Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Mapping of Flavonoid Grain Pigments in Durum Wheat

Pigmented cereal grains with high levels of flavonoid compounds have attracted the attention of nutritional science backing the development of functional foods with claimed health benefits. In this study, we report results on the genetic factors controlling grain pigmentation in durum wheat using a segregant population of recombinant inbred lines (RILs) derived from a cross between an Ethiopian purple grain accession and an Italian amber grain cultivar. The RIL population was genotyped by the wheat 25K SNP array and phenotyped for total anthocyanin content (TAC), grain color, and the L*, a*, and b* color index of wholemeal flour, based on four field trials. The mapping population showed a wide variation for the five traits in the different environments, a significant genotype x environment interaction, and high heritability. A total of 5942 SNP markers were used for constructing the genetic linkage map, with an SNP density ranging from 1.4 to 2.9 markers/cM. Two quantitative trait loci (QTL) were identified for TAC mapping on chromosome arms 2AL and 7BS in the same genomic regions of two detected QTL for purple grain. The interaction between the two QTL was indicative of an inheritance pattern of two loci having complementary effects. Moreover, two QTL for red grain color were detected on chromosome arms 3AL and 3BL. The projection of the four QTL genomic regions on the durum wheat Svevo reference genome disclosed the occurrence of the candidate genes Pp-A3, Pp-B1, R-A1, and R-B1 involved in flavonoid biosynthetic pathways and encoding of transcription factors bHLH (Myc-1) and MYB (Mpc1, Myb10), previously reported in common wheat. The present study provides a set of molecular markers associated with grain pigments useful for the selection of essential alleles for flavonoid synthesis in durum wheat breeding programs and enhancement of the health-promoting quality of derived foods.
Compact Reconfigurable Slot antenna for UWB, Wide band and Narrow band applications

this work presents a compact slot antenna with reconfigurable frequency for Ultra Wide Band (UWB), wide band and narrow band applications. A very compact size with 24mm x 8mm x 1.6mm is shown for the proposed compact slot antenna. A combination of the elementary slot is placed in the back side of the proposed antenna to realize a UWB band width. Three switches ON/OFF (PIN diodes) are integrated in the stepped slot, eight resonant modes are achieved by switching the three diodes in two states ON and OFF position. The UWB, the wide band, the dual band and the narrow modes are obtained. An UWB bandwidth is achieved between 2.9GHz and 11.6GHz in the first mode, in the second mode two frequency bands are realized between 2.65 GHz and 4.2GHz in the frequency center of 3.2GHz and between 4.95GHz and 6.88GHz in the frequency center of 6.2GHz. A wide impedance band width is obtained in the third mode; this mode covers various applications. In the last mode, two frequency bands are achieved in 2.55GHz and in 2.78GHz respectively. A good radiation pattern is observed and realized by the proposed antenna where, in H-Plan an omnidirectional radiating is achieved and bidirectional radiation in the E-Plan for the operating frequencies. Two simulators are used to realize the simulated results of proposed antenna. A good agreement is shown between the two simulators in term of impedance matching.
Design and Manufacture for Select Frequency Oscillation Grating in the Zero-Order Coupling Output of 9.77 μm Laser

The physical model of real ruled grating is proposed, the corresponding expression of diffraction efficiency is given, and design principle of blazed grating for frequency-selection oscillation in zero\|order output for an infrared (IR) laser is introduced. On the base of the result of theory analysis, the technologies are adopted that adjusting the ruled load to control the zero-order plane of the grating and bi-directionally gradually approach the design value of the diffraction efficiencies of the zero-order and first-order, the gilt blazed grating is made with the first-order diffraction efficiency of 86.5% and the zero-order diffraction efficiency of 10.3% in Littrow form. The testing result shows that the deviations of the zero-order and first-order diffraction efficiency from the designed central value are only 0.6% and 3%, respectively.
Emissive and reflective properties of curved displays in relation to image quality

Different aspects of the characterization of curved displays are presented. The limit of validity of viewing angle measurements without angular distortion on such displays using goniometer or Fourier optics viewing angle instrument is given. If the condition cannot be fulfilled the measurement can be corrected using a general angular distortion formula as demonstrated experimentally using a Samsung Galaxy S6 edge phone display. The reflective properties of the display are characterized by measuring the spectral BRDF using a multispectral Fourier optics viewing angle system. The surface of a curved OLED TV has been measured. The BDRF patterns show a mirror like behavior with and additional strong diffraction along the pixels lines and columns that affect the quality of the display when observed with parasitic lighting. These diffraction effects are very common on OLED surfaces. We finally introduce a commercial ray tracing software that can use directly the measured emissive and reflective properties of the display to make realistic simulation under any lighting environment.
State-oriented evaluation of observability and sensor placement for mechanical estimation applications

Optimal sensor selection and placement are of paramount importance in estimation and control applications. In this article, we exploit observability metrics based on the observability Gramian to effectively determine sensor locations that contribute significantly to the individual observability of a limited number of states of importance. In particular, the ellipsoid representation of the observability Gramian is utilized to ensure optimal sensor placement in conjunction with a state-based observability evaluation. The method is demonstrated on a numerical example of a mechanical linear time-invariant system. The results show that the method can highlight states with particularly good observability and place a set of sensors in such a way that the observability is maximised for an arbitrary set of interested states.
Efficient Re-configurable Multiply and Accumulate Unit for Convolutional Neural Network

Conventional Multiply and Accumulate (MAC) unit consists of multiplier, and accumulator. The hardware realization on programmable system on chip of the systems require trade-off between speed and area. In this paper, the Booth Multiplier, array multipliers, ripple-carry array multipliers are expiored with row bypassing technique, Vedic multiplier, Wallace-Tree multipliers, and DADDA multipliers in terms of area, delay, and power. The combination of Radix-4 Booth multiplier and carry-s adder has shown better performance in terms of area, and delay in the design of the MAC Unit. The design is then synthesized on ARTIX-7 FPGA using the Xilinx Vivado. Further, the MAC unit has been evaluated in implementing a 2D convolution engine application.
Compact and fully automated system for monitoring photodynamic therapy, based on two LEDs and a single CCD

Photodynamic therapy is one of the most promising methods for the treatment of oncological, inflammatory and degenerative diseases of the skin. This technique is based on light irradiation of a photosensitizer that has been injected into the patient’s body prior to the procedure, with determination of the efficacy of treatment requiring proper assessment of the drug concentration in the tissue lesion and the extent of sensitizer photobleaching during irradiation. We have developed a compact and low cost device based on a fluorescence imaging for localizing the tumor in the patient’s body, tracking the position of the tissue lesion during involuntary movements of the patient, estimating accumulation of the sensitizer in the tumor relative to the surrounding tissues and monitoring photobleaching of the sensitizer during laser irradiation. The system that has been created is compatible with any therapeutic laser and includes a single CCD camera and two LEDs, one in the excitation band and the other in the emission band of the ‘Photoditazin’ sensitizer. In this letter we also present the test results of the device in model experiments and in preliminary clinical trials. The results obtained clearly show the efficacy of the system for monitoring sensitizer photobleaching during photodynamic therapy.
Medical Image Segmentation Used Unsupervised Convolutional Neural Network

Segmenting skin lesions is a crucial step in computer-aided melanoma diagnosis, but it is also a highly difficult task because of the imprecise lesion boundaries and varied lesion textures. We describe a deep fully convolutional network-based, totally automatic technique for skin lesion segmentation (FCNs). In order to extend ConvNets for tasks requiring medical picture segmentation, this work provides unsupervised domain adaption techniques employing adversarial learning. By combining the prior knowledge stored by the shallow network with the deep FCNs, we are able to show that our newly built network may increase accuracy for lesion segmentation. Our approach is robust and does not require extensive parameter tuning or data augmentation. The experimental findings demonstrate the method's considerable potential for efficient model generalization.
Improved Light-Weight Target Detection Method Based on YOLOv5

Aiming at the problems of the YOLOv5, such as large model size, enormous amount of computation, and low accuracy of target box regression, a new model with fewer parameters, less computation, faster convergence speed, and higher accuracy was proposed. Firstly, the improved SKConv was used to greatly reduce the number of model parameters and increase the receptive field range of the network. Secondly, C-ECA, an optimized version of the channel attention module ECA, was added to the model to obtain attention information in a cross-channel way, so that the model could more accurately focus on important features among complex features. Then, the MSM structure is designed to effectively improve the feature extraction ability of the network. Finally, the backbone network is deepened to extract more intermediate features, and the depth of the feature pyramid and detection layer are increased accordingly so that the network can make full use of intermediate features and accurately detect more targets. The experimental results show that compared with YOLOv5s, the number of parameters of the final models is reduced by 18.6%, and the calculation amount is reduced by 8.1%, and when tested on the VOC2007 test dataset, mAP@0.5 and mAP@0.5:0.95 improved by 3.3 percentage points and 5.6 percentage points, respectively. The improved model has significantly improved the ability of target detection in various complex environments and realizes the target detection model with low parameters and high accuracy, which provides important ideas for the design of lightweight target detection networks.
Intelligent identification of radar active jamming type based on multi-domain information fusion

Feature fusion is beneficial to improving the recognition rate of radar active jamming types, which usually costs a lot of computation and network complexity, however. Contrapose the disadvantage of low computational power and resource allocation of missile-borne radar, this paper introduces data enhancement theory into the feature fusion method and proposes an intelligent identification model of radar active jamming type based on multi-domain information fusion. The model first analyzes the time-frequency domain features of the signal and one-dimensional features such as its real part, imaginary part, frequency spectrum, and power spectrum, and then uses the Cutout&Patchup algorithm to fuse the one-dimensional and two-dimensional features into a new multi-domain information fusion matrix as the input of the recognition network. The simulation results show that this method greatly improves the recognition accuracy of active jamming types. Under the WideResNet28_2 classifier, the classification accuracy of active interference type reaches 88.06%, which is 0.79% higher than that before fusion.
Research of Continuous Delta-Sigma Analog-to-Digital Converter for Bio-Electricity Signal Acquisition

In this paper, a continuous delta-sigma analog-to-digital converter with a low power, single-ring, third-order mixed integrator for bio-electricity signal acquisition is proposed. For the trade-off between low power consumption and high resolution, the Active-RC integrator is used in the first-order of the third-order feedforward modulator and the Gm-C integrator with improved linearity is adopted for the second and third order integrators. Because of the infinite resistance provided by the Gm-C integrator, the first-order integrator can replace the traditional second-order operational transconductance amplifier (OTA) with a single-stage OTA operating in the weak invert region to achieve low power consumption and ensure the output swing of the first-order integrator. The structure of cascade-of-integrators with feedforward (CIFF) can scale the output voltage values of three integrators and reduce the requirement of linearity of the Gm-C integrator, to reduce the design difficulty of the Gm-C integrator. In addition, the linearity of the second and third-order Gm-C integrator is improved by using the auxiliary difference pair OTA with source-level negative feedback. Therefore, the matching between the circuit structure and the model coefficients is improved significantly.
Bayesian Approach for Nonlinear Random Effects Models

SUMMARY In this paper, we propose a general model-determination strategy based on Bayesian methods for nonlinear mixed effects models. Adopting an exploratory data analysis viewpoint, we develop diagnostic tools based on conditional predictive ordinates that conveniently get tied in with Markov chain Monte Carlo fitting of models. Sampling-based methods are used to carry out these diagnostics. Two examples are presented to illustrate the effectiveness of these criteria. The first one is the famous Langmuir equation, commonly used in pharmacokinetic models, whereas the second model is used in the growth curve model for longitudinal data.
An Indoor Real-Time Visualization Exploration Unmanned Aerial Vehicle

Aiming at the scene of a rescue in an indoor environment, this paper proposes an indoor real-time visual exploration UAV. UAVs can model the unknown space in 3D space in real-time, obtain obstacle information and flight navigation information, avoid obstacles and fly to the region of interest in real-time. Real-time transmission of three-dimensional space information to the remote end to provide rescue personnel with timely arrangements for rescue plans. The 3D mapping uses a 32-line laser radar, combining high-density point clouds and IMU to obtain accurate and real-time navigation information. The point cloud is then made into a grid map for real-time planning so that the UAV can independently calculate the flight path to the designated area. Then it is transmitted to the remote interface in real-time through WIFI communication so that the rescuers can quickly get the location and route of the rescuers needed and complete the rapid rescue operation.
Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.
CASOG: Conservative Actor-critic with SmOoth Gradient for Skill Learning in Robot-Assisted Intervention

Robot-assisted intervention has shown reduced radiation exposure to physicians and improved precision in clinical trials. However, existing vascular robotic systems follow master-slave control mode and entirely rely on manual commands. This paper proposes a novel offline reinforcement learning algorithm, Conservative Actor-critic with SmOoth Gradient (CASOG), to learn manipulation skills from human demonstrations on vascular robotic systems. The proposed algorithm conservatively estimates Q-function and smooths gradients of convolution layers to deal with distribution shift and overfitting issues. Furthermore, to focus on complex manipulations, transitions with larger temporal-difference error are sampled with higher probability. Comparative experiments in a pre-clinical environment demonstrate that CASOG can deliver guidewire to the target at a success rate of 94.00\% and mean backward steps of 14.07, performing closer to humans and better than prior offline reinforcement learning methods. These results indicate that the proposed algorithm is promising to improve the autonomy of vascular robotic systems.
Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning

Parkinson's disease (PD) has been found to affect 1 out of every 1000 people, being more inclined towards the population above 60 years. Leveraging wearable-systems to find accurate biomarkers for diagnosis has become the need of the hour, especially for a neurodegenerative condition like Parkinson's. This work aims at focusing on early-occurring, common symptoms, such as motor and gait related parameters to arrive at a quantitative analysis on the feasibility of an economical and a robust wearable device. A subset of the Parkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has been utilised for feature-selection after a thorough analysis with various Machine Learning algorithms. Identified influential features has then been used to test real-time data for early detection of Parkinson Syndrome, with a model accuracy of 91.9%
Application of attention-based Siamese composite neural network in medical image recognition

Medical image recognition often faces the problem of insufficient data in practical applications. Image recognition and processing under few-shot conditions will produce overfitting, low recognition accuracy, low reliability and insufficient robustness. It is often the case that the difference of characteristics is subtle, and the recognition is affected by perspectives, background, occlusion and other factors, which increases the difficulty of recognition. Furthermore, in fine-grained images, the few-shot problem leads to insufficient useful feature information in the images. Considering the characteristics of few-shot and fine-grained image recognition, this study has established a recognition model based on attention and Siamese neural network. Aiming at the problem of few-shot samples, a Siamese neural network suitable for classification model is proposed. The Attention-Based neural network is used as the main network to improve the classification effect. Covid- 19 lung samples have been selected for testing the model. The results show that the less the number of image samples are, the more obvious the advantage shows than the ordinary neural network.
Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.
Category $\mathcal{O}$ for truncated current Lie algebras

In this paper we study an analogue of the Bernstein--Gelfand--Gelfand category $\mathcal{O}$ for truncated current Lie algebras $\mathfrak{g}_n$ attached to a complex semisimple Lie algebra. This category admits Verma modules and simple modules, each parametrised by the dual space of the truncated currents on a choice of Cartan subalgebra in $\mathfrak{g}$. Our main result describes an inductive procedure for computing composition multiplicities of simples inside Vermas for $\mathfrak{g}_n$, in terms of similar composition multiplicities for $\mathfrak{l}_{n-1}$ where $\mathfrak{l}$ is a Levi subalgebra. As a consequence, these numbers are expressed as integral linear combinations of Kazhdan--Lusztig polynomials evaluated at 1. This generalises recent work of the first author, where the case $n = 1$ was treated.
Graph Exploration for Effective Multi-agent Q-Learning

This paper proposes an exploration technique for multi-agent reinforcement learning (MARL) with graph-based communication among agents. We assume the individual rewards received by the agents are independent of the actions by the other agents, while their policies are coupled. In the proposed framework, neighbouring agents collaborate to estimate the uncertainty about the state-action space in order to execute more efficient explorative behaviour. Different from existing works, the proposed algorithm does not require counting mechanisms and can be applied to continuous-state environments without requiring complex conversion techniques. Moreover, the proposed scheme allows agents to communicate in a fully decentralized manner with minimal information exchange. And for continuous-state scenarios, each agent needs to exchange only a single parameter vector. The performance of the algorithm is verified with theoretical results for discrete-state scenarios and with experiments for continuous ones.