Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning and transfer learning in Biomedical relation extraction

Biomedical literature is growing exponentially every year, making it harder for researchers to find relevant literature for their research. With this huge growth in biomedical information, the challenge of keeping up to date with new discoveries is getting extremely hard. Databases consisting of structured biomedical knowledge are manually curated and they are often unable to keep pace with the rich amount of information available in fast growing biomedical texts. To cope with this huge amount of literature, text mining and knowledge extraction methods can be very useful. Biomedical relation extraction is the task of extraction biomedical relations from biomedical text. Existing methods mostly rely on hand-crafted features, and by the lack of annotated data, the methods have inadequate performance. The existing methods are mainly applied to a single application in Healthcare, for instance focusing on Drug-Drug Interactions. In this thesis a method is proposed to extract different types of biomedical relations by joining data from existing knowledge bases to unstructured text from PubMed. By using this method, a large dataset was created that contained information about drugs and diseases and different types of relations like Treats, Causes and Interacts. Recently, Neural networks have shown to achieve better or similar performance as models that depend on hand-crafted features. With the current trend of transfer learning in NLP (BERT), neural networks can also be used without the requirement of a large annotated dataset. In this thesis, we propose several methods that are all based on embedding features and do not require any explicit feature engineering. Our experimental results show that especially the transfer learning models have very promising performance on this task. The performance of this model outperforms all of the Deep learning models that before achieved state-of-the-art performance on many biomedical NLP tasks.
Aerodynamic Design Optimization of Transonic Natural-Laminar-Flow Airfoil at Low Reynolds Number

The position and size of laminar separation bubble on airfoil surfaces exert a profound impact on the efficiency of transonic natural-laminar-flow airfoil at low Reynolds number. Based on the particle swarm algorithm, an optimization methodology in the current work would be established with the aim of designing a high and robust performance transonic natural-laminar-flow airfoil at low Reynolds number. This methodology primarily includes two design processes: a traditional deterministic optimization at on-design point and a multi-objective of uncertainty-based optimization. First, a multigroup cooperative particle swarm optimization was used to obtain the optimal deterministic solution. The crowing distance multi-objective particle swarm optimization and the non-intrusive polynomial chaos expansion method were then adopted to determinate the Pareto-optimal front of uncertainty-based optimization. Additionally, the transition model was employed to predict the laminar-turbulent transition. Regarding to the established optimization methodology, a propeller tip airfoil of solar energy unmanned aerial vehicle was finally designed. During optimization processes, the minimized pressure drag was particularly chosen as the optimization objective, while the friction drag increment served as a constraint condition. The deterministic results indicate that the optimized airfoil has a good ability to control the separation and reattachment positions, and the pressure drag can be greatly reduced when the laminar separation bubble is weakened. The multi-objective results show that the uncertainty-based optimized airfoil possesses a significant robust performance by considering the uncertainty of Mach number. The findings evidently demonstrate that the proposed optimization methodology and mathematical model are valuable tools to design a high-efficiency airfoil for the propeller tip.
The Analysis of Indonesia Maize Trade Position and Imported Maize Dependency Level In Domestic Market

This paper attempts to analyze local maize performance compare with imported maize and examine some factors influencing imported maize dependency in Indonesia. This research employed the TSI method to measure trade position for competitive advantage perspective, the IDR method to measure percentage of imported maize dependency for comparative advantage, and VECM method to examine factor affecting import dependency. Based on TSI result indicates that Indonesia maize has lower competitiveness rather than maize exporter countries in domestic market. Based on IDR result indicates that Indonesia has low dependency for imported maize (6% 11%). Cropping area, world maize price, and exchange rate affect import dependency maize in Indonesia based on VECM analysis. However, it seems that Indonesian Government should be more oriented toward improving local maize based on domestic and international condition to compete with import maize and full fill domestic demand.
Tracing Molecular Properties Throughout Evolution: A Chemoinformatic Approach.

Evolution of metabolism is a longstanding yet unresolved question, and several hypotheses were proposed to address this complex process from a Darwinian point of view. Modern statistical bioinformatic approaches targeted to the comparative analysis of genomes are being used to detect signatures of natural selection at the gene and population level, as an attempt to understand the origin of primordial metabolism and its expansion. These studies, however, are still mainly centered on genes and the proteins they encode, somehow neglecting the small organic chemicals that support life processes. In this work, we selected steroids as an ancient family of metabolites widely distributed in all eukaryotes and applied unsupervised machine learning techniques to reveal the traits that natural selection has imprinted on molecular properties throughout the evolutionary process. Our results clearly show that sterols, the primal steroids that first appeared, have more conserved properties and that, from then on, more complex compounds with increasingly diverse properties have emerged, suggesting that chemical diversification parallels the expansion of biological complexity. In a wider context, these findings highlight the worth of chemoinformatic approaches to a better understanding the evolution of metabolism.
A Further Details from the Harmonic Analysis Framework for Boolean Functions A . 1 Discrete derivative

B.1 Proof of Lemma 1 Proof. We first derive a relation between the true gradient ∂pi Ep(z)[f(z)] and the degree-1 Fourier coefficients f̂ (i). This is an extension of the Margulis-Russo formula (Margulis, 1974; Russo, 1982; O’Donnell, 2014). We show that ∂pi Ep(z)[f(z)] = 2 σi f̂ (i). We follow O’Donnell (2014, §8.4). We work with two representations of the Boolean function f . The first is the Fourier expansion of f under the uniform Bernoulli distribution. This is also the representation obtained by expressing f as a polynomial in zi. Since the domain of the function f is the Boolean cube, the polynomial representation is multilinear. That is f(z) = ∑ S⊆[n] f̂(S) ∏ j∈S zj . To avoid confusion and to differentiate the representation from the Boolean function, we use f (z) to denote this representation in the following. Note that since this representation is a polynomial it is defined over any input in R. In particular,
Identification of Meloidogyne graminis on golf greens

In 2009,yellow patches emerged on golf greens of two golf courses in Yangjiang and Zhuhai of Guangdong Province.Root knots were observed in turfgrass roots of the golf greens,and numerous juvenile second stage(J2) worms belong to the genus Meloidogyne were isolated from soils around turf grass roots.The root knot nematodes were identified by using morphology,isozyme and mtDNA-PCR-sequence.The results indicated the root knot nematode was M.graminis. M.graminis are known to be an important pathogen of golf course greens turf in USA,however the nematode is found firstly on golf course in China.
A Laptop Ensemble Performance System using Recurrent Neural Networks

The popularity of applying machine learning techniques in musical domains has created an inherent availability of freely accessible pre-trained neural network (NN) models ready for use in creative applications. This work outlines the implementation of one such application in the form of an assistance tool designed for live improvisational performances by laptop ensembles. The primary intention was to leverage off-the-shelf pre-trained NN models as a basis for assisting individual performers either as musical novices looking to engage with more experienced performers or as a tool to expand musical possibilities through new forms of creative expression. The system expands upon a variety of ideas found in different research areas including new interfaces for musical expression, generative music and group performance to produce a networked performance solution served via a web-browser interface. The final implementation of the system offers performers a mixture of high and low-level controls to influence the shape of sequences of notes output by locally run NN models in real time, also allowing performers to define their level of engagement with the assisting generative models. Two test performances were played, with the system shown to feasibly support four performers over a four minute piece while producing musically cohesive and engaging music. Iterations on the design of the system exposed technical constraints on the use of a JavaScript environment for generative models in a live music context, largely derived from inescapable processing overheads.
Deep Lagrangian Networks for Robot Control Deep Lagrangian Networks für Roboterregelung

A dynamics model is essential for the control of a robot. It can encapsulate information about different properties of the robotic system like masses, joint configuration, friction, the center of gravity and inertia parameters in a mathematical model. Online model learning is a viable alternative for robotic manipulators in comparison to analytical models and models learned offline, as online learning offers the advantage of being able to dynamically react to a changing model during execution. During an exploration phase, the model gets learned by an online algorithm using a constant stream of data generated by the robot performing a desired trajectory. In this phase, it is important that the robot is robustly controlled, while simultaneously generating rich data to learn on, ideally using a trajectory providing maximal excitation of the parameters for the dynamics model. In this thesis, I will lay out the foundations of online model learning, develop a robust controller for the Barrett WAM 4DoF and will evaluate the performance of different trajectories that have been used for online model learning on the WAM 4DoF robot. Additionally, I will show that the analytical model of the WAM provided by Barrett is capturing the real dynamics only badly and that trajectories, which bear the excitation of dynamic parameters in mind, indeed perform superior to other, more trivial trajectories. Zusammenfassung Ein Dynamikmodell eines Roboters ist essentiell um diesen zu steuern. Es kann Informationen über den Roboter wie Masses, Konfiguration der Gelenke, Reibung, Schwerpunkte und Trägheitsparameter in einem mathematischen Modell vereinen. Das Online-Lernen von Modellen ist mittlerweile eine mögliche Alternative im Vergleich zu analytischen oder offline gelernten Modellen. Der Vorteil von online gelernten Modellen besteht darin, dass während der Ausführung dyamisch auf eine sich verändernde Umgebung, die damit das Dynamikmodell beeinflusst, reagiert werden kann. Während einer Explorationsphase wird das Modell von einem Online-Algorithmus gelernt, der dafür mit einem konstanten Strom von Daten versorgt wird, die von dem Roboter erzeugt werden, der eine gewünschte Trajektorie verfolgt. Während dieser Phase ist es wichtig, dass der Roboter robust geregelt wird und dass gleichzeitig die erzeugten Daten reich an Informationen sind, idealerweise indem eine Trajektorie verwendet wird, die die Parameter des Dynamikmodells maximal anregt. In dieser Thesis werde ich die Grundlagen des Online-Lernens von Modellen darlegen, einen robusten Regler für den Barret WAM 4DoF entwickeln und werde bewerten, was für einen Einfluss verschiedene, für das Online-Lernen verwendete, Trajektorien auf die Leistung des Online-Lernens am WAM 4DoF Roboter haben. Außerdem werde ich zeigen, dass das analytische Modell des WAM das von Barrett zur Verfügung gestellt wird, die tatsächlichen Dynamiken nur unzulänglich darstellt und dass Trajektorien, die designt wurden, um die dynamischen Parameter anzuregen, tatsächlich besser abschneiden, als andere einfachere Trajektorien.
An end-to-end spike-based image compression architecture

In this paper, we discover the special properties of neurons in terms of compression. Neurons are able to transform a visual stimulus into a sequence of discrete biphasic events, called spikes trains, forming the neural code. The neural spike generation properties are beneficial to image processing community as the neural code is very compact, yet informative enough, to be used in the input stimulus recovery. We show that the spike-based compression enables to improve the reconstruction quality in time which is a completely novel feature compared to compression standards. In addition, we mathematically prove that the proposed neuro-inspired mechanism behave either as a uniform or a non-uniform quantizer depending on its parameter. Last but not least, we build an end-to-end spike-based coding/decoding architecture that first transforms an image with a DCT filter and then, it generates spikes to compress the transformed coefficients. Based on these spike trains we reconstruct the input image and we estimate the rate-distortion performance of the whole system. Last but not least, we provide numerical results that confirm that our proposed architecture is much more efficient compared to the JPEG standards.
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains

Planning in Partially Observable Markov Decision Processes (POMDPs) inherently gathers the information necessary to act optimally under uncertainties. The framework can be extended to model pure information gathering tasks by considering belief-based rewards. This allows us to use reward shaping to guide POMDP planning to informative beliefs by using a weighted combination of the original reward and the expected information gain as the objective. In this work we propose a novel online algorithm, Information Particle Filter Tree (IPFT), to solve problems with belief-dependent rewards on continuous domains. It simulates particle-based belief trajectories in a Monte Carlo Tree Search (MCTS) approach to construct a search tree in the belief space. The evaluation shows that the consideration of information gain greatly improves the performance in problems where information gathering is an essential part of the optimal policy.
Estimation of Import Demand Elasticities for Nigeria: Implications for the Balance of Payments Adjustment Policies

Import demand elasticities have important implications for the formulation of effective balance of payments adjustment policies. Over the years, Nigeria had frequently registered severe balance of payments problems which had led the country into large indebtedness to the rest of the world and consequently depleting the country’s long accumulated foreign reserves. Regrettably, despite the governments’ enormous efforts to resolve the balance of payments problems in Nigeria, the balance of payments situation continues to worsen. The study, therefore, estimated the Nigeria’s imports demand elasticities as implications for the balance of payments adjustment policies using data from 1970 to 2019. The study employed the contemporary econometric techniques of cointegration and error correction mechanism, within the framework of the ARDL model. The results show that there is a longrun relationship between imports demand and the chosen explanatory variables (real GDP, import prices, domestic prices, naira/dollar exchange rate, import tariff rates, and domestic credits). The results further reveal that though imports demand is more sensitive to domestic income in Nigeria, it is generally inelastic since all the estimated imports demand elasticity coefficients are less than unity. Based on these findings, we conclude that the use of price-related imports demand management instruments (e.g. currency devaluation, tariffs, domestic price adjustment, etc.) is not validated for effective adjustment of the BoP position in Nigeria. Thus, the study recommends that the non-price import demand management instruments could be the best option to ensure that Nigerians substitute imported commodities with locally produced goods.
qu an tph ] 1 6 M ar 2 02 0 Continuous-variable gate decomposition for the Bose-Hubbard model

In this work, we decompose the time-evolution of the Bose-Hubbard model into a sequence of logic gates that can be implemented on a continuous-variable photonic quantum computer. We examine the structure of the circuit that represents this time-evolution for one-dimensional and two-dimensional lattices. The elementary gates needed for the implementation are counted as a function of lattice size. We also include the contribution of the leading dipole interaction term which may be added to the Hamiltonian, and its corresponding circuit.
University of Birmingham Volumetric generation of optical vortices with metasurfaces

: Recent advances in metasurfaces, i.e., two dimensional arrays of engineered nanoscale inclusions that are assembled onto a surface, have revolutionized the way to control electromagnetic waves with ultrathin, compact components. The generation of optical vortex beams, which carry orbital angular momentum, has emerged as a vital approach to applications ranging from high-capacity optical communication to parallel laser fabrication. However, the typically bulky elements used for the generation of optical vortices impose a fundamental limit toward on-chip integration with subwavelength footprints. Here, we investigate and experimentally demonstrate a three-dimensional vortex array generation based on the light-matter-interaction with a high-efficiency dielectric metasurface. By employing the concepts of Dammann vortex gratings and spiral Dammann zone plates, 3D optical vortex arrays with micrometer spatial separation are achieved from visible to near-infrared
The aesthetics of coupling : An impossible marriage

This paper is about embodied interaction with digital products and systems, and more specific, about its aesthetics. We establish a particular form of aesthetics: the aesthetics of coupling. We start with illustrating them by presenting two design research projects, the rich actions camera and the Experimental Payment Terminal. We discuss the characteristics of the aesthetics, and coin their corner stone concepts, the persistent and the temporal. After that, we present the design process of two conceptual products: a night lamp and a medication dispenser. Both projects demonstrate how to design for the aesthetics of coupling. In a reflection, we position the aesthetics of coupling among several existing perspectives on interaction aesthetics, and elaborate on their significance for the design community. We conclude this paper by highlighting the broad lines of our framework, and by indicating directions for future research.
In vitro Kinetic of Inhibition of Lipase, Antioxidant Activity, Glucose Entrapment and Polyphenolic Content of Solanum linnaeanum

Abstract Decoction as traditionally used and methanol extract of a commonly consumed vegetable Solanum linnaeanum (SL) was evaluated for any antioxidant potential (Ferric reducing power (FRAP) assay, nitric oxide, DPPH, and xanthine oxidase inhibitory activity). The inhibitory effect of SL against α-amylase, α-glucosidase and pancreatic lipase activity was studied and the type of inhibition was established by the Lineweaver-Burk plot in vitro. The entrapment ability in decreasing glucose movement in an in vitro model was also investigated. The total phenolic, flavonoid, anthocyanin and tannin content were determined. The aqueous extract (IC50=0.20 µg/ mL) was more active against pancreatic lipase than the methanol extract (IC50=0.40 µg/ mL) and the positive control Orlistat® (IC50=0.35 µg/ mL). An uncompetitive type of inhibition as indicated by a reduction in Vmax and Km was recorded. Methanol extract demonstrated a more efficient DPPH scavenging potential. The FRAP of the methanolic extract was 102.708±1.36 mM trolox equivalent (TE)/mg extract. Decoction extracts showed no significant difference (p>0.05) on glucose entrapment capacities (33.3 %) when compared to the positive control (Arabic gum). Phytochemical screening of SL indicated the presence of phenols (152.80±1.40 (GAE) [g/mL]) and flavonoids (4.34±0.16 Rutin equivalent[μg/mL]) in the methanol extract only and absence of tannin and anthocyanin in both methanol and aqueous extracts. This study is the first attempt to establish valuable baseline data on SL bioactivity. The results can be further exploited for the development of new therapeutic dietary approach for the management of diabetes and obesity.
Effects of public school education expenditure on income inequality: Evidence from 15 major States of rural India

Using panel data for 15 major states of India for the period 1983 to 2012, this paper empirically examines whether more funding on school education (both elementary and secondary levels) by various state governments and central government taken together can affect the distribution of income/expenditure in rural sector of India. From the findings it is observed that public expenditure on school education helps reducing income inequality whether we use the rightist view of inequality or relative inequality as measured by Gini coefficient or, the leftist view of inequality or absolute inequality as measured by either absolute Gini or by Standard Deviation of income in rural sector of India. These findings are robust to the inclusion of various socio-economic and demographic variables. The findings, therefore, suggest that devoting more public expenditure on school education may be one way to reduce income inequality in India. We have also tried to explain the nature of variation in all the variables, viz, the dependent variable (income inequality as measured by three methods) and all explanatory variables (as mentioned in section IV) across 15 major states of India and over the time period from 1983 to 2012. In our results we see that the inter-state variation is more significant than inter-temporal variation for all explanatory variables and explained variable except school education expenditure. For school education expenditure the inter-temporal variation is more significant than inter-state variation.
Innovation and Credit Constraints – Evidence from a Survey of Vietnamese Small and Medium Enterprises

This paper explores the impact of credit constraints on a firm’s performance and whether innovating firms face a tighter credit constraint. In the theoretical model of the paper, credit constraint arises from the asymmetric information problem where banks cannot observe a firm’s true productivity. The longer time frame and the higher risks of innovation projects result in tighter credit constraints for innovating firms than for non-innovating firms. Thus, the theoretical model predicts a positive relationship between a firm’s interest payment per worker and its revenue (and profits) per worker, as well as tighter credit constraints for innovating firms. Empirical evidence on a sample of Vietnamese small and medium enterprises supports these theoretical predictions.