Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating location parameters in entangled single-sample distributions

We consider the problem of estimating the common mean of independently sampled data, where samples are drawn in a possibly non-identical manner from symmetric, unimodal distributions with a common mean. This generalizes the setting of Gaussian mixture modeling, since the number of distinct mixture components may diverge with the number of observations. We propose an estimator that adapts to the level of heterogeneity in the data, achieving near-optimality in both the i.i. d. setting and some heterogeneous settings, where the fraction of ``low-noise'' points is as small as $\frac{\log n}{n}$. Our estimator is a hybrid of the modal interval, shorth, and median estimators from classical statistics; however, the key technical contributions rely on novel empirical process theory results that we derive for independent but non-i.i. d. data. In the multivariate setting, we generalize our theory to mean estimation for mixtures of radially symmetric distributions, and derive minimax lower bounds on the expected error of any estimator that is agnostic to the scales of individual data points. Finally, we describe an extension of our estimators applicable to linear regression. In the multivariate mean estimation and regression settings, we present computationally feasible versions of our estimators that run in time polynomial in the number of data points.
Adversarial Fault Tolerant Training for Deep Neural Networks

Deep Learning Accelerators are prone to faults which manifest in the form of errors in Neural Networks. Fault Tolerance in Neural Networks is crucial in real-time safety critical applications requiring computation for long durations. Neural Networks with high regularisation exhibit superior fault tolerance, however, at the cost of classification accuracy. In the view of difference in functionality, a Neural Network is modelled as two separate networks, i.e, the Feature Extractor with unsupervised learning objective and the Classifier with a supervised learning objective. Traditional approaches of training the entire network using a single supervised learning objective is insufficient to achieve the objectives of the individual components optimally. In this work, a novel multi-criteria objective function, combining unsupervised training of the Feature Extractor followed by supervised tuning with Classifier Network is proposed. The unsupervised training solves two games simultaneously in the presence of adversary neural networks with conflicting objectives to the Feature Extractor. The first game minimises the loss in reconstructing the input image for indistinguishability given the features from the Extractor, in the presence of a generative decoder. The second game solves a minimax constraint optimisation for distributional smoothening of feature space to match a prior distribution, in the presence of a Discriminator network. The resultant strongly regularised Feature Extractor is combined with the Classifier Network for supervised fine-tuning. The proposed Adversarial Fault Tolerant Neural Network Training is scalable to large networks and is independent of the architecture. The evaluation on benchmarking datasets: FashionMNIST and CIFAR10, indicates that the resultant networks have high accuracy with superior tolerance to stuck at "0" faults compared to widely used regularisers.
From weak to strong disorder in Weyl semimetals: Self-consistent Born approximation

We analyze theoretically the conductivity of Weyl semimetals within the self-consistent Born approximation (SCBA) in the full range of disorder strength, from weak to strong disorder. In the range of intermediate disorder, we find a critical regime which separates the semimetal and diffusion regimes. While the numerical values of the critical exponents are not expected to be exact within the SCBA, the approach allows us to calculate functional dependences of various observables (density of states, quasiparticle broadening, conductivity) in a closed form. This sheds more light on the qualitative behavior of the conductivity and its universal features in disordered Weyl semimetals. In particular, we have found that the vertex corrections in the Kubo formula are of crucial importance in the regime of strong disorder and lead to saturation of the dc conductivity with increasing disorder strength. We have also analyzed the evolution of the optical conductivity with increasing disorder strength, including its scaling properties in the critical regime.
Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data

Autoencoders are neural network formulations where the input and output of the network are identical and the goal is to identify the hidden representation in the provided datasets. Generally, autoencoders project the data nonlinearly onto a lower dimensional hidden space, where the important features get highlighted and interpretation of the data becomes easier. Recent studies have shown that even in the presence of noise in the input data, autoencoders can be trained to reconstruct the noisefree component of the data from the reduced-dimensional hidden space. In this paper, we explore the application of autoencoders within the scope of denoising geophysical datasets using a data-driven methodology. The autoencoder formulation is discussed, and a stacked variant of deep autoencoders is proposed. The proposed method involves locally training the weights first using basic autoencoders, each comprising a single hidden layer. Using these initialized weights as starting points in the optimization model, the full autoencoder network is then trained in the second step. The applicability of denoising autoencoders has been demonstrated on a basic mathematical example and several geophysical examples. For all the cases, autoencoders are found to significantly reduce the noise in the input data.
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning

In statistical data assimilation (SDA) and supervised machine learning (ML), we wish to transfer information from observations to a model of the processes underlying those observations. For SDA, the model consists of a set of differential equations that describe the dynamics of a physical system. For ML, the model is usually constructed using other strategies. In this paper, we develop a systematic formulation based on Monte Carlo sampling to achieve such information transfer. Following the derivation of an appropriate target distribution, we present the formulation based on the standard Metropolis-Hasting (MH) procedure and the Hamiltonian Monte Carlo (HMC) method for performing the high dimensional integrals that appear. To the extensive literature on MH and HMC, we add (1) an annealing method using a hyperparameter that governs the precision of the model to identify and explore the highest probability regions of phase space dominating those integrals, and (2) a strategy for initializing the state space search. The efficacy of the proposed formulation is demonstrated using a nonlinear dynamical model with chaotic solutions widely used in geophysics.
Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

The relative positions of the high and low surface brightness regions of radio-loud active galaxies in the 3CR sample were found by Fanaroff and Riley to be correlated with their luminosity. We revisit this canonical relationship with a sample of 5805 extended radio-loud active galactic nuclei (AGN) from the LOFAR Two-Metre Sky Survey (LoTSS), compiling the most complete data set of radio-galaxy morphological information obtained to date. We demonstrate that, for this sample, radio luminosity does not reliably predict whether a source is edge-brightened (FRII) or centre-brightened (FRI). We highlight a large population of low-luminosity FRIIs, extending three orders of magnitude below the traditional FR break, and demonstrate that their host galaxies are on average systematically fainter than those of high-luminosity FRIIs and of FRIs matched in luminosity. This result supports the jet power/environment paradigm for the FR break: low-power jets may remain undisrupted and form hotspots in lower mass hosts. We also find substantial populations that appear physically distinct from the traditional FR classes, including candidate restarting sources and ‘hybrids’. We identify 459 bent-tailed sources, which we find to have a significantly higher SDSS cluster association fraction (at z < 0.4) than the general radio-galaxy population, similar to the results of previous work. The complexity of the LoTSS faint, extended radio sources not only demonstrates the need for caution in the automated classification and interpretation of extended sources in modern radio surveys, but also reveals the wealth of morphological information such surveys will provide and its value for advancing our physical understanding of radio-loud AGN.
The jet of FSRQ PKS 1229−02 and its misidentification as a γ-ray AGN

Flat-spectrum radio quasar PKS 1229–02 with a knotty and asymmetric radio morphology was identified as the optical and radio counterpart of a γ-ray source. In this paper, we study the properties (e.g. morphology, opacity, polarization and kinematics) of the jet in PKS 1229–02 using radio interferometry. With our results, we find that the knotty and asymmetric morphology of this source may probably shaped by the interaction between its anterograde jet and the nonuniform dense ambient medium. By reproducing a Spectral Energy Distribution of PKS 1229–02 with the obtained kinematic parameters, we find that the relativistic beaming effect in PKS 1229–02 is not strong enough to produce the reported γ-ray emission, i.e. PKS 1229–02 may not be a γ-ray AGN. The misidentification may probably be due to the poor spatial resolution of the γ-ray detector of the previous generation.
Low-layer quasi-agreed order broadcast over local bus

Agreed order broadcast protocols are supposed to deliver messages in a group wide agreed total order. With large group sizes and a faulty communication environment (e.g., prone to reception misses), existing protocols can collapse due to imperfect synchronisation mechanisms. We propose and evaluate a MAC layer Jamming Protocol (JP), applicable in Ethernet type media, whereby a site jams an out of sequence message thus preventing all other sites from delivering it. The low layer approach yields higher group throughput at the price of introducing a practically negligible possibility of violating agreed order.
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the best prediction. Experimental results on the AVEC Sentiment Analysis in the Wild dataset indicate that the proposed DNN can achieve a higher level of Concordance Correlation Coefficient (CCC) than other state-of-the-art systems that perform early fusion of modalities at feature-level (i.e., concatenation) and late fusion at score-level (i.e., weighted average) fusion. The proposed DNN has achieved CCCs of 0.606, 0.534, and 0.170 on the development partition of the dataset for predicting arousal, valence and liking, respectively.
Early Discovery of Emerging Entities in Microblogs

Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research. Previous studies have attempted to detect unseen entities that are not registered in a particular knowledge base as emerging entities and consequently find non-emerging entities since the absence of entities in knowledge bases does not guarantee their emergence. We therefore introduce a novel task of discovering truly emerging entities when they have just been introduced to the public through microblogs and propose an effective method based on time-sensitive distant supervision, which exploits distinctive early-stage contexts of emerging entities. Experimental results with a large-scale Twitter archive show that the proposed method achieves 83.2% precision of the top 500 discovered emerging entities, which outperforms baselines based on unseen entity recognition with burst detection. Besides notable emerging entities, our method can discover massive long-tail and homographic emerging entities. An evaluation of relative recall shows that the method detects 80.4% emerging entities newly registered in Wikipedia; 92.8% of them are discovered earlier than their registration in Wikipedia, and the average lead-time is more than one year (578 days).
Riemann-Hilbert Problem for the Matrix Laguerre Biorthogonal Polynomials: Eigenvalue Problems and the Matrix Discrete Painlev\'e IV

In this paper the Riemann-Hilbert problem, with jump supported on a appropriate curve on the complex plane with a finite endpoint at the origin, is used for the study of corresponding matrix biorthogonal polynomials associated with Laguerre type matrices of weights ---which are constructed in terms of a given matrix Pearson equation. First and second order differential systems for the fundamental matrix, solution of the mentioned Riemann-Hilbert problem are derived. An explicit and general example is presented to illustrate the theoretical results of the work. Related matrix eigenvalue problems for second order matrix differential operators and non-Abelian extensions of a family of discrete Painleve IV equations are discussed.
Robust Guarantees for Perception-Based Control

Motivated by vision based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, can only be extracted from high-dimensional data, such as an image. Our approach is to learn a perception map from high-dimensional data to partial-state observation and its corresponding error profile, and then design a robust controller. We show that under suitable smoothness assumptions on the perception map and generative model relating state to high-dimensional data, an affine error model is sufficiently rich to capture all possible error profiles, and can further be learned via a robust regression problem. We then show how to integrate the learned perception map and error model into a novel robust control synthesis procedure, and prove that the resulting perception and control loop has favorable generalization properties. Finally, we illustrate the usefulness of our approach on a synthetic example and on the self-driving car simulation platform CARLA.
Cyclic structures and broken cycles

We introduce a new way to encode semicyclic structures using a stack of broken cycles. (We also prove an analogue for paracyclic structures.) This was motivated not only by higher algebra but also by Fukaya-categorical considerations. We also openly speculate about some Fukaya-categorical implications. For example, this stack sees moduli of stopped Liouville disks, and hence yields another platform for gluing together Fukaya categories. We also see that Lagrangian cobordisms with multiple ends may not only serve to detect $K_0$ groups of Fukaya categories, but higher $K$-theory groups as well. Along the way, we include brief expositions of (i) basic techniques in infinity-categories and (ii) the translation between exit path categories and constructible sheaves. These may be of independent interest.
Tidal Disruption Events, Main-sequence Extreme-mass Ratio Inspirals, and Binary Star Disruptions in Galactic Nuclei

The Galactic Center has been under intense scrutiny in recent years thanks to pioneering missions aiming to measure the gas and star dynamics near the supermassive black hole (SMBH) and to find the gravitational-wave (GW) signatures of inspiraling stellar black holes. In the crowded environment of galactic nuclei, the two-body interactions alter the distribution of stars on long timescales, making them drift in energy and angular momentum. We present a simplified analytical treatment of the scattering processes in galactic stellar nuclei, assuming all stars have the same mass. We have discussed how the interplay between two-body relaxation and GW emission modifies the slope of the inner stellar cusp within the SMBH sphere of influence, and calculated the rates of tidal disruption events (TDEs) and main-sequence extreme-mass ratio inspirals (MS-EMRIs) of stars that are tidally disrupted by the SMBH. We find that typically the ratio of the TDE and MS-EMRI rates is the square of the ratio of the tidal and Schwarzschild radii. For our Galaxy, this implies that the rate of MS-EMRIs is just about a percent of the TDE rate. We then consider the role of stars injected on highly eccentric orbits in the vicinity of the SMBH due to the Hills binary disruption mechanism, and show that the MS-EMRI rate can almost approach the TDE rate if the binary fraction at the SMBH influence radius is close to unity. Finally, we discuss how physical stellar collisions affect a large area of phase space.
The prudence transmission method I (PTM I): a retransmission cut-off method for contention based multiple-access communication systems

A transmission policy is studied for the random access slotted ALOHA channel in terms of the number of transmissions. A stable operation of slotted ALOHA can be achieved by reducing the transmission number for a specific limit of the new packet generation rate. Our results show that the control of transmission number is not needed if the new packet generation rate from all active users is less than e/sup -1/ packet per time slot. We have introduced an optimum transmission method called the "prudence transmission method (PTM)" which gives the maximum throughput. This can be done by adjusting the number of transmission for a given new packet generation rate, when it is greater than e/sup -1/ packet per time slot. The result shows that the number of transmissions decrease abruptly with the increase of the new packet generation rate. For the stable operation of slotted ALOHA, the maximum allowable new packet generation rate is 1 packet per time slot. The results of this study are portrayed on graphs and may be used as a guideline of a contention based multiple access scheme with and without retransmission cut-off.
Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions

We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., “largest elephant standing behind baby elephant”. This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context — visual attributes (e.g., “largest”, “baby”) and relationships (e.g., “behind”) that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Specifically, our framework exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. In addition to reciprocity, our framework considers the semantic information of context, i.e., the referring expression can be reproduced based on the estimated context. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings.
Implementation and analysis of speech recognition front-ends

We have developed a comprehensive front-end module integrating several signal modeling algorithms common to state-of-the-art speech recognition systems. The algorithms presented in this work include mel-frequency cepstra, perceptual linear prediction, filter bank amplitudes, and delta features. The framework for the front-end system was carefully designed to ensure simple integration into speech processing software. The modular design of the software along with an intuitive GUI provide a powerful tutorial by allowing a wide selection of algorithms. The software is written in a tutorial fashion, with a direct correlation between algorithmic lines of code and equations in the technical paper.
Nonlinear properties of IMPATT devices

The basic principles of IMPATT diodes as microwave devices are reviewed and the current status of these devices concerning power output and efficiency is given. The main purpose of this paper, however, is to discuss the nonlinear properties of these diodes which are useful in the design of amplifiers, oscillators, and other microwave devices. The main results of this paper are obtained from a digital computer analysis where an approximate, but realistic, diode model is employed. A detailed comparison of complementary silicon diodes as well as GaAs diodes concerning power output and efficiency is given. The effects of doping profile, current density, temperature, and material parameters on the performance of these devices have been investigated and are summarized. Saturation effects which limit the efficiency and power output of these devices are described and optimum efficiencies which can be achieved for various doping profiles are given. A comparison between single-sided and double-drift diodes in both silicon and GaAs is also presented.
Ensembles of multilayer feedforward: a new comparison

As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several methods to construct the ensemble. In this paper we present some new results in a comparison of twenty different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually low. Also, the best method for a ensemble of 3 networks is called "Decorrelated" and uses a penalty term in the usual Backpropagation function to decorrelate the network outputs in the ensemble. For the case of 9 and 20 networks the best method is conservative boosting. And finally for 40 networks the best method is Cels.
Tests of the Kerr Hypothesis with GRS 1915+105 Using Different relxill Flavors

In a previous paper, we tried to test the Kerr nature of the stellar-mass black hole in GRS 1915+105 by analyzing NuSTAR data of 2012 with our reflection model relxill_nk. We found that the choice of the intensity profile of the reflection component is crucial and eventually we were not able to get any constraint on the spacetime metric around the black hole in GRS 1915+105. In the present paper, we study the same source with Suzaku data of 2007. We confirm that the intensity profile plays an important role, but now we find quite stringent constraints consistent with the Kerr hypothesis. The key differences with respect to our previous study are likely the lower disk temperature in the Suzaku observation and the higher energy resolution near the iron line of the Suzaku data. We also apply different relxill flavors (different descriptions of the coronal spectrum and variable disk electron density) obtaining essentially the same results. We thus conclude that this choice is not very important for our tests of the Kerr hypothesis while the intensity profile does play an important role, and that with high-quality data it is possible to measure both the spacetime metric and the intensity profile.