Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model for the Fast Blue Optical Transient AT2018cow: Circumstellar Interaction of a Pulsational Pair-instability Supernova

The fast blue optical transient (FBOT) ATLAS18qqn (AT2018cow) has a light curve as bright as that of superluminous supernovae (SLSNe) but rises and falls much faster. We model this light curve by circumstellar interaction of a pulsational pair-instability (PPI) supernova (SN) model based on our PPISN models studied in previous work. We focus on the 42 M⊙ He star (core of a 80 M⊙ star) which has circumstellar matter (CSM) of mass 0.50 M⊙. With the parameterized mass cut and the kinetic energy of explosion E, we perform hydrodynamical calculations of nucleosynthesis and optical light curves of PPISN models. The optical light curve of the first ∼20 days of AT2018cow is well reproduced by the shock heating of CSM for the 42 M⊙ He star with E = 5 × 1051 erg. After day 20, the light curve is reproduced by the radioactive decay of 0.6 M ⊙ 56 Co, which is a decay product of 56Ni in the explosion. We also examine how the light-curve shape depends on the various model parameters, such as CSM structure and composition. We also discuss (1) other possible energy sources and their constraints, (2) the origin of the observed high-energy radiation, and (3) how our result depends on the radiative transfer codes. Based on our successful model for AT2018cow and the model for SLSN with CSM mass as large as 20 M ⊙ , we propose the working hypothesis that PPISN produces SLSNe if the CSM is massive enough and FBOTs if CSM is less than ∼1 M⊙.
Measuring the spectral index of turbulent gas with deep learning from projected density maps

Turbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of their initial conditions, better constraints on turbulence can result in windfalls in Galactic archaeology, star cluster dynamics and star formation. Observationally, constraining the spectral index of turbulent gas usually involves computing spectra from velocity maps. Here we suggest that information on the spectral index might be directly inferred from column density maps (possibly obtained by dust emission/absorption) through deep learning. We generate mock density maps from a large set of adaptive mesh refinement turbulent gas simulations using the hydro-simulation code RAMSES. We train a convolutional neural network (CNN) on the resulting images to predict the turbulence index, optimize hyper-parameters in validation and test on a holdout set. Our adopted CNN model achieves a mean squared error of 0.024 in its predictions on our holdout set, over underlying spectral indexes ranging from 3 to 4.5. We also perform robustness tests by applying our model to altered holdout set images, and to images obtained by running simulations at different resolutions. This preliminary result on simulated density maps encourages further developments on real data, where observational biases and other issues need to be taken into account.
DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Degraded Audio Signals

Automatic speaker recognition algorithms typically use pre-defined filterbanks, such as Mel-Frequency and Gammatone filterbanks, for characterizing speech audio. The design of these filterbanks is based on domain-knowledge and limited empirical observations. The resultant features, therefore, may not generalize well to different types of audio degradation. In this work, we propose a deep learning-based technique to induce the filterbank design from vast amounts of speech audio. The purpose of such a filterbank is to extract features robust to degradations in the input audio. To this effect, a 1D convolutional neural network is designed to learn a time-domain filterbank called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet mining technique is developed to efficiently mine the data samples best suited to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX filterbanks reveals the presence of both vocal source and vocal tract characteristics in the extracted features. Experimental results on VOXCeleb2, NIST SRE 2008 and 2010, and Fisher speech datasets demonstrate the efficacy of the DeepVOX features across a variety of audio degradations, multi-lingual speech data, and varying-duration speech audio. The DeepVOX features also improve the performance of existing speaker recognition algorithms, such as the xVector-PLDA and the iVector-PLDA.
On Structured-Closed-Loop versus Structured-Controller Design: the Case of Relative Measurement Feedback

We consider optimal distributed controller design problems with two separate structural constraints: locality and relative feedback. The locality constraint accounts for subcontroller interactions restricted to local neighborhoods as specified by an underlying graph structure. We provide a detailed characterization of such locality constraints imposed on an implementation of a controller (sparsity of state space matrices), in contrast to locality constraints on the sparsity of the controller input-output mapping (transfer function), which is the main tool used in the recently developed System Level Synthesis (SLS) framework. We formally show how the latter problem is a convex relaxation of the former, original problem. We then formulate and show how to append relative feedback constraints that are natural in many problems where only differential sensors are available, such as in vehicular formation and consensus-type problems. We show that for a class of plants, these constraints can be imposed in a convex manner. Our main result demonstrates that when relative feedback constraints are imposed in addition to locality, the SLS convex relaxation can become infeasible. We use these insights to provide a comparative context for the various structured distributed control design methods such as SLS, funnel causality and quadratic invariance, as well as the open problems of structured and networked realizations.
High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality

We develop the first parallel graph coloring heuristics with strong theoretical guarantees on work and depth and coloring quality. The key idea is to design a relaxation of the vertex degeneracy order, a well-known graph theory concept, and to color vertices in the order dictated by this relaxation. This introduces a tunable amount of parallelism into the degeneracy ordering that is otherwise hard to parallelize. This simple idea enables significant benefits in several key aspects of graph coloring. For example, one of our algorithms ensures polylogarithmic depth and a bound on the number of used colors that is superior to all other parallelizable schemes, while maintaining workefficiency. In addition to provable guarantees, the developed algorithms have competitive run-times for several real-world graphs, while almost always providing superior coloring quality. Our degeneracy ordering relaxation is of separate interest for algorithms outside the context of coloring.
Growth-laws and invariants from ribosome biogenesis in lower Eukarya

Eukarya and Bacteria are the most evolutionarily distant domains of life, which is reflected by differences in their cellular structure and physiology. For example, Eukarya feature membrane-bound organelles such as nuclei and mitochondria, whereas Bacteria have none. The greater complexity of Eukarya renders them difficult to study from both an experimental and theoretical perspective. However, encouraged by a recent experimental result showing that budding yeast (a unicellular eukaryote) obeys the same proportionality between ribosomal proteome fractions and cellular growth rates as Bacteria, we derive a set of relations describing eukaryotic growth from first principles of ribosome biogenesis. We recover the observed ribosomal protein proportionality, and then continue to obtain two growth-laws for the number of RNA polymerases synthesizing ribosomal RNA per ribosome in the cell. These growth-laws, in turn, reveal two invariants of eukaryotic growth, i.e. quantities predicted to be conserved by Eukarya regardless of growth conditions. The invariants, which are the first of their kind for Eukarya, clarify the coordination of transcription and translation kinetics as required by ribosome biogenesis, and link these kinetic parameters to cellular physiology. We demonstrate application of the relations to the yeast S. cerevisiae and find the predictions to be in good agreement with currently available data. We then outline methods to quantitatively deduce several unknown kinetic and physiological parameters. The analysis is not specific to S. cerevisiae and can be extended to other lower (unicellular) Eukarya when data become available. The relations may also have relevance to certain cancer cells which, like bacteria and yeast, exhibit rapid cell proliferation and ribosome biogenesis.
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA

The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a viable approach to reduce the memory pressure issue, significant modification of the source code and considerations for algorithms are required. An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.We propose a performance model based on the concurrency analysis of out-of-core training behavior, and derive a strategy that combines layer swapping and redundant recomputing. We achieve an average of 1. 52x speedup in six different models over the state-of-the-art out-of-core methods. We also introduce the first method to solve the challenging problem of out-of-core multi-node training by carefully pipelining gradient exchanges and performing the parameter updates on the host. Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g. Megatron-LM and Turning-NLG.
Establishment of a radioresistant subline from human nasopharyngeal carcinoma cell line and its cell cycle distribution pattern

OBJECTIVE:To establish a radioresistant subline from human nasopharyngeal carcinoma cell line CNE-2,so as to supply comparable pair cells for investigating the mechanism of radioresistance.METHODS:CNE-2 human nasopharyngeal carcinoma cells were exposed repeatedly to γ-rays and then screened for a new radioresistant subline named CNE-2R.Double times of CNE-2 and CNE-2R cells were determined by MTT assays.The relative radiosensitivities of tumor cells were assessed by standard colony formation assays.Survival rates after exposed to irradiation were measured by MTT assays.The time courses of cell cycle distribution before and after irradiation were investigated by flow cytometry(FCM). RESULTS:The double time of CNE-2R cells was longer than that of parental generation CNE-2 cells(4.5 d vs 3.6 d,P=0.047). By using the colony-forming assay and line-quadratic model,the parameters of CNE-2 and CNE-2R were obtained.The radioresistance of CNE-2R cells was stronger than that of CNE-2 cells.After exposed to irradiation respectively,the survival rate in CNE-2R was also higher than that in CNE-2(89.0% vs 71.7%,P=0.001). The CNE-2R subline showed higher percentage of cells in S phase,and lower percentage of cells in G0/G1 and G2/M phases(χ2=7.312,P=0.034). A marked accumulation of cells in G2/M phase was observed in CNE-2R cells during the period from 12 to 36 h after irradiation A little increase of cells in G2/M phase was observed in CNE-2 cells from 12 to 24 h after irradiation.CONCLUSION:The new subline CNE-2R is more radioresistant comparable to its pa
Temporal Action Localization with Variance-Aware Networks

This work addresses the problem of temporal action localization with Variance-Aware Networks (VAN), i.e., DNNs that use second-order statistics in the input and/or the output of regression tasks. We first propose a network (VANp) that when presented with the second-order statistics of the input, i.e., each sample has a mean and a variance, it propagates the mean and the variance throughout the network to deliver outputs with second order statistics. In this framework, both the input and the output could be interpreted as Gaussians. To do so, we derive differentiable analytic solutions, or reasonable approximations, to propagate across commonly used NN layers. To train the network, we define a differentiable loss based on the KL-divergence between the predicted Gaussian and a Gaussian around the ground truth action borders, and use standard back-propagation. Importantly, the variances propagation in VANp does not require any additional parameters, and during testing, does not require any additional computations either. In action localization, the means and the variances of the input are computed at pooling operations, that are typically used to bring arbitrarily long videos to a vector with fixed dimensions. Second, we propose two alternative formulations that augment the first (respectively, the last) layer of a regression network with additional parameters so as to take in the input (respectively, predict in the output) both means and variances. Results in the action localization problem show that the incorporation of second order statistics improves over the baseline network, and that VANp surpasses the accuracy of virtually all other two-stage networks without involving any additional parameters.
5G Utility Pole Planner Using Google Street View and Mask R-CNN

With the advances of fifth-generation (5G) [1] cellular networks technology, many studies and work have been carried out on how to build 5G networks for smart cities. In the previous research, street lighting poles and smart light poles are capable of being a 5G access point[2]. In order to determine the position of the points, this paper discusses a new way to identify poles based on Mask R-CNN[3], which extends Fast R-CNNs[4] by making it employ recursive Bayesian filtering and perform proposal propagation and reuse. The dataset contains 3,000 high-resolution images from google map. To make training faster, we used a very efficient GPU implementation of the convolution operation. We achieved a train error rate of 7.86% and a test error rate of 32.03%. At last, we used the immune algorithm [5] [6] to set 5G poles in the smart cities.
Extending Fibre Nonlinear Interference Power Modelling to Account for General Dual-Polarisation 4D Modulation Formats

In optical communications, four-dimensional (4D) modulation formats encode information onto the quadrature components of two arbitrary orthogonal states of polarisation of the optical field. Many analytical models available in the optical communication literature allow, within a first-order perturbation framework, the computation of the average power of the nonlinear interference (NLI) accumulated in coherent fibre-optic transmission systems. However, all such models only operate under the assumption of transmitted polarisation-multiplexed two-dimensional (PM-2D) modulation formats, which only represent a limited subset of the possible dual-polarisation 4D (DP-4D) formats. Namely, only those where data transmitted on each polarisation channel are mutually independent and identically distributed. This paper presents a step-by-step mathematical derivation of the extension of existing NLI models to the class of arbitrary DP-4D modulation formats. In particular, the methodology adopted follows the one of the popular enhanced Gaussian noise model, albeit dropping most assumptions on the geometry and statistic of the transmitted 4D modulation format. The resulting expressions show that, whilst in the PM-2D case the NLI power depends only on different statistical high-order moments of each polarisation component, for a general DP-4D constellation, several other cross-polarisation correlations also need to be taken into account.
SIGL: Securing Software Installations Through Deep Graph Learning

Many users implicitly assume that software can only be exploited after it is installed. However, recent supply-chain attacks demonstrate that application integrity must be ensured during installation itself. We introduce SIGL, a new tool for detecting malicious behavior during software installation. SIGL collects traces of system call activity, building a data provenance graph that it analyzes using a novel autoencoder architecture with a graph long short-term memory network (graph LSTM) for the encoder and a standard multilayer perceptron for the decoder. SIGL flags suspicious installations as well as the specific installation-time processes that are likely to be malicious. Using a test corpus of 625 malicious installers containing real-world malware, we demonstrate that SIGL has a detection accuracy of 96%, outperforming similar systems from industry and academia by up to 87% in precision and recall and 45% in accuracy. We also demonstrate that SIGL can pinpoint the processes most likely to have triggered malicious behavior, works on different audit platforms and operating systems, and is robust to training data contamination and adversarial attack. It can be used with application-specific models, even in the presence of new software versions, as well as application-agnostic meta-models that encompass a wide range of applications and installers.
Automatic Integration Issues of Tabular Data for On-Line Analysis Processing

Companies and individuals produce numerous tabular data. The objective of this position paper is to draw up the challenges posed by the automatic integration of data in the form of tables so that they can be cross-analyzed. We provide a first automatic solution for the integration of such tabular data to allow On-Line Analysis Processing. To fulfil this task, features of tabular data should be analyzed and the challenge of automatic multidimensional schema generation should be addressed. Hence, we propose a typology of tabular data and discuss our idea of an automatic solution.
Gravilon: Applications of a New Gradient Descent Method to Machine Learning

Gradient descent algorithms have been used in countless applications since the inception of Newton's method. The explosion in the number of applications of neural networks has re-energized efforts in recent years to improve the standard gradient descent method in both efficiency and accuracy. These methods modify the effect of the gradient in updating the values of the parameters. These modifications often incorporate hyperparameters: additional variables whose values must be specified at the outset of the program. We provide, below, a novel gradient descent algorithm, called Gravilon, that uses the geometry of the hypersurface to modify the length of the step in the direction of the gradient. Using neural networks, we provide promising experimental results comparing the accuracy and efficiency of the Gravilon method against commonly used gradient descent algorithms on MNIST digit classification.
CAN: A Causal Adversarial Network for Learning Observational and Interventional Distributions

We propose a generative Causal Adversarial Network (CAN) for learning and sampling from observational (conditional) and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph for the labels to be given, our proposed framework learns the causal relations from the data and generates samples accordingly. In addition to the relationships between labels, our model also learns the label-pixel and pixel-pixel dependencies and incorporate them in sample generation. The proposed CAN comprises a two-fold process namely Label Generation Network (LGN) and Conditional Image Generation Network (CIGN). The LGN is a novel GAN architecture which learns and samples from the causal graph over multi-categorical labels. The sampled labels are then fed to CIGN, a new conditional GAN architecture, which learns the relationships amongst labels and pixels and pixels themselves and generates samples based on them. This framework additionally provides an intervention mechanism which enables the model to generate samples from interventional distributions. We quantitatively and qualitatively assess the performance of CAN and empirically show that our model is able to generate both interventional and observational samples without having access to the causal graph for the application of face generation on CelebA data.
Search for dormant black holes in ellipsoidal variables I. Revisiting the expected amplitudes of the photometric modulation

Ellipsoidal variables present light-curve modulations caused by stellar distortion, induced by tidal interaction with their companions. An analytical approximated model of the ellipsoidal modulation is given as a discrete Fourier series by Morris & Naftilan. Based on numerical simulations using the phoebe code, we present here updated amplitudes of the first three harmonics of the model. The expected amplitudes are given as a function of the mass ratio and inclination of the binary system and the fillout factor of the primary – the ratio between the stellar radius and that of its Roche lobe. The corrections can get up to 30 per cent relative to the Morris & Naftilan model for fillout factors close to unity. The updated model can be instrumental in searching for short-period binaries with compact-object secondaries in large data sets of photometric light curves. As shown in one OGLE light-curve example, the minimum mass ratio can be obtained by using only the amplitudes of the three harmonics and an estimation of the stellar temperature. High enough amplitudes can help to identify binaries with mass ratios larger than unity, some of which might have compact companions.
Combinatorial Communication in the Locker Room

The reader may be familiar with various problems involving prisoners and lockers. A typical set-up is that there are $n$ lockers into which a random permutation of $n$ cards are inserted. Then $n$ prisoners enter the locker room one at a time and are allowed to open half of the lockers in an attempt to find their own card. The team of prisoners wins if every one of them is successful. The surprising result is that there is a strategy which wins with probability about $1-\ln 2$. A modified problem in which helpful Alice enters before the prisoners, inspects the whole permutation and swaps two cards improves the winning probability to $1$. In our problem, there are $n$ lockers and $n$ cards and a helpful Alice just as before, but there is only one prisoner, Bob. If Bob may only open one locker, their chance of success is less than $\frac{2.4}{n}$, but our main result is that, if Bob can open two lockers, their chance of success is $\frac{c_n}{n}$ where $c_n\rightarrow \infty$ as $n\rightarrow \infty$. For this, Alice and Bob have to achieve effective communication within the locker room. We show asymptotically matching upper and lower bounds for their optimal probability of success. Our analysis relies on a close relationship of this problem to some intrinsic properties of random permutations related to the rencontres number (which is the number of $n$-permutations with a given number of fixed points).