Miracle-dz/UniFAR-FTUs huggingface dataset:
Reasoning over Different Types of Knowledge Graphs: Static, Temporal and Multi-Modal

—Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to signiﬁcantly beneﬁt the usage of KGs in many AI applications, such as question answering and recommendation systems, etc. According to the graph types, the existing KGR models can be roughly divided into three categories, i.e., static models, temporal models, and multi-modal models. The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task. However, these models are not suitable for more complex but practical tasks, such as inductive static KGR, temporal KGR, and multi-modal KGR. To this end, multiple works have been developed recently, but no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To ﬁll the gap, we conduct a survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the preliminaries, summaries of KGR models, and typical datasets are introduced and discussed consequently. Moreover, we discuss the challenges and potential opportunities. The corresponding open-source repository is shared on GitHub: https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.
Structure formation in the universe

Preface Part I. The Smooth Universe: 1. Introducing the Universe 2. The Friedmann model 3. Thermal history of the Universe Part II. The Clumpy Universe: 4. Growth of linear perturbations 5. Statistical properties of the density fluctuations 6. The microwave background radiation 7. The linear theory: velocity fields 8. The non-linear evolution 9. The non-linear theory: high redshift objects Part III. Towards a More Complete Picture: 10. The origin of initial perturbations 11. Dark matter 12. Epilogue Appendices Notes and references Index.
Another article titled “Should I Stay or Should I Go?” or, the mass production of academic research titles

Abstract What kinds of titles are appropriate for research articles? Does creativity have a place in titles or should titles be descriptive and scientific? This article examines the 408 articles titled “Should I Stay or Should I Go? ” and asks why there are so many articles with the same title. The academic culture of publication pressure can lead to researchers choosing creative titles, including popular song titles, to stand out from the crowd. Furthermore, risk assessment leads researchers to choose the same songs as others, because well-known, easy-to-understand cultural references are better rewarded than more obscure references. The collective outcome of this, many researchers choosing the same title for their articles, reflects the mass production of cultural products, wherein creativity is standardized and panders to the largest possible audience.
Parametric amplifiers in phase-noise-limited optical communications

Gordon and Mollenauer [ Opt. Lett.15, 1351 ( 1990)] have shown that the nonlinear Kerr effect limits the range of coherent communications systems using laser amplifiers. We show that parametric amplifiers avoid this limitation. Our method is novel in that we use quantum-optical master equations to model the communications systems. These are solved numerically for systems with either laser amplifiers or parametric amplifiers, with and without the nonlinear Kerr effect. Parametric amplifiers perform better because they preserve the signal-to-noise ratio and decrease the phase noise.
Data-Driven Interval Granulation Approach Based on Uncertainty Principle for Efficient Classification

Granular computing (GrC) is an efficient way to reveal descriptions of data in line with human cognition and plays a critical role in knowledge discovery. Information granules (IGs), the basic computing unit of GrC, is the key component of knowledge representation and processing. Rough sets are one of the classical GrC models and generate IGs based on indiscernibility relations. The relations can effectively achieve the granulation of nominal attributes and generate desirable IGs, but they may cause information loss when achieving the granulation of numerical attributes. To overcome this issue, fuzzy rough sets (FRS) and neighborhood rough sets (NRS) were developed based on the rough sets. However, to generate high-performance IGs in practice, the FRS model requires prior knowledge to determine a fuzzy operation in advance, and NRS needs to calculate an optimal neighborhood radius. In addition, regardless of FRS or NRS, each object is taken as a computing unit to generate IGs that constitute a covering rather than a partition for the universe. This process is not only time-consuming but also prone to generate redundant IGs. Therefore, in this study, a data-driven interval granulation approach based on the uncertainty principle is proposed to generate justifiable interval neighborhood IGs with flexibility and tolerance. First, the interval granulation of attribute values and interval equivalence relation are defined. Next, with the interval equivalence relation, a novel interval rough sets model is developed to unify numerical and nominal attributes into one framework, and a membership function is developed without requiring prior knowledge in advance. Then, a highly effective classifier named CAR-ING integrating attribute reduction technique is developed from the perspective of interval neighborhood IGs. Finally, experiments and comparisons on 17 widely used UCI benchmark datasets and 3 real Biobank medical datasets from the U.K. demonstrated that CAR-ING performs significantly better than four state-of-the-art classifiers based on GrC and five classical classifiers in machine learning. Additionally, the efficiency of CAR-ING is demonstrated on 20 datasets.
Collateral and risk sharing in group lending: evidence from an urban microcredit program

Empirical research on the impact and determinants of group lending is by now substantial. However, very little is known about the possible role of collateral to mitigate incentive problems in group lending. This is because microcredit programs have normally been implemented in rural areas of developing countries. Indeed, the reason for this choice is lack of credit access since agents with collateral are very rare. Also, to the extent that rural communities have tight-knit hierarchical structures information about borrowers is accessible and the enforcement of sanctions via social networks makes collateral superfluous for default mitigation. Yet, in an urban setting in which information is more atomized and social sanctions are not as powerful, collateral may have an important role in group lending. First, we illustrate in a model the role of collateral to mitigate group default. Second, we use data from a group lending program implemented in 2001 in Cotonou, the largest city in Benin with more than one million inhabitants. We empirically explore the risk profile of individual borrowers and resulting group heterogeneity to identify the role of personal contributions to investment projects. Our evidence suggests that while diversification within groups facilitates risk pooling, it also increases expected bailout or group default costs for low risk borrowers. Collateral helps offset and alleviate potential negative spillovers from group default induced by membership of borrowers with risky projects. The presence of borrowers with collateral facilitates access to credit for group members without collateral, who in turn provide insurance against group default. We find joint liability to be a mechanism for risk sharing in a setting where poor households lack resources for collateral and insurance markets are missing.
Location-Aware Reinforcement Learning-Based Live Virtual Machine Migration in Fog Computing

One of the most existing problems in live virtual machine migration in a fog environment is the type of devices. The studies assume that all fog nodes in the fog layer are homogeneous and dedicated to fog functionality. However, fog nodes can be implemented over different types of devices (routers, switches, servers, etc.). They are naturally heterogeneous and originally erected to do specific functions. This would create an issue while migrating the virtual machines. Appropriate destinations should be chosen before the migration to avoid any delay in accomplishing the time-critical tasks and to increase the success rate of migrations. Further, fog nodes are location-aware devices. This feature needs to be incorporated while modeling any VM migration solution. To address these challenges, we propose a novel approach to live VM migration in a fog environment that employs Reinforcement Learning (RL). Extensive experiments show that our approach significantly reduces the latency of time- critical applications. Our proposed system, called EVM_MIG, outperforms other models in terms of reduced latency by approximately 50% and this proved that considering device type, function, and location in deciding VM migration in a fog environment can effectively reduce the response time in time- critical applications.
Architecture of a Nascent Sphingomonas sp. Biofilm under Varied Hydrodynamic Conditions

ABSTRACT The architecture of a Sphingomonas biofilm was studied during early phases of its formation, using strain L138, a gfp-tagged derivative of Sphingomonas sp. strain LB126, as a model organism and flow cells and confocal laser scanning microscopy as experimental tools. Spatial and temporal distribution of cells and exopolymer secretions (EPS) within the biofilm, development of microcolonies under flow conditions representing varied Reynolds numbers, and changes in diffusion length with reference to EPS production were studied by sequential sacrificing of biofilms grown in multichannel flow cells and by time-lapse confocal imaging. The area of biofilm in terms of microscopic images required to ensure representative sampling varied by an order of magnitude when area of cell coverage (2 × 105 μm2) or microcolony size (1 × 106 μm2) was the biofilm parameter under investigation. Hence, it is necessary to establish the inherent variability of any biofilm metric one is attempting to quantify. Sphingomonas sp. strain L138 biofilm architecture consisted of microcolonies and extensive water channels. Biomass and EPS distribution were maximal at 8 to 9 μm above the substratum, with a high void fraction near the substratum. Time-lapse confocal imaging and digital image analysis showed that growth of the microcolonies was not uniform: adjacently located colonies registered significant growth or no growth at all. Microcolonies in the biofilm had the ability to move across the attachment surface as a unit, irrespective of fluid flow direction, indicating that movement of microcolonies is an inherent property of the biofilm. Width of water channels decreased as EPS production increased, resulting in increased diffusion distances in the biofilm. Changing hydrodynamic conditions (Reynolds numbers of 0.07, 52, and 87) had no discernible influence on the characteristics of microcolonies (size, shape, or orientation with respect to flow) during the first 24 h of biofilm development. Inherent factors appear to have overriding influence, vis-à-vis environmental factors, on early stages of microcolony development under these laminar flow conditions.
Application of Terrestrial Laser Scanning in the Preservation of Fortified Caves

Fortified caves are unique to mountain ranges of north Mediterranean area, hidden in inaccessible parts of those mountains and served as secret forts for the people. They are not only pearls of defensive architecture but also a great example of symbioses between man and nature. Research of the Croatian fortified caves plays a major role in the European Union project "Karst Underground Protection". The reason being that, the confirmed number of these objects located in Croatia is second only to France. So far, the existence of more than 50 fortified caves has been established and pending further exploration and documentation. Most of those finds are based on oral conveyance. The significance of preserving cultural heritage for future generations calls for locating and documenting such objects. Considering the complexity of cave structures, their inaccessibility and the rate of decay a suitable method for collecting comprehensive data was considered to be laser scanning. The advantages of laser scanning over traditional surveying techniques have already been proven in many past projects involving survey of geometrically complex objects lacking significant texture differences. Laser scanning provides a non-destructive, comprehensive and efficient method of survey that allows experts to conduct detailed inspection, analysis and visualization of complex structures. The progress of laser scanners has ensured that future projects of this type will play a major role in preservation of cultural heritage objects such as these. This paper describes the importance of subterranean forts to past generations, in light of need for their preservation, and the methodology applied for their 3D documenting and visualization, on the example of objects explored and documented so far. This provides a basis for interdisciplinary geodetic, archaeological, geological and anthropological research.
Inductive Displacement Sensors with a Notch Filter for an Active Magnetic Bearing System

Active magnetic bearing (AMB) systems support rotating shafts without any physical contact, using electromagnetic forces. Each radial AMB uses two pairs of electromagnets at opposite sides of the rotor. This allows the rotor to float in the air gap, and the machine to operate without frictional losses. In active magnetic suspension, displacement sensors are necessary to detect the radial and axial movement of the suspended object. In a high-speed rotating machine equipped with an AMB, the rotor bending modes may be limited to the operating range. The natural frequencies of the rotor can cause instability. Thus, notch filters are a useful circuit for stabilizing the system. In addition, commercial displacement sensors are sometimes not suitable for AMB design, and cannot filter the noise caused by the natural frequencies of rotor. Hence, implementing displacement sensors based on the AMB structure is necessary to eliminate noises caused by natural frequency disturbances. The displacement sensor must be highly sensitive in the desired working range, and also exhibit a low interference noise, high stability, and low cost. In this study, we used the differential inductive sensor head and lock-in amplifier for synchronous demodulation. In addition, an active low-pass filter and a notch filter were used to eliminate disturbances, which caused by natural frequencies. As a consequence, the inductive displacement sensor achieved satisfactory linearity, high sensitivity, and disturbance elimination. This sensor can be easily produced for AMB applications. A prototype of these displacement sensors was built and tested.
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout

Training deep belief networks (DBNs) requires optimizing a non-convex function with an extremely large number of parameters. Naturally, existing gradient descent (GD) based methods are prone to arbitrarily poor local minima. In this paper, we rigorously show that such local minima can be avoided (upto an approximation error) by using the dropout technique, a widely used heuristic in this domain. In particular, we show that by randomly dropping a few nodes of a one-hidden layer neural network, the training objective function, up to a certain approximation error, decreases by a multiplicative factor. On the flip side, we show that for training convex empirical risk minimizers (ERM), dropout in fact acts as a "stabilizer" or regularizer. That is, a simple dropout based GD method for convex ERMs is stable in the face of arbitrary changes to any one of the training points. Using the above assertion, we show that dropout provides fast rates for generalization error in learning (convex) generalized linear models (GLM). Moreover, using the above mentioned stability properties of dropout, we design dropout based differentially private algorithms for solving ERMs. The learned GLM thus, preserves privacy of each of the individual training points while providing accurate predictions for new test points. Finally, we empirically validate our stability assertions for dropout in the context of convex ERMs and show that surprisingly, dropout significantly outperforms (in terms of prediction accuracy) the L2 regularization based methods for several benchmark datasets.
The case for species selection

The mere existence of speciation and extinction make macro-evolutionary processes possible. Speciation and extinction introduce discontinuities in the microevolutionary change within lineages by initiating, disrupting, and terminating the continuity of species lineages. Within a clade, speciation and extinction become potent means of macroevolution in and of themselves. This process, termed species selection, is a macroevolutionary analogue of natural selection, with species playing an analogous part akin to that played by organisms in microevolution. That said, it has proven difficult to think about levels of selection. The concept of species sorting was introduced to help our thinking on this issue by identifying two aspects inherent in hierarchical systems can confuse our attempts to understand them: uncertainty in the level that selection acts and uncertainty about if the pattern of selection is in fact caused at all. Thanks to insights from evolutionary transitions in individuality, we now know more about how to identify the level of selection and how to parse the causal structure in hierarchical evolutionary circumstances. We know that if the fitness of organisms causes the fitness of more inclusive species then they must covary. However, there is no evidence of such a covariance between fitnesses at these two levels. This covariance is just not observed; neither between cells and organisms nor between organisms and species. Rather, speciation and extinction rates appear to be completely divorced from organismal fitness. With this insight, the concept of species sorting shrinks so that it only covers the two processes of species selection and drift. I argue that we are better off focusing on understanding the processes of species selection and drift and that there is therefore no further need for the concept of species sorting.
In Mold Flow of Long Fibers in Compression Molding Process

Long Fiber Thermoplastics (LFT) are promising new materials with high physical properties and low density. These high properties are obtained by embedding very long fibers (∼100 mm) into a thermoplastic matrix. Such a high fiber length dictates the use of a compression molding process for manufacturing as the length of discontinuous fibers in injection molding is limited by pellet length. LFT composites are of great interest for the automotive industry. These materials are already used in some interior and exterior car parts such as bumpers, seat structures, door module etc. This research is inspired by the desire to manufacture load carrying parts for vehicles such as wheel rims which would dramatically reduce vehicle weight and subsequently save fuel. This, however, requires a much better understanding of long fiber orientation and distribution during compression molding. Current orientation models were developed for short fibers (< 1mm). Initially these models were extended to cases that were considered long fibers (several millimetres). Recently these models are being extended even for the LFT-D case fibers which can reach up to 80 mm. Since several of the governing assumptions for short fiber models are not suitable for long fibers, the models can not provide accurate results for long fibers. Due to this limitation long fibers require independent treatments. This thesis presents a new model which is specifically designed for long flexible fibers. This model is confirmed by comparing results obtained for simple shear flow to results found in the literature. The model was implemented in a rheometric squeeze flow, which is defined as flow between two approaching to each other parallel plates, and provided results previously not seen in the literature. Interactions were implemented into the model and tested for rheometric squeeze flow and simple shear flow cases. In addition to providing insight into fiber orientation and deformation in rheometric squeeze flow, which was not previously studied in the literature, the proposed model shows more predictive results than previously found in the literature.
A History of Spike-Timing-Dependent Plasticity

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.