Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of One-Step Electrodeposited Cu(In 1- x , Ga x )Se 2 Thin Films

One鄄step electrodeposition (ED) of Cu (In1 -x, Gax)Se2 (CIGS) thin films on Mo/glass substrates from aqueous solutions containing CuCl2, InCl3, GaCl3, and H2SeO3 was studied. In order to stabilize the solutions, they were buffered using a potassium biphthalate/sulfamic acid mixture giving a bath of pH 2.5. The type of the solutions would influence Ga concentration in the CIGS films. In general, CIGS thin film is called stoichiometric compound when the molar ratio of Cu to In+Ga is 1, and when the ratio is in the range of 0.8-1, it is called near stoichiometric and slightly Cu鄄poor or In鄄 rich CIGS compound. As鄄deposited films were near stoichiometric and slightly Cu鄄poor CIGS precursors with smooth, compact, crack鄄free surface by the optimization of the solution composition and the deposition condition. The electrodeposition mechanisms of CIGS on Mo substrates were studied by cyclic voltammetry. The results showed that Se4 + was first reduced to Se, and then Cu2 + , In3 + , and Ga3 + were deposited via the induced co鄄 deposition mechanism at more positive potentials than they were reduced. As鄄deposited films were selenized and recrystallized at 550 益 in Se vapor with a Se source temperature of 280 益 , which improved crystal structure of the films and changed little the compositions of ED鄄CIGS thin films, but resulted in cracking of the films.
Batch adsorptive removal of metanil yellow from simulated dye wastewater using nitric acid modified-H3PO4-activated carbon (NAMPAAC) from Gmelina arborea bark: equilibrium and isotherm studies

The aim of this work was to investigate the capacity of nitric acid modified-H3PO4-activated carbon produced from Gmelina arborea bark in the removal of metanil yellow from aqueous solution. The effects of initial dye concentration, C0, contact time, adsorbent dosage, and initial solution pH on the adsorption capacity qe were studied. Experimental data were analyzed with the Langmuir, Freundlich, Flory–Huggins and Temkin isotherm models. Results show low equilibrium adsorption capacities which decreased with increase in Co. The optimum experimental qe obtained was 2.35 mg/g by Co 25 mg/L. The optimum contact time of 300 min was obtained. Correlation coefficient R2, values (0.861–0.9781) obtained, show that the four applied isotherm models analyzed experimental data well. The optimum pH and adsorbent dosage were 2 and 0.01 g/25 mL solution, respectively. The ΔGads value of –11.248 kJ/mol based on the Flory–Huggins isotherm, and the separation factor, RL values 0.489, 0.332, and 0.199 for Co 25, 50, and 100 mg/L, respectively, show the adsorption to be spontaneous and favourable. The H3PO4-activated carbon was modified with nitric acid, to determine the effect of the modification on the surface area and adsorption capacity. Compared with a similar work with unmodified H3PO4-activated carbon (yet unpublished), there was a decrease in surface area, and qe by 109.59 m2/g and 12.363 mg/g, respectively. These results show that the modification of the activated carbon with nitric acid had a negative effect.
Effect of clinoptilolite supplementation on the nutritive value of oil palm frond

In this paper, the effect of clinoptilolite with and without supplementation on the nutritive value of oil palm fronds (OPF) was studied. Five different levels of clinoptilolite (0-5%) were supplemented on OPF and the effects on the nutritive values were determined; dry matter (DM), total nitrogen (N), crude protein (CP), crude fat (EE), crude fibre (CF), total ash (ASH), calcium (Ca) and phosphorus (P) using the AOAC standard methods. Nitrogen free extract (NFE), total digestible nutrient (TDN) and metabolizable energy (ME) were calculated by differences. Increasing levels of clinoptilolite resulted in some changes in the nutritive values of OPF, except in its EE content.
Computer-controlled Raman microspectroscopy (CC-Raman): A method for the rapid characterization of individual atmospheric aerosol particles

ABSTRACT The ability of an atmospheric aerosol particle to impact climate by acting as a cloud condensation nucleus (CCN) or an ice nucleus (IN), as well as scatter and absorb solar radiation is determined by its physicochemical properties at the single particle level, specifically size, morphology, and chemical composition. The identification of the secondary species present in individual aerosol particles is important as aging, which leads to the formation of these species, can modify the climate relevant behavior of particles. Raman microspectroscopy has a great deal of promise for identifying secondary species and their mixing with primary components, as it can provide detailed information on functional groups present, morphology, and internal structure. However, as with many other detailed spectroscopic techniques, manual analysis by Raman microspectroscopy can be slow, limiting single particle statistics and the number of samples that can be analyzed. Herein, the application of computer-controlled Raman (CC-Raman) for detailed physicochemical analysis that increases throughput and minimizes user bias is described. CC-Raman applies automated mapping to increase analysis speed allowing for up to 100 particles to be analyzed in an hour. CC-Raman is applied to both laboratory and ambient samples to demonstrate its utility for the analysis of both primary and, most importantly, secondary components (sulfate, nitrate, ammonium, and organic material). Reproducibility and precision are compared to computer controlled-scanning electron microscopy (CCSEM). The greater sample throughput shows the potential for CC-Raman to improve particle statistics and advance our understanding of aerosol particle composition and mixing state, and, thus, climate-relevant properties. © 2017 American Association for Aerosol Research
TO STUDY OF ESSENTIAL OIL COMPOSITION AND ANTIOXIDANT ACTIVITY OF PIMPINELLA PLANT

In this research we have investigated essential oil constituents and antioxidant property of Pimpinella plant. aerial part of plant are collected from around sari, mazandaran, provience of mazandaran (North of Iran). The chemical composition of the essential oil plant obtained by hydrodistillation using a Clevenger. Composite was analysed by GC-MS.the extraction was carried out with soxhlet apparatus. For antioxidant activity investigation used of TPC, DPPH and FTC, TBA method . then result by comparison of vitamin c antioxidant activity (natural antioxidant) and BHT (synthetic antioxidant). 20 components were identified in the essential Oil, that 94.5% of total composition. The major components of essential oil from Pimpinella plant were: α-pinene0.8%, couminalchol55.6%, carvacrol0.9%, terpinolene14.9%, limonen7.8%. Absorption read at plant phenolic compound method 0.298, amount of phenolic compound in this plant 37.63mgGAEg-1 and according DPPH test amount of antioxidant with IC50 is 334μg/ml. α-pinene and limonene observed in composition have Antimicrobial and fungicidal properties. Limonene also used in providing of polymer, paste and perfume. In totally this Plant used that in cure of whooping cough and instigator cough. Percentage of antioxidant were determined for ethanolic extract of plant under study 27% by FTC method , 63% for TBA method . By regards of this plant in Iran natural ,essence of this plant as an natural antioxidant in industrial food and drugs.
Siamese Neural Network with Joint Bayesian Model Structure for Speaker Verification

Generative probability models are widely used for speaker verification (SV). However, the generative models are lack of discriminative feature selection ability. As a hypothesis test, the SV can be regarded as a binary classification task which can be designed as a Siamese neural network (SiamNN) with discriminative training. However, in most of the discriminative training for SiamNN, only the distribution of pair-wised sam-ple distances is considered, and the additional discriminative information in joint distribution of samples is ignored. In this paper, we propose a novel SiamNN with consideration of the joint distribution of samples. The joint distribution of samples is first formulated based on a joint Bayesian (JB) based generative model, then a SiamNN is designed with dense layers to approx-imate the factorized affine transforms as used in the JB model. By initializing the SiamNN with the learned model parameters of the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task for SV. We carried out SV experiments on data corpus of speakers in the wild (SITW) and VoxCeleb. Experimental results showed that our proposed model improved the performance with a large margin compared with state of the art models for SV.
An ideal rapid-cycle Thouless pump

Thouless pumping is a fundamental instance of quantized transport, which is topologically protected. Although its theoretical importance, the adiabaticity condition is an obstacle for further practical applications. Here, focusing on the Rice-Mele model, we provide a family of finite-frequency examples that ensure both the absence of excitations and the perfect quantization of the pumped charge at the end of each cycle. This family, which contains an adiabatic protocol as a limiting case, is obtained through a mapping onto the zero curvature representation of the Euclidean sinh-Gordon equation.
Neural Articulated Radiance Field

We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images. While recent advances in 3D implicit representation have made it possible to learn models of complex objects, learning pose-controllable representations of articulated objects remains a challenge, as current methods require 3D shape supervision and are unable to render appearance. In formulating an implicit representation of 3D articulated objects, our method considers only the rigid transformation of the most relevant object part in solving for the radiance field at each 3D location. In this way, the proposed method represents pose-dependent changes without significantly increasing the computational complexity. NARF is fully differentiable and can be trained from images with pose annotations. Moreover, through the use of an autoencoder, it can learn appearance variations over multiple instances of an object class. Experiments show that the proposed method is efficient and can generalize well to novel poses. The code is available for research purposes at https://github.com/nogu-atsu/NARF.
Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems

Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snapshot. The performance of compressive imaging systems based on CAs mostly depends on two factors: the properties of the mask's attenuation pattern, that we refer to as "codification", and the computational techniques used to recover the quantity of interest from the coded snapshot. In this work, we introduce the idea of using time-varying CAs synchronized with spatially varying pixel shutters. We divide the exposure of a sensor into sub-exposures at the beginning of which the CA mask changes and at which the sensor's pixels are simultaneously and individually switched "on" or "off". This is a practically appealing codification as it does not introduce additional optical components other than the already present CA but uses a change in the pixel shutter that can be easily realized electronically. We show that our proposed time-multiplexed coded aperture (TMCA) can be optimized end to end and induces better coded snapshots enabling superior reconstructions in two different applications: compressive light field imaging and hyperspectral imaging. We demonstrate both in simulation and with real captures (taken with prototypes we built) that this codification outperforms the state-of-the-art compressive imaging systems by a large margin in those applications.
Structural organization of space polymers

Extra-terrestrial polymers of glycine with iron have been characterized by mass spectrometry to have a core mass of 1494Da with dominant rod-like variants at m/z 1567 and m/z 1639 [1]. Several principal macro-structural morphologies are observed in solvent extracts from CV3 class meteoritic material. The first is an extended sheet of linked triskelia containing the 1494Da core entity that encloses gas bubbles in the solvent [1]. A second is of fiber-like crystals found here, via X-ray diffraction, to be multiple-walled nanotubes made from a square lattice of the 1494Da polymer. A third is a dispersion of floating phantom-like short tubes of up to 100micron length [1] with characteristic angled bends that suggest the influence of a specific underlying protein structure. Here it is proposed that the angled tubes are the observable result of a space-filling super-polymerization of 1638Da polymer subunits guided by the tetragonal symmetry of linking silicon bonds. Distorted hexagonal sheets are linked by perpendicular subunits in a three-dimensional hexagonal diamond structure to fill the largest possible volume. This extended very low-density structure is conjectured to have dominated in a process of chemical selection because it captured a maximum amount of molecular raw material in the ultra-low density of molecular clouds or of the proto-solar nebula. This could have led ultimately to the accretion of the earliest planetary bodies.
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems

Sensor and control data of modern mechatronic systems are often available as heterogeneous time series with different sampling rates and value ranges. Suitable classification and regression methods from the field of supervised machine learning already exist for predictive tasks, for example in the context of condition monitoring, but their performance scales strongly with the number of labeled training data. Their provision is often associated with high effort in the form of person-hours or additional sensors. In this paper, we present a method for unsupervised feature extraction using autoencoder networks that specifically addresses the heterogeneous nature of the database and reduces the amount of labeled training data required compared to existing methods. Three public datasets of mechatronic systems from different application domains are used to validate the results.
C2CL: Contact to Contactless Fingerprint Matching

Matching contactless fingerprints or finger photos to contact-based fingerprint impressions has received increased attention in the wake of COVID-19 due to the superior hygiene of the contactless acquisition and the widespread availability of low cost mobile phones capable of capturing photos of fingerprints with sufficient resolution for verification purposes. This paper presents an end-to-end automated system, called C2CL, comprised of a mobile finger photo capture app, preprocessing, and matching algorithms to handle the challenges inhibiting previous cross-matching methods; namely i) low ridge-valley contrast of contactless fingerprints, ii) varying roll, pitch, yaw, and distance of the finger to the camera, iii) non-linear distortion of contact-based fingerprints, and vi) different image qualities of smartphone cameras. Our preprocessing algorithm segments, enhances, scales, and unwarps contactless fingerprints, while our matching algorithm extracts both minutiae and texture representations. A sequestered dataset of 9, 888 contactless 2D fingerprints and corresponding contact-based fingerprints from 206 subjects (2 thumbs and 2 index fingers for each subject) acquired using our mobile capture app is used to evaluate the cross-database performance of our proposed algorithm. Furthermore, additional experimental results on 3 publicly available datasets show substantial improvement in the state-of-the-art for contact to contactless fingerprint matching (TAR in the range of 96.67% to 98.30% at FAR=0.01%).
Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications

—The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards trafﬁc classiﬁcation. While classiﬁcation of known trafﬁc is a well investigated subject with supervised classiﬁcation tools (such as ML and DL models) are known to provide satisfactory performance, detection of unknown (or zero-day) trafﬁc is more challenging and typically handled by unsupervised techniques (such as clustering algorithms). In this paper, we share our experience on a commercial-grade DL trafﬁc classiﬁcation engine that is able to (i) identify known applications from encrypted trafﬁc, as well as (ii) handle unknown zero-day applications. In particular, our contribution for (i) is to perform a thorough assessment of state of the art trafﬁc classiﬁers in commercial-grade settings comprising few thousands of very ﬁne grained application labels, as opposite to the few tens of classes generally targeted in academic evaluations. Additionally, we contribute to the problem of (ii) detection of zero-day applications by proposing a novel technique, tailored for DL models, that is signiﬁcantly more accurate and light-weight than the state of the art. Summarizing our main ﬁndings, we gather that (i) while ML and DL models are both equally able to provide satisfactory solution for classiﬁcation of known trafﬁc, however (ii) the non-linear feature extraction process of the DL backbone provides sizeable advantages for the detection of unknown classes.
Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, it's genuinely unpredictable to mimic sub-atomic communication in the air, which brings about off-base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D-CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.
Local Strain Induced Band Gap Modulation and Photoluminescence Enhancement of Multilayer Transition Metal Dichalcogenides

The photocarrier relaxation between direct and indirect band gaps along the high symmetry K−Γ line in the Brillion zone reveals interesting electronic properties of the transition metal dichalcogenides (TMDs) multilayer films. In this study, we reported on the local strain engineering and tuning of an electronic band structure of TMDs multilayer films along the K−Γ line by artificially creating one-dimensional wrinkle structures. Significant photoluminescence (PL) intensity enhancement in conjunction with continuously tuned optical energy gaps was recorded at the high strain regions. A direct optical band gap along K–K points and an indirect optical gap along Γ–K points measured from the PL spectra of multilayer samples monotonically decreased as the strain increased, while the indirect band gap along Λ–Γ was unaffected owing to the same level of local strain in the range of 0%–2%. The experimental results of band gap tuning were in agreement with the density functional theory calculation results. Local s.
ARC: A Vision-based Automatic Retail Checkout System

Retail checkout systems employed at supermarkets primarily rely on barcode scanners, with some utilizing QR codes, to identify the items being purchased. These methods are time-consuming in practice, require a certain level of human supervision, and involve waiting in long queues. In this regard, we propose a system, that we call ARC, which aims at making the process of check-out at retail store counters faster, autonomous, and more convenient, while reducing dependency on a human operator. The approach makes use of a computer vision-based system, with a Convolutional Neural Network at its core, which scans objects placed beneath a webcam for identification. To evaluate the proposed system, we curated an image dataset of one-hundred local retail items of various categories. Within the given assumptions and considerations, the system achieves a reasonable test-time accuracy, pointing towards an ambitious future for the proposed setup. The project code and the dataset are made publicly available.
A Question-answering Based Framework for Relation Extraction Validation

Relation extraction is an important task in knowledge acquisition and text understanding. Existing works mainly focus on improving relation extraction by extracting effective features or designing reasonable model structures. However, few works have focused on how to validate and correct the results generated by the existing relation extraction models. We argue that validation is an important and promising direction to further improve the performance of relation extraction. In this paper, we explore the possibility of using question answering as validation. Specifically, we propose a novel question-answering based framework to validate the results from relation extraction models. Our proposed framework can be easily applied to existing relation classifiers without any additional information. We conduct extensive experiments on the popular NYT dataset to evaluate the proposed framework, and observe consistent improvements over five strong baselines.
Community Detection With Contextual Multilayer Networks

In this paper, we study community detection when we observe <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> sparse networks and a high dimensional covariate matrix, all encoding the same community structure among <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> subjects. In the asymptotic regime where the number of features <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and the number of subjects <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> grow proportionally, we derive an exact formula of asymptotic minimum mean square error (MMSE) for estimating the common community structure in the balanced two block case using an orchestrated approximate message passing algorithm. The formula implies the necessity of integrating information from multiple data sources. Consequently, it induces a sharp threshold of phase transition between the regime where detection (i.e., weak recovery) is possible and the regime where no procedure performs better than random guess. The asymptotic MMSE depends on the covariate signal-to-noise ratio in a more subtle way than the phase transition threshold. In the special case of <inline-formula> <tex-math notation="LaTeX">$m=1$ </tex-math></inline-formula>, our asymptotic MMSE formula complements the pioneering work Deshpande et al., (2018) which found the sharp threshold when <inline-formula> <tex-math notation="LaTeX">$m=1$ </tex-math></inline-formula>. A practical variant of the theoretically justified algorithm with spectral initialization leads to an estimator whose empirical MSEs closely approximate theoretical predictions over simulated examples.