Miracle-dz/UniFAR-FTUs huggingface dataset:
Annual reproductive cycle of gafftopsail catfish, Bagre marinus (Ariidae) in a tropical coastal environment in the Gulf of Mexico Ciclo reproductivo anual del bagre bandera Bagre marinus (Ariidae) en un ambiente tropical costero del Golfo de México

Reproductive cycle of gafftopsail catfish (Bagre marinus) and its relation to environmental variables were analyzed, based on specimens collected randomly from commercial landings of long line artisan fleet of the Chiltepec Barrier, Tabasco State. Gonadosomatic index showed that this species had one reproductive period per year (May - August). Reproduction occurs in shallow coastal waters at high temperatures (27.0 - 29.0°C) under heavy precipitation conditions (170- 333 mm). Female condition factor had a similar behavior than the gonadosomatic index, reaching high values (0.61 to 0.63) during May through July, although a high value of 0.64 was observed during March. A decrease on the condition factor in males from July (0.55) to October (0.50) was related to the energetic cost of oral incubation. This finding was consistent with a decrease in the proportion of captured males during these months (from 50.7% in July to 23.9% in November) when males migrated from fishing areas to estuarine zones to release their progeny. Gafftopsail catfish presents a synchronic ovocitary development in two groups with total spawning. The species is characterized by low fecundity. The mean number of mature hydrated oocytes per female was 33, ranging from 21 to 62, which is related to the mouth brooding tactic wich allowed ariid species to survive under a variety of environmental conditions. Gafftopsail catfish take advantages of the different habitats present in its distribution area, by using a reproductive strategy with local seasonal migrations.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL) Exploring a Hybrid Algorithm for Price Volatility Prediction of Exploring a Hybrid Algorithm for Price Volatility Prediction of Bitcoin Bitcoin

: In recent years, the Bitcoin investment market has become increasingly popular. We collected existing literature on Bitcoin and found that predictions about the role of Bitcoin in investment portfolios and the volatility of Bitcoin price as well as return have become advanced research topics. This study shows our current work on the prediction of Bitcoin price volatility and proposes an idea for predicting the price volatility. We have designed an experiment that compares different combinations of machine learning algorithms with GARCH-type models, intending to compare the effects of these models in the prediction of Bitcoin time series and finally implement an optimized algorithm.
Radial Growth of Picea schrenkiana Influenced by Increasing Temperature in the Tianshan Mountains

ABSTRACT There is a tendency of warming and wetting in northwestern China in recent decades. However, less is known about whether or not tree growth shows an increasing trend. In this study, we developed three tree-ring width chronologies of Picea schrenkiana in the northern Tianshan Mountains to assess changes in the radial growth of P. schrenkiana and to discuss the stability of the relationships between tree growth and climate. Three chronologies all showed that the tree-ring index has declined significantly since the 1960s. At two western sites, the growth of P. schrenkiana was negatively affected by the summer temperature of the previous year. At the eastern site, early summer temperature-induced drought of the current year was the key factor affecting tree growth. The result of moving-window correlations was consistent with correlation analyses. Spatial correlation analyses revealed that variations in tree-ring width could respond to a wide range of temperature changes in northwestern China, especially in the past half century. We expect that climate warming hinders the radial growth of P. schrenkiana in the northern Tianshan Mountains. Our study also helps to clarify the characteristics of tree growth in northwestern China under the influence of westerlies.
Structure of the Cerambycidae (Coleoptera) Community in a Tropical Deciduous Forest in Northern Morelos, Mexico

Abstract The purpose of this study was to determine the structure of the community and seasonality of the Cerambycidae family in the tropical deciduous forest (TDF) of San Andrés de la Cal, Tepoztlán in Morelos, Mexico. Following a year of systematic collections, and with the addition of data obtained from breeding chambers and sporadic collections, 155 species, 91 genera, 35 tribes, and 4 subfamilies were recorded. Thirty-three of these species, >21% are new records for the state of Morelos. Sample coverage was 97% and, according to the Chao 1 richness estimator, we found 80% of the species present in the study area. Greater species richness, abundance, and diversity were found during the rainy season, which generates a significant difference between seasons. The ordering of faunal similarities between months indicated a marked seasonality in the composition. The data suggest that the diversity of TDF insects is not completely known (i.e., new records were found), but comprises a diverse community, composed of a high proportion (78%) of rare species. Our findings reinforce the need to continue documenting insect diversity in TDF, especially considering that this is among the least protected of the ecosystems, and one that is negatively affected to a greater degree by anthropogenic activities and climate change. Resumen El propósito de este estudio fue conocer la estructura de la comunidad de la familia Cerambycidae del bosque tropical caducifolio (BTC) de San Andrés de la Cal,Tepoztlán, Morelos, México. Después de un año de recolectas sistemáticas, además de datos adicionales de cámaras de cría y recolectas esporádicas, se registraron 155 especies, 91 géneros, 35 tribus y 4 subfamilias. Treinta y tres especies (>21%) son nuevos registros para el estado de Morelos. La cobertura de muestra fue del 97% y según el estimador de riqueza Chao 1, el 80% de las especies del área de estudio están representadas. La mayor riqueza, abundancia y diversidad de especies ocurrió durante la temporada de lluvias y el ordenamiento entre meses indicó una marcada estacionalidad. La estructura de la comunidad refleja una gran diversidad, compuesta por una alta proporción (78%) de especies raras. Los datos muestran que la diversidad de insectos en el BTC es alta y que los datos de distribución están incompletos (e. g. nuevos registros) lo que refuerza la necesidad de seguir documentándolo, especialmente considerando que este ecosistema se encuentra entre los menos protegidos y con mayor riesgo de desaparecer por la presión de las actividades antropogénicas y el cambio climático.
Reply to the Comment on "General Non-Markovian Dynamics of Open Quantum System"

This is the reply to the 2nd version of the comment, [arXiv.1312. 5286v2], by McCutcheon {\it et al.} on our paper [PRL 109, 170402 (2012)]. Our letter presents three examples. For the steady-state solution of the first example, i.e., the dissipationless part of Eq.(12) in the PRL, the 2nd version of the Comment claimed that "this [dissipationless] regime exists if and only if the total Hamiltonian is unbounded from below, casting serious doubts on the usefulness of this result. " In this reply, we show again that the 2nd version of the Comment is also incorrect.
Development of high-performance filter from Agave americana fibre/polyacrylonitrile nanofibre membrane for Cd, Pb (II) and organic contaminants removal from aqueous solution

Constructing an easily recyclable and reusable filter is highly efficient for removing Cd, Pb2+ and organic contaminants from aqueous solution. This study investigates the optimization of Cd, Pb2+ adsorption process by thiol functionalized Agave americana fibre. Additionally, polyacrylonitrile (PAN) nanofibre membrane is used as a substrate to filter the organic contaminants. The properties of adsorbents are characterized by various techniques such as Attenuated Total Reflection- Fourier transforms infrared spectroscopy (ATR-FTIR), tensile tester and scanning electron microscopy (SEM). The filter’s performance has been investigated with three variables of thiol functionalized fibre packing density, length of the functionalized fibre and nanofibre spinning time. The fibre packing density and nanofibre spinning time are the most significant parameters that affect all responses. The impact of independent variables on responses has been critically analysed. The optimized filter is identified by regression analysis. The developed filter satisfies heavy metal ion adsorption (Cd, Pb2+) and organic contaminants.
Data-Centric Factors in Algorithmic Fairness

Notwithstanding the widely held view that data generation and data curation processes are prominent sources of bias in machine learning algorithms, there is little empirical research seeking to document and understand the specific data dimensions affecting algorithmic unfairness. Contra the previous work, which has focused on modeling using simple, small-scale benchmark datasets, we hold the model constant and methodically intervene on relevant dimensions of a much larger, more diverse dataset. For this purpose, we introduce a new dataset on recidivism in 1.5 million criminal cases from courts in the U.S. state of Wisconsin, 2000-2018. From this main dataset, we generate multiple auxiliary datasets to simulate different kinds of biases in the data. Focusing on algorithmic bias toward different race/ethnicity groups, we assess the relevance of training data size, base rate difference between groups, representation of groups in the training data, temporal aspects of data curation, including race/ethnicity or neighborhood characteristics as features, and training separate classifiers by race/ethnicity or crime type. We find that these factors often do influence fairness metrics holding the classifier specification constant, without having a corresponding effect on accuracy metrics. The methodology and the results in the paper provide a useful reference point for a data-centric approach to studying algorithmic fairness in recidivism prediction and beyond.
Constraint-based Simulation of Passive Suction Cups

In this paper, we propose a physics-based model of suction phenomenon to achieve simulation of deformable objects like suction cups. Our model uses a constraint-based formulation to simulate the variations of pressure inside suction cups. The respective internal pressures are represented as pressure constraints which are coupled with anti-interpenetration and friction constraints. Furthermore, our method is able to detect multiple air cavities using information from collision detection. We solve the pressure constraints based on the ideal gas law while considering several cavity states. We test our model with a number of scenarios reflecting a variety of uses, for instance, a spring loaded jumping toy, a manipulator performing a pick and place task, and an octopus tentacle grasping a soda can. We also evaluate the ability of our model to reproduce the physics of suction cups of varying shapes, lifting objects of different masses, and sliding on a slippery surface. The results show promise for various applications such as the simulation in soft robotics and computer animation.
Resume Format, LinkedIn URLs and Other Unexpected Influences on AI Personality Prediction in Hiring: Results of an Audit

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the reliability of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. We develop a methodology for an external audit of stability of algorithmic personality tests, and instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Rather than challenging or affirming the assumptions made in psychometric testing -- that personality traits are meaningful and measurable constructs, and that they are indicative of future success on the job -- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability on key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text, violating the assumption that the output of an algorithmic personality test is stable across job-irrelevant input variations. Among other notable findings is evidence of persistent --- and often incorrect --- data linkage by Humantic AI. An open-source implementation of our auditing methodology, and of the audits of Humantic AI and Crystal, is available at https://github.com/DataResponsibly/hiring-stability-audit.
FINS Auditing Framework: Group Fairness for Subset Selections

Subset selection is an integral component of AI systems that is increasingly affecting people's livelihoods in applications ranging from hiring, healthcare, education, to financial decisions. Subset selections powered by AI-based methods include top-k analytics, data summarization, clustering, and multi-winner voting. While group fairness auditing tools have been proposed for classification systems, these state-of-the-art tools are not directly applicable to measuring and conceptualizing fairness in selected subsets. In this work, we introduce the first comprehensive auditing framework, FINS, to support stakeholders in interpretably quantifying group fairness across a diverse range of subset-specific fairness concerns. FINS offers a family of novel measures that provide a flexible means to audit group fairness for fairness goals ranging from item-based, score-based, and a combination thereof. FINS provides one unified easy-to-understand interpretation across these different fairness problems. Further, we develop guidelines through the FINS Fair Subset Chart, that supports auditors in determining which measures are relevant to their problem context and fairness objectives. We provide a comprehensive mapping between each fairness measure and the belief system (i.e., worldview) that is encoded within its measurement of fairness. Lastly, we demonstrate the interpretability and efficacy of FINS in supporting the identification of real bias with case studies using AirBnB listings and voter records.
A faster elastic-net algorithm for the traveling salesman problem

The elastic-net algorithm of R. Durbin and D. Willshaw (Nature, vol.326, p.689-91, 1987) for the traveling salesman problem works by deforming an elastic band as some of its points are attracted toward the cities. In each iteration, the influence of all cities on all points of the band is computed and the points displaced accordingly. A filtering mechanism is proposed. It is called the gamma -filter for gamma epsilon (0, 1), and allows points to be displaced under the attraction of only those cities that exert a significant influence on them, with performance advantages for both sequential and distributed parallel implementations. Experimental results are provided for instances of up to 1000 cities, revealing that the filtering mechanism does accelerate the elastic-net algorithm, without compromising the quality of its solutions. Results are compared with the Lin-Kernighan heuristic for the traveling salesman problem. <<ETX>>
Fuzzing of Embedded Systems: A Survey

Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the article introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed.
Electromagnetic scattering by two bodies of revolution

ABSTRACT A systematic procedure is presented for computing the electromagnetic scattering by two arbitrarily oriented bodies of revolution when the size of the objects are in the resonance region. The analysis uses a System Transfer Operator (T-matrix) approach and iteratively solves for the scattering of the two-body configuration. A modified unimoment technique is employed to generate the system transfer matrix of each body of revolution. Results are presented and compared to measured data to demonstrate the versatility and accuracy of the analysis. Applications of the two-body analysis stem from extending it to N-bodies. Some potential applications are discussed along with the major problems that need to be addressed.
Comment on 'Simultaneous gravity and gradient measurements from a recoil-compensated absolute gravimeter'

The article (Niebauer et al. 2011 Metrologia 48 154-163) reports on the important innovations enhancing the ability of absolute gravimeter to measure vertical gravity gradient along with the gravity acceleration. This comment suggests experiments to further assess the improvements and the results obtained with the modified instrument, considers some limitations of non-linear models in metrology and ways to overcome them, and discusses possible applications of the described instrument.
Conditional entropy-constrained vector quantization of linear predictive coefficients

Vector quantization (VQ) followed by entropy coding is used to compress linear predictive coefficients (LPCs) of speech into a variable-rate representation with distortion. It is shown in the LPC case that when a conditional entropy coder is used i.e., when the entropy code used for the current codeword is conditioned on the previous codeword, then a conditional version of entropy constrained vector quantizer (ECVQ) outperforms a conditional version of the straightforward approach by over 27%. Thus, conditioning restores the usual gain of ECVQ over standard VQ. This 27% reduction in bit rate is over and above the 42% reduction in bit rate already obtained by using a conditional rather than memoryless entropy coder in the straightforward approach. The product of these reductions is an overall reduction of 60% in average rate from the original, fixed-rate LPC system. <<ETX>>
Théorie d'Iwasawa des représentations p-adiques semi-stables

Soient F une extension finie non ramifiee de Q p et V une representation p-adique galoisienne semi-stable sur F de dimension d. On developpe dans ce texte la theorie d'Iwasawa relative a V et a la Z p -extension cyclotomique. En particulier, on construit un « logarithme » (regulateur) du module d'Iwasawa local associe a V (construit a partir de sa cohomologie galoisienne) dans un module tres explicite sur l'algebre engendree par les fonctions analytiques sur la couronne {p -1/(p-1) < |x| < 1} et log x.
Learning Constraint Programming Models from Data Using Generate-And-Aggregate

Constraint programming (CP) is used widely for solving real-world problems. However, designing these models require substantial expertise. In this paper, we tackle this problem by synthesizing models automatically from past solutions. We introduce COUNT-CP, which uses simple grammars and a generate-and-aggregate approach to learn expressive first-order constraints typically used in CP as well as their parameters from data. The learned constraints generalize across instances over different sizes and can be used to solve unseen instances – e
DUELMIPs: Optimizing SDN Functionality and Security

Software defined networks (SDNs) define a programmable network fabric that can be reconfigured to respect global networks properties. Securing against adversaries who try to exploit the network is an objective that conflicts with providing functionality. This paper proposes a two-stage mixed-integer programming framework. The first stage automates routing decisions for the flows to be carried by the network while maximizing readability and ease of use for network engineers. The second stage is meant to quickly respond to security breaches to automatically decide on network counter-measures to block the detected adversary. Both stages are computationally challenging and the security stage leverages large neighborhood search to quickly deliver effective response strategies. The approach is evaluated on synthetic networks of various sizes and shown to be effective for both its functional and security objectives.
Inter-Annual Climate Variability Impact on Oil Palm Mapping

: The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future·studies.