Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of nest occupancy and nest reproduction in two sympatric raptors: common buzzard Buteo buteo and goshawk Accipiter gentilis

Nest site selection can have important fitness consequences in birds. I analysed the habitat characteristics of 392 nests of two sympatric raptor species (common buzzard Buteo buteo and goshawk Accipiter gentilis) in Germany and their relation to nest occupation rate and nest reproductive success. For common buzzard, multivariate models explained only small proportions of the variance in nest occupation rate and nest reproductive success (13-19%). Important variables related to nest occupation rate were human disturbances, intra- and interspecific neighbour density, the amount of forested area and nest tree crown cover. Variables related to nest site reproductive success also included human disturbance, intra- and interspecific neighbour density and nest tree crown cover as well as nest distance to the nearest forest edge. In contrast, models for the goshawk explained a much higher proportion of the variation in nest occupation rate and nest reproductive success (41-43%). Important variables related to nest occupation rate were the remoteness of the nest site and direct human disturbance. Variables related to nest site reproductive success were remoteness of the nest site and good hunting habitat. Goshawks seem to be more sensitive to human disturbance than buzzards. A multiple discriminant analysis showed that nest site characteristics substantially overlapped between the species and there is a good evidence that competition for optimal nest sites occurs. Thus, buzzards might be constrained by the dominant goshawk in their nest site selection.
Granularity as a modelling approach to investigate hypothesized emergence in biology

Informal usage of emergence in biological discourse tends towards being of the epistemic type, but not ontological emergence, primarily due to our lack of knowledge about nature and limitations to how to model it. Philosophy adds clarification to better characterise the fuzzy notion of emergence in biology, but paradoxically it is the methodology of conducting scientific experiments that can give decisive answers. A renewed interest in whole-ism in (molecular) biology and simulations of complex systems does not imply emergent properties exist, but illustrates the realisation that things a more dicult and complex than initially anticipated. Usage of (weak- and epistemological) emergence in bioscience is a shorthand for ‘we have a gap in our knowledge about the precise relation(s) between the whole and its parts and possibly missing something about the parts themselves as well’, which amounts to absence of emergence in the philosophical sense. Given that the existence of emergent properties is not undisputed, we need better methodologies to investigate such claims. Granularity serves as one of these approaches to investigate postulated emergent properties. Specification of levels of granularity and their contents can provide a methodological modelling framework to enable structured examination of emergence from both a formal ontological modelling approach and the computational angle, and helps elucidating the required level of granularity to explain away emergence. I discuss some modelling considerations for a granularity framework and its relevance for the testability of emergence in computational implementations such as simulations.
Socially Responsible Fixed-Income Funds

A hand-held device, such as a hammer drill, has a replaceable axially extending holder for a working tool. The device includes a sensor and a signal transmitter for adjusting its operating characteristics. The holder has through openings containing locking elements and control elements. The locking elements and control elements are radially displaceable within the through openings by a locking sleeve. The position of the locking sleeve in the axial direction of and relative to the holder determines the cooperation of the sensor and the signal transmitters.
Optimal Capital Structure in Real Estate Investment: A Real Options Approach

This article employs a real options approach to investigate the determinants of an optimal capital structure in real estate investment. An investor has the option to delay the purchase of an income-producing property because the investor incurs sunk transaction costs and receives stochastic rental income. At the date of purchase, the investor also chooses a loan-to-value ratio, which balances the tax shield benefit against the cost of debt financing resulting from a higher borrowing rate and a lower rental income. An increase in the sunk cost or the risk of investment will not affect the financing decision, but will delay investment. An increase in the income tax rate or a decrease in the depreciation allowance will encourage borrowing and delay investment, while an increase in the penalty from borrowing, a decrease in the investor's required rate of return, or worse real estate performance through borrowing, will discourage borrowing and delay investment.
Does `Smart Growth' Matter to Public Finance?

This paper addresses four fundamental questions about the relationship between `smart growth', a fiscally motivated anti-sprawl policy movement, and public finance. Do low-density, spatially extensive land use patterns cost more to support? If so, how large an influence does sprawl actually have? How does the influence differ among types of spending? And, how does it compare with the influence of other relevant factors? The analysis, which is based on the entire continental US and uses a series of spatial econometric models to evaluate one aggregate (total direct) and nine disaggregate (education, fire protection, housing and community development, libraries, parks and recreation, police protection, roadways, sewerage, and solid waste disposal) measures of spending, provides the most detailed evidence to date of how sprawl affects the vast sum of revenue that local governments spend every year.
Microsoft Kinect-based differences in lower limb kinematics during modified timed up and go test phases between men with and without Parkinson’s disease

The aim of the study was to analyse with Microsoft Kinect (Kinect) the differences in lower limb kinematics during sub-phases of modified Timed Up and Go test (modTUG) in men with Parkinson’s disease (PD) compared to healthy age-matched male individuals. Eight men with mild-to-moderate PD (age 67.5±4.5 yrs) and eight healthy men (age 69.8±8.0 yrs) participated. Kinect along with KinectPsyManager (v1.0) and Matlab2016b software was used for data collection. Selected lower limb kinematics and gait speed (GS) were calculated during sittingto- walking (STW) transition while performing modTUG. According to Kinect men with mild to moderate PD did not differ from healthy counterparts in aspects of postural characteristics of STW, with the exception of smaller distance between knees while sitting (p<0.001). Men with PD were found to perform the walking phase of STW transition slower (p<0.01) and with slower GS (p<0.01) comparing to healthy men. In conclusion, compared to healthy men, Kinect detects smaller distance between knees during sitting before transitioning from STW in men with mild to moderate PD. In addition, men with PD also demonstrated slower GS and a longer walking phase of STW transition in comparison to healthy men.
Study of Word-Level Accent Classication and Gender Factors

In this work, we conduct word-level accent classication. Dierent features, words, and learning methods are explored for accent classication, and results show that HMM-MFCC models show promising performance. Besides, we also explore the eect of gender on accent classication. Results show that models trained on male data do not generalized well on female data; Models trained on both male and female data is not always better than models trained on female data only. At last, we propose to use stacked ensemble classier to classify gender rstly and then classify accent to improve accuracy.
Hungarian vowel quantity neutralisation as a potential social marker

Hungarian is a language with distinctive vowel quantity, but it seems that quantity discrimination in acoustic and perceptual terms is less robust for high vowels than for low ones. In this paper, we argue that the unstable behaviour of high vowels could refer to a sound change from below in Labov’s terminology. Due to our results, quantity loss was observed both for high and partly also for mid vowels, especially in unstressed position. The extent of quantity neutralisation showed an interaction with speech style, linguistic attitude, and partly also social status, but not with gender, age-dependent occupation or regional affiliation.
. 15 No . 4 A ( 2006 ) , 6 6-6 8 Refractive and Chiroptical Properties of Clinical Dextrans

The refractive index dispersion and optical rotatory dispersion of two clinical dextrans is discussed. Temperature dependence of n and specific rotation do not show evidence of conformational phase transition in the temperature range of 278 ÷ 347 K. Two characteristic concentrations for both dextrans were observed. One with c ~ 0.02 g/dL and second with c ~ 2 g/dL which may be related with an aggregation and an overlapping processes of polymer chains respectively. Negligible influence of K and Ca ions on conformational structure of dextran polymer chain was found.
Early Intervention of Autism: A Case for Floor Time Approach

Autism is a developmental disorder that affects a child’s perception of the world and how the child learns from his or her experiences. Even among the most complex disabili‐ ties, autism remains an enigma. Autism is the frequently occurring form of a group of disorders known as Autism Spectrum Disorders (ASD). The term Autism Spectrum Dis‐ orders (ASD) covers diagnostic labels which include Autistic Disorder, High Functioning Autism, Asperger's Syndrome, and Pervasive Developmental Disorder – Not Otherwise Specified (PDD-NOS).
Partial Order on the set of Boolean Regulatory Functions

Logical models have been successfully used to describe regulatory and signaling networks without requiring quantitative data. However, existing data is insufficient to adequately define a unique model, rendering the parametrization of a given model a difficult task. Here, we focus on the characterization of the set of Boolean functions compatible with a given regulatory structure, i.e. the set of all monotone nondegenerate Boolean functions. We then propose an original set of rules to locally explore the direct neighboring functions of any function in this set, without explicitly generating the whole set. Also, we provide relationships between the regulatory functions and their corresponding dynamics. Finally, we illustrate the usefulness of this approach by revisiting Probabilistic Boolean Networks with the model of T helper cell differentiation from Mendoza & Xenarios.
SC-Fano Decoding of Polar Codes

For finite-length polar codes, the standard successive cancellation (SC) decoding has been improved, such as SC-List (SCL), SC-Stack (SCS), and SC-Flip (SCF) decodings. In this paper, we present an alternative improvement of SC decoding by incorporating the Fano sequential decoding into SC decoding. This is referred to as SC-Fano decoding. Specifically, it can address the major drawback of SC decoding by enabling moving-backward when the reliability of an on-going path is not good enough. The SCS and SC-Fano decodings can be viewed as the sequential decoding for polar codes. In addition, for cyclic-redundancy-check (CRC) concatenated polar codes, we enhance SC-Fano decoding by leveraging the bit-flipping idea of SCF decoding. The simulation results demonstrate that the proposed SC-Fano decoding can provide better performance-complexity tradeoff than the existing decoding methods.
Coded Caching via Projective Geometry: A new low subpacketization scheme

Coded Caching is a promising solution to reduce the peak traffic in broadcast networks by prefetching the popular content close to end users and using coded transmissions. One of the chief issues of most coded caching schemes in literature is the issue of large subpacketization, i.e., they require each file to be divided into a large number of subfiles. In this work, we present a coded caching scheme using line graphs of bipartite graphs in conjunction with projective geometries over finite fields. The presented scheme achieves a rate $\Theta \left( {\frac{K}{{{{\log }_q}K}}} \right)$ (K being the number of users, q is some prime power) with subexponential subpacketization ${q^{O\left( {{{\left( {{{\log }_q}K} \right)}^2}} \right)}}$ when cached fraction is upper bounded by a constant ($\tfrac{M}{N} \leq \tfrac{1}{{{q^\alpha }}}$, for some positive integer α). Compared to earlier schemes, the presented scheme has a lower subpacketization (albeit possessing a higher rate). We also present a new subpacketization dependent lower bound on the rate for caching schemes in which each subfile is cached in the same number of users. Compared to the previously known bounds, this bound seems to perform better for a range of parameters of the caching system.
DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD

We propose a novel diminishing learning rate scheme, coined Decreasing-Trend-Nature (DTN), which allows us to prove fast convergence of the Stochastic Gradient Descent (SGD) algorithm to a first-order stationary point for smooth general convex and some class of nonconvex including neural network applications for classification problems. We are the first to prove that SGD with diminishing learning rate achieves a convergence rate of $\mathcal{O}(1/t)$ for these problems. Our theory applies to neural network applications for classification problems in a straightforward way.
Nature, Aesthetic Appreciation, and Knowledge

L'A. defend la these selon laquelle une appreciation esthetique adequate de la nature requiert une connaissance sceintifique du monde naturel. Analysant la these adverse, illustree par le modele de l'eveil chez N. Carroll et le modele du mystere chez S. Godlovitch, l'A. montre que celui-ci rend impossible toute connaissance de la nature, mais que celui-la la rend necessaire
Robust Photogeometric Localization Over Time for Map-Centric Loop Closure

Map-centric Simultaneous Localization And Mapping (SLAM) is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the process of loop closure differs from that of conventional SLAM and the result of incorrect loop closure is more destructive and is not reversible. In this letter, we present a tightly coupled photogeometric metric localization for the loop closure problem in map-centric SLAM. In particular, our method combines complementary constraints from LiDAR and camera sensors, and validates loop closure candidates with sequential observations. The proposed method provides a visual evidence-based outlier rejection where failures caused by either place recognition or localization outliers can be effectively removed. We demonstrate that the proposed method is not only more accurate than the conventional global ICP methods but is also robust to incorrect initial pose guesses.
Fast and Robust Distributed Subgraph Enumeration

We study the subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them impractical for very large graphs. Most of them follow a synchronous model where the performance is often bottlenecked by the machine with the worst performance. Motivated by this, in this paper, we propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system. RADS first identifies results that can be found using single-machine algorithms. This strategy not only improves the overall performance but also reduces network communication and memory cost. Moreover, RADS employs a novel region-grouped multi-round expand verify & filter framework which does not need to shuffle and exchange the intermediate results, nor does it need to replicate a large part of the data graph in each machine. This feature not only reduces network communication cost and memory usage, but also allows us to adopt simple strategies for memory control and load balancing, making it more robust. Several optimization strategies are also used in RADS to further improve the performance. Our experiments verified the superiority of RADS to state-of-the-art subgraph enumeration approaches.
Deep Clustering with a Dynamic Autoencoder

In unsupervised learning, there is no obvious straightforward loss function which can capture the major factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised loss function remains static during the training process. The absence of concrete supervision suggests that smooth complex dynamics should be integrated as a substitute to the classical static loss functions to better make use of the gradual and uncertain knowledge acquired through self-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a new model for deep clustering that allows to solve a clustering-reconstruction trade-off by gradually and smoothly eliminating the reconstruction objective in favor of a construction one while preserving the space topology. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to all the other autoencoder-based clustering methods.
“How Else Should It Work?” A Grounded Theory of Pre-College Students’ Understanding of Computing Devices

In order to understand and evaluate computing technology in their environment, students first need to be able to identify it. This task becomes increasingly difficult, however, as computing systems become more and more ubiquitous and invisible. Based on the analysis of semi-structured focus interviews with 28 German pre-college students, we present a grounded theory of their conceptions and reasoning related to the identification of computing within technical devices. At its core is the finding that many students seemed to differentiate technical artifacts with respect to three conceived levels of capability. Many household appliances, for instance, were very well seen as electronic and programmed, but still as too limited in their capability to warrant the presence of a “real” computer or to be related to informatics. Given the increasing versatility, power, and associated risks of modern embedded systems as well as the advent of the internet of things, this issue should clearly be addressed. Based on our grounded theory, we propose some first ideas for how this might be done.
GroupSense

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense using smartphone sensors, smartwatch sensors, and embedded sensors in things, where we have a protocol for these different devices to exchange information required for GAR. A range of continuous group activities (from simple to fairly complex) illustrates our approach and demonstrates the feasibility of our model and richness of the proposed specialization. We then conclude with lessons learned for GAR and future work.
Rapid Triggering Capability Using an Adaptive Overlay during FPGA Debug

Field Programmable Gate Array (FPGA) technology is rapidly gaining traction in a wide range of applications. Nonetheless, FPGAs still require long design and debug cycles. To debug hardware circuits, trace-based instrumentation is inserted into the design that enables capturing data during the circuit execution into on-chip memories for later offline analysis. Since on-chip memories are limited, a trigger circuitry is used to only record data related to specific events during the execution. However, during debugging, a circuit recompilation is required on modifying these instruments. This can be very slow, reducing debug productivity. In this article, we propose a non-intrusive and rapid triggering solution with a tailored overlay fabric and mapping algorithm that seeks to enable fast debug iterations without performing a recompilation. This overlay is specialized for small combinational and sequential circuits with a single output; such circuits are typical of common trigger functions. We present an adaptive strategy to construct the overlay fabric using spare FPGA resources at compile time. At debug time, our proposed trigger mapping algorithms adapt to this specialized overlay to rapidly implement combinational and sequential trigger circuits. Our results show that the overlay fabric can be reconfigured to map different triggering scenarios in less than 40s instead of recompiling the circuit during debug iterations, increasing debug productivity.
Développement saprotrophe de fusarium graminearum : rôle respectif de différents habitats naturels du champignon dans le processus d'infection du blé en Bourgogne ; recherche d'indicateurs prédictifs du risque de fusariose

La fusariose est une des maladies les plus importantes alterant le ble en Bourgogne. L’espece fongique Fusarium graminearum est l’un des agents principaux de la maladie. L’interaction hote-pathogene peut entrainer la production de mycotoxines toxiques pour l’homme et l’animal.La seule alternative pour prevenir le developpement de la maladie est de controler l’inoculum primaire dans son habitat naturel : les adventices, le sol et les residus de culture. En raison de la tendance a la reduction du travail du sol, une attention particuliere doit etre portee au role des residus de culture dans la survie et le developpement de F. graminearum. Dans ce travail de these, deux approches ont ete choisies pour mieux comprendre le developpement saprotrophe du champignon et ses consequences. La premiere, a travers un essai en champ dans le contexte pedoclimatique Bourguignon, avait pour but de definir la part relative des differentes sources d’inoculum dans le developpement de la fusariose et l’accumulation des mycotoxines dans les grains. Cet essai devait en outre permettre de determiner si des indicateurs precoces du developpement de la maladie sur epi et de l’accumulation de mycotoxines pouvaient etre identifies. La seconde, a travers un essai en microcosmes, avait pour but de suivre le developpement de F. graminearum dans le sol et les residus.Ce travail a permis de souligner l’importance de la gestion des residus de culture dans le developpement de la fusariose du ble. Favoriser une decomposition biologique rapide des residus de culture et/ou introduire des cultures intermediaires assainissantes constituent des perspectives de recherche sur lesquelles doivent porter nos efforts
Dynamic Checkpoint Architecture for Reliability Improvement on Distributed Frameworks

Fault tolerant mechanisms are essential to provide reliable feature for distributed systems. Checkpoint and Recovery is a widely used technique that consists on saving data states for a fast recovery in case of failure. On Apache Hadoop and Apache Spark – distributed high performance frameworks –, checkpoint aims to help on recovery steps after failures. However, wrong configuration of checkpoint attributes can degrade system performance and reliability, thus losing checkpoint purpose. This work proposes a dynamic architecture for checkpoint based on system monitoring and alerts. In order to avoid checkpoint problems on Hadoop and Spark, one implementation of dynamic mechanism is defined for each framework.