Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for Interspecific Competition in Natural Populations of the Monarch and Queen Butterflies, Danaus Plexippus and D. Gilippus Berenice in South Central Florida

Daia us plexippus (Linne) and Danauis gilippus berenice (Cramer), the Monarch and Queen butterflies, overlap broadly in their ecological requirements (Brower 1961a). Further evidence of their similarity was indicated by comparative laboratory analyses of egg cannibalism (Brower 1961b), developmental rates, and food consumption (Brower in prep.). Briefly summarized, these investigations showed that in south central Florida the adult butterflies of both are highly attracted to the same flowers from which they obtain nectar and also oviposit on the same plants which are shared as food by their larvae. Moreover, their larvae are highly prone to egg cannibalism and the 2 species eat each other's eggs to approximately the same extent as their own, although the larvae of the Queen are nearly twice as cannibalistic as those of the Monarch. Their rates of development from egg to adult differ by less than 10%, but the Monarch requires more than one anad a half times as much larval food as the Queen to complete its development. Indirect evidence that the species compete in nature was suggested by the fact that there is a periodic change between extensive sympatry and nearly complete allopatry as the aninual migration cycle of the Monarch occurs. Nevertheless, before almost completely vacating the southeastern wintering grounds as summer advances, the Monarch breeds extensively in the same areas as the Queen. That they are actively competing was suggested by the fact that the reduction in numbers of the Monarch coinci(le(l with a build-up of the Queen population, and that durini, the heavy spring breeding of the Monarch, the Queen females for the most part avoided the most common species of Asclepias foodplant but later readily oviposited on it after most of the Monarchs had left (Brower 1961a). This paper will present further evidence of competition based on additional field studies of their breeding populations in south central Florida during the summer of 1960.
The Regulatory Role of Interleukin-1 Receptor Associated Kinase M in Toll-Like Receptor Signaling

Toll-like receptors (TLRs) are one of the major groups of pattern recognition receptors. TLRs are able to recognize the pathogen-associated molecular patterns and transduce their signals through the adaptor molecule MyD88 and members of the IL-1Rassociated kinase family (IRAK1, 2, M and 4). IRAKM was previously known to function as a negative regulator that prevents the dissociation of IRAKs from MyD88, thereby inhibiting downstream signaling. However, we now found that IRAKM was also able to interact with MyD88-IRAK4 to form IRAKM Myddosome to mediate TLR7-induced MEKK3-dependent second wave NFκB activation. As a result, the IRAKM-dependent pathway only induced expression of genes that are not regulated at the posttranscriptional levels (including inhibitory molecules SOCS1, SHIP1, A20 and IκBα), exerting an overall inhibitory effect on inflammatory response. On the other hand, through interaction with IRAK2, IRAKM inhibited TLR7-mediated production of cytokines and chemokines at translational levels. While the TLR-mediated inflammatory response is critical for innate immunity and host defense against infections, uncontrolled inflammation is detrimental to the host, leading to chronic inflammatory diseases. Alcohol-induced liver injury is induced by necrosis of hepatocytes and increased translocation of endotoxin from the intestinal tract
A Novel Hybrid Mitigation Technique against DoS Attacks in Software defined Network with Entropy, SVM and Reinforcement Learning

: Software Defined Network (SDN), also known as a Smart Network, as it performs significant role in regulating and managing number of heterogeneous networks. Tragically, SDN faces a great deal of security issues that may seriously influence the system activities if not appropriately tended to. In the SDN network, there is centralization of the controller and hence any DoS attack will cause the entire system to collapse. The DoS attack on centralized model brings about huge challenge of communication overhead, packet delay and loss of genuine packets. Another challenge is there is a lack of research on finding a common methodology for intelligently evaluating the security of SDN controllers. Thus, the paper contributes by evaluating an intelligent hybrid method for detection and mitigation of DoS attack with Entropy, SVM and Reinforcement Learning with Markovian Process model. The paper proposes a profound reinforcement learning based system, which can intelligently gain proficiency in learning the optimal mitigation policies under various attack scenarios and mitigate the DoS flooding attack in real time. Practical experiments are conducted in the Mininet environment, to defend against a wide range of DoS flooding attacks such as TCP SYN, UDP, and ICMP flooding and proves that the proposed novel hybrid mechanism can be an effective against DoS attacks, causing benign traffic to keep flowing, keeping the network working. The framework also proposes a novel flow based algorithm which can determine the attacker in crucial attack
Reduced Power Consumption in Wireless Sensor Networks with Min (N, T) Policy based M/G/1 Queue in Rayleigh Fading Channel

: The purpose of this study is to reduce the consumption of power at a wireless sensor node in a spectrum sharing environment by employing queuing theory. Spectrum is an expensive and scarce commodity. Spectrum sharing is an efficient way to utilize the under-utilized spectrum. To this effect, we first analyze the effect of number of primary users in a spectrum sharing environment on the average waiting time experienced by packets at any secondary user by utilizing the M/G/1 queuing model. Further, this scenario is extended to a Wireless Sensor Network operating in a spectrum sharing environment where the wireless sensor network is considered to be the secondary user. Due to presence of other users in the shared spectrum, the channel used by the sensor nodes suffers from fading effects. To counter and mitigate the effects of spectrum sharing, we propose using an M/G/1 queue with min (N, T) policy at the sensor node in a WSN. We analyze the power consumed by wireless sensor nodes by adopting an M/G/1 queue with min (N, T) policy by assuming that the wireless channel experiences Rayleigh fading. In this scenario, it is found that both the queuing policy and the constraint imposed on the peak interference power due to the presence of the primary user have a pronounced impact on the consumption of power at the wireless sensor node. The implementation of the Min (N, T) policy M/G/1 queue successfully reduces the average power consumption of a sensor node. The policy suggested in this paper helps reduce the power consumption at the sensor node in a spectrum sharing environment that operates in the presence of signal fading.
Technical Session – Hospitality Tourism and Events Management Table of Contents Oral Session Development through International Events: a case study on Commonwealth Heads of Government Meeting (CHOGM) in Sri Lanka

Oral Session Development through International Events: a case study on Commonwealth Heads of Government Meeting (CHOGM) in Sri Lanka 2013. 1 The Effects of Motivation, Satisfaction and Revisit Intention of Tourists on Ayurvedic Tourist Hotels (With Special Reference to Galle District) . 4 Influence of Sustainable Development in Foreign Policy Creation: Realist Framework . 8 Identifying the potential to develop Nuwara Eliya as a tea tourism destination Demand and supply perspectives . 11 Consumer awareness on sustainable apparel product consumption . 15 A Study on the Tourist Attractions in Colombo City . 18 Identification of potentials to promote gem tourism in Sri Lanka . 21
Conformal predictive distributions: an approach to nonparametric ﬁducial prediction

The subject of this chapter is conformal predictive distributions, which represent the only approach to nonparametric ﬁducial prediction that is available at this time. It starts from reviewing parts of traditional ﬁducial inference that are relevant to conformal predictive distributions and places the latter in the more general context of ﬁducial prediction. Among key desiderata for ﬁducial prediction procedures are their validity and eﬃciency; the ﬁrst is usually attained automatically, and the other one require careful design. We will discuss various formalizations of these desiderata. The requirement to present results of the process of prediction in the form of predictive distributions imposes severe restrictions on the process and is not always possible or even desirable. At the end of the chapter we discuss the wider area of conformal prediction, whose predictions can be interpreted as families of p-values; we no longer insist on the kind of consistency of the p-values implicit in conformal predictive distributions
Comparative Study of the Geomagnetic Activity Effect on foF2 Variation as Defined by the Two Classification Methods at Dakar Station over Solar Cycle Phases

This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods; and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N; Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence () between both classifications is ᆟ. 1% (for the negative value which is observed during the increasing phase) and +16.74% (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to 37% and for the NC at the maximum phase with a percentage of 54.47%. The maximum gap of occurrence () between both classifications is ᆥ. 85% (for the negative value which is observed at maximum phase) and +13.53% (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly 37% for the NC. The maximum gap of occurrence () between both classifications is ᆞ% (for the negative value which is observed during the decreasing phase) and +20.11% (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.
Failure of the MacWilliams Identities

where hwe refers to the Hamming weight enumerator [5]. The same statement of the MacWilliams identities is valid for linear codes over a finite Frobenius ring of size q with respect to the Hamming weight [8]. We are interested in the question of whether there is some version of the MacWilliams identities for other alphabets and other weight functions. In this paper we consider the Lee weight over Zm, the integers modulo m. The Lee weight and the Hamming weight are equal when m “ 2 or m “ 3; thus the MacWilliams identities are valid in those cases. For codes over Z4, it is known from [3] that the Lee weight enumerator of a linear code C over Z4 and its dual are related: lweCKpX,Y q “ 1 |C| lweCpX ` Y,X ́ Y q.
Spin-orbit and spin-lattice dynamics in magnetic nanostructures

We studied the influence of the interplay of spin-orbit interactions and spin-lattice couplings on the dynamics and transport of magnetic structures, such as domain walls and spin currents in alloys. We related these to observations of Spin Seebeck effects and spin pumping. 1. Research Objectives Spin-orbit interactions constitute the microscopic mechanism for many key effects in both pure and applied magnetism, involving coupling of orbital and spin degrees of freedom. Spinorbit effects are also basic to the effects of crystalline anisotropy in magnetic systems and the coupling between magnetism and lattice effects. The interplay between lattice vibrations, quantized as phonons and magnetic degrees of freedom leads to couplings between heat and spin currents which can lead to a new generation of devices, for example spin-current injectors. Our aim is to relate the more microscopic measurements, for example of neutron scattering to nano-magnetic transport as is exploited in novel devices. 2. Research
Aspectos biológicos y poblacionales de Sipha Maydis (Passerini) y Schizaphis Graminum (Rondani) en cebada - Biological and populational aspect of Sipha Maydis (Passerini) y Schizaphis Graminum (Rondani) on barley

The object of this study was to obtain the bioetological parameters of Sipha maydis and Schizaphis graminum on barley, Hordeum vulgare cv. Pampa. The aphids were bred in laboratory conditions at 20±1 °C, 14:10 photophase hours and 50-70 % of relative humidity. The cohorts were obtained from populations of these aphids, on which a daily register of their status changes, number of dead individuals and births once they reached their adult state was performed. Curves of survival per age (lx) and fecundity (mx) were analized, as well as the following vital statistics. Net reproductive rate (Ro); natural intrinsec growth rate (rm); mean generational time (T) and duplicational time (D). In order to compare the rm were obtained together with the standard error by means of «jackknife» procedure. Although the S. graminum has a higher reproductive efficiency, S. maydis has shown relevant survival and fecundity values, which could indicate that this aphid may become a potential peste on cultured gramineous.
Eosinophils and Type 2 Cytokine Signaling in Macrophages Support the Biogenesis of Cold-induced Beige Fat

Brown adipose generates heat via oxidation of fatty acids by a mitochondrial uncoupling protein 1 (UCP1)-dependent process. In addition, a subpopulation of cells within subcutaneous white adipose tissue, known as beige adipocytes, also plays a role in thermogenesis. The biogenesis of beige adipocytes is induced by thermogenic signals, such as chronic cold exposure. Recently, it has been reported that eosinophils, type 2 cytokines of IL-4/13, and alternatively activated macrophages control the thermogenic cycle of beige adipocytes. Alternatively, activated macrophages induce UCP1 beige adipocytes through secretion of catecholamines. These results define the role of type 2 immune responses in the regulation of energy homeostasis.
The First Robust Constraints on the Relationship between Dust-to-gas Ratio and Metallicity in Luminous Star-forming Galaxies at High Redshift

We present rest-optical spectroscopic properties of a sample of four galaxies in the Atacama Large Millimeter/submillimeter Array Hubble Ultra Deep Field (ALMA HUDF). These galaxies span the redshift range and the stellar mass range . They have existing far-infrared and radio measurements of dust-continuum and molecular gas emission from which bolometric star formation rates (SFRs), dust masses, and molecular gas masses have been estimated. We use new H- and K-band near-infrared spectra from the Keck/Multi-object Spectrometer for Infrared Exploration (MOSFIRE) to estimate SFRs from dust-corrected Hα emission (SFR(Hα)) and gas-phase oxygen abundances from the ratio [N ii] /Hα. We find that the dust-corrected SFR(Hα) is systematically lower than the bolometric SFR by a factor of several, and measure gas-phase oxygen abundances in a narrow range, ( ). Relative to a large z ∼ 2 comparison sample from the MOSFIRE Deep Evolution Field (MOSDEF) survey, the ALMA HUDF galaxies scatter roughly symmetrically around the best-fit linear mass–metallicity relation, providing tentative evidence for a flattening in the SFR dependence of metallicity at high stellar mass. Combining oxygen abundances with estimates of dust and molecular gas masses, we show that there is no significant evolution in the normalization of the dust-to-gas ratio (DGR) versus metallicity relation from z ∼ 0 to z ∼ 2. This result is consistent with some semi-analytic models and cosmological simulations describing the evolution of dust in galaxies. Tracing the actual form of the DGR versus metallicity relation at high redshift now requires combined measurements of dust, gas, and metallicity over a significantly wider range in metallicity.
End-to-End Speech Recognition and Disfluency Removal

Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the task of end-to-end speech recognition and disfluency removal. We specifically explore whether it is possible to train an ASR model to directly map disfluent speech into fluent transcripts, without relying on a separate disfluency detection model. We show that end-to-end models do learn to directly generate fluent transcripts; however, their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. We also propose two new metrics for evaluating integrated ASR and disfluency removal models. The findings of this paper can serve as a benchmark for further research on the task of end-to-end speech recognition and disfluency removal in the future.
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network

Recently, smart cities, healthcare system, and smart vehicles have raised challenges on the capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for the devices in hotspots area. Multiaccess edge computing (MEC) can enhance the ability of emerging resource-intensive IoT applications and has attracted much attention. However, due to the time-varying network environments, as well as the heterogeneous resources of network devices, it is hard to achieve stable, reliable, and real-time interactions between edge devices and their serving edge servers, especially in the 5G ultradense network (UDN) scenarios. Ultradense edge computing (UDEC) has the potential to fill this gap, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: 1) efficient utilization of multiple 5G resources (e.g., computation, communication, storage, and service resources); 2) low overhead offloading decision making and resource allocation strategies; and 3) privacy and security protection schemes. Thus, we first propose an intelligent UDEC (I-UDEC) framework, which integrates blockchain and artificial intelligence (AI) into 5G UDEC networks. Then, in order to achieve real-time and low overhead computation offloading decisions and resource allocation strategies, we design a novel two-timescale deep reinforcement learning (2Ts-DRL) approach, consisting of a fast-timescale and a slow-timescale learning process, respectively. The primary objective is to minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation, and service caching placement. We also leverage federated learning (FL) to train the 2Ts-DRL model in a distributed manner, aiming to protect the edge devices’ data privacy. Simulation results corroborate the effectiveness of both the 2Ts-DRL and FL in the I-UDEC framework and prove that our proposed algorithm can reduce task execution time up to 31.87%.
Physical approach to the marginalization of LIGO calibration uncertainties

The data from ground based gravitational-wave detectors such as Advanced LIGO and Virgo must be calibrated to convert the digital output of photodetectors into a relative displacement of the test masses in the detectors, producing the quantity of interest for inference of astrophysical gravitational wave sources. Both statistical uncertainties and systematic errors are associated with the calibration process, which would in turn affect the analysis of detected sources, if not accounted for. Currently, source characterization algorithms either entirely neglect the possibility of calibration uncertainties or account for them in a way that does not use knowledge of the calibration process itself. We present physiCal, a new approach to account for calibration errors during the source characterization step, which directly uses all the information available about the instrument calibration process. Rather than modeling the overall detector's response function, we consider the individual components that contribute to the response. We implement this method and apply it to the compact binaries detected by LIGO and Virgo during the second observation run, as well as to simulated binary neutron stars for which the sky position and distance are known exactly. We find that the physiCal model performs as well as the method currently used within the LIGO-Virgo collaboration, but additionally it enables improving the measurement of specific components of the instrument control through astrophysical calibration.
SQuARE: Semantics-based Question Answering and Reasoning Engine

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) and from its early days, it has received significant attention through question answering (QA) tasks. We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework. The framework is based on the denotational semantics approach widely used in programming language research. In our framework, valuation function maps syntax tree of the text to its commonsense meaning represented using basic knowledge primitives (the semantic algebra) coded using answer set programming (ASP). We illustrate an application of this framework by using VerbNet primitives as our semantic algebra and a novel algorithm based on partial tree matching that generates an answer set program that represents the knowledge in the text. A question posed against that text is converted into an ASP query using the same framework and executed using the s(CASP) goal-directed ASP system. Our approach is based purely on (commonsense) reasoning. SQuARE achieves 100% accuracy on all the five datasets of bAbI QA tasks that we have tested. The significance of our work is that, unlike other machine learning based approaches, ours is based on "understanding" the text and does not require any training. SQuARE can also generate an explanation for an answer while maintaining high accuracy.