Miracle-dz/UniFAR-FTUs huggingface dataset:
A new interpolation error expansion based reversible watermarking algorithm considering the human visual system

Reversible watermarking has merged over the past few years as a promising solution for copyright protection, especially for applications like remote sensing, medical imaging and military applications which require lossless recovery of the host media. In this paper, we aim to extend the additive interpolation error expansion technique in [16]. We will consider the human visual system (HVS) to improve the embedding rate while maintaining the image visual quality. To this end, the just noticeable difference (JND) is used to embed more watermark bits. The experimental results show that the proposed algorithm can improve the embedding rate while preserving the image visual quality.
A graph-based signal processing approach for non-intrusive load disaggregation

Graph-based signal processing (GSP) is an emerging field that has been used in many domains. Getting inspiration from the successful applications of GSP in signal filtering and image processing, in this paper, we demonstrate how GSP can serve as a feasible approach to non-intrusive appliance load monitoring (NALM). Specifically, NALM means disaggregating household's gross energy consumption down to single appliances through purely software solutions. Since NALM was proposed over 30 years ago, it has got a lot of attention. However, despite the fact that many solutions has been proposed, the majority of approaches can't work well without training and are prone to appliance variations requiring re-training on a regular basis. In this paper, we tackle this challenge by applying a GSP-based NALM approach that can perform well with no training. This algorithm uses GSP three times, which represents the datasets of active power measurements with 1 min resolution using graphs to perform adaptive thresholding, signal clustering and feature matching respectively. Simulation results using publicly available REDD dataset demonstrate the feasibility and potential of the GSP for NALM.
Adaptive navigation of vehicles in congested road networks

We examine the problem of routing vehicles in a road network where traffic congestion affects the time required to traverse an edge. We propose a fully distributed approach that uses only the computational resources and communication capabilities of vehicles and requires no fixed infrastructure or centralized servers. Our approach bases its operation on wireless ad-hoc communications and offers a protocol for alerting vehicles regarding traffic conditions in areas to be travelled through. Vehicles exchange estimations for the time required to reach areas of the road network and every vehicle dynamically determines the path it will follow based on estimations received from fellow travellers. Considering vehicles as selfish players in a game-theoretic framework, we relate the steady-state of our protocol with theoretical results. In this direction, our simulation focuses on confirming that the protocol adjusts rapidly to congestion variation, leading to a steady state. We also evaluate the performance of our protocol, compared to a system which uses static navigation to route vehicles, and as experimental results show, our approach achieves a better traffic distribution on the road network and provides improved average latency.
A fluorescence assay for detecting amyloid-β using the cytomegalovirus enhancer/promoter

Robust assays for detecting the effects of elevated concentrations of amyloid-β (Aβ) may facilitate Alzheimer’s disease research. An appropriate assay would be high-throughput and enable identification of drugs and genetic mutations that block the effects of Aβ, potentially leading to treatments for Alzheimer’s disease. We discovered that the commonly used cytomegalovirus (CMV) enhancer/promoter is sensitive to the effects of Aβ. By combining the CMV enhancer/promoter with a fluorescent protein, we created a reporter system that produces changes in intracellular fluorescence in response to Aβ. Using hippocampal neurons, we quantified the ability of a CMV-fluorescent protein recombinant reporter to detect both exogenously applied and overexpressed Aβ. This is the first report of a high-throughput enhancer/promoter-based Aβ detection method. The reporter is able to detect the effects of elevated concentrations of Aβ in a high-throughput fashion, providing a new tool for Alzheimer’s disease research and important knowledge about the commonly used CMV enhancer/promoter.
Lineage- and stage-specific expressed CYCD7;1 coordinates the single symmetric division that creates stomatal guard cells

ABSTRACT Plants, with cells fixed in place by rigid walls, often utilize spatial and temporally distinct cell division programs to organize and maintain organs. This leads to the question of how developmental regulators interact with the cell cycle machinery to link cell division events with particular developmental trajectories. In Arabidopsis leaves, the development of stomata, two-celled epidermal valves that mediate plant-atmosphere gas exchange, relies on a series of oriented stem cell-like asymmetric divisions followed by a single symmetric division. The stomatal lineage is embedded in a tissue in which other cells transition from proliferation to postmitotic differentiation earlier, necessitating stomatal lineage-specific factors to prolong competence to divide. We show that the D-type cyclin, CYCD7;1, is specifically expressed just prior to the symmetric guard cell-forming division, and that it is limiting for this division. Further, we find that CYCD7;1 is capable of promoting divisions in multiple contexts, likely through RBR1-dependent promotion of the G1/S transition, but that CYCD7;1 is regulated at the transcriptional level by cell type-specific transcription factors that confine its expression to the appropriate developmental window. Highlighted Article: Stomatal transcription factors define GMC-specific expression of the D-type cyclin CYCD7;1, which promotes the G1/S transition in partnership with RBR1, enabling a direct link between cell cycle control and developmental fate decisions in Arabidopsis leaves.
FPGA implementation of image enhancement technique for Automatic Vehicles Number Plate detection

In this paper, a simple and effective FPGA based image enhancement method for Automatic Vehicles Number Plate (AVNP) detection has been proposed. In this technique, image enhancement achieved by estimating optimum values of image sharpness, brightness and contrast components. One of the most challenging problems for AVNP Detection is image quality of the images captured in low light generally in the night, due to low light image got blur or noisy and may be out of focus due to movement of vehicles. Pre-processing of image is highly recommended before optical character recognition (OCR). Field Programmable Gate Array (FPGA) has advantage to be implement the algorithm at hardware level, that required less time to process the image compare to clock based processor and can be used for parallel processing.
The Gendering of Emotional Flexibility

Research on emotional labor has consistently shown that women’s jobs require the suppression of anger. But in the debt settlement firms we studied, the women who negotiated with creditors were expected to express anger. We show that what made their anger acceptable was that its expression was preceded and followed by positive emotions. Women were praised for their ability to rapidly shift from anger to warmth and back to anger again. But this ability to shift emotional registers was also seen by employers and coworkers as a function of women’s natural emotional plasticity, and was contrasted unfavorably with men’s emotional consistency. What was gendered was not an emotion but an emotional pattern, with the consequence that women’s emotional labor was simultaneously valued and devalued.
Embedded laser profile measurement based on Zynq

This paper discusses on the process about how to design a laser measuring instrument based on embedded system. For that, Zynq-7000 is chosen as the system hardware platform that incorporates a dual core ARM Cortex-A9 based Processing System (PS) and Xilinx Programmable Logic (PL) in a single device, which is used as the data acquisition controller parts and integrated controller in this design. Then the real-time image is displayed on the display by HDMI interface. The whole measuring process are as follows, firstly we capture the original image of the laser bar on the surface of the measured object by a Gige camera which is controlled by ARM Cortex-A9 and then transfer image to Programmable Logic which is another part of Zynq for preprocessing the image. And then the preprocessed image data is transferred back to ARM again and used to calculate the image contour based on threshold-weighted centroid algorithm. Finally we will optimize structure parameters calibration for the precision. At the end of the paper, contour data is used to reconstruct the 3D image, and the measurement error is also evaluated. Finally, the embedded laser profile measurement system with the working range of ±5mm, the measurement accuracy achieved ±5μm and real-time processing speed reached 30 frame/second is realized.
How does mutation affect the distribution of phenotypes?

The potential for mutational processes to influence patterns of neutral or adaptive phenotypic evolution is not well understood. If mutations are directionally biased, shifting trait means in a particular direction, or if mutation generates more variance in some directions of multivariate trait space than others, mutation itself might be a source of bias in phenotypic evolution. Here, we use mutagenesis to investigate the affect of mutation on trait mean and (co)variances in zebrafish, Danio rerio. Mutation altered the relationship between age and both prolonged swimming speed and body shape. These observations suggest that mutational effects on ontogeny or aging have the potential to generate variance across the phenome. Mutations had a far greater effect in males than females, although whether this is a reflection of sex‐specific ontogeny or aging remains to be determined. In males, mutations generated positive covariance between swimming speed, size, and body shape suggesting the potential for mutation to affect the evolutionary covariation of these traits. Overall, our observations suggest that mutation does not generate equal variance in all directions of phenotypic space or in each sex, and that pervasive variation in ontogeny or aging within a cohort could affect the variation available to evolution.
Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera

In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles’ camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images. The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated. Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps. Finally, a dense 3D point cloud can be obtained using the depth–map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.
Feasibility Study of a 1000+ Electrode Array in Epiretinal Prosthesis

To improve resolution and achieve functional vision, it is required 1000 electrodes with minimum feature size at epiretinal prosthetic devices. This challenging aim, however, has a limitation as to accommodate 1000+ electrode array and be within the limits of charge density and temperature increase at the device. In this work, we simulated a ganglion cell ON-model with PEDOT-NaPSS arranged electrode array in a three-dimensional retina model using COMSOL-Matlab interface. We varied electrode size, pulse width and inter electrode-ganglion cell distance to analyze charge density and temperature increase at the device. With our results, we investigated the feasibility of using 1024 electrode array attaching 16 scalable chips of 64 electrodes each with a daisy chain configuration. For IEGD less than 10 μm, it is feasible to use 1024 array of electrodes with the following requirements: i) reduce electrode diameter to 2μm; ii) maximum output voltage of 1 V; iii) work with either 50 or 100μs low pulse duration; iv) 11.3mm electrode carrier area; v) PEDOTNaPSS electrode deposition and vi) circular electrodes. 
Optical and water repellant properties of Ag/SnO2 bilayer thin films

The optical and water repellant properties of single layer and bilayer films of Ag and SnO 2 deposited on glass substrates by thermal evaporation have been reported. Ag/SnO 2 bilayers were deposited in two sequences wherein the deposition of SnO 2 layer was followed by Ag deposition and vice versa. X-ray diffraction studies show that the Ag films crystallize in the FCC structure and SnO 2 is amorphous, while atomic force microscopy images indicate the formation of large clusters of the order of 12 nm. The single layer Ag films exhibit localized surface plasmon resonance (LSPR) that shifts from visible region to the infrared with increase in thickness from 5 to 12 nm. It is observed that, only the Ag films of thickness ≤ 8 nm exhibits LSPR peak whereas the critical thickness is 5 nm for Ag/SnO 2 films. A blue shift is observed in the LSPR peak position when the SnO 2 layer caps the Ag film. Whereas, the LSPR of Ag is suppressed significantly when the SnO 2 layer is introduced between the glass and the Ag film and also when Ag and SnO 2 were co-evaporated. Water repellant properties indicate that the pure Ag film has an average contact angle of 104 o which decreases to 100 o when SnO 2 caps the Ag layer and 97 o when Ag is deposited on top of the SnO 2 buffer layer. Co-evaporated Ag-SnO 2 films show a contact angle of 93 o .
Variable DC-Link Control Loop Design for an Integrated Two-Stage AC/DC Converter

This paper presents an innovative technique of synthesizing variable dc-link voltage control loop for a two-stage integrated ac-dc converter, which is a cascaded combination of a single-phase continuous conduction mode interleaved totem-pole power factor correction and a half-bridge CLLC resonant converter. Although the variable dc-link control exhibits a better overall efficiency than the fixed dc-link control, it faces issues of: 1) loss of output voltage regulation and 2) low-frequency oscillations in the dc voltages and inductor current. In order to address and analyze these issues, both time-domain and frequency-domain studies of the control loop are performed, which led to two different voltage loop controller configurations, resolving the oscillation and regulation issues. As verification to the proof-of-concept, a hardware prototype of an integrated ac-dc converter is developed and tested up to 2 kW. The experimental results exhibit a power factor of 0.992, peak efficiency of >94% and a total harmonic distortion of <5%, with the elimination of low-frequency oscillation of the dc voltage waveforms.
Multi-view network-based social-tagged landmark image clustering

The multiple types of social media data have abundant information, but learning multi-modal social data is challenging due to data heterogeneity and noise in usergenerated data. To address this problem, we propose a multiview network-based clustering approach that is robust to noise and fully reflects the underlying structure of the comprehensive network. To demonstrate the proposed approach, we experimented with clustering challenging tagged images of landmarks. The results show that the proposed method outperforms other previously reported multi-view clustering algorithms and better utilizes the advantages of the network for each view. Furthermore, the tagged-image network constructed by the proposed method and the clustering results are extensively analyzed.
Parameterized Traveling Salesman Problem: Beating the Average

In the traveling salesman problem (TSP), we are given a complete graph $K_n$ together with an integer weighting $w$ on the edges of $K_n$, and we are asked to find a Hamilton cycle of $K_n$ of minimum weight. Let $h(w)$ denote the average weight of a Hamilton cycle of $K_n$ for the weighting $w$. Vizing in 1973 asked whether there is a polynomial-time algorithm which always finds a Hamilton cycle of weight at most $h(w)$. He answered this question in the affirmative and subsequently Rublineckii, also in 1973, and others described several other TSP heuristics satisfying this property. In this paper, we prove a considerable generalization of Vizing's result: for each fixed $k$, we give an algorithm that decides whether, for any input edge weighting $w$ of $K_n$, there is a Hamilton cycle of $K_n$ of weight at most $h(w)-k$ (and constructs such a cycle if it exists). For $k$ fixed, the running time of the algorithm is polynomial in $n$, where the degree of the polynomial does not depend on $k$ (i.e., the gen.
Reliable and confidential cloud storage with efficient data forwarding functionality

Cloud computing is a promising computing paradigm which has drawn extensive attention. Serious concerns over the reliability and confidentiality of the outsourced data are arising. Traditional encryption methods can, guarantee data confidentiality, however, it also limits the cloud's functionality as few operations are supported over encrypted data. In this study, the authors construct an enhanced cloud that not only provides secure and robust data storage, but also supports the functionality that the cipher data can be forwarded without being retrieved back. Specifically, they design an all-or-nothing-transform-based encryption and a variant of ElGamal-based proxy re-encryption algorithms, blending them with Reed–Solomon code, the authors’ scheme is quite more efficient compared with previous studies because it only needs to update partial data blocks instead of the whole file for data forwarding. Besides, the authors’ scheme also satisfies another practical property that the original data owner can no longer decrypt or forward the re-encrypted data to others after a complete forwarding instance, which is termed to be ‘original inaccessibility’ in this study. Analysis shows that the authors’ scheme is secure and satisfactory. Finally, the authors theoretically and experimentally evaluate its performance and the results indicate that their scheme is efficient during file dispersal, forward and retrieval.
The Geometry of Random Features

We present an in-depth examination of the effectiveness of radial basis function kernel (beyond Gaussian) estimators based on orthogonal random feature maps. We show that orthogonal estimators outperform state-of-the-art mechanisms that use iid sampling under weak conditions for tails of the associated Fourier distributions. We prove that for the case of many dimensions, the superiority of the orthogonal transform can be accurately measured by a property we define called the charm of the kernel, and that orthogonal random features provide optimal (in terms of mean squared error) kernel estimators. We provide the first theoretical results which explain why orthogonal random features outperform unstructured on downstream tasks such as kernel ridge regression by showing that orthogonal random features provide kernel algorithms with better spectral properties than the previous state-of-the-art. Our results enable practitioners more generally to estimate the benefits from applying orthogonal transforms.
Space-Time Pre-Filtering Based Soft-Input Soft-Output Detectors in Frequency-Selective MIMO Channels

This paper investigates low-complexity soft-input soft-output (SISO) detectors for multiple-input multiple-output (MIMO) transmission over frequency-selective channels. The detectors are developed with a unified detection procedure, that is space-time pre-filtering followed by a reduced dimension MAP detection. The condition of perfect pre-filtering is discussed and several suboptimal pre-filtering methods are proposed. Simulation results show that the proposed SISO detectors can make a good trade-off between performance and computational complexity in frequency-selective fading MIMO channels with various spatial correlating factor and Ricean components.
Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks

Common medical conditions are often associated with sleep abnormalities. Patients with medical disorders often suffer from poor sleep quality compared to healthy individuals, which in turn may worsen the symptoms of the disorder. Accurate detection of sleep/wake patterns is important in developing personalized digital markers, which can be used for objective measurements and efficient disease management. Big Data technologies and advanced analytics methods hold the promise to revolutionize clinical research processes, enabling the effective blending of digital data into clinical trials. Actigraphy, a non-invasive activity monitoring method is heavily used to detect and evaluate activities and movement disorders, and assess sleep/wake behavior. In order to study the connection between sleep/wake patterns and a cluster headache disorder, activity data was collected using a wearable device in the course of a clinical trial. This study presents two novel modeling schemes that utilize Deep Convolutional Neural Networks (CNN) to identify sleep/wake states. The proposed methods are a sequential CNN, reminiscent of the bi-directional CNN for slot filling, and a Multi-Task Learning (MTL) based model. Furthermore, we expand standard "Sleep" and "Wake" activity states space by adding the "Falling asleep" and "Siesta" states. We show that the proposed methods provide promising results in accurate detection of the expanded sleep/wake states. Finally, we explore the relations between the detected sleep/wake patterns and onset of cluster headache attacks, and present preliminary observations.
Analyzing word embeddings and improving POS tagger of tigrinya

In this paper, we analyze word embeddings for a morphologically rich language, Tigrinya. Tigrinya is a Semitic language spoken natively in Eritrea and Ethiopia by over seven million people. The unique and complex morphology of Semitic languages, which includes Arabic, Amharic, and Hebrew, is commonly known as 'root and template pattern' morphology. This morphology generates a large number of inflected forms that often cause out-of-vocabulary (OOV) challenges in language processing. This problem is more challenging for low resource languages, such as Tigrinya, that offers very little support of annotated resources. Word embedding methods, given a large raw text corpus, form semantic and syntactic vector representation of words. Therefore, we construct a new text corpus and investigate the optimal settings for generating word vectors for Tigrinya. We also utilize word embeddings to improve the performance of a Tigrinya part-of-speech tagger created from a small tagged corpus.