Miracle-dz/UniFAR-FTUs huggingface dataset:
Hermetia illucens (Diptera: Stratiomyidae) larvae in waste valorization and diet-based shifts in their gut microbiome

Fast-moving consumerism of food products in industrial states requires constant adaptations to fulfill the market’s demands. Negative side effects of such developments include an increase of municipal biodegradable waste in the EU from 14 M tons in 1995 to 40 M tons in 2016 (Eurostat, 2018). Aside from conventional methods like anaerobic fermentation and composting, people are constantly on the lookout for economic and ecologically worthwhile ways to valorize and process organic waste in an efficient manner. Some insects are infamous for their unquenchable hunger either in clearing out vast areas of agricultural crop land (e.g. locusts, van Huis et al, 2007) or in the disintegration of housing (e.g. termites, Querner, 2015). This appetite can be utilized to give the insects a remarkable role in valorizing organic wastes and providing a basis for novel varieties of feedstuff, as the Black Soldier Fly (BSF, Hermetia illucens) proves (Wang and Shelomi, 2017). Although the fly is not native to temperate zones, an increasing number of European companies are discovering its potential for waste treatment and for the production of animal feedstuff and are tying in with the industrialization of insect farming otherwise happening mostly in south-east Asia. However, the transition from lab-scale experiments to smoothly working large-scale applications still is not methodologically sound and holds various stumbling blocks that have yet to be overcome by gaining knowledge in process management and fly biology (Rafieenia, 2016). While figuring out the optimal rearing conditions for a maximum yield in larval protein with a minimum amount of residues is crucial, it is also important to target interactions with associated microbial communities (Bienenstock et al, 2018). In a series of feeding experiments, we investigated the effect of organic-waste related diets on the development and mass balance of BSF larvae and analyzed via next generation sequencing the involved impact on the microbial gut communities in larvae. We hypothesized that (i) although BSF larvae won’t show the same biomass gain when offered organic wastes as with a diet based on balanced chickenfeed, they will still be able to efficiently degrade the offered substrates. (ii) Depending on the diet, the composition of microbial gut communities will be shaped by the available feed.
Failure to Share Natural Disaster Risk

I test whether asset prices reflect the preferences of financial intermediaries in a setting that is well suited to tackling concerns about omitted risk factors. I analyze catastrophe bonds whose cash flows are linked to the occurrence of natural disasters and find that 71% of the variation in their expected returns can be explained by a theoretically-motivated measure of financial intermediaries' marginal rate of substitution. Assuming that natural disasters are independent of aggregate wealth, this pricing result is inconsistent with any explanation based on macroeconomic risk factors. However, the result is consistent with intermediary asset pricing models suggesting that financial intermediaries are marginal investors in capital markets. I also show that the premium on natural disaster risk has decreased significantly in recent years and has become less responsive to the occurrence of disasters, suggesting that intermediaries' access to outside capital has improved over time.
Demystifying the Coherence Index in Compressive Sensing [Lecture Notes]

The existence and uniqueness conditions are a prerequisite to ensure the reliable reconstruction of sparse signals from reduced sets of measurements within the compressive sensing (CS) paradigm. However, despite their underpinning role in practical applications, the existing uniqueness relations are either computationally prohibitive to implement [the restricted isometry property (RIP)] or involve mathematical tools that are beyond the standard background of engineering graduates (the coherence index). This may introduce conceptual and computational obstacles in the development of engineering intuition, design of suboptimal practical solutions, and understanding of theoretical and practical limitations of the CS framework.
A Biaswalk Based RDF Entity Embeddings

Resource Description Framework(RDF) graph has become an important data source for many knowledge discoveries and data mining tasks. However, to enable complex analytic, most of knowledge discovery algorithms require data in vector representation. Therefore, several works have been recently proposed which aim to represent entities in the RDF graph as low dimensional vectors by graph walking. However, sequences generated by graph walking capture only structure related context, it cannot capture latent context such as semantically related information which is an important property of RDF data. In this paper, we proposed a new method to map each entity in RDF to vector using word2vec as a language modeling to learn embedding. In order to use word2vec, we produce a bias random walk, to generate sequences as node context. In this paper, we provide a new concept of similar entities which trade-off between the label of outgoing edge and outgoing nodes. By using entity similarity, we provide a structural similarity that calculates the similarity of two entities in each case of the current sequence. Moreover, we proposed a latent sequences which cannot be generated by traveling the graph, but provide more semantic sentences. Experimental results and the case study on real graphs demonstrates that our method achieves better quality and efficiency.
Multiple Recommendation for Bayesian optimization via Multi-Scale Search

Bayesian optimization operates sequentially recommending single evaluation setting each time. Many practical applications, however, have the facility of evaluating a batch of multiple settings simultaneously. Current batch Bayesian methods are mostly heuristic based and none have considered heteroscedasticity of the unknown objective function. We base our method on extracting the most promising batch of recommendations by searching across different smoothness assumptions which is realized through different length-scales of the Gaussian process covariance function. Theoretical analysis suggests that the proposed batch method has tighter regret bound than a pure sequential approach. Further improvement is brought by introducing a novel multi-armed bandit (MAB) based length-scale selection procedure, resulting in a more computationally efficient algorithm. We evaluate our method by minimizing three heteroscedastic benchmarked test functions and tuning the hyperparameters of two machine learning algorithms.
Corporate sector restructuring, Productivity growth and the Role of Foreign- owned enterprises in China

This study examines the productivity consequences of two important developments, namely the large-scale restructuring of and substantial foreign direct investment inflow into China’s corporate sector since the late 1990s, using firm-level data from 1998 to 2007. The ownership affiliations of a large number of manufacturing firms changed from the state to the private sector, from domestically owned to foreign owned, and from one type of foreign ownership (Hong Kong, Macao, Taiwan) to the opposite (termed the HMT and non-HMT groups, respectively) during this period. We first examine the key characteristics of firm-level total factor productivity changes and decompose the total factor productivity growth of each ownership category by using the dynamic Olley–Pakes method. Second, we estimate the productivity spillover effects of foreign-owned enterprises (FOEs), paying special attention to the role of newly acquired FOEs in the HMT and non-HMT groups. The horizontal and vertical spillover effects associated with incumbent FOEs differ from those associated with new FOEs in some cases. Newly acquired FOEs, who have more local inter-industry supply chain connections, appear to have played a positive role as a conduit of sharing enhanced productivity after joining the FOE sector. However, acquisitions of highly productive local firms by non-HMT FOEs (i.e., cream skimming foreign direct investment) appear to have had negative effects on the average productivity of some industries in the short run.
A Factor-Based Stochastic Implied Volatility Model

We present a factor-based model of the stochastic evolution of the implied volatility surface. The model allows for the integrated and consistent pricing and hedging, risk management, and trading of equity index derivatives as well as volatility derivatives. We develop a unifying theory for the analysis of contingent claims under both the realworld measure and the risk-neutral measure in an environment of stochastic implied volatility. On the basis of transaction data, we provide extensive statistical analyses on the dynamics of the implied volatility surface of German DAX options and propose a four-factor model to describe its evolution. The model is validated and tested on market data. JEL classi cation: G10; G12; G13
Hush City – A Novel Mobile Application to Crowdsource and Access “Everyday Quiet Areas” in Cities.

: This paper presents the Hush City app: a novel mobile application to crowd-source and evaluate quietness in cities. Hush City app has been developed in the framework of the “Beyond the Noise: Open Source Soundscapes” project, grounded on the soundscape approach and citizen science. This paper firstly outlines the project’s theoretical frame - work; secondly it describes the Hush City app’s concept, its technology and advancement on the state of the art; lastly it discusses initial results and introduces future work. The soundscape is a huge musical composition, unfolding around us cease-lessly [where] we are simultaneously its audience, its performers and its composers. (Schafer, 1977)
Artificial Neural Networks (ANN) to Predict Overall Heat Transfer Coefficient and Pressure Drop on a Simulated Heat Exchanger

Computational Fluid Dynamics (CFD) numerical simulations were performed to calculate the maximum overall heat transfer coefficient (U) and minimum pressure drop (Δp) for a crossflow heat exchanger using four materials: stainless steel, copper, aluminum and titanium. Transversal and longitudinal sections were modified, obtaining 143 geometries for analysis. With the simulated data, an Artificial Neural Network (ANN) was built to predict the overall heat transfer coefficient and pressure drop as a function of the heat exchanger material. The ANN exhibits maximum deviations, between the predicted and simulated data, below 0.9 y 0.3 % for the pressure drop and air overall heat transfer coefficient respectively. This assisted model reference strategy can be used for material selection in the heat exchanger design considering replacement and cleaning cycles due to corrosion and fouling in other thermal analysis tasks in engineering applications.
Data Visualisation using Augmented Reality with a Focus set on Head Mounted Displays and Collaborative Tasks

For data analytics a proper visualisation of the data is important to provide the user a good overview and help him to find and understand relationships in the data. Furthermore, appropriate visualisations can help to improve the efficiency of tasks. There are a lot of different approaches for 2D as well as 3D visualisation, which can be applied to existing rich client applications. Many of these rules are applicable for augmented and virtual reality, too. But especially when head mounted displays are used additional factors have to be considered and new possibilities exist. This paper will therefore describe related work regarding 2D and 3D visualisation. Factors like perspective, performed task, ambiguity, depth cues and the general user interface are discussed. Examples will be given for map centric applications and tasks, like the perception accuracy of objects on 3D maps. Furthermore, head mounted displays are categorised. They can provide stereoscopic vision, which is essential for human’s binocular vision and adds benefits for some tasks. With augmented and virtual reality new concepts for collaborative tasks could be created. This paper will provide an overview of related literature and discuss the advantages and disadvantages of augmented and virtual reality. Especially augmented reality can support collaboration which is linked to the fact that all users can see each other.
Exploring design guidelines for fostering citizen engagement through information sharing: Local playgrounds in The Hague

Citizens’ engagement in their neighbourhood community is pivotal for cities to effectively deal with future transitions. Knowing what is going on and having access to the neighbourhood network are important conditions for this. Although prior research has studied ways to foster information sharing between citizens, the underlying assumptions and design choices are often not made explicit. This research identifies design guidelines for playgrounds: physical and virtual spaces where citizens can exchange information about their neighbourhood. A focus group, a workshop and a case study of an existing playground design were performed in The Hague, NL, the context of this research. A set of eight guidelines was identified, covering how to select playground locations, which information to include, and how to design the interaction between citizens. These guidelines inform designers how to create urban playgrounds for citizens to meet, interact, and collaborate to create engaged communities.
Pricing Model for Owner ’ s Payment Bond in China

To reveal the pricing mechanism of an owner’s payment bond and improve warrantor risk management, a pricing model of an owner’s payment bond was developed in a risk neutral valuation framework. The pricing model combines the unique characteristics of an owner’s payment bond using a binomial tree method. A numerical simulation using a real estate project as an example was created by compiling a program in the C# language based on the model. The findings of the simulation not only verified the reliability of the model but also described explicitly the relationship between bond prices and the main influencing factors.
Soft Materials with Broadband and Near-Total Absorption of Sound

Lowand broadband-frequency sound absorption promotes myriad practical applications and scientific endeavors. Yet emerging metamaterial concepts enable such sound absorption only by the use of a complex assembly of attenuator constituents or by the addition of heavy undesirable mass. In this study, multiple low-frequency hybrid resonances are achieved in a soft-lightweight-material-based Helmholtz resonator. By replacing the rigid walls of a traditional Helmholtz resonator with compliant walls and by tailoring the relative structural and acoustic compliances, a unique multiphysics coupling among the material, structure, and sound is realized. These coupling mechanisms yield hybrid resonances that can be used for subwavelength, broadband, and near-total absorption of sound. As a result, the compliant-material resonator exploits a lightweight-material monolithic design that exerts dramatic and tunable control over low-frequency and broadband acoustic-energy transfer. This paper details analytical and experimental investigations to test the concept and reveals strategies for near-total absorption at arbitrary subwavelength frequencies.
Nonlinear simulation of toroidal Alfvén eigenmodes in the presence of a tearing mode

J. Zhu, Z. W. Ma(a), S. Wang, and W. Zhang Institute for Fusion Theory and Simulation, Zhejiang University, Hangzhou, Zhejiang 310027, China Abstract A hybrid simulation is carried out to study nonlinear dynamics of n = 1 toroidal Alfvén eigenmodes (TAEs) with the m/n = 2/1 tearing mode. It is found that the n = 1 TAE is first excited by isotropic energetic particles at the linear stage and reaches the first steady state due to waveparticle interaction. After the saturation of the n = 1 TAE, the m/n=2/1 tearing mode grows continuously and reaches its steady state due to nonlinear mode-mode coupling, especially, the n = 0 component plays a very important role in the tearing mode saturation. The results suggest that the enhancement of the tearing mode activity with increase of the resistivity could weaken the TAE frequency chirping through the interaction between the p = 1 TAE resonance and the p = 2 tearing mode resonance for passing particles in the phase space, which is opposite to the classical physical picture of the TAE frequency chirping that is enhanced with dissipation increase.
An Optimised Thermal Design and Development Process for Passenger Compartments of Vehicles

Published work indicates that neither a single 1D tool, a 3D tool nor a co-simulation approach alone will fulfil the financial pressures of today’s automotive industry to achieve tough time and quality targets. Virtual Product Development (VPD) has to be optimised individually for each stage of the design process, while all simulation methods and software tools have to work together towards a best-in-class VPD process.
W A Novel Scheme to Identify the Word Sense in Question Answering Systems

A novel approach to identify the sense of the word appearing in a sentence is proposed in this paper. The basic idea is to facilitate the natural language processing through the broad manifold of unsupervised observations. The target word is tokenized and a relationship with other words is constructed through mapping to constitute the training set using specific domain wordnet. It constitutes the corpus from where similar word senses are arrived at and there from be-hives its role into the question answering system. The scheme is evaluated through precise performance indices for two specific domains to illustrate its applicability in the present day context.
CAN A RELIABILITY CHARGE SECURE ELECTRICITY SUPPLY? An SD-based assessment of the Colombian power market

The deregulation of the Colombian electricity market took place in 1994 and the pool started operation in 1995. The Colombian market adopted a capacity charge mechanism to increase incentives for investment in new capacity. The capacity charge proved to be weak in terms of transparency and incentives, causing negative effects on investment. Nowadays, the application of the capacity charge is over and a new mechanism is in place. The new mechanism, the reliability charge, intends to provide reliability to the system. We have previously used a non-standard system dynamics approach to evaluate alternative regulation schemes for the Colombian Electricity Market. In this paper, we have updated the system dynamic model, formerly built, to evaluate alternatives to the capacity charge mechanism. We have also assessed the effect that the reliability charge mechanism may have over the market and found that the proposed scheme may actually overcome some of the drawbacks of the previous scheme; however, simulations indicate that this may not have the desirable effects prompt enough to avoid blackouts.
The Multi-Entity Variational Autoencoder

Representing the world as objects is core to human intelligence. It is the basis of people’s sophisticated capacities for reasoning, imagining, planning, and learning. Artiﬁcial intelligence typically assumes human-deﬁned representations of objects, and little work has explored how object-based representations can arise through unsupervised learning. Here we present an approach for learning probabilistic, object-based representations from data, called the “multi-entity variational au-toencoder” (MVAE), whose prior and posterior distributions are deﬁned over a set of random vectors. We demonstrate that the model can learn interpretable representations of visual scenes that disentangle objects and their properties.
Toxic Comments Identification in Arabic Social Media

The usage of social media increases day by day. Both individuals and organizations use social media for different purposes. Problems increase in association with social media technologies. Toxic comments bots create a negative impression about people, companies and products. These kinds of toxic comment bots are created by the attackers. This research work is carried out to identify these toxic comments in Arabic social media. For that, Machine learning techniques are used. Mainly gradient boosting technique (XGBoost algorithm) has been utilized to effectively identify the comments created by the toxic comment bots. XGBoost can efficiently divide toxic comments into the following categories, toxic, severe toxic, obscene, threat, insult, and identity hate. The accuracy achieved by the proposed
Recognition and Positional Identity in an Elementary Professional Learning Community: A Case Study

Professional learning communities are typically conceived of as spaces for reducing teacher isolation, supporting informed and committed teachers, and fostering student academic gains. Focusing on a professional learning community that supported the teaching and learning of engineering in elementary schools, we also conceived of this learning environment as a space for negotiating a teacher-ofengineering identity. Calling attention to emergent issues of power and status through a lens of positional identity, this article examines a Black female educator’s sense of self as a teacher-of-engineering and how this perception was informed by participation in the professional learning community. Findings reveal that racialized and gendered positionings informed the teacher’s perception of having limited access to being recognized in this space as a teacher-of-engineering. Implications for this work include reimagining the design of professional learning spaces in engineering education that intentionally account for teachers’ identity development, as well as supporting the identity development for teachers from historically marginalized communities.
Dynamic User Recruitment with Truthful Pricing for Mobile CrowdSensing

Mobile CrowdSensing (MCS) is a promising paradigm that recruits users to cooperatively perform various sensing tasks. In most realistic scenarios, users dynamically participate in MCS, and hence, we should recruit them in an online manner. In general, we prefer to recruit a user who can make the maximum contribution at the least cost, especially when the recruitment budget is limited. The existing strategies usually formulate the user recruitment as the budgeted optimal stopping problem, while we argue that not only the budget but also the time constraints can greatly influence the recruitment performance. For example, if we have less remaining budget but plenty of time, we should recruit users with more patience. In this paper, we propose a dynamic user recruitment strategy with truthful pricing to address the online recruitment problem under the budget and time constraints. To deal with the two constraints, we first estimate the number of users to be recruited and then recruit them in segments. Furthermore, to correct estimation errors and utilize newly obtained information, we dynamically re-adjust the recruiting strategy and also prove that the proposed strategy achieves a competitive ratio of (1 − 1/e)2/7. Finally, a reverse auction-based online pricing mechanism is lightly built into the proposed user recruitment strategy, which achieves truthfulness and individual rationality. Extensive experiments on three real-world data sets validate the proposed online user recruitment strategy, which can effectively improve the number of completed tasks under the budget and time constraints.