Miracle-dz/UniFAR-FTUs huggingface dataset:
Central limit theorem for a partially observed interacting system of Hawkes processes

We observe the actions of a $K$ sub-sample of $N$ individuals up to time $t$ for some large $K\le N$. We model the relationships of individuals by i.i. d. Bernoulli($p$)-random variables, where $p\in (0,1]$ is an unknown parameter. The rate of action of each individual depends on some unknown parameter $\mu> 0$ and on the sum of some function $\phi$ of the ages of the actions of the individuals which influence him. The parameters $\mu$ and $\phi$ are considered as nuisance parameters. The aim of this paper is to obtain a central limit theorem for the estimator of $p$ that we introduced in \cite{D}, both in the subcritical and supercritical cases.
A Comparison of Speckle Reduction Techniques in Medical Ultrasound Imaging

Speckle noise is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In addition, it limits the efficient application of intelligent image processing algorithms, such as segmentation techniques. Thus, speckle noise reduction is considered an essential pre-processing step. The objective of this paper is to carry out a comparative evaluation of speckle filtering techniques, based on two image quality evaluation metrics, the Peak Signal to Noise Ratio (PSNR), and the Structural SIMilarity (SSIM) index, and visual evaluation.
On a new queue backoff fair algorithm for ad hoc networks

The medium access control (MAC) protocol through which mobile nodes can share a common broadcast channel is essential in ad hoc networks. Due to the existence of hidden terminals and multihop feature of ad hoc networks, contention among nodes is not homogeneous. Some nodes are at a disadvantage in access to the shared channel and suffer severe throughput degradation when load to channel is high. This is known as the "fairness problem". A new simple yet effective queue backoff fair algorithm is proposed. To quantify the degree of the fairness effect of the algorithm, an improved fairness index (IFI) is also defined. Based on IFI, the goal of achieving channel access fairness relative to effective aggregate throughput becomes equivalent to minimizing the improved fairness index. Analysis and simulation results show that, by choosing appropriate parameters, the new algorithm achieve lower improved fairness index and consequently improve the fairness relative to effective aggregate throughput, compared with the IEEE 802.11 binary exponential backoff (BEB) algorithm and other algorithms.
Convergence revisit on generalized symmetric ADMM

In this note, we show a sublinear nonergodic convergence rate for the algorithm developed in Bai et al. [Generalized symmetric ADMM for separable convex optimization. Comput Optim Appl. 2018;70:129–170], as well as its linear convergence under assumptions that the sub-differential of each component objective function is piecewise linear and all the constraint sets are polyhedra. These remaining convergence results are established for the stepsize parameters of dual variables belonging to a special isosceles triangle region, which aims to strengthen our understanding for convergence of the generalized symmetric ADMM.
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates

Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters’ preferences. In practice, voters do not provide a full ranking; instead, each voter provides their favorite K candidates, potentially in ranked order. The election organizer must choose K and an aggregation rule. We provide a theoretical framework to make these choices. Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election recovers the asymptotic outcome. Given the voter choice distribution, the election planner can thus identify the rate optimal mechanism. Earlier work in this area provides coarse order-of-magnitude guaranties which are not sufficient to make such choices. Our framework further resolves questions of when randomizing between multiple mechanisms may improve learning for arbitrary voter noise models. Finally, we use data from 5 large participatory budgeting elections that we organized across several US cities, along with other ranking data, to demonstrate the utility of our methods. In particular, we find that historically such elections have set K too low and that picking the right mechanism can be the difference between identifying the ultimate winner with only a 80% probability or a 99.9% probability after 400 voters.
Engineering and Business Applications of Sum of Squares Polynomials

Optimizing over the cone of nonnegative polynomials, and its dual counterpart, optimizing over the space of moments that admit a representing measure, are fundamental problems that appear in many different applications from engineering and computational mathematics to business. In this paper, we review a number of these applications. These include, but are not limited to, problems in control (e.g., formal safety verification), finance (e.g., option pricing), statistics and machine learning (e.g., shape-constrained regression and optimal design), and game theory (e.g., Nash equilibria computation in polynomial games). We then show how sum of squares techniques can be used to tackle these problems, which are hard to solve in general. We conclude by highlighting some directions that could be pursued to further disseminate sum of squares techniques within more applied fields. Among other things, we briefly address the current challenge that scalability represents for optimization problems that involve sum of squares polynomials and discuss recent trends in software development.
Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning

1 Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China 2 Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China Dong.Liang@siat.ac. cn 3 Department of Electronic Information Engineering, Nanchang University, Nanchang, Jiangxi, China 4 Departments of Biomedical Engineering and Electrical Engineering, University at Buffalo, the State University of
Local Bures-Wasserstein Transport: A Practical and Fast Mapping Approximation

Optimal transport (OT)-based methods have a wide range of applications and have attracted a tremendous amount of attention in recent years. However, most of the computational approaches of OT do not learn the underlying transport map. Although some algorithms have been proposed to learn this map, they rely on kernel-based methods, which makes them prohibitively slow when the number of samples increases. Here, we propose a way to learn an approximate transport map and a parametric approximation of the Wasserstein barycenter. We build an approximated transport mapping by leveraging the closed-form of Gaussian (Bures-Wasserstein) transport; we compute local transport plans between matched pairs of the Gaussian components of each density. The learned map generalizes to out-of-sample examples. We provide experimental results on simulated and real data, comparing our proposed method with other mapping estimation algorithms. Preliminary experiments suggest that our proposed method is not only faster, with a factor 80 overall running time, but it also requires fewer components than state-of-the-art methods to recover the support of the barycenter. From a practical standpoint, it is straightforward to implement and can be used with a conventional machine learning pipeline.
Large-Scale Speaker Diarization of Radio Broadcast Archives

This paper describes our initial efforts to build a large-scale speaker diarization (SD) and identification system on a recently digitized radio broadcast archive from the Netherlands which has more than 6500 audio tapes with 3000 hours of Frisian-Dutch speech recorded between 1950-2016. The employed large-scale diarization scheme involves two stages: (1) tape-level speaker diarization providing pseudo-speaker identities and (2) speaker linking to relate pseudo-speakers appearing in multiple tapes. Having access to the speaker models of several frequently appearing speakers from the previously collected FAME! speech corpus, we further perform speaker identification by linking these known speakers to the pseudo-speakers identified at the first stage. In this work, we present a recently created longitudinal and multilingual SD corpus designed for large-scale SD research and evaluate the performance of a new speaker linking system using x-vectors with PLDA to quantify cross-tape speaker similarity on this corpus. The performance of this speaker linking system is evaluated on a small subset of the archive which is manually annotated with speaker information. The speaker linking performance reported on this subset (53 hours) and the whole archive (3000 hours) is compared to quantify the impact of scaling up in the amount of speech data.
Separating line emission from star formation, shocks, and AGN ionization in NGC 1068

In the optical spectra of galaxies, the separation of line emission from gas ionized by star formation and an active galactic nucleus (AGN), or by star formation and shocks, are very well-understood problems. However, separating line emission between AGN and shocks has proven difficult. With the aid of a new three-dimensional diagnostic diagram, we show the simultaneous separation of line emission from star formation, shocks, and AGN in NGC 1068, and quantify the ratio of star formation, shocks, and AGN in each spaxel. The AGN, shock, and star formation luminosity distributions across the galaxy accurately align with X-ray, radio, and CO(3–2) observations, respectively. Comparisons with previous separation methods show that the shocked emission heavily mixes with the AGN emission. We also show that if the H α flux is to be used as a star formation rate indicator, separating line emission from as many sources as possible should be attempted to ensure accurate results.
Characterisation of the continuum and kinematical properties of nearby NLS1

Aims. In order to study the slope and strength of the non-stellar continuum, we analysed a sample from nearby Narrow Line Seyfert 1 (NLS1). Also, we re-examined the location of NLS1 galaxies on the MBH − σ⋆ relation, using the stellar velocity dispersion and the [OIII]λ5007 emission line as a surrogate of the former. Methods. We studied spectra of a sample of 131 NLS1 galaxies taken from the Sloan Digital Sky Survey (SDSS) DR7. We approached determining the non-stellar continuum by employing the spectral synthesis technique, which uses the code STARLIGHT, and by adopting a power-law base to model the non-stellar continuum. Composite spectra of NLS1 galaxies were also obtained based on the sample. In addition, we obtained the stellar velocity dispersion from the code and by measuring Calcium II Triplet absorption lines and [OIII] emission lines. From Gaussian decomposition of the Hβ profile we calculated the black hole mass. Results. We obtained a median slope of β = −1.6 with a median fraction of contribution of the non-stellar continuum to the total flux of 0.64. We determined black hole masses in the range of log(MBH/M⊙) = 5.6–7.5, which is in agreement with previous works. We found a correlation between the luminosity of the broad component of Hβ and black hole mass with the fraction of a power-law component. Finally, according to our results, NLS1 galaxies in our sample are located mostly underneath the MBH − σ⋆ relation, both considering the stellar velocity dispersion (σ⋆) and the core component of [OIII]λ5007.
The Effect of Translationese in Machine Translation Test Sets

The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data. We study in depth the effect of translationese on test data, using the test sets from the last three editions of WMT’s news shared task, containing 17 translation directions. We show evidence that (i) the use of translationese in test sets results in inflated human evaluation scores for MT systems; (ii) in some cases system rankings do change and (iii) the impact translationese has on a translation direction is inversely correlated to the translation quality attainable by state-of-the-art MT systems for that direction.
DESIGN AND CALIBRATION OF OPTICAL TWEEZERS FOR SINGLE-MOLECULE STUDIES

This version is a PDF produced from the source Microsoft Word document. Figure 13, on logical page 36 (PDF page 46), is missing. It was a CorelDraw drawing of the optical layout. Please see the supplemental files "20000710 tweezers1xx AI9.emf" and "20000710 tweezers1xx AI9.ai" for enhanced metafile and Adobe Illustrator 9 versions of that figure.
Contact phase-field modeling for chemo-mechanical degradation processes. Part I: Theoretical foundations

As phase-field modelling is booming across various disciplines and has been proven fitted for numerically modeling interfacial problems, we aim at taking a step back to revisit its fundamental validity, especially for far-from-equilibrium problems. This questioning is relevant for rapid phase change and pattern formation, which is often the case in the processes modelled by PFM, although it is designed for slow dynamics and therefore not to deviate too much from local thermodynamic equilibrium. Starting from the main assumptions of PFM, non-equilibrium thermodynamics and maximum dissipation principle, the gradient flow equation usually obtained from variational formulation can be extended to generalized relaxation equations, based on a generalized free energy, which long term manifolds correspond to dynamic equilibria. For that, a general contact thermodynamic framework is derived from contact geometry, thus extending Gibbs seminal geometrical representation of thermostatics. The obtained viscous Allen-Cahn equation allows both the PFM kinematic degrees of freedom, the order parameter and its gradient, to be fully dissipative. The model is also extended to include chemo-mechanical coupling, corresponding respectively to endothermic and exothermic processes, leading to a phase change bidirectionality. This contact phase-field model will be applied in the second part of this work to irregular microstructures like geomaterials, valid for porous media in general.
Optical properties of magnetized transient low-pressure plasma

A transient optically thick plasma under the influence of a constant external magnetic field was investigated taking self-absorption into account. Self-absorption which defines the excited state-densities is an important mechanism and can be used with other methods to describe the state densities for an optically thick plasma. This method is an established tool to retrieve state-density and plasma parameters. To measure each magnetic sub-level density of argon 1s4 and 1s5 (in Paschen’s notation) a tunable diode laser absorption spectroscopy was used. Based on reconstructed excited state densities, the self-absorption coefficient was calculated for individual magnetic sub-levels. A decrease in self-absorption with an external magnetic field was noticed indicating a higher transparency of the plasma. Furthermore a polarization dependent self-absorption was found. The presented results can help to model optical properties and interpret the absorption of a low-pressure optically thick magnetized plasma.
Exploiting Weak Head Movements for Camera-based Respiration Detection

In recent years, considerable progress has been made in the non-contact based detection of the respiration rate from video sequences. Common techniques either directly assess the movement of the chest due to breathing or are based on analyzing subtle color changes that occur as a result of hemodynamic properties of the skin tissue by means of remote photoplethysmography (rPPG). However, extracting hemodynamic parameters from rPPG is often difficult especially if the skin is not visible to the camera. In contrast, extracting respiratory signals from chest movements turned out to be a robust method. However, the detectability of chest regions cannot be guaranteed in any application scenario, for instance if the camera setting is optimized to provide close-up images of the head. In such a case an alternative method for respiration detection is required.It is reasonable to assume that the mechanical coupling between chest and head induces minor movements of the head which, like in rPPG, can be detected from subtle color changes as well. Although the strength of these movements is expected to be much smaller in scale, sensing these intensity variations could provide a reasonably suited respiration signal for subsequent respiratory rate analysis.In order to investigate this coupling we conducted an experimental study with 12 subjects and applied motion-and rPPG-based methods to estimate the respiratory frequency from both head regions and chest. Our results show that it is possible to derive signals correlated to chest movement from facial regions. The method is a feasible alternative to rPPG-based respiratory rate estimations when rPPG-signals cannot be derived reliably and chest movement detection cannot be applied as well.
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility scores. Two of these algorithms make use of structural information in the form of a class hierarchy, which is often available in prediction problems with many classes. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency.
Extended probabilistic Rand index and the adjustable moving window-based pixel-pair sampling method

The probabilistic Rand (PR) index has the following three problems: It lacks variations in its value over images; the normalized probabilistic Rand (NPR) index to address this is theoretically unclear, and the sampling method of pixel-pairs was not proposed concretely. In this paper, we propose methods for solving these problems. First, we propose extended probabilistic Rand (EPR) index that considers not only similarity but also dissimilarity between segmentations. The EPR index provides twice as wide effective range as the PR index does. Second, we propose an adjustable moving window-based pixel-pair sampling (AWPS) method in which each pixel-pair is sampled adjustably by considering granularities of ground truth segmentations. Results of experiments show that the proposed methods work effectively and efficiently.
Clustering with Fairness Constraints: A Flexible and Scalable Approach

This study investigates a general variational formulation of fair clustering, which can integrate fairness constraints with a large class of clustering objectives. Unlike the existing methods, our formulation can impose any desired (target) demographic proportions within each cluster. Furthermore, it enables to control the trade-off between fairness and the clustering objective. We derive an auxiliary function (tight upper bound) of our KL-based fairness penalty via its concave-convex decomposition and Lipschitz-gradient property. Our upper bound can be optimized jointly with various clustering objectives, including both prototype-based such as K-means and graph-based such as Normalized Cut. Interestingly, at each iteration, our general fair-clustering algorithm performs an independent update for each assignment variable, while guaranteeing convergence. Therefore, it can be easily distributed for large-scale data sets. Such scalability is important as it enables to explore different trade-off levels between fairness and clustering objectives. Unlike existing fairness-constrained spectral clustering, our formulation does not need storing an affinity matrix and computing its eigenvalue decomposition. Moreover, unlike existing prototype-based methods, our experiments reveal that fairness does not come at a significant cost of the clustering objective. In fact, several of our tests showed that our fairness penalty helped to avoid weak local minima of the clustering objective (i.e., with fairness, we obtained better clustering objectives). We demonstrate the flexibility and scalability of our algorithm with comprehensive evaluations over both synthetic and real world data sets, many of which are much larger than those used in recent fair-clustering methods.
Chemical evolution models for the Galactic disk based on H II region abundances derived from a direct method and a temperature independent method

We present two chemical evolution models of our galaxy, both models are built to fit the O/H ratios derived from H II regions, using two different methods. One model is based on abundances obtained from the [O III] 4363/5007 temperatures (direct method, DM) and the other on abundances obtained from the recombination line ratios of [O II/H I] (temperature independent method, TIM). The differences between the O/H values obtained from these two methods are about 0.25 dex. We find that the model based on the TIM values produces an excellent fit to the observational stellar constraints (B-stars, Cepheids, and the Sun), while the model based on the DM fails to reproduce each of them. Moreover, the TIM model can explain the flattening of the O/H gradient observed in the inner disk due to the assumption of an inside-out star formation quenching, in the 3 - 6 kpc galactocentric range, starting ~ 9 Gyr ago.
A simple and effective postprocessing method for image classification

Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstream applications completion more efficient. Taking image recognition as an example, whether it is hand-crafted low-level feature representation or feature representation extracted by a convolutional neural networks(CNNs), the goal is to extract features that better represent image features, thereby improving classification accuracy. However, we observed that image feature representations share a large common vector and a few top dominating directions. To address this problems, we propose a simple but effective postprocessing method to render off-the-shelf feature representations even stronger by eliminating the common mean vector from off-the-shelf feature representations. The postprocessing is empirically validated on a variety of datasets and feature extraction methods.such as VGG, LBP, and HOG. Some experiments show that the features that have been post-processed by postprocessing algorithm can get better results than original ones.