during the study period. States such as Arizona, Florida, Georgia, Massachusetts, New York, and Texas showed simultaneous increases in the number of COVID-19 cases, consistent with data from the Centers for Disease Control and Prevention. Results from DMD analysis indicate that certain areas in the US shared similar trends and similar spatiotemporal transmission patterns of COVID-19. These results provide valuable insights into the spread of COVID-19 and can inform policy makers and public health authorities in designing and implementing mitigation interventions.

Detection of Mooney faces is robust to image asymmetries produced by illumination.

Face detection relies on the visual features that are shared across different faces. An important component of the basic spatial configuration of a face is symmetry around the vertical midline. Although human faces are structurally symmetrical, they can be asymmetrical in an image due to the direction of lighting or the position of the face. In the experiments presented here, we examined how face detection from simple contrast patterns that occur across the face is affected by the image asymmetries associated with variations in the horizontal lighting direction. We presented observers with two-tone images of faces (Mooney faces) that isolated the unique pattern of contrast in the shading and shadows on a face, illuminated from a wide range of horizontal directions. In two experiments, we found that face detection is surprisingly robust to these lighting changes, with sensitivity in discriminating between face and non-face patterns reduced only at the most extreme lighting directions. This tolerance to changes in the horizontal lighting direction depended partly on the orientation of the face, vertical lighting direction, and contrast polarity. Our results provide insight into how contrast cues produced by shading and shadows occurring across the facial surface are utilized by the visual system to detect human faces.

Effective Approach for Fabricating Highly Precise High-Curvature Structural Patterns via Air-Bubble Induction.

Developing a new master mold-based patterning technology that can be used to accurately, precisely, and uniformly create large-area micropatterns while controlling the micropatterns of curved structures is essential for promoting innovative developments in various application fields. This study develops a new top-down lithographic process that can effectively produce structural patterns with high curvatures by growing isolated microbubbles in the master pattern holes. The isolated air-pocket lithography (IAL) we developed is based on the controlled behavior of micrometer-sized air pockets trapped between the grooves of the master pattern and the curable polymer. We successfully fabricated a concave array polydimethylsiloxane (PDMS) film and a convex array polymer film. In addition, the IAL mechanism was proven by confirming the expansion process of micrometer-sized air pockets trapped between the deep groove of the silicon master pattern and the PDMS coating film by using optical microscopy images. We successfully obtained complex three-dimensional structural patterns containing both 3D hollow spherical concave and ring-shaped two-dimensional convex patterns. This simple, fast, and effective high-curvature patterning technique is expected to provide innovative solutions for future applications such as nanoelectronics, optical devices, displays, and photovoltaics.

The application of artificial intelligence technology in game design

With the continuous development of both games and artificial intelligence (AI) technology, their integration is inevitable. Traditional game design faces issues such as simple character behavior patterns, fixed gameplay, and a lack of immersive game scenes, which have found better solutions with the incorporation of AI technology. This paper focuses on the application of AI technology in game design from three aspects: character behavior patterns, environmental interaction and scene changes, and emotional perception and feedback. Firstly, two game cases are analyzed to highlight the important role of AI character behavior patterns in games, and two design schemes for character behavior patterns are summarized. Secondly, the dynamic changes in environment are analyzed through two game cases, demonstrating the qualitative impact of dynamic environment changes on a game. Thirdly, this paper focuses on emotional perception and feedback, specifically analyzing the automatic adjustment of game effects through facial recognition technology and physiological indicators. Then, the challenges and shortcomings that current artificial intelligence technology still faces in game design are analyzed and addressed. Finally, this paper concludes with a summary of the research and an outlook for future development trends.

An exact chiral amorphous spin liquid

Topological insulator phases of non-interacting particles have been generalized from periodic crystals to amorphous lattices, which raises the question whether topologically ordered quantum many-body phases may similarly exist in amorphous systems? Here we construct a soluble chiral amorphous quantum spin liquid by extending the Kitaev honeycomb model to random lattices with fixed coordination number three. The model retains its exact solubility but the presence of plaquettes with an odd number of sides leads to a spontaneous breaking of time reversal symmetry. We unearth a rich phase diagram displaying Abelian as well as a non-Abelian quantum spin liquid phases with a remarkably simple ground state flux pattern. Furthermore, we show that the system undergoes a finite-temperature phase transition to a conducting thermal metal state and discuss possible experimental realisations.

ANALYZING USER PASSWORDS WORLDWIDE IN TERMS OF CYBER THREATS

Analysing user passwords used around the world is a very broad topic and involves a variety of factors. Password analysis is usually based on detailed studies by security experts, cyber security researchers and encryption experts. Weak passwords, repeated passwords, simple patterns, short passwords, personal information are critical contents that create vulnerabilities and are ignored by users. Attackers or attack tools working for password interception can easily intercept passwords that contain security measures below a certain level, that is, passwords that are not complex. The purpose of this study is to analyse global password usage, to increase users' awareness about password security and to encourage users to use stronger passwords. Small program snippets and new tools have been coded to achieve this goal. In future studies, it is aimed to improve the current application by automating the control of the distance of the texts from each other as a new password suggestion method and hybridising machine learning methods.

Aiding Ukraine in the Russian war: unity or new dividing line among Europeans?

The Russian invasion of Ukraine has caused a seemingly high level of unity amongst Europeans in support of Ukraine. However, this article uncovers some inter- and intra-country fault-lines in public opinion across and within 16 EU countries and the UK regarding pro-Ukraine aid initiatives by using a two-wave design with data from the EUI-YouGov survey conducted in April and September 2022. Findings show that support is relatively stable but varies a lot depending on the specific measure and between countries. We uncover lowest support for measures that go against the self-interest of Europeans such as deploying troops and accepting higher energy costs. Frontrunners of Ukraine support are geographically close to Russia and located in both Western and Eastern Europe (though not exclusively), whereas laggards are countries of Eastern and Southern Europe with a history of Russian ties during the Cold War. Yet within countries, Ukraine support does not follow a simple pre-determined ideological pattern of the left and right. Most countries with lower overall support for Ukraine display a higher level of polarization between supporters of the incumbent versus the opposition party. Understanding these fault-lines is important for insights on current and future levels of Ukraine aid across Europe.

3D solid of SARS-CoV-2 viral particles applying Legendre polynomials from tomography Fourier analysis.

We show the construction of 3D solids (volumetric 3D models) of SARS-CoV-2 viral particles from the tomographic studies (videos) of SARS-CoV-2-infected tissues. To this aim, we propose a video analysis (tomographic images) by frames (medical images of the virus), which we set as our metadata. We optimize the frames by means of Fourier analysis, which induces a periodicity with simple structure patterns to minimize noise filtering and to obtain an optimal phase of the objects in the image, focusing on the SARS-CoV-2 cells to obtain a medical image under study phase (MIS) (process repeated over all frames). We build a Python algorithm based on Legendre polynomials called "2DLegendre_Fit," which generates (using multilinear interpolation) intermediate images between neighboring MIS phases. We used this code to generate m images of size M×M, resulting in a matrix with size M×M×M (3D solid). Finally, we show the 3D solid of the SARS-CoV-2 viral particle as part of our results in several videos, subsequently rotated and filtered to identify the glicoprotein spike protein, membrane protein, envelope, and the hemagglutinin esterase. We show the algorithms in our proposal along with the main MATLAB functions such as FourierM and Results as well as the data required for the program execution in order to reproduce our results.

Reinforcement of colon anastomosis healing with leukocyte platelet-rich fibrin in rabbit model

AimThis study investigated the regenerative efficacy of leukocyte platelet-rich fibrin (L-PRF) on colon anastomotic healing in rabbits. Main methodsThirty-six healthy male white New Zealand rabbits were subjected to complete transactions of the ascending colon. The rabbits were equally divided into two groups: the control group, where the transected colon ends were anastomosed by a simple interrupted suture pattern, and the L-PRF-treated group, in which L-PRF was wrapped entirely around the anastomotic line. The postoperative acute pain scale was assessed using the Bristol Rabbit Pain Scale before surgery and at each four-hour interval post-operatively. After euthanizing the rabbits, the adhesion degree score, anastomotic bursting pressure, and stenosis degree of the anastomotic colon were assessed, and histopathological examination at the 7th, 14th, and 28th days postoperatively. Key findingsRabbits in both groups showed a significant increase in pain scores compared to baseline. Postoperatively, the L-PRF group exhibited significantly lower pain scores, adhesion scores, and stenosis degrees than the control group. However, the anastomotic bursting pressure was significantly higher in the L-PRF group. Re-epithelialization, polymorphonuclear neutrophil infiltration, granulation tissue formation, and collagen deposition scores were improved considerably in the L-PRF group compared to the control group. Immunostaining of growth factor expression was significantly lower in the control than in the L-PRF group. SignificanceThe L-PRF can augment collagen deposition, re-epithelialize the mucosa, promote angiogenesis, reduce adhesions, and diminish the stenosis degree scores. Therefore, it can be considered a promising aid in healing bowel anastomoses.

Translation Analysis In Sentence Patterns Found In The Novel Entitled “The Prince”

This study aims to: (i) analyze the tree diagram structure of sentence patterns found in the novel entitled “The Prince”, (ii) analyze the types of translation shifts of sentence patterns in the translation process found in the novel entitled “The Prince”. This research focuses on The Syntax Analysis and its Translation Found on Sentence Patterns in the Novel entitled “The Prince”. This study aims at analyzing the tree diagram structure and the types of translation shifts found in the novel entitled “The Prince”. In analyzing data, this study applied the theory of sentence patterns proposed by Quirk and Greenbaum and the theory of translations shift proposed by Catford. In the method and technique of collecting data, the process of collecting data was started by reading the entire data source to get the story in the novel entitled “The Prince” and observe the data of sentence patterns that can be taken from the story entitled “The Prince”. Then, the data source was read to get simple sentence patterns found in the data source. This research study found that there are seven sentence patterns translated by using unit shift of translation proposed by Catford. The researcher applied the theory of syntax, types of sentences, sentence pattern, tree diagram, and reviewed the previous studies. The implication of the study is that it can give the understanding of translation analysis found in the data source and can be used as teaching materials

Assessment of primary schoolchildren’s fluid intelligence using Raven's Colored progressive matrices: A pilot cross-sectional study in Lebanon

This study aims to assess children’s reasoning ability, namely following the crises in Lebanon, and to identify those in need. A pilot study was conducted in April-May 2023, targeting children between 6 and 11 attending grades 1 to 5. The sample (130 students) comprised more females (60.5%) than males (39.5%). The score for simple pattern completion was significantly higher among students aged 8-11 (9.58 over 11) than their younger peers (8.38; p=0.007). This score showed statistically significant variation depending on the student’s grade, with the lowest score at grade 1 (6.88), which significantly increased at grade 2 (9.44) and attained its maximum value at grade 4 (10.72), then decreased again at grade 5 (9.03; p<0.001). Male students (9.33) had higher scores than females (8.84) with no statistical significance (p>0.05). The parent’s characteristics did not statistically affect these scores, but students with older and married parents and those with better economic situations had higher scores. The score in discrete and continuous pattern completion was significantly higher among older students (10.68) than their younger peers (7.24; p<0.001) and per grade increase. Educators can support the development of fluid intelligence in schoolchildren through activities that encourage problem-solving, critical thinking, and creative exploration.

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data.

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

One‐Stop Fabrication of Flexible Sensors Based on Aqueous Printable Triboelectric Layer‐Electrode Integrated Ink with Enhanced Heat‐Resistance

AbstractTriboelectric nanogenerator (TENG) sensors can meet various application‐scenario requirements for their portability and sustainability. However, the multiple steps from manufacturing to assembly are generally tedious and manpower‐consuming, limiting further development of TENGs in industrialization. Herein, a printable heat‐resistant triboelectric layer‐electrode integration ink is prepared by doping MXene into Polyvinylpyrrolidone and methyl cellulose system for the first time, which is simple and environmentally friendly. And a facile method for manufacturing integrated‐forming TENG (IF‐TENG) sensors without additional electrode assembly based on 3D printing technology is reported, the resulting TENG is flexible and heat‐resistant (≤200 °C). On this basis, IF‐TENGs with simple and complex patterns are rapidly constructed without manual assembly, which can perceive different pressures and bending angles of arms efficiently in high‐temperature conditions. To improve the sensing accuracy, the prepared IF‐TENG sensors are implanted on the four main stress positions on an insole. The support vector machine (SVM) is used to process and analyze the output signals to improve the accuracy of gaits recognition. The mean area under the receiver operating characteristic curve (AUC) value of 0.99 is obtained after parameter optimization by the GridSearch algorithm, and the recognization of various gaits achieves a high accuracy of 96.7%. This work provides an advanced fabrication strategy for TENG with low labor costs and thermal stability, demonstrating great prospects of TENG in mass manufacturing and safety motion monitoring in high‐temperature environments.

Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.

Analysis of the Pattern Shapes Obtained By Micro/Nanospherical Lens Photolithography.

Micro/nanospherical lens photolithography (SLPL) constitutes an efficient and precise micro/nanofabrication methodology. It offers advantages over traditional nanolithography approaches, such as cost-effectiveness and ease of implementation. By using micrometer-sized microspheres, SLPL enables the preparation of subwavelength scale features. This technique has gained attention due to its potential applications. However, the SLPL process has a notable limitation in that it mostly produces simple pattern shapes, mainly consisting of circular arrays. There has been a lack of theoretical analysis regarding the possible shapes that can be created. In our experiments, we successfully prepared annular and ring-with-hole pattern shapes. To address this limitation, we applied the Mie scattering theory to systematically analyze and summarize the various patterns that can be obtained through the SLPL process. We also proposed methods to predict and obtain different patterns. This theoretical analysis enhances the understanding of SLPL and expands its potential applications, making it a valuable area for further research.