Array Representation Research Articles

Benchmarking various nonadiabatic semiclassical mapping dynamics methods with tensor-train thermo-field dynamics.

Accurate quantum dynamics simulations of nonadiabatic processes are important for studies of electron transfer, energy transfer, and photochemical reactions in complex systems. In this comparative study, we benchmark various approximate nonadiabatic dynamics methods with mapping variables against numerically exact calculations based on the tensor-train (TT) representation of high-dimensional arrays, including TT-KSL for zero-temperature dynamics and TT-thermofield dynamics for finite-temperature dynamics. The approximate nonadiabatic dynamics methods investigated include mixed quantum-classical Ehrenfest mean-field and fewest-switches surface hopping, linearized semiclassical mapping dynamics, symmetrized quasiclassical dynamics, the spin-mapping method, and extended classical mapping models. Different model systems were evaluated, including the spin-boson model for nonadiabatic dynamics in the condensed phase, the linear vibronic coupling model for electronic transition through conical intersections, the photoisomerization model of retinal, and Tully's one-dimensional scattering models. Our calculations show that the optimal choice of approximate dynamical method is system-specific, and the accuracy is sensitively dependent on the zero-point-energy parameter and the initial sampling strategy for the mapping variables.

Finite impulse response design based on two‐level transpose Vedic multiplier for medical image noise reduction

AbstractMedical signal processing requires noise and interference‐free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two‐level transpose Vedic multiplier. The proposed architecture identifies the zero‐noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero‐noise segregation. This is performed independently for the zero‐ and nonzero‐noise intervals. Finally, the finite impulse response is estimated as the sum of zero‐ and nonzero‐noise inputs at any finite classification.

Does a row of objects comprise a boundary? How children miss the forest for the trees in spatial navigation.

Unlike children's early ability to navigate by continuous boundaries, their ability to extract geometric information from an array of objects emerges gradually over childhood. To investigate children's developing representation of object arrays for navigation and its relation to their mental representation of the global spatial layout, reorientation behavior was tested in 146 children (4-9 years, 78 male children and 68 female children, Italian) with rectangular arrays made up of 20 objects. Posttest questions on children's spatial language and their mental and pictorial representation of the environment were administered. Although children of all ages navigated by the geometry of continuous boundary-like arrays, they only succeeded with separated object arrays at around 7 years of age. This developmental change was predicted by children's individual ability to extract the abstract geometry of the spatial layout in a two-dimensional picture of the room. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cramer-Rao bound investigation of a novel virtually extended nested arc array for 2D DOA estimation

This paper exploits a sparse array representation, a novel nested arc array, to generate a virtual non-uniform circular arrays that have been presented and investigated. To increase the array aperture, the virtual array has been developed using higher-order statistics, namely, the Forth-Order Cumulants (FOC). The extended array contains a combination of physical as well as virtual sensors. The increased degrees of freedom (DOFs) in the resultant virtual array make it suitable for over-and under-determined direction of arrival (DOA) estimation. The Cramer-Rao bound (CRB) and the (DOA) estimation for such an array are developed and investigated for various arrays. The proposed array performance is superior to that of the uniform one. Further, the offered array is suitable for two-dimensional (2D) DOA estimation problems.

Abstract ED9-2A: Can ctDNA substitute tissue testing

Abstract Circulating tumor DNA (ctDNA) is at the forefront of liquid biopsy technology. A key advantage of ctDNA is being able to achieve genomic profiling from a blood test rather than from a more invasive tissue biopsy, reducing risk and discomfort for patients as well as costs and logistical complexity. Increasing evidence supports ctDNA as a useful clinical tool in specific indications, for example in the detection of PIK3CA mutations to identify patients suitable for alpelisib plus fulvestrant.The expectation is that indications for routine ctDNA testing will expand as more data become available and ctDNA is embedded into clinical trials and paired with novel therapies. Biological and technical factors both contribute to the feasibility of ctDNA expansion into the clinic. Representation of heterogeneity might be a key strength of ctDNA analysis over tumour biopsy analysis, as sampling the circulation compartment can in principle allow representation of a wider array of disease sites than a tumor sample acquired with a single needle. However, the factors influencing how different metastases might contribute to the circulating compartment are poorly understood, presenting a challenge to clinical interpretation. Moreover, the limit of detection of ctDNA assays, the timing of treatment and stochastic effects can contribute to uncertainty, particularly in the interpretation of negative results. Recent work analyzing tumor mutational burden using ctDNA has highlighted these challenges as applied to biomarker development. Circulating tumour DNA is likely to complement tissue biopsy in the future, and may substitute tumor biopsy in specific indications supported by the data. Ultimately, clinical utility for different ctDNA indications, as opposed to tissue biopsy, will need ongoing confirmation within clinical trials. Citation Format: Ben O’Leary. Can ctDNA substitute tissue testing [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED9-2A.

Bootstrap inference under cross‐sectional dependence

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm‐level regression application investigating the relationship between firms' sales growth and the import activity in their local markets using unique firm‐level and imports data for Canada.

Two‐port network compact representation of resonator arrays for wireless power transfer with variable receiver position

SummaryThis paper presents an equivalent circuit characterization of an array of resonators for wireless power transfer (WPT) applications. These apparatuses are composed of magnetically coupled resonant coils acting as a transmitter which feeds a receiver coil that can be placed over them at any position, allowing the tolerance of the system to the receiver misalignment to be dramatically enhanced. This advantage makes resonator arrays a suitable solution for both low‐ and high‐power applications. The latter usually involve battery charging, with the resonator array acting as a transformer stage, ensuring galvanic isolation. An equivalent two‐port network can be defined for any receiver position, allowing the array to be treated as a traditional transformer. Thereby, the simulation of the system can be simplified, as it is required in the design steps of a converter and control strategy. Analytical expressions of the two‐port network parameters are proposed for arrays of any size and length. The formulas have been numerically and experimentally validated.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network.

Semantic feature recognition in colour images is required for identifying uneven patterns in object detection and classification. The semantic features are identified by segmenting the colorimetric sensor array features through machine learning paradigms. Semantic segmentation is a method for identifying distinct elements in an image. This can be considered a task involving image classification at the pixel level. This article introduces a semantic feature-dependent array segmentation method (SFASM) to improve recognition accuracy due to irregular semantics. The proposed method incorporates a deep convolutional neural network for detecting the semantic and un-semantic features based on sensor array representations. The colour distributions per array are identified for horizontal and vertical semantics analysis. In this analysis, deep learning classifies the uneven patterns based on colour distribution, i.e. the consecutive and scattered colour distribution pixels in an array are correlated for their similarity. This similarity identification is maximized through max-pooling and recurrent iterations, preventing detection errors. The proposed method classifies the semantic features for further correlation sections, improving the accuracy. The proposed method's performance is thus validated using the metrics precision, analysis time and F1-Score.

Portable CPU implementation of Wilson, Brillouin and Susskind fermions in lattice QCD

A modern Fortran implementation of three Dirac operators (Wilson, Brillouin, Susskind) in lattice QCD is presented, based on OpenMP shared-memory parallelization and SIMD pragmas. The main idea is to apply a Dirac operator to Nv vectors simultaneously, to ease the memory bandwidth bottleneck. All index computations are left to the compiler and maximum weight is given to portability and flexibility. The lattice volume, NxNyNzNt, the number of colors, Nc, and the number of right-hand sides, Nv, are parameters defined at compile time. Several memory layout options are compared. The code performs well on modern many-core architectures (480 Gflop/s, 880 Gflop/s, and 780 Gflop/s with Nv=12 for the three operators in single precision on a 72-core KNL processor, a 2×24-core Skylake node yields similar results). Explicit run-time tests with CG/BiCGstab inverters confirm that the memory layout is relevant for the KNL, but less so for the Skylake architecture. The ancillary code distribution contains all routines, including the single, double, and mixed precision Krylov space solvers, to render it self-contained and ready-to-use. Program summaryProgram title: testknl_main.f90CPC Library link to program files:https://doi.org/10.17632/7km9wcspn3.1Licensing provisions: Creative Commons by 4.0Programming language: fortran 2008Supplementary material: Detailed description of each module of the code distribution.Nature of problem: Solving the Dirac equation Du=b for three choices of the Dirac operator D relevant to Lattice QCD. The choices are known as Wilson fermions, Brillouin fermions and Susskind or staggered fermions, respectively.Solution method: Standard solution through sparse iterative solvers CG and BiCGstab, albeit with several right-hand vectors packed into a right-hand matrix. The number of colors, Nc, the number of right-hand sides, Nv, and the box lengths, Nx, Ny, Nz and Nt, can be chosen freely.Additional comments including restrictions and unusual features: The code allows the user to compare several memory layout options for the block of right-hand sides, that is several orderings of the slots pertinent to Nc, Nv and possibly the Dirac spinor, in the array representation of u and b. No machine dependent optimizations are used, only standard OpenMP shared-memory parallelization and SIMD pragmas are used.

Integrated systems modeling of severe asthma: Exploration of IL‐33/ST2 antagonism

Asthma is a complex, heterogeneous disease with a high unmet medical need, despite therapies targeting a multitude of pathways. The ability to quantitatively integrate preclinical and clinical data on these pathways could aid in the development and testing of novel targets and therapeutics. In this work, we develop a computational model of asthma biology, including key cell types and mediators, and create a virtual population capturing clinical heterogeneity. The simulated responses to therapies targeting IL‐13, IL‐4Rα, IL‐5, IgE, and TSLP demonstrate agreement with clinical endpoints and biomarkers of type 2 (T2) inflammation, including blood eosinophils, FEV1, IgE, and FeNO. We use the model to explore the potential benefit of targeting the IL‐33 pathway with anti‐IL‐33 and anti‐ST2. Model predictions are compared with data on blood eosinophils, FeNO, and FEV1 from recent anti‐IL‐33 and anti‐ST2 trials and used to interpret trial results based on pathway biology and pharmacology. Results of sensitivity analyses on the contributions of IL‐33 to the predicted biomarker changes suggest that anti‐ST2 therapy reduces circulating blood eosinophil levels primarily through its impact on eosinophil progenitor maturation and IL‐5‐dependent survival, and induces changes in FeNO and FEV1 through its effect on immune cells involved in T2 cytokine production. Finally, we also investigate the impact of ST2 genetics on the conferred benefit of anti‐ST2. The model includes representation of a wide array of biologic mechanisms and interventions that will provide mechanistic insight and support clinical program design for a wide range of novel therapies during drug development.

Flow effects of finite-sized tidal turbine arrays in the Chacao Channel, Southern Chile

To characterize energy resources and study of hydrodynamic effects induced by marine hydrokinetic devices in tidal channels, numerical models need to provide reliable representations of turbine arrays. In regions disconnected from the grid, near coastal protected areas and other relevant economic activities, there is a pressing need to evaluate the impacts of limited-size arrays. Here, we use the emblematic Chacao Channel in Southern Chile to understand the effects of bathymetry and array placement on energy extraction in strongly tidal channels. We implement in FVCOM a parameterization from a previously derived high-resolution model to represent a group of turbines in different locations. We first analyze the complexity of the bathymetry to define the appropriate grid size and obtain a correct representation of the interaction of turbines with the bed morphology. We simulate a base case to identify three suitable locations in the channel where we analyze the effects of the turbines: From simulations we compute the changes in the mean velocity, turbulent kinetic energy (TKE), and bed shear stress. The results show that baseline velocities and TKE are the main factors on the momentum extraction despite the bed complexity. However, in flatter bathymetries, changes on TKE and bottom shear are significantly larger compared to complex morphologies, since turbine arrays modify considerably the original flow conditions. Simulations also provide additional insights that are critical to evaluate the local impacts, showing the directionally-dependent flow resistance of tidal channels, in which the interactions with bathymetry change the downstream effects of turbine arrays in flood or ebb regimes.

Integration of Environmental Issues in Axiomatic Design to Pursue Sustainable Products

Product design is an activity that must be supported by information in order to allow designers to conceive solutions to real problems that do not introduce further issues, first of all, environmental concerns. Axiomatic design is an approach that provides the possibility to check whether a design solution is functionally valid and it can also be extended considering eco-design elements. In a synthetic representation of 1D and 2D arrays, it can inform designers about the level of sustainability of the product on which they have been working on since the first phase of design when they start to embody functionalities by introducing real components, and first assemblies appear. To achieve this task, the domains considered in the original formulation of Axiomatic Design have been revised, and a new domain has been introduced. This allows designers to take into account all the phases of product life and improve design solutions to avoid introducing structures, components, and functionalities that might be the cause of environmental problems. The paper describes such new mapping among domains and applies it to design a daily life device. The contents of the new data structure will be presented and discussed.

Novel Static Multi-Layer Forest Approach and Its Applications

The existing multi-layer tree is of dynamic linked list type which has many limitations and is complicated due to the pointer-node structure. Static array representation gives more flexibility in programming of algorithms and operations like insertion, deletion, and search. It also reduces the storage space. This paper presents a new method for representing multi-layer forest data structure in array format. It also explains various tree operations, unique data compression algorithm and migration algorithm between traditional approach and the proposed data structure. Most of the fundamental algorithms like those from artificial intelligence that employ decision trees are based on trees/forest data structure. The current paper brings a completely new idea in the representation of these data structures without employing recursion and targeting memory optimizations with reduced code complexities. The applications of forest data structures are many and span over various interdisciplinary areas of Engineering, Medicine, Aviation, Locomotive, Marine, etc. The proposed novel approach not just introduces a new method to look at the tree data structure but also provides the flexibility to adapt to the existing methods as per the user needs. A few such applications in Simulink debugging and the Forest visualisation have been discussed in detail in this paper.

Logistics networks: a sparse matrix application for solving the transshipment problem

Effective and efficient supply chain management is an important concern for most companies primarily in the Retail Industry. The reduced cost and enhanced customer service levels stemming from a better inventory control approach presents opportunities for the implementation of new approaches like using Sparse Matrix for solving transshipment problems. A Sparse matrix with a significant number of zeros can better represent a transshipment model. This is represented by three parallel linear arrays viz., row_no., col_no., value . The present paper is an attempt to efficiently represent the lateral transshipment model in the form of three linear arrays of the Sparse Matrix. At the end of the paper, algorithms to read, display, and add sparse matrices using the linear array representations are also discussed.

Itemset Representation and Mining the Rules for Huntington’s Dataset

Association rule mining does not restrict to market basket application but it is also employed in many applications such as health, industrial, network domain and etc. In this paper, an association mining algorithm is applied to the health management domain. It helps in the decision making by producing the rules for the early detection of the disease. By checking the personal details and symptoms of the patient, association rule mining will help in prediction and diagnosing the disease at an early stage. The dataset used in this experiment is the Huntington Disease (HD) dataset, which is one of the rare diseases. The dataset needs to be stored in the memory for the computation and generation of rules. Storing the items in the memory will take 4 bytes if the array data structure is used. Furthermore, if the dataset is very large, storing each and every detail in the memory becomes speculative. It is also not cost-effective and consumes a lot of resources. One of the solutions is to present the itemset in such a way that the memory consumed is concise. The items are represented using the set representation that takes less time and memory as compared to the traditional methods. The dataset is mine using the Apriori Algorithm which produces only those itemsets which are more frequent or have a high probability of occurrence. The algorithm gives a prior knowledge of the frequent itemsets. Then, the rules will be generated from these frequent itemsets. The memory and time consumption using the set representation is compared with the array representation of itemsets. Doi: 10.28991/esj-2021-01284 Full Text: PDF

Division rules for tissue P systems inspired by space filling curves

We propose a new variant of tissue P systems, with oriented division rules and external inputs. The oriented division rules are based on Hilbert’s space filling curve, more precisely on a new variant of parallel array rewriting rules, which generate Hilbert words of different ’resolutions’ in array representation. The P systems introduced here have also the capability to receive input from an external source. This is illustrated with a simple problem, that of approximating the contour of a 2D picture.

Disentangling feedforward versus feedback processing in numerosity representation

Numerosity is a fundamental aspect of the external environment, needed to guide our behavior in an effective manner. Previous studies show that numerosity processing involves at least two temporal stages (~100 and ~150 msec after stimulus onset) in early visual cortex. One possibility is that the two stages reflect an initial feedforward processing followed by feedback signals from higher-order cortical areas that underlie segmentation of visual inputs into perceptual units that define numerosity. Alternatively, multiple stages of feedforward processing might progressively refine the input leading to the segmented representation. Here, we distinguish these two hypotheses by exploiting the connectedness illusion (i.e., the systematic underestimation of pairwise-connected dots), backward masking (to suppress feedback signals), and serial dependence (i.e., a perceptual bias making a stimulus appear to be more similar to its preceding one). Our results show that a connected dot array biases the numerosity representation of the subsequent dot array based on its illusory perception, irrespective of whether it is visible or suppressed by masking. These findings demonstrate that feedback processing is not strictly necessary for the perceptual segmentation that gives rise to perceived numerosity, and instead suggest that different stages of feedforward activity presumably carrying low and high spatial frequency information are sufficient to create a numerosity representation in early visual areas.

Strong Consistency of the Risk Estimator in Multiple Hypothesis Testing with the FDR Threshold

Procedures for multiple hypothesis testing are widely used in analyzing data arrays. Transformation is done first, producing an economic (sparse) array representation. Data is then processed by testing hypotheses of data significance and using inverse transformation. Noise suppression and/or data compression are the main goals of this approach. Such techniques are used in the theory of communication, astronomy, physics, biology, medicine, and a number of areas of practical interest. Risk (error) analysis for such procedures is one of the most important challenges, as it is essential for estimating the quality of applied techniques and equipment. In this work, we consider the Benjamini-Hochberg algorithm of multiple hypothesis testing in selecting the FDR threshold. The strong consistency of the risk estimator is also proved for the case of sparse data.

Matrix application for multi-radar processing of radar data arrays

In the processing of dynamically changing data, for example, radar data (RD), a crucial part is made by the representation of various data sets containing information about routes and signs of air objects. In the practical implementation of the computational process, it previously seemed natural that RD processing in data arrays was carried out by the elementwise search method. However, the representation of data arrays in the form of matrices and the use of matrix math allow optimal calculations to be formed during tertiary processing. Forming matrices and working with them requires a significant computational resource, so the authors can assume that a certain gain in calculation time may be achieved if there is a large amount of data in the arrays, at least several thousand messages. The article shows the sequences of the most frequently repeated operations of tertiary network processing, such as searching for and replacing an array element. The simulation results show that the processing efficiency (relative reduction of processing time and saving of computing resources) with the use of matrices, in comparison with elementwise search and replacement, increases in proportion to the number of messages received by the information processing device. The most significant gain is observed when processing several thousand messages (array elements). Thus, the use of matrices and the mathematical apparatus of matrix math for processing arrays of dynamically changing data can reduce processing time and save computational resources. The proposed matrix method of organizing calculations can also find its place in the modeling of complex information systems.