Set Of Patterns Research Articles

Monkey Bar-Associated Pediatric Fractures: A National Epidemiologic Study of Emergency Department Visits.

Emergency department (ED) visits because of pediatric fractures are not only burdensome for patients and their families but also result in an increased healthcare expenditure. Almost half of all children experience at least one fracture by the age of 15. Many fractures occur in playgrounds, with monkey bars and other climbing apparatuses noted as frequent mechanisms of injury. Our purpose was to identify the pattern of injury and the population sustaining monkey bar-associated fractures. We queried the National Electronic Injury Surveillance System database for all monkey bar-associated injuries in patients aged 0 to 18 years from January 1, 2009, to December 31, 2019. We described demographic data, patient disposition from the ED, fracture pattern, and injury setting using unweighted and weighted estimates. Weighted results that more closely reflect national estimates were calculated. During the study period, 30,920 (862,595 weighted) monkey bar-associated injuries presented to EDs; 16,410 (53.1%) (weighted injuries: 408,722 [47.4%]) were fracture injuries. The average age of kids sustaining fractures was 6.5 years, with most injuries (66.4%) occurring in kids between 6 and 12 years. A higher percentage of male patients presented to the ED with fractures compared with female patients (53% versus 47%). Upper extremity fractures were most common, 382,672 (94%) with forearm fractures constituting the majority (156,691 [38%]). Most children were treated and released (354,323 [87%]), with only 35,227 children (9%) being admitted for treatment. Places of recreation/sports were the most common setting of fractures (148,039 [36%]), followed by schools (159,784 [39%]). A notable association was observed between year and ethnicity and between month period and injury setting. Monkey bar-associated injuries are a major cause of upper extremity fractures in children, with most injuries occurring in recreational areas or schools. Young elementary school children are at the highest risk of injury. ED visits because of monkey bar-associated fractures have increased over the study period, and these injuries continue to be a major cause of fractures in children. Additional measures should be installed to decrease these preventable fractures among children, with schools as a potential starting point.

Text-to-Image Generation for Abstract Concepts

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are primarily trained on concrete physical objects and often struggle to visualize abstract concepts. Inspired by the three-layer artwork theory that identifies critical factors, intent, object and form during artistic creation, we propose a framework of Text-to-Image generation for Abstract Concepts (TIAC). The abstract concept is clarified into a clear intent with a detailed definition to avoid ambiguity. LLMs then transform it into semantic-related physical objects, and the concept-dependent form is retrieved from an LLM-extracted form pattern set. Information from these three aspects will be integrated to generate prompts for T2I models via LLM. Evaluation results from human assessments and our newly designed metric concept score demonstrate the effectiveness of our framework in creating images that can sufficiently express abstract concepts.

Pandemic, sanctions and anxiety in Russia’s regions: Business expectations nowcasting

The study develops a methodology of business expectations index nowcasting with testing on data for the Russian economy as a whole and its regions. This methodology differs from the existing solutions in that it introduces a Bayesian averaging approach to define a set of search patterns for nowcasting and solves the issue of aggregation of time series by individual queries. The developed indices have shown a high level of adequacy, serving as effective tools to reflect shock events in the country’s economic and political landscape and also as predictors of fluctuations in survey­based expectation indices. The application of the presented methodology has revealed the factors that affect the volatility of business expectations indices depending on the level of development and sectoral specialization of Russian regions. In particular, financial and economic centers of highly developed regions and developed regions with diversified economy show high volatility, while business expectations indices in less developed agrarian and commodity regions show low volatility. These results can be useful for economic policy decisions and are of interest to researchers concerned with economic stability and forecasting.

Evaluation of variability in performance and paw placement patterns by dogs completing the dog walk obstacle in an agility competition.

The objective of this study was to describe paw placement patterns for canine athletes completing the dog walk obstacle during canine agility trials. It was hypothesized that dogs would demonstrate defined sets of paw placement patterns as they complete the dog walk obstacle and that those could be classified based on end contact behavior. Videos of 296 dogs attempting the dog walk obstacle at the 2021 UK Agility International (UKI) US Open were reviewed online. Data observed from video evaluation included front and rear limb paw placement across the dog walk and time to complete the obstacle. Results showed a high variability in obstacle performance. Mean time to complete the entire obstacle was 2.26 seconds (sd = 1.03). Mean and median completion times were qualitatively similar across all height classes. A slight majority of dogs hit the up ramp with their right foot first indicating running on their left lead (n = 185, 63%) with some variation observed between heights. Likewise, a slight majority (58%) of dogs hit the down ramp with their right front foot first (151/262). Given the high variation in completion times and paw placements, we could not identify clear patterns of dog walk performance. The large amount of variation observed with the dog walk obstacle suggests a need for future studies to employ alternative methods for objective gait analysis and to strategically select dogs to reflect the large variety in obstacle performance observed here.

Weakly Supervised Depth Estimation for 3D Imaging with Single Camera Fringe Projection Profilometry.

Fringe projection profilometry (FPP) is widely used for high-accuracy 3D imaging. However, employing multiple sets of fringe patterns ensures 3D reconstruction accuracy while inevitably constraining the measurement speed. Conventional dual-frequency FPP reduces the number of fringe patterns for one reconstruction to six or fewer, but the highest period-number of fringe patterns generally is limited because of phase errors. Deep learning makes depth estimation from fringe images possible. Inspired by unsupervised monocular depth estimation, this paper proposes a novel, weakly supervised method of depth estimation for single-camera FPP. The trained network can estimate the depth from three frames of 64-period fringe images. The proposed method is more efficient in terms of fringe pattern efficiency by at least 50% compared to conventional FPP. The experimental results show that the method achieves competitive accuracy compared to the supervised method and is significantly superior to the conventional dual-frequency methods.

Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks

Inferring microbial interaction networks from abundance patterns is an important approach to advance our understanding of microbial communities in general and the human microbiome in particular. Here we suggest discriminating two levels of information contained in microbial abundance data: (1) the quantitative abundance values and (2) the pattern of presences and absences of microbial organisms. The latter allows for a binary view on microbiome data and a novel interpretation of microbial data as attractors, or more precisely as fixed points, of a Boolean network.Starting from these attractors, our aim is to infer an interaction network between the species present in the microbiome samples. To accomplish this task, we introduce a novel inference method that combines the previously published ESABO (Entropy Shifts of Abundance vectors under Boolean Operations) method with an evolutionary algorithm. The key idea of our approach is that the inferred network should reproduce the original set of (observed) binary abundance patterns as attractors.We study the accuracy and runtime properties of this evolutionary method, as well as its behavior under incomplete knowledge of the attractor sets. Based on this theoretical understanding of the method we then show an application to empirical data.

Automatic optimization of projection intensity for high dynamic range 3D surface measurement

Stripe projection profilers are widely used in many fields because of their high speed, high accuracy, and clear surface textures. In the structured light projection method, the complex reflectance distribution on the surface of the high-dynamic-range objective is an important factor that affects the measurement accuracy, as it can result in image over- and under-exposure. In this study, a method is proposed to adaptively determine the number of stripe patterns and the corresponding number of light intensities of the stripe patterns based on the complex reflectance of the surface of the object being measured. A multi threshold Otsu algorithm is used to classify the histogram of the surface reflectance distribution of the measured object, and several sets of stripe patterns with optimal light intensity are generated according to the light intensity response function. The original stripe patterns acquired at different light intensities are synthesized pixel by pixel into a high dynamic range (HDR) stripe image, and a four-step phase-shifting algorithm is used to obtain the unwrapped phase from the synthesized image. Experimental results show that this method can accurately measure the target of a surface-reflectance-transformed HDR.

Constraint-based high utility mobile trajectory pattern mining for security systems

In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.

Method for and Analysis of Early-Stage Firm Growth Patterns Using World Bank Data

Venture growth and entrepreneurial activity impact aspects of modern societies, such as wealth creation, employment, and innovation, and also potentially influence sustainable development, a key focus in the social sciences. The study of firms has benefitted from solid theoretical foundations dating back to Edith Penrose’s The Theory of the Growth of the Firm. Yet, early-stage firms, as one of the fastest-growing types of firms, continue to be poorly understood. Therefore, we focus our analysis on the specificities of the growth patterns of new firms. Our contribution to the literature is to derive an empirically based set of growth patterns observed among early-stage firms that can be used for theory testing and in proofs. Our analysis, grounded in data from the World Bank Enterprise Surveys (WBES) encompassing over 140,000 entries, provides insights into growth patterns in early-stage firms, indirectly indicating potential intersections with sustainable business practices related, for example, to management team experience. More importantly, to make our research applicable to current real-world decisions, we evaluate the predictive capacity of these growth patterns and provide a series of conclusions that could help practitioners better evaluate the growth potential of early-stage firms.

Neural networks with functional inputs for multi-class supervised classification of replicated point patterns

A spatial point pattern is a collection of points observed in a bounded region of the Euclidean plane or space. With the dynamic development of modern imaging methods, large datasets of point patterns are available representing for example sub-cellular location patterns for human proteins or large forest populations. The main goal of this paper is to show the possibility of solving the supervised multi-class classification task for this particular type of complex data via functional neural networks. To predict the class membership for a newly observed point pattern, we compute an empirical estimate of a selected functional characteristic. Then, we consider such estimated function to be a functional variable entering the network. In a simulation study, we show that the neural network approach outperforms the kernel regression classifier that we consider a benchmark method in the point pattern setting. We also analyse a real dataset of point patterns of intramembranous particles and illustrate the practical applicability of the proposed method.

Synthesis of the neuro-fuzzy regulator with genetic algorithm

Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data.

The World Health Organization has reported that approximately 300 million individuals suffer from the mood disorder known as MDD. Non-invasive measurement techniques have been utilized to reveal the mechanism of MDD, with rsfMRI being the predominant method. The previous functional connectivity and energy landscape studies have shown the difference in the coactivation patterns between MDD and HCs. However, these studies did not consider oscillatory temporal dynamics. In this study, the dynamic mode decomposition, a method to compute a set of coherent spatial patterns associated with the oscillation frequency and temporal decay rate, was employed to investigate the alteration of the occurrence of dynamic modes between MDD and HCs. Specifically, The BOLD signals of each subject were transformed into dynamic modes representing coherent spatial patterns and discrete-time eigenvalues to capture temporal variations using dynamic mode decomposition. All the dynamic modes were disentangled into a two-dimensional manifold using t-SNE. Density estimation and density ratio estimation were applied to the two-dimensional manifolds after the two-dimensional manifold was split based on HCs and MDD. The dynamic modes that uniquely emerged in the MDD were not observed. Instead, we have found some dynamic modes that have shown increased or reduced occurrence in MDD compared with HCs. The reduced dynamic modes were associated with the visual and saliency networks while the increased dynamic modes were associated with the default mode and sensory-motor networks. To the best of our knowledge, this study showed initial evidence of the alteration of occurrence of the dynamic modes between MDD and HCs. To deepen understanding of how the alteration of the dynamic modes emerges from the structure, it is vital to investigate the relationship between the dynamic modes, cortical thickness, and surface areas.

A dependable and resource-frugal ring oscillator physically unclonable function

ABSTRACT Silicon Physical Unclonable Function (PUF) is a crucial component for hardware security, providing unique device identification in networks. PUF provides robust security for cryptographic keys and IC digital fingerprints. However, resource constraints in network devices limit its use in security protocols. To address this challenge, PUF offers a lightweight hardware security solution with two main types: strong PUF (e.g. arbiter PUF) and Ring Oscillator (RO) PUF. The first option produces large CRPs with improved security but incurs hardware overhead, whereas the second option offers lower overhead with smaller CRP size, leading to reduced security. This paper introduces a novel, cost-effective solution to enhance RO PUF security while reducing hardware limitations. Our proposal involves a resource sharing technique with scan flip-flops, multiplexers, and inverter logic. This approach takes advantage of existing scan chains in circuit netlists to reduce PUF area without sacrificing uniqueness. A specific set of test patterns and odd number of inverters transform the memory-based PUF into an RO PUF. Our solution also leverages pass transistor-based implementation to significantly reduce power consumption and area overhead. Simulation results show that our approach effectively reduces area and power consumption while maintaining PUF uniqueness.

Low grade oncocytic renal tumor (LOT): Clinicopathological characterization of 13 cases

ContextLow grade oncocytic tumor (LOT) is a recently recognized renal oncocytic neoplasm with unique morphologic and immunohistochemical pattern (CK7 +, CD117 -) that differentiates them from oncocytoma and chromophobe renal cell carcinoma (ChrRCC). ObjectiveTo further evaluate the histomorphological characteristics as well as the clinical outcome of low grade oncocytic tumors, retrospectively. DesignThirteen cases of LOT were identified from 463 cases of renal oncocytic neoplasm in our pathology archive. All tumors were immunostained with CK7, CD117 and other relevant markers. The pathohistological features and follow up data of these cases were recorded. ResultsMedian age of patients was 76 years old (range from 36 to 86), with male to female ratio of 2:11. None of the patients had a syndromic association/hereditary condition. Eleven tumors were unifocal in each affected kidney, and two were multifocal with 2 and 3 separated tumors, respectively. On microscopic examination, tumors show variety of growth patterns, namely solid, compact nested, focal tubular/tubuloreticular and trabecular patterns. The stroma can be hypocellular and edematous where the tumor cells are loosely arranged exhibiting cords and scattered single cell arrangement. Immunohistochemically, all thirteen cases displayed strong and diffuse CK7 positivity in tumor cells. Eleven cases were CD117 negative and the other two showed focal and weak CD117 positivity (< 5% of tumor cells). Uniform tumor cell positivity was found for AE1/3, EMA, PAX8, and e-cadherin. Negative staining results include CAIX, AMACR, CD10 and vimentin. All cases in our cohort demonstrate indolent behavior and show no evidence of disease recurrence, progression, or metastases during the follow-up period up to 96 months. ConclusionLOT is an emerging new entity of renal oncocytic neoplasm and demonstrates indolent clinical behavior. Its unique morphologic features and immunohistochemical patterns (CK7 +, CD117 -) set them apart from oncocytoma and ChrRCC.

Digital Three-dimensional Smocking Design

We develop an optimization-based method to model smocking , a surface embroidery technique that provides decorative geometric texturing while maintaining stretch properties of the fabric. During smocking, multiple pairs of points on the fabric are stitched together, creating non-manifold geometric features and visually pleasing textures. Designing smocking patterns is challenging, because the outcome of stitching is unpredictable: The final texture is often revealed only when the whole smocking process is completed, necessitating painstaking physical fabrication and time consuming trial-and-error experimentation. This motivates us to seek a digital smocking design method. Straightforward attempts to compute smocked fabric geometry using surface deformation or cloth simulation methods fail to produce realistic results, likely due to the intricate structure of the designs, the large number of contacts and high-curvature folds. We instead formulate smocking as a graph embedding and shape deformation problem. We extract a coarse graph representing the fabric and the stitching constraints and then derive the graph structure of the smocked result. We solve for the three-dimensional embedding of this graph, which in turn reliably guides the deformation of the high-resolution fabric mesh. Our optimization based method is simple, efficient, and flexible, which allows us to build an interactive system for smocking pattern exploration. To demonstrate the accuracy of our method, we compare our results to real fabrications on a large set of smocking patterns.

Coexistence of Cyclic Sequential Pattern Recognition and Associative Memory in Neural Networks by Attractor Mechanisms.

Neural networks are developed to model the behavior of the brain. One crucial question in this field pertains to when and how a neural network can memorize a given set of patterns. There are two mechanisms to store information: associative memory and sequential pattern recognition. In the case of associative memory, the neural network operates with dynamical attractors that are point attractors, each corresponding to one of the patterns to be stored within the network. In contrast, sequential pattern recognition involves the network memorizing a set of patterns and subsequently retrieving them in a specific order over time. From a dynamical perspective, this corresponds to the presence of a continuous attractor or a cyclic attractor composed of the sequence of patterns stored within the network in a given order. Evidence suggests that the brain is capable of simultaneously performing both associative memory and sequential pattern recognition. Therefore, these types of attractors coexist within the neural network, signifying that some patterns are stored as point attractors, while others are stored as continuous or cyclic attractors. This article investigates the coexistence of cyclic attractors and continuous or point attractors in certain nonlinear neural networks, enabling the simultaneous emergence of various memory mechanisms. By selectively grouping neurons, conditions are established for the existence of cyclic attractors, continuous attractors, and point attractors, respectively. Furthermore, each attractor is explicitly represented, and a competitive dynamic emerges among these coexisting attractors, primarily regulated by adjustments to external inputs.

Notice of Violation of IEEE Publication Principles: Connected Optimal Two-Coverage of Sensor Networks

Notice of Violation of IEEE Publication Principles<br><br>"Connected Optimal Two-Coverage of Sensor Networks,"<br>by Z. Yun, J. Teng, Z. Yu, D. Xuan, B. Chen, W. Zhao,<br>in the IEEE/ACM Transactions on Networking, vol.PP, no.99<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. One author (Ziqiu Yun) was responsible for the violation, and this violation was done without the knowledge of the other authors.<br><br>"Planar Thinnest Deployment Pattern of Congruent Discs which Achieves 2-coverage"<br>by Ge Jun,<br>in his thesis presented at the School of Mathematical Sciences, Soochow University, Suzhou 215006, P. R. China, June 2010.<br><br> <br/> In wireless sensor networks, multiple-coverage, in which each point is covered by more than one sensor, is often required to improve detection quality and achieve high fault tolerance. However, finding optimal patterns that achieve multiple-coverage in a plane remains a long-lasting open problem. We first derive the optimal deployment density bound for two-covered deployment patterns where Voronoi polygons generated by sensor nodes are congruent. We then propose optimal two-coverage patterns based on the optimal bound. We further extend these patterns by considering the connectivity requirement and design a set of optimal patterns that achieve two-coverage and one-, two-, and three-connectivity. We also study optimal patterns under practical considerations. To our knowledge, our work is the very first that proves the optimality of multiple-covered deployment patterns.

Frequent Pattern Mining in Continuous-Time Temporal Networks.

Networks are used as highly expressive tools in different disciplines. In recent years, the analysis and mining of temporal networks have attracted substantial attention. Frequent pattern mining is considered an essential task in the network science literature. In addition to the numerous applications, the investigation of frequent pattern mining in networks directly impacts other analytical approaches, such as clustering, quasi-clique and clique mining, and link prediction. In nearly all the algorithms proposed for frequent pattern mining in temporal networks, the networks are represented as sequences of static networks. Then, the inter- or intra-network patterns are mined. This type of representation imposes a computation-expressiveness trade-off to the mining problem. In this paper, we propose a novel representation that can preserve the temporal aspects of the network losslessly. Then, we introduce the concept of constrained interval graphs ( CIGs). Next, we develop a series of algorithms for mining the complete set of frequent temporal patterns in a temporal network data set. We also consider four different definitions of isomorphism for accommodating minor variations in temporal data of networks. Implementing the algorithm for three real-world data sets proves the practicality of the proposed approach and its capability to discover unknown patterns in various settings.

Climate variability on Fit for 55 European power systems.

The use of variable renewable energy sources to generate electricity introduces a dependency on meteorological factors into power systems. With the renewables share growing globally, often driven by political pressures, the reliability and efficiency of power systems are increasingly affected by this dependency. In this paper, we investigate the impact of the natural variability of meteorological parameters on the European power system in 2030. We specifically focus on (1) analysing the main European weather patterns affecting renewable energy production and (2) understanding the co-variability of this production among European countries. The identification of a set of patterns in the behaviour of key power system operation indicators allows us to analyse the relationship between large-scale weather regimes and daily power system operations in a 2030 European energy context. Regarding renewable generation, analysis of the co-variability shows that European power systems tend to form two clusters, in each of which all the regions tend to show a positive correlation among themselves and a negative correlation with the other cluster. Our analysis of the most important large-scale weather regimes shows that during cyclonic patterns, the carbon intensity of all the European power systems is lower than normal, while the opposite happens during blocking regimes.

Démonette-2, a derivational database for French with broad lexical coverage and fine-grained morphological descriptions

Morphological databases play an important role in linguistic research today. While several exist for the study of inflectional morphology in French, there is still a lack of resources for derivational morphology. This article presents Démonette-2, a new release of the French derivational morphology database Démonette. Developed in the context of the Démonext project, this database provides a framework in which it is possible to integrate various existing derivational resources. Démonette is characterized by its relational nature, which makes it possible to describe an extensive set of word formation patterns, including suffixation, prefixation, and a variety of non-canonical derivational processes such as conversion and parasynthetic formations. It reconciles broad coverage and fine-grained descriptions and is suitable for different audiences: morphologists working in different theoretical frameworks, teachers, speech therapists, NLP specialists, etc. The article presents in detail the structure and content of Démonette, its evolution since the first version, and its query interface.