Miracle-dz/UniFAR-FTUs huggingface dataset:
HOMOLOGY BETWEEN BACTERIOPHAGE X DNA AND E. COLI DNA

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Deep Learning Strategies for User Clustering in MIMO-NOMA Networks

The user clustering problem in an uplink MIMO Non-Orthogonal Multiple Access (NOMA) scheme is considered here. The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. At the ﬁrst stage, the receiver is designed to recover the transmission from a cluster of selected users. The contribution of these users is then subtracted from the received signal and the remaining user transmissions are then linearly recovered. The determination of which users should be detected during the ﬁrst stage is formulated as a deep learning based multiple classiﬁcation problem. In order to guarantee that the selection is robust to fast fading, the input to the neural network is based on second order channel statistics. Furthermore, the training process is sim-pliﬁed by using a large system approximation of the resulting sum-rates. Simulation results indicate that the proposed deep learning-based solution is able to achieve a signiﬁcant rate advantage with respect to other lazy approaches, such as ﬁxed or random cluster assignments.
Manuscript for Genetics Selective sweeps reveal candidate genes for adaptation to drought and salt tolerance in common sunflower, Helianthus annuus

Here we report the results of an analysis of variation at 128 EST-based microsatellites in wild Helianthus annuus, using populations from the species' typical plains habitat in Kansas and Colorado, as well two arid desert and two distinct brackish marsh areas in Utah. The test statistics lnRV and lnRH were used to find regions of the genome that were significantly less variable in one population relative to the others, and thus are likely to contain genes under selection. A small but detectable percentage (1.5 – 6%) of genes showed evidence for selection from both statistics in any particular environment, and a total of 17 loci showed evidence of selection in at least one environment. Distance-based measures provided additional evidence of selection for 15 of the 17 loci. Global FST values were significantly higher for candidate loci, as expected under divergent selection. However, pairwise FST values were lower for populations which shared a selective sweep. Moreover, while spatially separated populations undergoing similar selective pressures showed evidence of divergence at some loci, they evolved in concert at other loci. Thus, this study illustrates how selective sweeps might contribute both to the integration of conspecific populations and to the differentiation of races or species. INTRODUCTION Because plants are sessile during a significant fraction of their life cycle, they are subject to strong selection for optimal performance under local environmental conditions. As a consequence, most plant populations are expected to exhibit significant local adaptation, an expectation that has been corroborated by a century of reciprocal transplant and common garden studies (ANGERT and SCHEMSKE 2005; CLAUSEN 1940;
Smell dysfunction from symptoms to diagnosis – recent methods of olfactory function assessment

Sense of smell poses an incredibly significant aspect of our life, it has been present from the beginning of our existence and its role in maintaining the well-being of our organism is nonsustainable. Olfactory system dysfunctions have a negative influence on general health status, psychological condition and quality of life. Smell detection disturbances can not only lead to loss of pleasant feelings or basic defence mechanism deficiency, but also may be symptoms of serious ongoing or commencing diseases. The authors aimed to present in this article the most important information on physiology of olfaction system, most recent methods of examination and possible causes of smell dysfunction.
Performance analysis of adaptive algorithms for spaceâ•’time adaptive processor (STAP) in phased array radar

The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array.
An invasive prey provides long-lasting silver spoon effects for an endangered predator

The natal environment can have long-term fitness consequences for individuals, particularly via ‘silver spoon’ or ‘environmental matching’ effects. Invasive species could alter natal effects on native species by changing species interactions, but this potential remains unknown. Using 17 years of data on 2588 individuals across the entire US breeding range of the endangered snail kite (Rostrhamus sociabilis), a wetland raptor that feeds entirely on Pomacea snails, we tested for silver spoon and environmental matching effects on survival and movement and whether the invasion of a non-native snail may alter outcomes. We found support for silver spoon effects, not environmental matching, on survival that operated through body condition at fledging, explained by hydrology in the natal wetland. When non-native snails were present at the natal site, kites were in better condition, individual condition was less sensitive to hydrology, and kites fledged across a wider range of hydrologic conditions, leading to higher survival that persisted for at least 10 years. Movement between wetlands was driven by the current (adult) environment, and birds born in both invaded and uninvaded wetlands preferred to occupy invaded wetlands post-fledging. These results illustrate that species invasions may profoundly impact the role of natal environments on native species.
Evolution of toxins as a public good in phytoplankton

Toxic phytoplankton blooms have increased in many waterbodies worldwide with well-known negative impacts on human health, fisheries and ecosystems. However, why and how phytoplankton evolved toxin production is still a puzzling question, given that the producer that pays the costs often shares the benefit with other competing algae and thus provides toxins as a ‘public good’ (e.g. damaging a common competitor or predator). Furthermore, blooming phytoplankton species often show a high intraspecific variation in toxicity and we lack an understanding of what drives the dynamics of coexisting toxic and non-toxic genotypes. Here, by using an individual-based two-dimensional model, we show that small-scale patchiness of phytoplankton strains caused by demography can explain toxin evolution in phytoplankton with low motility and the maintenance of genetic diversity within their blooms. This patchiness vanishes for phytoplankton with high diffusive motility, suggesting different evolutionary pathways for different phytoplankton groups. In conclusion, our study reveals that small-scale spatial heterogeneity, generated by cell division and counteracted by diffusive cell motility and turbulence, can crucially affect toxin evolution and eco-evolutionary dynamics in toxic phytoplankton species. This contributes to a better understanding of conditions favouring toxin production and the evolution of public goods in asexually reproducing organisms in general.
Exploring the dimensionality of speech using manifold learning and dimensionality reduction methods

Many previous investigations have indicated that speech data has inherent low-dimensional structure and that it may be possible to efficiently represent speech using only a small number of parameters. This view is motivated by the fact that articulatory movement is limited by physiological constraints and thus the speech production apparatus has only limited degrees of freedom. Also, the set of sounds used in human spoken communication is only a small subset of all producible sounds. A number of dimensionality reduction methods capable of discovering such underlying structure have previously been applied to speech. However, if speech lies on a manifold nonlinearly embedded in high-dimensional space, as has been proposed in the past, classic linear dimensionality reduction methods would be unable to discover this embedding. In this dissertation a number of manifold learning, also referred to as nonlinear dimensionality reduction, methods are applied to speech to explore the possibility of underlying nonlinear manifold structure. This dissertation describes a number of existing manifold learning methods and details the application of these methods to high-dimensional feature representations of speech data. Representations derived from the conventional magnitude spectrum and less widely used phase spectrum are investigated. The manifold learning methods used in this study are locally linear embedding, Isomap, and Laplacian eigenmaps. The classic linear method, principal component analysis (PCA), is also applied to facilitate the comparison of linear and nonlinear methods. The resulting low-dimensional representations are analysed through visualisation, phone recognition, and speaker recognition experiments. The recognition experiments are used as a means of evaluating how much meaningful discriminatory information is contained in the low-dimensional representations produced by each method. These experiments also serve to display the potential value of these methods in speech processing applications. The manifold learning methods are shown to be capable of producing meaningful lowdimensional representations of speech data suggesting speech has low-dimensional manifold structure. In general, these methods are found to outperform PCA in low dimensions, indicating that speech may lie on a manifold nonlinearly embedded in high-dimensional space. Phone classification experiments show that Isomap can offer improvements over standard features and PCA-transformed features. Investigation of magnitude and phase spectrum representations found both to have similar low-dimensional structure and confirm that the phase spectrum contains useful information for phone discrimination. Results indicate that combining magnitude and phase spectrum information yields improvements in phone classification tasks. A method to combine magnitude and phase spectrum features for increased phone classification accuracy without large increases in feature dimensionality is also described.
Characterizing phenotypes for Mental Health Disorders with Wrapper-typed Feature selection techniques:: Comparison of RF-RFE and Fuzzy Forest

Random Forest is a popular feature selection method suitable for handling “small n, large p” problem but lacking capability of dealing collinearity. To compensate the gap of removing highly correlated features, wrapper-typed feature selection methods: Fuzzy Forest (FF) and Random Forest- Recursive Feature Elimination (RF-RFE) have been developed. These two methods are similar in multiple ways but implement different strategies to deal with features. Meanwhile, the field of clinical psychiatry is changing in the way it characterizes mental health disorders. Thus, the aims of our paper are to compare the impact of FF and RF-RFE and to study phenotypic features relevant to three mental health disorders: schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD). We specify the classification problem as “one versus rest” (OVR) and implement phenotype data from Consortium for Neuropsychiatric Phenomics. FF and RF-RFE are applied to select the optimal feature subsets separately, which are then evaluated by Support Vector Machines (SVM) and Extreme Learning Machines (ELM) classifiers respectively. The evaluation criteria include precision, recall, accuracy, F-measure and Area under the curve. As a result, RF-RFE showed superior feature selection performance over FF. Also, we found that the features of the original data are informative in diseases from most to least: schizophrenia, ADHD and bipolar disorder.
Apple Leaf Disease Recognition Based on Attention Mechanics and Multi-Scale Feature Fusion

Early diagnosis and accurate identification of apple diseases play a major role in reducing growing costs and curbing economic losses. The diagnosis and identification of apple diseases are more difficult in the natural farming environment. A large amount of background noise in complex natural environments makes apple disease features relatively inconspicuous and makes the features of different diseases less distinguishable. A single-scale feature extraction network will be more difficult to extract effective information. In order to solve this problem, this paper proposes an apple leaf classification network based on attention mechanism and multi-scale feature fusion. First, the residual unit of ResNet50 is improved by replacing the second convolution in the residual unit with a pyramidal convolution modified by using dilated convolution to obtain multi-scale fused features. Then a channel attention module is added to the residual bypass to enhance the weighting of the disease features and improve the classification accuracy. The experiments in this paper first validate the role of the attention mechanism and pyramidal convolution separately and find that both improve the model performance. Then the combination of attention mechanism and pyramidal convolution is validated, and the optimized model has stronger noise immunity and the classification accuracy on the validation set is 94.96%. The results show that the optimized model has a better classification effect and higher robustness for apple leaf disease pictures in the natural environment.
HAIR: Halving the Area of the Integer Register File with Odd/Even Banking

This article proposes a new microarchitectural scheme for reducing the hardware complexity of the integer register file of a superscalar processor. The register file is split into two banks holding even-numbered and odd-numbered physical registers, respectively. Each bank provides one read port to each two-input integer execution unit. This way, each bank has half the total number of read ports, and the register file area is roughly halved, which reduces the energy dissipated per register access and the register access time. However, a bank conflict occurs when both inputs of a two-input micro-operation lie in the same bank. Bank conflicts hurt performance, and we propose a simple solution to remove most bank conflicts, thus recovering most of the lost performance.
Optimization and Reconstruction for EPMA Image Compressed Sensing Based on Chaotic Measurement Matrix

Image reconstruction is an important part of today's image processing. The quality and efficiency of image re-construction are one of the research hotspots in today's image processing field. Image reconstruction using compressed sensing can greatly reduce the sampling rate and break the constraint of traditional Nyquist sampling law, which is of great breakthrough significance for image reconstruction. The quality of the measurement matrix in compressed sensing has a great influence on the effect of the reconstruction. Therefore, the construction of the measurement matrix is an important direction of the current research on compressed sensing. This paper is using the deterministic Monte Carlo pseudo-random number sampling method to construct a chaotic measurement matrix for compressed sensing. This measurement matrix can solve the uncertainty of the random matrix. The experimental results show that the reconstruction effect of this method on the EPMA image has a better reconstruction performance than other measurement matrices and achieves the super-resolution recovery.
Expressive querying for accelerating visual analytics

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and time-consuming. In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching. To address them, we first present our work on Zenvisage that helps enumerate large collections of visualizations and supports simple visualization matching with the help of an interactive interface and an expressive visualization query language. For more finegrained and flexible visualization matching, including search for underspecified and approximate patterns, we extend Zenvisage to develop ShapeSearch. ShapeSearch supports a novel shape querying algebra that helps express a large class of pattern queries that are hard to specify with existing systems. ShapeSearch exposes multiple specification mechanisms: sketch, natural-language, and visual regular expressions that help users easily issue shape queries, while applying query-aware and perceptually-aware optimizations to efficiently execute them within interactive response times. To conclude, we discuss a number of open research problems to further improve the usability and performance of both Zenvisage and ShapeSearch.
Spherical Tree Structured Self-Organizing Map

∗ In order to speed up the search for winner nodes in a Self-Organizing Map (SOM), Tree Structured SOM(TS-SOM), which applies the tree search method to the SOM, was proposed. Since TS-SOM has the edges of the map like the SOM, the learning is biased depending on the position of the winner node. Therefore, in this paper, in order to eliminate the edges of TS-SOM’s map, we propose Spherical TS-SOM(S-TS-SOM) in which nodes are placed on spheres and the tree search method is applied. Also, we evaluate the performance of S-TS-SOM.
Microgrids Energy Management Using Robust Convex Programming

This paper presents an energy management system (EMS) for single-phase or balanced three-phase microgrids via robust convex optimization. Along a finite planning horizon, the solution provided by the proposed microgrids EMS remains feasible under adverse conditions of random demands and renewable energy resources. The proposed model is represented as a convex mixed-integer second-order cone programming model. Two operation modes are considered: grid-connected and isolated. In grid-connected mode, the proposed EMS minimizes the costs of energy imports, dispatches of distributed generation (DG) units, and the operation of the energy storage systems. In isolated mode, the proposed EMS minimizes the unsupplied demand considering consumer priorities. Global robustness of the proposed mathematical model is adjusted using a single parameter $\zeta $ . The robustness of the solutions provided by the robust EMS are assessed using the Monte Carlo simulation method. In this case, DG units are set to operate in frequency and voltage droop control to support network fluctuations. Simulations are deployed using a microgrid with 136-nodes and several distributed energy resources. Results showed that the proposed model is suitable for the short-term microgrids energy management system. The robustness of the final solution was directly proportional to the operational costs, and it can be effectively controlled by the proposed parameter $\zeta $ for both operation modes. When compared to stochastic approaches, the proposed formulation proved to be more flexible and less time-consuming.
Conditional Generative Adversarial Networks for Hyperspectral Image Classification

Though Hyperspectral Image (HSI) classification has been extensively investigated over recent decades, it is still a challenging task especially when the labeled samples are extremely limited. In this paper, we overcome the obstacle by using Conditional Generative Adversarial Networks (CGAN) to generate trainable data set with complete spectral and spatial information. Through comparing generated images of different shape and classification map for Indian pines, the most suitable data are selected and used to train the common model of neural network. Second, three common and latest neural network methods including two-dimensional Convolution (Conv2D), three-dimensional Convolution (Conv3D), Hybrid spectral CNN (Hybrid SN) used for HSI classification, are proposed. After repeating experiments and cross-validation, we have found that the proposed method, enhancing original data, can make model achieve better and robust performance for HSI classification compared to complete original data set, especially when the labeled data is limited.