Miracle-dz/UniFAR-FTUs huggingface dataset:
CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification

We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the convolutional layers to represent an image as an optimal weighted collaboration of features learned from training samples as a whole rather than one at a time. This gives CoCoNet more power to encode the fine-grained nature of the data with limited samples. We perform a detailed study of the performance with 1-stage and 2-stage transfer learning. The ablation study shows that the proposed method outperforms its constituent parts consistently. CoCoNet also outperforms few state-of-the-art competing methods. Experiments have been performed on the fine-grained bird species classification problem as a representative example, but the method may be applied to other similar tasks. We also introduce a new public dataset for fine-grained species recognition, that of Indian endemic birds and have reported initial results on it.
Fast Multilevel Quantization for Distributed Detection Based on Gaussian Approximation

An iterative algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, where each sensor transmits a summary of its observation to the fusion center and the fusion center makes the final decision. The proposed scheme is composed of a person-by-person optimum quantization at each sensor and a Gaussian approximation to the distribution of the test statistic at the fusion center. The complexity of the algorithm is linear both for identically and non-identically distributed independent sensors. Experimental results indicate that the proposed scheme is promising in comparison to the current state-of-the-art.
Crystal structures of tryptamine picrate and d,l‐tryptophan picrate–methanol, two indole donor–acceptor complexes

The crystal structures of the red picric acid salts of tryptamine and of D,L-tryptophan-methanol were determined by X-ray diffraction methods. Crystals of tryptamine picrate (C10HIaN2.C6H2N307) are monoclinic, space group P21/c, with a= 15.927 (2), b=6.913 (1), c=21.841 (2) A, fl= 133.88 (1) ° and Z= 4. Crystals of O,L-tryptophan picrate-methanol (C11HI3N202. C6H2N307. CH40) are triclinic, space group P1, with a= 11-733 (1), b= 11-547 (1), c=7.971 (1) ,~, a= 100.34 (3), fl=81.31 (1), y=97.98 (2) °, and Z--2. Three-dimensional X-ray intensity data (2882 reflections for the tryptamine complex and 3458 reflections for the tryptophan complex) were measured with an automated diffractometer by use of nickel-filtered copper radiation. Trial structures, obtained by direct methods, were refined by leastsquares calculations to R indices of 0.056 and 0.088 for the tryptamine and tryptophan complexes, respectively. Both crystal structures contain distinct indole-picrate pairs. The indole and picrate planes are stacked, with interplanar spacings of 3.3-3.5 A. The stacking interaction appears to be of the donoracceptor (charge-transfer) type. However, contrary to the continous columns of stacked rings usually found in crystal structures of aromatic donor-acceptor complexes, the stacked indole-picrate pairs are relatively isolated and without re-re interactions between adjacent pairs. The stacking patterns are similar in the tryptamine and tryptophan complexes. The tryptamine and tryptophan cations assume the same conformation found for serotonin in the crystal structure of the serotonin picrate monohydrate donor-acceptor complex.
Charge Transport and Dipolar Relaxations in Hyperbranched Polyamide Amines

Broadband dielectric spectroscopy, pulsed field gradient nuclear magnetic resonance (PFG NMR), and differential scanning calorimetry are combined to study charge transport and dipolar relaxations in novel hyperbranched polyamide amines. The dielectric spectra are dominated by conductivity contributions at higher temperatures (masking out the structural α-relaxation process), whereas two secondary dipolar relaxation processes are observed at lower temperatures for the two samples investigated. Based on Einstein and Einstein−Smoluchowski relations, the diffusion coefficient is extracted from the dielectric spectra—in quantitative agreement with independent PFG NMR measurements. It exhibits a Vogel−Fulcher−Tammann temperature dependence, while the effective number density of the charge carriers varies only weakly with temperature. Charge transport and structural α-relaxation are found to be characterized by a decoupling index of ∼7 for the hyperbranched polymers studied.
Drivers’ Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities.
Energy Efficient Decentralized Geographical Load Balancing via Dynamic Deferral of Workload

Integration of on-demand Cloud services has become an orthodox approach adopted by enterprises of all scales, especially over the past decade. Thus, efficient load balancing techniques in data centers that perform intensive cloud computations become increasingly necessary to reduce energy costs. The essence of cloud computing lies within data centers being located in diverse locations, thus in different time zones. Thus, we take advantage of spatial and temporal variations of the data centers in our load balancing decisions. We demonstrate our design of a decentralized online algorithm for geographical load balancing via dynamic deferral of arriving workload from the data center. It responds with reduced energy costs by adapting to the dynamic price changes in its region via a head start of predicting prices in the real market. We compare our algorithm with the centralized approach without future price predictions and have achieved cost savings. We validate our model by experiments run on Google traces, and show that geographical load balancing with dynamic deferral can provide up to 32% reduction in costs to execute arriving load in data centers with both temporal and geographical variations.
The water dimer interaction energy: Convergence to the basis set limit at the correlated level

The water dimer interaction energy and its convergence to the basis set limit was investigated, with electron correlation effects treated at the level of second order Mo/ller-Plesset perturbation theory (MP2). ANO-type and large uncontracted basis sets were used, spreading over a wide range in size; the biggest set included 1046 functions with angular momentum up to (l=7). Core correlation effects were treated accurately by augmenting the original valence basis with extended sets of core polarization functions. The MP2 dimer interaction energy at the basis set limit was determined to −4.94±0.02 kcal/mol, with a contribution due to core correlation of −0.04 kcal/mol. Furthermore, based on some elementary considerations from intermolecular perturbation theory, a simple procedure was devised, which brings the counterpoise corrected interaction energies of moderate basis set calculations closer to the basis set limit. The interaction energies so obtained turned out be surprisingly stable with respect to extensions of the basis set.The water dimer interaction energy and its convergence to the basis set limit was investigated, with electron correlation effects treated at the level of second order Mo/ller-Plesset perturbation theory (MP2). ANO-type and large uncontracted basis sets were used, spreading over a wide range in size; the biggest set included 1046 functions with angular momentum up to (l=7). Core correlation effects were treated accurately by augmenting the original valence basis with extended sets of core polarization functions. The MP2 dimer interaction energy at the basis set limit was determined to −4.94±0.02 kcal/mol, with a contribution due to core correlation of −0.04 kcal/mol. Furthermore, based on some elementary considerations from intermolecular perturbation theory, a simple procedure was devised, which brings the counterpoise corrected interaction energies of moderate basis set calculations closer to the basis set limit. The interaction energies so obtained turned out be surprisingly stable with respect to exten.
A Degenerate Spatial ARMA Process of External Sources Impinging Upon an Azimuth-Only ULA and the Estimation of Doas and Noise Variances

Array processing is an interdisciplinary topic of both physics and signal processing. Physical basis of array processing is the orderly-setup of sensors in space that induces regular change of the phase of external sources incident upon sensor array. In the current paper the direction-of-arrival (DoA) estimation of an azimuth-only uniform linear array (ULA) is investigated in theory and method from the fused perspective of signal and physical properties of the incident sources. By organically fusing the stationary assumption on source signals and noises with difference operation on the phases, a distinctive system of linear equations satisfied by the incident sources is theoretically derived for the azimuth-only ULA with the Hankel-block-matrix of signal correlations as coefficient matrix and the elementary power-sum symmetric functions of the propagators of incident sources as the unknowns. Based on the derived system of linear equations, signal model of the incident sources is first proved as a degenerate spatial ARMA process subject to the identical autoregressive and moving average parameters and simultaneously obeying the dimensional homogeneity principle (DHP) in physics. The explicit root-finding polynomial is proposed with the unknowns of the system of linear equations as polynomial coefficients and the propagators as the roots. No extraneous roots and conjugate symmetry constraint on polynomial coefficients are involved. The DoAs and noise variances can be separately estimated under the backgrounds of spatially white and colored noises, which are numerically analyzed with the different coherent lengths of noises. A simple sound experiment is designed and performed to verify the proposed DoA estimation method. It is promising to investigate the DoA estimation of the ULA model, particularly of the ULAs of the multi-dimensional array from the fused perspective of physical and signal properties of the incident sources.
A Heuristic Algorithm to Find a Path to be Blocked by Minimizing Traffic Disruption

This paper discusses the problem of finding a path to be blocked from the source to the destination for a vehicle to pass by in such a way that the traffic disruption caused is minimum. The traffic disruption caused by blocking a path is measured by estimating the number of vehicles that would have crossed any of the vertices in the path if the path had not been blocked. It also presents a heuristic algorithm “Aggregate Traffic Minimization” (ATM) to solve the above problem. The traffic disruption caused by the path chosen by the ATM algorithm was compared with that of a popular baseline algorithm and was found that ATM outperforms the baseline alzorithm in most cases.
Multi-model Deep Learning Ensemble for ECG Heartbeat Arrhythmia Classification

Managing and treating cardiovascular diseases can be substantially improved by automatic detection and classification of the heart arrhythmia. In this paper, we introduced a novel deep learning system for classifying the electrocardiogram (ECG) signals. The heartbeats are classified into different arrhythmia types using two proposed deep learning models. The first model is integrating the convolutional neural network (CNN) and long short-term memory (LSTM) network to extract useful features within the ECG signal. The second model combines several classical features with LSTM in order to effectively recognize abnormal classes. These deep learning models are trained using a bagging model then aggregated by a fusion classifier to form a robust unified model. The proposed system is evaluated on the MIT-BIH arrhythmia database and produces an overall accuracy of 95.81%, which significantly outperforms the state-of-the-art.
Automated Dysarthria Severity Classification Using Deep Learning Frameworks

Dysarthria is a neuro-motor speech disorder that renders speech unintelligible, in proportional to its severity. Assessing the severity level of dysarthria, apart from being a diagnostic step to evaluate the patient's improvement, is also capable of aiding automatic dysarthric speech recognition systems. In this paper, a detailed study on dysarthia severity classification using various deep learning architectural choices, namely deep neural network (DNN), convolutional neural network (CNN) and long short-term memory network (LSTM) is carried out. Mel frequency cepstral coefficients (MFCCs) and its derivatives are used as features. Performance of these models are compared with a baseline support vector machine (SVM) classifier using the UA-Speech corpus and the TORGO database. The highest classification accuracy of 96.18% and 93.24% are reported for TORGO and UA-Speech respectively. Detailed analysis on performance of these models shows that a proper choice of a deep learning architecture can ensure better performance than the conventionally used SVM classifier.
Synchronization in Quantum-Dot Cellular Automata Circuits and Systems

Signal synchronization of large scale Quantum-dot Cellular Automata (QCA) circuits is one of the most complex QCA design challenges. More specifically, the QCA circuits synchronization problem, especially in the large circuits, is characterized as rather complex due to technology constraints. In this paper, by extensively analyzing the most important properties of the signal synchronization problem in QCA circuits, we propose an efficient design methodology to tackle the problem, based on the well-known from computer science, Firing Squad Synchronization Problem (FSSP). Comparing FSSP with the QCA circuits synchronization problem many similarities can be found. Among the numerous FSSP's algorithmic solutions in literature, the Mazoyer algorithm has proven to be the most efficient one. In this paper, a novel design and implementation in QCA technology of this algorithm is presented. Moreover, by the appropriate modification of the Mazoyer algorithm, we are able to propose a generic synchronization design methodology for QCA circuits and systems. This method is enhanced by a novel freezing technique, that makes it applicable to any QCA circuit and system as manifested by our corresponding simulation results. The proposed synchronization methodology is a universal design tool, that can be applied to exiting designs without increasing the complexity.
Sequential Sensor Placement using Bayesian Compressed Sensing for Source Localization

This paper deals with the sensor placement problem for an array designed for source localization. When it involves the identification of a few sources, the compressed sensing framework is known to find directions effectively thanks to sparse approximation. The present contribution intends to provide an answer to the following question: given a set of observations, how should we make the next measurement to minimize (some form of) uncertainty on the localization of the sources? More specifically, we propose a methodology for sequential sensor placement inspired from the "Bayesian compressive sensing" framework introduced by Ji et al. Our method alternates between a step of sparse source localization estimation, and a step to choose the sensor position that minimizes the covariance of the estimation error. Numerical results show that an array designed by the proposed procedure leads to better performance than sensors positioned at random.
Confidence-based Local Feature Selection for Material Classification

With the rise of Convolutional Neural Network (CNN) in the recent years, image classification has shown outstanding performances in the computer vision field. Many wellknown state of the art’s CNN architectures such as the ResNet family are applying a Global Average Pooling (GAP) to reduce the number of parameters of the fully connected layers. Most of the time, this pooling operation helps to prevent overfitting but we claim that it has a serious weakness for specific images where small details are crucial to predict their category, such as material images. In this case, the details are lost in the global average, providing non accurate global features. In this paper, we propose to select the most important local features before applying the GAP. In this aim, we add a branch in the classification network that predicts the confidence the network should have in each local feature vector. The less confident features are filtered out before applying the GAP. Experimental results on three datasets show that our approach outperforms recent alternatives in terms of classification accuracy and output probability calibration.
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series

Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model performance is evaluated on two public sleep datasets (SHHS and Sleep-EDF) with different available channels. The results have shown that our model achieves an overall accuracy of 85.2% on the SHHS dataset and an accuracy of 85% on the Sleep-EDF dataset. We have also demonstrated that the proposed architecture not only is able to handle various numbers of input channels and several signal modalities from different datasets but also exhibits short runtimes and low computational cost.
Automatic Ship Recognition Chain on Satellite Multispectral Imagery

This article elaborates a processing chain devised to recognize the ships existing on medium resolution multispectral imageries (MSI). The chain consists of the following three steps. Firstly, an adaptive local saliency mapping technique is instigated on open ocean regions to obtain all floating objects. Secondly, to extract the ship candidates, two-step verification is applied based on specific spectral and geometric information of the ships. Lastly, a calculation to determine the properties of the ships, including their length, breadth, and heading, is then carried out. Furthermore, we propose a novel method for correcting miscalculated ship heading; by combining wake segmentation and Radon Transform (RT) approaches to locate the position and estimate the length of the wake generated by the ships. With the detected wake length, ship velocity can also be assessed. The developed chain is then tested using imageries acquired by LAPAN-A3 microsatellite, and the results are compared to those reported by the Automatic Identification System (AIS). Experimental results indicate that the proposed chain achieves higher detection performance and can produce better heading information compared to the existing methods.
Dessorção de fósforo avaliada por extrações sucessivas em amostras de solo provenientes dos sistemas plantio direto e convencional

The phosphorus is retained in the soil solid phase with several degrees of energy, which depends on the characteristics and amount of the soil colloids. The no tillage system (NT) may promote phosphorus accumulation in different forms from those observed in the conventional tillage (CT) and thus, may modify the dessorption capacity of the soil. The objective of this work was to estimate the phosphorus dessorption capacity of different soils submitted to NT and CT. Samples from three soils with variable clay contents (Rhodic Hapludox - 680g kg-1 clay, Rhodic Hapludox - 530g kg-1 clay and Rhodic Paleudult - 220g kg-1 clay) cultivated under NT and CT were submitted to successive extractions with anion exchange resin (AER) and adjusted first order kinetics equations to estimate the maximum phosphorus dessorption of the soil. The dessorption by the first extraction and maximum dessorption were higher in the superficial layer of the soil under NT, comparatively to CT. Only one extraction with AER represented, on average, 38, 46 and 49% of the maximum dessorption on soils with high, medium and low clay content, respectively, while that estimated by Mehlich 1 extractor represented, for the same soils, 32, 46 and 89% of the maximum dessorption.
Numerical simulation of a bilayer organic solar cell based on boron chromophore compounds as acceptors

Organic solar cells fabricated with non-fullerene acceptors have proven to be a solution to reduce manufacturing costs. Besides the material selection, another way to reduce such costs is by optimizing the properties of both the materials and the interfaces which in turn would contribute to enhance the solar cells efficiency. This can be done with numerical simulations. Among non-fullerene materials, boron chromophores are relatively stable and chemically versatile compounds which have been usually used as donors in solar cells. In this work, planar solar cells based on PTB7 and boron compounds are proposed and simulated but by using the latter as acceptors. Specifically, we show the functionality of our proposed devices by analyzing the variation of both the Lowest Unoccupied Molecular Orbital (LUMO) and the influence of the non-intentional doping of the boron compounds with regard to the photovoltaic parameters of the solar cells. The properties of the materials such as the dielectric constant, energy levels, and non-intentional doping are taken from literature. We show that there are optimal values for LUMO and doping concentration to maximize the device efficiency. The causes of this behavior are analyzed using band diagram simulations.
Hardware Architecture for the Regular Interpolation Filter of the AV1 Video Coding Standard

This work proposes a hardware architecture for fractional-pixel interpolation filter defined in the royalty-free AV1 video coding standard. Analysis conducted in this work shows that the AV1 Regular family of filters has the highest usage especially when considering high resolution videos. The proposed architecture implements the 15 interpolation filters of the AV1 Regular family and is capable to interpolate videos of up to 8K video resolution at 120 fps. The proposed architecture achieves the highest throughput compared to related works.