Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach And Design To Enhance Video Denoising Algorithms

video denoising is highly desirable in many real world applications. It can enhance the perceived quality of video signals, and can also help improve the performance of subsequent processes such as com-press ion, segmentation, and object recognition. Here the proposed algorithm is a effective strategy that aims to enhance the performance of existing video denoising algorithms. The idea is to denoise the noisy video as a 3-D volume using a given base 2-D denoising algorithm but applied from multiple views (front, top, and side views). A fusion algorithm is then designed to merge the resulting multiple denoised videos into one, so that the visual quality of the fused video is improved. And the extensive tests using a variety of base video-denoising algorithms show that the proposed method leads to surprisingly significant and consistent gain in terms of PSNR. Where the improvement over state-of-the-art denoising algorithms is often more than 2 dB in PSNR. Keywords— polyview fusion, video denoising, video quality enhancement, PSNR.
IJSRD-International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613

This paper focuses on various approaches to load forecasting, which is essential for developing power supply method to improve the reliability of the power line and provide optimal load scheduling for many developing countries where the demand can be increased day by day. Load forecast is extremely important in electric energy generation, transmission, distribution and markets. Electricity suppliers use forecasting models for prediction of load demand of the consumers to increase or decrease the power generated and to minimize the operating cost of producing electricity. This paper shows study of different methods for load forecasting such as wavelet method, ANN method and fuzzy logic method.
Flexible memristor‐based LUC and its network integration for Boolean logic implementation

Memristor is a nanoscale electronic element with variable resistance that depends on the amount and direction of the charge passing through it. As a promising candidate, this memristive element opens up a new approach for the implementation of Boolean logic operations. In this study, a flexible logic unit circuit (LUC) based on a practical memristor model is proposed, which is able to perform the AND, OR, NOT, NOR, and NAND gate operations by different switch settings. Unlike existing memristor-based logic implementation, the initialisation is not necessary for the proposed method, and the total delay can be effectively reduced, especially for time-sequence inputs. Furthermore, the concept of the memristor-based logic network is developed by multiple memristor-based LUCs connected in series and parallel. The circuit simulation demonstrates that the presented logic network is capable of realising multi-input-multi-output logic operations with compact structure, high efficiency, and sufficient accuracy.
Genetic diversity of mocó cotton (Gossypium hirsutum race marie-galante) from the northeast of Brazil: implications for conservation

Moco cotton (Gossypium hirsutum L. race marie-galante (Watt) Hutch. ) is a potential source of valuable alleles for breeding programs, mainly because of its great adaptability to semi-arid conditions. With the aim of quantifying moco cotton genetic variability, 187 plants collected in the northeast of Brazil were evaluated using 12 microsatellite markers. A total of 62 alleles were amplified, ranging from three to eight polymorphic alleles per locus. Total genetic diversity was high (0.52), and when measured on a per state basis, was of 0.37 on average. The population showed a low level of heterozygozity (HO = 0.16), reflecting a high level of endogamy (FIS = 0.67). Phylogenetic analysis using the neighbor-joining method revealed that plants sampled in different states tended to cluster according to their geographic origin, except for those collected in the states of Paraiba and Rio Grande do Norte, which grouped together. Plants from the state of Piaui formed two groups, one with an apparent allelic contr.
A NEW PATTERN FOR NUTATING RETICLES TO SIMPLIFY SEEKER ELECTRONICS

Reticle systems have widely been used in IR seekers because of their simple structure and low cost; and they often employ stationary reticles with conical scan optics, for their extensive field of view (FOV) and high accuracy. The typical reticle pattern in these systems is the Wagon-Wheel reticle. This paper presents a new reticle pattern instead of Wagon-Wheel pattern in order to simplify the seeker electronics, while keeping the extended field of view. The proposed pattern requires AM demodulation instead of current FM demodulation. We have simulated target detection system by using the proposed reticle pattern. The results have confirmed our claim. The static gain curves have been obtained for different target sizes and positions for the proposed reticle and have been compared with that’s of WagonWheel reticle.
The Impact of Fairness Constraints on Information Routing in Energy-Limited Wireless Sensor Networks

In the area of wireless sensor networks (WSN) there is still a signiﬁcant gap between theory and practice: system designs and protocols are rapidly out-pacing mathematical understanding. We develop formal mathematical models of a WSN based on non-linear optimization and use them to analyze the impact of fairness constraints on network performance. The mathematical framework presented is very general and can also be used to analyze the overall performance of WSN subject to other design parameters such as the topology, number of nodes, energy levels, source rates, reception power, etc. Our results show that the maximum information that can be extracted for a ﬁxed amount of energy increases and that the minimum energy required to output a ﬁxed amount of information decreases as we reduce the fairness requirement in the network. We present these functions for a ﬁxed network topology and observe that they exhibit sharp changes in gradient due to qualitative changes in optimal routes.
AGRICULTURAL PRODUCTIVITY AND ECONOMIC GROWTH: IMPACT ANALYSIS FROM NIGERIA

This study examined the impact of agricultural productivity on economic growth in Nigeria between the periods of 1981 to 2015. The Johansen cointegration test was employed to determine the existence of long run relationship between agricultural productivity and economic growth. Error Correction Model (ECM) was employed to determine the short run impact of agricultural productivity on economic growth. From the results, it was found that the agricultural labour productivity and agricultural value added were the positive determinants of economic growth. The study concluded that improvement in the performance of the agricultural sector has a significant effect on economic growth in Nigeria. Therefore, the government should encourage labour force participation in the agricultural sector by increasing investment in the agricultural sector.
Robust Uncertainty-Aware Multiview Triangulation

We propose a robust and efficient method for multiview triangulation and uncertainty estimation. Our contribution is threefold: First, we propose an outlier rejection scheme using two-view RANSAC with the midpoint method. By prescreening the two-view samples prior to triangulation, we achieve the state-of-the-art efficiency. Second, we compare different local optimization methods for refining the initial solution and the inlier set. With an iterative update of the inlier set, we show that the optimization provides significant improvement in accuracy and robustness. Third, we model the uncertainty of a triangulated point as a function of three factors: the number of cameras, the mean reprojection error and the maximum parallax angle. Learning this model allows us to quickly interpolate the uncertainty at test time. We validate our method through an extensive evaluation.
Viral cultures for COVID-19 infectivity assessment. Systematic review

We report the results of a review of the evidence from studies comparing SARS-CoV-2 culture with reverse transcriptase polymerase chain reaction (rt-PCR), as viral culture represents the best indicator of current infection and infectiousness of the isolate. We identified fourteen studies succeeding in culturing or observing tissue invasion by SARS-CoV in sputum, naso or oropharyngeal, urine, stool and environmental samples from patients diagnosed with Covid-19. The data are suggestive of a relation between the time from collection of a specimen to test, copy threshold, and symptom severity, but the quality of the studies was moderate with lack of standardised reporting and lack of testing of PCR against viral culture or infectivity in animals. This limits our current ability to quantify the relationship between viral load, cycle threshold and viable virus detection and ultimately the usefulness of PCR use for assessing infectiousness of patients. Prospective routine testing of reference and culture specimens are necessary for each country involved in the pandemic to establish the usefulness and reliability of PCR for Covid-19 and its relation to patients factors such as date of onset of symptoms and copy threshold, in order to help predict infectivity.
Gravitational lensing of binary systems in wave optics

We present the first detailed computations of wave optics effects in the gravitational lensing of binary systems. The field is conceptually rich, combining the caustic singularities produced in classical gravitational lensing with quantum (wave) interference effects. New techniques have enabled us to overcome previous barriers to computation. Recent developments in radio astronomy present observational opportunities which, while still futuristic, appear promising.
1st Place Solutions of Waymo Open Dataset Challenge 2020 - 2D Object Detection Track

In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Neck and Double-Head are used for performance improvements. In order to handle the small object detection problem in WOD, we use very large image scales for both training and testing. Using our methods, our team RW-TSDet achieved the 1st place in the 2D Object Detection Track.
Optimized Processing of Localized Collisions in Projective Dynamics

We present a method for the efficient processing of contact and collision in volumetric elastic models simulated using the Projective Dynamics paradigm. Our approach enables interactive simulation of tetrahedral meshes with more than half a million elements, provided that the model satisfies two fundamental properties: the region of the model's surface that is susceptible to collision events needs to be known in advance, and the simulation degrees of freedom associated with that surface region should be limited to a small fraction (e.g. 5%) of the total simulation nodes. In such scenarios, a partial Cholesky factorization can abstract away the behaviour of the collision‐safe subset of the face model into the Schur Complement matrix with respect to the collision‐prone region. We demonstrate how fast and accurate updates of bilateral penalty‐based collision terms can be incorporated into this representation, and solved with high efficiency on the GPU. We also demonstrate iterating a partial update of the element rotations, akin to a selective application of the local step, specifically on the smaller collision‐prone region without explicitly paying the cost associated with the rest of the simulation mesh. We demonstrate efficient and robust interactive simulation in detailed models from animation and medical applications.
Plasmonic nanowire coupled to zero-dimensional nanostructures: A brief review.

Metal nanowires and nanoparticles that facilitate surface plasmons are of contemporary interest in nanophotonics, thermoplasmonics and optoelectronics. They facilitate not only subwavelength light propagation and localization capabilities, but also provide an excellent platform for opto-thermal effects confined to volumes down to the nanoscale. This brief review article aims to provide an overview of a specific nanophotonic geometry: a plasmonic nanowire coupled to a zero-dimensional nanostructure. We discuss the methods to prepare such nano-architectures and review some interesting nanophotonic applications that arise out of it. We conclude with a discussion on some emerging research directions that can be facilitated by employing the coupled nanostructures.
Faithful Autoencoder Interpolation by Shaping the Latent Space

One of the fascinating properties of deep learning is the ability of the network to reveal the underlying factors characterizing elements in datasets of different types. Autoencoders represent an effective approach for computing these factors. Autoencoders have been studied in the context of their ability to interpolate between data points by decoding mixed latent vectors. However, this interpolation often incorporates disrupting artifacts or produces unrealistic images during reconstruction. We argue that these incongruities are due to the manifold structure of the latent space where interpolated latent vectors deviate from the data manifold. In this paper, we propose a regularization technique that shapes the latent space following the manifold assumption while enforcing the manifold to be smooth and convex. This regularization enables faithful interpolation between data points and can be used as a general regularization as well for avoiding overfitting and constraining the model complexity.
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams

Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do not fit the expected behaviour. Recent work has coalesced on variations of (random) $k$\emph{d-trees} to summarise data for anomaly detection. However, these methods rely on ad-hoc score functions that are not easy to interpret, making it difficult to asses the severity of the detected anomalies or select a reasonable threshold in the absence of labelled anomalies. To solve these issues, we contextualise these methods in a probabilistic framework which we call the Mondrian \Polya{} Forest for estimating the underlying probability density function generating the data and enabling greater interpretability than prior work. In addition, we develop a memory efficient variant able to operate in the modern streaming environments. Our experiments show that these methods achieves state-of-the-art performance while providing statistically interpretable anomaly scores.
Quantitative comparison of magnon transport experiments in three-terminal YIG/Pt nanostructures acquired via dc and ac detection techniques

All-electrical generation and detection of pure spin currents is a promising way towards controlling the diffusive magnon transport in magnetically ordered insulators. We quantitatively compare two measurement schemes, which allow to measure the magnon spin transport in a three-terminal device based on a yttrium iron garnet thin film. We demonstrate that the dc charge current method based on the current reversal technique and the ac charge current method utilizing first and second harmonic lock-in detection can both efficiently distinguish between electrically and thermally injected magnons. In addition, both measurement schemes allow to investigate the modulation of magnon transport induced by an additional dc charge current applied to the center modulator strip. However, while at low modulator charge current both schemes yield identical results, we find clear differences above a certain threshold current. This difference originates from nonlinear effects of the modulator current on the magnon conductance.
Minimizing Electricity Cost through Smart Lighting Control for Indoor Plant Factories

Smart plant factories incorporate sensing technology, actuators and control algorithms to automate processes, reducing the cost of production while improving crop yield many times over that of traditional farms. This paper investigates the growth of lettuce (Lactuca Sativa) in a smart farming setup when exposed to red and blue light-emitting diode (LED) horticulture lighting. An image segmentation method based on K-means clustering is used to identify the size of the plant at each stage of growth, and the growth of the plant modelled in a feed forward network. Finally, an optimization algorithm based on the plant growth model is proposed to find the optimal lighting schedule for growing lettuce with respect to dynamic electricity pricing. Genetic algorithm was utilized to find solutions to the optimization problem. When compared to a baseline in a simulation setting, the schedules proposed by the genetic algorithm can achieved between 40-52% savings in energy costs, and up to a 6% increase in leaf area.
A Comparative Study on Transformation of UML/OCL to Other Specifications

Static verification is a sound programming methodology that permits automated reasoning about the correctness of an implementation with respect to its formal specification before its execution. Unified Modelling Language is most commonly used modelling language which describes the client’s requirement. Object Constraint Language is a formal language which allows users to express textual constraints regarding the UML model. Therefore, UML/OCL express formal specification and helps the developers to implement the code according to the client’s requirement through software design. This paper aims to compare the existing approaches generating Java, C++, C# code or JML, Spec# specifications from UML/OCL. Nowadays, software system is developed via automatic code generation from software design to implementation when using formal specification and static analysis. In this paper, the study considers transformation from design to implementation and vice versa using model transformation, code generation or other techniques. The related tools, which generate codes, do not support verification at the implementation phase. On the other hand, the specification generation tools do not generate all the required properties which are needed for verification at the implementation phase. If the generated system supports the verification with all required properties, code developer needs less efforts to produce correct software system. Therefore, this study recommends introducing a new framework which can act as an interface between design and implementation to generate verified software systems.
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning

New advances in technology have greatly improved the monitoring and controlling of power networks, but these advances leave the system open to cyber attacks. One common attack is known as a False Data Injection Attacks (FDIAs), which poses serious threats to the operation and control of power grids. Hence, recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to detect attacks. Inspired by these advancements, we have developed a new methodology for not only identifying AC FDIAs but, more importantly, for correction as well. Our methodology utilizes a LongShort Term Memory Denoising Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the attacked measurements. The method was evaluated using the IEEE 30 system, and the experiments demonstrated that the proposed method was successfully able to identify the corrupted states and correct them with high accuracy.
Vector-Like Leptons in Light of the Cabibbo-Angle Anomaly

The first row of the Cabibbo-Cobayashi-Maskawa (CKM) matrix shows a discrepancy of $\sim $$3\,\sigma$ with unitarity, known as the"Cabibbo Angle Anomaly"(CAA). This tension can be explained via modified $W$ couplings to leptons, but in order to consistently assess the validity of this solution, a global fit including the electroweak (EW) precision constraints and the low energy tests of lepton flavour universality (LFU) is necessary. Performing such a fit for gauge-invariant dimension-6 operators, we find that even when assuming LFU, including the CKM elements $V_{us}$ and $V_{ud}$ into the electroweak fit has a relevant impact, shifting the best fit point significantly. Vector-like leptons (VLLs) are prime candidates for a corresponding UV completion since they can affect the $W$ and $Z$ couplings to leptons already at tree-level. Studying each pattern of new physics (NP) given by the six possible representations of VLLs (under the SM gauge group) we find that any single representation describes experimental data at most slighlty better than the SM hypothesis. However, allowing for several representations of VLLs at the same time, we find a simple scenario consisting of a singlet, $N$, coupling to electrons and a triplet, $\Sigma_1$, coupling to muons which not only explains the CAA but also improves the electroweak fit in such a way that its best fit point is preferred by more than $4\,\sigma$ with respect to the SM.
Fisher Discriminative Least Squares Regression for Image Classification.

Discriminative least squares regression (DLSR) has been shown to achieve promising performance in multi-class image classification tasks. Its key idea is to force the regression labels of different classes to move in opposite directions by means of the proposed the joint use of the $\epsilon$-draggings technique, yielding discriminative regression model exhibiting wider margins, and the Fisher criterion. The $\epsilon$-draggings technique ignores an important problem: its non-negative relaxation matrix is dynamically updated in optimization, which means the dragging values can also cause the labels from the same class to be uncorrelated. In order to learn a more powerful discriminative projection, as well as regression labels, we propose a Fisher regularized DLSR (FDLSR) framework by constraining the relaxed labels using the Fisher criterion. On one hand, the Fisher criterion improves the intra-class compactness of the relaxed labels during relaxation learning. On the other hand, it is expected further to enhance the inter-class separability of $\epsilon$-draggings technique. FDLSR for the first time ever attempts to integrate the Fisher discriminant criterion and $\epsilon$-draggings technique into one unified model because they are absolutely complementary in learning discriminative projection. Extensive experiments on various datasets demonstrate that the proposed FDLSR method achieves performance that is superior to other state-of-the-art classification methods. The Matlab codes of this paper are available at this https URL.
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling

Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer. This attention is a token-wise design and its complexity is quadratic to the length of sequence, limiting its application potential for long sequence tasks. In this paper, we propose a dimension-wise attention mechanism based on which a novel language modeling approach (namely TensorCoder) can be developed. The dimension-wise attention can reduce the attention complexity from the original $O(N^2d)$ to $O(Nd^2)$, where $N$ is the length of the sequence and $d$ is the dimensionality of head. We verify TensorCoder on two tasks including masked language modeling and neural machine translation. Compared with the original Transformer, TensorCoder not only greatly reduces the calculation of the original model but also obtains improved performance on masked language modeling task (in PTB dataset) and comparable performance on machine translation tasks.