Selective Mapping Research Articles

Fast and effective molecular property prediction with transferability map.

Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties.

Filtered Ofdm System Model for Papr Reduction in the Growth Of 5g

A proposed waveform for fifth generation (5G) communications is the filtered orthogonal frequency division multiplexing (F-OFDM) system. Filtering-based waveform frameworks distinguish themselves by suppressing out-of-band emission and asynchronous transmission. Meanwhile, the high Peak-To-Average Power Ratio (PAPR) remains a barrier to new waveform possibilities. Partials transmit sequence (PTS) is a successful strategy for reducing high PAPR in multicarrier networks. This work presents a multicarrier in OFDM and UFMC based on the Filter Bank Multicarrier (FBMC) waveform for communication systems. Furthermore, FBMC waveforms have limited orthogonality between subcarriers, which can mitigate Doppler and multipath effects. This FBMC approach uses the cosine law to filter the traffic signal, and the Welch algorithm is offered for segmentation, which lowers noise in estimated power spectra. As a result, a hybrid Selective Mapping (SLM) with Cuckoo Search and Ant Lion Optimization (CS-ALO) algorithms is suggested using the FBMC framework to reduce PAPR in the OFDM system. The proposed work is implemented, and detailed experimental research is carried out to show the increased performance in 5G networks. Based on a fair comparison, the F-OFDM scheme outperforms previous approaches in terms of BER and multicarrier system capacity. Simulation results show that the proposed waveform design and processing method outperform standard OFDM and F-OFDM methods in PAPR reduction and communication integrated systems.

PAPR reduction techniques optimization-based OFDM signal for wireless communication systems

OFDM (Orthogonal Frequency Division Multiplexing) is a widely used modulation technique in many standards such as Wi-Fi (Wireless Fidelity), LTE (Long Term Evolution), and 4 G. It allows for high transmission data rates and excellent spectral efficiency. However, one of the main limitations of OFDM is its high PAPR (Peak-to-Average Power Ratio). In this paper, we will first describe some methods to reduce the PAPR associated with OFDM signal. We will discuss some methods such as the clipping, the Selective mapping SLM, the Partial Transmit Sequence (PTS), and the Tone Reservation (TR). Let's list their advantages and disadvantages before focusing on the TR method which we will detail. This work aims to improve the performance of the TR method in terms of speed of convergence and reduction of the PAPR, using the conjugate different algorithms while respecting the frequency specifications of the IEEE 802.11a standard. The proposed algorithm which is based on the gradient method, Conjugate gradient methods, and Quasi-Newton method shows good performance regarding PAPR reduction and speed of convergence. The choice of an appropriate technique depends on several factors: required PAPR reduction, acceptable complexity, and performances (spectral efficiency, latency, etc.). A trade-off must be found in practice between these different criteria. It has been shown that the Quasi-Newton method algorithm with its two variants converges more quickly.

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.

PAPR reduction using model‐driven hybrid algorithms in the 6G NOMA waveform

AbstractIn the evolving landscape of sixth‐generation (6G) network technologies, Non‐Orthogonal Multiple Access (NOMA) systems are pivotal for achieving enhanced spectral efficiency and network capacity. However, a significant challenge in NOMA systems is the high Peak‐to‐Average Power Ratio (PAPR), which undermines system efficiency by necessitating high‐power amplifiers (HPAs) to operate in their less efficient, non‐linear range. Addressing this, we introduce a novel hybrid approach, the Selective Mapping‐Circular Transformation Method (SLM‐CTM), which ingeniously amalgamates the strengths of Selective Mapping (SLM) and the Circular Transformation Method (CTM) to mitigate PAPR issues. SLM is renowned for its peak power reduction capabilities without adding to system complexity, whereas CTM is valued for its simplicity and controlled signal distortion. The proposed SLM‐CTM strategy employs a blend of linear and nonlinear techniques to effectively lower PAPR in non‐orthogonal NOMA configurations, thereby reducing high‐power peaks while simultaneously enhancing signal quality. This paper delineates the application of the SLM‐CTM algorithm to evaluate critical NOMA parameters such as Power Spectral Density (PSD), Bit Error Rate (BER), and PAPR. Simulation results highlight the efficacy of SLM‐CTM over conventional SLM, demonstrating a significant throughput improvement of 3.2 dB and a PAPR reduction of 4.6 dB, underscoring the potential of SLM‐CTM in elevating the performance of NOMA systems within 6G network.

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

In silico EST-SSR Identification and Development through EST Sequences from Metroxylon sagu Rottb. for Genetic Diversity Analysis.

Sago plant (Metroxylon sagu Rottb.) is one of the most carbohydrate-producing plants in the world. Microsatellites or simple sequence repeats (SSRs) play an important role in the genome and are used extensively compared to other molecular markers. For the first time, we are exploiting data expressed sequence tags (EST) of sago plants to identify and characterise markers in this species. EST data about sago plants are obtained through the EST database on the National Center for Biotechnology Information (NCBI) website. We obtained data of 458 Kb (412 contig) with a maximum and minimum length of 1,138 and 124 nucleotides, respectively. We successfully identified 820 perfectly patterned SSR using Phobos 3.3.12 software. The type characterisation of EST-SSR was dominated by tri-nucleotides 36% (294), followed by hexa-nucleotides 24% (202), tetra-nucleotides 15% (120), penta-nucleotides 13% (108) and di-nucleotides 12% (96). The most frequency of SSR motifs in each type is AG, AAG and AAAG. Analysis of synteny on the EST sequence with the online application Phytozome found that sequences were distributed on 12 Oryza sativa chromosomes with a likeness percentage between 63% to 100% and e-value between 0 to 0.094. We developed the primer and generated 19 primers. Furthermore, we validated 7 primers that all generated polymorphic alleles. To our knowledge, this report is the first identification and characterisation of EST-SSR for sago species and these markers can be used for genetic diversity analysis, marker assisted selection (MAS), cultivar identification, kinship analysis and genetic mapping analysis.

Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals

AbstractElectroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.

Retinal origin of orientation but not direction selective maps in the superior colliculus

Neurons in the mouse superior colliculus ("colliculus") are arranged in ordered spatial maps. While orientation-selective (OS) neurons form a concentric map aligned to the center of vision, direction-selective (DS) neurons are arranged in patches with changing preferences across the visual field. It remains unclear whether these maps are a consequence of feedforward input from the retina or local computations in the colliculus. To determine whether these maps originate in the retina, we mapped the local and global distribution of OS and DS retinal ganglion cell axon boutons using invivo two-photon calcium imaging. We found that OS boutons formed patches that matched the distribution of OS neurons within the colliculus. DS boutons displayed fewer regional specializations, better reflecting the organization of DS neurons in the retina. Both eyes convey similar orientation but different DS inputs to the colliculus, as shown in recordings from retinal explants. These data demonstrate that orientation and direction maps within the colliculus are independent, where orientation maps are likely inherited from the retina, but direction maps require additional computations.

A Theoretical Study on the Thermal Performance of an Increasing Pressure Endothermic Cycle for Geothermal Power Generation

In this study, a power cycle (IPEC), with an increasing pressure endothermic process in a downhole heat exchanger (DHE) and a CO2-based working fluid mixture, was developed for geothermal power generation. The increasing pressure endothermic process, which cannot be achieved in a conventional evaporator on the ground, was realized using the gravitational potential energy in the DHE. The parameters of the power cycle and the structural size of the DHE were optimized simultaneously. Using CO2-R32 as the working fluid of the IPEC provides the highest net power output. The net power generated with the IPEC was compared with a single-flash (SF) system, a trans-critical CO2 (t-CO2) system, and an organic Rankine cycle (ORC) under the same heat source and sink conditions. Six selection maps were generated for choosing the optimum power cycle for electricity production, in which four power generation systems (ORC, t-CO2, IPEC, and SF) were included, and two DHE diameters (0.155 m and 0.22 m) were investigated. It was found that the IPEC system had more net power output than the other three systems (ORC, t-CO2, and SF) under the conditions that the geofluid’s mass flow rate was less than 10 kg/s and its temperature was lower than 180 °C.

Uncovering the architecture of selection in two Bos taurus cattle breeds.

Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

Recognition of map activities using eye tracking and EEG data

Recognizing the activities being performed on a map is crucial for adaptive map design based on user context. Despite eye tracking (ET) demonstrating potential in recognizing map activities and electroencephalography (EEG) measuring map users’ cognitive load, no studies have yet combined ET and EEG for recognition of the user’s activity on maps. Our study collected participants’ ET and EEG data during four types of map activities. After feature extraction and selection, we trained LightGBM (light Gradient-Boosting Machine) to classify these activities, and achieved 88.0% accuracy when combining ET and EEG features in the entire map usage trial, which is higher than using ET (85.9%) or EEG (53.9%) alone. Acceptable recognition accuracy could also be achieved with the early time windows (73.1% when using the first 3 seconds). Saccade features of ET were the most important for differentiating map activities, indicating selective map content for different tasks. Our findings demonstrate the feasibility and advantages of combining ET and EEG for activity recognition in map use. The results not only improve our understanding of visual patterns and cognitive processes in map use, but also enable the design of adaptive maps that can automatically adapt to the activities a map user is performing.

Enhanced PAPR reduction in DCO-OFDM using multi-point constellations and DPSO optimization

DC-biased optical OFDM (DCO-OFDM) is a commonly used method of OFDM in visible light communication (VLC). Unfortunately, VLC systems that use OFDM often experience a high peak-to-average power ratio (PAPR). To address this issue, this study proposes a novel method called the multi-point constellation method (MPC) to reduce PAPR in DCO-OFDM. The MPC method involves adding extra alternative constellation points around the existing points and using the discrete particle swarm optimization (DPSO) algorithm to select the constellation points with the lowest PAPR. The proposed MPC method is also combined with selective mapping (SLM), a well-known PAPR reduction technique in the literature. Simulation results show that the proposed MPC method outperforms the SLM method in reducing PAPR in 4-QAM and 16-QAM modulations when used in combination with SLM. Furthermore, increasing the number of iterations and particles in the DPSO algorithm improves the PAPR reduction performance of the proposed method even further.

A mathematical analysis of the PAPR in the NOMA waveform using SLM algorithm for 64-QAM

In Non-Orthogonal Multiple Access (NOMA) technology, a paradigm shift in wireless communication is observed, as it allows numerous users to simultaneously transmit data over the same spectral resources in a non-orthogonal manner. Despite the advancements it brings to the table, NOMA is faced with a notable technical obstacle, that being the elevated Peak-to-Average Power Ratio (PAPR). Elevated PAPR levels can lead to less efficient use of power and a deterioration in the fidelity of the signal. We proposed a Selective Mapping (SLM) as a powerful solution to address this issue. SLM is a PAPR reduction technique that applies controlled phase rotations to the symbols of different users before transmission, aiming to minimize the PAPR of the combined signal. In the context of NOMA, the application of SLM offers several notable advantages. SLM modifies the Power Spectrum Density (PSD), leading to a more uniform and spectrally efficient power distribution over the frequency spectrum. This enhancement in spectral efficiency concurrently lowers the risk of disrupting nearby frequency channels. Moreover, SLM exerts a favorable influence on the Bit Error Rate (BER) in NOMA systems, which contributes to improved transmission accuracy. Utilizing SLM to achieve lower PAPR values significantly diminishes the probability of symbol errors, thus enhancing the dependability. Additionally, it has been observed that the method in question delivers an improvement in PAPR, BER, and PSD with gains of 2.2 dB, 4 dB, and -620 respectively, in comparison to traditional SLM techniques.

Dynamics of a lipid vesicle across a microfluidic constriction: how do its membrane properties and microenvironment matter?

Constriction in the flow passage of the physiological circulatory system is central to the occurrence of several diseased conditions such as thrombosis and is also pivotal towards the understanding of several regulatory processes in the human microvasculature. It is, therefore, imperative to advance a mechanistic insight into the dynamics of lipid vesicles, cell mimicking, fluid-filled compartments, through a physiologically relevant microconfinement, with particular focus on deciphering the role of its mechanophysical properties. Here we bring out the role of membrane bending rigidity and the initial deflation (deviation in shape from sphericity) on the transient shape evolution of a lipid vesicle as it migrates through a microfluidic constriction, a paradigm that is unexplored thus far. Based on our experimental observations as well as theoretical insights, we construct a regime map to elucidate the range of the key dimensionless parameters orchestrating this dynamical transition. Furthermore, our observations on the vesicle's stretching dynamics emerging from selective mapping with viscosity contrast between the encapsulated and the suspending fluid medium offer potential physiologically relevant cues on the impact of cell aging on its deformability across a constricted path. Such mechanistic insights may help in establishing quantitative correlations between the dynamical transition of a lipid vesicle and its membrane mechanics, which may in turn have decisive implications in health and disease while circulating across microvascular fluidic pathways.

РЕЗУЛЬТАТЫ МОНИТОРИНГА ДЕФОРМАЦИОННЫХ ПРОЦЕССОВ МЕТОДАМИ ВЫСОКОТОЧНОЙ ГЕОДЕЗИИ И РАДАРНОЙ ИНТЕРФЕРОМЕТРИИ НА ГЕОДИНАМИЧЕСКОМ ПОЛИГОНЕ ЗАПОЛЯРНОГО НЕФТЕГАЗОКОНДЕНСАТНОГО МЕСТОРОЖДЕНИЯ

To obtain information on the presence and magnitude of deformation processes during the development of the Polar oil and gas condensate field, an integrated method of surveying and geodetic works was applied, including: 4 high-precision geodetic satellite observations at all points of the field's observation network to determine global rock movements expressed by changes in the coordinates of points of the geodynamic polygon; 4 high-precision leveling of a local section of a geodynamic polygon to determine vertical movements of rocks in the area of a geo-dynamically active zone; 4 application of the radar interferometry method of stable reflectors to the oil and gas production region with a large number of man-made objects. In this paper, a comparative analysis of the anthropogenic impact of field development (based on accumulated maps of reservoir pressure selection and drop) on modern deformations of the Earth's surface (based on the results of satellite observations and radar inter-ferometry) is performed.

An encoding framework for binarized images using hyperdimensional computing.

Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to be applied in the wearable Internet of Things, near-sensor artificial intelligence applications, and on-device processing. HDC is computationally less complex than traditional deep learning algorithms and typically achieves moderate to good classification performance. A key aspect that determines the performance of HDC is encoding the input data to the hyperdimensional (HD) space. This article proposes a novel lightweight approach relying only on native HD arithmetic vector operations to encode binarized images that preserves the similarity of patterns at nearby locations by using point of interest selection and local linear mapping. The method reaches an accuracy of 97.92% on the test set for the MNIST data set and 84.62% for the Fashion-MNIST data set. These results outperform other studies using native HDC with different encoding approaches and are on par with more complex hybrid HDC models and lightweight binarized neural networks. The proposed encoding approach also demonstrates higher robustness to noise and blur compared to the baseline encoding.

Transcutaneous Imaging of Rabbit Kidney Using 3-D Acoustic Wave Sparsely Activated Localization Microscopy with a Row-Column-Addressed Array.

Super-resolution ultrasound (SRUS) imaging through localizing and tracking microbubbles, also known as ultrasound localization microscopy (ULM), can produce sub-diffraction resolution images of micro-vessels. We have recently demonstrated 3-D selective SRUS with a matrix array and phase change contrast agents (PCCAs). However, this method is limited to a small field of view (FOV) and by the complex hardware required. This study proposed 3-D acoustic wave sparsely activated localization microscopy (AWSALM) using PCCAs and a 128+128 row-column-addressed (RCA) array, which offers ultrafast acquisition with over 6 times larger FOV and 4 times reduction in hardware complexity than a 1024-element matrix array. We first validated this method on an in-vitro microflow phantom and subsequently demonstrated non-invasively on a rabbit kidney in-vivo. Our results show that 3-D AWSALM images of the phantom covering a 25×25×40 mm 3 volume can be generated under 5 seconds with an 8 times resolution improvement over the system point spread function. The full volume of the rabbit kidney can be covered to generate 3-D microvascular structure, flow speed and direction super-resolution maps under 15 seconds, combining the large FOV of RCA with the high resolution of SRUS. Additionally, 3-D AWSALM is selective and can visualize the microvasculature within the activation volume and downstream vessels in isolation. Sub-sets of the kidney microvasculature can be imaged through selective activation of PCCAs. Our study demonstrates large FOV 3-D AWSALM using an RCA probe. 3-D AWSALM offers an unique in-vivo imaging tool for fast, selective and large FOV vascular flow mapping.

Three-dimensional non-orthogonal multiple access high-security seven-core transmission system based on constellation chaotic selection mapping

Three-dimensional non-orthogonal multiple access high-security seven-core transmission system based on constellation chaotic selection mapping

Constructing solidification microstructure selection map for AlSi10Mg alloy processed by laser powder bed fusion

The cellular structures, which consist of cells and eutectic networks at the cell boundaries, determine the mechanical properties of the Al–Si alloys fabricated by laser powder bed fusion (L-PBF). Therefore, it is scientifically interesting and technologically important to establish the relationship between cellular structures and processing parameters, which is applicable to all kinds of processing methods involving rapid directional solidification, such as L-PBF. In this work, a rapid solidification microstructure selection map (SMSM) of the Al–10Si system in temperature gradient (G)–solidification velocity (V) space is constructed by a synergistic calculation with dendritic and eutectic growth models compatible to the L-PBF process. Considering that the divorced eutectic reaction should occur as the width of the liquid film entrapped in the α-Al cell boundaries becomes smaller than the Al–Si eutectic spacing, the G–V domain suitable for acquiring the cellular structures decorated with divorced eutectic Si networks is determined on the SMSM. The microstructure evolution trends predicted by the SMSM are well consistent with those in the L-PBFed AlSi10Mg melt pools with different processing parameters. The length scales of actual microstructures observed by the experiments from this work and the literature are quantitatively compared to those estimated by the SMSM, and the experimental results are basically in accord with the SMSM. It is demonstrated that divorced eutectic Si networks prefer to form at the bottom of the melt pool, where G is large (> 5 × 106 K/m) and V is small (3 × 10−4–6 × 10−3 m/s) in the SMSM. The outcomes of this work may provide fundamental guidance for tailoring the microstructures of the L-PBFed Al–Si alloys for achieving optimal mechanical properties.