Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nature of the Eccentric Double-lined Eclipsing Binary System KIC 2306740 with Kepler Space Photometry

We present a detailed study of KIC 2306740, an eccentric double-lined eclipsing binary system. Kepler satellite data were combined with spectroscopic data obtained with the 4.2 m William Herschel Telescope (WHT). This allowed us to determine precise orbital and physical parameters of this relatively long period (P = 10.ͩ3) and slightly eccentric (e = 0.3) binary system. The physical parameters have been determined to be M 1 = 1.194 ± 0.008 M ⊙, M 2 = 1.078 ± 0.007 M ⊙, R 1 = 1.682 ± 0.004 R ⊙, R 2 = 1.226 ± 0.005 R ⊙, L 1 = 2.8 ± 0.4 L ⊙, L 2 = 1.8 ± 0.2 L ⊙, and orbital separation a = 26.20 ± 0.04 R ⊙ through simultaneous solutions of the Kepler light curves and WHT radial velocity data. Binarity effects were extracted from the light curve in order to study intrinsic variations in the residuals. Five significant and more than 100 combination frequencies were detected. We modeled the binary system assuming nonconservative evolution models with the Cambridge stars (twin) code, and we show the evolutionary tracks of the components in the logL–logT plane, the logR–logM plane, and the logP–age plane for both spin and orbital periods together with eccentricity e and logR1 . The model of the nonconservative processes in the code led the system to evolve to the observed system parameters in roughly 5.1 Gyr.
Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications

We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. For this, we propose a low-complexity recurrent neural network (RNN)-based estimator that allows channel equalization of a sequence of channel observations based on independent time- and frequency-domain long short-term memory (LSTM) cells. Motivated by Vehicle-to-Everything (V2X) applications, we simulate time- and frequency-selective channels with orthogonal frequency division multiplex (OFDM) and extend our channel models in such a way that a continuous degradation from line-of-sight (LoS) to non-line-of-sight (NLoS) conditions can be emulated. It turns out that the NN-based system cannot just compete with the LMMSE equalizer, but it also can be trained w.r. t. resilience against system parameter mismatch. We thereby showcase the conceptual simplicity of such a data-driven system design, as this not only enables more robustness against, e.g., signal-to-noise-ratio (SNR) or Doppler spread estimation mismatches, but also allows to use the same equalizer over a wider range of input parameters without the need of re-building (or re-estimating) the filter coefficients. Particular attention has been paid to ensure compatibility with the existing IEEE 802.11p piloting scheme for V2X communications. Finally, feeding the payload data symbols as additional equalizer input unleashes further performance gains. We show significant gains over the conventional LMMSE equalization for highly dynamic channel conditions if such a data-augmented equalization scheme is used.
Compressibility and Fractal Dimension of Fine Coal Particles in Relation to Pore Structure Characterisation Using Mercury Porosimetry

Mercury porosimetry has been applied to characterize the pore structure of fine coals particles. Interparticle voids and compressibility effects on the mercury intrusion data were examined. It is found that coal compressibility has a significant effect on mercury porosimetry data when pressure P > 20 MPa. The compressibility of the two coals used was determined to be 3.13 x 10(-10) m(2) N-1 and 2.50 x 10(-10) m(2) N-1 for CA and GO coals, respectively. Fractal dimension analysis provides a fingerprint to distinguish the effect of coal compression from the pore filling process during mercury intrusion. It is shown that fractal dimension can be evaluated from the compressibility corrected pore volume data. Results from the present study suggest that statistic self-similarity of the fractal dimension perspective is limited by certain artificial effects, such as crushing and grinding. Different surface irregularities exist over different pore size ranges, and a single fractal dimension value can only be used to describe the surface irregularity within a limited pore size range. The average fractal dimensions in the pore size range of 6-60 nm were found to be 2.71 and 2.43 for CA and GO coals, respectively.
Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of training-evaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2 cm to 10 cm better position accuracy than known NN structures used as a reference.
ChainCQG: Flow-Aware Conversational Question Generation

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy. ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.
Semi-Synchronous Federated Learning

There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. For example, data present in users’ cellphones, manufacturing data of companies in a given industrial sector, or medical records located at different hospitals. Federated Learning (FL) provides an approach to learn a joint model over all the available data across silos. In many cases, participating sites have different data distributions and computational capabilities. In these heterogeneous environments previous approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence; conversely, asynchronous FL protocols have faster convergence, but at a higher communication cost. Here we introduce a novel Semi-Synchronous Federated Learning protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments that our approach significantly outperforms previous work in data and computationally heterogeneous environments.
Dyck Paths Categories And Its Relationships With Cluster Algebras

Dyck paths categories are introduced as a combinatorial model of the category of representations of quivers of Dynkin type An. In particular, it is proved that there is a bijection between some Dyck paths and perfect matchings of some snake graphs. The approach allows us to give formulas for cluster variables in cluster algebras of Dynkin type An in terms of Dyck paths.
A database of spectral energy distributions of progenitors of core-collapse supernovae*

This paper presents a database of the spectroscopic- and photometric-spectral energy distributions (spec-SEDs and phot-SEDs respectively) of the progenitors of core-collapse supernovae (CCSNe). Both binary- and single-star progenitors are included in the database. The database covers the initial metallicity (Z) range of 0.0001–0.03, mass range of 8–25 M ⊙, binary mass ratio range of 0–1, and orbital period range of 0.1–10 000 d. The low-resolution spec-SEDs and phot-SEDs of single- and binary-star CCSN progenitors are included in the database. These data can be used for studying the basic parameters, e.g., metallicity, age, and initial and final masses of CCSN progenitors. It can also be used for studying the effects of different factors on the determination of parameters of CCSN progenitors. When the database is utilized for fitting the SEDs of binary-star CCSN progenitors, it is strongly suggested to determine the metallicity and orbital period in advance, but this is not necessary for single-star progenitors.
Unsupervised Novel View Synthesis from a Single Image

Novel view synthesis from a single image aims at generating novel views from a single input image of an object. Several works recently achieved remarkable results, though require some form of multi-view supervision at training time, therefore limiting their deployment in real scenarios. This work aims at relaxing this assumption enabling training of conditional generative model for novel view synthesis in a completely unsupervised manner. We first pre-train a purely generative decoder model using a GAN formulation while at the same time training an encoder network to invert the mapping from latent code to images. Then we swap encoder and decoder and train the network as a conditioned GAN with a mixture of auto-encoder-like objective and self-distillation. At test time, given a view of an object, our model first embeds the image content in a latent code and regresses its pose w.r. t. a canonical reference system, then generates novel views of it by keeping the code and varying the pose. We show that our framework achieves results comparable to the state of the art on ShapeNet and that it can be employed on unconstrained collections of natural images, where no competing method can be trained.
Stability of overshoots of Markov additive processes

We prove precise stability results for overshoots of Markov additive processes (MAPs) with finite modulating space. Our approach is based on the Markovian nature of overshoots of MAPs whose mixing and ergodic properties are investigated in terms of the characteristics of the MAP. On our way we extend fluctuation theory of MAPs, contributing among others to the understanding of the Wiener-Hopf factorization for MAPs by generalizing Vigon's \'equations amicales invers\'es known for L\'evy processes. Using the Lamperti transformation the results can be applied to self-similar Markov processes. Among many possible applications, we study the mixing behavior of stable processes sampled at first hitting times as a concrete example.
Nonequilibrium statistical mechanics of crystals

The local equilibrium approach previously developed by the authors (J Mabillard and P Gaspard 2020 J. Stat. Mech. 103203) for matter with broken symmetries is applied to crystalline solids. The macroscopic hydrodynamics of crystals and their local thermodynamic and transport properties are deduced from the microscopic Hamiltonian dynamics. In particular, the Green–Kubo formulas are obtained for all the transport coefficients. The eight hydrodynamic modes and their dispersion relation are studied for general and cubic crystals. In the same twenty crystallographic classes as those compatible with piezoelectricity, cross effects coupling transport between linear momentum and heat or crystalline order are shown to split the degeneracy of damping rates for modes propagating in opposite generic directions.
On the Sample Complexity of Causal Discovery and the Value of Domain Expertise

Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the seminal works in this area is the Inferred Causation algorithm, which guarantees successful causal discovery under the assumption of a conditional independence (CI) oracle: an oracle that can states whether two random variables are conditionally independent given another set of random variables. Practical implementations of this algorithm incorporate statistical tests for conditional independence, in place of a CI oracle. In this paper, we analyze the sample complexity of causal discovery algorithms without a CI oracle: given a certain level of confidence, how many data points are needed for a causal discovery algorithm to identify a causal structure? Furthermore, our methods allow us to quantify the value of domain expertise in terms of data samples. Finally, we demonstrate the accuracy of these sample rates with numerical examples, and quantify the benefits of sparsity priors and known causal directions.
Quantifying Bounds of Model Gap for Synchronous Generators

In practice, uncertainties in parameters and model structures always cause a gap between a model and the corresponding physical entity. Hence, to evaluate the performance of a model, the bounds of this gap must be assessed. In this paper, we propose a trajectory-sensitivity--based approach to quantify the bounds of the gap. The trajectory sensitivity is expressed as a linear time-varying system. We thus first derive several bounds for a general linear time-varying system in different scenarios. The derived bounds are then applied to obtain bounds of the model gap for generator plant models with different types of structural information, e.g., models of different orders. Case studies are carried out to show the efficacy of the bounds through synchronous generator models on different accuracy levels.
Fast and Memory Efficient Differentially Private-SGD via JL Projections

Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requires computation of per-sample gradients norms which is extremely slow and memory intensive in practice. In this paper, we present a new framework to design differentially private optimizers called DP-SGD-JL and DP-Adam-JL. Our approach uses Johnson-Lindenstrauss (JL) projections to quickly approximate the per-sample gradient norms without exactly computing them, thus making the training time and memory requirements of our optimizers closer to that of their non-DP versions. Unlike previous attempts to make DP-SGD faster which work only on a subset of network architectures or use compiler techniques, we propose an algorithmic solution which works for any network in a black-box manner which is the main contribution of this paper. To illustrate this, on IMDb dataset, we train a Recurrent Neural Network (RNN) to achieve good privacy-vs-accuracy tradeoff, while being significantly faster than DP-SGD and with a similar memory footprint as non-private SGD. The privacy analysis of our algorithms is more involved than DP-SGD, we use the recently proposed f-DP framework of Dong et al. (2019) to prove privacy.
Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms

We propose perturbed proximal algorithms that can provably escape strict saddles for nonsmooth weakly convex functions. The main results are based on a novel characterization of -approximate local minimum for nonsmooth functions, and recent developments on perturbed gradient methods for escaping saddle points for smooth problems. Specifically, we show that under standard assumptions, the perturbed proximal point, perturbed proximal gradient and perturbed proximal linear algorithms find -approximate local minimum for nonsmooth weakly convex functions in O( −2 log(d)) iterations, where d is the dimension of the problem. Keywords— Nonsmooth Optimization, Saddle Point, Perturbed Proximal Algorithms
Removing biased data to improve fairness and accuracy

Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data will also be biased. We propose a black-box approach to identify and remove biased training data. Machine learning models trained on such debiased data (a subset of the original training data) have low individual discrimination, often 0%. These models also have greater accuracy and lower statistical disparity than models trained on the full historical data. We evaluated our methodology in experiments using 6 real-world datasets. Our approach outperformed seven previous approaches in terms of individual discrimination and accuracy.
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers

The size of Transformer models is growing at an unprecedented pace. It has only taken less than one year to reach trillion-level parameters after the release of GPT-3 (175B). Training such models requires both substantial engineering efforts and enormous computing resources, which are luxuries most research teams cannot afford. In this paper, we propose PipeTransformer, which leverages automated and elastic pipelining and data parallelism for efficient distributed training of Transformer models. PipeTransformer automatically adjusts the pipelining and data parallelism by identifying and freezing some layers during the training, and instead allocates resources for training of the remaining active layers. More specifically, PipeTransformer dynamically excludes converged layers from the pipeline, packs active layers into fewer GPUs, and forks more replicas to increase data-parallel width. We evaluate PipeTransformer using Vision Transformer (ViT) on ImageNet and BERT on GLUE and SQuAD datasets. Our results show that PipeTransformer attains a 2.4 fold speedup compared to the state-of-the-art baseline. We also provide various performance analyses for a more comprehensive understanding of our algorithmic and system-wise design. We also develop open-sourced flexible APIs for PipeTransformer, which offer a clean separation among the freeze algorithm, model definitions, and training accelerations, hence allowing it to be applied to other algorithms that require similar freezing strategies.
F eb 2 02 1 UPR-1308-T MI-TH-212 Conformal Symmetries for Extremal Black Holes with General Asymptotic Scalars in STU

We present a construction of the most general BPS black holes of STU supergravity (N = 2 supersymmetric D = 4 supergravity coupled to three vector super-multiplets) with arbitrary asymptotic values of the scalar fields. These solutions are obtained by acting with a subset of of the global symmetry generators on STU BPS black holes with zero values of the asymptotic scalars, both in the U-duality and the heterotic frame. The solutions are parameterized by fourteen parameters: four electric and four magnetic charges, and the asymptotic values of the six scalar fields. We also present BPS black hole solutions of a consistently truncated STU supergravity, which are parameterized by two electric and two magnetic charges and two scalar fields. These latter solutions are significantly simplified, and are very suitable for further explicit studies. We also explore a conformal inversion symmetry of the Couch-Torrence type, which maps any member of the fourteen-parameter family of BPS black holes to another member of the family. Furthermore, these solutions are expected to be valuable in the studies of various swampland conjectures in the moduli space of string compactifications.
How Enzymes, Proteins, and Antibodies Recognize Extended DNAs; General Regularities

X-ray analysis cannot provide quantitative estimates of the relative contribution of non-specific, specific, strong, and weak contacts of extended DNA molecules to their total affinity for enzymes and proteins. The interaction of different enzymes and proteins with long DNA and RNA at the quantitative molecular level can be successfully analyzed using the method of the stepwise increase in ligand complexity (SILC). The present review summarizes the data on stepwise increase in ligand complexity (SILC) analysis of nucleic acid recognition by various enzymes—replication, restriction, integration, topoisomerization, six different repair enzymes (uracil DNA glycosylase, Fpg protein from Escherichia coli, human 8-oxoguanine-DNA glycosylase, human apurinic/apyrimidinic endonuclease, RecA protein, and DNA-ligase), and five DNA-recognizing proteins (RNA helicase, human lactoferrin, alfa-lactalbumin, human blood albumin, and IgGs against DNA). The relative contributions of structural elements of DNA fragments “covered” by globules of enzymes and proteins to the total affinity of DNA have been evaluated. Thermodynamic and catalytic factors providing discrimination of unspecific and specific DNAs by these enzymes on the stages of primary complex formation following changes in enzymes and DNAs or RNAs conformations and direct processing of the catalysis of the reactions were found. General regularities of recognition of nucleic acid by DNA-dependent enzymes, proteins, and antibodies were established.
Natural Herbal Estrogen-Mimetics (Phytoestrogens) Promote the Differentiation of Fallopian Tube Epithelium into Multi-Ciliated Cells via Estrogen Receptor Beta

Phytoestrogens are herbal polyphenolic compounds that exert various estrogen-like effects in animals and can be taken in easily from a foodstuff in daily life. The fallopian tube lumen, where transportation of the oocyte occurs, is lined with secretory cells and multi-ciliated epithelial cells. Recently, we showed that estrogen induces multi-ciliogenesis in the porcine fallopian tube epithelial cells (FTECs) through the activation of the estrogen receptor beta (ERβ) pathway and simultaneous inhibition of the Notch pathway. Thus, ingested phytoestrogens may induce FTEC ciliogenesis and thereby affect the fecundity. To address this issue, we added isoflavones (genistein, daidzein, or glycitin) and coumestan (coumestrol) to primary culture FTECs under air–liquid interface conditions and assessed the effects of each compound. All phytoestrogens except glycitin induced multi-ciliated cell differentiation, which followed Notch signal downregulation. On the contrary, the differentiation of secretory cells decreased slightly. Furthermore, genistein and daidzein had a slight effect on the proportion of proliferating cells exhibited by Ki67 expression. Ciliated-cell differentiation is inhibited by the ERβ antagonist, PHTPP. Thus, this study suggests that phytoestrogens can improve the fallopian tube epithelial sheet homeostasis by facilitating the genesis of multi-ciliated cells and this effect depends on the ERβ-mediated pathway.