Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates

In this paper, we study smooth stochastic multi-level composition optimization problems, where the objective function is a nested composition of $T$ functions. We assume access to noisy evaluations of the functions and their gradients, through a stochastic first-order oracle. For solving this class of problems, we propose two algorithms using moving-average stochastic estimates, and analyze their convergence to an $\epsilon$-stationary point of the problem. We show that the first algorithm, which is a generalization of [22] to the $T$ level case, can achieve a sample complexity of $\mathcal{O}(1/\epsilon^6)$ by using mini-batches of samples in each iteration. By modifying this algorithm using linearized stochastic estimates of the function values, we improve the sample complexity to $\mathcal{O}(1/\epsilon^4)$. This modification also removes the requirement of having a mini-batch of samples in each iteration. To the best of our knowledge, this is the first time that such an online algorithm designed for the (un)constrained multi-level setting, obtains the same sample complexity of the smooth single-level setting, under mild assumptions on the stochastic first-order oracle.
Portfolio Selection under Median and Quantile Maximization

In this paper, we study a portfolio selection problem in which an agent trades a risk-free asset and multiple risky assets with deterministic mean return rates and volatility and wants to maximize the $\alpha$-quantile of her wealth at some terminal time. Because of the time inconsistency caused by quantiles, we consider intra-personal equilibrium strategies. We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when $\alpha>1/2$, leads to zero investment in the risky assets when $\alpha<1/2$, and is a portfolio insurance strategy when $\alpha=1/2$. We then compare the intra-personal equilibrium strategy in the case of $\alpha=1/2$, namely under median maximization, to some other strategies and apply it to explain why more wealthy people invest more precentage of wealth in risky assets. Finally, we extend our model to account for multiple terminal time.
Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks

We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bitrates. Although GAN can help to reconstruct sharp images, there are two drawbacks. First, GAN makes training unstable. Second, the reconstructions often contain unpleasing noise or artifacts. To address both of the drawbacks, our method adopts two-stage training and network interpolation. The two-stage training is effective to stabilize the training. Moreover, the network interpolation utilizes the models in both stages and reduces undesirable noise and artifacts, while maintaining important edges. Hence, we can control the trade-off between perceptual quality and fidelity without re-training models. The experimental results show that our model can reconstruct high quality images. Furthermore, our user study confirms that our reconstructions are preferable to state-of-the-art GAN-based image compression model. Our source code is available at https://github.com/iwa-shi/fidelity_controllable_compression
Extended mean flow analysis of the circular cylinder flow

A new eigenvalue analysis is developed and applied to the circular cylinder laminar flow configuration to investigate the various mechanisms at play in the nonlinear saturation of perturbations yielding to limit cycles for supercritical values of the Reynolds number. Unlike the mean-flow analysis, which only accounts for the interaction of the first-harmonic of the time-periodic flow with its mean-flow, the so-called extended mean-flow analysis also accounts for its interaction with the second-harmonic. The results reveal the existence of two eigenvalues both having a growth rate exactly equal to zero. The high-frequency eigenmode gives better estimations of the frequency and spatial structure of the first-harmonic than the marginal modes obtained with the mean-flow analysis, especially when the Reynolds number is increased.
Forecasting of the Montreal Subway Smart Card Entry Logs with Event Data

One of the major goals of transport operators is to adapt the transport supply scheduling to the passenger demand for existing transport networks during each specific period. Another problem mentioned by operators is accurately estimating the demand for disposable ticket or pass to adapt ticket availability to passenger demand. In this context, we propose generic data shaping, allowing the use of well-known regression models (basic, statistical and machine learning models) for the long-term forecasting of passenger demand with fine-grained temporal resolution. Specifically, this paper investigates the forecasting until one year ahead of the number of passengers entering each station of a transport network with a quarter-hour aggregation by taking planned events into account (e.g., concerts, shows, and so forth). To compare the models and the quality of the prediction, we use a real smart card and event data set from the city of Montr\'eal, Canada, that span a three-year period with two years for training and one year for testing.
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology

Conversational interfaces are increasingly popular as a way of connecting people to information. Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpusbased conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence or on single sentences instead of an entire dialogue. In this paper, we define the task of Malevolent Dialogue Response Detection and Classification (MDRDC). We make three contributions to advance research on this task. First, we present a Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical classification task over this taxonomy. Third, we apply stateof-the-art text classification methods to the MDRDC task and report on extensive experiments aimed at assessing the performance of these approaches.
Hierarchical deep learning of multiscale differential equation time-steppers

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration expensive. In this work, we develop a hierarchy of deep neural network time-steppers to approximate the dynamical system flow map over a range of time-scales. The model is purely data-driven, enabling accurate and efficient numerical integration and forecasting. Similar ideas can be used to couple neural network-based models with classical numerical time-steppers. Our hierarchical time-stepping scheme provides advantages over current time-stepping algorithms, including (i) capturing a range of timescales, (ii) improved accuracy in comparison with leading neural network architectures, (iii) efficiency in long-time forecasting due to explicit training of slow time-scale dynamics, and (iv) a flexible framework that is parallelizable and may be integrated with standard numerical time-stepping algorithms. The method is demonstrated on numerous nonlinear dynamical systems, including the Van der Pol oscillator, the Lorenz system, the Kuramoto–Sivashinsky equation, and fluid flow pass a cylinder; audio and video signals are also explored. On the sequence generation examples, we benchmark our algorithm against state-of-the-art methods, such as LSTM, reservoir computing and clockwork RNN. This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
Quantum Hierarchical Systems: Fluctuation Force by Coarse-Graining, Decoherence by Correlation Noise

While the issues of dissipation, fluctuations, noise and decoherence in open quantum systems (with autocratic divide) analyzed via Langevin dynamics are familiar subjects, the treatment of corresponding issues in closed quantum systems is more subtle, as witnessed by Boltzmann's explanation of dissipation in a macroscopic system made up of many equal constituents (a democratic system). How to extract useful physical information about a closed democratic system with no obvious ways to distinguish one constituent from another, nor the existence of conservation laws governing certain special kinds of variables, e.g., the hydrodynamic variables -- this is the question we raise in this essay. Taking the inspirations from Boltzmann and Langevin, we study a) how a hierarchical order introduced to a closed democratic system -- defined either by substance or by representation, and b) how hierarchical coarse-graining, executed in a specific order, can facilitate our understanding in how macro-behaviors arise from micro-dynamics. We give two examples in: a) the derivation of correlation noises in the BBGKY hierarchy and how using a Boltzmann-Langevin equation one can study the decoherence of the lower order correlations; and b) the derivation of quantum fluctuation forces by ordered coarse-grainings of the relevant variables in the medium, the quantum field and the internal degrees of freedom of an atom.
EVALUTION OF THE ANTIEIMERIAN EFFECT OF SOME PLANT EXTRACTS IN QUAIL EIMERIOSIS

The intensive breeding of quails knows large development perspectives. The japonese quail Coturnix coturnix japonica, the smallest tamed avian specie is used not only for egg production but also for meat production. Fast growing ritm, early sexual maturity, short period between generations, great egg production small fadder needs, and small space comparativ to other bird species exploited for the same purpose, are the main characteristics of this avian specie. This study is the gathering of data regarding the evolution and pathogeny of eimeriosis in quail, as well as the establishment of some phitoterapeutical programs in order to reduce as much as posible the incidence of this diseases in quail breeding compexes.
The Homophily Principle in Social Network Analysis

In recent years, social media has become a ubiquitous and integral part of social networking. One of the major attentions made by social researchers is the tendency of like-minded people to interact with one another in social groups, a concept which is known as Homophily. The study of homophily can provide eminent insights into the flow of information and behaviors within a society and this has been extremely useful in analyzing the formations of online communities. In this paper, we review and survey the effect of homophily in social networks and summarize the state of art methods that has been proposed in the past years to identify and measure the effect of homophily in multiple types of social networks and we conclude with a critical discussion of open challenges and directions for future research.
Efficient neural speech synthesis for low-resource languages through multilingual modeling

Recent advances in neural TTS have led to models that can produce high-quality synthetic speech. However, these models typically require large amounts of training data, which can make it costly to produce a new voice with the desired quality. Although multi-speaker modeling can reduce the data requirements necessary for a new voice, this approach is usually not viable for many low-resource languages for which abundant multi-speaker data is not available. In this paper, we therefore investigated to what extent multilingual multi-speaker modeling can be an alternative to monolingual multi-speaker modeling, and explored how data from foreign languages may best be combined with low-resource language data. We found that multilingual modeling can increase the naturalness of low-resource language speech, showed that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models, and saw that the target language naturalness was affected by the strategy used to add foreign language data.
Correlations with tailored extremal properties

Recently, Chatterjee has introduced a new coefficient of correlation which has several natural properties. In particular, the coefficient attains its maximal value if and only if one variable is a measurable function of the other variable. In this paper, we seek to define correlations which have a similar property, except now the measurable function must belong to a pre-specified class, which amounts to a shape restriction on the function. We will then look specifically at the correlation corresponding to the class of monotone nondecreasing functions, in which case we can prove various asymptotic results, as well as perform local power calculations.
Universality of Linearized Message Passing for Phase Retrieval With Structured Sensing Matrices

In the phase retrieval problem one seeks to recover an unknown <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> dimensional signal vector <inline-formula> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> measurements of the form <inline-formula> <tex-math notation="LaTeX">$y_{i} = |(\mathbf {A} \mathbf {x})_{i}|$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> denotes the sensing matrix. Many algorithms for this problem are based on approximate message passing. For these algorithms, it is known that if the sensing matrix <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> is generated by sub-sampling <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> columns of a uniformly random (i.e., Haar distributed) orthogonal matrix, in the high dimensional asymptotic regime (<inline-formula> <tex-math notation="LaTeX">$m,n \rightarrow \infty, n/m \rightarrow \kappa $ </tex-math></inline-formula>), the dynamics of the algorithm are given by a deterministic recursion known as the state evolution. For a special class of linearized message-passing algorithms, we show that the state evolution is universal: it continues to hold even when <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> is generated by randomly sub-sampling columns of the Hadamard-Walsh matrix, if the signal is drawn from a Gaussian prior.
Country-wide Mobility Changes Observed Using Mobile Phone Data During COVID-19 Pandemic

In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian public and private life came to a sudden halt. Here we assess the effect of the lock-down quantitatively for all regions in Austria and present an analysis of daily changes of human mobility throughout Austria using near-real-time anonymized mobile phone data. We describe an efficient data aggregation pipeline and analyze the mobility by quantifying mobile-phone traffic at specific point of interests (POIs), analyzing individual trajectories and investigating the cluster structure of the origin-destination graph. We found a reduction of commuters at Viennese metro stations of over 80% and the number of devices with a radius of gyration of less than 500 m almost doubled. The results of studying crowd-movement behavior highlight considerable changes in the structure of mobility networks, revealed by a higher modularity and an increase from 12 to 20 detected communities. We demonstrate the relevance of mobility data for epidemiological studies by showing a significant correlation of the outflow from the town of Ischgl (an early COVID-19 hotspot) and the reported COVID-19 cases with an 8-day time lag. This research indicates that mobile phone usage data permits the moment-by-moment quantification of mobility behavior for a whole country. We emphasize the need to improve the availability of such data in anonymized form to empower rapid response to combat COVID-19 and future pandemics.
Dietary Fats and Metabolic Syndrome

The quantity and quality of fats consumed in the diet can have important effects on prevention and/or improvement clustering metabolic abnormalities of the metabolic syndrome. Fatty acids as food ingredients regulate cholesterol homeostasis and concentrations of blood lipoproteins, and affect the levels of other cardiometabolic risk factors, such as blood pressure, haemostasis, and body weight, through various mechanisms. There is convincing evidence that exchanging dietary saturated fat with monounsaturated fat produces beneficial effects on metabolic control, i.a. it lowers LDL-cholesterol concentrations, improves the postprandial lipid profile and lowers the blood pressure. Serum fatty acid composition mainly reflects dietary fat intake, but also endogenous fatty acid synthesis catalyzed by desaturases. Furthermore, high fat intake and serum fatty acid profile may influence the progression of obesity and insulin sensitivity. This review highlights current dietary guidance of fats quality recommendation in health and cardio metabolic risk.
DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model’s accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition
Learning Personalized Models of Human Behavior in Chess

Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they may need to collaborate with them, or they may expect them to serve as partners in an extended interaction. Motivated by this goal of human-like AI systems, the problem of predicting human actions -- as opposed to predicting optimal actions -- has become an increasingly useful task. We extend this line of work by developing highly accurate personalized models of human behavior in the context of chess. Chess is a rich domain for exploring these questions, since it combines a set of appealing features: AI systems have achieved superhuman performance but still interact closely with human chess players both as opponents and preparation tools, and there is an enormous amount of recorded data on individual players. Starting with an open-source version of AlphaZero trained on a population of human players, we demonstrate that we can significantly improve prediction of a particular player's moves by applying a series of fine-tuning adjustments. The differences in prediction accuracy between our personalized models and unpersonalized models are at least as large as the differences between unpersonalized models and a simple baseline. Furthermore, we can accurately perform stylometry -- predicting who made a given set of actions -- indicating that our personalized models capture human decision-making at an individual level.
The effect of one-week intensive feed restriction and age on the carcass composition and meat quality of growing rabbits.

The effect of one-week intensive feed restriction on the carcass characteristics, meat tenderness, cooking loss, and muscle fibre characteristics of growing rabbits was evaluated. Weaned rabbits (35 days) were divided into three groups: rabbits fed ad libitum (ADL), rabbits fed 50 g (R50), and rabbits fed 65 g (R65) of feed per animal per day in days 42-49 of age. Dressing out percentage was lower (-1.3% and -2.3% for R50 and R65, respectively) in restricted rabbits (P ≤ 0.001) but increased with age (P ≤ 0.001). Cooking loss signifi- cantly increased in restricted rabbits. A significant interaction between feeding regime and age was observed in the number of type αR muscle fibres, the highest number (P ≤ 0.05) was observed in the ADL rabbits (114.5) at 49 days, and the lowest was found in the R50 group at 70 days of age (25.0). Feed restriction increased the percentage of αW glycolytic fibres (P ≤ 0.01). A significant negative correlation was found between cooking loss and the cross sectional area of αW fibres (-0.486) and αR (-0.325). It could be concluded that one-week intensive feed restriction did not have a negative effect on meat tenderness and cooking loss, despite the fact that it affected muscle fibre type distribution.
Hankel hyperpfaffian calculations and Selberg integrals

In the previous paper (J. Combin. Theory Ser. A, 120, 2013, 1263--1284) H. Tagawa and the two authors proposed an algebraic method to compute certain Pfaffians whose form resemble to Hankel determinants associated with moment sequences of the classical orthogonal polynomials. At the end of the paper they offered several conjectures. In this work we employ a completely different method to evaluate this type of Pfaffians. The idea is to apply certain de Bruijn type formula and convert the evaluation of the Pfaffians to the certain Selberg type integrals. This approach works not only for Pfaffians but also for hyperpfaffians. Hence it enables us to establish much more generalized identities than those conjectured in the previous paper. We also attempt q-analogues.
Mapping the Galactic Disk with the LAMOST and Gaia Red Clump Sample. V. On the Origin of the “Young” [α/Fe]-enhanced Stars

Using a sample of nearly 140,000 primary red-clump stars selected from the LAMOST and Gaia surveys, we have identified a large sample of “young” [α/Fe]-enhanced stars with stellar ages younger than 6.0 Gyr and [α/Fe] ratios greater than 0.15 dex. The stellar ages and [α/Fe] ratios are measured from LAMOST spectra, using a machine-learning method trained with common stars in the LAMOST–APOGEE fields (for [α/Fe]) and in the LAMOST–Kepler fields (for stellar age). The existence of these “young” [α/Fe]-enhanced stars is not expected from the classical Galactic chemical evolution models. To explore their possible origins, we have analyzed the spatial distribution, and the chemical and kinematic properties of those stars and compared the results with those of the chemically thin and thick disk populations. We find that those “young” [α/Fe]-enhanced stars have distributions in number density, metallicity, [C/N] abundance ratio, velocity dispersion, and orbital eccentricity that are essentially the same as those of the chemically thick disk population. Our results clearly show those so-called “young” [α/Fe]-enhanced stars are not really young but genuinely old. Although other alternative explanations can not be fully ruled out, our results suggest that the most possible origin of these old stars is the result of stellar mergers or mass transfer.