Miracle-dz/UniFAR-FTUs huggingface dataset:
Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives

Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MOHO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and −92% for the required running time.
Isotropic Inhomogeneous Cosmological Model with Torsion

An isotropic, inhomogeneous cosmology with torsion and cosmological constant is considered in the context of the Lemâitre-Tolman-Bondi metric. The form of the torsion tensor is determined from the isotropy of space. The field equations are derived by varying the Einstein-CartanHilbert action with respect to the metric tensor and considering the torsion as a parameter. Consistency of the field equation implies that only the axial part of the torsion is of relevance and that torsion differs from pression by an arbitrary time dependent function. If such function is taken constant, the scheme admits inflationary phases, but torsion is irrelevant because it amounts to a re-definition of the cosmological constant. On the contrary, if that function is not constant, the scheme is much more difficult to be integrate, but torsion results to be an effective suitable parameter to fit observational data.
A Novel GNSS Repeater Architecture for Indoor Positioning Systems in ISM Band

For indoor positioning using customary Global Navigation Satellite Systems (GNSS) receivers, the use of GNSS repeaters can be considered. However, regulations and provisions limit the use of GNSS repeaters. In order to circumvent these limitations, we propose a novel repeater architecture that transmits the Global Positioning System (GPS) signals in 433 MHz ISM band by frequency down-conversion. In addition, a receiver front end is proposed to upconvert GPS signals back to 1575.42 MHz, which allows any off-the-shelf GPS receiver to be used for indoor positioning. Measurement results with the RF hardware show that when the GPS signals are downconverted and then upconverted back to its original frequency, GPS signal fidelity and positioning accuracy are preserved.
Plasmonic Enhanced Photodetectors for Near Infra-red Light Detection

Silicon based single photon avalanche diodes (SPAD) are able to detect single photons in the visible part of the spectrum with high detection efficiency and high timing resolution. They also provide both single-photon sensitivity and fast responsivity in large-area detectors if arranged in extended arrays as Silicon Photomultipliers (SiPM). However, in applications exploiting near infrared light like light detection and ranging (LiDAR), the detector performance is hindered by the limited Si absorption coefficient. The latter implies absorption depths much larger than the typical active thickness of these devices (10-100 μm against few micrometers) resulting in a quantum efficiency (QE) too low for most of the previous applications. The exploitation of Surface Plasmon Polaritrons (SPP) can convert light in highly-confined modes and enhance the absorption of NIR photons. In this contribution, the first results on the integration of plasmonics nanostructures on thin silicon photodiodes are reported. Electro-optical measurements were carried out and the QE has been measured in the full 400-1100 nm spectrum. The resulting QE on the first prototypes is higher than 7% at 950 nm, an enhancement of about 45% with respect to the reference structure, paving the way for the application of metallic nanograting to SPADs and SiPMs devices.
Effects of sinusoidal turbulator in cylindrical channel on heat transfer and flow characteristics

Effects of heat transfer and flow characteristics on Reynolds numbers of different sinusoidal turbulators placed separately from a pipe in the turbulent flow were investigated experimentally. The experiments were carried out under forced flow and constant heat flow conditions. The elements, which drew a sine curve used as a turbulator, were positioned axially to the channel centre. The experiments were repeated for 3 width values of sinusoidal turbulators that correspond to 3D/4, D/2 and D/4 (D = inner diameter of test pipe). The experiments showed that although there were increases in the rate of heat transfer with the turbulator, they caused considerable pressure drop. As a result, as the width of the turbulator increased, the heat transfer ratio (the Nusselt number) and coefficient of friction increased. Increase in Nusselt number was about 39-86% at D/4 width, 70-119% at D/2 width and 72170% at 3D/4 width. It was observed that the coefficient of friction was significantly affected by the width of the sinusoidal turbulator, being about 0.02 in the case of straight pipe, 0.184 0.242 at D/4 width, 0.345 0.415 at D/2 width, and 0.416 0.451 at 3D/4 width. In addition, increasing coefficient of friction increased pressure drop and power required for pumping.
Comparative analysis of football statistics data clustering algorithms based on deep learning and Gaussian mixture model

—The paper considers the Gaussian mixtures model and the possibilities of its application for solving clustering tasks. First, the case is considered when the Gaussian mixtures model is formed in such a way that all the parameters of the model are known. Next, the case is considered when the approximation of normally distributed data occurs using the Gaussian mixtures model. Finally, the article presents a study of the accuracy of clustering two-dimensional data of football statistics of medal-position teams, middle-table teams and worst teams of the top 5 European football championships such as English Premier League, Spanish La Liga, German Bundesliga, Italian Serie A and French League One. The results of the algorithm based on the Gaussian mixtures models are compared with the results of clustering performed using neural networks.
Plumb: Efficient stream processing of multi‐user pipelines

Operational services run 24×7 and require analytics pipelines to evaluate performance. In mature services such as domain name system (DNS), these pipelines often grow to many stages developed by multiple, loosely coupled teams. Such pipelines pose two problems: first, computation and data storage may be duplicated across components developed by different groups, wasting resources. Second, processing can be skewed, with structural skew occurring when different pipeline stages need different amounts of resources, and computational skew occurring when a block of input data requires increased resources. Duplication and structural skew both decrease efficiency, increasing cost, latency, or both. Computational skew can cause pipeline failure or deadlock when resource consumption balloons; we have seen cases where pessimal traffic increases CPU requirements 6‐fold. Detecting duplication is challenging when components from multiple teams evolve independently and require fault isolation. Skew management is hard due to dynamic workloads coupled with the conflicting goals of both minimizing latency and maximizing utilization. We propose Plumb, a framework to abstract stream processing as large‐block streaming (LBS) for a multi‐stage, multi‐user workflow. Plumb users express analytics as a DAG of processing modules, allowing Plumb to integrate and optimize workflows from multiple users. Many real‐world applications map to the LBS abstraction. Plumb detects and eliminates duplicate computation and storage, and it detects and addresses both structural and computational skew by tracking computation across the pipeline. We exercise Plumb using the analytics pipeline for B‐Root DNS. We compare Plumb to a hand‐tuned system, cutting latency to one‐third the original, and requiring 39% fewer container hours, while supporting more flexible, multi‐user analytics and providing greater robustness to DDoS‐driven demands.
Fe b 20 20 Nuclear structure uncertainties in coherent elastic neutrino-nucleus scattering

The effects of the nuclear structure uncertainties on the description of processes induced by coherent scattering of neutrinos on nuclei are investigated. A reference calculation based on a specific nuclear model is defined and the cross sections and also the expected number of events produced by neutrinos generated by the explosion of a supernova in our galaxy, and by a spallation neutron source are evaluated. By changing the input parameters of the reference calculation their relevance on cross sections and on the number of the detected events is estimated. Seven spherical nuclei with different proton to neutron ratios are considered as possible targets of the neutrinos in the detector, the lightest being C and the heaviest Pb. The effects generated by the uncertainties of the nuclear model are much smaller than those due to the neutrino properties. This makes the coherent elastic neutrino-nucleus scattering a reliable tool to investigate the details of the neutrino sources, the neutrino-nucleus interaction, and, eventually, also to extract information about neutron distributions in nuclei.
Automating the Classification of Hysteresis in Event Concentration-Discharge Relationships

The response of in-stream sediment concentration and discharge during rainfall-runoff events provides information about dominant watershed processes as it represents the amalgamation of the connectivity, erodibility, and the spatial location of sediment sources. A common way to collapse the sediment and streamflow response into a readily interpretable visualization is to utilize an event concentration-discharge (C-Q) plots which frequently exhibit patterns of hysteresis. However, challenges exist given the subjective nature of visual classifications and when scaling to large data sets. Hysteresis indices have been used to facilitate an automated and objective analysis method. In this study, we present an alternative method for automating hysteresis classification utilizing all the information present in the event C-Q plots. Thus, avoiding the loss of information that may occur when collapsing data into metrics and enabling the local sediment dynamics to be interpreted to a greater extent. We developed an automated machine learning tool using images of event C-Q plots to classify storm events into pre-defined hysteresis pattern types. The classifier utilizes a convolutional neural network, a machine learning method that has achieved excellent predictive accuracy in image classification tasks. We then applied this tool using surrogate suspended sediment data from turbidity monitoring in eight watersheds within the Lake Champlain Basin in Vermont encompassing 760 individual storm events. The tool accurately and efficiently classifies events and represents an advancement over manual visual classification.
The Effects of Anatomic Enhancing Base Layer Garments on Knee Performance and Injury Reduction Among Professional Ski Instructors

There are nearly 30 million individuals engaged in downhill skiing in the United States every year. Injury rates currently range between 3.7 and 9.1 injuries per 1000 skier days, and there has been a well-documented increase in the number of trauma cases and fatalities associated with this sport (Langran & Selvaraj, 2002; Rossi et al., 2003). Significant technological improvements in bindings and boots over the past decade have surpassed advances in knee support and traumatic knee injuries are at a higher rate now than 20 years ago (Pecina 2002). The need for new injury prevention technology is compounded by the fact that the majority of traumatic lower extremity injuries are due to muscular fatigue and performance errors rather than equipment issues (Girardi et al., 2010).
System Design and Optimisation Study on a Novel CCHP System Integrated with a Hybrid Energy Storage System and an ORC

For achieving higher energy transferring efficiency from the resources to the load, the Combined Cooling, Heating, and Power (CCHP) systems have been widely researched and applied as an efficient approach. The key idea of this study is designing a novel structure of a hybrid CCHP system and evaluating its performance. In this research, there is a hybrid energy storage unit enhancing the whole system’s operation flexibility while supplying cooling, heating, and power. An ORC system is integrated into the CCHP system which takes responsibility of absorbing the low-temperature heat source for electricity generation. There are a few research studies focusing on the CCHP systems’ performance with this structure. In order to evaluate the integrated system’s performance, investigation and optimisation work has been conducted with the approaches of experimental studies and modelling simulation. The integrated system’s configuration, the model building process of several key components, the optimisation method, and the case studies are discussed and analysed in this study. The design of the integrated system and the control strategy are displayed in detail. Several sets of dynamic energy demand profiles are selected to evaluate the performance of the integrated system. The simulation study of the system supplying selected scenarios of loads is conducted. A comprehensive evaluation report indicates that the system’s efficiency during each study process differs while supplying different loads. The results include the power supplied by each component, the energy consumed by each type of load, and the efficiency improvements. It is found that the integrated system fully satisfies the selected domestic loads and various selected scenarios of loads with high efficiency. Compared to conventional power plants or CHP systems, the system efficiency enhancement comes from higher amount of recovery waste heat. Especially, the ORC system can absorb the low-temperature heat source for electricity generation. Compared to the original following electrical load (FEL) control strategy, the optimisation process brings overall efficiency improvements. The system’s overall efficiency was increased by from 3%, 3.18%, 2.85%, 17.11%, 8.89%, and 21.7% in the second case studies. Through the whole study, the main challenge lies within the design and the energy management of the integrated system.
Description of a new species of the "brown" Agrodiaetus com- plex from South-East Turkey (Lycaenidae)

Agrodiaetus dantchenkoi sp. n. is described from Van Province in Turkey. The new taxon belongs to the "brown" complex of the genus Agrodiaetus Hubner, 1822. This complex includes several sibling species which are extremely uniform in their morphology but have distinct chromosome numbers. The karyotype of Agrodiaetus dantchenkoi sp. n. (n=40-42) is investigated. The new species is compared with A. eriwanensis Forster, 1960 (n=34), A. humedasae Toso & Balletto, 1976 (n=38) and A. aroanien- sis Brown, 1976 (n=48). Zusammenfassung. Agrodiaetus dantchenkoi sp. n. wird aus der Provinz Van in der Turkei neu beschrieben. Das neue Taxon gehort zum "braunen" Artenkomplex der Gattung Agrodiaetus Hubner, 1822. Dieser Komplex enthalt mehrere Zwillingsarten, die sich morphologisch auserst ahnlich sind, aber verschiedene Chromosomenzahlen besitzen. Der Karyotyp von Agrodiaetus dantchenkoi sp. n. (n=40-42) wird untersucht und die neue Art wird mit A. eriwanensis Forster, 1960 (n=34), A. humedasae Toso & Balletto, 1976 (n=38) und A. aroaniensis Brown, 1976 (n=48) verglichen.
Évaluation de l’impact de l’extrapolation de bioréacteur sur la physiologie de Yarrowia lipolytica pour la production de lipase

Bioreactor scale-up often pose a serious issue during the industrial development of a bioprocess considering the numerous physical and biological phenomena occurring in the reacting volume. The basic principles of scale-up coming from the traditional chemical and process engineering approaches will be first reviewed and will be then compared to a new one involving recent development at the level of microbial strain manipulation. This "physiological" approach of scale-up involves directly a biological component of the system
Consequences of natal philopatry for reproductive success and mate choice in an Alpine rodent

Quantifying the interaction between dispersal, kinship, and genetic structure can provide insights into the factors that shape kin-structured mammal societies. Here, we first employ a combination of 8 years of capture–mark–recapture and molecular data to characterize the spatial and genetic relationships among female snow voles (Chionomys nivalis) in a population located in the Swiss Alps. Subsequently, we examine the individual-level consequences of kin structure in terms of fitness and mating patterns. Behavioral data, relatedness estimates, and spatial autocorrelation analyses indicate that females show strong philopatry, with spatially clustered females being characterized by high levels of genetic relatedness, leading to significant small-scale (<30 m) spatial genetic structure (SGS). In line with selection favoring female philopatry, dispersing females had a lower fitness compared with philopatric individuals. However, we found a negative association between female reproductive success and the number of neighboring females. This suggests that female kin clustering does not constitute an adaptive strategy in this species, but rather that site tenacity is a by-product of the costs of dispersal. Although dispersal is frequently invoked as a means to avoid inbreeding, our results provide no evidence for premating inbreeding avoidance, which is in line with previous studies on mammals. Instead, in the majority of years, we observed that pairs were more-closely related than expected by chance. However, we found that both males and females with related partners had reduced reproductive success, suggesting the existence of inbreeding depression and/or postmating inbreeding avoidance mechanisms. On the whole, our results show how quantification of SGS within populations can provide insights into individual dispersal behavior and its fitness consequences, and into the ways in which social and genetic structure interacts to shape the evolution of free-living populations.
Random Walk Graph Neural Networks

In recent years, graph neural networks (GNNs) have become the de facto tool for performing machine learning tasks on graphs. Most GNNs belong to the family of message passing neural networks (MPNNs). These models employ an iterative neighborhood aggregation scheme to update vertex representations. Then, to compute vector representations of graphs, they aggregate the representations of the vertices using some permutation invariant function. Therefore, MPNNs treat graphs as sets and thus seem to ignore features emanating from the graph topology. In this paper, we propose a more intuitive and transparent architecture for graph-structured data, so-called Random Walk Graph Neural Network (RWNN). The ﬁrst layer of the model consists of a number of trainable “hidden graphs” which are compared against the input graphs using a random walk kernel to produce graph representations. These representations are then passed on to a fully-connected neural network which produces the output. The employed random walk kernel is differentiable, and therefore, the proposed model is end-to-end trainable. We demonstrate the model’s transparency on synthetic datasets. Furthermore, we empirically evaluate the model on graph classiﬁcation datasets and show that it achieves strong performance.
The labor market in real business cycle theory

The standard real business cycle model fails to adequately account for two facts found in the U.S. data: the fact that hours worked fluctuate considerably more than productivity and the fact that the correlation between hours worked and productivity is close to zero. In this paper, in a unified framework, the authors describe and analyze four extensions of the standard model, by introducing nonseparable leisure, indivisible labor, government spending, and household production.
Etude de la structure électronique et magnétique de CrO2

The electronic and magnetic properties of CrO2 were investigated using the selfconsistent A-S-Wmethod in a new approach to study the evolution of its magnetic properties at decreasing volume and to assess recent photoemission results on thin films. The results show that a magnetic transition of ferro ~ antiferromagnetic type is likely to be induced under pressure. Experimental results could be explained by a compression of the CrO~ octahedron within the cell.
Vortex-in-cell analysis of wing wake roll-up

A vortex-in-cell (VIC) method was coupled with a wing vortex-lattice model to compute steady-state wake roll-up. The vorticity concentrated on the wing and wake vortex segments is distributed to a Cartesian grid through the application of a spreading function. A sub vortex technique is introduced to refine the wake description without altering the number of wing panels. The velocity field induced by the spread vorticity is computed using an infinite domain fast Poisson solver. Interpolation provides velocities at any point inside the grid. The VIC method is used to compute velocities for a wake relaxation procedure and to correct wing panel circulations. The iterative method developed can be applied to configurations with several wings. The method was tested for various oneand two-wing problems and compared with results from experiments and from other theories with very good agreement. Detailed descriptions for wake geometry and accurate load distributions were obtained, even for cases where wakes intercepted wings directly.