Miracle-dz/UniFAR-FTUs huggingface dataset:
Keeping the Strings Attached: The Intra-Organizational Management of Transnational Network Behaviour

Due to the internationalization of markets and growing interdependence of policy issues, many forms of transnational collaboration have emerged, enmeshing domestic agencies in a wide variety of (formal and informal) transnational policy settings. However, the internal problems of management and coordination this potentially creates for domestic agencies are rarely studied by public administration scholars. This paper applies the concept of boundary-spanning and connects it to organizational structure, as to provide a better understanding of the different ways in which external network activities can be internally organized and what potential tensions might emerge. The analysis demonstrates how domestic agencies use network coordinators to resolve tensions between the differentiation needed to operate in complex transnational environments and the integration needed to keep them accountable. The discussion notes several challenges resulting from a reliance on such network coordinators and sets out directions for future research. To be presented at the PMRC Conference, Panel 14D, Chapel Hill, June 11-14 2019
Third Session Summaries of the Phosphate-Caries Literature

Considerable evidence has accumulated during the past decade to indicate that phosphates may cause significant decreases in the development of dental caries when added to the caries-producing diets of young hamsters and rats. These animal species have been used extensively in dental caries research since 1931 when Hoppert, Webber, and Cannif' reported that an adequate diet produced dental caries in rats. Though one must be careful in extrapolating results from rodents to human beings, the experience of the past 50 years has shown that valuable qualitative and quantitative data can be derived by experiments with animals which, with minor modification, are usually applicable to many of the problems of mankind (e.g., diabetes, rickets, scurvy, beriberi, nutritional anemias, etc.). In this survey we propose (1) to line the early evidence that led to the development of the phosphorus-caries relationship, (2) to summarize literature in which the results of approximately 100 feeding trials with phosphate compounds are reported, and (2) to discuss and interpret these data in terms of the dental caries problem. EARLY LITERATURE RELATING TO THE PHOSPHORUS-CARIES RELATIONSHIP. -It is apparent from the literature, and we know it to be a fact, that the phosphorus-caries relationship was discovered by several groups of investigators independently. Major credit for the discovery of this relationship cannot be ascribed to a single individual or research group. Though the cariostatic potential of phosphates was recognized when phosphates were added to diets that were either deficient or adequate in phosphorus, the results obtained with phosphorus-adequate diets are more meaningful, since they are not complicated by the effects of phosphorus deficiency. The earliest report on the effects of low P diets on experimental caries that we have found was that by Klein and McCollum,2 who in 1931 reported an increase in dental caries in rats when the P content of their ration was decreased from 0.48 per cent P, while the Ca content was held at 0.34 per cent. In a subsequent paper3 they suggested that this cariogenic effect may have been due to the coarseness of the particles of the
Tolerance for Cheating From the Classroom to the Boardroom: A Study of Underlying Personal and Cultural Drivers

Professors face increasingly diverse student bodies that exhibit divergent understandings and motivations to engage in academic dishonesty. Research suggests that collectivism/individualism is the cultural dimension underlying such differences. This study measures this dimension at the individual level using two constructs—agency-communion and self-construal—and their relationships to tolerance for academic cheating and unethical corporate behavior. Analyses show a positive relationship between tolerance for academic cheating and for unethical corporate behavior. Both measures of collectivism (interdependent self-construal and communion) exhibit positive relationships to tolerance for unethical business behavior, while interdependence is also positively related to tolerance for academic cheating.
Fostering Savings by Commitment: Evidence from a Quasi-Natural Experiment at the Small Enterprise Foundation in South Africa

We study the effects of a pilot project that strengthened the saving incentive mechanisms set up by the Small Enterprise Foundation (SEF), a leading microfinance institution based in South Africa. The program aimed at introducing a stimulus to save in the form of the possession of “Goal Card” whereby clients owning this tool were asked to identify a saving goal and to commit to a regular saving amount. The experiment had quasinatural approach, as it has been implemented by SEF in selected locations starting from May 2015. Difference in differences estimates show an improvement in the savings performance of the SEF customers treated with the program, compared to the counterfactual. Besides the evaluation of the program’s main effects, we investigate the clients’ understanding of the pilot and their attitude towards saving, as well as the quality of the pilot’s administration and its operational challenges, through the administration and analysis of surveys conducted on both the treated and control groups of clients.
Factors Affecting Profitability: An Emprical Study on Ethiopian Banking Industry

The main purpose of this study was to examine factors affecting profitabilityEthiopian banking industry. The study adopted quantitative research approach and the statistical tool was used to estimate the profitability, which was measured by returnon asset as a function of balance sheet, industry specific and macroeconomic explanatoryvariables. The finding of the study showed that loan and advance, current deposit, otherliabilities and gross domestic product have statistically significant and positiverelationship with banks’ profitability. On the other hand, variables like fixed deposit, market concentration have a negative and statistically significant relationship withbanks’ profitability. However, the relationship of deposit with other banks, sum ofinvestment, saving deposit and inflation is found to be statistically insignificant. As aresult, the study recommended that Ethiopian Banking Industry must focus on increasingpublic awareness to mobilize more savings this will enhance their performance inprovision of loans and advance to customers. Finally, Ethiopian Banking Industry shouldnot only be concerned about internal structures and policies, but they must consider boththe internal environment and the macroeconomic environment together in fashioning out strategies to improve their profitability.
Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps from a single RGB-D image of the target object, which is provided as input to both networks. By using the geometric reconstruction to refine the candidate grasp produced by the grasp proposal network, our system is able to accurately grasp both known and unknown objects, even when the grasp location on the object is not visible in the input image.This paper presents the network architectures, training procedures, and grasp refinement method that comprise our system. Experiments demonstrate the efficacy of our system at grasping both known and unknown objects (91% success rate in a physical robot environment, 84% success rate in a simulated environment). We additionally perform ablation studies that show the benefits of combining a learned grasp proposal with geometric reconstruction for grasping, and also show that our system outperforms several baselines in a grasping task.
Kraken reveals itself – the merger history of the Milky Way reconstructed with the E-MOSAICS simulations

Globular clusters (GCs) formed when the Milky Way experienced a phase of rapid assembly. We use the wealth of information contained in the Galactic GC population to quantify the properties of the satellite galaxies from which the Milky Way assembled. To achieve this, we train an artificial neural network on the E-MOSAICS cosmological simulations of the co-formation and co-evolution of GCs and their host galaxies. The network uses the ages, metallicities, and orbital properties of GCs that formed in the same progenitor galaxies to predict the stellar masses and accretion redshifts of these progenitors. We apply the network to Galactic GCs associated with five progenitors: Gaia-Enceladus, the Helmi streams, Sequoia, Sagittarius, and the recently discovered ‘low-energy’ GCs, which provide an excellent match to the predicted properties of the enigmatic galaxy ‘Kraken’. The five galaxies cover a narrow stellar mass range [M⋆ = (0.6–4.6) × 108 M⊙], but have widely different accretion redshifts ($\mbox{$z_{\rm acc}$}=0.57\! -\! 2.65$). All accretion events represent minor mergers, but Kraken likely represents the most major merger ever experienced by the Milky Way, with stellar and virial mass ratios of $\mbox{$r_{M_\star }$}=1$:$31^{+34}_{-16}$ and $\mbox{$r_{M_{\rm h}}$}=1$:$7^{+4}_{-2}$, respectively. The progenitors match the z = 0 relation between GC number and halo virial mass, but have elevated specific frequencies, suggesting an evolution with redshift. Even though these progenitors likely were the Milky Way’s most massive accretion events, they contributed a total mass of only log (M⋆, tot/M⊙) = 9.0 ± 0.1, similar to the stellar halo. This implies that the Milky Way grew its stellar mass mostly by in-situ star formation. We conclude by organizing these accretion events into the most detailed reconstruction to date of the Milky Way’s merger tree.
Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance

The ability of animals to interact with complex dynamics is unmatched in robots. Especially important to the interaction performances is the online adaptation of body dynamics, which can be modelled as an impedance behaviour. However, variable impedance control still continues to be a challenge in the current control frameworks due to the difficulties of retaining stability when adapting the controller gains. The fractal impedance controller has recently been proposed to solve this issue. However, it still has limitations such as sudden jumps in force when it starts to converge to the desired position and the lack of a force feedback loop. In this manuscript, two improvements are made to the control framework to solve these limitations. The force discontinuity has been addressed introducing a modulation of the impedance via a virtual antagonist that modulates the output force. The force tracking has been modelled after the parallel force/position controller architecture. In contrast to traditional methods, the fractal impedance controller enables the implementation of a search algorithm on the force feedback to adapt its behaviour to the external environment instead of on relying on a priori knowledge of the external dynamics. Preliminary simulation results presented in this paper show the feasibility of the proposed approach, and it allows to evaluate the trade-off that needs to be made when relying on the proposed controller for interaction. In conclusion, the proposed method mimics the behaviour of an agonist/antagonist system adapting to unknown external dynamics, and it may find application in computational neuroscience, haptics, and interaction control.
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger’s structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94% objects within the ± 6 mm error and measured 80% of the structural strains within ± 0.1 mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.
Non-orientable Slice Surfaces and Inscribed Rectangles

We give a unified proof of existence of inscribed squares and inscribed rectangles with aspect ratio $\sqrt3$ in Jordan curves with a regularity condition, generalizing a result by Hugelmeyer. We also discuss differences between genera of smooth and locally-flat non-orientable surfaces in the 4-ball with boundary a given torus knot or 2-bridge knot. In particular, we establish that a result by Batson on the smooth non-orientable 4-genus of torus knots does not hold in the locally-flat category.
MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With Applications

Geometric deep learning has attracted significant attention in recent years, in part due to the availability of exotic data types for which traditional neural network architectures are not well suited. Our goal in this paper is to generalize convolutional neural networks (CNN) to the manifold-valued image case which arises commonly in medical imaging and computer vision applications. Explicitly, the input data to the network is an image where each pixel value is a sample from a Riemannian manifold. To achieve this goal, we must generalize the basic building block of traditional CNN architectures, namely, the weighted combinations operation. To this end, we develop a tangent space combination operation which is used to define a convolution operation on manifold-valued images that we call, the Manifold-Valued Convolution (MVC). We prove theoretical properties of the MVC operation, including equivariance to the action of the isometry group admitted by the manifold and characterizing when compositions of MVC layers collapse to a single layer. We present a detailed description of how to use MVC layers to build full, multi-layer neural networks that operate on manifold-valued images, which we call the MVC-net. Further, we empirically demonstrate superior performance of the MVC-nets in medical imaging and computer vision tasks.
Hierarchically Decoupled Imitation for Morphological Transfer

Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning. For such tasks, we argue that transferring learned information from a morphologically simpler agent can massively improve the sample efficiency of a more complex one. To this end, we propose a hierarchical decoupling of policies into two parts: an independently learned low-level policy and a transferable high-level policy. To remedy poor transfer performance due to mismatch in morphologies, we contribute two key ideas. First, we show that incentivizing a complex agent's low-level to imitate a simpler agent's low-level significantly improves zero-shot high-level transfer. Second, we show that KL-regularized training of the high level stabilizes learning and prevents mode-collapse. Finally, on a suite of publicly released navigation and manipulation environments, we demonstrate the applicability of hierarchical transfer on long-range tasks across morphologies. Our code and videos can be found at this https URL.
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards the design of key technologies both in healthcare and other industries. MI data is generated when a subject imagines movement of limbs and can be used to aid rehabilitation as well as in autonomous driving scenarios. Thus, classification of MI signals is vital for EEG-based BCI systems. Recently, MI EEG classification techniques using deep learning have shown improved performance over conventional techniques. However, due to inter-subject variability, the scarcity of unseen subject data, and low signal-to-noise ratio, extracting robust features and improving accuracy is still challenging. In this context, we propose a novel two-way few shot network that is able to efficiently learn how to learn representative features of unseen subject categories and how to classify them with limited MI EEG data. The pipeline includes an embedding module that learns feature representations from a set of samples, an attention mechanism for key signal feature discovery, and a relation module for final classification based on relation scores between a support set and a query signal. In addition to the unified learning of feature similarity and a few shot classifier, our method leads to emphasize informative features in support data relevant to the query data, which generalizes better on unseen subjects. For evaluation, we used the BCI competition IV 2b dataset and achieved an 9.3% accuracy improvement in the 20-shot classification task with state-of-the-art performance. Experimental results demonstrate the effectiveness of employing attention and the overall generality of our method.
Non-Hermitian topology of one-dimensional spin-torque oscillator arrays

Magnetic systems have been extensively studied both from a fundamental physics perspective and as building blocks for a variety of applications. Their topological properties, in particular those of excitations, remain relatively unexplored due to their inherently dissipative nature. The recent introduction of non-Hermitian topological classifications opens up new opportunities for engineering topological phases in dissipative systems. Here, we propose a magnonic realization of a non-Hermitian topological system. A crucial ingredient of our proposal is the injection of spin current into the magnetic system, which alters and can even change the sign of terms describing dissipation. We show that the magnetic dynamics of an array of spin-torque oscillators can be mapped onto a non-Hermitian Su-Schrieffer-Heeger model exhibiting topologically protected edge states. Using exact diagonalization of the linearized dynamics and numerical solutions of the non-linear equations of motion, we find that a topological magnonic phase can be accessed by tuning the spin current injected into the array. In the topologically nontrivial regime, a single spin-torque oscillator on the edge of the array is driven into auto-oscillation and emits a microwave signal, while the bulk oscillators remain inactive. Our findings have practical utility for memory devices and spintronics neural networks relying on spin-torque oscillators as constituent units.
ORDERS OF GROWTH AND GENERALIZED ENTROPY

Abstract We construct the complete set of orders of growth and define on it the generalized entropy of a dynamical system. With this object, we provide a framework wherein we can study the separation of orbits of a map beyond the scope of exponential growth. We show that this construction is particularly useful for studying families of dynamical systems with vanishing entropy. Moreover, we see that the space of orders of growth in which orbits are separated is wilder than expected. We achieve this with different types of examples.
Iterate Averaging Helps: An Alternative Perspective in Deep Learning

Iterate averaging has a rich history in optimisation, but has only very recently been popularised in deep learning. We investigate its effects in a deep learning context, and argue that previous explanations on its efficacy, which place a high importance on the local geometry (flatness vs sharpness) of final solutions, are not necessarily relevant. We instead argue that the robustness of iterate averaging towards the typically very high estimation noise in deep learning and the various regularisation effects averaging exert, are the key reasons for the performance gain, indeed this effect is made even more prominent due to the over-parameterisation of modern networks. Inspired by this, we propose Gadam, which combines Adam with iterate averaging to address one of key problems of adaptive optimisers that they often generalise worse. Without compromising adaptivity and with minimal additional computational burden, we show that Gadam (and its variant GadamX) achieve a generalisation performance that is consistently superior to tuned SGD and is even on par or better compared to SGD with iterate averaging on various image classification (CIFAR 10/100 and ImageNet 32$\times$32) and language tasks (PTB).
The Gamow Explorer: A Gamma-Ray Burst Mission to Study the High Redshift Universe

Long Gamma Ray Bursts (LGRBs) can be used to address key questions on the formation of the modern universe including: How does the star formation rate evolve at high redshift? When and how did the intergalactic medium become re-ionized? What processes governed its early chemical enrichment? A LGRB signals when a massive star collapses to form a black hole and in doing so provides an independent tracer of the star formation rate. The LGRB afterglow is a bright back-light to view the host galaxy and intergalactic medium in absorption. The Gamow Explorer will be optimized to search for high redshift LGRBs, with a z>6 detection rate at least ten times the Neil Gehrels Swift Observatory. Furthermore it will go beyond Swift by using the photo-z technique to autonomously identify >80% of z>6 redshift LGRBs to enable rapid follow up by large ground based telescopes and JWST for spectroscopy and host galaxy identification. The Gamow Explorer will be proposed to the 2021 NASA MIDEX opportunity for launch in 2028.
When Gas Dynamics Decouples from Galactic Rotation: Characterizing ISM Circulation in Disk Galaxies

In galactic disks, galactic rotation sets the bulk motion of gas, and its energy and momentum can be transferred toward small scales. Additionally, in the interstellar medium, random and noncircular motions arise from stellar feedback, cloud–cloud interactions, and instabilities, among other processes. Our aim is to comprehend to what extent small-scale gas dynamics is decoupled from galactic rotation. We study the relative contributions of galactic rotation and local noncircular motions to the circulation of gas, Γ, a macroscopic measure of local rotation, defined as the line integral of the velocity field around a closed path. We measure the circulation distribution as a function of spatial scale in a set of simulated disk galaxies and model the velocity field as the sum of galactic rotation and a Gaussian random field. The random field is parameterized by a broken power law in Fourier space, with a break at the scale . We define the spatial scale at which galactic rotation and noncircular motions contribute equally to Γ. For our simulated galaxies, the gas dynamics at the scale of molecular clouds is usually dominated by noncircular motions, but in the center of galactic disks galactic rotation is still relevant. Our model shows that the transfer of rotation from large scales breaks at the scale , and this transition is necessary to reproduce the circulation distribution. We find that , and therefore the structure of the gas velocity field, is set by the local conditions of gravitational stability and stellar feedback.