Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic hand gesture recognition : from traditional handcrafted to recent deep learning approaches

Hand gestures are the most natural and intuitive non-verbal communica- tion medium while using a computer, and related research efforts have recently boosted interest. Additionally, data provided by current com- mercial inexpensive depth cameras can be exploited in various gesture recognition based systems. The area of hand gesture analysis covers hand pose estimation and gesture recognition. Hand pose estimation is con- sidered to be more challenging than other human part estimation due to the small size of the hand, its greater complexity and its important self occlusions. Beside, the development of a precise hand gesture recognition system is also challenging due to high dissimilarities between gestures derived from ad-hoc, cultural and/or individual factors of users. First, we propose an original framework to represent hand gestures by using hand shape and motion descriptors computed on 3D hand skeletal fea- tures. We use a temporal pyramid to model the dynamic of gestures and a linear SVM to perform the classification. Additionally, we create the Dynamic Hand Gesture dataset containing 2800 sequences of 14 gesture types. Evaluation results show the promising way of using hand skele- tal data to perform hand gesture recognition. Experiments are carried out on three hand gesture datasets, containing a set of fine and coarse heterogeneous gestures. Furthermore, results of our approach in terms of latency demonstrated improvements for a low-latency hand gesture recog- nition systems, where an early classification is needed. Then, we extend the study of hand gesture analysis to online recognition. Using a deep learning approach, we employ a transfer learning strategy to learn hand posture and shape features from depth image dataset originally created for hand pose estimation. Second, we model the temporal variations of the hand poses and its shapes using a recurrent deep learning technology. Finally, both information are merged to perform accurate prior detection and recognition of hand gestures. Experiments on two datasets demon- strate that the proposed approach is capable to detect an occurring gesture and to recognize its type far before its end.
Self-pulsing discharges in pre-heated air at atmospheric pressure

The paper presents investigations of self-pulsing discharges in atmospheric pressure air pre-heated to 300–1000 K. Despite using a direct-current power supply, two self-pulsing discharge regimes, a repetitive transient spark (TS) and a repetitive streamer (RS) were generated. The pulse repetition frequency, on the order of a few kHz, can be controlled by adjusting the generator voltage. The TS is a discharge initiated by a streamer, followed by a short (tens of ns) spark current pulse (∼ 1 A), associated with the total discharging of the internal capacity of the electric circuit. The TS is suitable for the study of ‘memory’ effects (pre-heating, pre-ionization) on the mechanisms of streamer-to-spark transition and electrical breakdown in atmospheric pressure air. The TS regime was stable below ∼600 K. Above ∼600 K, a stable repetitive streamer (RS) regime was observed. In this regime, the breakdown and spark did not occur. After the initial streamer, the internal capacity of the electrical circuit discharged partially. With further pre-heating of the gas, the stable TS appeared again at ∼1000 K.
Facile Synthesis of Gold Nanoplates by Citrate Reduction of AuCl4^- at Room Temperature

Single-crystalline, regular-edged gold nanoplates are synthesized through chemical reduction of AuCl4 by a suitable amount of citrate at room temperature, without additional capping agents or surfactants. The suitable molar ratio of sodium citrate to HAuCl4, low reaction temperature and the presence of natural light are critical factors for the formation of the regularly shaped nanoplates.
Kinetics of Cyclooctene Epoxidation with tert‐Butyl Hydroperoxide in the Presence of [MoO2X2L]‐Type Catalysts (L = Bidentate Lewis Base)

MoVI complexes with the general formula [MoO2X2L] [X = Cl, Br; L = 4,4'-dimethyl-2,2'-bipyridine (dmbp) or 4-hexyl-4'-methyl-2,2'-bipyridine (hmbp)] have been prepared and characterized by IR and solution NMR (1H, 13C, 17O and 95Mo) spectroscopy. The complexes were applied as catalysts in the homogeneous phase for the epoxidation of cyclooctene, with tert-butyl hydroperoxide (TBHP) as the oxygen source. The desired epoxide was the only product and turnover frequencies of up to about 9000 h–1 could be reached. The catalytic activities increased in the order [MoO2Br2(dmbp)] < [MoO2Br2(hmbp)] < [MoO2Cl2(dmbp)] < [MoO2Cl2(hmbp)]. A kinetic model was built up for a homogeneous batch reactor based on a simplified mechanism involving three steps: (i) reversible coordination of TBHP to the starting MoVI complex to give a MoVI alkylperoxide; (ii) irreversible oxidation of cyclooctene to cyclooctene oxide by the species formed in step 1, with formation of the starting complex and tert-butyl alcohol; (iii) reversible coordination of tert-butyl alcohol to the starting complex. This model is consistent with the observed kinetics. The first step in this reaction mechanism was characterized in more detail by studying the kinetics of the reaction of the starting complexes with TBHP in the absence of any reductant by UV/Vis spectroscopy. Rate constants, equilibrium constants, and activation parameters were determined. All ΔS‡ values were negative and therefore support an associative mechanism in which a seven-coordinate intermediate is formed. The results also suggest that the first step is not always the rate-limiting step of cyclooctene epoxidation with these complexes. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2005)
Electronic and magnetic properties of (Bi0.5Na0.5)(Mn0.5Nb0.5)O3

(Bi0.5Na0.5)(Mn0.5Nb0.5)O3 has been sintered by hot-sintering method at ambient air. Composition has been checked with use of a scanning microscope. Magnetic behavior of (Bi0.5Na0.5)(Mn0.5Nb0.5)O3 has been investigated by DC magnetization and AC susceptibility techniques. (Bi0.5Na0.5)(Mn0.5Nb0.5)O3 exhibits a magnetic phase transition at 6.8 K. Below this temperature, the interaction between magnetic moments is antiferromagnetic. The resultant non-zero magnetic moment emerges because of weak precipitation of manganese oxide with ordering temperature at T ≈ 40 K. From the fit of the Curie–Weiss law to the AC susceptibility, the Weiss temperature θ = –2.3 K has been determined and Curie constant C = 1.15 emu K/molMn, close to the value expected for spin S = 2 of Mn3+, has been estimated. The antiferromagnetic state with spin-glass type features has been deduced. Crystal structures and partial density of states (DOS) have been simulated by ab initio method. DOS simulations showed half-metal electronic structure of (Bi0.5Na0.5)(Mn0.5Nb0.5)O3.
Glucose fermentation in the presence of linoleic, oleic and stearic acids by a mixed culture

Long Chain Fatty Acid (LCFA) mixtures containing linoleic, oleic and stearic acids plus carbohydrates are found in a variety of effluents arising from fried food manufacture and milk processing. Accumulation of Volatile Fatty acids (VFAs) due to the presence of LCFAs may impair the operation of an anaerobic system treating effluents containing a mixture of triglycerides and carbohydrates. In this study, the effects of linoleic (C18:2), oleic (C18:1), and stearic (C18:0) acids on glucose fermentation were investigated at 21 °C using a culture acclimated to glucose. In cultures receiving ≥300 mg dm−3 LCFAs, residual amounts of glucose remained after approximately 8 h and none was detected after 24 h. Acetate degradation was inhibited in the presence of 300 or more mg dm−3 linoleic acid (LA), oleic acid (OA), or stearic acid (SA) with more acetate accumulation observed in cultures receiving LA. In comparison to the controls, similar amounts of propionate accumulation were observed in cultures receiving ≤100 mg dm−3 of each LCFA. However, in cultures receiving ≥300 mg dm−3 LCFAs, more propionate accumulated with complete removal observed within 20 days for only those cultures receiving oleic or stearic acids. Butyrate accumulation was observed only in cultures receiving ≥300 mg dm−3 LA and none was detected after 10 days of incubation. Copyright © 2004 Society of Chemical Industry
The Effect of Mercury on Lipid Peroxidation and Its Relation with Vitamin (A, E) and Essential Elements in Dentals Serum

In the present study, the authors attempted to shed a light on the possible relationships between lipid per oxidation markers, serum malondialdehyde (MDA) with the effect of mercury exposure using serum samples obtained from 100 mercury exposed dentals due to their occupation. These dentals were divided into two groups according to their occupational period. The third group consists of healthy volunteers that were working outside the mercury factories as a control group. The present results showed that mercury levels increased with the increasing of their occupation period. The highest level was 5.7 μg/dL compared to the control group which was 0.1 μg/dL. The level of MDA was elevated in the workers and the highest level was found to be 41.00 nmol/dL for workers with longest period compared to the control group 8.6 nmol/dL. Some serum antioxidants such as vitamin A, E and albumin were evaluated and were found to be lower in all workers compared to the control group. The present results showed that Mg levels increased with the increasing of their occupation period and were found to be lower in Zn, Se of all workers compared to the control group.
PACAP and VIP Modulation of Neuroexcitability in Rat Intracardiac Neurons

xi Chapter 1 Background and Significance 1 Mammalian Intracardiac Ganglia 1 PACAP and VIP effects on the Heart 4 PACAP and VIP receptors 6 PACAP and VIP relevance to cardiac function 7 Chapter 2 Heterogeneity of PACAP and VIP receptors in rat intrinsic cardiac neurons 12 Introduction 12 Methods 15 Cell culture 15 RT-PCR 16 Cytoplasm harvest of individual neurons 17 ii Results 18 PAC1 receptor mRNA isoforms detected in cardiac tissue . 18 PAC1 mRNA expression in isolated rat intracardiac neurons . 19 VPAC1 and VPAC2 receptor transcripts detected in cardiac tissue 20 VPAC mRNA expression in isolated rat intracardiac neurons . 21 Individual intracardiac neuron expression pattern of PAC1 and VPAC mRNA 22 Discussion 31 Chapter 3 VPAC receptor modulation of neuroexcitability in intracardiac neurons: dependence on intracellular Ca mobilization and synergistic enhancement by PAC1 receptor activation . 33 Introduction 33 Methods 37 Microfluorometric measurements 37 Electrophysiology 38 Reagents and statistical analysis 40 Results 41 PACAP and VIP directly increase free [Ca]i 41 Increases in free [Ca]i evoked by PACAP and VIP are mediated by a VPAC receptor 42 PACAP and VIP mobilize Ca from intracellular stores and evoke Ca entry through the plasma membrane 44 PACAP induces mobilization of [Ca]i from caffeineand ryanodine-sensitive stores 46 PACAP and VIP Induced Excitability in Rat Intracardiac Neurons 49
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering

Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works have investigated the application of deep neural networks in recommendation domain, they mostly extract entity features by exploiting unstructured auxiliary data such as visual and textual information, and when it comes to using user-item rating matrix, feature extraction is done by using matrix factorization. As matrix factorization has some limitations, some works have been done to replace it with deep neural network. but these works either need to exploit unstructured data such item's reviews or images, or are specially designed to use implicit data and don't take user-item rating matrix into account. In this paper, we investigate the usage of novel representation learning algorithms to extract users and items representations from rating matrix, and offer a deep neural network for Collaborative Filtering. Our proposed approach is a modular algorithm consisted of two main phases: REpresentation eXtraction and a deep neural NETwork (RexNet). Using two joint and parallel neural networks in RexNet enables it to extract a hierarchy of features for each entity in order to predict the degree of interest of users to items. The resulted predictions are then used for the final recommendation. Unlike other deep learning recommendation approaches, RexNet is not dependent to unstructured auxiliary data such as visual and textual information, instead, it uses only the user-item rate matrix as its input. We evaluated RexNet in an extensive set of experiments against state of the art recommendation methods. The results show that RexNet significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.
ESAD: End-to-end Semi-supervised Anomaly Detection

This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new KL-divergence based objective function for semi-supervised anomaly detection, and show that two factors: the mutual information between the data and latent representations, and the entropy of latent representations, constitute an integral objective function for anomaly detection. To resolve the contradiction in simultaneously optimizing the two factors, we propose a novel encoder-decoder-encoder structure, with the first encoder focusing on optimizing the mutual information and the second encoder focusing on optimizing the entropy. The two encoders are enforced to share similar encoding with a consistent constraint on their latent representations. Extensive experiments have revealed that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, including medical diagnosis and several classic anomaly detection benchmarks.
Robust Improvement of the Age of Information by Adaptive Packet Coding

We consider a wireless communication network with an adaptive scheme to select the number of packets to be admitted and encoded for each transmission, and characterize the information timeliness. For a network of erasure channels and discrete time, we provide closed form expressions for the Average and Peak Age of Information (AoI) as functions of admission control and adaptive coding parameters, the feedback delay, and the maximum feasible end-to-end rate that depends on channel conditions and network topology. These new results guide the system design for robust improvements of the AoI when transmitting time sensitive information in the presence of topology and channel changes. We illustrate the benefits of using adaptive packet coding to improve information timeliness by characterizing the network performance with respect to the AoI along with its relationship to throughput (rate of successfully decoded packets at the destination) and per-packet delay. We show that significant AoI performance gains can be obtained in comparison to the uncoded case, and that these gains are robust to network variations as channel conditions and network topology change.
Fine-Grained Network Analysis for Modern Software Ecosystems

Modern software development is increasingly dependent on components, libraries, and frameworks coming from third-party vendors or open-source suppliers and made available through a number of platforms (or forges). This way of writing software puts an emphasis on reuse and on composition, commoditizing the services that modern applications require. On the other hand, bugs and vulnerabilities in a single library living in one such ecosystem can affect, directly or by transitivity, a huge number of other libraries and applications. Currently, only product-level information on library dependencies is used to contain this kind of danger, but this knowledge often reveals itself too imprecise to lead to effective (and possibly automated) handling policies. We will discuss how fine-grained function-level dependencies can greatly improve reliability and reduce the impact of vulnerabilities on the whole software ecosystem.
Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer

Nuclear pleomorphism is the degree of change in nuclear morphology, one of the components of the three-tiered breast cancer grading, along with tubular differentiation and mitotic counting. We consider the degree of nuclear pleomorphism as a continuum; a continuous spectrum of change in tumor morphology. We train a deep learning network on a large variety of tumor regions from the collective knowledge of several pathologists without constraining the network to the traditional three-category classification. We also motivate an additional approach in which we discuss the additional benefit of normal epithelium as baseline, following the routine clinical practice where pathologists are trained to score nuclear pleomorphism in tumor, having the normal breast epithelium as baseline. In multiple experiments, our fully-automated approach could achieve top pathologist-level performance in select regions of interest as well as at whole slide images, compared to ten and four pathologists, respectively.
Coupled Schrödinger equations as a model of interchain torsional excitation transport in the DNA model

In this report we consider two weakly coupled Schrödinger equations as a reduced model of interchain energy transport in the DNA double-helix torsional model. We employ a reduction of the Yakushevich-type model that considers the torsional dynamics of the DNA as effective chains of pendula. In previous works, only small amplitude excitations and stationary dynamics were investigated, whereas we focus on the nonstationary dynamics of the double helix. We consider the system to be a model of two weakly interacting DNA strands. Assuming that initially only one of the chains is excited in the form of a breather, we demonstrate the existence of an invariant that allows us to reduce the order of the problem and examine the system of the phase plane. The analysis demonstrates the utility of an analytical tool for predicting the periodic interchain excitation transitions of its localisation on one of the chains. The technique also takes into account the spreading of the excitations over time.
Short paths in PU(2)

Parzanchevski and Sarnak recently adapted an algorithm of Ross and Selinger for factorization of PU(2)-diagonal elements to within distance $\varepsilon$ into an efficient probabilistic algorithm for any PU(2)-element, using at most $3\log_p\frac{1}{\varepsilon^3}$ factors from certain well-chosen sets. The Clifford+$T$ gates are one such set arising from $p=2$. In that setting, we leverage recent work of Carvalho Pinto and Petit to improve this to $\frac{7}{3}\log_2\frac{1}{\varepsilon^3}$, and implement the algorithm in Haskell.
Numerical Calabi-Yau metrics from holomorphic networks

We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\"ahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods.
Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy–galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a convolutional neural network (CNN) that analyses the outputs of semi-analytic methods that parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialized lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically reinitialize the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sérsic sources, accurately classifies source reconstructions of the same type with a precision P > 0.99 and recall R > 0.99. The same CNN, without retraining, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed Hubble Ultra-Deep Field (HUDF) sources. Using the CNN predictions to reinitialize the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.
Scale-dependent halo bias and the squeezed limit bispectrum in the presence of radiation

We investigate the gravitational effect of large-scale radiation perturbations on small-scale structure formation. In addition to making the growth of matter perturbations scale dependent, the free-streaming of radiation also affects the coupling between structure formation at small and large scales. We study this using Separate Universe N-body simulations to compute the (isotropized) squeezed-limit matter bispectrum and the linear halo bias. Our results show that the scale dependence in the growth of long-wavelength matter perturbations, caused by radiation, translates into these quantities acquiring a non-trivial scale-dependence at $k\lesssim 0.05$ Mpc$^{-1}$. In a universe with radiation composed of cosmic microwave background photons and three species of massless neutrinos, the bias of halos with $b = 2$ at high $k$ will decrease by $0.29\%,\ 0.45\%$ and $0.8\%$ between $k = 0.05$ Mpc$^{-1}$ and $k = 0.0005$ Mpc$^{-1}$ at redshifts $z=0,\ 1$, and $3$ respectively. For objects with $b\gg1$, these differences approach $0.43\%,\ 0.68\%$ and $1.2\%$ respectively.
Convex Regularization Behind Neural Reconstruction

Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. To cope with this challenge, this paper advocates a convex duality framework that makes a two-layer fully-convolutional ReLU denoising network amenable to convex optimization. The convex dual network not only offers the optimum training with convex solvers, but also facilitates interpreting training and prediction. In particular, it implies training neural networks with weight decay regularization induces path sparsity while the prediction is piecewise linear filtering. A range of experiments with MNIST and fastMRI datasets confirm the efficacy of the dual network optimization problem.