Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical analogies: An engine for understanding the transformations of the boundaries between economics and physics

To date, the relation between economics and physics has been analyzed through the influence of physics on economics. However, in the last years this relation has become more complex, because economic models are nowadays used in physics (minority game, GARCH model, etc.). The aim of this paper is to explain the origin of this new relation between physics and economics. It shows how mathematical analogies have contributed to progressively reshape the disciplinary boundaries of economics and physics, making them more permeable. It investigates three examples: Frisch’s “rocking horse” model (1933); the use of the Ising model for creating econophysics in the 1990s; the minority game, created by econophysicists in 1997 for solving an economic problem and nowadays used in physics. Our investigation demonstrates how mathematical analogies have started to influence the flow of thinking between physics and economics in both directions.
Notes On Some Indo-Pacific Pontoniinae. I. Periclimenes Tosaensis Kubo 1)

[De nouveaux specimens de la crevette Periclimenes tosaensis Kubo, qui jusqu'a present n'etait connue que par un holotype endommage, ont ete obtenus de la Mer de Chine du Sud. Ils sont decrits ici et la disposition des couleurs, ainsi que les renseignements ecologiques, sont notes. Les affinites de l'espece sont discutees et il a ete remarque qu'elle presente des traits communs avec les deux sous-genres du genre Periclimenes. Bien que l'espece soit classee dans le sous-genre Harpilius Dana, il est manifeste que la separation entre sous-genres n'est pas precise. , De nouveaux specimens de la crevette Periclimenes tosaensis Kubo, qui jusqu'a present n'etait connue que par un holotype endommage, ont ete obtenus de la Mer de Chine du Sud. Ils sont decrits ici et la disposition des couleurs, ainsi que les renseignements ecologiques, sont notes. Les affinites de l'espece sont discutees et il a ete remarque qu'elle presente des traits communs avec les deux sous-genres du genre Periclimenes. Bien que l'espece soit classee dans le sous-genre Harpilius Dana, il est manifeste que la separation entre sous-genres n'est pas precise. ]
The Perception of Earnings Management According to an Econometric-Accounting Analysis: The Case of Tunisia

AbstractAccounting information provides support for decisions made by the company's management and its partners. Potential investors, financial backers as well as authorities (financial and judicial) make their decisions based on this information, which itself is supposed to be drawn up in accordance with generally accepted accounting standards and principles. However, the existence of accounting choices and accounting policies that are diversified and standardized by the accounting system create the freedom for managers to manipulate the quality of the information. In other words, a situation of information asymmetry may tempt the managers of failing companies to adopt choices in order to influence the perception of risk by its partners.
RATES OF MITOSIS IN A PARTIALLY SYNCHRONOUS MERISTEM

Summary In root apices of Sinapis alba mitosis is partially synchronous under certain cultural conditions, with two maxima and two minima of mitotic index each day. But the phase of the partial synchrony is different in the stele and cortex from that in the cap initials and that in the quiescent centre. The average rates of mitosis vary from once in 35 hours in the cap initials to once in 500 hours in the quiescent centre. Cells of the cortex and stele divide more slowly near the quiescent centre than at 150 microns from it. In this respect Sinapis differs from Zea and the dissimilarity in organization may be due to the possession of partial synchrony and the opportunity it gives for phasic differences in synthesis.
Manfaat Unsur N, P, dan K Bagi Tanaman

Pertumbuhan tanaman tidak hanya dikontrol oleh faktor dalam (internal), tetapi juga ditentukan oleh faktor luar (eksternal). Salah satu faktor eksternal tersebut adalah unsur hara esensial. Unsur hara esensial adalah unsur-unsur yang diperlukan bagi pertumbuhan tanaman. Apabila unsur tersebut tidak tersedia bagi tanaman, maka tanaman akan menunjukkan gejala kekurangan unsur tersebut dan pertumbuhan tanaman akan terganggu. Berdasarkan jumlah yang diperlukan, kita mengenal unsur hara makro dan mikro. Unsur hara makro diperlukan bagi tanaman dalam jumlah yang lebih besar (0,5-3% berat tubuh tanaman). Sedangkan unsur hara mikro diperlukan oleh tanaman dalam jumlah yang relatif kecil (beberapa ppm/ part per-million dari berat keringnya).
Effects of Water Temperature and Silicate on the Winter Blooming Diatom Stephanodiscus hantzschii (Bacillariophyceae) Growing in Eutrophic Conditions in the Lower Han River, South Korea

ABSTRACT We investigated the relationships between fluctuations in environmental factors and blooms of the diatom Stephanodiscus hantzschii by bi-weekly monitoring from February 2001 to February 2002 and from February 2004 to February 2005, in the eutrophic lower Han River (LHR), South Korea. With the exception of the summer rainy season when nutrients were lower, diatoms were abundant. Initial development of S. hantzschii populations (the most dominant diatom species in the LHR) started in late autumn, peaked in winter to early spring, and then gradually decreased in mid-spring. The peak of S. hantzschii abundance occurred at low water temperatures (approximately 5–10°C) and caused a significant decrease in silicate concentration, whereas other nutrients remained at eutrophic levels. The dynamics of S. hantzschii under the perennially eutrophic conditions of the LHR appears to be primarily affected by changes in water temperature, but growth of the diatom may be restricted by depletion of silicate.
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks

Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks. We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy.
Self-organization of multi-layer spiking neural networks

Living neural networks in our brains autonomously self-organize into large, complex architectures during early development to result in an organized and functional organic computational device. A key mechanism that enables the formation of complex architecture in the developing brain is the emergence of traveling spatio-temporal waves of neuronal activity across the growing brain. Inspired by this strategy, we attempt to efficiently self-organize large neural networks with an arbitrary number of layers into a wide variety of architectures. To achieve this, we propose a modular tool-kit in the form of a dynamical system that can be seamlessly stacked to assemble multi-layer neural networks. The dynamical system encapsulates the dynamics of spiking units, their inter/intra layer interactions as well as the plasticity rules that control the flow of information between layers. The key features of our tool-kit are (1) autonomous spatio-temporal waves across multiple layers triggered by activity in the preceding layer and (2) Spike-timing dependent plasticity (STDP) learning rules that update the inter-layer connectivity based on wave activity in the connecting layers. Our framework leads to the self-organization of a wide variety of architectures, ranging from multi-layer perceptrons to autoencoders. We also demonstrate that emergent waves can self-organize spiking network architecture to perform unsupervised learning, and networks can be coupled with a linear classifier to perform classification on classic image datasets like MNIST. Broadly, our work shows that a dynamical systems framework for learning can be used to self-organize large computational devices.
EFEITO DA ÉPOCA DA DESMAMA E DA PASTAGEM NO DESEMPENHO DE VACAS E TERNEIROS DE CORTE. 1 - DESEMPENHO DAS VACAS

RESUMO Foi avaliado o desempenho de vacas de corte desterneiradas antecipadamente, aos 101 dias apos o parto, e que no periodo anterior ao desterneiramento haviam permanecido em campo nativo (CN) ou pastagem cultivada (PC) e apos o desterneiramento, em CN. Foi mantido ainda um grupo de vacas em CN desterneiradas aos 213 dias, seguindo o metodo de criacao normalmente empregado na regiao. Considerando todo o periodo experimental, foi verificado um maior ganho de peso (P < 0,01) e melhor estado corporal (P < 0,05) nas vacas cujo desterneiramento precoce foi precedido pela alimentacao em PC. A taxa de aparecimento de cio pos-parto, foi de 100% nas vacas desterneiradas aos 101 dias e mantidas previamente em PC, 41,7% para as vacas desterneiradas antecipadamente e que permaneceram em CN, e de 14,3% naquelas desterneiradas aos 213 dias.
Asymptotic Singular Value Distribution of Linear Convolutional Layers

In convolutional neural networks, the linear transformation of multi-channel two-dimensional convolutional layers with linear convolution is a block matrix with doubly Toeplitz blocks. Although a "wrapping around" operation can transform linear convolution to a circular one, by which the singular values can be approximated with reduced computational complexity by those of a block matrix with doubly circulant blocks, the accuracy of such an approximation is not guaranteed. In this paper, we propose to inspect such a linear transformation matrix through its asymptotic spectral representation - the spectral density matrix - by which we develop a simple singular value approximation method with improved accuracy over the circular approximation, as well as upper bounds for spectral norm with reduced computational complexity. Compared with the circular approximation, we obtain moderate improvement with a subtle adjustment of the singular value distribution. We also demonstrate that the spectral norm upper bounds are effective spectral regularizers for improving generalization performance in ResNets.
Environments of dwarf galaxies with optical AGN characteristics

This study aims to explore the relation between dwarf galaxies (M* ≤ 5 × 109 M⊙) with active galactic nuclei (AGNs) and their environment by comparing neighbourhood parameters of AGN and non-AGN samples. Using the NASA-Sloan Atlas, both the local environment and the immediate environment of dwarf galaxies with z ≤ 0.055 are analysed. Of the 145 155 galaxies in the catalogue, 62 258 of them are classified as dwarf galaxies, and by employing two AGN selection methods based on emission-line fluxes (BPT and WHAN), 4476 are found to have AGN characteristics in their optical spectra. Regardless of selection method, this study finds no discernible differences in environment between AGN and non-AGN host dwarf galaxies and these results indicate that environment is not an important factor in triggering AGN activity in dwarf galaxies. This is in line with existing literature on environments of regular galaxies with AGNs and suggests universality in terms of reaction to environment across the mass regime. The biases of AGN selection in low-mass galaxies, and the biases of different measures of environment are also considered. It is found that there are several mass trends in emission-line ratios and that the SDSS fibre covers galaxies non-uniformly with redshift. These biases should be accounted for in future work by possibly including other wavelength regimes or mass weighting of emission-line ratios. Lastly, a discussion of the environment estimation methods is included since they may not gauge the desired properties due to factors such as time delay or using loosely constrained proxy parameters.
On Correctness of Automatic Differentiation for Non-Differentiable Functions

Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these systems have been developed to compute derivatives of differentiable functions, but in practice, they are commonly applied to functions with non-differentiabilities. For instance, neural networks using ReLU define non-differentiable functions in general, but the gradients of losses involving those functions are computed using autodiff systems in practice. This status quo raises a natural question: are autodiff systems correct in any formal sense when they are applied to such non-differentiable functions? In this paper, we provide a positive answer to this question. Using counterexamples, we first point out flaws in often-used informal arguments, such as: non-differentiabilities arising in deep learning do not cause any issues because they form a measure-zero set. We then investigate a class of functions, called PAP functions, that includes nearly all (possibly non-differentiable) functions in deep learning nowadays. For these PAP functions, we propose a new type of derivatives, called intensional derivatives, and prove that these derivatives always exist and coincide with standard derivatives for almost all inputs. We also show that these intensional derivatives are what most autodiff systems compute or try to compute essentially. In this way, we formally establish the correctness of autodiff systems applied to non-differentiable functions.
Effects of Jack Pine Plantation Management on Barrens Flora and Potential Kirtland's Warbler Nest Habitat

Jack pine barrens, once common in northern lower Michigan, mostly have been converted to managed jack pine plantations. Management of the disturbances associated with logging provides the opportunity to maintain the unique plant assemblages of jack pine barrens and nest habitat of the federally endangered Kirtland's warbler. Studies indicate that Carex pensylvanica can develop into dense mats and strongly compete with other barrens species such as Vaccinium angustifolium, which seem to be important species for Kirtland's warbler nest locations. According to forest managers, the most important factors facilitating high cover of V. angustifolium and reducing cover of C. pensylvanica are the amount of shade produced by tree crowns before harvest (pre‐harvest shade), the length of time between harvest and planting (planting delay), and fire. We found that high or low levels of pre‐harvest shade had no effect on cover of either V. angustifolium or C. pensylvanica. Planting delays of at least three years following prescribed burns generally increased cover of V. angustifolium in forest plots, which are important for warbler nesting. Analysis of community composition in openings indicated that burning enhanced the growth of barrens species. We found only weak evidence for a negative correlation between the cover of V. angustifolium and C. pensylvanica on our study sites. The openings created in the jack pine plantation are important refugia for barrens flora that would likely be lost under forests managed strictly for jack pine. Maintenance of jack pine barrens flora and Kirtland's warbler nest habitat is possible within the context of a heavily managed forest plantation system.
On The Hecke Orbit Conjecture for PEL Type Shimura Varieties

The Hecke orbit conjecture plays an important role in understanding the geometric structure of Shimura varieties. First postulated by Chai and Oort in 1995, the Hecke orbit conjecture predicts that prime-to-p Hecke correspondences on mod p reductions of Shimura varieties characterize the foliation structure formed by Oort's central leaves. In other words, every prime-to-p Hecke orbit is Zariski dense in the central leaf containing it. Roughly speaking, a central leaf is the locus in a Shimura variety consisting of all points whose corresponding Barsotti-Tate groups belong to a fixed geometric isomorphism class. On the other hand, the prime-to-p Hecke orbit of a closed point x is the (countable) set consisting of all points y such that there is a prime-to-p quasi-isogeny from x to y. In 2005, Chai and Yu proved the Hecke orbit conjecture for Hilbert modular varieties, followed by a proof for Siegel modular varieties by Chai and Oort in the same year. The major purpose of the present work is to generalize the method of Chai and Oort to Shimura varieties of PEL type. We show that the Hecke orbit conjecture holds for points in certain irreducible components of Newton strata under our assumptions.
First-order methods for large-scale market equilibrium computation

Market equilibrium is a solution concept with many applications such as digital ad markets, fair division, and resource sharing. For many classes of utility functions, equilibria are captured by convex programs. We develop simple first-order methods that are suitable for solving these programs for large-scale markets. We focus on three practically-relevant utility classes: linear, quasilinear, and Leontief utilities. Using structural properties of a market equilibrium under each utility class, we show that the corresponding convex programs can be reformulated as optimization of a structured smooth convex function over a polyhedral set, for which projected gradient achieves linear convergence. To do so, we utilize recent linear convergence results under weakened strong-convexity conditions, and further refine the relevant constants, both in general and for our specific setups. We then show that proximal gradient (a generalization of projected gradient) with a practical version of linesearch achieves linear convergence under the Proximal-PL condition. For quasilinear utilities, we show that Mirror Descent applied to a specific convex program achieves sublinear last-iterate convergence and recovers the Proportional Response dynamics, an elegant and efficient algorithm for computing market equilibrium under linear utilities. Numerical experiments show that proportional response is highly efficient for computing an approximate solution, while projected gradient with linesearch can be much faster when higher accuracy is required.
Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition

In many real-life tasks of application of supervised learning approaches, all the training data are not available at the same time. The examples are lifelong image classification or recognition of environmental objects during interaction of instrumented persons with their environment, enrichment of an online-database with more images. It is necessary to pre-train the model at a "training recording phase" and then adjust it to the new coming data. This is the task of incremental/continual learning approaches. Amongst different problems to be solved by these approaches such as introduction of new categories in the model, refining existing categories to sub-categories and extending trained classifiers over them, . we focus on the problem of adjusting pre-trained model with new additional training data for existing categories. We propose a fast continual learning layer at the end of the neuronal network. Obtained results are illustrated on the opensource CIFAR benchmark dataset. The proposed scheme yields similar performances as retraining but with drastically lower computational cost.
Adaptive Sampling to Reduce Disparate Performance

Existing methods for reducing disparate performance of a classifier across different demographic groups assume that one has access to a large data set, thereby focusing on the algorithmic aspect of optimizing overall performance subject to additional constraints. However, poor data collection and imbalanced data sets can severely affect the quality of these methods. In this work, we consider a setting where data collection and optimization are performed simultaneously. In such a scenario, a natural strategy to mitigate the performance difference of the classifier is to provide additional training data drawn from the demographic groups that are worse off. In this paper, we propose to consistently follow this strategy throughout the whole training process and to guide the resulting classifier towards equal performance on the different groups by adaptively sampling each data point from the group that is currently disadvantaged. We provide a rigorous theoretical analysis of our approach in a simplified one-dimensional setting and an extensive experimental evaluation on numerous real-world data sets, including a case study on the data collected during the Flint water crisis.
Multi Layer Neural Networks as Replacement for Pooling Operations

Pooling operations are a layer found in almost every modern neural network, which can be calculated at low cost and serves as a linear or nonlinear transfer function for data reduction. Many modern approaches have already dealt with replacing the common maximum value selection and mean value operations by others or even to provide a function that includes different functions which can be selected through changing parameters. Additional neural networks are used to estimate the parameters of these pooling functions. Therefore, these pooling layers need many additional parameters and increase the complexity of the whole model. In this work, we show that already one perceptron can be used very effectively as a pooling operation without increasing the complexity of the model. This kind of pooling allows to integrate multi-layer neural networks directly into a model as a pooling operation by restructuring the data and thus learning complex pooling operations. We compare our approach to tensor convolution with strides as a pooling operation and show that our approach is effective and reduces complexity. The restructuring of the data in combination with multiple perceptrons allows also to use our approach for upscaling, which is used for transposed convolutions in semantic segmentation.
Continuous data assimilation applied to a velocity-vorticity formulation of the 2D Navier-Stokes equations

We study a continuous data assimilation (CDA) algorithm for a velocity-vorticity formulation of the 2D Navier-Stokes equations in two cases: nudging applied to the velocity and vorticity, and nudging applied to the velocity only. We prove that under a typical finite element spatial discretization and backward Euler temporal discretization, application of CDA preserves the unconditional long-time stability property of the velocity-vorticity method and provides optimal long-time accuracy. These properties hold if nudging is applied only to the velocity, and if nudging is also applied to the vorticity then the optimal long-time accuracy is achieved more rapidly in time. Numerical tests illustrate the theory, and show its effectiveness on an application problem of channel flow past a flat plate.