Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative Arbitrage Opportunities in $N$ Investors and Mean-Field Regimes

This paper analyzes the market behavior and optimal investment strategies to attain relative arbitrage both in the $N$ investors and mean field regimes. An investor competes with a benchmark of market and peer investors, expecting to outperform the benchmark and minimizing the %proportion of initial capital. With market price of risk processes depending on the stock market and investors respectively, the minimal initial capital required is the optimal cost in the $N$-player games and mean field games. It can be characterized as the smallest nonnegative continuous solution of a Cauchy problem. The measure flow of wealth appears in the cost, while the joint flow of wealth and strategy is in state processes. We modify the extended mean field game with common noise and its notion of the uniqueness of Nash equilibrium. There is a unique equilibrium in $N$-player games and mean field games with mild conditions on the equity market.
Switchblade - a Neural Network for Hard 2D Tasks

Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural network model, named Switchblade, that can efficiently exploit long-range dependencies in 2D data and solve much more challenging tasks. It has close-to-optimal $\mathcal{O}(n^2 \log{n})$ complexity for processing $n \times n$ data matrix. Besides the common image classification and segmentation, we consider a diverse set of algorithmic tasks on matrices and graphs. Switchblade can infer highly complex matrix squaring and graph triangle finding algorithms purely from input-output examples. We show that our model is likewise suitable for logical reasoning tasks -- it attains perfect accuracy on Sudoku puzzle solving. Additionally, we introduce a new dataset for predicting the checkmating move in chess on which our model achieves 72.5% accuracy.
Ecuadorian frogs of the genus Colostethus (Anura: Dendrobatidae) /

The present knowledge of dendrobatid frogs of the genus Colostethus in Ecuador is summarized. The taxonomic history of the genus is discussed and taxonomically useful characters (e.g., external morphology, gonads, tadpoles, and calls) are described. Thirty-one species are recognized, including five new species described herein. Eight nominal species are assigned to its synonymy; they
Fatores de mortalidade na fase de ovo de Tuta absoluta em sistemas de produção orgânica e convencional de tomate

A traca-do-tomateiro Tuta absoluta (Meyrick) (Lep.: Gelechiidae) esta sujeita a fatores de mortalidade bioticos como predadores e parasitoides e fatores abioticos como agua da irrigacao e da chuva que influenciam sua dinâmica populacional. O objetivo deste trabalho foi quantificar a importância desses fatores na sobrevivencia de ovos da traca-do-tomateiro. Os experimentos foram desenvolvidos na Embrapa Hortalicas, de 1/8/2004 a 7/11/2005, em plantio de tomate e tomate consorciado em sistema orgânico e tomate em sistema convencional. As avaliacoes consistiram em: 1) coleta de ovos no campo em diferentes epocas do ciclo da cultura e sua incubacao em laboratorio ate a emergencia do parasitoide ou eclosao da lagarta; 2) exposicao de ovos sentinelas no campo por 72 horas; 3) ovos marcados e acompanhados a cada 24 horas ate eclosao das lagartas em campo. Ovos coletados no campo e incubados em laboratorio mostraram que a mortalidade por Trichogramma sp. foi de 32%. A mortalidade dos ovos que permaneceram no campo (metodologia 2 e 3), evidenciaram que o parasitismo por Trichogramma sp. foi de 12% e que os predadores e o efeito mecânico da agua produziram 36% de mortalidade dos ovos. Essa diferenca revela que a permanencia dos ovos no campo os expoe a competicao com predadores e a mortalidade causada pelo efeito mecânico da agua reduzindo o recrutamento de Trichogramma sp. Parasitoides de ovos, predadores generalistas associados a acao da agua acarretaram maior mortalidade de T. absoluta em sistemas de producao orgânica de tomate.
Biological thin layer formation: interactions between the larval decapod, Neotrypaea californiensis, haloclines and light

As the average concentration of food in the ocean is often too low to support the growth observed in zooplankton, the ability to exploit patchily distributed resources is linked to zooplankton survival (Bainbridge, 1953; Mullin and Brooks, 1976; Daro, 1988; Tiselius, 1992; Leising, 2001). Biological thin layers of phytoplankton are often found at density discontinuities in the water column and can range in average thickness from tens of centimetres to several metres, can persist for several days, and span several kilometres horizontally (Nielsen et al., 1990; Cowles and Desiderio, 1993; Dekshenieks et al., 2001; Rines et al., 2002; McManus et al., 2008; Bochdansky and Bollens, 2009). Field observations demonstrate that planktonic grazers aggregate at thin layers (Castro et al., 1991; Tiselius et al., 1994; Holliday et al., 1998), and several experimental studies have shown that zooplankton respond to thin layers of food (Bochdansky and Bollens, 2004; Clay et al., 2004; Ignoffo et al., 2005; Bochdansky et al., 2010). Other studies show that aggregations can occur at haloclines in the absence of food, suggesting that some species use physical cues to maintain position (Lance, 1962; Tiselius, 1992; Lougee et al., 2002; Woodson et al., 2007). Higher trophic levels aggregate at oceanographic discontinuities (Genin, 2004) and this can also occur at thin layers (Harder, 1968; Batty, 1994; Clay et al., 2004). However, many questions remain as to how responses to thin layers and haloclines interact with predator avoidance behaviours, e.g. diel vertical migration (DVM; Bollens and Frost, 1989, 1991; and Hays 2003). We investigated the effects of the presence of haloclines and diel light cycles on the vertical distribution of the first zoeae of the Callianasid decapod, Neotrypaea californiensis. Most previous behavioural research related to thin layers has focused on copepods (Saiz et al., 1993; Leising and Franks, 2002; Lougee et al., 2002; Bochdansky and
Progressive Generation of Long Text

Large-scale language models pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text ($>$1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. To overcome the limitation, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution. Our method first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages. The simple design allows our approach to take advantage of pretrained language models at each stage and effectively adapt to any target domain given only a small set of examples. We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned GPT-2 in terms of domain-specific quality and sample efficiency. The coarse-to-fine nature of progressive generation also allows for a higher degree of control over the generated content.
Difference Necklaces

An $(a,b)$-difference necklace of length $n$ is a circular arrangement of the integers $0, 1, 2, \ldots , n-1$ such that any two neighbours have absolute difference $a$ or $b$. We prove that, subject to certain conditions on $a$ and $b$, such arrangements exist, and provide recurrence relations for the number of $(a,b)$-difference necklaces for $( a, b ) = ( 1, 2 )$, $( 1, 3 )$, $( 2, 3 )$ and $( 1, 4 )$. Using techniques similar to those employed for enumerating Hamiltonian cycles in certain families of graphs, we obtain these explicit recurrence relations and prove that the number of $(a,b)$-difference necklaces of length $n$ satisfies a linear recurrence relation for all permissible values $a$ and $b$. Our methods generalize to necklaces where an arbitrary number of differences is allowed.
Multi-sided Exposure Bias in Recommendation

Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' perspective. However, many real-world recommenders are often multi-stakeholder environments in which the needs and interests of several stakeholders should be addressed in the recommendation process. In this paper, we focus on the popularity bias problem which is a well-known property of many recommendation algorithms where few popular items are over-recommended while the majority of other items do not get proportional attention and address its impact on different stakeholders. Using several recommendation algorithms and two publicly available datasets in music and movie domains, we empirically show the inherent popularity bias of the algorithms and how this bias impacts different stakeholders such as users and suppliers of the items. We also propose metrics to measure the exposure bias of recommendation algorithms from the perspective of different stakeholders.
Submodular Combinatorial Information Measures with Applications in Machine Learning

Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize independence, (conditional) entropy, (conditional) mutual information, and total correlation defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property. This turns out to include a number of practically useful cases such as the facility location and set-cover functions. We study specific instantiations of the submodular information measures on these, as well as the probabilistic coverage, graph-cut, and saturated coverage functions, and see that they all have mathematically intuitive and practically useful expressions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy-preserving summarization -- and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning.
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa

This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task 3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58% in task 2 which is 12% more than the average score b) relaxed F1-score of 70.1% in ADR extraction of task 3 which is 13.7% more than the average score and relaxed F1-score of 35% in ADR extraction + normalization of task 3 which is 5.8% more than the average score. Overall, our models achieve promising results in both the tasks with significant improvements over average scores.
Modules of constant Jordan type from Galois extensions of local fields

In this paper, we describe the Galois module structure of $J=\mathbf{K}^{\times}/\mathbf{K}^{\times p}$, where $\mathbf{K}$ is the maximal $p$-elementary abelian extension of a local field $\mathbf{k}$ containing a primitive $p$-th root of unity; in fact, we show that $J$ is a module of constant Jordan type, with stable Jordan type $[1]^2$, which, in a way, extends the result of Minac and Swallow. Also, we take profit from our proof and compute the cohomology groups of the Galois group $Gal(\mathbf{K}/\mathbf{k})$ with coefficients in $J$, which leads us to give a description by generators and relation of modules which are stably isomorphic to the Heller shifts of the trivial module.
Breathing k-Means

We propose a new algorithm for the $k$-means problem which repeatedly increases and decreases the number of centroids by $m$ in order to find an approximate solution. New centroids are inserted in areas where they will likely reduce the error. The subsequent removal of centroids is done such that the resulting raise in error is small. After each increase or decrease step standard $k$-means is performed. Termination is guaranteed by decrementing $m$ after each increase/decrease cycle unless the overall error was lowered. In experiments with Gaussian mixture distributions the new algorithm produced on average solutions several percent better than $k$-means++.
CHARACTERIZATION OF FUSARIUM ISOLATES FROM RICE, SUGARCANE AND MAIZE USING RFLP-IGS

Isolates of Fusarium from rice, sugarcane and maize were identified as F. verticillioides, F. sacchari, F. proliferatum F. subgluti- nans, F. fujikuroi and F. oxysporum. The species were then characterized by restriction analysis of intergenic spacer (RFLP-IGS) using AluI, Eco88I, RsaI and XhoI. Twenty-five haplotypes were identified among the isolates of Fusarium which indicated high levels of variations. UPGMA cluster analysis was conducted to cluster the isolates and to estimate the intraspecific and interspecific variability. Isolates of F. fujikuroi from rice were clustered together with isolates of F. proliferatum from rice and maize with a similarity value of 88-100%. Isolates of F. verticilliodes from maize and sugarcane were clustered together with a similarity value ranging from 92-100%, and two isolates from rice formed another cluster. Isolates of F. oxysporum from maize and rice were clustered together with a simi- larity value ranging from 87-95%. Isolates of F. subglutinans from rice and maize, and F. sacchari from rice and sugarcane were also clustered together with a similarity value of 77-100%. Based on RFLP-IGS analysis, variability was observed within and between species of Fusarium from rice, maize and sugarcane and the technique could be use to complement morphological characterization and to determine genetic relationships between the species.
Thermodynamic machine learning through maximum work production

Adaptive systems—such as a biological organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients—must somehow embody relevant regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machine learning algorithms estimate models to capture predictable structure and identify irrelevant noise in training data. This happens through optimization of performance metrics, such as model likelihood. If such learning is physically implemented, is there a sense in which computational models estimated through machine learning are physically preferred? We introduce the thermodynamic principle that work production is the most relevant performance measure for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning. Within the class of physical agents that most efficiently harvest energy from their environment, we demonstrate that an efficient agent’s model explicitly determines its architecture and how much useful work it harvests from the environment. We then show that selecting the maximum-work agent for given environmental data corresponds to finding the maximum-likelihood model. This establishes an equivalence between nonequilibrium thermodynamics and dynamic learning. In this way, work maximization emerges as an organizing principle that underlies learning in adaptive thermodynamic systems.
Asymptotic enumeration of digraphs and bipartite graphs by degree sequence

We provide asymptotic formulae for the numbers of bipartite graphs with given degree sequence, and of loopless digraphs with given in‐ and out‐degree sequences, for a wide range of parameters including the biregular case. In particular, for bipartite graphs with part sizes that are not highly unequal, our results cover an existing gap in known results between the sparse and dense cases that were proved by Greenhill, McKay, and Wang in 2006 and by Canfield, Greenhill, and McKay in 2008, respectively. For the range of parameters which our results cover, they imply that the degree sequence of a random bipartite graph with m$$ m $$ edges and given part sizes is accurately modeled by a sequence of independent binomial random variables, conditional upon the sum of variables in each part being equal to m$$ m $$ . This extends the known behavior in the sparse and dense cases to essentially the full spectrum of possible densities. A similar model also holds for loopless digraphs.
A Measurement of Transportation Ban inside Wuhan on the COVID-19 Epidemic by Vehicle Detection in Remote Sensing Imagery

Wuhan, the biggest city in China's central region with a population of more than 11 million, was shut down to control the COVID-19 epidemic on 23 January, 2020. Even though many researches have studied the travel restriction between cities and provinces, few studies focus on the transportation control inside the city, which may be due to the lack of the measurement of the transportation ban. Therefore, we evaluate the implementation of transportation ban policy inside the city by extracting motor vehicles on the road from two high-resolution remote sensing image sets before and after Wuhan lockdown. In order to detect vehicles from the remote sensing image datasets with the resolution of 0.8m accurately, we proposed a novel method combining anomaly detection, region grow and deep learning. The vehicle numbers in Wuhan dropped with a percentage of at least 63.31% caused by COVID-19. Considering fewer interferences, the dropping percentages of ring road and high-level road should be more representative with the value of 84.81% and 80.22%. The districts located in city center were more intensively affected by the transportation ban. Since the public transportations have been shut down, the significant reduction of motor vehicles indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city.
The effects of relative humidity and turning in incubators machines on the incubation yield and chick performance

Due to the high demands and production costs of the poultry industry it is imperative to study alternatives to modify the practice of incubation in order to maximise productivity by ensuring the conditions necessary for the development of embryos of current strains. Such changes in the management of incubation cannot affect the quality of chicks and broiler performance during early growth. To achieve these goals, several factors that work together during artificial incubation should be considered, for example, the relative humidity and turning machine incubators. Some research has already been conducted to define the optimum for these factors. This article reviews the results of studies where relative humidity and use of turning systems in incubators and their impact on incubation yield and chick performance were examined.
Zero-sum Switching Game, Systems of Reflected Backward SDEs and Parabolic PDEs with bilateral interconnected obstacles

In this paper we study a zero-sum switching game and its verification theorems expressed in terms of either a system of Reflected Backward Stochastic Differential Equations (RBSDEs in short) with bilateral interconnected obstacles or a system of parabolic partial differential equations (PDEs in short) with bilateral interconnected obstacles as well. We show that each one of the systems has a unique solution. Then we show that the game has a value.
The Generalized Independent and Dominating Set Problems on Unit Disk Graphs

In this article, we study a generalized version of the maximum independent set and minimum dominating set problems, namely, the maximum $d$-distance independent set problem and the minimum $d$-distance dominating set problem on unit disk graphs for a positive integer $d>0$. We first show that the maximum $d$-distance independent set problem and the minimum $d$-distance dominating set problem belongs to NP-hard class. Next, we propose a simple polynomial-time constant-factor approximation algorithms and PTAS for both the problems.
Caracterización del proceso de fermentación y del efecto de inhibición de Lactobacillus lactis en Staphylococcus aureus y Staphylococcus epidermidis

The fermentative process and in vitro inhibition of L. lactis in Staphylococcus aureus and Staphylococcus epidermidis were assessed. The growth of L. lactis at three pH (2.5, 4.5 and 7), bile salts (0.5, 1 and 2 %), bovine bile (1 and 1.2 %) and two temperatures (38 and 45 °C) were evaluated. Peptides and organic acids in supernatant of L. lactis by HPLC were determined. Fermentation kinetics was carried out, evaluating: pH, total sugar, protein and lactic acid. An antibiogram of dicloxacilin, cefepime, penilicin and cefalotin was made. The inhibition of L. lactis and its supernatant were defined in pathogenic strains. The best growth was at a pH of 2.5 (3 × 10 12 UFC/ml); of 1 × 10 10 and 4 × 10 9 UFC/ml for 0.5 % of bile salts and 1.2 % of bovine bile, respectively; of 3.5 × 10 13 and 3.4 × 10 13 UFC/ml for 38 and 45 °C, respectively.
Ehrlich and Raven Revisited: Mechanisms Underlying Codiversification of Plants and Enemies

After almost 50 years of scrutiny, the ideas that Ehrlich and Raven presented in their classical paper on the coevolution between butterflies and plants are still very much alive. Much of this interest has involved the potential for codiversification, both in how the interaction itself diversifies and how the interaction affects modes and rates of speciation. Despite high levels of conservatism and specialization, diversification of the interaction appears to be mainly a consequence of host shifts, but this somewhat paradoxical conclusion can be understood by an appreciation of the ecological as well as genetic mechanisms behind host shifts. There are several ways that the interaction can influence speciation, with or without host-plant-based divergent selection on reproductive barriers. One current debate is over the relative importance of radiations following shifts to new adaptive zones and elevated rates of speciation in groups with plastic and diverse host use.