Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of temperature and salinity on the gastric evacuation of juvenile sole Solea solea and Solea senegalensis

Summary The juveniles of Senegal sole, Solea senegalensis, Kaup 1858, and common sole, Solea solea (Linnaeus 1758) concentrate in estuarine and coastal nurseries of widely differing temperatures and salinities. Yet, little is known about the effect of these physiologically important variables on the gastric evacuation rates of these species. Gastric evacuation experiments were performed on juveniles of S. senegalensis and S. solea. Three temperatures were tested, 26, 20 and 14� C at a salinity of 35&. A low salinity experiment was also carried out at 15& ,a t 26� C. Experimental conditions intended to reflect conditions in estuarine and coastal nurseries where juveniles of these species spend their first years of life. The relation between stomach contents and time was best described by exponential regression models for both species. An analysis of covariance (ANCOVA) was performed in order to test differences in evacuation rate due to temperature and salinity (slope of evacuation time against stomach contents) for each species. While increasing temperature increased evacuation rates in both species (although not at 26� Ci nS. solea), the effect of low salinity differed among species, leading to a decrease in gastric evacuation rate in that of S. senegalensis and an increase in S. solea. Differences in gastric evacuation rate between species were related to its metabolic optimums and to its distribution in the nursery area where fish were captured. Implications for the habitat use of estuarine and coastal nurseries are discussed.
Comparison of aerobic heterotrophic taxa isolated from four root domains of mature sugar beet (Beta vulgaris)

The distribution of bacteria in the rhizosphere, rhizoplane, interior root tissues (core) and lower root (all tissues) of mature sugar beet roots (Beta vulgaris) was compared. Of 556 isolates, 102 species from 40 genera were identified by fatty acid methyl ester gas-chromatographic (FAME-GC) analysis. The ten most common genera (Bacillus, 14%; Arthrobacter, 12%; Pseudomonas, 11%; Aureobacterium, 9%; Micrococcus, 6%; Xanthomonas, 5%; Alcaligenes, 4%; Flavobacterium, 3%; Agrobacterium, 3%; Microbacterium, 3%) accounted for 70% of isolates, and were found in each of three root domains (rhizosphere, rhizoplane and interior root tissues) on the two principal sampling occasions. Gram-positive strains were more abundant in the rhizosphere than the rhizoplane. Compared to the rhizoplane, rhizosphere bacterial communities were represented by a less diverse, more hierarchical distribution of species where twice as many isolates formed late developing colonies on isolation plates. Between October and January, the bacteria isolated from root interior tissues acquired a distinct change in taxonomic pattern, with decreased diversity and increased hierarchy. A bacterial continuum of similar taxa was observed which extended from the rhizosphere to interior root tissues.
Emergent geometry and path integral optimization for a Lifshitz action

Extending the background metric optimization procedure for Euclidean path integrals of two-dimensional conformal field theories, introduced by Caputa et al. (Phys. Rev. Lett. 119, 071602 (2017)), to a $z=2$ anisotropically scale-invariant $(2+1)$-dimensional Lifshitz field theory of a free massless scalar field, we find optimal geometries for static and dynamic correlation functions. For the static correlation functions, the optimal background metric is equivalent to an AdS metric on a Poincare patch, while for dynamical correlation functions, we find Lifshitz like metric. This results suggest that a MERA-like tensor network, perhaps without unitarity, would still be considered an optimal background spacetime configuration for the numerical description of this system, even though the classical action we start with is not a conformal field theory.
SITE-DEPENDENT REGULATION OF POPULATION SIZE:A NEW SYNTHESIS

The nature and extent of population regulation remains a principal unanswered question for many types of organisms, despite extensive research. In this paper, we provide a new synthesis of theoretical and empirical evidence that elucidates and extends a mechanism of population regulation for species whose individuals preemptively use sites that differ in suitability. The sites may be territories, refuges from predation, oviposition sites, etc. The mechanism, which we call site dependence, is not an alternative to density dependence; rather, site dependence is one of several mechanisms that potentially generate the negative feedback required for regulation. Site dependence has two major features: (1) environmentally caused heterogeneity among sites in suitability for reproduction and/or survival; and (2) preemptive site occupancy, with the tendency for individuals to move to sites of higher quality as they become available. Simulation modeling shows that these two features, acting in concert, generate nega.
SoK: The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing

Greybox fuzzing has been the most scalable and practical approach to software testing. Most greybox fuzzing tools are coverage guided as code coverage is strongly correlated with bug coverage. However, since most covered codes may not contain bugs, blindly extending code coverage is less efficient, especially for corner cases. Unlike coverage-based fuzzers who extend the code coverage in an undirected manner, a directed fuzzer spends most of its time budget on reaching specific target locations (e.g., the bug-prone zone) without wasting resources stressing unrelated parts. Thus, directed greybox fuzzing is particularly suitable for scenarios such as patch testing, bug reproduction, and special bug hunting. In this paper, we conduct the first in-depth study of directed greybox fuzzing. We investigate 28 state-of-the-art fuzzers (82% are published after 2019) closely related to DGF, which have various directed types and optimization techniques. Based on the feature of DGF, we extract 15 metrics to conduct a thorough assessment of the collected tools and systemize the knowledge of this field. Finally, we summarize the challenges and provide perspectives of this field, aiming to facilitate and boost future research on this topic.
Spatial patterns of seed dispersal and seedling recruitment in Corema album (Empetraceae): the importance of unspecialized dispersers for regeneration

1 Spatial patterns of seed dispersal and seedling recruitment of Corema album were examined among and within habitats to determine the quantitative importance of different dispersers in each type of habitat, and their effectiveness in carrying seeds to suitable habitats for seedling recruitment. 2 Gulls, blackbirds and rabbits were, respectively, the main dispersers (45%, 40% and 15% of Corema album seeds). Within habitats, blackbirds disperse seeds mainly to female Corema album shrubs, while gulls and rabbits disperse seeds mainly to open ground. 3 The quantitative role of dispersers varies among habitats because of their habitat preferences, causing the spatial pattern of seed rain to differ. 4 Open ground has the highest density of seedlings and the highest seedling‐to‐seed ratios. Regeneration is more active in the pioneer scrub than in the mature scrub and the herbaceous vegetation. 5 Gulls, rather than specialist frugivores, are the most effective dispersers in carrying seeds to suitable sites for recruitment.
Population dynamics of Siphoninus philyreae in California in the presence and absence of a parasitoid, Encarsia partenopea

Abstract. 1. Densities of the whitefly, Siphoninus phillyreae Haliday, remained at low levels at two sites where a parasitoid, Encarsia parknopea (Walker), was released on whitefly‐infested ash, Fraxinus sp. , and pomegranate, Punica granatum. Populations of S.phillyreae at control sites, where parasitoids were absent, were increasing in density at the beginning of the summer.
DeepSQLi: deep semantic learning for testing SQL injection

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is se- mantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster.
Compendium of recurrent selection methods and their application

Recurrent selection is a breeding method that has been used for the genetic improvement of germplasm. Although recurrent selection methods were suggested over 40 years ago, there use has not been as extensive as the other classical plant breeding methods. Recurrent selection is used for the improvement of traits inherited in a quantitative manner, and the primary objective of the method is to gradually increase the frequency of favorable alieles and maintain genetic variability for future selection. Different methods of recurrent selection have been suggested, depending on the types of gene action considered important in the inheritance of the traits under selection, relative efficiency of selection, and the complexity of the trait under selection. Most methods of recurrent selection include three phases conducted in a repetitive manner; development of progenies, evaluation of progenies in replicated trials, and recombination of the superior progenies based on the evaluation trials. Most information sugge.
Unexpected late-time temperature increase observed in the two neutron star crust-cooling sources XTE J1701−462 and EXO 0748−676

Transient low-mass X-ray binaries (LMXBs) that host neutron stars (NSs) provide excellent laboratories for probing the dense matter physics present in NS crusts. During accretion outbursts in LMXBs, exothermic reactions may heat the NS crust, disrupting the crust-core equilibrium. When the outburst ceases, the crust cools to restore thermal equilibrium with the core. Monitoring this cooling evolution allows us to probe the dense matter physics in the crust. Properties of the deeper crustal layers can be probed at later times after the end of the outburst. We report on the unexpected late-time temperature evolution (≳2000 days after the end of their outbursts) of two NSs in LMXBs, XTE J1701−462 and EXO 0748−676. Although both these sources exhibited very different outbursts (in terms of duration and the average accretion rate), they exhibit an unusually steep decay of ∼7 eV in the observed effective temperature (occurring in a time span of ∼700 days) around ∼2000 days after the end of their outbursts. Furthermore, they both showed an even more unexpected rise of ∼3 eV in temperature (over a time period of ∼500–2000 days) after this steep decay. This rise was significant at the 2. 4σ and 8. 5σ level for XTE J1701−462 and EXO 0748−676, respectively. The physical explanation for such behaviour is unknown and cannot be straightforwardly be explained within the cooling hypothesis. In addition, this observed evolution cannot be well explained by low-level accretion either without invoking many assumptions. We investigate the potential pathways in the theoretical heating and cooling models that could reproduce this unusual behaviour, which so far has been observed in two crust-cooling sources. Such a temperature increase has not been observed in the other NS crust-cooling sources at similarly late times, although it cannot be excluded that this might be a result of the inadequate sampling obtained at such late times.
Enhanced PID: Adaptive Feedforward RBF Neural Network Control of Robot manipulators with an Optimal Distribution of Hidden Nodes

This paper focus on three inherent demerits of adaptive feedback RBFNN control with lattice distribution of hidden nodes: 1) The approximation area of adaptive RBFNN is difficult to be obtained in priori; 2) Only partial persistence of excitation (PE) can be guaranteed; 3) The number of hidden nodes is the exponential growth with the increase of the dimension of the input vectors and the polynomial growth with the increase of the number of the hidden nodes in each channel which is huge especially for the high dimension of inputs of the RBFNN. Adaptive feedforward RBFNN control with lattice distribution of hidden node can improve solve the demerits 1) but just improve demerits 2) and 3) slightly. This paper proposes an adaptive feedforward RBFNN control strategy with an optimal distribution of hidden nodes. It solves the demerits 2) and 3) that the standard PE can be guaranteed and the number of hidden nodes is linear increase with the complexity of the desired state trajectory rather than the exponential growth with the increase of the dimension of the input vectors. In addition, we articulate that PID is the special case of adaptive feedforward RBFNN control for the set points tracking problem and we named the controller is enhanced PID. It is very easy tuning our algorithm which just more complex than PID slightly and the tuning experience of PID can be easily transferred to our scheme. In the case of the controller implemented by digital equipment, the control performance can equal or even better than it in model-based schemes such as computed torque control and feedforward nonlinear control after enough time to learn. Simulations results demonstrate the excellent performance of our scheme. The paper is a significant extension of deterministic learning theory.
How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning

Searching for information about a specific person is an online activity frequently performed by many users. In most cases, users are aided by queries containing a name and sending back to the web search engines for finding their will. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Currently, most solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic approach which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings. This sophisticated and innovative embeddings captures the way people pronounce names in any language and accent. Utilizing the name pronunciation can be helpful for both differentiating and detecting names that sound alike, but are written differently. The proposed approach was demonstrated on a large-scale dataset consisting of 250,000 forenames and evaluated using a machine learning classifier and 7,399 names with their verified synonyms. The performance of the proposed approach was found to be superior to 10 other algorithms evaluated in this study, including well used phonetic and string similarity algorithms, and two recently proposed algorithms. The results obtained suggest that the proposed approach could serve as a useful and valuable tool for solving the similar name suggestion problem.
Large-Scale Co-Simulation of Power Grid and Communication Network Models with Software in the Loop

Power grids are transitioning from an infrastructure model based on reactive electronics towards a smart grid that features complex software stacks with intelligent, pro-active and decentralized control. As the power grid infrastructure becomes a platform for software, so does the need for a reliable roll-out of software updates on a large scale. In order to validate resilient large-scale software roll-out protocols, corresponding test beds are needed, which mirror not only ICT networks, but also include the actual software being deployed, and show the interaction between the power grid and the ICT network during the roll-out, and especially during roll-out failures. In this paper, we describe the design implementation of a large-scale co-simulation test bed that combines ICT and power grid simulators. We pay specific attention to the details of integrating containerized software in the simulation loop.
Eye Gaze Controlled Robotic Arm for Persons with SSMI

BACKGROUND: People with severe speech and motor impairment (SSMI) often uses a technique called eye pointing to communicate with outside world. One of their parents, caretakers or teachers hold a printed board in front of them and by analyzing their eye gaze manually, their intentions are interpreted. This technique is often error prone and time consuming and depends on a single caretaker. OBJECTIVE: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking. The eye gaze tracker is used to develop a video see through based AR (augmented reality) display that controls a robotic device with eye gaze and deployed for a fabric printing task. METHODS: We undertook a user centred design process and separately evaluated the web cam based gaze tracker and the video see through based human robot interaction involving users with SSMI. We also reported a user study on manipulating a robotic arm with webcam based eye gaze tracker. RESULTS: Using our bespoke eye gaze controlled interface, able bodied users can select one of nine regions of screen at a median of less than 2 secs and users with SSMI can do so at a median of 4 secs. Using the eye gaze controlled human-robot AR display, users with SSMI could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs using a COTS eye tracker and at an average time of 2 mins using the webcam based eye gaze tracker. CONCLUSION: The proposed system allows users with SSMI to manipulate physical objects without any dedicated eye gaze tracker. The novelty of the system is in terms of non-invasiveness as earlier work mostly used glass based wearable trackers or head/face tracking but no other earlier work reported use of webcam based eye tracking for controlling robotic arm by users with SSMI.
Characterizing High-Energy $pp$ Collisions at the Large Hadron Collider using Thermal Properties

High-multiplicity $pp$ collisions at the Large Hadron Collider energies have created a new domain of research to look for possible formation of Quark-Gluon Plasma in these events. In this work, we have estimated various thermal properties of the matter formed in high-energy $pp$ collisions, like mean free path ($\Lambda$), isobaric expansivity ($\alpha$), thermal pressure ($\frac{\partial P}{\partial T}$), heat capacity ($C_{\rm V}$) and the degrees of freedom ($\epsilon/T^{4}$) using a thermodynamically consistent Tsallis distribution function, which happens to describe the identified particle spectra very well. Particle species dependent mean free path and isobaric expansivity are studied as a function of final state charged particle multiplicity for $pp$ collisions at $\sqrt{s}$ = 7 TeV. The findings are confronted to the theoretical expectations. The role of degree of non-equilibrium, baryo-chemical potential and temperature on these thermal properties are studied.
DISPERSIVE ESTIMATES FOR THE SCHR¨ODINGER FLOW ON COMPACT SEMISIMPLE GROUPS AND TWO APPLICATIONS

In this note, we prove “scale-invariant” L p -estimates for the Schr¨odinger kernel on compact semisimple groups for major arcs of the time variable and give two applications. The ﬁrst application is to improve the range of exponent for scale-invariant Strichartz estimates on compact semisimple groups. For such a group M of dimension d and rank r , let s be the largest among the numbers 2 d 0 / ( d 0 − r 0 ), where d 0 ,r 0 are respectively the dimension and rank of a simple factor of M . We establish k e it ∆ f k L p ( I × M ) . k f k H d/ 2 − ( d +2) /p ( M ) for p > 2 + 8( s − 1) /sr when r ≥ 2. The second application is to prove some eigenfunction bounds for the Laplace-Beltrami operator on compact semisimple groups. For any eigenfunction f of eigenvalue − λ , we establish k f
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation

Although deep-learning-based methods have markedly improved the performance of speech separation over the past few years, it remains an open question how to integrate multi-channel signals for speech separation. We propose two methods, namely, early-fusion and late-fusion methods, to integrate multi-channel information based on the time-domain audio separation network, which has been proven effective in single-channel speech separation. We also propose channel-sequential-transfer learning, which is a transfer learning framework that applies the parameters trained for a lower-channel network as the initial values of a higher-channel network. For fair comparison, we evaluated our proposed methods using a spatialized version of the wsj0-2mix dataset, which is open-sourced. It was found that our proposed methods can outperform multi-channel deep clustering and improve the performance proportionally to the number of microphones. It was also proven that the performance of the late-fusion method is consistently higher than that of the single-channel method regardless of the angle difference between speakers.
Self-supervised Robust Object Detectors from Partially Labelled datasets

In the object detection task, merging various datasets from similar contexts but with different sets of Objects of Interest (OoI) is an inexpensive way (in terms of labor cost) for crafting a large-scale dataset covering a wide range of objects. Moreover, merging datasets allows us to train one integrated object detector, instead of training several ones, which in turn resulting in the reduction of computational and time costs. However, merging the datasets from similar contexts causes samples with partial labeling as each constituent dataset is originally annotated for its own set of OoI and ignores to annotate those objects that are become interested after merging the datasets. With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets. More specifically, we propose a computationally efficient self-supervised framework to create on-the-fly pseudo-labels for the unlabeled positive instances in the merged dataset in order to train the object detector jointly on both ground truth and pseudo labels. We evaluate our proposed framework for training Yolo on a simulated merged dataset with missing rate $\approx\! 48\%$ using VOC2012 and VOC2007. We empirically show that generalization performance of Yolo trained on both ground truth and the pseudo-labels created by our method is on average $4\%$ higher than the ones trained only with the ground truth labels of the merged dataset.