Miracle-dz/UniFAR-FTUs huggingface dataset:
ZiMM: a deep learning model for long term adverse events with non-clinical claims data

This paper considers the problem of modeling long-term adverse events following prostatic surgery performed on patients with urination problems, using the French national health insurance database (SNIIRAM), which is a non-clinical claims database built around healthcare reimbursements of more than 65 million people. This makes the problem particularly challenging compared to what could be done using clinical hospital data, albeit a much smaller sample, while we exploit here the claims of almost all French citizens diagnosed with prostatic problems (with between 1.5 and 5 years of history). We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) to capture such long-term adverse events, and we build a deep-learning architecture on top of it to deal with the complex, highly heterogeneous and sparse patterns observable in such a large claims database. This architecture combines several ingredients: embedding layers for drugs, medical procedures, and diagnosis codes; embeddings aggregation through a self-attention mechanism; recurrent layers to encode the health pathways of patients before their surgery and a final decoder layer which outputs the ZiMM's parameters.
TOI-677b: A Warm Jupiter (P = 11.2 days) on an Eccentric Orbit Transiting a Late F-type Star

We report the discovery of TOI-677 b, first identified as a candidate in light curves obtained within Sectors 9 and 10 of the Transiting Exoplanet Survey Satellite (TESS) mission and confirmed with radial velocities. TOI-677 b has a mass of = , a radius of = , and orbits its bright host star (V = 9.8 mag) with an orbital period of d, on an eccentric orbit with . The host star has a mass of = , a radius of = , an age of Gyr and solar metallicity, properties consistent with a main-sequence late-F star with K. We find evidence in the radial velocity measurements of a secondary long-term signal, which could be due to an outer companion. The TOI-677 b system is a well-suited target for Rossiter–Mclaughlin observations that can constrain migration mechanisms of close-in giant planets.
Lightning Impulse Current Tests on Conductive Fabrics

The large amount of electric current associated to lightning discharges is hazardous for living beings, equipment, structures and buildings. To protect those targets against lightning are used Lightning Protection Systems (LPS). However, there are some temporary outdoor activities and backcountry places where an adequate LPS cannot be set up mainly due to the large dimensions of its components and its heavy weight. On the search of lightweight lightning protection materials that can be used as part of special LPS, we research some types of electroconductive fabrics by applying high lightning impulse currents in laboratory. The fabric samples checked were pieces of 10 cm x 10 cm: two rip-stop type, a plain-weave, a non-woven and a carbon-impregnated polymeric, all of them obtained commercially. Under laboratory conditions, these samples were subject to subsequent lightning impulse currents registering the voltage and current signals. Optical and scanning electron microscope inspections were performed after tests. Despite some changes visualized as marks left on the fabric surface, the results show that investigated conductive textiles can endure ground currents produced by atmospheric lightning since they withstand the several applied laboratory lightning impulse currents. The outcomes suggest that the weave pattern of the conductive fabric influences the lightning current tolerance, enabling some conductive fabrics to be used in heavy-current applications and as part of personal LPS for outdoor, backcountry and mobile shelters, particularly when lightweight and portability are mandatory.
Locking-free Simulation of Isometric Thin Plates

To efficiently simulate very thin, inextensible materials like cloth or paper, it is tempting to replace force-based thin-plate dynamics with hard isometry constraints. Unfortunately, naive formulations of the constraints induce membrane locking---artificial stiffening of bending modes due to the inability of discrete kinematics to reproduce exact isometries. We propose a simple set of meshless isometry constraints, based on moving-least-squares averaging of the strain tensor, which do not lock, and which can be easily incorporated into standard constrained Lagrangian dynamics integration.
Affective Computing for Large-scale Heterogeneous Multimedia Data

The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., images, music, and videos), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.
Context-aware Dynamic Assets Selection for Online Portfolio Selection based on Contextual Bandit

Online portfolio selection is a sequential decision-making problem in financial engineering, aiming to construct a portfolio by optimizing the allocation of wealth across a set of assets to achieve the highest return. In this paper, we present a novel context-aware dynamic assets selection problem and propose two innovative online portfolio selection methods named Exp4.ONSE and Exp4.EGE respectively based on contextual bandit algorithms to adapt to the dynamic assets selection scenario. We also provide regret upper bounds for both methods which achieve sublinear regrets. In addition, we analyze the computational complexity of the proposed algorithms, which implies that the computational complexity can be significantly reduced to polynomial time. Extensive experiments demonstrate that our algorithms have marked superiority across representative real-world market datasets in term of efficiency and effectiveness.
Running Head : Poverty , Inequality , and Calorie Intake 1 1 2 3 4 Poverty , inequality , and increased consumption of high calorie food : Experimental evidence 5 for a causal link

29 Rising obesity represents a serious, global problem. It is now well established that obesity is 30 associated with poverty and wealth inequality, suggesting that these factors may promote 31 caloric intake. Whereas previous work has examined these links from an epidemiological 32 perspective, the current paper examined them experimentally. In Study 1 we found that 33 people experimentally induced to view themselves as poor (v. wealthy) exhibited increased 34 calorie intake. In Study 2, participants who believed that they were poorer or wealthier than 35 their interaction partners exhibited higher levels of anxiety compared to those in an equal 36 partners condition; this anxiety in turn led to increased calorie consumption for people who 37 had a strong need to belong. The findings provide causal evidence for the poverty-intake and 38 inequality-intake links. Further, we identify social anxiety and a strong need to belong as 39 important social psychological factors linking inequality to increased calorie intake. 40 Word Count: 146 41
Going Negative Online? - A Study of Negative Advertising on Social Media

A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social media and have pressured congress to restrict political advertising on social media. Following the related legislation, social media companies began disclosing their political ads archive for transparency during the summer of 2018 when the midterm election campaign was just beginning. This research collects the data of the related political ads in the context of the U.S. midterm elections since August to study the overall pattern of political ads on social media and uses sets of machine learning methods to conduct sentiment analysis on these ads to classify the negative ads. A novel approach is applied that uses AI image recognition to study the image data. Through data visualization, this research shows that negative advertising is still the minority, Republican advertisers and third party organizations are more likely to engage in negative advertising than their counterparts. Based on ordinal regressions, this study finds that anger evoked information-seeking is one of the main mechanisms causing negative ads to be more engaging and effective rather than the negative bias theory. Overall, this study provides a unique understanding of political advertising on social media by applying innovative data science methods. Further studies can extend the findings, methods, and datasets in this study, and several suggestions are given for future research.
2 Steady-State Performance Analyses of Adaptive Filters

Adaptive filters have become a vital part of many modern communication and control systems, which can be used in system identification, adaptive equalization, echo cancellation, beamforming, and so on [l]. The least mean squares (LMS) algorithm, which is the most popular adaptive filtering algorithm, has enjoyed enormous popularity due to its simplicity and robustness [2] [3]. Over the years several variants of LMS have been proposed to overcome some limitations of LMS algorithm by modifying the error estimation function from linearity to nonlinearity. Sign-error LMS algorithm is presented by its computational simplicity [4], least-mean fourth (LMF) algorithm is proposed for applications in which the plant noise has a probability density function with short tail [5], and the LMMN algorithm achieves a better steady state performance than the LMS algorithm and better stability properties than the LMF algorithm by adjusting its mixing parameter [6], [7]. The performance of an adaptive filter is generally measured in terms of its transient behavior and its steady-state behavior. There have been numerous works in the literature on the performance of adaptive filters with many creationary results and approaches [3]-[20]. In most of these literatures, the steady-state performance is often obtained as a limiting case of the transient behavior [13]-[16]. However, most adaptive filters are inherently nonlinear and time-variant systems. The nonlinearities in the update equations tend to lead to difficulties in the study of their steady-state performance as a limiting case of their transient performance [12]. In addition, transient analyses tend to require some more simplifying assumptions, which at times can be restrictive. Using the energy conservation relation during two successive iteration update , N. R. Yousef and A. H. Sayed re-derived the steady-state performance for a large class of adaptive filters [11],[12], such as sign-error LMS algorithm, LMS algorithm, LMMN algorithm, and so on, which bypassed the difficulties encountered in obtaining steady-state results as the limiting case of a transient analysis. However, it is generally observed that most works for analyzing the steady-state performance study individual algorithms separately. This is because different adaptive schemes have different nonlinear update equations, and the particularities of each case tend to require different arguments and assumptions. Some authors try to investigate the steadystate performance from a general view to fit more adaptive filtering algorithms, although that is a challenge task. Based on Taylor series expansion (TSE), S. C. Douglas and T. H. Meng obtained a general expression for the steady-state MSE for adaptive filters with error
Strong self-concordance and sampling

Motivated by the Dikin walk, we develop aspects of the interior-point theory for sampling in high dimension. Specifically, we introduce the notions of strong self-concordance and symmetry for a barrier. These properties imply that the Dikin walk defined using a strongly self-concordant barrier with symmetry parameter ν mixes in Õ(nν) steps from a warm start for a convex body in ℝ n . For many natural barriers, ν is roughly bounded by ν, the standard self-concordance parameter. We also show that these properties hold for the Lee-Sidford barrier. As a consequence, we obtain the first walk that mixes in Õ(n 2) steps for an arbitrary polytope in ℝ n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection — for the universal and entropic barriers, it is implied by the KLS conjecture.
Energy-Efficient UAV Backscatter Communication With Joint Trajectory Design and Resource Optimization

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV’s limited on-board energy and the CEs’ transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV’s trajectory, the BDs’ scheduling, and the CEs’ transmission power, subject to the BDs’ throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach’s method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the hover-and-fly scheme, the state-of-the-art scheme, and the CE-relay scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained.
Overconstrained gravitational lens models and the Hubble constant

It is well known that measurements of H0 from gravitational lens time delays scale as H0 ∝ 1 − κE, where κE is the mean convergence at the Einstein radius RE but that all available lens data other than the delays provide no direct constraints on κE. The properties of the radial mass distribution constrained by lens data are RE and the dimensionless quantity ξ = REα″(RE)/(1 − κE), where α″(RE) is the second derivative of the deflection profile at RE. Lens models with too few degrees of freedom, like power-law models with densities ρ ∝ r−n, have a one-to-one correspondence between ξ and κE (for a power-law model, ξ = 2(n − 2) and κE = (3 − n)/2 = (2 − ξ)/4). This means that highly constrained lens models with few parameters quickly lead to very precise but inaccurate estimates of κE and hence H0. Based on experiments with a broad range of plausible dark matter halo models, it is unlikely that any current estimates of H0 from gravitational lens time delays are more accurate than ${\sim} 10{{\ \rm per\ cent}}$, regardless of the reported precision.
Warm dark matter model with a few keV mass is bad for the too-big-to-fail problem

Theoretical studying of the very inner structure of faint satellite galaxy requires very high-resolution hydro-dynamical simulations with realistic model for star formation, which are beginning to emerge recently. In this work, we present an analytical description to model the inner kinematic of satellites in the Milky Way (MW). We use a Monte Carlo method to produce merger trees for MW mass halo and analytical models to produce stellar mass in the satellite galaxies. We consider two important processes which can significantly modify the inner mass distribution in satellite galaxy. The first is baryonic feedback which can induce a flat inner profile depending on the star formation efficiency in the galaxy. The second is the tidal stripping to reduce and re-distribute the mass inside satellite. We apply this model to MW satellite galaxies in both CDM and thermal relic WDM models. It is found that tidal heating must be effective to produce a relatively flat distribution of the satellite circular velocities, to agree with the data. The constraint on WDM mass depends on the host halo mass. For a MW halo with dark matter mass lower than $2\times 10^{12}\, \text{ M}_\odot$, a 2 keV WDM model can be safely excluded as the predicted satellite circular velocities are systematically lower than the data. For WDM with mass of 3.5 keV, it requires the MW halo mass to be larger than $1.5\times 10^{12}\, {\text{ M}}_{\odot }$.
Heterogeneity, control, social infrastructure, and possibilities of participation: Their interplay in modern understandings of heredity and in interpretation of science

This working paper is a prospectus for research, writing, and engagement. It consists of vignettes, sketches of lines of inquiry, and proposals for engagement, all of which concern modern understandings of heredity and development over the life course as well as the social interpretation of science. The various items address a range of areas of science and of its interpretation: heritability studies, the social uses of genetic information, gene-by-environment interaction, personalized medicine, IQ paradoxes, racial group membership, biobanks, and life events and difficulties research. Fresh perspectives in these areas are opened up by examining the ways that research and application of resulting knowledge address—or suppress—heterogeneity in a range of senses (including individual particularity and variation around a mean). The prospectus is presented in the spirit of unfinished and open inquiry, aiming to encourage readers to make further contributions of their own. Adopting or adapting items in the prospectus is encouraged, as is critical commentary on practical and theoretical issues raised by the
Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction

Charge prediction, determining charges for criminal cases by analyzing the textual fact descriptions, is a promising technology in legal assistant systems. In practice, the fact descriptions could exhibit a significant intra-class variation due to factors like non-normative use of language, which makes the prediction task very challenging, especially for charge classes with too few samples to cover the expression variation. In this work, we explore to use the charge definitions from criminal law to alleviate this issue. The key idea is that the expressions in a fact description should have corresponding formal terms in charge definitions, and those terms are shared across classes and could account for the diversity in the fact descriptions. Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation. The generated auxiliary representations are created through the interaction of fact description with the relevant charge definitions and terms in those definitions by integrated sentence- and word-level attention scheme. Experimental results on two datasets show that our model achieves significant improvement than baselines, especially for classes with few samples.
Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps

We develop and approach to unsupervised semantic medical image segmentation that extends previous work with generative adversarial networks. We use existing edge detection methods to construct simple edge diagrams, train a generative model to convert them into synthetic medical images, and construct a dataset of synthetic images with known segmentations using variations on extracted edge diagrams. This synthetic dataset is then used to train a supervised image segmentation model. We test our approach on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset. We show that our unsupervised approach is more accurate than previous unsupervised methods, and performs reasonably compared to supervised image segmentation models. All code and trained models are available at this https URL.
Sparse linear discriminant analysis for multiview structured data

Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two‐step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.
Deep learning decoding of mental state in non-invasive brain computer interface

Brain computer interface (BCI) has been popular as a key approach to monitor our brains recent year. Mental states monitoring is one of the most important BCI applications and becomes increasingly accessible. However, the mental state prediction accuracy and generality through encephalogram (EEG) are not good enough for everyday use. Here in this paper we present a deep learning-based EEG decoding method to read mental states. We propose a novel 1D convolutional neural network with different filter lengths to capture different frequency bands information. To improve the prediction accuracy, we also used a resnet-like structure to train a relatively deep convolutional neural network to promote feature extraction. Compared with traditional ways of predictions such as KNN and SVM, we achieved a significantly better result with an accuracy of 96.40%. Also, in contrast with some already published open source deep neural network structures, our methods achieved the state of art prediction accuracy on a mental state recognition dataset. Our results demonstrate using only 1D convolution could extract the features of EEG and the possibility of mental state prediction using portable EEG devices.