Simple Representation Research Articles

The Space–Time Representation of Extraordinary Rainfall Events

ABSTRACTExtraordinary events are rarely observable in a single rainfall gauge, and this make extremely challenging the correct prediction of their arrivals. However, it may be possible to develop a more robust approach by employing a space–time modelling scheme that is able to capture the spatial dynamics of such phenomena. Therefore, a space–time Poisson model of rainfall cells with circular shape and random depth has been exploited for the first time to interpret the behaviour of this family of extraordinary events. This category of events that may be connected to larger meteorological phenomena not necessarily connected with local heterogeneity of the landscape. Following the identification of the observed extraordinary event across southern Italy, six zones with significantly different dynamics in terms of the frequency of such extremes were identified. Subsequently, a simple mathematical representation was adopted to calibrate the model parameters, leading to an estimate of regional probability distributions defined on the space–time occurrences of extraordinary events over homogeneous zones. The approach allows to overcome the limitations posed by point observations allowed the definition of a probability distribution that pertains to an entire area rather than just a point. The obtained quantiles of rainfall estimated seems to align well with the upper bound of the probability distribution of the annual maxima observed over the areas of interests.

Frogs recognize prey: a causal-behavioral teleosemantics

I propose a version of teleosemantics for simple animal representations that combines Millikan’s suggestion that they represent the most crucial “normal conditions” required for them to bring about their beneficial behavioral effects with Neander’s suggestion that they represent their “normal causes”. The content of a simple representation is the most crucial among the normal conditions responsible for its tokens having beneficial behavioral effects that also causes such tokens. I argue that this version delivers more plausible contents than other versions of teleosemantics. Frogs’ prey detectors represent the animals they prey on rather than nutritious or small moving things.

Integrating Art in Medical Visualization Techniques

The incorporation of art with medical visualisation techniques offers a significant improvement in the communication of medical information. Traditional methods of medical illustration were primarily concerned with anatomical precision, but introducing artistic aspects can expand knowledge, increase emotional resonance, and produce more effective visual storytelling. This study investigates how artistic principles such as composition, colour theory, and dynamic symmetry can enhance the clarity and effectiveness of medical images. Medical visualisation can go beyond simple representation by combining scientific rigour and artistic flair, resulting in a more profound link between the spectator and the medical facts, which improves patient understanding, physician communication, and educational outcomes. Case studies are offered to demonstrate the multidisciplinary approach’s potential, as well as the obstacles and opportunities it brings for future research. Keywords: Medical visualization, Medical illustration, Artistic integration, Data visualization, Visual communication in medicine

A Data-Driven Bayesian Koopman Learning Method for Modeling Hysteresis Dynamics.

Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the applications of hysteresis systems for high-speed and high-precision positioning, detection, execution, and other operations. In this article, a Bayesian Koopman (B-Koopman) learning algorithm is therefore developed to characterize hysteresis dynamics. Essentially, the proposed scheme establishes a simplified linear representation with time delay for hysteresis dynamics, where the properties of the original nonlinear system are preserved. Furthermore, model parameters are optimized via sparse Bayesian learning together with an iterative strategy, which simplifies the identification procedure and reduces modeling errors. Extensive experimental results on piezoelectric positioning are elaborated to substantiate the effectiveness and superiority of the proposed B-Koopman algorithm for learning hysteresis dynamics.

Plastic Surgeons and Portraits Before the Invention of Photography.

The aim of this paper is to review portraits from the Renaissance to the Romantic eras, created before the invention of photography, and to discuss their usefulness to plastic surgeons. Before photography and film were invented, portraits played a critical role in documenting individuals, conveying power, and immortalizing people's appearance and identity. Portraits were not merely simple representations of a person's physical appearance; they played a crucial role in conveying the subject's inner qualities and social position, as well as the cultural values of the era. Artists often idealized or embellished certain features to align with the cultural and aesthetic ideals of their time. Portraits of the Renaissance to Romantic eras could have a significant impact on plastic surgeons' understanding of beauty and aesthetics. A plastic surgeon drawing on Renaissance ideals might prioritize symmetry and balanced facial features, which continue to be regarded as attractive in contemporary beauty standards. Beauty standards during these times favored curvaceous and lively figures, potentially inspiring modern plastic surgeons to focus on creating more dramatic or unique appearances. A plastic surgeon inspired by Romantic art might value uniqueness and avoid rigid beauty standards, focusing instead on enhancing a person's natural traits rather than conforming to a singular "ideal" appearance. Plastic surgeons could gain a richer, more classical perspective on aesthetics by studying historical art, potentially leading to more personalized and balanced outcomes. However, integrating these classical ideals into modern practice must also consider the diverse and varying definitions of beauty prevalent in today's multicultural society.

Simulation-based inference for parameter estimation of complex watershed simulators

Abstract. High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

Dual role for Headcase in hemocyte progenitor fate determination in Drosophila melanogaster.

The hematopoietic organ of the Drosophila larva, the lymph gland, is a simplified representation of mammalian hematopoietic compartments, with the presence of hemocyte progenitors in the medullary zone (MZ), differentiated hemocytes in the cortical zone (CZ), and a hematopoietic niche called the posterior signaling centre (PSC) that orchestrates progenitor differentiation. Our previous work has demonstrated that the imaginal cell factor Headcase (Hdc, Heca) is required in the hematopoietic niche to control the differentiation of hemocyte progenitors. However, the downstream mechanisms of Hdc-mediated hematopoietic control remained unknown. Here we show that Hdc exerts this function by negatively regulating the insulin/mTOR signaling in the niche. When Hdc is depleted in the PSC, the overactivation of this pathway triggers reactive oxygen species (ROS) accumulation and, in turn, the differentiation of effector lamellocytes non-cell-autonomously. Although overactivation of insulin/mTOR signaling normally leads to an increase in the size of the hematopoietic niche, this effect is concealed by cell death caused by hdc loss-of-function. Moreover, we describe here that hdc silencing in progenitors causes cell-autonomous ROS elevation and JNK pathway activation, resulting in decreased MZ size and differentiation of lamellocytes. Similarly to the PSC niche, knocking down hdc in the MZ also leads to caspase activation. Notably, depleting Hdc in the progenitors triggers proliferation, an opposing effect to what is observed in the niche. These findings further our understanding of how progenitor maintenance in the larval lymph gland is controlled autonomously and non-cell-autonomously, and point towards new mechanisms potentially regulating HSC maintenance across vertebrates.

WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

Activity Identification, Classification, and Representation of Wheelchair Sport Court Tasks: A Method Proposal.

Monitoring player mobility in wheelchair sports is crucial for helping coaches understand activity dynamics and optimize training programs. However, the lack of data from monitoring tools, combined with a lack of standardized processing approaches and ineffective data presentation, limits their usability outside of research teams. To address these issues, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements, utilizing kinematic data from standard wheelchair mobility tests. Each participant's wheelchair was equipped with inertial measurement units-two on the wheel axes and one on the frame. A total of 36 wheelchair tennis and badminton players completed at least one of three proposed tests: the star test, the figure-of-eight test, and the forward/backward test. Locomotor tasks were identified using a five-step procedure involving data reduction, symbolic approximation, and logical pattern searching. This method successfully identified 99% of locomotor tasks for the star test, 95% for the figure-of-eight test, and 100% for the forward/backward test. The proposed method offers a valuable tool for the simple and clear identification and representation of locomotor tasks over extended periods. Future research should focus on applying this method to wheelchair court sports matches and daily life scenarios.

Tropical Intraseasonal Variability as a Leading Moisture Dynamic Mode of the Warm-Pool Background State

Abstract Tropical intraseasonal variability (ISV) is dominated by the Madden–Julian oscillation (MJO), and its spatiotemporal characteristics vary with the Indo-Pacific warm-pool background on seasonal and longer timescales. Previous works have suggested ISV dynamics in various frameworks, whereas a unifying view remains challenging. Motivated by the recent advance in moisture mode theory, we revisit the ISV as a leading moisture mode modulated by varying background states derived from a reanalysis, using a moist linear baroclinic model (mLBM) improved with a simple convective scheme relating convective precipitation to tropospheric and boundary-layer moisture anomalies and simple cloud–radiative feedback representations. Under a boreal winter background state, this mLBM yielded a large-scale but local eastward-propagating mode with a phase speed of 3–5 m/s over the warm-pool region, resembling the MJO. Background lower-tropospheric winds and thermodynamic fields are important in determining the growth rate and periodicity of the leading mode, whose stability depends on cloud–radiative feedback and background state variations. We further demonstrate why the MJO is locally contained in the Indo-Pacific warm-pool region. The local thermal/moisture condition and Walker circulation greatly enhance its instability, but outside this region, this mode is heavily damped. Thus, the expansion/contraction of this warm-pool condition may enhance/reduce its instability and expand/reduce its domain of activity. Prescribing El Niño background causes eastward displacement of the wintertime ISV activity, reminiscent of the observed MJO modulations by El Niño. Under a summer background state, the eastward-propagating leading mode resembles the boreal summer ISV but biased, requiring further model improvements.

Analysis of signals from air conditioner compressors with ordinal patterns and machine learning

Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment.

Fisher Information-Based Optimization of Mapped Fourier Grid Methods

The mapped Fourier grid method (mapped-FGM) is a simple and efficient discrete variable representation (DVR) numerical technique for solving atomic radial Schrödinger differential equations. It is set up on equidistant grid points, and the mapping, a suitable coordinate transformation to the radial variable, deals with the potential energy peculiarities that are incompatible with constant step grids. For a given constrained number of grid points, classical phase space and semiclassical arguments help in selecting the mapping function and the maximum radial extension, while the energy does not generally exhibit a variational extremization trend. In this work, optimal computational parameters and mapping quality are alternatively assessed using the extremization of (coordinate and momentum) Fisher information. A benchmark system (hydrogen atom) is employed, where energy eigenvalues and Fisher information are traced in a standard convergence procedure. High-precision energy eigenvalues exhibit a correlation with the extrema of Fisher information measures. Highly efficient mapping schemes (sometimes classically counterintuitive) also stand out with these measures. Same trends are evidenced in the solution of Dalgarno–Lewis equations, i.e., inhomogeneous counterparts of the radial Schrödinger equation occurring in perturbation theory. A detailed analysis of the results, implications on more complex single valence electron Hamiltonians, and future extensions are also included.

Nonprojective Bell-state measurements

The Bell-state measurement (BSM) is the projection of two qubits onto four orthogonal maximally entangled states. Here we first propose how to appropriately define more general BSMs, which have more than four possible outcomes, and then study whether they exist in quantum theory. We observe that nonprojective BSMs can be defined in a systematic way in terms of equiangular tight frames of maximally entangled states, i.e., a set of maximally entangled states, where every pair is equally, and in a sense maximally, distinguishable. We show that there exists a five-outcome BSM through an explicit construction and find that it admits a simple geometric representation. Then we prove that there exists no larger BSM on two qubits by showing that no six-outcome BSM is possible. We also determine the most distinguishable set of six equiangular maximally entangled states and show that it falls only somewhat short of forming a valid quantum measurement. Finally, we study the nonprojective BSM in the contexts of both local state discrimination and entanglement-assisted quantum communication. Our results put forward natural forms of nonprojective joint measurements and provide insight into the geometry of entangled quantum states. Published by the American Physical Society 2024

Semiconcept and concept representations

In FCA, we often deal with a formal context K=(G,M,I) that is only partially known, i.e. only the attributes that belong to an observable set N⊂M are known. There must also exist a part H of the object set G – called a training set – that consists of elements with all attributes known. The concepts of K have to be determined using the subcontexts corresponding to the training object set H and to the observable attribute set N. In our paper, this problem is examined within the extended framework of the semiconcepts of the original context, which are generalizations of its concepts. Each semiconcept of the original context induces a semiconcept in both subcontexts. In this way, each semiconcept of the context is represented by an induced pair of semiconcepts, which can also be considered its approximations — as in the case of rough sets. We describe the properties of the mapping defined by this representation and prove that the poset formed by these semiconcept pairs is a union of two complete lattices. We show that these induced semiconcept pairs can be generated by using a simplified representation of them. As the number of semiconcepts grows exponentially with the size of the training set and the observable attribute set, an algorithm that selects the representation pairs for which their support and relevance reach a certain threshold is also presented.

A Genetic Programming Approach With Building Block Evolving and Reusing to Image Classification

Genetic programming (GP) has shown promising results in image classification in the last decade. However, most existing GP-based image classification methods often have a complex tree/program structure and a large search space, which may lead to poor performance. To address this, this paper develops a two-stage-based GP approach to automatically evolving solutions/ensembles for image classification. In the new approach, the process of constructing an image classification solution is divided into two stages, i.e., evolving small building blocks for feature extraction and evolving ensembles of classifiers by reusing these blocks. Accordingly, at each stage, a simple tree structure can be designed to facilitate the search. In the first stage, a simple block representation and a new search mechanism including a population updating strategy are developed to evolve diverse and effective blocks. In the second stage, a small set of diverse blocks are selected and transformed into primitives, which produces a new tree representation to evolve ensembles of classifiers for image classification. The new designs allow the proposed approach to construct sufficiently but not over complex solutions for difficult tasks by using/searching small trees. The new approach outperforms most GP-based and non-GP-based comparison methods on five image datasets including CIFAR10, Fashion_MNIST and SVHN. Deep analysis is conducted to provide more insights into the proposed approach.

Simple and Accurate Representation of Cumulative Nighttime Leaf Respiratory CO2 Efflux.

Leaf respiratory carbon loss decreases independent of temperature as the night progresses. Detailed nighttime measurements needed to quantify cumulative respiratory carbon loss at night are challenging under both lab and field conditions. We provide a simple yet accurate approach to represent variation in nighttime temperature-independent leaf respiratory CO2 efflux in environments with both stable and fluctuating temperatures, which requires no detailed measurements throughout the night. We demonstrate that the inter- and intraspecific variation in the cumulative leaf respiratory CO2 efflux at constant temperature, at any length of night, scales linearly with the inter- and intraspecific variation in initial measurement of leaf respiratory CO2 efflux at the same temperature at the beginning of the night. This approach informs large-scale predictions of cumulative leaf respiratory CO2 efflux, which is needed to understand plant carbon economy in global change studies as well as in global modeling and eddy covariance monitoring of the land-atmosphere exchange of CO2.

On the influence of heat-induced evaporation over interfacial atomization driven by surface acoustic waves

In this work, the influence of evaporative flux over the atomization driven by surface acoustic waves (SAWs) of a Newtonian water drop is studied. The drop is placed on a heated substrate at constant temperature, higher than the saturation temperature at a given vapor pressure. In this manner, an interfacial temperature distribution arises along the drop free surface in terms evaporative mass flux and vapor recoil, which repercussion over aerosol size is studied by determining the asymptotic evolution equation governing the acoustically driven free surface. At such scenario, the connection between surface tension and temperature is also considered; thus, thermocapillary flow is incorporated into our drop model, described in terms of fundamental parameters, like the evaporation number, Marangoni number, and acoustic capillary number. Numerical solution of the evolution equation led us to obtain a simplified representation of the drop interfacial deformation mechanism, capable of predicting atomization and portraying the influence of evaporation over atomization. Subsequent analysis shows that the incorporation of evaporation at SAW atomization traduces in normal stresses counteracting the acoustic and thermocapillary effect, leading to the development of smaller drop aspect ratios with respect to the no-evaporative case. Being aware that the aerosol size is deeply related to the aspect ratio, we propose an analytical expression to estimate aerosol diameter under evaporative conditions. The results show that aspect ratio reduction leads to a decrement on aerosol size, up to two orders of magnitude, with respect to the no-evaporative case. Our study is a first approach providing insight about the importance of evaporation on aerosol regulation at SAW atomization.

Relation measurement between semantic fields by metric approach.

The article considers a numerical research of approach for semantic metric between lexical units calculation. Received a set of statistical characteristics of the lexicographic semantic trees. Simplified representation of tree as a semantic field is proposed and operations for relation measurement between fields is described. This approach can be used for explainable language model creation for natural language processing tasks.

Transformer-Based Regression Models for Assessing Reading Passage Complexity: A Deep Learning Approach in Natural Language Processing

Natural Language Processing (NLP) is a vital area in deep learning, widely applied in tasks like text classification, virtual assistants, speech recognition, and autocorrect features in digital devices. It allows machines to understand and generate human language, enhancing user interactions with software. This paper presents a deep learning model using the Transformer architecture for a regression task to predict the complexity of reading passages based on text excerpts. By leveraging the Transformer’s capability to identify complex patterns in text, the model achieves a relative error rate of about 10%. The paper also examines how different architectural choices influence model performance, focusing on one-hot encoding and embeddings. While one-hot encoding provides a simple text representation, embeddings offer a richer, more nuanced understanding of word relationships. The findings highlight the significance of model design and data representation in optimizing NLP tasks, providing insights for future advancements in the field.

Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks.

Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol-1 for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.