Miracle-dz/UniFAR-FTUs huggingface dataset:
What Do Kernel Density Estimators Optimize?

Some linkages between kernel and penalty methods of density estimation are explored. It is recalled that classical Gaussian kernel density estimation can be viewed as the solution of the heat equation with initial condition given by data. We then observe that there is a direct relationship between the kernel method and a particular penalty method of density estimation. For this penalty method, solutions can be characterized as a weighted average of Gaussian kernel density estimates, the average taken with respect to the bandwidth parameter. A Laplace transform argument shows that this weighted average of Gaussian kernel estimates is equivalent to a fixed bandwidth kernel estimate using a Laplace kernel. Extensions to higher order kernels are considered and some connections to penalized likelihood density estimators are made in the concluding sections.
Empirical Evaluation of Stochastikon Magister

Abstract In this paper we evaluate the learnability of the virtual classroom Stochastikon Magister [International Journal of Emerging Technologies in Learning (iJET) 4: 52–57, 2009, International Journal of Emerging Technologies in Learning (iJET) 4: 44–50, 2009] by comparing the examination scores of teacher candidates who selected the Magister E-Learning system to learn Bernoulli stochastics with those who selected a regular course in statistics. The evaluation result shows that Stochastikon Magister may well replace traditional classroom teaching in statistics.
Amelioration of Aflatoxin B1-induced gastrointestinal injuries by Eucalyptus oil in rats

Abstract Background Eucalyptus oil (EO), derived from Eucalyptus species, possesses vast remedial and healing properties, although its gut health-promoting properties have not been well investigated. In this study, we investigated the chemical composition of a commercial EO formulation and its potential role in protecting against aflatoxin B1 (AfB1)-induced gastrointestinal damage in rats. Methods Male Wistar rats were divided into six groups with eight rats each. Control rats were administered with the vehicle (1% Tween 80) for 14 days, while another group was exposed to two oral doses of AFB1 on days 12 and 14. Two other groups were pre-treated with oral doses of EO (50 and 100 mg/kg b.w.) for 14 consecutive days, along with two oral doses of AfB1 (5 mg/kg b.w.) on days 12 and 14. The remaining two groups were treated with EO alone at the two doses for 14 days. At the end of the experiment, blood samples, stomach and intestinal tissues were collected for measurement of oxidative stress and antioxidant parameters and light microscopic examination. Results Gas chromatography-mass spectrometry analysis revealed Eucalyptol (1, 8-cineole) as the main constituent (67.48%) of the oil. AfB1 administration induced oxidative and inflammatory disturbances, indicated by significantly (p<0.05) increased serum nitric oxide level and myeloperoxidase activity; increased tissue contents of hydrogen peroxide, malondialdehyde and protein carbonyls, accompanied with corresponding histological alterations. AfB1 also induced significant (p<0.05) reductions in glutathione peroxidase and superoxide dismutase (SOD) activities. Treatment with EO produced significant improvements in the biochemical parameters as well as the appearance of the gastric and intestinal mucosa. EO alone, at the two doses tested did not produce any significant changes in the parameters investigated. Conclusion The findings from this study showed that EO demonstrated protective activity against Aflatoxin-induced toxicity in stomach and intestinal tissues and may thus find application in treatment of gastrointestinal disorders.
OPTICAM: A TRIPLE-CAMERA OPTICAL SYSTEM DESIGNED TO EXPLORE THE FASTEST TIMESCALES IN ASTRONOMY

We report the development of a high-time resolution, 3-colour, simultaneous optical imaging system for the 2.1 m telescope in the San Pedro Mártir Observatory, México. OPTICAM will be equipped with three 2,048×2,048 pixel Andor Zyla 4.2-Plus sCMOS cameras and a set of SDSS filters allowing optical coverage in the 320–1,100 nm range. OPTICAM will nominally allow sub-second exposures. Given its instrumental design, a wide range of fast-variability astrophysical sources can be targeted with OPTICAM including X-ray binaries, pulsating white dwarfs, accreting compact objects, eclipsing binaries and exoplanets. OPTICAM observations will be proprietary for only six months and will then be made publicly available to the astronomical community.
New method of soft modes investigation by Little-Parks effect

As is known, Little-Parks effect concerned with oscillations of the critical temperature of a superconducting transition was one of the first effects which suggested the existence of Cooper pairing in conventional superconductors. It is shown that in high-temperature superconductors (HTSC) the Little-Parks effect based on bipolaron mechanism which in HTSC is the equivalent of Cooper pairing can be anomalously high. The results obtained can be used as a new method for detecting soft phonon modes in nanostructures on the basis of HTSC. The results can be also used to enhance the critical temperature of a superconducting transition.
How Sequence-to-Sequence Models Perceive Language Styles?

Style is ubiquitous in our daily language uses, while what is language style to learning machines? In this paper, by exploiting the second-order statistics of semantic vectors of different corpora, we present a novel perspective on this question via style matrix, i.e. the covariance matrix of semantic vectors, and explain for the first time how Sequence-to-Sequence models encode style information innately in its semantic vectors. As an application, we devise a learning-free text style transfer algorithm, which explicitly constructs a pair of transfer operators from the style matrices for style transfer. Moreover, our algorithm is also observed to be flexible enough to transfer out-of-domain sentences. Extensive experimental evidence justifies the informativeness of style matrix and the competitive performance of our proposed style transfer algorithm with the state-of-the-art methods.
Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs

Recent approaches to the Automatic Post-Editing (APE) research have shown that better results are obtained by multi-source models, which jointly encode both source (src) and machine translation output (mt) to produce post-edited sentence (pe). Along this trend, we present a new multi-source APE model based on the Transformer. To construct effective joint representations, our model internally learns to incorporate src context into mt representation. With this approach, we achieve a significant improvement over baseline systems, as well as the state-of-the-art multi-source APE model. Moreover, to demonstrate the capability of our model to incorporate src context, we show that the word alignment of the unknown MT system is successfully captured in our encoding results.
Gaia parallax of Milky Way globular clusters – A solution of mixture model

We have established a mixture model approach to derive the parallax of the Milky Way globular clusters. It avoids the problem of cluster membership determination and provides a completely independent astrometrical solution by purely using the parallax data. This method is validated with simulated clusters of Pancino et al. We have resolved 120 real globular clusters by the mixture model using parallaxes of the second data release of Gaia . They construct the largest direct parallax sample up to now. In comparison with other direct parallax results based on cluster members, including 75 clusters of Gaia Collaboration, our method presents its accuracy, especially for some particular clusters. A systematic offset of −27.6 ± 1.7 μas, together with a scatter of 22.8 ± 1.3 μas is found in comparison with other indirect parallax measurements. They are consistent with the global value and the variation of the zero-point of current Gaia parallaxes. Distances of several specific nearby globular clusters are discussed while the closest ones can reach high precisions, even taking the systematic error into account.
Sub-spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion

Environmental Sound Classification (ESC) is an important and challenging problem, and feature representation is a critical and even decisive factor in ESC. Feature representation ability directly affects the accuracy of sound classification. Therefore, the ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. In this paper, we propose a sub-spectrogram segmentation based ESC classification framework. In addition, we adopt the proposed Convolutional Recurrent Neural Network (CRNN) and score level fusion to jointly improve the classification accuracy. Extensive truncation schemes are evaluated to find the optimal number and the corresponding band ranges of sub-spectrograms. Based on the numerical experiments, the proposed framework can achieve 81.9% ESC classification accuracy on the public dataset ESC-50, which provides 9.1% accuracy improvement over traditional baseline schemes.
Edge Computing-Enabled Cell-Free Massive MIMO Systems

Mobile edge computing (MEC) has been introduced to provide additional computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell-free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the cloud computing capability. After deriving successful communication and computing probabilities using stochastic geometry and queueing theory, we present the successful edge computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs

Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices. However, the leading optimization algorithm in such settings, i.e., federated averaging (FedAvg), suffers from heavy communication costs and the inevitable performance drop, especially when the local data is distributed in a non-IID way. To alleviate this problem, we propose two potential solutions by introducing additional mechanisms to the on-device training. The first (FedMMD) is adopting a two-stream model with the MMD (Maximum Mean Discrepancy) constraint instead of a single model in vanilla FedAvg to be trained on devices. Experiments show that the proposed method outperforms baselines, especially in non-IID FL settings, with a reduction of more than 20% in required communication rounds. The second is FL with feature fusion (FedFusion). By aggregating the features from both the local and global models, we achieve higher accuracy at fewer communication costs. Furthermore, the feature fusion modules offer better initialization for newly incoming clients and thus speed up the process of convergence. Experiments in popular FL scenarios show that our FedFusion outperforms baselines in both accuracy and generalization ability while reducing the number of required communication rounds by more than 60%.
Linear in Temperature Resistivity and Associated Mysteries

Recent experimental results: (i) the measurement of the singular specific heat in cuprates and the earlier such results in some heavy fermion compounds, (ii) the measurement of the single-particle scattering rates, (iii) the density fluctuation spectrum in cuprates and (iv) the long standing results on the linear temperature dependence of the resistivity, are used to show that a theory of the quantum-criticality in these compounds based on the solution of the dissipative 2D - XY model {\it quantitatively} gives the temperature and frequency dependence of such properties with one parameter - the dimensionless coupling constant of the fermions to the quantum-critical fluctuations. The asymptotic low frequency or temperature dependences persist to an upper cut-off scale which is given by another parameter in the theory - the upper cut-off of the derived fluctuations. This can be read from the measured singularity in the specific heat or the saturation of the single-particle relaxation rate. The two parameter had earlier been estimated from microscopic parameters to within a factor of about 2. The cross-over exponent from quantum-criticality to Fermi-liquid is estimated from specific heat and resistivity data to be about 1/2, also in agreement with the results on the model. The simplicity of the results depends on the discovery that orthogonal topological excitations in space and in time determine the fluctuations near criticality such that the space and time metrics are free of each other. Only then do the interacting fermions form a marginal Fermi-liquid.
ScarletNAS: Bridging the Gap Between Scalability and Fairness in Neural Architecture Search

One-shot neural architecture search features fast training of a supernet in a single run. A pivotal issue for this weight-sharing approach is the lacking of scalability. A simple adjustment with identity block renders a scalable supernet but it arouses unstable training, which makes the subsequent model ranking unreliable. In this paper, we introduce linearly equivalent transformation to soothe training turbulence, providing with the proof that such transformed path is identical with the original one as per representational power. The overall method is named as SCARLET (SCAlable supeRnet with Linearly Equivalent Transformation). We show through experiments that linearly equivalent transformations can indeed harmonize the supernet training. With an EfﬁcientNet-like search space and a multi-objective reinforced evolutionary backend, it generates a series of competitive models: SCARLET-A achieves 76.9% top-1 accuracy on ImageNet which outperforms EfﬁcientNet-B0 by a large margin; the shallower SCARLET-B exempliﬁes the proposed scalability which attains the same accuracy 76.3% as EfﬁcientNet-B0 with much fewer FLOPs; SCARLET-C scores competitive 75.6% with comparable sizes. The models and evaluation code are released online 1 .
Statistical analysis on X-ray flares from the nucleus and HST-1 knot in the M87 jet

The statistical properties of X-ray flares from two separate locations (nucleus and HST-1) in the M87 jet are investigated to reveal the physical origin of the flares. We analyse the archival Chandra data for M87, and identify 14 flares in the nucleus and nine flares in HST-1. The peak intensity (IP) and the flaring duration time (Tfl) for each flare are obtained. It is found that the distributions of both IP and Tfl for the nucleus obey a power law form with a similar index. A similar result is also obtained for HST-1, and no significant inconsistency between the nucleus and HST-1 is found for the indices. Similar to solar X-ray flares, the power-law distributions of the flare event parameters can be well explained by a self-organized criticality system, which are triggered by magnetic reconnection. Our results suggest that the flares from nucleus and HST-1 are possibly triggered by magnetic reconnection process. The consistent indices for the distributions of IP and Tfl in the nucleus and HST-1 indicate that the dimensions of the energy dissipation of the magnetic reconnection are identical in the two regions. A strong correlation between the flares in the two regions also suggests a similar physical origin for the flares.
Quantum geometry from higher gauge theory

Higher gauge theories play a prominent role in the construction of 4D topological invariants and have been long ago proposed as a tool for 4D quantum gravity. The Yetter lattice model and its continuum counterpart, the BFCG theory, generalize BF theory to 2-gauge groups and—when specialized to 4D and the Poincaré 2-group—they provide an exactly solvable topologically-flat version of 4D general relativity. The 2-Poincaré Yetter model was conjectured to be equivalent to a state sum model of quantum flat spacetime developed by Baratin and Freidel after work by Korepanov (KBF model). This conjecture was motivated by the origin of the KBF model in the theory of two-representations of the Poincaré 2-group. Its proof, however, has remained elusive due to the lack of a generalized Peter–Weyl theorem for 2-groups. In this work we prove this conjecture. Our proof avoids the Peter–Weyl theorem and rather leverages the geometrical content of the Yetter model. Key for the proof is the introduction of a kinematical boundary Hilbert space on which 1- and two-Lorentz invariance is imposed. Geometrically this allows the identification of (quantum) tetrad variables and of the associated (quantum) Levi-Civita connection. States in this Hilbert space are labelled by quantum numbers that match the two-group representation labels. Our results open exciting opportunities for the construction of new representations of quantum geometries. Compared to loop quantum gravity, the higher gauge theory framework provides a quantum representation of the ADM—Regge initial data, including an identification of the intrinsic and extrinsic curvature. Furthermore, it leads to a version of the diffeomorphism and Hamiltonian constraints that acts on the vertices of the discretization, thus providing a prospect for a quantum realization of the hypersurface deformation algebra in 4D.
The Angel is in the Priors: Improving GAN based Image and Sequence Inpainting with Better Noise and Structural Priors

Contemporary deep learning based inpainting algorithms are mainly based on a hybrid dual stage training policy of supervised reconstruction loss followed by an unsupervised adversarial critic loss. However, there is a dearth of literature for a fully unsupervised GAN based inpainting framework. The primary aversion towards the latter genre is due to its prohibitively slow iterative optimization requirement during inference to find a matching noise prior for a masked image. In this paper, we show that priors matter in GAN: we learn a data driven parametric network to predict a matching prior for a given image. This converts an iterative paradigm to a single feed forward inference pipeline with a massive 1500X speedup and simultaneous improvement in reconstruction quality. We show that an additional structural prior imposed on GAN model results in higher fidelity outputs. To extend our model for sequence inpainting, we propose a recurrent net based grouped noise prior learning. To our knowledge, this is the first demonstration of an unsupervised GAN based sequence inpainting. A further improvement in sequence inpainting is achieved with an additional subsequence consistency loss. These contributions improve the spatio-temporal characteristics of reconstructed sequences. Extensive experiments conducted on SVHN, Standford Cars, CelebA and CelebA-HQ image datasets, synthetic sequences and ViDTIMIT video datasets reveal that we consistently improve upon previous unsupervised baseline and also achieve comparable performances(sometimes also better) to hybrid benchmarks.
stdgpu: Efficient STL-like Data Structures on the GPU

Tremendous advances in parallel computing and graphics hardware opened up several novel real-time GPU applications in the fields of computer vision, computer graphics as well as augmented reality (AR) and virtual reality (VR). Although these applications built upon established open-source frameworks that provide highly optimized algorithms, they often come with custom self-written data structures to manage the underlying data. In this work, we present stdgpu, an open-source library which defines several generic GPU data structures for fast and reliable data management. Rather than abandoning previous established frameworks, our library aims to extend them, therefore bridging the gap between CPU and GPU computing. This way, it provides clean and familiar interfaces and integrates seamlessly into new as well as existing projects. We hope to foster further developments towards unified CPU and GPU computing and welcome contributions from the community.
Using Schematic Models to Understand the Microscopic Basis for Inverted Solubility in γD-crystallin.

Inverted solubility--melting a crystal by cooling--is observed in a handful of proteins, such as carbomonoxy hemoglobin and $\gamma$D-crystallin. In human $\gamma$D-crystallin, the phenomenon is associated with the mutation of the 23$^\mathrm{rd}$ residue, a proline, to a threonine, serine or valine. One proposed microscopic mechanism entails an increase in surface hydrophobicity upon mutagenesis. Recent crystal structures of a double mutant that includes the P23T mutation allow for a more careful investigation of this proposal. Here, we first measure the surface hydrophobicity of various mutant structures of $\gamma$D-crystallin and discern no notable increase in hydrophobicity upon mutating the 23$^\mathrm{rd}$ residue. We then investigate the solubility inversion regime with a schematic patchy particle model that includes one of three variants of temperature-dependent patch energies: two of the hydrophobic effect, and one of a more generic nature. We conclude that while solubility inversion due to the hydrophobic effect may be possible, microscopic evidence to support it in $\gamma$D-crystallin is weak. More generally, we find that solubility inversion requires a fine balance between patch strengths and their temperature-dependent component, which may explain why inverted solubility is not commonly observed in proteins. We also find that the temperature-dependent interaction has only a negligible impact on liquid-liquid phase boundaries of $\gamma$D-crystallin, in line with previous experimental observations.
Double-Coupling Learning for Multi-Task Data Stream Classification

Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handling them separately, it is beneficial to consider the correlations among the multi-task data streams for data stream modeling tasks. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM), is proposed for classifying them simultaneously. DC-SVM considers the external correlations between multiple data streams, while handling the internal relationship within the individual data stream. Experimental results on artificial and real-world multi-task data streams demonstrate that the proposed method outperforms traditional data stream classification methods.