Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Communities in Gossip Model with Stubborn Agents

We consider a community detection problem in a gossip model, where agents randomly interact pairwise, with stubborn agents never changing their states. It is assumed that the agents can be divided into two communities based on their interaction probability with others. Such a model can illustrate how disagreement and opinion ﬂuctuation arise in a social network. The considered problem is twofold: to infer which community each agent belongs to, and to estimate interaction probabilities between agents, by only observing their state evolution. First, stability and limit theorems of the model are derived. An integrated detection and estimation algorithm is then proposed to infer the two communities and to estimate the interaction probabilities, based on agent states. It is veriﬁed that the community detector of the algorithm converges in ﬁnite time, and the interaction estimator converges almost surely. In addition, non-asymptotic property is obtained for the former, and convergence rate is analyzed for the latter. Simulations are presented to illustrate the performance of the proposed algorithm.
Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming

In this work, we propose a deep reinforcement learning (DRL) model for finding a feasible solution for (mixed) integer programming (MIP) problems. Finding a feasible solution for MIP problems is critical because many successful heuristics rely on a known initial feasible solution. However, it is in general NP-hard. Inspired by the feasibility pump (FP), a well-known heuristic for searching feasible MIP solutions, we develop a smart feasibility pump (SFP) method using DRL. In addition to multi-layer perception (MLP), we propose a novel convolution neural network (CNN) structure for the policy network to capture the hidden information of the constraint matrix of the MIP problem. Numerical experiments on various problem instances show that SFP significantly outperforms the classic FP in terms of the number of steps required to reach the first feasible solution. Moreover, the CNN structure works without the projection of the current solution as the input, which saves the computational effort at each step of the FP algorithms to find projections. This highlights the representational power of the CNN structure.
Convolutional Normalization

As the deep neural networks are being applied to complex tasks, the size of the networks and architecture increases and their topology becomes more complicated too. At the same time, training becomes slow and at some instances inefficient. This motivated the introduction of various normalization techniques such as Batch Normalization and Layer Normalization. The aforementioned normalization methods use arithmetic operations to compute an approximation statistics (mainly the first and second moments) of the layer's data and use it to normalize it. The aforementioned methods use plain Monte Carlo method to approximate the statistics and such method fails when approximating the statistics whose distribution is complex. Here, we propose an approach that uses weighted sum, implemented using depth-wise convolutional neural networks, to not only approximate the statistics, but to learn the coefficients of the sum.
Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast

Segmenting audio into homogeneous sections such as music and speech helps us understand the content of audio. It is useful as a pre-processing step to index, store, and modify audio recordings, radio broadcasts and TV programmes. Deep learning models for segmentation are generally trained on copyrighted material, which cannot be shared. Annotating these datasets is time-consuming and expensive and therefore, it significantly slows down research progress. In this study, we present a novel procedure that artificially synthesises data that resembles radio signals. We replicate the workflow of a radio DJ in mixing audio and investigate parameters like fade curves and audio ducking. We trained a Convolutional Recurrent Neural Network (CRNN) on this synthesised data and outperformed state-of-the-art algorithms for music-speech detection. This paper demonstrates the data synthesis procedure as a highly effective technique to generate large datasets to train deep neural networks for audio segmentation.
Recognition of Road Surface by Low-THz Frequencies

Recognition of road surface should always be the at the forefront of intelligent transportation systems (ITSs) for the enhancement of transportation safety and efficiency. When roads are covered by ice or snow, accident rate would increase due to the reduction of road surface roughness and also friction between tire and road. High-resolution recognition of natural and manmade surfaces has been proved to be achievable by employing radars operating at low-terahertz (low-THz) frequencies. In this work, we present theoretical investigations on surface recognition of an icy road by employing low-THz frequencies. A theoretical model combining integral equation method (IEM), radiative transfer equation (RTE) and Rayleigh scattering theory is developed. Good agreement between the calculation results and measured data confirms the applicability of low-THz frequencies for the evaluation of icy road surface in winter. The influence of carrier frequency, ambient temperature, impurities inside the ice layer and frozen soil surface conditions on the efficiency of this method is presented and discussed.
Scaling Creative Inspiration with Fine-Grained Functional Facets of Product Ideas

Web-scale repositories of products, patents and scientific papers offer an opportunity for building automated systems that scour millions of existing ideas and assist users in discovering novel inspirations and solutions to problems. Yet the current way ideas in such repositories are represented is largely in the form of unstructured text, which is not amenable to the kind of user interactions required for creative innovation. Prior work has pointed to the importance of functional representations – capturing the mechanisms and purposes of inventions – for allowing users to discover structural connections across ideas and creatively adapt existing technologies. However, previous work exploring the use of functional representations was either very coarse-grained and limited in expressivity, or dependent on manually curated knowledge bases with poor coverage and significant manual effort from users. To help bridge this gap and unlock the potential of large-scale idea mining, we propose a novel computational representation that automatically breaks up products into fine-grained functional facets . We train a model to extract these facets from a challenging real-world corpus of invention descriptions, and represent each product as a set of facet embeddings. We design similarity metrics that support granular matching between functional facets across ideas, and use them to build a novel functional search capability that enables expressive queries for mechanisms and purposes. We construct a graph capturing hierarchical relations between purposes and mechanisms across an entire corpus of products, and use the graph to help problem-solvers explore the design space around a focal problem and view related problem perspectives. In empirical user studies, our approach leads to a significant boost in search accuracy and in the quality of creative inspirations, outperforming strong baselines and state-of-art representations of product texts by 50-60%.
Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT

This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our final approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four subtasks, and we were ranked second in MSA identification subtasks and fourth in DA identification subtasks.
Rethinking the Funding Line at the Swiss National Science Foundation: Bayesian Ranking and Lottery

Abstract Funding agencies rely on peer review and expert panels to select the research deserving funding. Peer review has limitations, including bias against risky proposals or interdisciplinary research. The inter-rater reliability between reviewers and panels is low, particularly for proposals near the funding line. Funding agencies are also increasingly acknowledging the role of chance. The Swiss National Science Foundation (SNSF) introduced a lottery for proposals in the middle group of good but not excellent proposals. In this article, we introduce a Bayesian hierarchical model for the evaluation process. To rank the proposals, we estimate their expected ranks (ER), which incorporates both the magnitude and uncertainty of the estimated differences between proposals. A provisional funding line is defined based on ER and budget. The ER and its credible interval are used to identify proposals with similar quality and credible intervals that overlap with the provisional funding line. These proposals are entered into a lottery. We illustrate the approach for two SNSF grant schemes in career and project funding. We argue that the method could reduce bias in the evaluation process. R code, data and other materials for this article are available online.
Supernova 1987A: 3D Mixing and Light Curves for Explosion Models Based on Binary-merger Progenitors

Six binary-merger progenitors of supernova 1987A (SN 1987A) with properties close to those of the blue supergiant Sanduleak −69°202 are exploded by neutrino heating and evolved until long after shock breakout in 3D and continued for light-curve calculations in spherical symmetry. Our results confirm previous findings for single-star progenitors: (1) 3D neutrino-driven explosions with SN 1987A-like energies synthesize 56Ni masses consistent with the radioactive light-curve tail; (2) hydrodynamic models mix hydrogen inward to minimum velocities below 40 km s−1 compatible with spectral observations of SN 1987A; and (3) for given explosion energy the efficiency of outward radioactive 56Ni mixing depends mainly on high growth factors of Rayleigh–Taylor instabilities at the (C+O)/He and He/H composition interfaces and a weak interaction of fast plumes with the reverse shock occurring below the He/H interface. All binary-merger models possess presupernova radii matching the photometric radius of Sanduleak −69°202 and a structure of the outer layers allowing them to reproduce the observed initial luminosity peak in the first ∼7 days. Models that mix about 0.5 M ⊙ of hydrogen into the He-shell and exhibit strong outward mixing of 56Ni with maximum velocities exceeding the 3000 km s−1 observed for the bulk of ejected 56Ni have light-curve shapes in good agreement with the dome of the SN 1987A light curve. A comparative analysis of the best representatives of our 3D neutrino-driven explosion models of SN 1987A based on single-star and binary-merger progenitors reveals that only one binary model fulfills all observational constraints, except one.
Three-dimensional Reconstruction of Weak-lensing Mass Maps with a Sparsity Prior. I. Cluster Detection

We propose a novel method to reconstruct high-resolution three-dimensional mass maps using data from photometric weak-lensing surveys. We apply an adaptive LASSO algorithm to perform a sparsity-based reconstruction on the assumption that the underlying cosmic density field is represented by a sum of Navarro–Frenk–White halos. We generate realistic mock galaxy shear catalogs by considering the shear distortions from isolated halos for the configurations matched to the Subaru Hyper Suprime-Cam Survey with its photometric redshift estimates. We show that the adaptive method significantly reduces line-of-sight smearing that is caused by the correlation between the lensing kernels at different redshifts. Lensing clusters with lower mass limits of 1014.0 h−1 M ⊙, 1014.7 h−1 M ⊙, 1015.0 h−1 M ⊙ can be detected with 1. 5σ confidence at the low (z < 0.3), median (0.3 ≤ z < 0.6), and high (0.6 ≤ z < 0.85) redshifts, respectively, with an average false detection rate of 0.022 deg−2. The estimated redshifts of the detected clusters are systematically lower than the true values by Δz ∼ 0.03 for halos at z ≤ 0.4, but the relative redshift bias is below 0.5% for clusters at 0.4 < z ≤ 0.85. The standard deviation of the redshift estimation is 0.092. Our method enables direct three-dimensional cluster detection with accurate redshift estimates.
MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks

Molecular machine learning bears promise for efﬁcient molecule property prediction and drug discovery. However, due to the limited labeled data and the giant chemical space, machine learning models trained via supervised learning perform poorly in generalization. This greatly limits the applications of machine learning methods for molecular design and discovery. In this work, we present MolCLR : Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework for large unlabeled molecule datasets. Speciﬁcally, we ﬁrst build a molecular graph, where each node represents an atom and each edge represents a chemical bond. A GNN is then used to encode the molecule graph. We propose three novel molecule graph augmentations: atom masking, bond deletion, and subgraph removal. A contrastive estimator is utilized to maximize the agreement of different graph augmentations from the same molecule. Experiments show that molecule representations learned by MolCLR can be transferred to multiple downstream molecular property prediction tasks. Our method thus achieves state-of-the-art performance on many challenging datasets. We also prove the efﬁciency of our proposed molecule graph augmentations on supervised molecular classiﬁcation tasks.
Control Variate Approximation for DNN Accelerators

In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Monte Carlo methods to achieve variance reduction. Our approach significantly decreases the induced error due to approximate multiplications in DNN inference, without requiring time-exhaustive retraining compared to state-of-the-art. Leveraging our control variate method, we use highly approximated multipliers to generate power-optimized DNN accelerators. Our experimental evaluation on six DNNs, for Cifar-10 and Cifar100 datasets, demonstrates that, compared to the accurate design, our control variate approximation achieves same performance and 24% power reduction for a merely 0.16% accuracy loss.
20 yr of observations of PM 1-188: its chemical abundances and extraordinary kinematics

The analysis of 20 years of spectrophotometric data of the double shell planetary nebula PM\,1-188 is presented, aiming to determine the time evolution of the emission lines and the physical conditions of the nebula, as a consequence of the systematic fading of its [WC\,10] central star whose brightness has declined by about 10 mag in the past 40 years. Our main results include that the [\ion{O}{iii}], [\ion{O}{ii}], [\ion{N}{ii}] line intensities are increasing with time in the inner nebula as a consequence of an increase in electron temperature from 11,000 K in 2005 to more than 14,000 K in 2018, due to shocks. The intensity of the same lines are decreasing in the outer nebula, due to a decrease in temperature, from 13,000 K to 7,000 K, in the same period. The chemical composition of the inner and outer shells was derived and they are similar. Both nebulae present subsolar O, S and Ar abundances, while they are He, N and Ne rich. For the outer nebula the values are 12+log He/H= 11.13$\pm$0.05, 12+log O/H = 8.04$\pm$0.04, 12+log N/H= 7.87$\pm$0.06, 12+log S/H = 7.18$\pm$0.10 and 12+log Ar = 5.33$\pm$0.16. The O, S and Ar abundances are several times lower than the average values found in disc non-Type I PNe, and are reminiscent of some halo PNe. From high resolution spectra, an outflow in the N-S direction was found in the inner zone. Position-velocity diagrams show that the outflow expands at velocities in the $-$150 to 100 km s$^{-1}$ range, and both shells have expansion velocities of about 40 km s$^{-1}$.
Finite-size effects in response functions of molecular systems

We consider an electron in a localized potential submitted to a weak external, timedependent field. In the linear response regime, the response function can be computed using Kubo's formula. In this paper, we consider the numerical approximation of the response function by means of a truncation to a finite region of space. This is necessarily a singular approximation because of the discreteness of the spectrum of the truncated Hamiltonian, and in practice a regularization (smoothing) has to be used. Our results provide error estimates for the response function past the ionization threshold with respect to both the smoothing parameter and the size of the computational domain.
CatNet: music source separation system with mix-audio augmentation

Music source separation (MSS) is the task of separating a music piece into individual sources, such as vocals and accompaniment. Recently, neural network based methods have been applied to address the MSS problem, and can be categorized into spectrogram and time-domain based methods. However, there is a lack of research of using complementary information of spectrogram and time-domain inputs for MSS. In this article, we propose a CatNet framework that concatenates a UNet separation branch using spectrogram as input and a WavUNet separation branch using time-domain waveform as input for MSS. We propose an end-to-end and fully differentiable system that incorporate spectrogram calculation into CatNet. In addition, we propose a novel mix-audio data augmentation method that randomly mix audio segments from the same source as augmented audio segments for training. Our proposed CatNet MSS system achieves a state-of-the-art vocals separation source distortion ratio (SDR) of 7.54 dB, outperforming MMDenseNet of 6.57 dB evaluated on the MUSDB18 dataset.
On Connectivity of Solutions in Deep Learning: The Role of Over-parameterization and Feature Quality

It has been empirically observed that, in deep neural networks, the solutions found by stochastic gradient descent from diﬀerent random initializations can be often connected by a path with low loss. Recent works have shed light on this intriguing phenomenon by assuming either the over-parameterization of the network or the dropout stability of the solutions. In this paper, we reconcile these two views and present a novel condition for ensuring the connectivity of two arbitrary points in parameter space. This condition is provably milder than dropout stability, and it provides a connection between the problem of ﬁnding low-loss paths and the memorization capacity of neural nets. This last point brings about a trade-oﬀ between the quality of features at each layer and the over-parameterization of the network. As an extreme example of this trade-oﬀ, we show that (i) if subsets of features at each layer are linearly separable, then almost no over-parameterization is needed, and (ii) under generic assumptions on the features at each layer, it suﬃces that the last two hidden layers have Ω( √ N ) neurons, N being the number of samples. Finally, we provide experimental evidence demonstrating that the presented condition is satisﬁed in practical settings even when dropout stability does not hold.
Neuromorphic Pattern Generation Circuits for Bioelectronic Medicine

Chronic diseases can greatly benefit from bio-electronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central Pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker.
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory

A neural network model of a differential equation, namely neural ODE, has enabled the learning of continuous-time dynamical systems and probabilistic distributions with high accuracy. The neural ODE uses the same network repeatedly during a numerical integration. The memory consumption of the backpropagation algorithm is proportional to the number of uses times the network size. This is true even if a checkpointing scheme divides the computation graph into sub-graphs. Otherwise, the adjoint method obtains a gradient by a numerical integration backward in time. Although this method consumes memory only for a single network use, it requires high computational cost to suppress numerical errors. This study proposes the symplectic adjoint method, which is an adjoint method solved by a symplectic integrator. The symplectic adjoint method obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The experimental results demonstrate that the symplectic adjoint method consumes much less memory than the naive backpropagation algorithm and checkpointing schemes, performs faster than the adjoint method, and is more robust to rounding errors.
Electron-impact excitation of Fe II

We report in this paper the computation of accurate total collision strengths and effective collision strengths for electron-impact excitation of FeII, using the parallel R-matrix program PRMAT. Target states corresponding to the 3d64s, 3d7, 3d64p and 3d54s4s basis configurations were included in the calculations giving rise to a 113 LS state 354 coupled channel problem. Following a detailed systematic study of correlation effects in both the target state and collision wavefunctions, it was found that an additional 21 configuration functions needed to be included in the Configuration Interaction expansion to obtain significantly more accurate target states and collision wavefunctions. This much improved 26-configuration model has been used to calculate converged total effective collision strengths for all sextet to quartet transitions among these levels with total spin S = 2, giving a total of 1785 lines. These calculations have laid the foundation for an approach which may be adopted in the study of electron collisions with the low ionization stages of other iron peak elements. The work has been further extended with the commencement of a Breit-Pauli relativistic calculation for one of the smaller models and includes 262 fine-structure levels and over 1800 coupled channels. At the same time the PRMAT parallel R-matrix package is being extended to include relativistic effects which will allow us to attempt the more sophisticated 26-configuration model and produce for the first time the amount and quality of atomic data required to perform a meaningful synthesis of the Fe II spectrum.