Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical properties and asset modelling at high frequency

This thesis explores theoretical and empirical aspects of price formation and evolution at high frequency. We begin with the study of the joint dynamics of an option and its underlying. The high frequency data making observable the realized volatility process of the underlying, we want to know if this information is used to price options. We find that the market does not process this information to fix option prices. The stochastic volatility models are then to be considered as reduced form models. Nevertheless, this study tests the relevance of an empirical hedging parameter that we call effective delta. This is the slope of the regression of option price increments on those of the underlying. It proves to be a satisfactory model-independent hedging parameter. For the price dynamics, we turn our attention in the following chapters to more explicit models of market microstructure. One of the characteristics of the market activity is its clustering. Hawkes processes are point processes with this characteristic, therefore providing an adequate mathematical framework for the study of this activity. Moreover, the Markov property associated to these processes when the kernel is exponential allows to use powerful analytical tools such as the infinitesimal generator and the Dynkin formula to calculate various quantities related to them, such as moments or autocovariances of the number of events on a given interval. We begin with a monovariate framework, simple enough to illustrate the method, but rich enough to enable applications such as the clustering of arrival times of market orders, prediction of future market activity knowing past activity, or characterization of unusual shapes, but nevertheless observed, of signature plot, where the measured volatility decreases when the sampling frequency increases. Our calculations also allow us to make instantaneous calibration of the process by relying on the method of moments. The generalization to the multidimensional case then allow us to capture, besides the clustering, the phenomenon of mean reversion, which also characterizes the market activity observed in high frequency. General formulas for the signature plot are then obtained and used to connect its shape to the relative importance of clustering or mean reversion. Our calculations also allow to obtain the explicit form of the volatility associated with the diffusive limit, therefore connecting the dynamics at microscopic level to the macroscopic volatility, for example on a daily scale. Additionally, modelling buy and sell activity by Hawkes processes allows to calculate the market impact of a meta order on the asset price. We retrieve and explain the usual concave form of this impact as well as its relaxation with time. The analytical results obtained in the multivariate case provide the adequate framework for the study of the correlation. We then present generic results on the Epps effect as well as on the formation of the correlation and the lead lag.
Ermes: Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Most existing sentiment analysis approaches heavily rely on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations. The distribution of this labeled data is significantly imbalanced among languages, e.g., more English texts are labeled than texts in other languages, which presents a major challenge to cross-lingual sentiment analysis. There have been several cross-lingual representation learning techniques that transfer the knowledge learned from a language with abundant labeled examples to another language with much fewer labels. Their performance, however, is usually limited due to the imperfect quality of machine translation and the scarce signal that bridges two languages. In this paper, we employ emojis, a ubiquitous and emotional language, as a new bridge for sentiment analysis across languages. Specifically, we propose a semi-supervised representation learning approach through the task of emoji prediction to learn cross-lingual representations of text that can capture both semantic and sentiment information. The learned representations are then utilized to facilitate cross-lingual sentiment classification. We demonstrate the effectiveness and efficiency of our approach on a representative Amazon review data set that covers three languages and three domains.
Detecting abnormal and collusive bids in capped tendering

Recent developments in the area of Bid Tender Forecasting have enabled bidders to implement new types of easy-to-use tools for increasing their chances of winning contracts. Although these new tools (such as iso-Score Curve Graphs, Scoring Probability Graphs, and Position Probability Graphs) are designed for bidders in capped tendering (tenders with an upper price limit), some of their principles can also be applied by a Contracting Authority to detect which bidders do not follow a standard pattern, that is, their bids are extremely high or low. Since a collusive bid generally needs to be sufficiently high or low to make an impact on the bid distribution, any person in charge of supervising capped tenders can be alerted to any bidder that might be involved in a cartel after identifying the same abnormal behavior in a series of tenders through simple calculations and a new type of graph.
Twistor spaces over the connected sum of 3 projective planes

© Foundation Compositio Mathematica, 1992, tous droits réservés. L’accès aux archives de la revue « Compositio Mathematica » (http: //http://www.compositio.nl/) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Binary Constrained Deep Hashing Network for Image Retrieval without Human Annotation

Learning compact binary codes for image retrieval problem using deep neural networks has attracted increasing attention recently. However, training deep hashing networks is challenging due to the binary constraints on the hash codes, the similarity preserving properties, and the requirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-toend deep hashing approach, which is trained to produce binary codes directly from image pixels without human intervention. In particular, our main contribution is to propose a novel pairwise loss function, which simultaneously encodes the distances between pairs of binary codes, and the binary quantization error. We propose an efficient parameter learning algorithm for this loss function. In addition, to provide similar/dissimilar images for our pairwise loss function, we exploit 3D models reconstructed from unlabeled images for automatic generation of enormous similar/dissimilar pairs. Extensive experiments on three image retrieval benchmark datasets demonstrate the superior performance of the proposed method.
A unified CFD‐DEM approach for modeling of debris flow impacts on flexible barriers

This paper presents a unified modeling framework to investigate the impacts of debris flow on flexible barriers, based on coupled computational fluid dynamics and discrete element method (CFD‐DEM). We consider a debris flow as a mixture of fluid and particles where the fluid and particle phases are modeled by the CFD and the DEM, respectively. The fluid‐particle coupling is considered by the exchange of interaction forces between CFD and DEM calculations. The flexible barrier is simulated by the DEM as a network of bonded particles with remote interactions. The proposed coupled CFD‐DEM approach enables us to conveniently handle the complicated three‐way interactions among the fluid, the particles, and the flexible barrier structure for debris flow impact simulations. The proposed approach is first used to investigate the influences of channel inclination and the volumetric solid fraction in a debris mixture on the impact force, the resultant deformation, and the retained mass in a flexible barrier. The predictions agree well with existing experimental and numerical studies. We further examine the possible failure modes of a flexible barrier under debris flow impact and their underlying mechanisms. The performance of different components in a flexible barrier system, including single wires, double twists and cables, and their load sharing mechanisms, are carefully evaluated. The proposed unified framework offers a novel, promising pathway towards physically based, quantitative analysis and design of flexible barriers for debris flow mitigation.
First-order Methods Almost Always Avoid Saddle Points

We establish that first-order methods avoid saddle points for almost all initializations. Our results apply to a wide variety of first-order methods, including gradient descent, block coordinate descent, mirror descent and variants thereof. The connecting thread is that such algorithms can be studied from a dynamical systems perspective in which appropriate instantiations of the Stable Manifold Theorem allow for a global stability analysis. Thus, neither access to second-order derivative information nor randomness beyond initialization is necessary to provably avoid saddle points.
Simplicial Chern-Weil theory for coherent analytic sheaves, part I

In"Chern classes for coherent sheaves", H.I. Green constructs Chern classes in de Rham cohomology of coherent analytic sheaves. We construct here a formal $(\infty,1)$-categorical framework into which we can place Green's work and generalise it, also obtaining a better idea as to what exactly a simplicial connection should be. The result will be the ability to work with generalised invariant polynomials (which will be introduced in the sequel to this paper) evaluated at the curvature of so-called admissible simplicial connections to get explicit \v{C}ech representatives in de Rham cohomology of characteristic classes of coherent analytic sheaves.
Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass

"In the spirit of Nickel and Dimed, a necessary and revelatory expose of the invisible human workforce that powers the web—and that foreshadows the true future of work. Hidden beneath the surface of the web, lost in our wrong-headed debates about AI, a new menace is looming. Anthropologist Mary L. Gray and computer scientist Siddharth Suri team up to unveil how services delivered by companies like Amazon, Google, Microsoft, and Uber can only function smoothly thanks to the judgment and experience of a vast, invisible human labor force. These people doing "ghost work" make the internet seem smart. They perform high-tech piecework: flagging X-rated content, proofreading, designing engine parts, and much more. An estimated 8 percent of Americans have worked at least once in this “ghost economy,” and that number is growing. They usually earn less than legal minimums for traditional work, they have no health benefits, and they can be fired at any time for any reason, or none. There are no labor laws to govern this kind of work, and these latter-day assembly lines draw in—and all too often overwork and underpay—a surprisingly diverse range of workers: harried young mothers, professionals forced into early retirement, recent grads who can’t get a toehold on the traditional employment ladder, and minorities shut out of the jobs they want. Gray and Suri also show how ghost workers, employers, and society at large can ensure that this new kind of work creates opportunity—rather than misery—for those who do it. "
Multiplicative Conditional Correlation Models for Realized Covariance Matrices

We introduce a class of multiplicative dynamic models for realized covariance matrices assumed to be conditionally Wishart distributed. The multiplicative structure enables consistent three-step estimation of the parameters, starting by covariance targeting of a scale matrix. The dynamics of conditional variances and correlations are inspired by specifications akin to the consistent dynamic conditional correlation model of the multivariate GARCH literature, and estimation is performed by quasi maximum likelihood. Simulations show that in finite samples the three-step estimator has smaller bias and root mean squared error than the full estimator when the cross-sectional dimension increases. An empirical application illustrates the flexibility of these models in a low-dimensional setting, and another one illustrates their e ectiveness and practical usefulness in high dimensional portfolio allocation strategies.
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition —Supplementary Material—

In this supplementary material, we provide more details and experimental results to complement the manuscript. Firstly, we report the frequency of different attributes when attacking diverse targets with our importance-aware attribute selection. Secondly, we plot the variations of the overall loss with or without our multi-objective optimization during optimization. Thirdly, we provide the complete proof of Pareto-stationary solution. Finally, we illustrate the comparison of edited faces by the original StyleGAN [1] and the proposed Adv-Attribute attack. A Important-aware Attribute Selection When editing the same source face, different attributes could play a different role on the identification of FR models. During training, the proposed important-aware attribute selection can choose the optimal attribute for the different pairs of target faces and source faces. Due to the limited page in the submission, we supply more frequency of different attributes when attacking different target faces from FFHQ [2] and CelebA-HQ [3], as illustrated in Figure A. We report the frequency of five attributes (i.e., smiling, eyeglass, mustache, blurry and pale skin) between different source faces and different target faces. When attacking the same target face, diverse source faces choose different attributes in each step. In other words, the same source face also selects different attributes for different target faces.
Heap Abstraction Beyond Context-Freeness

The aim of shape analysis is to discover precise abstractions of the reachable data structures in a program's heap. This paper develops a shape analysis for reasoning about relational properties of data structures, such as balancedness of trees or lengths of lists. Both the concrete and the abstract domain are represented by hypergraphs. The analysis is based on user-defined indexed graph grammars to guide concretization and abstraction. This novel extension of context-free graph grammars is powerful enough to model complex data structures, such as balanced binary trees with parent pointers, while preserving most desirable properties of context-free graph grammars. One strength of our analysis is that no artifacts apart from grammars are required from the user; it thus offers a high degree of automation. In particular, indexed graph grammars naturally describe how a data structure evolves and require no deep knowledge about relational properties. We have implemented a prototype of our analysis and report on first experimental results.
THE RANK REVERSAL PROBLEM IN MULTI-CRITERIA DECISION MAKING: A LITERATURE REVIEW

Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field.
The political economy of corporate governance reform in South Africa

This study describes the political-economic dimension of corporate governance reform in South Africa. It then investigates the relationship between corporate governance institutions and systems on the one hand and the political, economic and historical context of South African society on the other. The study establishes the political, economic and historical determinants of corporate governance reform as they evolved in the course of South African corporate history. The study concludes that South African corporate governance reform and such reform in the Commonwealth economic systems have a lot in common in terms of their historical evolution. This is despite the reasons for such reform being vastly different. The outcome of the political process in South Africa, for very specific reasons, is that a specific shareholder model of corporate governance became the corporate governance system in South Africa.
nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks

Converting time domain waveforms to frequency domain spectrograms is typically considered to be a prepossessing step done before model training. This approach, however, has several drawbacks. First, it takes a lot of hard disk space to store different frequency domain representations. This is especially true during the model development and tuning process, when exploring various types of spectrograms for optimal performance. Second, if another dataset is used, one must process all the audio clips again before the network can be retrained. In this paper, we integrate the time domain to frequency domain conversion as part of the model structure, and propose a neural network based toolbox, nnAudio, which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion during feed-forward. It allows on-the-fly spectrogram generation without the need to store any spectrograms on the disk. This approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, which implies that this transformation process can be made trainable, and hence further optimized by gradient descent. nnAudio reduces the waveforms-to-spectrograms conversion time for 1,770 waveforms (from the MAPS dataset) from $10.64$ seconds with librosa to only $0.001$ seconds for Short-Time Fourier Transform (STFT), $18.3$ seconds to $0.015$ seconds for Mel spectrogram, $103.4$ seconds to $0.258$ for constant-Q transform (CQT), when using GPU on our DGX work station with CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz Tesla v100 32Gb GPUs. (Only 1 GPU is being used for all the experiments.) We also further optimize the existing CQT algorithm, so that the CQT spectrogram can be obtained without aliasing in a much faster computation time (from $0.258$ seconds to only $0.001$ seconds).
Escaping Saddles with Stochastic Gradients

We analyze the variance of stochastic gradients along negative curvature directions in certain non-convex machine learning models and show that stochastic gradients exhibit a strong component along these directions. Furthermore, we show that - contrary to the case of isotropic noise - this variance is proportional to the magnitude of the corresponding eigenvalues and not decreasing in the dimensionality. Based upon this observation we propose a new assumption under which we show that the injection of explicit, isotropic noise usually applied to make gradient descent escape saddle points can successfully be replaced by a simple SGD step. Additionally - and under the same condition - we derive the ﬁrst convergence rate for plain SGD to a second-order stationary point in a number of iterations that is independent of the problem dimension.
Evolutionary development: a model for the design, implementation, and evaluation of ICT for education programmes

The impact of information and communication technology (ICT) in primary and secondary education is still an open question. Following review of the available literature, we classify the causes of the lack of impact on students' attainment in four dimensions: (1) the design and implementation of ICT in educational settings; (2) the evaluation of its impact; (3) the scaling up of these kinds of innovations; and (4) the cost-effectiveness of technology-enhanced learning environments. Based on this evidence, we proposed the evolutionary development model (EDM), which aims to produce a cost-effective and sustainable ICT for education (ICT4E) programme in three steps: efficacy, effectiveness, and efficiency. In each step, one component of the programme is built and validated in real educational settings. Therefore, the resultant ICT4E programme is ready to be replicated across the school system. We also show how the EDM guided the development of a programme based on mobile computer supported collaborative learning, known as Eduinnova. Finally, we discuss how EDM can serve as an analysis tool for researchers and policy makers. © 2012 Wiley Periodicals, Inc.
Title : Performance of statistical and machine learning-based methods for predicting 1 biogeographical patterns of fungal productivity in forest ecosystems

Affiliations: 7 1 Department of Crop and Forest Sciences, University of Lleida, Av. Alcalde Rovira 8 Roure 191, E-25198 Lleida, Spain 9 2 Joint Research Unit CTFC – AGROTECNIO, Av. Alcalde Rovira Roure 191, E-25198 10 Lleida, Spain 11 3 Forest Science and Technology Centre of Catalonia, Ctra. Sant Llorenç de Morunys km 12 2, 25280 Solsona, Spain 13 4 Forest Advanced Computing and Artificial Intelligence Laboratory, Department of 14 Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA 15
Political Connections and Firm Value : Evidence from Close Gubernatorial Elections

The external networks of directors significantly impact firm value and decisions. Surrounding close gubernatorial elections, local firms with directors connected to winners increase value by 4.1% over firms connected to losers. Director network’s value increases with network strength and activities, and is not due to network homophily. Connected firms are more likely to receive state subsidies, loans, and tax credits. They obtain better access to bank loans, borrow more, pay lower interest, invest and employ more, and enjoy better long-term performance. Network benefits are concentrated on connected firms, possibly through quid pro quo deals, and unlikely spread to industry competitors.
Northumbria Research

This paper provides estimates of the impact of an unanticipated child during adolescence on labour supply and earnings using data for women who gave birth between 1976 and 2015 drawn from the 1990 and 2000 censuses and the American Community Surveys. Twins at first birth are used as an instrument to avoid the problems of fertility endogeneity. Estimates from our IV models indicate that the arrival of a second-born twin had severe economic consequences for adolescent women over most of our data.
Remarks on solutions to the functional equations of the radical type

This is an expository paper containing remarks on solutions to some functional equations of a form, that could be called of the radical type. Simple natural examples of them are the following two functional equations fn √xn + yn= f(x) + f(y),f n √xn + yn+ fn p|xn −yn|= 2f(x) + 2f(y) considered recently in several papers, for real functions and with given positive integer n, in connection with the notion of Ulam (or Hyers-Ulam) stability. We provide a general method allowing to determine solutions to them.