Miracle-dz/UniFAR-FTUs huggingface dataset:
Task specialization across research careers

Scientific careers are conceived as one unique pathway which scientists must follow to succeed. We report the diversity of profiles scientists exhibit based on their contributorship and look into biases in their career trajectory. We use Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals representing 347,136 distinct authors and their associated contribution statements. This model is used to predict the contributions of 222,925 authors in 6,236,239 publications, and apply a robust archetypal analysis to profile scientists by career stage. We divide scientific careers into four stages: junior, early-career, mid-career and late-career. Three scientific archetypes are found throughout the four career stages: leader, specialized, and supporting. All three archetypes are encountered for the early- and mid-career stages, whereas for junior and late-career stages only two archetypes are found. Scientists assigned to the leader and specialized archetypes tend to have longer careers than researchers who belong to the supporting archetype. There is consistent gender bias at all stages: the majority of male scientists belong to the leader archetype, while the larger proportion of women belong to the specialized archetype, especially for early and mid-career researchers.
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

The ability to base current computations on memories from the past is critical for many cognitive tasks such as story understanding. Hebbian-type synaptic plasticity is believed to underlie the retention of memories over medium and long time scales in the brain. However, it is unclear how such plasticity processes are integrated with computations in cortical networks. Here, we propose Hebbian Memory Networks (H-Mems), a simple neural network model that is built around a core hetero-associative network subject to Hebbian plasticity. We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations. H-Mems can one-shot memorize associations between stimulus pairs and use these associations for decisions later on. Furthermore, they can solve demanding question-answering tasks on synthetic stories. Our study shows that neural network models are able to enrich their computations with memories through simple Hebbian plasticity processes.
The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
Pollution status of a tropical forest river using aquatic insects as indicators

Aquatic insects inhabiting Okhuo River, in a tropical forest near Benin City, Southern Nigerian, were studied between January and December 2006 to determine the taxa composition, diversity, EPT index, relative proportions of the various groups and hence the pollution level of the waterbody. Three stations were selected and sampled monthly using the kick sampling technique. A total of 3235 individual aquatic insects belonging to 24 taxa distributed among 23 genera in six orders were collected. The insect orders occurred in the following order of dominance: Ephemeroptera > Odonata > Coleoptera > Diptera > Plecoptera > Tricoptera. Based on the diverse composition of the community dominated by organisms intolerant of organic enrichment, and the high diversity and EPT index, water quality in Okhuo River is not significantly degraded. Community composition varies seasonally, with a trend toward a declining proportion during the rainy season and increasing proportions during the dry season. Aquatic insect composition in Okhuo River compares favourably with those in similar, relatively undisturbed forest streams and rivers in Nigeria, but the diversity and proportional distribution of taxa vary considerably between streams.
Building Decentralized Fog Computing-Based Smart Parking Systems: From Deterministic Propagation Modeling to Practical Deployment

The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the simulation stage and then deploy a system that can provide fast responses and decentralized services.
Model Study of Design Possibilities for Optimizing the Microwave Antenna

A finite-element mathematical model of a microwave mirror parabolic antenna is considered, taking into account the influence of its structural elements (the reflecting mirror, the irradiator, and its mounting elements) on the characteristics radiation. Using this model, we evaluated of the design possibilities of optimizing the microwave antenna to minimize the influence of external the electromagnetic perturbations of the irradiator and its mounting elements on the characteristics radiation was carried out.
Cyber in War: Assessing the Strategic, Tactical, and Operational Utility of Military Cyber Operations

The study analyzes the use of cyber capabilities in war and conflict situations. The research question is: What good is cyber in war? What is the utility of military cyber operations in conflict situations and what obstacles exist? The paper analyzes a small set of cases where cyber capabilities have been used for military purposes. Using the ‘three levels of warfare’ heuristic, the study outlines the potentials and operational restrictions of military cyber operations. The analysis proposes a set of variables and hypotheses, such as the timing of use of cyber capabilities and the operational complexity of a cyber operation, for further theory building.
Immediate and long-term effects of transcranial direct-current stimulation in the mouse primary somatosensory cortex

Transcranial direct-current stimulation (tDCS) is a non-invasive brain stimulation technique consisting in the application of weak electric currents on the scalp. Although previous studies have demonstrated the clinical value of tDCS for modulating sensory, motor, and cognitive functions, there are still huge gaps in the knowledge of the underlying physiological mechanisms. To define the immediate impact as well as the after-effects of tDCS on sensory processing, we first performed electrophysiological recordings in primary somatosensory cortex (S1) of alert mice during and after administration of S1-tDCS, and followed up with immunohistochemical analysis of the stimulated brain regions. During the application of cathodal and anodal transcranial currents we observed polarity-specific bidirectional changes in the N1 component of the sensory-evoked potentials (SEPs) and associated gamma oscillations. Regarding the long-term effects observed after 20 min of tDCS, cathodal stimulation produced significant after-effects including a decreased SEP amplitude for up to 30 min, a power reduction in the 20-80 Hz range and a decrease in gamma event related synchronization (ERS). In contrast, no significant long-term changes in SEP amplitude or power analysis were observed after anodal stimulation except for a significant increase in gamma ERS after tDCS cessation. The polarity-specific differences of these long-term effects were corroborated by immunohistochemical analysis, which revealed an unbalance of GAD 65-67 immunoreactivity between the stimulated vs. non-stimulated S1 region only after cathodal tDCS. These results highlight the differences between immediate and long-term effects of tDCS, as well as the asymmetric long-term changes induced by anodal and cathodal stimulation. Significance Statement Here we provide a first glimpse at the immediate and long-term impact of tDCS on neural processing in alert animals. The obtained results highlight the complexity of tDCS-associated effects, which include both bidirectional as well as asymmetrical modulation depending on the polarity of the stimulation. This asymmetry suggests the implication of different mechanisms underlying the long-term effects induced by anodal and cathodal transcranial currents. Identifying and defining these effects and its associated mechanisms is crucial to help design effective protocols for clinical applications.
Load balance algorithm for an OPSquare datacenter network under real application traffic

To cope with the dynamic datacenter (DC) traffic matrix generated by multiple applications, load balancing algorithms can be deployed to improve the datacenter network (DCN) performance. OPSquare, as a promising optical DCN architecture, adopts fast optical switches to provide high bandwidth, low latency, large scalability, and cost and energy efficiency. Current load balancing algorithms are not optimal for improving the network performance of an OPSquare DCN. To optimize the network performance of OPSquare, a dedicated load balancing algorithm considering the peculiarity of OPSquare should be addressed. Moreover, network virtualization in today’s data DC creates new heterogeneous traffic patterns different from what so far have been reported in the literature. A synthetic virtualized DC traffic model is needed to properly evaluate the performance of the load balancing algorithms. In this paper, we provide a synthetic traffic model by capturing the traffic traces of the virtualized DC applications running in a DC. The captured traffic model is used in the simulator to quantitatively evaluate the network performance of an OPSquare DCN. Then we propose and evaluate the lowest path buffer (LPB) algorithm to optimize the OPSquare DCN performance. LPB considers the buffer occupation in the whole path (from the source to the destination) rather than the local buffer occupation. The network performance improvements of LPB are demonstrated under different central controller update periods, traffic destination distributions, and network sizes. Compared with the round-robin, DRILL, and LocalFlow load balancing algorithms, LPB could achieve 23.7%, 46%, and 32.1% less latency, respectively. Besides, LPB provides lower packet loss in comparison with round-robin, DRILL, and LocalFlow.
Device-Aware Test for Emerging Memories: Enabling Your Test Program for DPPB Level

This paper introduces a new test approach: device-aware test (DAT) for emerging memory technologies such as MRAM, RRAM, and PCM. The DAT approach enables accurate models of device defects to obtain realistic fault models, which are used to develop high-quality and optimized test solutions. This is demonstrated by an application of DAT to pinhole defects in STT-MRAMs and forming defects in RRAMs.
Computing the k-Dominant Skyline Efficiently in the Dynamic Environments

Most information these days is being put away in the system, in this manner, there are considerable need of a proficient methodology on finding significant data from the information. One of the advancements called horizon question is being utilized in numerous applications, for example, multi-criteria basic leadership and client inclination inquiries. In any case, as the quantity of measurement increment, the likelihood that a point dominates another point diminishes. Subsequently, there are a great deal of horizon focuses will be recovered in a high dimensional dataset. Consequently, the idea of k-predominant horizons was proposed to manage this issue. In genuine application, information is continued evolving progressively, along these lines a productive methodology on figuring dynamic information is vital required. In this paper, a methodology of keeping up k-prevailing horizon in a powerful domain is proposed. By keeping some data of the first k-prevailing horizons, we can figure the changing horizon efficiently. In addition, a lot of investigations is performed to show the productivity of our methodology.
Dietary Recommendations for Familial Hypercholesterolaemia: an Evidence-Free Zone

We have evaluated dietary recommendations for people diagnosed with familial hypercholesterolaemia (FH), a genetic condition in which increased low-density lipoprotein cholesterol (LDL-C) is associated with an increased risk for coronary heart disease (CHD). Recommendations for FH individuals have emphasised a low saturated fat, low cholesterol diet to reduce their LDL-C levels. The basis of this recommendation is the ‘diet-heart hypothesis’, which postulates that consumption of food rich in saturated fat increases serum cholesterol levels, which increases risk of CHD. We have challenged the rationale for FH dietary recommendations based on the absence of support for the diet-heart hypothesis, and the lack of evidence that a low saturated fat, low cholesterol diet reduces coronary events in FH individuals. As an alternative approach, we have summarised research which has shown that the subset of FH individuals that develop CHD exhibit risk factors associated with an insulin-resistant phenotype (elevated triglycerides, blood glucose, haemoglobin A1c (HbA1c), obesity, hyperinsulinaemia, high‐sensitivity C reactive protein, hypertension) or increased susceptibility to develop coagulopathy. The insulin-resistant phenotype, also referred to as the metabolic syndrome, manifests as carbohydrate intolerance, which is most effectively managed by a low carbohydrate diet (LCD). Therefore, we propose that FH individuals with signs of insulin resistance should be made aware of the benefits of an LCD. Our assessment of the literature provides the rationale for clinical trials to be conducted to determine if an LCD would prove to be effective in reducing the incidence of coronary events in FH individuals which exhibit an insulin-resistant phenotype or hypercoagulation risk.
Citizen Satisfaction With Mandatory E-Government Services: A Conceptual Framework and an Empirical Validation

Few studies have examined the antecedents of citizen satisfaction in relation to mandatory e-government services pertaining to promising sectors such as education. This study was thus aimed at developing and empirically test a model based on a blend of information systems success models, in conjunction with trust theory. In Saudi Arabia, the Ministry of Education has launched a mandatory e-government service intended to assist high school graduates in their university academic admission process. To test the model, a questionnaire was constructed and data from 780 university students were collected. The findings show that the perceived usefulness of and trust in e-government mediated the indirect effect of both system quality and information quality on citizen satisfaction. System quality exhibited the strongest such overall total effect on citizen satisfaction. Policymakers can take advantage of the findings in adjusting the resources required to increase citizen satisfaction with mandatory services in education.
Brief, Protracted or Flexible? Quantitative Versus Qualitative Classification of Spawning for Fish in the Murray-Darling Basin

Abstract Knowledge of the duration of the spawning period for individual fish species is central to management interventions, including timing of environmental water allocations. A quantitative approach to the classification of fish species into spawning categories is provided based on a dataset resulting from five years of intensive sampling for larvae in the lower River Murray. The six most abundant native fish species were first classified empirically into brief and protracted spawners based on three different ‘thresholds’ of two, three and four months. A statistical classification was then implemented using receiver operating characteristic (ROC) analysis. A threshold of four months was able tominimise the proportion of incorrect classifications, thereby classifying correctly the spawning categories for the six species. Unspecked hardyhead, carp gudgeons, flathead gudgeon and Australian smelt were classified as flexible spawners, whereas Murray cod and Murray River rainbowfish were classified as brief spawners. Attention is drawn to the fact that a species classified as flexible spawner would actually result from a combination of brief and protracted spawner cases, leading to a ‘Sorites paradox’. This would be ultimately reconcilable by group consensus aided by quantitative approaches, such as the one provided in the present study.
Phylogenetic significance of the kinethmoid‐associated Y‐shaped ligament and long intercostal ligaments in the Cypriniformes (Actinopterygii: Ostariophysi)

Liao, T.Y. & Kullander, S.O. (2012). Phylogenetic significance of the kinethmoid‐associated Y‐shaped ligament and long intercostal ligaments in the Cypriniformes (Actinopterygii: Ostariophysi). —Zoologica Scripta, 42, 71–87.
Impact of Acidified Sodium Chlorite and Enzyme Treatment on the Microbial Load and Energy Bioavailability of Feedstuffs

The presence of microbial load and high fiber content in various non conventional feedstuffs limit their utilization in poultry feed. In the present study, the feedstuffs were treated with acidified sodium chlorite (ASC) and its impact on the microbial load and metabolizable energy availability was assessed in the chickens. The effect of supplementation of feed grade enzyme was also evaluated either alone or in combination with ASC treatment. ASC was prepared by adding citric acid to an aqueous solution of sodium chlorite (625 g/liter). The sanitizing effect of ASC was assessed at 0, 100, 250 and 500 ppm levels in the meat cum bone meal (MBM) and sunflower meal (SFM), while its impact on the energy bioavailability from SFM and de-oiled rice bran (DORB) was assessed at 0, 100 and 250 ppm levels with (0.3 g/kg) and without exogenous enzyme supplementation. The results revealed that ASC treatment was effective in reducing the microbial load in MBM and SFM. Regarding the metabolizable energy availability, ASC treatment produced appreciable improvement both in SFM and DORB, while enzyme supplementation was effective only with SFM. It may be concluded that ASC treatment has a sanitizing effect and improves the energy bioavailability from feed ingredients like SFM and DORB in the chicken.
GeSe Evanescent Field Saturable Absorber for Mode-Locking in a Thulium/Holmium Fiber Laser

A mode-locked Thulium-Holmium doped fiber laser (THDFL) with a Germanium Selenide (GeSe) saturable absorber (SA) is demonstrated for operation in the <inline-formula> <tex-math notation="LaTeX">$2.0~\mu \text{m}$ </tex-math></inline-formula> wavelength region. The SA device is fabricated by drop-casting the GeSe onto an arc-shaped fiber which is then incorporated into the THDFL to induce mode-locked pulses. Stable mode-locking is attained at 1908.78 nm, with pulse duration of 1.67 ps and output power of 2.74 mW at a maximum pump power of 476 mW. The results of this work show that the GeSe can be a viable alternative for ultrafast photonics applications in the <inline-formula> <tex-math notation="LaTeX">$2.0~\mu \text{m}$ </tex-math></inline-formula>. region.
Efficient Digital Predistortion Using Sparse Neural Network

This paper proposes an efficient neural-network-based digital predistortion (DPD), named as envelope time-delay neural network (ETDNN) DPD. The method complies with the physical characteristics of radio-frequency (RF) power amplifiers (PAs) and uses a more compact DPD model than the conventional neural-network-based DPD. Additionally, a structured pruning technique is presented and used to reduce the computational complexity. It is shown that the resulting ETDNN obtained after applying pruning becomes so sparse that its complexity is comparable to that of conventional DPDs such as memory polynomial(MP) and generalized memory polynomial (GMP), while the degradation in performance due to the pruning is negligible. In an experiment on a 3.5-GHz GaN Doherty power amplifier (PA), our method with the proposed pruning had only one-thirtieth the computational complexity of the conventional neural-network-based DPD for the same adjacent channel leakage ratio (ACLR). Moreover, compared with memory-polynomial-based digital predistortion, our method with the proposed pruning achieved a 7-dB improvement in ACLR, despite it having lower computational complexity.