Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Cost High-Speed Neuromorphic Hardware Based on Spiking Neural Network

Neuromorphic is a relatively new interdisciplinary research topic, which employs various fields of science and technology, such as electronic, computer, and biology. Neuromorphic systems consist of software/hardware systems, which are utilized to implement the neural networks based on human brain functionalities. The goal of neuromorphic systems is to mimic the biologically inspired concepts of the nervous systems, envisioned to provide advantages, such as lower power consumption, fault tolerance, and massive parallelism for the next generation of computers. This brief presents a neural computing hardware unit and a neuromorphic system architecture based on a modified leaky integrate and fire neuron model in a spiking neural network for a pattern recognition task in register-transfer level. The neuron model and the spiking network are explored, considering digital implementation, targeting low-cost high-speed large-scale systems. Results of the hardware synthesis and implementation on field-programmable gate array are presented as a proof of concept. Accordingly, the maximum frequency of the implemented neuron model and spiking network are 412.371 MHz and 189.071 MHz, respectively.
LPI: Learned Positional Invariances for Transfer of Task Structure and Zero-shot Planning

Real-world tasks often include interactions with the environment where our actions can drastically change the available or desirable long-term outcomes. One formulation of this in the reinforcement learning setting is in terms of non-Markovian rewards. Here the reward function, and thus the available rewards, are themselves history-dependent, and dynamically change given the agent-environment interactions. An important challenge for navigating such environments is to be able to capture the structure of this dynamic reward function, in a way that is interpretable and allows for optimal planning. This structure, in conjunction with the particular task setting at hand, then determines the optimal order in which actions should be executed, or subtasks completed. Planning methods face the challenge of combinatorial explosion if all such orderings need to be evaluated, however, learning invariances inherent in the task structure can alleviate this pressure. Here we propose a solution to this problem by allowing the planning method to recognise task segments where temporal ordering is irrelevant for predicting reward outcomes downstream. To facilitate this, our agent simultaneously learns to segment a task and predict the changing reward function resulting from its actions, while also learning about the permutation invariances in the its history that are relevant for this prediction. This dual approach can allow zero-shot or few-shot generalisation for complex, dynamic re-inforcement learning tasks.
European Union Digital Education quality standard framework and companion evaluation toolkit

ABSTRACT The Covid-19 pandemic positioned digital education in a new light. The need for educational institutions to develop strategies, standards and establish quality assurance across digital education became even more evident. This paper describes the four-step process of designing an interactive European Union (EU) Digital Education Quality Standard Framework and Companion Evaluation Toolkit to guide the design, delivery and evaluation of effective digital education. (1) A review of literature of existing digital education frameworks and models is presented. (2) Variables and sub-variables inherent in designing, delivering and evaluating effective digital education are identified. (3) Next the variables and sub-variables in the framework are defined. (4) The process of designing the interactive framework diagram is described with the companion evaluation toolkit outlined. The proposed framework is flexible and applicable to entities and audiences regardless of where they are in the online learning adoption process.
Influence of the degree of dietary fatty acid unsaturation on rumen fermentation parameters

SUMMARY – The influence of the degree of dietary fatty acid unsaturation on rumen fermentative activity was tested by comparing two extruded oilseeds (rapeseed or R and linseed or L) incorporated at a level of 8% dry matter (DM) in ruminant diets. With the R diet (rich in C 18:1 ), the ruminal pH of fistulated goats was statistically lower and the number of protozoa was higher than with the L diet (rich in C 18:3 ). However, the molar composition of the volatile fatty acid (VFA) did not differ between diets at the different sampling times. The in sacco dry matter degradability of the diets was measured on fistulated cows adapted to the experimental diets. It was higher with the R diet. In conclusion, the increase in the degree of dietary fatty acid unsaturation seems to decrease the ruminal fermentative activity.
only: A Case Study In Projective Meaning

I offer an integrated theory of the meaning of only in which the prejacent, while not presupposed, is both entailed and backgrounded, hence tends to project (following a general proposal about projection due to Simons et al. 2010). Moreover, I argue, contra Beaver & Brady (2008), that only is not conventionally associated with focus, the focus effects arising instead pragmatically. But I do adopt aspects of their semantics for only, including the presupposition of a pre-order over the elements of
Chaos , Solitons & Fractals Nonlinear Science , and Nonequilibrium and Complex Phenomena

Much of human cooperation remains an evolutionary riddle. There is evidence that individuals are often organized into groups in many social situations. Inspired by this observation, we propose a simple model of evolutionary public goods games in which individuals are organized into networked groups. Here, nodes in the network represent groups; the edges, connecting the nodes, refer to the interactions between the groups. Individuals establish public goods games with partners in the same group and migrate among neighboring groups depending on their payoffs and expectations. We show that the paradigmatic public goods social dilemma can be resolved and high cooperation levels are attained in structured groups, even in relatively harsh conditions for cooperation. Further, by means of numerical simulations and mean-field analysis, we arrive at the result: larger average group size and milder cooperation environment would lead to lower cooperation level but higher average payoffs of the entire population. Altogether, these results emphasize that our understanding of cooperation can be enhanced by investigations of how spatial groups of individuals affect the evolution dynamics, which might help in explaining the emergence and evolution of cooperation. 2011 Elsevier Ltd. All rights reserved.
Evolution of Dynamic and Thermodynamic Structures before and during Rapid Intensification of Tropical Cyclones: Sensitivity to Vertical Wind Shear

This study explores the spatial and temporal changes in tropical cyclone (TC) thermodynamic and dynamic structures before, near, and during rapid intensification (RI) under different vertical wind shear conditions through four sets of convection-permitting ensemble simulations. A composite analysis of TC structural evolution is performed by matching the RI onset time of each member. Without background flow, the axisymmetric TC undergoes a gradual strengthening of the inner-core vorticity and warm core throughout the simulation. In the presence of moderate environmental shear (5–6 m s−1), both the location and magnitude of the asymmetries in boundary layer radial flow, relative humidity, and vertical motion evolve with the tilt vector throughout the simulation. A budget analysis indicates that tilting is crucial to maintaining the midlevel vortex while stretching and vertical advection are responsible for the upper-level vorticity generation before RI when strong asymmetries arise. Two warm anomalies are observed before the RI onset when the vortex column is tilted. When approaching the RI onset, these two warm anomalies gradually merge into one. Overall, the most symmetric vortex structure is found near the RI onset. Moderately sheared TCs experience an adjustment period from a highly asymmetric structure with updrafts concentrated at the down-tilt side before RI to a more axisymmetric structure during RI as the eyewall updrafts develop. This adjustment period near the RI onset, however, is found to be the least active period for deep convection. TC development under a smaller environmental shear (2.5 m s−1) condition displays an intermediate evolution between ensemble experiments with no background flow and with moderate shear (5–6 m s−1).
Tensor Mixed Effects Model with Applications in Nanomanufacturing Inspection

Raman mapping technique has been used to do in-line quality inspections of nanomanufacturing process. In such an application, a massive high-dimensional Raman mapping data with mixed effects are generated. In general, fixed effects and random effects in the multi-array Raman data are associated with different quality characteristics such as fabrication consistency, uniformity, defects, et al. The existing tensor decomposition methods cannot separate mixed effects, and existing mixed effects model can only handle matrix data instead of high-dimensional multi-array data. In this paper, we propose a tensor mixed effects (TME) model to analyze massive high-dimensional Raman mapping data with complex structure. The proposed TME model can (i) separate fixed effects and random effects in a tensor domain; (ii) exploit the correlations along different dimensions; and (iii) realize efficient parameter estimation by a proposed iterative double Flip-Flop algorithm. Properties of the TME model, existence and identifiability of parameter estimation are investigated. The numerical analysis demonstrates the efficiency and accuracy of the parameter estimation in the TME model. Convergence and asymptotic properties are discussed in the simulation and surrogate data analysis. The Real case study shows an application of the TME model in quantifying the influence of alignment on carbon nanotubes buckypaper.
Sobre la presencia en Canarias de varias especies de braquiópodos (Brachiopoda: Rhynchonellata) en cuevas y cornisas submarinas

Four species of Recent articulated brachiopods collected from shallow-water caves and ledges in the Canary Islands (El Hierro and Tenerife) are studied. These species are Pajaudina atlantica Logan, 1988, Argyrotheca cuneata (Risso, 1826), A. cordata (Risso, 1826) and Argyrotheca sp. The specimens from El Hierro were collected in 1996, while working on the project "Levantamiento de la carta batimetrica y de bionomia bentonica de la futura reserva marina del mar de las Calmas (El Hierro) y su integracion en un sistema de informacion geografica (SIG)". The brachiopods from Tenerife were found whilst monitoring the impact on the environment of the marine LIC of "los Cerebros" cave, in the west coast of Tenerife (San Juan beach), from 2003 until present. The four species, and the associated fauna, are described and illustrated.
Suite for Smooth Particle Hydrodynamic Code Relevant to Spherical Plasma Liner Formation and Implosion

Three-dimensional (3D) modeling of magneto-inertial fusion (MIF) is at a nascent stage of development. A suite of test cases relevant to plasma liner formation and implosion is presented to present the community with some exact solutions for verification of hydrocodes pertaining to MIF confinement concepts. MIF is of particular interest to fusion research, as it may lead to the development of smaller and more economical reactor designs for power and propulsion. The authors present simulated test cases using a new smoothed particle hydrodynamic (SPH) code called SPFMax. These test cases consist of a total of six problems with analytical solutions that incorporate the physics of radiation cooling, heat transfer, oblique-shock capturing, angular-momentum conservation, and viscosity effects. These physics are pertinent to plasma liner formation and implosion by merging of a spherical array of plasma jets as a candidate standoff driver for MIF. An L2 norm analysis was conducted for each test case. Each test case was found to converge to the analytical solution with increasing resolution, and the convergence rate was on the order of what has been reported by other SPH studies.
Performance comparison of representative methods for few-shot speech gender analysis

As one of the basic tasks of the famous and important speech emotion recognition system, gender recognition based on speech analysis has attracted a lot of research interests in recent year. This paper considers the model performance of different machine learning methods on gender recognition in few-shot learning. In this article, we use three types of methods, namely Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Random forest. Among them, in the experiments of the SVM method, we report the linear SVM and nonlinear SVM models separately. Different experiments were performed with respect to the number of features and number of iterations and the results and possible issues were evaluated.
Instrumental variables matter: towards causal inference using deep learning

Causal inference requires knowing causal connections between treatment and outcome variables, and DeepIV, a causal inference framework, is the pioneer work to predict such connections by crossing deep learning with causal inference in applying instrumental variables(IVs) to deep neural network. DeepIV has been proved to be one of the best methods in this field theoretically, but how the framework performs on real-life problems still remains unclear. This paper provides an implementation of DeepIV, and use the framework to predict causal effect from people’s educational background on their annual income. DeepIV framework allows us to take advantage of neural network to estimate causal effect by adjusting loss function. To evaluate the performace of DeepIV in solving real-life problems, our experiment is based on real datasets. The result of our experiment shows that DeepIV’s ability to predict causal effect on real data is at least as good as those of other casual inference models’ whose reliability has been verified in practice. Meanwhile, DeepIV does not have obvious shortcoming in predicting outcomes compared with other supervised learning methods.
Subduction and accumulation of lawsonite eclogite and 1 garnet blueschist in eastern Australia 2

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Celebrity 2.0: Lil Miquela and the rise of a virtual star system

ABSTRACT In this essay, we interrogate the boundary between the highly controlled Hollywood celebrity star system and the highly liberated social media influencer. By examining the case of Lil Miquela, a virtual social media influencer created through computer-generated imagery (CGI), we introduce the notion of a re-imagined virtual star system, which is defined by techno-human cultural intermediaries and the non-agentic persona as property. The virtual star system calls attention to contradictions between the real and the artifice and questions the significance of authenticity in both celebrity practice and the influencer industry.
Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris‐flow channel

Detection of surface change is a fundamental task in geomorphology. Terrestrial laser scanners are increasingly used for monitoring surface change resulting from a variety of geomorphic processes, as they allow the rapid generation of high‐resolution digital elevation models. Irrespective of instrument specifics, survey design or data processing, such data are subject to a finite level of ambiguity in position measurement, a consideration of which must be taken into account when deriving change. The propagation of errors is crucial in change detection because even very small uncertainties in elevation can produce large uncertainties in volume when extrapolated over an area of interest. In this study we propose a methodology to detect surface change and to quantify the resultant volumetric errors in areas of complex topography such as channels, where data from multiple scan stations must be combined. We find that a commonly proposed source of error – laser point elongation at low incidence angles – has a negligible effect on the quality of the final registered point cloud. Instead, ambiguities in elevation inherent to registered datasets have a strong effect on our ability to detect and measure surface change. Similarly, we find that changes in surface roughness between surveys also reduce our ability to detect change. Explicit consideration of these ambiguities, when propagated through to volume calculations, allows us to detect volume change of 87 ± 5 m3, over an area of ∼ 4900 m2, due to passage of a debris flow down a 300 m reach of the Illgraben channel in Switzerland. Copyright © 2011 John Wiley & Sons, Ltd.
Empirical prior latent Dirichlet allocation model

In this study, empirical prior Dirichlet allocation (epLDA) model that uses latent semantic indexing framework to derive the priors required for topics computation from data is presented. The parameters of the priors so obtained are related to the parameters of the conventional LDA model using exponential function. The model was implemented and tested with benchmarked data and it achieves a prediction accuracy of 92.15%. It was observed that the epLDA model consistently outperforms the conventional LDA model on different datasets with an average percentage accuracy of 6.33%; this clearly demonstrates the advantage of using side information obtained from data for the computation of the mixture components. Keywords: latent Dirichlet allocation; semantic indexing; empirical prior; hidden structures; Prediction accuracy
A novel markov random field segmentation method for SAR images

The synthetic aperture radar(SAR) images is difficult to be segmented by traditional Hidden Markow Random Field(HMRF)-based segmentation method, a novel HMRF-based segmentation method for SAR images is proposed in this article. The method utilizes more spacial information of SAR image model by involving the intensity differences between pixels in the potential function. An initial segmentation result is obtained by K-means method. Then accurate segmentation is carried out by the novel MRF-based segmentation method. The result showed that this method is better than the traditional HMRF one both in noise filtering and miss classification ratio.
Seismic behaviour of shallow foundations on layered liquefiable soils

Earthquakes have been historically perceived as one of the most damaging natural hazards. Seismic soil liquefaction is often one of the major sources of damage and disruptions, and has been observed to severely affect key lifelines. Settlement and tilting of shallow foundations resting on saturated sandy/silty soils has been repeatedly observed throughout the world as a consequence of liquefaction or softening of the foundation soil. Such settlements and tilts can render structures unusable, and homes uninhabitable, causing significant economic losses. Despite the undoubted relevance of this phenomenon, field data on the liquefaction induced settlement of shallow foundations are scarce. New data from 24 buildings that suffered settlement and tilting as a consequences of soil liquefaction during the February 27th 2010 Maule earthquake in Chile, are presented in this work to supplement the existing field cases database. Due to the complexity of this phenomenon, field data are not sufficient to fully understand the mechanisms controlling the settlement of structures resting on liquefied or softened ground.In this framework, centrifuge modelling provides a valuable tool for research by reproducing field conditions in a controlled environment. A series of 10 dynamic centrifuge tests were performed as part of this work. Thanks to the University of Dundee newly installed centrifuge-mounted servohydraulic earthquake simulator, scaled version of field earthquake motions were reproduced in the models tested, enhancing the reliability of experimental results. Particular attention was given to the effect of key parameters on the observed foundation settlement. These parameters are the bearing pressure of the foundation, the thickness of the liquefied soil layer and the soil’s relative density. The effect of the soil layering pattern was also investigated, with particular attention to the effect of a low permeability soil crust overlying the liquefied soil. Results suggest that the excess pore pressure generation in the foundation soil is significantly influenced by the stress distribution due to the presence of the foundation itself. In particular, lower excess pore pressure where measured in soil subjected to high static shear stresses (i.e. below the edge of a footing). The soil stratification pattern, and the relative thicknesses of the liquefied and un-liquefied portions of the soil profile, were also found to play a crucial role in determining the seismic demand at foundation level and the type of failure mechanism leading to foundation settlement. Observed differences between centrifuge (i.e. field) and element testing soil response are also discussed. Experimental results are compared to field observations, with the aim of improving the current understanding of the behaviour of structures built on shallow foundations in the eventuality of seismic induced liquefaction of their foundation soil.
ERDC/CERL TR-05-30, Guidelines for Developing Habitat-based Threatened and Endangered Species Population Goals on Army Installations

This document provides an overview of species-habitat modeling approaches, including recent recommendations and criticisms of model shortcomings. Rather than provide an exhaustive treatment of species-habitat modeling, this report provides enough information that an installation could hire or supervise an expert to conduct appropriate modeling, and anticipate and avoid common pitfalls and errors associated with current approaches. The overview is based on a hierarchical structure for organizing species-habitat modeling approaches. Modeling approaches are grouped into three classes, based on data requirements, effort, expense, technical difficulty, application uses, and output characteristics. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN IT IS NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR ERDC/CERL TR-05-30 iii