Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised Semantic Role Labeling for Brazilian Portuguese

Semantic Role Labeling (SRL) is a natural language processing task that detects the arguments of predicates (usually verbs) and their semantic roles. Such roles characterize semantic relationships between an event and its participants, as who did what to whom, where, when and how, which is very useful to improve a wide range of tasks, such as information extraction and plagiarism dectection to name a few. Commonly, a supervised classifier is trained over large English annotated resources in order to perform the prediction of unlabeled sentences. However, most part of non-English languages suffers from scarcity of annotated data, as the labeling process is expensive, time consuming and requires the efforts of human annotators. Although such limitation makes harder the training of supervised methods for those languages, it indicates an appropriate scenario to apply semi-supervised learning (SSL) methods, which are able to learn not only from labeled data, but also from the unlabeled ones. In this article, we investigate SSL methods in the classification of semantic roles for the Brazilian Portuguese, a relatively resource-poor language. Specifically, a representative set of SSL methods based on low density separation, graphs and self-training are considered. Experiments on the PropBank-br, a Brazilian Portuguese corpus built with text from Brazilian newspapers, were performed varying the number of labeled arguments. Additionaly, the SSL methods were compared against state-of-the-art SRL methods. The results demonstrated that self-training heuristic outperforms other SSL and supervised methods, even when the latter are trained on a high number of labeled arguments.
Grüneisen-stress-induced ablation of biological tissue

The objective of biomedical applications of lasers is frequently to remove tissue in a controlled manner. However, for ablation induced by thermal- or photo-decomposition, damage to surrounding tissue may be excessive in some instances. Tissue can also be ablated by a hydrodynamic process referred to as front surface spallation, in which a thin layer next to a free surface is heated to levels, below vaporization but, so rapidly that it cannot undergo thermal expansion during laser heating. This generates a stress pulse, which propagates away from the heated region, with an initial amplitude that can be calculated using the Gruneisen coefficient. As the pulse reflects from the free surface, a tensile tail can develop of sufficient amplitude, exceeding the material strength, that a layer will be spalled off, taking much of the laser-deposited energy with it. Because tissue is generally a low strength material, this process has the potential of producing controlled ablation with reduced damage to the remaining tissue. However, to achieve these conditions, the laser pulse length, absorption depth and fluence must be properly tailored. This paper presents hydrodynamic calculations and analytical modeling relating to both stress- and thermal-induced ablation as a function of laser and tissue properties to illustrate the potential benefits of stress induced ablation. Also, guidance is given for tailoring the exposure parameters to enhance front surface spallation.
Gas Sensing Studies of an n-n Hetero-Junction Array Based on SnO2 and ZnO Composites

A composite metal oxide semiconductor (MOS) sensor array based on tin dioxide (SNO2) and zinc oxide (ZnO) has been fabricated using a straight forward mechanical mixing method. The array was characterized using X-ray photoelectron spectroscopy, scanning electron microscopy, Raman spectroscopy and X-ray diffraction. The array was evaluated against a number of environmentally important reducing and oxidizing gases across a range of operating temperatures (300–500 °C). The highest response achieved was against 100 ppm ethanol by the 50 wt% ZnO–50 wt% SnO2 device, which exhibited a response of 109.1, a 4.5-fold increase with respect to the pure SnO2 counterpart (which displayed a response of 24.4) and a 12.3-fold enhancement with respect to the pure ZnO counterpart (which was associated with a response of 8.9), towards the same concentration of the analyte. Cross sensitivity studies were also carried out against a variety of reducing gases at an operating temperature of 300 °C. The sensors array showed selectivity towards ethanol. The enhanced behaviour of the mixed oxide materials was influenced by junction effects, composition, the packing structure and the device microstructure. The results show that it is possible to tune the sensitivity and selectivity of a composite sensor, through a simple change in the composition of the composite.
Comparison and Analysis between Distinguish Clustering Protocols of Wireless Sensor Networks (A Survey)

as a finest having rules i.e. energy (power) validation from every sensor hub is comprised the battery or energy device with a restricted energy (power) provide to job. Likewise, not easy task to replace the battery in different environment there may be very rough or exhaustive in a few different situations. On the other side, the wireless sensor network says that there may be enough energy to complete the task or can gather the information easily. There are so many clustering protocol to maximize the life cycle of network in WSN. This will be a typical to task to create the mind relax system that can help to collect the data and as well as less energy usage. In this paper, focus on different clustering protocols with different parameters that suggest which one protocol is better for the nodes into hostile area & further to improvement of energy usage should be less.
Demand Shocks as Productivity Shocks

We provide a macroeconomic model where demand for goods has a productive role. A search friction prevents perfect matching between potential customers and producers, and larger demand induces more search, which in turn increases output in the economy. Consequently, when viewed through the lens of a standard neoclassical aggregate production function, an increase in demand will appear as an increase in the Solow residual. We estimate the model using standard Bayesian techniques, allowing for business cycles being driven by both preference shocks – which we interpret as shocks to demand – and true technology shocks. Technology shocks account for less than 18% of the fluctuations in output and the measured Solow residual. Our model also provides a novel theory for important macroeconomic variables such as the relative price of consumption and investment, Tobin’s Q, and capacity utilization.
Hyperactivity of Anterior Cingulate Cortex Areas 24a/24b Drives Chronic Pain-Induced Anxiodepressive-like Consequences

Pain associates both sensory and emotional aversive components, and often leads to anxiety and depression when it becomes chronic. Here, we characterized, in a mouse model, the long-term development of these sensory and aversive components as well as anxiodepressive-like consequences of neuropathic pain and determined their electrophysiological impact on the anterior cingulate cortex (ACC, cortical areas 24a/24b). We show that these symptoms of neuropathic pain evolve and recover in different time courses following nerve injury in male mice. In vivo electrophysiological recordings evidence an increased firing rate and bursting activity within the ACC when anxiodepressive-like consequences developed, and this hyperactivity persists beyond the period of mechanical hypersensitivity. Whole-cell patch-clamp recordings also support ACC hyperactivity, as shown by increased excitatory postsynaptic transmission and contribution of NMDA receptors. Optogenetic inhibition of the ACC hyperactivity was sufficient to alleviate the aversive and anxiodepressive-like consequences of neuropathic pain, indicating that these consequences are underpinned by ACC hyperactivity. SIGNIFICANCE STATEMENT Chronic pain is frequently comorbid with mood disorders, such as anxiety and depression. It has been shown that it is possible to model this comorbidity in animal models by taking into consideration the time factor. In this study, we aimed at determining the dynamic of different components and consequences of chronic pain, and correlated them with electrophysiological alterations. By combining electrophysiological, optogenetic, and behavioral analyses in a mouse model of neuropathic pain, we show that the mechanical hypersensitivity, ongoing pain, anxiodepressive consequences, and their recoveries do not necessarily exhibit temporal synchrony during chronic pain processing, and that the hyperactivity of the anterior cingulate cortex is essential for driving the emotional impact of neuropathic pain.
Impact of native and non-native aquatic plants on methane emission and phytoplankton growth

Freshwater plants affect the ecosystem functioning of shallow aquatic ecosystems. However, because native plants are threatened by environmental change such as eutrophication, global warming and biological invasions, continued ecosystem functioning may be at risk. In this study, we explored how the growth of native and non-native plant species in eutrophic, warm conditions impacts two plant ecosystem functions: regulation of phytoplankton growth and methane emission. We expected that plants would inhibit phytoplankton growth, while for methane emission both inhibition and stimulation are possible. We conducted an outdoor experiment using monocultures of four native and four non-native freshwater plant species planted at three different densities, as well as a no-plant control. Monocultures of each species were planted in 65 L mesocosms and after three weeks of acclimatisation each mesocosm was inoculated with phytoplankton. Subsequently, we added nutrients twice a week for eight weeks, before harvesting the plant biomass. During these eight weeks, we measured chlorophyll-a concentration thirteen times and the diffusive methane emissions once after four weeks. The mesocosms amplified the temperature of a warm summer so that plants were exposed to higher-than-average temperatures. We found that five plant species lost biomass, two species increased their biomass only at the highest initial plant density (native Myriophyllum spicatum and non-native Lagarosiphon major) and a single species increased its biomass at all densities (on average 14 times its initial mass; amphibious non-native Myriophyllum aquaticum). Overall, the mean biomass change of non-natives was positive, whereas that of natives was negative. This difference in biomass change between native and non-native plants did not relate to overall differences in phytoplankton mass or diffusive methane emissions. In mesocosms where submerged plant species gained biomass, chlorophyll-a concentration was lower than in the noplant control and mesocosms with biomass loss. Diffusive methane emissions were highest in mesocosms where plants lost considerable biomass, likely because it increased substrate availability for methanogenesis. However, mesocosms where plant biomass increased had emissions similar to the no-plant control, hence we found no inhibitory effects of plant presence on diffusive methane emission. We conclude that plant growth in eutrophic, warm conditions varies strongly with plant identity. Our results furthermore suggest that plant identity determines whether the replacement of native by non-native freshwater plants will alter ecosystem functions such as regulation of phytoplankton growth and methane emission.
Ensembles based on the Rich-Club and how to use them to build soft-communities

Ensembles of networks are used as null-models to discriminate network structures. We present an efficient algorithm, based on the maximal entropy method to generate network ensembles defined by the degree sequence and the rich-club coefficient. The method is applicable for unweighted, undirected networks. The ensembles are used to generate correlated and uncorrelated null--models of a real networks. These ensembles can be used to define the partition of a network into soft communities.
A three-dimensional microfabrication system for biodegradable polymers with high resolution and biocompatibility

Biodegradable polylactide polymers are already widely used in medicine. While further applications are eagerly awaited, progress has been impeded by a lack of appropriate processing methods. Our group attempted to construct a novel three-dimensional microfabrication system for the free processing of three-dimensional micro-level forms. The system processed the polymers with extremely high precision using a fine nozzle. An evaluation of one of the piled-up layers of the extruded lines revealed lateral and depth resolutions of 40 µm and 45 µm, respectively. Micro-pipes, micro-bends and micro-coil springs were fabricated in less than 15 min by stacking up melted polymers from a nozzle. A batch process was adopted for the supply of the materials in order to eliminate the use of toxic solvents, one of the necessary evils with earlier fabrication techniques. Microstructures fabricated by our system and by a conventional method were tested in tensile strength tests to compare the mechanical strengths. The biocompatibility of a newly fabricated structure was tested by cultivating a cell line (PC12) in a small poly(lactic acid) (PLA) vessel with a transparent base, then comparing the morphology and growth with those of the same PC12 line cultivated by a standard vessel.
Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming

In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal controller with convergence analysis in terms of cost function and control law. In order to carry out the iterative algorithm, a neural network is constructed first to identify the unknown controlled system. Then, based on the learned system model, two other neural networks are employed as parametric structures to facilitate the implementation of the iterative algorithm, which aims at approximating at each iteration the cost function and its derivatives and the control law, respectively. Finally, a simulation example is provided to verify the effectiveness of the proposed optimal control approach. Note to Practitioners-The increasing complexity of the real-world industry processes inevitably leads to the occurrence of nonlinearity and high dimensions, and their mathematical models are often difficult to build. How to design the optimal controller for nonlinear systems without the requirement of knowing the explicit model has become one of the main foci of control practitioners. However, this problem cannot be handled by only relying on the traditional dynamic programming technique because of the "curse of dimensionality". To make things worse, the backward direction of solving process of dynamic programming precludes its wide application in practice. Therefore, in this paper, the iterative adaptive dynamic programming algorithm is proposed to deal with the optimal control problem for a class of unknown nonlinear systems forward-in-time. Moreover, the detailed implementation of the iterative ADP algorithm through the globalized dual heuristic programming technique is also presented by using neural networks. Finally, the effectiveness of the control strategy is illustrated via simulation study.
A knowledge level software engineering methodology for agent oriented programming

Our goal in this paper is to introduce and motivate a methodology, called \emph{Tropos}, for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all the related mentalistic notions (for instance: beliefs, goals, actions and plans) are used in all phases of software development, from the early analysis down to the actual implementation. Second, Tropos covers also the very early phases of requirements analysis, thus allowing for a deeper understanding of the environment where the software must operate, and of the kind of interactions that should occur between software and human agents. The methodology is illustrated with the help of a case study.
Progressive Brain Atrophy Despite Persistent Viral Suppression in HIV Patients Older Than 60 Years

Background: Current HIV treatments are successful at suppressing plasma HIV RNA to undetectable levels for most adherent patients. Yet, emerging evidence suggests that viral suppression will inadequately control inflammation and mitigate risk for progressive brain injury. We sought to quantify differences in longitudinal brain atrophy rates among older virally suppressed HIV-infected participants compared with that of healthy aging participants. Methods: We examined longitudinal structural brain magnetic resonance imaging atrophy rates using region of interest assessments and voxel-wise tensor-based morphometry in HIV-infected participants older than 60 years (n = 38) compared with age-matched HIV-uninfected healthy and cognitively normal controls (n = 24). Results: The mean age of participants was 63 years, the mean estimated duration of infection was 21 years, and the median duration of documented viral suppression was 3.2 years. Average proximal and nadir CD4 counts were 550 and 166, respectively; 15/38 (39%) met criteria for HIV-associated neurocognitive disorder. In models adjusting for age and sex, HIV serostatus was associated with more rapid average annualized rates of atrophy in the cerebellum (0.42% vs. 0.02%, P = 0.016), caudate (0.74% vs. 0.03%, P = 0.012), frontal lobe (0.48% vs. 0.01%, P = 0.034), total cortical gray matter (0.65% vs. 0.16%, P = 0.027), brainstem (0.31% vs. 0.01%, P = 0.026), and pallidum (0.73% vs. 0.39%, P = 0.046). Among those with HIV, atrophy rates did not differ statistically by cognitive status. Conclusions: Despite persistent control of plasma viremia, these older HIV-infected participants demonstrate more rapid progressive brain atrophy when compared with healthy aging. Either HIV or other factors that differ between older HIV-infected participants and healthy controls could be responsible for these differences.
Teratozoospermia and asthenozoospermia are associated with specific epigenetic signatures

Semen analysis is commonly used as a tool to assess the fertility potential of a male, despite its relatively low predictive power. In this study, we have assessed associations between semen analysis findings (low count, low motility, low viability, poor sperm penetration assay results, poor morphology, and increased DNA damage) and DNA methylation patterns in mature spermatozoa. DNA methylation patterns in the mature spermatozoa are thought to be indicative of patterns in the adult germline stem cells and may offer insight into potential perturbations to cellular pathways involved in spermatogenesis. In this study, sperm DNA methylation at >480,000 CpGs was assessed in 94 men using the Illumina 450k HumanMethylation Array and compared to standard measures of sperm quality. We did not identify any global changes to methylation profiles that were associated with reduced semen parameters. Similarly, we found no significant difference in methylation variability that was associated with any abnormal semen analysis parameter, although sperm displaying abnormal parameters tended to have an increased coefficient of variability, suggesting that, in some samples, this may be a contributing factor. Analysis of methylation at single CpGs and genomic regions did identify associations for low viability and low motility, and to a smaller extent, low count. Interestingly, based on GO Term analysis, differentially methylated regions associated with low viability were over‐represented in regions important in meiosis, spermatogenesis, and genomic imprinting. These results suggest that while there are not global alterations to the sperm methylome associated with semen abnormalites, some viability associated regional alterations do exist that may be indicative of perturbed cellular pathways during spermatogenesis.
Time-varying behavior of long-run among stock markets in Western Europe

This paper aims to investigate the potential time-varying behavior of long-run stock market relationship in Western Europe Markets (DAX, FTSE100 and CAC40). To do this we apply the Engle-Granger methodology with and no structural breaks. It is shown that indices from these three markets are cointegrated and the change in long-term relationship between them is more unstable for DAX/FTSE100. Also we identified that there is an opposite relationship in long-term changes in these markets, i.e., when the relationship between DAX/CAC40 weakens the relationship between DAX/FTSE100 intensifies. Key-Words: cointegration, stock markets, structural breaks, Engle-Granger, long-rum, crisis.
Towards Completeness via Proof Search in the Linear Time mu-Calculus

Modal µ-calculus is one of the central languages of logic and verification , whose study involves notoriously complex objects: au-tomata over infinite structures on the model-theoretical side; infinite proofs and proofs by (co)induction on the proof-theoretical side. Nevertheless, axiomatizations have been given for both linear and branching time µ-calculi, with quite involved completeness arguments. We come back to this central problem, considering it from a proof search viewpoint, and provide some new completeness arguments in the linear time µ-calculus. Our results only deal with restricted classes of formulas that closely correspond to (non-alternating) ω-automata but, compared to earlier proofs, our completeness arguments are direct and constructive. We first consider a natural circular proof system based on sequent calculus, and show that it is complete for inclusions of parity automata directly expressed as formulas, making use of Safra's construction directly in proof search. We then consider the corresponding finitary proof system, featuring (co)induction rules, and provide a partial translation result from circular to finitary proofs. This yields completeness of the finitary proof system for inclusions of sufficiently determin-istic parity automata, and finally for arbitrary Buchi automata.
Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population

Flowering time is a well-known complex trait in crops and is influenced by many interacting genes. In this study, Mathew et al. identify two-way and. Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait.