Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetocapacitance and loss factor of GaAs quantum Hall effect devices

We present a simple and accurate coaxial bridge capable of measuring the magnetocapacitance and the associated loss factor of a quantum Hall effect device, with and without an external Hall current—a situation where commercial instruments are limited. We interpret the results in terms of the model of compressible and incompressible regions in the two-dimensional electron gas and we deduce a novel empirical relation between the loss factor and the voltage dependence of the magnetocapacitance. This highlights the reason for the linear voltage dependence of the loss factor whose elimination is the basis for all metrological applications of the quantum Hall resistance at alternating current.
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning

Recent work has highlighted the advantage of jointly learning grounded sentence representations from multiple languages. However, the data used in these studies has been limited to an aligned scenario: the same images annotated with sentences in multiple languages. We focus on the more realistic disjoint scenario in which there is no overlap between the images in multilingual image--caption datasets. We confirm that training with aligned data results in better grounded sentence representations than training with disjoint data, as measured by image--sentence retrieval performance. In order to close this gap in performance, we propose a pseudopairing method to generate synthetically aligned English--German--image triplets from the disjoint sets. The method works by first training a model on the disjoint data, and then creating new triples across datasets using sentence similarity under the learned model. Experiments show that pseudopairs improve image--sentence retrieval performance compared to disjoint training, despite requiring no external data or models. However, we do find that using an external machine translation model to generate the synthetic data sets results in better performance.
Magnetic Doppler imaging of the roAp star HD 24712

Aims. We present the first magnetic Doppler images of a rapidly oscillating Ap (roAp) star. Methods. We deduce information about magnetic field geometry and abundance distributions of a number of chemical elements on the surface of the hitherto best studied roAp star, HD 24712, using the magnetic Doppler imaging (MDI) code, invers10, which allows us to reconstruct simultaneously and consistently the magnetic field geometry and elemental abundance distributions on a stellar surface. For this purpose we analyse time series spectra obtained in Stokes I and V parameters with the SOFIN polarimeter at the Nordic Optical Telescope and recover surface abundance structures of sixteen different chemical elements, respectively ions, including Mg, Ca, Sc, Ti, Cr, Fe, Co, Ni, Y, La, Ce, Pr, Nd, Gd, Tb, and Dy. For the rare earth elements (REE) Pr and Nd separate maps were obtained using lines of the first and the second ionization stage. Results. We find and confirm a clear dipolar structure of the surface magnetic field and an unexpected correlation of elemental abundance with respect to this field: one group of elements accumulates solely where the positive magnetic pole is visible, whereas the other group avoids this region and is enhanced where the magnetic equatorial region dominates the visible stellar surface. We also observe relative shifts of abundance enhancement- or depletion regions between the various elements exhibiting otherwise similar behaviour.
Universal Flying Objects (UFOs): Modular Multirotor System for Flight of Rigid Objects

We introduce UFO, a modular aerial robotic platform for transforming a rigid object into a multirotor robot. To achieve this, we develop flight modules, in the form of a control module and propelling modules, that can be affixed to an object. The object, or payload, serves as the airframe of the vehicle. The modular design produces a highly versatile platform as it is reconfigurable by the addition or removal of flight modules, adjustment of the modules' arrangement, or change of payloads. To facilitate the flight control, we propose an IMU-based estimation strategy for rapid computation of the robot's configuration. When combined with the adaptive geometric controller for further refinement of uncertain parameters, stable flights are accomplished with minimal manual intervention or tuning required by a user. To this end, we demonstrate hovering and trajectory tracking flights through various robot's configurations with different dummy payloads, weighing ~200-800 grams, using four to eight propelling modules. The results reveal that stable flights are attainable thanks to the proposed IMU-based estimation method. The flight performance is markedly improved over time through the adaptive scheme, with the position errors of a few centimeters after the parameter convergence.
Distilling the Knowledge of BERT for Text Generation

Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks. However, it remains an open question how to utilize BERT for text generation tasks. In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting. We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset. The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation. By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization. Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets.
GraphDefense: Towards Robust Graph Convolutional Networks

In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have shown that the original GCNs are very unstable to adversarial perturbations. In particular, we can observe a severe performance degradation by slightly changing the graph adjacency matrix or the features of a few nodes, making it unsuitable for security-critical applications. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate. We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset.
Learning GENERAL Principles from Hundreds of Software Projects

Managers and practitioners become dubious about software analytics when its conclusions keep changing as we look at new projects. GENERAL is a new approach for quickly finding conclusions that generalize across hundreds of projects. This algorithm (a) removes spurious attributes via feature selection; (b) fixes training data imbalance via synthetic instances; (c) recursively clusters the project data; (d) finds the best model within any cluster, then promotes it up the cluster tree; (e) returns the model promoted to the top. GENERAL is much faster than prior methods (4.8 hours versus 204 hours our case studies) and theoretically scales better (O(N^2/m) versus O(N^2), which is a large reduction since often we find m>20 clusters). When tested on 756 Github projects, a single defect prediction model generalized over all those projects while also being useful and insightful and generalizable; i.e. that model worked just as well as 756 separate models learned from each project; and that model succinctly show what key factors most contributed to defects. Hence, when exploring hundreds of projects, we endorse GENERAL reasoning.
Utilizing Intrinsic Motivation in the Design of Instruction.

A synthesis of the flow theory of intrinsic motivation of Csikszentmihalyi and the motivational design theory of instruction of Keller is presented. The ccnceptual integration of these theories is the basis for prescriptions aimed at increasing the level of intrinsic motivation in instruction. The flow phenomenon is described in terms of challenge and skills. When these are in balance, people feel that they are able to achieve a challenging but realistic goal. Flow is perceived as a positive experience because mastery of an achievable challenge stretches capabilities by promoting the development of new skills and increasing self-esteem and personal complexity. If the flow theory is integrated into the categories of Keller's theory (effort, performance, and consequences), effort can be seen to correspond to the challenge parameter, whereas performance corresponds to skills and consequences correspond to feedback in the flow model. A literature review identifies the recommendations and prescriptions of various authors into the variables of the synthesis, which are conceptualized as challenges (incorporating goals), means (including skills), and feedback. T40 figures and three tables illustrate the synthesis. (Contains 16 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** U.S. DEPARTMENT Of EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) O Th document has been reproduced as received from the person or organization originating a O Minor changes have been mach to improve (*production quality Points of view or opinions stated in this document do not necessarily represent official OERI position or policy
(When) Is Truth-telling Favored in AI Debate?

For some problems, humans may not be able to accurately judge the goodness of AI-proposed solutions. Irving et al. (2018) propose that in such cases, we may use a debate between two AI systems to amplify the problem-solving capabilities of a human judge. We introduce a mathematical framework that can model debates of this type and propose that the quality of debate designs should be measured by the accuracy of the most persuasive answer. We describe a simple instance of the debate framework called feature debate and analyze the degree to which such debates track the truth. We argue that despite being very simple, feature debates nonetheless capture many aspects of practical debates such as the incentives to confuse the judge or stall to prevent losing. We then outline how these models should be generalized to analyze a wider range of debate phenomena.
In-cloud formation of secondary species in iron-containing particles

Abstract. The increase in secondary species through cloud processing potentially increases aerosol iron (Fe) bioavailability. In this study, a ground-based counterflow virtual impactor coupled with a real-time single-particle aerosol mass spectrometer was used to characterize the formation of secondary species in Fe-containing cloud residues (dried cloud droplets) at a mountain site in southern China for nearly 1 month during the autumn of 2016. Fe-rich, Fe-dust, Fe-elemental carbon (Fe-EC), and Fe-vanadium (Fe-V) cloud residual types were obtained in this study. The Fe-rich particles, related to combustion sources, contributed 84 % (by number) to the Fe-containing cloud residues, and the Fe-dust particles represented 12 %. The remaining 4 % consisted of the Fe-EC and Fe-V particles. It was found that above 90 % (by number) of Fe-containing particles had already contained sulfate before cloud events, leading to no distinct change in number fraction (NF) of sulfate during cloud events. Cloud processing contributed to the enhanced NFs of nitrate, chloride, and oxalate in the Fe-containing cloud residues. However, the in-cloud formation of nitrate and chloride in the Fe-rich type was less obvious relative to the Fe-dust type. The increased NF of oxalate in the Fe-rich cloud residues was produced via aqueous oxidation of oxalate precursors (e.g., glyoxylate). Moreover, Fe-driven Fenton reactions likely increase the formation rate of aqueous-phase OH, improving the conversion of the precursors to oxalate in the Fe-rich cloud residues. During daytime, the decreased NF of oxalate in the Fe-rich cloud residues was supposed to be due to the photolysis of Fe-oxalate complexes. This work emphasizes the role of combustion Fe sources in participating in cloud processing and has important implications for evaluating Fe bioavailability from combustion sources during cloud processing.
Unsupervised Deep Contrast Enhancement With Power Constraint for OLED Displays

Various power-constrained contrast enhancement (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display while preserving the image quality. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Experimental results show that the proposed method is superior to conventional ones in terms of image quality assessment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).
The Relationship between Labor Market Conditions and Welfare Receipt in Australia : A Stock-Flow Analysis

This paper estimates the role of labor market condi tions in the recent decline in welfare receipt among the working age population in Austral ia. A stock-flow model is used, which involves modeling the underlying welfare flows and using the results to simulate the effect of labor market conditions on the welfare stock. The s imulation analysis suggests that improvements in the labor market explain the majori ty of the decline. A range of robustness checks are undertaken including using alternative l e ls of geographic disaggregation to deal with likely measurement error in labor market data. JEL classifications: C59, J11, I38. + This paper is derived from my PhD thesis. I am ind ebted to my supervisors Jeff Borland, John Creedy and Lixin Cai for their constructive gu idance. Thanks are also due to Paul Miller, Richard Disney, Tue Gorgens, Joshua Chan an d Cain Polidano for their invaluable comments. I acknowledge the Commonwealth Department of Education, Employment and Workplace Relation (DEEWR) for the sample of the LD S data and the assistance provided in using the sample. The research findings and the vie ws expressed in this paper are my own, and should not be attributed to the Commonwealth de partment.
Sharp test for equilibrium uniqueness in discrete games with a flexible information structure

I propose a test for an assumption commonly maintained when estimating static discrete games of incomplete information, i.e. the assumption of equilibrium uniqueness in the data generating process. The test is appealing for several reasons. It allows for discrete common knowledge payoff-relevant unobserved heterogeneity (henceforth unobserved heterogeneity, for short). In that sense, it is more general than tests which attribute all correlation between players’ decisions to multiple equilibria. Furthermore, the test does not require the estimation of payoffs to separate multiplicity of equilibria from unobservable heterogeneity. It is therefore useful in empirical applications leveraging multiple equilibria to identify the model’s primitives when commonly-used exclusion restrictions are not available. Finally, it makes no parametric assumption on the payoff functions nor the distribution of players’ private information. The main identifying assumption is the existence of an observable variable that plays the role of a proxy for the unobserved heterogeneity. The procedure boils down to testing the emptiness of the set of data generating processes that can rationalize the sample through a single equilibrium and a finite mixture over unobserved heterogeneity. Simulation evidence is provided to study the test’s properties.
Locally equivalent Floer complexes and unoriented link cobordisms

We show that the local equivalence class of the collapsed link Floer complex $cCFL^\infty(L)$, together with many $\Upsilon$-type invariants extracted from this group, is a concordance invariant of links. In particular, we define a version of the invariants $\Upsilon_L(t)$ and $\nu^+(L)$ when $L$ is a link and we prove that they give a lower bound for the slice genus $g_4(L)$. Furthermore, in the last section of the paper we study the homology group $HFL'(L)$ and its behaviour under unoriented cobordisms. We obtain that a normalized version of the $\upsilon$-set, introduced by Ozsvath, Stipsicz and Szabo, produces a lower bound for the 4-dimensional smooth crosscap number $\gamma_4(L)$.
Title Optimised analytical models of the dielectric properties ofbiological tissue

The interaction of electromagnetic fields with the human body is quantified by the dielectric properties of biological tissues. These properties are incorporated into complex numerical simulations using parametric models such as Debye and Cole-Cole, for the computational investigation of electromagnetic wave propagation within the body. These parameters can be acquired through a variety of optimisation algorithms to achieve an accurate fit to measured data sets. A number of different optimisation techniques have been proposed, but these are often limited by the requirement for initial value estimations or by the large overall error (often up to several percentage points). In this work, a novel two-stage genetic algorithm proposed by the authors is applied to optimise the multi-pole Debye parameters for 54 types of human tissues. The performance of the two-stage genetic algorithm has been examined through a comparison with five other existing algorithms. The experimental results demonstrate that the two-stage genetic algorithm Email addresses: S.salah-ud-din1@nuigalway.ie (Saqib Salahuddin), E.porter3@nuigalway.ie (Emily Porter), F.krewer1@nuigalway.ie (Finn Krewer), Martin.ohalloran@nuigalway.ie (Martin O’ Halloran) Preprint submitted to Elsevier January 3, 2017 produces an accurate fit to a range of experimental data and efficiently outperforms all other optimisation algorithms under consideration. Accurate values of the three-pole Debye models for 54 types of human tissues, over 500 MHz to 20 GHz, are also presented for reference.
Universal Communication, Universal Graphs, and Graph Labeling

We introduce a communication model called universal SMP, in which Alice and Bob receive a function $f$ belonging to a family $\mathcal{F}$, and inputs $x$ and $y$. Alice and Bob use shared randomness to send a message to a third party who cannot see $f, x, y$, or the shared randomness, and must decide $f(x,y)$. Our main application of universal SMP is to relate communication complexity to graph labeling, where the goal is to give a short label to each vertex in a graph, so that adjacency or other functions of two vertices $x$ and $y$ can be determined from the labels $\ell(x),\ell(y)$. We give a universal SMP protocol using $O(k^2)$ bits of communication for deciding whether two vertices have distance at most $k$ on distributive lattices (generalizing the $k$-Hamming Distance problem in communication complexity), and explain how this implies an $O(k^2\log n)$ labeling scheme for determining $\mathrm{dist}(x,y) \leq k$ on distributive lattices with size $n$; in contrast, we show that a universal SMP protocol for determining $\mathrm{dist}(x,y) \leq 2$ in modular lattices (a superset of distributive lattices) has super-constant $\Omega(n^{1/4})$ communication cost. On the other hand, we demonstrate that many graph families known to have efficient adjacency labeling schemes, such as trees, low-arboricity graphs, and planar graphs, admit constant-cost communication protocols for adjacency. Trees also have an $O(k)$ protocol for deciding $\mathrm{dist}(x,y) \leq k$ and planar graphs have an $O(1)$ protocol for $\mathrm{dist}(x,y) \leq 2$, which implies a new $O(\log n)$ labeling scheme for the same problem on planar graphs.
Understanding Young Students' Problem Solving Pathways: Building a Design Process Model Based on Sequential Analysis

is in design and cognition seeking to better understand how young students learn design and how design improves STEM education. team of researchers to create a program to improve learning STEM in elementary grades, and the team NSF Math and Science called Science Learning Through Engineering Design (SLED). Kelley currently the PI on an NSF I-Test project called Teachers and Researchers Advancing Integrated Lessons in STEM (TRAILS). TRAILS prepares science and technology education teachers to integrate STEM content through biomimicry inspired engineering design within the context of entomology. engineering/technology teacher Dr. Kelley the second year Design Thinking course for the Polytechnic Institute. The course is a collaboration between the Polytechnic and Anthropology to integrate ethnographic approaches by developing technological and engineering design human centered design solutions. Abstract Many STEM-related K-12 education standards, such as the Next Generation Science Standards, Standards for Technological Literacy, and Common Core Mathematics Standards, place great emphasis on designing as a way of delivering their core concepts. However, the classroom delivery of the design process has been the focus of very few studies. Particularly, most design-based lessons use a design process model, which poses a challenge for many engineering and technology educators. Therefore, the purpose of this study was to examine the actual process of engineering design using the sequential pattern analysis method. The researchers collected ten Concurrent Think-Aloud (CTA) protocols from fourth grade elementary students. The collected CTA sessions were coded using Halfin’s codes, and then analyzed using the sequential pattern analysis method. The study results suggested there exist iterative patterns of design cycles when participant students were more likely to follow specific iterative sequential patterns. To better illustrate these design behaviors, the researchers created a design pattern model based on the sequential analysis.