Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight Convolutional Neural Network for Breast Cancer Classification Using RNA-Seq Gene Expression Data

Gene expressions are considered among the most used features in cancer classification. The available gene expression data has a small number of samples and a relatively big number of dimensions, and that makes it not suitable for deep Convolutional Neural Networks (CNN) architectures, which exhibit state-of-the-art performance in many fields. In this paper, we propose a lightweight CNN architecture for breast cancer classification using gene expression data downloaded from Pan-Cancer Atlas using “Illumina HiSeq” platform. The downloaded gene expression data is preprocessed and then transformed into 2D-images. We started the preprocessing by removing the outlier samples, which are determined based on the Array-Array Intensity Correlation (AAIC), which defines a symmetric square matrix of Spearman correlation. Then we applied a normalization process on the gene expression data to ensure that we can infer the expression level from it correctly and avoid biases in the expression measures. Finally, filtering is applied on the data. Model selection or a parameters search strategy is conducted to choose the values of the CNN hyper-parameters that give optimal performance. Our experiments show that our proposed method achieves an accuracy of 98.76%, which is the highest compared to other competing methods.
Mathematical and Computer Modeling of a New Type of Two-Link Manipulator

For a new type of two-link manipulator with two nonlinear geometric constraints, a mathematical model of dynamics is developed in the form of vector-matrix equations M.F. Shulgin. The consideration as carried out taking into account transients in electric drives - DC collector motors with independent excitation. The results are used to solve the problem of stabilization of a given position of a grip of a manipulator.
A Review of Game Theoretical Resource Allocation Methods in Wireless Communications

The allocation and control of network resources play an important role in improving users’ satisfaction with network services and optimizing the overall performance of the communication system. Game theory, as a branch of economics, provides an interdisciplinary technique for efficiently allocating wireless resources, such as channels, bandwidth, and spectrum level for mobile devices. This paper reviews the applications of game theory in mobile systems in recent years. Based on the different game models, categorized by cooperative game, non-cooperative game and their combination, this paper summarizes the most effective resource allocation methods for typical mobile communication scenarios, such as cognitive radio, cloud computing environment, device-to-device communications and cooperative relay networks.
Design of Real-Time Hardware for Edge Detection

In this paper, we present a method to efficiently detect the edge of an image by using hardware. Canny edge detection algorithm which is strong against. However it has a disadvantage that the processing speed is slower than other detection methods. Sobel detector can be implemented in hardware with less memory and faster than Canny edge detector. In software, each result is stored in the memory, but hardware uses line buffers to reduce memory size and increase speed. The length of the line buffer is equal to the size of the vertical pixels of the image. To implement in hardware, we use Verilog HDL and the function of the HDL code was verified by a FPGA board and a monitor. Using our method, full HD video can be processed up to 48 frames per second, and the size of the memory can be reduced to 33 percent of the conventional method.
Multi-Spectral Imaging for Artificial Ripened Banana Detection

Ripening is a natural process that makes fruits edible and nutritious. With increasing demand, the practice to employ artificial ripening of fruits have been increased recently in the market chain to fulfill the needs of the consumer. Artificial ripening not only reduces the quality of fruits but also increases the health-related risk especially Calcium carbide(CaC2), an artificial ripening agent, inherits the carcinogenic properties. Although the problem of detecting artificial ripening of fruits is important, the conventional methods based on chemical analysis are not feasible. In this paper, we present the use of multi-spectral imaging in eight narrow spectrum bands across VIS and NIR range to detect the artificial ripened banana. To present this approach, we introduce a newly constructed multi-spectral images collected from naturally and artificially ripened banana samples. The extensive experiments are performed on the large scale data set consists of 5760 banana samples by performing 10 fold cross-validation. The obtained average classification accuracy of 97.5% demonstrates the significance of multi-spectral imaging for differentiating natural and artificial ripened banana.
Pure-CNN: A Framework for Fruit Images Classification

Automation in fields like robot harvesting, farming, health and education require object classification using machine learning and computer vision techniques. Among these fruit classification is a challenging task because of its several varieties and similarity in color, shape, size and texture features. In order to recognize multiple fruits more accurately, we proposed a Pure Convolutional Neural Network (PCNN) with minimum number of parameters. The PCNN consists of 7 convolutional layers, some of them followed with stride. Additionally, to reduce overfitting and taking average of whole feature maps we employed recently developed Global Average Pooling (GAP) layer that verified to be very effective. We demonstrate our classification performance using PCNN on recently introduced fruit-360 dataset. The experimental results of the 55244 color fruit images from the 81 categories, show that the PCNN achieve a classification accuracy of 98.88%.
Weight and Metabolic Outcomes After 2 Years on a Low-Carbohydrate Versus Low-Fat Diet

BACKGROUND Previous studies comparing low-carbohydrate and low-fat diets have not included a comprehensive behavioral treatment, resulting in suboptimal weight loss. OBJECTIVE To evaluate the effects of 2-year treatment with a low-carbohydrate or low-fat diet, each of which was combined with a comprehensive lifestyle modification program. DESIGN Randomized parallel-group trial. (ClinicalTrials.gov registration number: NCT00143936) SETTING 3 academic medical centers. PATIENTS 307 participants with a mean age of 45.5 years (SD, 9.7 years) and mean body mass index of 36.1 kg/m(2) (SD, 3.5 kg/m(2)). INTERVENTION A low-carbohydrate diet, which consisted of limited carbohydrate intake (20 g/d for 3 months) in the form of low-glycemic index vegetables with unrestricted consumption of fat and protein. After 3 months, participants in the low-carbohydrate diet group increased their carbohydrate intake (5 g/d per wk) until a stable and desired weight was achieved. A low-fat diet consisted of limited energy intake (1200 to 1800 kcal/d; <or=30% calories from fat). Both diets were combined with comprehensive behavioral treatment. MEASUREMENTS Weight at 2 years was the primary outcome. Secondary measures included weight at 3, 6, and 12 months and serum lipid concentrations, blood pressure, urinary ketones, symptoms, bone mineral density, and body composition throughout the study. RESULTS Weight loss was approximately 11 kg (11%) at 1 year and 7 kg (7%) at 2 years. There were no differences in weight, body composition, or bone mineral density between the groups at any time point. During the first 6 months, the low-carbohydrate diet group had greater reductions in diastolic blood pressure, triglyceride levels, and very-low-density lipoprotein cholesterol levels, lesser reductions in low-density lipoprotein cholesterol levels, and more adverse symptoms than did the low-fat diet group. The low-carbohydrate diet group had greater increases in high-density lipoprotein cholesterol levels at all time points, approximating a 23% increase at 2 years. LIMITATION Intensive behavioral treatment was provided, patients with dyslipidemia and diabetes were excluded, and attrition at 2 years was high. CONCLUSION Successful weight loss can be achieved with either a low-fat or low-carbohydrate diet when coupled with behavioral treatment. A low-carbohydrate diet is associated with favorable changes in cardiovascular disease risk factors at 2 years. PRIMARY FUNDING SOURCE National Institutes of Health.
Numerical Computation of Dispersion Curves for Both Symmetric and Asymmetric Modes in Metal Coaxial Slow Wave Structures

In the overmoded coaxial slow wave structures (SWSs), the asymmetric mode is easier to be excited by the electron beam. For conveniently studying the high frequency characteristics of coaxial asymmetric modes, we derive and solve the universal dispersion equation for both symmetric and asymmetric modes in coaxial SWSs. In principle, this numerical formula can be applied to arbitrary even-function SWSs, arbitrary corrugated depth and arbitrary transverse dimension. By this dispersion equation, the dispersion curves of asymmetric modes can be calculated more quickly and conveniently than the simulation software based on finite-element method.
Friction pre-sliding control and sliding impulse compensation

Precision position control in the order of micro metres demands that friction is either compensated for or included in the control system loop. The dynamic friction models including LuGre, Elasto-plastic and Generalized Maxwell-Slip (GMS) have been shown to model friction. Each of these dynamic models have the state variables of bristle displacement (z) and bristle displacement velocity dz/dt. They each have an equation that relates the bristle displacement velocity to the sliding velocity (v). This principle is disputed. It is shown that the presliding friction force can be analytically calculated from the applied electrical force, without having to have any relationship of the sliding velocity to the bristle displacement velocity. This leads to a feedback control system for presliding bristle displacement, without actual measurement of it.The dynamic friction models are currently used in real time control to provide feed forward compensation. Feed forward compensation is pre-emptive in that it provides anticipated friction force based on the planned velocity. A new control system strategy and method of overcoming the sliding friction forces called “impulse control” is developed which can be applied pre-emptively. Impulse control means the control of the time integral of a state variable. The term is not used to refer to control by short force pulses in this paper. The impulse control strategy is more generally applicable than just for the control of systems with friction. This friction control methodology is still awaiting a full practical implementation.
A Novel Filter Design for Edge Enhancement

Effective Image denoising algorithms require efficient and fast noise removal from any digital image. However, edge preservation or enhancement is more important in many applications such as computer vision and 3D imaging. A new family of linear filters for edge emphasizing and edge enhancement in image processing is introduced. Significant results of the study are demonstrated.
Investigation of Different Controllers and Observers Combinations for Grid-Tied LCL Filter

The problem of controlling the LCL filter remains an active topic since single loop proportional integral (PI) controllers fail to guarantee the stability of the system under changes in the grid impedance. This limitation can be tackled by adding an active damping to the system through various control techniques. In this paper, the first controller is obtained using feedback linearization with disturbance-observer-based controller (DOBC), while the second is designed based on the linear quadratic regulator (LQR) theory. A reduced-order observer, full-order observer, and Kalman filter (KF) are designed for both controllers. The two controllers with the different observers are compared in terms of total harmonic distortion (THD), and their transient performance is tested against changes in the grid impedance. Simulations show that the LQR with KF and DOBC with KF are able to achieve low THD with lower number of sensors, and the latter can maintain its performance if the design is adapted according to the changes in the grid impedance. However, for non-adaptive designs, sensor-based controllers offer a higher robustness against changes in grid impedance.
A local contrast fusion based 3D Otsu algorithm for multilevel image segmentation

To overcome the shortcomings of 1D and 2D Otsuʼ s thresholding techniques, the 3D Otsu method has been developed. Among all Otsuʼ s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.
Robust Deep Feature Extraction Method for Acoustic Scene Classification

In recent years, increasing number of acoustic scene classification (ASC) methods are based on deep learning models. In these models, the extraction of robust deep feature plays an important role on the classification accuracy. However the complex combination of acoustic phenomena in an acoustic scene results in overlapping of the analysis features, which degrades the performance of ASC. To enhance the compactness of feature and fit the multi-classification task, we explored the data label learning for deep feature extraction. And we combined the method of label smoothing(LS) and the additive margin softmax loss (AM-softmax) to extract deep feature based on VGG-style deep neural network. The comparison experiments show that the best classification results are obtained by the proposed method, which accuracy on ESC-50 dataset is 81.9%, which is beyond human performance.
What motives are important for participation in leisure-time activities at Swedish youth centres?

Objective: This study aimed to explore the motives of young people in multicultural suburbs for participating in youth-centre activities. Design and setting: The study employed practice-based research with a focus on collaboration and methodological diversity. Data on motives for participation were collected in spring 2013 at two non-governmental organisation (NGO)-run youth centres located in the suburbs of two cities in Sweden using surveys and focus-group interviews. Method: The study used mixed methods, with qualitative data being used to help explain initial quantitative findings. Statistical analysis was conducted using measures of competence and social motives. Qualitative analysis used both deductive and inductive content analysis. Results: Study findings suggest that motives concerning competence development and socialising are insufficient to account for why young people in multicultural, socially disadvantaged suburbs participate in youth-centre activities. The study highlights the importance of additional motives influencing participation in leisure-time activities. The additional motives of ‘fun/undemanding’ and ‘support’ were found to be important to most young people in this study. Conclusion: Study findings suggest that motives for participation in youth-centre activities have to do with characteristics of the participants, of the neighbourhood in which the centre is located and the specific type of unstructured leisure-time activity. Future motive measurement scales should include items concerning socioeconomic status, activities provided and young people’s degree of influence over the activities in which they participate.
Predicting non-linear dynamics: a stable local learning scheme for recurrent spiking neural networks.

Brains need to predict how our muscles and body react to motor commands. How networks of spiking neurons can learn to reproduce these non-linear dynamics, using local, online and stable learning rules, is an important, open question. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics, while an online and local rule changes the weights; hence we call the scheme FOLLOW (Feedback-based Online Local Learning Of Weights) The rule is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the post-synaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Using the Lyapunov method, and under reasonable assumptions and approximations, we show that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.
The ESO-Spitzer Imaging extragalactic Survey (ESIS) I: WFI B,V,R deep observations of ELAIS-S1 and comparison to

Context. The ESO-Spitzer extragalactic Imaging Survey (ESIS) is the optical follow up of the Spitzer Wide-Area InfraRed Extragalactic (SWIRE) survey in the ELAIS-S1 area. Aims. The multiwavelength study of galaxy emission is the key to understand the interplay of the various components of galaxies and to trace their role in cosmic evolution. ESIS provides optical identification and colors of Spitzer IR galaxies and builds the bases for photometric redshift estimates. Methods. This paper presents B, V, R Wide Field Imager observations of the first 1.5 square degree of the ESIS survey. Data reduction is described including astrometric calibration, illumination and color corrections. Synthetic sources are simulated in scientific and super-sky-flat images, with the purpose of estimating completeness and photometric accuracy for the survey. Number counts and color distributions are compared to literature observational and theoretical data, including ◦ : ′ : ′′ ′′ ′′ ′′ ]
A Short-Term Clinical Evaluation of L-697,661, a Non-Nucleoside Inhibitor of HIV-1 Reverse Transcriptase

Background The non-nucleoside reverse transcriptase inhibitors are novel antiretroviral agents with selective activity in vitro against human immunodeficiency virus type 1 (HIV-1). They act through direct inhibition of reverse transcriptase and are not incorporated into DNA. Methods We evaluated a pyridinone non-nucleoside reverse transcriptase inhibitor, L-697,661, in separate six-week double-blind trials in patients with HIV-1 infection whose CD4 counts ranged from 200 to 500 cells per cubic millimeter (68 patients) or less than 200 cells per cubic millimeter (67 patients). Eligible patients were randomly assigned to receive L-697,661 orally in one of three doses (25 mg twice a day, 100 mg three times a day, or 500 mg twice a day) or zidovudine (100 mg five times a day). Clinical and laboratory assessments were performed weekly. Viral isolates were obtained from a subgroup of patients before and after treatment and were evaluated for in vitro sensitivity to L-697,661. Results Both L-697,661 and zidovudi.
Root-exchange property of constrained linear predictive models

In recent works, we have studied linear predictive models constrained by time-domain filters. In the present study, studied the one-dimensional case in more detail. Firstly, we obtain root-exchange properties between the roots of an all-pole model and corresponding constraints. Secondly, using the root-exchange property we can construct a novel matrix decomposition A/sup T/RA/sup #/ = I, where R is a real positive definite symmetric Toeplitz matrix, superscript /sup #/ signifies reversal of rows and I is the identity matrix. In addition, there exists also an inverse matrix decomposition C/sup T/R/sup -1/C/sup #/ = I, where C /spl isin/ C is a Vandermonde matrix. Potential applications are discussed.
transformation of the

: Sclerosing angiomatoid nodular transformation (SANT) is a benign, proliferative vascular lesion affecting the spleen. Few reports detailing the cross sectional and PET appearance of this lesion are available, and the lesion’s behavior with 99mTc-sulfur colloid scintigraphy is previously unreported. Sclerosing nodular transformation of the spleen shows increased tracer accumulation on positron emission tomography, and a central scar-like appearance with an enhancing capsule and radiating septae on CT and MR studies that reflects the gross and histopathological features of the lesion may be visible. An understanding of this pathological finding may allow prospective recognition of the sclerosing nodular transformation of the spleen on cross sectional imaging studies. Abstract Sclerosing angiomatoid nodular transformation (SANT) is a benign, proliferative vascular lesion affecting the spleen. Few reports detailing the cross sectional and PET appearance of this lesion are available, and the lesion’s behavior with 99m Tc-sulfur colloid scintigraphy is previously unreported. Sclerosing nodular transformation of the spleen shows increased tracer accumulation on positron emission tomography, and a central scar-like appearance with an enhancing capsule and radiating septae on CT and MR studies that reflects the gross and histopathological features of the lesion may be visible. An understanding of this pathological finding may allow prospective recognition of the sclerosing nodular transformation of the spleen on cross sectional imaging studies.
What powers Ly α blobs

Lyα blobs (LABs) are spatially extended Lyα? nebulae seen at high redshift. The origin of Lyα emission in the LABs is still unclear and under debate. To study their heating mechanism(s), we present Australia Telescope Compact Array (ATCA) observations of the 20 cm radio emission and Herschel PACS and SPIRE measurements of the far-infrared (FIR) emission toward the four LABs in the protocluster J2143-4423 at z = 2.38. Among the four LABs, B6 and B7 are detected in the radio with fluxes of 67±17 μJy and 77±16 μJy, respectively, and B5? is marginally detected at 3σ (51±16 μJy). For all detected sources, their radio positions are consistent with the central positions of the LABs. Among them, B6 and B7 are obviously also detected in the FIR. By fitting the data with different templates, we obtained redshifts of 2.20+0.30-0.35 for B6 and 2.20+0.45-0.30 for B7, which are consistent with the redshift of the Lyα emission within uncertainties, indicating that both FIR sources are likely associated with the LABs. The associated FIR emission in B6 and B7 and high star formation rates strongly favor star formation in galaxies as an important powering source for the Lyα emission in both LABs. However, the other two, B1 and B5, are predominantly driven by the active galactic nuclei or other sources of energy still to be specified, but not mainly by star formation. In general, the LABs are powered by quite diverse sources of energy. © ESO, 2015.