Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time 3D Facial Tracking via Cascaded Compositional Learning

We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models with each is a boosted ferns initially trained to predict partial motion parameters of the same modality, and then concatenated together via a global optimization step to form a singular strong boosted ferns that can effectively handle the whole regression target. It can explicitly cope with the modality variety in output variables, while manifesting increased fitting power and a faster learning speed comparing against the conventional boosted ferns. By further cascading a sequence of GoMBFs (GoMBF-Cascade) to regress facial motion parameters, we achieve competitive tracking performance on a variety of in-the-wild videos comparing to the state-of-the-art methods which either have higher computational complexity or require much more training data. It provides a robust and highly elegant solution to real-time 3D facial tracking using a small set of training data and hence makes it more practical in real-world applications. We further deeply investigate the effect of synthesized facial images on training non-deep learning methods such as GoMBF-Cascade for 3D facial tracking. We apply three types synthetic images with various naturalness levels for training two different tracking methods, and compare the performance of the tracking models trained on real data, on synthetic data and on a mixture of data. The experimental results indicate that, i) the model trained purely on synthetic facial imageries can hardly generalize well to unconstrained real-world data, ii) involving synthetic faces into training benefits tracking in some certain scenarios but degrades the tracking model’s generalization ability. These two insights could benefit a range of non-deep learning facial image analysis tasks where the labelled real data is difficult to acquire.
Origin of the hardening in AMS-02 nuclei spectra at a few hundred GV

Many experiments have confirmed the spectral hardening in a few hundred GV of cosmic ray (CR) nuclei spectra, and 3 different origins have been proposed: the primary source injection, the propagation, and the superposition of different kinds of sources. In this work, the break power law has been employed to fit each of the AMS-02 nuclei spectra. The fitting results show complicated relationships between different nuclei species, which could not been reproduced naturally by the primary source injection and propagation scenarios. However, with a natural and simple assumption, the superposition of different kinds of sources could explain the fitting results successfully. CR nuclei spectra from one single experiment in future will provide us the opportunity to do cross checks and reveal the properties of the different kinds of sources.
Transfer and confusion deep learning in frustrated spin systems.

The transfer learning of a neural network is one of its most outstanding aspects, and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. By means of case studies, we show the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using dense and convolutional neural networks. In addition, we explore a recently-introduced technique, which is at the middle point of supervised and unsupervised learning. It consists in evaluating the performance of a neural network which has been deliberately 'confused' during its training. To properly demonstrate the capability of the 'confusion' and transfer learning techniques, we apply them to a paradigmatic model of frustrated magnetism in two dimensions, to determine its phase diagram and compare it with high-performance Monte Carlo simulations.
Intraspecific morphological diversity of anostracan resting eggs: Chirocephalus ruffoi Cottarelli & Mura, 1984 as a study case

The extent of intraspecific morphological variability in the egg shells of anostracans, never previously assessed, was investigated in the 13 known Chirocephalus ruffoi populations, a species endemic to Italy. Twelve sampling sites were chosen in the Northern Apennines, with distances between neighbouring sampling sites ranging from a minimum of 23 m to a maximum of 63 km. Another site was located in the Calabrian Apennines at a distance between 643 and 703 km from the previous set of sampling points. The SEM (scanning electron microscopy) observations of 300 cysts per population revealed the existence of great morphological diversity for most of the analysed populations. Up to 12 different morphotypes were described, with highly variable frequencies among the populations. Outer and inner cyst diameters and height of the “ridges” ornamenting cyst surface were calculated on a randomly selected sub-sample of 391 cysts. Statistically significant differences among populations were found for all the measured characters. No relationships between either occurrence of morphotypes or morphometric profiles and characteristics of sampling sites were found. In addition, the amount of morphological variability of the cysts did not seem to depend on the distances between sites. The results of this study are discussed in the light of the debate on the taxonomic value of the anostracan cyst ornamentation and the factors that can be invoked to explain the presence of high intraspecific morphological diversity.
Identification of skill in an online game: The case of Fantasy Premier League

In all competitions where results are based upon an individual’s performance the question of whether the outcome is a consequence of skill or luck arises. We explore this question through an analysis of a large dataset of approximately one million contestants playing Fantasy Premier League, an online fantasy sport where managers choose players from the English football (soccer) league. We show that managers’ ranks over multiple seasons are correlated and we analyse the actions taken by managers to increase their likelihood of success. The prime factors in determining a manager’s success are found to be long-term planning and consistently good decision-making in the face of the noisy contests upon which this game is based. Similarities between managers’ decisions over time that result in the emergence of ‘template’ teams, suggesting a form of herding dynamics taking place within the game, are also observed. Taken together, these findings indicate common strategic considerations and consensus among successful managers on crucial decision points over an extended temporal period.
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT

Machine based text comprehension has always been a significant research field in natural language processing. Once a full understanding of the text context and semantics is achieved, a deep learning model can be trained to solve a large subset of tasks, e.g. text summarization, classification and question answering. In this paper we focus on the question answering problem, specifically the multiple choice type of questions. We develop a model based on BERT, a state-of-the-art transformer network. Moreover, we alleviate the ability of BERT to support large text corpus by extracting the highest influence sentences through a semantic similarity model. Evaluations of our proposed model demonstrate that it outperforms the leading models in the MovieQA challenge and we are currently ranked first in the leader board with test accuracy of 87.79%. Finally, we discuss the model shortcomings and suggest possible improvements to overcome these limitations.
A perception centred self-driving system without HD Maps

Building a fully autonomous self-driving system has been discussed for more than 20 years yet remains unsolved. Previous systems have limited ability to scale. Their localization subsystem needs labor-intensive map recording for running in a new area, and the accuracy decreases after the changes occur in the environment. In this paper, a new localization method is proposed to solve the scalability problems, with a new method for detecting and making sense of diverse traffic lines. Like the way human drives, a self-driving system should not rely on an exact position to travel in most scenarios. As a result, without HD Maps, GPS or IMU, the proposed localization subsystem relies only on detecting driving-related features around (like lane lines, stop lines, and merging lane lines). For spotting and reasoning all these features, a new line detector is proposed and tested against multiple datasets.
In Search of an Interface between Warm and Hot Gas within the Local Bubble

We have examined UV spectra recorded by the Space Telescope Imaging Spectrograph on the Hubble Space Telescope for three stars, HD32309, 41 Ari, and η Tel, that are located well inside the boundary of the Local Hot Bubble in our search for absorption features of Si iv, C iv, and N v that could reveal the presence of an interface between the local warm (T ∼ 7000 K), neutral medium, and a more distant hot (T ∼ 106 K) interstellar medium. In all cases, we failed to detect such ions. Our most meaningful upper limit is that for log N(C iv) 11.86 toward HD32309, which is below the expectation for a sight line that penetrates either a conductive/evaporative interface or a turbulent mixing layer. We offer conjectures on the reasons for these negative results in terms of either a suppression of a conductive layer caused by the shielding of the local cloud by other clouds, which may make it more difficult for us to sense discrete absorption features from gases at intermediate temperatures, or by the presence of a tangential magnetic field at most locations on the surface of the local cloud.
Influence of point sources on heavy metal accumulation in different apartments of Jiaozhou Bay

The pollution and enrichment states of heavy metals in suspended particulate matter(SPM),sediment core and benthic organisms were analyzed and evaluated in Jiaozhou Bay.Results showed that:(1) The discharge of a large amount of untreated effluents and the insufficient diffusion of the terrestrial contamination to outer bay directly led to point source contamination,which caused the heavy metal enrichment in water and in benthic organism;(2) Anthropogenic activities significantly affected the heavy metals enrichment in SPM.The anthropogenic factors(AF) of sediment cores showed that the pollution at the estuary was more serious than that of other investigated areas;(3) The concentration of heavy metals in benthic organism had close relationship with the distance from the sampling site to the pollution sources.Venerupis Philippinarum,a benthic organism,was estimated to have higher accumulating capacity to cadmium as compared to any other tested heavy metals.
Antiviral Effect of Polysaccharide from Scutellaria Baicalensis Georgi on Porcine Respiratory and Reproductive Syndrome Virus

Objective:To show that polysaccharide from Scutellaria baicalensis Georgi has antiviral property by investigating its effect on replication of porcine reproductive and respiratory syndrome virus (PRRSV) in cells of the permanent MARC-145 cell line.Methods:Cells infected with virus were treated with various concentration of polysaccharide and incubated for some hours, then Trypan blue exclusive assay, immunofluorescence assay (IFA), plaque forming assay, polymerase chain reaction (PCR) was performed.Results:Number of plaques, the death rate of cells and percentage of IFA positive cells reduced and band of PCR product became weaker and undetectable with the concentration of polysaccharide increasing after infection with PRRSV. Conclusion:It was indicated that polysaccharide from Scutellaria baicalinsis Georgi could inhibit the replication of virus at cell level.
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams

Attaining prototypical features to represent class distributions is well established in representation learning. However, learning prototypes online from streaming data proves a challenging endeavor as they rapidly become outdated, caused by an ever-changing parameter space during the learning process. Additionally, continual learning does not assume the data stream to be stationary, typically resulting in catastrophic forgetting of previous knowledge. As a first, we introduce a system addressing both problems, where prototypes evolve continually in a shared latent space, enabling learning and prediction at any point in time. To facilitate learning, a novel objective function synchronizes the latent space with the continually evolving prototypes. In contrast to the major body of work in continual learning, data streams are processed in an online fashion without task information and can be highly imbalanced, for which we propose an efficient memory scheme. As an additional contribution, we propose the learner-evaluator framework that i) generalizes existing paradigms in continual learning, ii) introduces data incremental learning, and iii) models the bridge between continual learning and concept drift. We obtain state-of-the-art performance by a significant margin on eight benchmarks, including three highly imbalanced data streams. Code is publicly available. 1
Composition analysis of essential oil of Curcuma wenyujin Rhizoma from Hainan Province

Objective To analyze the essential oil compositions of Curcuma wenyujin Rhizoma from Hainan Province in order to provide identifiable ground for the use of resources and quality evaluation.Methods Essential oil of Curcuma wenyujin Rhizoma from Hainan Province and from Zhejiang Province was selected as the research objects.These essential oil were extracted by steam distillation.They were analyzed and determined by gas chromatography-mass spectrometry(GC-MS) and high performance liquid chromatography(HPLC),compared with the quality standards of "China pharmacopoeia". Results Essential oil compositions of Curcuma wenyujin Rhizoma from Hainan Province and Zhejiang Province were consistent.Similarity of characteristic chromatogram of two essential oils reached more than 95% compared with Chinese pharmacopoeia characteristic chromatogram(2010) of Curcuma wenyujin Rhizoma.Conclusion Essential oil compositions of different origin are similar.The study provided scientific basis for the development and utilization of Curcuma wenyujin Rhizoma from Hainan Province.
A flux and speed observer for induction motors with unknown rotor resistance and load torque and no persistent excitation requirement

In this article, we address the problems of flux and speed observer design for voltage‐fed induction motors with unknown rotor resistance and load torque. The only measured signals are stator current and control voltage. Invoking the recently reported Dynamic Regressor Extension and Mixing‐Based Adaptive Observer (DREMBAO), we provide the first global solution to this problem. The proposed DREMBAO achieves asymptotic convergence under an excitation condition that is strictly weaker than persistent excitation. If the latter condition is assumed the convergence is exponential.
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation

Adversarial examples have shown that albeit highly accurate, models learned by machines, differently from humans,have many weaknesses. However, humans' perception is also fundamentally different from machines, because we do not see the signals which arrive at the retina but a rather complex recreation of them. In this paper, we explore how machines could recreate the input as well as investigate the benefits of such an augmented perception. In this regard, we propose Perceptual Deep Neural Networks ($\varphi$DNN) which also recreate their own input before further processing. The concept is formalized mathematically and two variations of it are developed (one based on inpainting the whole image and the other based on a noisy resized super resolution recreation). Experiments reveal that $\varphi$DNNs can reduce attacks' accuracy substantially, surpassing state-of-the-art defenses in 87% of the tests for adversarial training variations and 100% of the tests when only comparing with other pre-processing type of defenses. Moreover, the recreation process intentionally corrupts the input image. Interestingly, we show by ablation tests that corrupting the input is, although counter-intuitive,beneficial. This suggests that the blind-spot in vertebrates might also be, analogously, the precursor of visual robustness. Thus, $\varphi$DNNs reveal that input recreation has strong benefits for artificial neural networks similar to biological ones, shedding light into the importance of the blind-spot and starting an area of perception models for robust recognition in artificial intelligence.
Floquet engineering of multifold fermions

Using Floquet theory, we investigate the effect of light on triple fold fermions. We study a low energy model as well as a simplified tight binding model under illumination. We find that the three fold degeneracy remains symmetry protected even after applying light if the original band structure is rotationally symmetric around the degeneracy. Otherwise, light can lift the degeneracy and open up a gap. We further investigate the effect of light on the topological Fermi arcs by means of numerical computations. The changes caused by illumination are reflected in experimentally detectable signatures, such as the density of states and the anomalous Hall conductivity, which we calculate.
Using a solid-state Quartz Crystal {\mu}-balance as a kinetic energy sensor in laser cooling experiments

We present here the development of a new and innovative experimental method to fully characterize a solenoidal "Spin-Flip" Zeeman slower (ZS) using a Quartz Crystal $\mu$-balance (QCM) as a kinetic energy sensor. In this experiment, we focus a 447.1 nm laser into a counter-propagating beam of Gd(I) atoms in order to drive the dipole transition between ground $^{9}$D$^{0}_{2}$ state and $^{9}$D$_{3}$ exited stated. Also, during the process, we continuously measure the change of the oscillation frequency signal of a QCM due to the deposition of the Gd(I) atoms in its surface. We use these measurements to study the time-evolution of the velocity distribution of the Gd(I) atom beam during a cooling process. We obtain a maximum atom average velocity reduction of (43.45 $\pm$ 6.44)$\%$ produced by our apparatus. Moreover, we estimate an experimental lifetime $\tau_{e}$ = 8.1988 ns and compare it with the reported lifetime for 443.06 nm and 451.96 nm electronic transitions of Gd(I). These results confirm that the QCM offers an accessible and simple solution to have in direct overview for laser cooling experiments. Therefore, a new field of research in the borderland between solid state physics and ultra-cold atoms physics will enable a highly sensitive test of postulates from fundamental physics.
COMPUTATIONAL ANALYSIS OF MUTATIONS IN REALLY INTERESTING NEW GENE FINGER DOMAIN AND BRCA1 C TERMINUS DOMAIN OF BREAST CANCER SUSCEPTIBILITY GENE

Objective: Breast Cancer 1 (BRCA1), Early Onset and Breast Cancer 2, Early Onset (BRCA2) genes are involved in pathways important for DNA damage recognition, double-strand break repair, checkpoint control, transcription regulation, and chromatin remodeling. These functions are essential and important for all cell types. Germline mutations in these genes increase the risk of breast and ovarian cancer in women. In this study, we did an analysis of the functional and structural impact of all known single nucleotide polymorphisms (SNPs) in BRCA1 and BRCA2 using publicly available computational prediction tools. Methods: We analyzed the mutations using two mutation tolerance prediction approaches: Sorting intolerant from tolerant (SIFT), and polymorphism phenotyping (PolyPhen-2). In addition, stability of the protein was analyzed by I-Mutant. Affinity and stability of really interesting new gene (RING) and BRCA1 C-terminus (BRCT) domains were also analyzed by BioLuminate tool. Results: Out of 486 SNPs in BRCA retrieved from functional SNP, a total of 10 SNPs were found to be deleterious by SIFT and PolyPhen. I-Mutant results indicate that C27F, A1708V could increase the stability of protein, whereas other mutations decrease the stability. Predicted changes in stability and affinity of RING and BRCT domains of BRCA were computed using residue scanning functionality in bioluminate for all 10 SNPs. The mutation C61R could affect the stability of RING domain and all mutations in BRCT domain were affecting the inter subunit affinity and stability of the complex. Conclusion: The combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the BRCA protein stability and affinity. Based on our investigation, we report potential candidate SNPs for future studies of BRCA mutations. Keywords: Breast cancer susceptibility gene, BRCA1 C terminus domain, Zinc finger domain, Sorting intolerant from tolerant, Polymorphism phenotyping, I-mutant, BioLuminate.
Computational Identification of MicroRNAs for Targeting Long and Short Segments of Lassa Virus

MicroRNAs (miRNAs), are a class of approximately 22 nucleotide long non coding RNAs which play critical role in different biological processes. The mature microRNA is usually 19-27 nucleotides long and is derived from a bigger precursor that folds into an flawed stem-loop structure which are involve in many cellular process that encompass development, proliferation, stress response, apoptosis, fat metabolism by gene regulation. Resent finding reveal that certain viruses encode their own miRNA that processed by cellular RNAi machinery. In recent research indicates that cellular microRNA can target the genetic material of invading viruses were reported. Cellular miRNA can be used in virus life cycle; either to up regulate or down regulate viral gene expression. Computational tools utilize in miRNA target prediction has been changing drastically in recently. Where many of them are available on the web and can be used by researcher and scientist without of bioinformatics. The development genomes analysis technologies recorded during the previous decade has tremendously cleared the biology of miRNA. In this study nucleotide sequences of long (23343512) and short segments (23343509) sequences of Lassa viruses; L and S segment; composing the genome of Lassa virus which naturally affect human were analyzed using VMir analyzer program (computational approach) to predict the counter human cellular miRNAs candidates targeting viral genome. The 50 nt minimum hairpin size, 90 nt maximum hairpin size and 50 minimum hairpin score were used for the filter of sequence, as well as pairing energy less than 10 kcal/mol was utilize as cutoff score. The results of RNA hybrid were categories in terms of pairing energy (minimum free energy) and hybridization pattern. Four types of hybridization patterns were obtain from RNA hybrid analysis. According to L segment analysis, 34 potent miRNA divided into three groups, subsequently, hsa-miR-608, hsa-miR-3692-5p, hsa-miR-557, hsa-miR-1273d, hsa- miR-136-5p and hsa-miR-3164 are selected as potential human cellular miRNAs. From the miRBase database 16 potential miRNA were predicted as potential miRNA targeting S segment, therefore, hsa-miR-4691-5p, hsa-miR-581 and hsa-miR-622 were selected as potential human cellular miRNAs on the basis of pairing energy. The results suggested that microRNAs from MD30, MR19 and MR30 as well as MD15 and MR1 from L segment and S segment, respectively, might be best candidate to targeting human cellular miRNAs.