Miracle-dz/UniFAR-FTUs huggingface dataset:
How to prove your model belongs to you: a blind-watermark based framework to protect intellectual property of DNN

Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep neural networks is computationally expensive and requires both human and intellectual resources. Therefore, it is necessary to protect the intellectual property of the model and externally verify the ownership of the model. However, previous studies either fail to defend against the evasion attack or have not explicitly dealt with fraudulent claims of ownership by adversaries. Furthermore, they can not establish a clear association between the model and the creator's identity. To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility. Our framework accepts ordinary samples and the exclusive logo as inputs, outputting newly generated samples as watermarks, which are almost indistinguishable from the origin, and infuses these watermarks into DNN models by assigning specific labels, leaving the backdoor as the basis for our copyright claim. We evaluated our IPP framework on two benchmark datasets and 15 popular deep learning models. The results show that our framework successfully verifies the ownership of all the models without a noticeable impact on their primary task. Most importantly, we are the first to successfully design and implement a blind-watermark based framework, which can achieve state-of-art performances on undetectability against evasion attack and un-forgeability against fraudulent claims of ownership. Further, our framework shows remarkable robustness and establishes a clear association between the model and the author's identity.
An Algorithm for Routing Capsules in All Domains

Building on recent work on capsule networks, we propose a new, general-purpose form of "routing by agreement" that activates output capsules in a layer as a function of their net benefit to use and net cost to ignore input capsules from earlier layers. To illustrate the usefulness of our routing algorithm, we present two capsule networks that apply it in different domains: vision and language. The first network achieves new state-of-the-art accuracy of 99.1% on the smallNORB visual recognition task with fewer parameters and an order of magnitude less training than previous capsule models, and we find evidence that it learns to perform a form of "reverse graphics. " The second network achieves new state-of-the-art accuracies on the root sentences of the Stanford Sentiment Treebank: 58.5% on fine-grained and 95.6% on binary labels with a single-task model that routes frozen embeddings from a pretrained transformer as capsules. In both domains, we train with the same regime. Code is available at this https URL along with replication instructions.
Composing and Embedding the Words-as-Classifiers Model of Grounded Semantics

The words-as-classifiers model of grounded lexical semantics learns a semantic fitness score between physical entities and the words that are used to denote those entities. In this paper, we explore how such a model can incrementally perform composition and how the model can be unified with a distributional representation. For the latter, we leverage the classifier coefficients as an embedding. For composition, we leverage the underlying mechanics of three different classifier types (i.e., logistic regression, decision trees, and multi-layer perceptrons) to arrive at a several systematic approaches to composition unique to each classifier including both denotational and connotational methods of composition. We compare these approaches to each other and to prior work in a visual reference resolution task using the refCOCO dataset. Our results demonstrate the need to expand upon existing composition strategies and bring together grounded and distributional representations.
Change Your Singer: A Transfer Learning Generative Adversarial Framework for Song to Song Conversion

Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by generative adversarial and transfer learning, which allows users to listen to a selected target singer singing any song. SCM-GAN first separates songs into vocals and instrumental music using a U-Net network, then converts the vocal segments to the target singer using advanced CycleGAN-VC, before merging the converted vocals with their corresponding background music. SCM-GAN is first initialized with feature representations learned from a state-of-the-art voice-to-voice conversion and then trained on a dataset of non-parallel songs. After that, SCM-GAN is evaluated against a set of metrics including global variance GV and modulation spectra MS on the 24 Mel-cepstral coefficients (MCEPs). Transfer learning improves the GV by 35% and the MS by 13% on average. A subjective comparison is conducted to test the output’s similarity to the target singer and its naturalness. Results show that the SCM-GAN’s similarity between its output and the target reaches 69%, and its naturalness reaches 54%.
Trade and Human Capital Accumulation-Evidence from U . S . Immigrants

This study provides empirical evidence that trade increases on-the-job human capital accumulation by estimating the effect of home country openness on estimated returns to home country experience of U.S. immigrants. The positive effect of trade on on-the-job human capital accumulation remains significant when controlling for GDP, educational attainment and institutional quality. It is not the result of self-selection, heterogeneity in returns to experience, English speaking origin or cultural background. The effect persists when restricting the sample to non-OECD countries, thereby resolving the theoretical ambiguity whether trade increases or decreases learning-by-doing. The role of trade in generating economic growth is therefore likely to be more important than generally considered. JEL Classiffication: O15, F00 World Bank Policy Research Working Paper 4144, March 2007 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org. *I am very grateful to Antonio Ciccone for excellent advice and suggestions and thank Jaume Garcia, Christian Haefke, Adriana Kugler, Martin Menner, Joel Shapiro, Ernesto Villanueva, Lutz Weinke and Michael Wolf for very useful comments. Email: ddomelandnarvaez@worldbank.org. Tel: ++1-202-458-1238 WPS4144 P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed P ub lic D is cl os ur e A ut ho riz ed Human capital accumulation takes place in school, in research organizations and in the course of producing goods and engaging in trade.  Robert Lucas (1993)
TESS observations of Be stars: a new interpretation

Light curves of 57 classical Be stars in TESS sectors 1–15 are examined. In most Be stars, the periodogram shows groups at a fundamental and one or more harmonics, which we attribute to rotation. In about 40 per cent of the stars, the group is just a single narrow or slightly broadened peak. In about 30 per cent, it consists of a multiple, closely spaced peaks. These groups can be interpreted as non-coherent variations most likely associated with photospheric gas clouds. Approximate rotational frequencies for about 74 per cent of the stars can be derived. Comparison with the projected rotational velocities shows that the photometric frequency is consistent with rotation. The first harmonic plays a prominent role in many Be stars and manifests itself in either single-wave or double-wave light curves. The reduction in amplitude of β Cep pulsations in a few Be stars during an outburst and their subsequent recovery is most likely an obscuration effect. Other instances of possible obscuration of the photosphere are suspected. A simple model, which attempts to explain these observations and other general properties of Be stars, is proposed.
A general method for constructing essential uniform algebras

A general method for constructing essential uniform algebras with prescribed properties is presented. Using the method, the following examples are constructed: an essential, natural, regular uniform algebra on the closed unit disc; an essential, natural counterexample to the peak point conjecture on each manifold of dimension at least three; and an essential, natural uniform algebra on the unit sphere in C^3 containing the ball algebra and invariant under the action of the 3-torus. These examples show that a smoothness hypothesis in some results of Anderson and Izzo cannot be omitted.
The effects of peculiar velocities in SN Ia environments on the local H0 measurement

The discrepancy between estimates of the Hubble constant (H0) measured from local (z ≲ 0.1) scales and from scales of the sound horizon is a crucial problem in modern cosmology. Peculiar velocities (vpec) of standard candle distance indicators can systematically affect local H0 measurements. We here use 2MRS galaxies to measure the local galaxy density field, finding a notable z < 0.05 underdensity in the SGC-6dFGS region of 27 ± 2 per cent. However, no strong evidence for a ‘Local Void’ pertaining to the full 2MRS sky coverage is found. Galaxy densities are used to measure a density parameter, Δϕ+−, which we introduce as a proxy for vpec that quantifies density gradients along a supernova (SN) line of sight. Δϕ+− is found to correlate with local H0 estimates from 88 Pantheon Type Ia supernovae (SNe Ia; 0.02 < z < 0.05). Density structures on scales of ∼50 Mpc are found to correlate strongest with H0 estimates in both the observational data and in mock data from the MDPL2-Galacticus simulation. Using trends of H0 with Δϕ+−, we can correct for the effects of density structure on local H0 estimates, even in the presence of biased vpec. However, the difference in the inferred H0 estimate with and without the peculiar velocity correction is limited to < 0.1 per cent. We conclude that accounting for environmentally induced peculiar velocities of SN Ia host galaxies does not resolve the tension between local and CMB-derived H0 estimates.
Extreme value theory – An application to the South African equity market 1

Extreme value theory is a statistical technique that is especially useful in modelling the tail of a distribution. It has been applied in many areas including hydrology, oceanography and structural engineering. More recently it has found a number of applications in finance (See, for example, Embrechts, Klüppelberg and Mikosh (1997), McNeil (1997, 1999) and Gençay, Selçuk and Ulugülyağci (2002) for applications in insurance and financial risk management. ) EVT focuses on the observations that lie in the tail and attempts to fit a distribution to these observations. It then provides the functionality to extrapolate into unknown areas of the tail.
Regional Authority in Cross Border Dynamics

The Öresund Region is a historical cross border region working over the national borders of Sweden and Denmark. The regional administration, the Öresund Committee, is the only political forum that encompasses the whole region and serves its interests collectively. Not only is it cooperating with the national governments but also has a close relationship with the Nordic Council and especially with the European Union. According to a method developed by Liesbet Hooghe, Gary Marks and Arjan H. Schakel, it is possible to retrieve a Regional Authority Index which mirrors the formal authority of a region. The method is evaluated in accordance to the complications of the Öresund Region’s dual nationality and finds that the Öresund Committee has a rather concealed role with greater indirect impact than it is given credit for. However, in terms of formal authority, the Öresund Committee has a weak position and shares no rule with higher political entities, such as the Danish, Swedish and European central administrations.
Aalborg Universitet Internationalization in schools-perspectives of school leaders

This paper explores how internationalization ideas in primary and lower secondary schools can be developed through the acquisition of international experience abroad by leaders. The study was inspired by existing literature on internationalization and leadership, and theories of experiential learning and reflection. Empirically, qualitative material was derived from a study of nineteen Danish school leaders participating in an eight-day delegation visit to China. This study shows that international experience for leaders can be used to develop ideas for internationalization at the school level through reflections of lived experiences, participation in meaningful activities, and active engagement in interaction with international and local colleagues. However, the realization of ideas depends on various elements, including leadership, teacher engagement, policy support, and financial support. © 2017 Elsevier Ltd. All rights reserved.
MENGGUNAKAN PENDEKATAN STRUCTURAL EQUATION MODELLING DENGAN PARTIAL LEAST SQUARES . ( STUDI KASUS PADA PT . “ XYZ ” )

Abstrak— Krisis ekonomi yang berdampak pada pertumbuhan ekonomi di Indonesia mengalami kontraksi diberbagai sektor. Salah satu yang terkena gelombang krisis adalah Badan Pengelolah Industri Strategis (BPIS) yang menaungi PT. “XYZ” di sektor manufaktur logam dan peralatan berat. Terjadinya optimalisasi atau downsizing berdampak pada kondisi psikologis perusahaan dengan karyawan. Pada penelitian Samsul Arifin (2013), menganalisis pola hubungan antara manajemen karier organisasi dan manajemen karier individu terhadap komitmen organisasi karyawan di PT. “XYZ” menggunakan Structural Equation Modeling (SEM), akan tetapi tidak memenuhi asumsi distribusi normal multivariate. Sehingga dikembangkan pendekatan SEM menggunakan Partial Least Squares tanpa asumsi parametrik dan metode yang digunakan bootstraps atau “resampling”. Selain menganalisis pengaruh manajemen karier organisasi dan manajemen karier individu terhadap komitmen organisasi, dilakukan pembentukan variabel moderasi dari interaksi manajemen karier organisasi dan manajemen karier individu terhadap komitmen organisasi. Koefisien determinasinya (R 2 ) dari komitmen organisasi sebesar 0,4633, berarti hubungan model yang dibentuk pada variabel endogen “lemah” dan perlu adanya evaluasi terhadap perkembangan perusahaan. Berdasarkan nilai Q 2 , model memilki nilai prediksi relevan yang baik.
Composition-based Multi-Relational Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it. Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only. In this paper, we propose CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph. CompGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations. It also generalizes several of the existing multi-relational GCN methods. We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results. We make the source code of CompGCN available to foster reproducible research.
Minibands in twisted bilayer graphene probed by magnetic focusing

Detection of magnetic focusing peaks allows probing of minibands in the energy spectrum of twisted bilayer graphene. Magnetic fields force ballistic electrons injected from a narrow contact to move along skipping orbits and form caustics. This leads to pronounced resistance peaks at nearby voltage probes as electrons are effectively focused inside them, a phenomenon known as magnetic focusing. This can be used not only for the demonstration of ballistic transport but also to study the electronic structure of metals. Here, we use magnetic focusing to probe narrowbands in graphene bilayers twisted at ~2°. Their minibands are found to support long-range ballistic transport limited at low temperatures by intrinsic electron-electron scattering. A voltage bias between the layers causes strong minivalley splitting and allows selective focusing for different minivalleys, which is of interest for using this degree of freedom in frequently discussed valleytronics.
Interactive Refinement of Cross-Lingual Word Embeddings

Cross-lingual word embeddings transfer knowledge between languages: models trained for a high-resource language can be used in a low-resource language. These embeddings are usually trained on general-purpose corpora but used for a domain-specific task. We introduce CLIME, an interactive system that allows a user to quickly adapt cross-lingual word embeddings for a given classification problem. First, words in the vocabulary are ranked by their salience to the downstream task. Then, salient keywords are displayed on an interface. Users mark the similarity between each keyword and its nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We experiment clime on a cross-lingual text classification benchmark for four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME also improves test accuracy faster than an active learning baseline, and a simple combination of CLIME with active learning has the highest test accuracy.
Low-Resource Machine Translation using Interlinear Glosses

Neural Machine Translation (NMT) does not handle low-resource translation well because NMT is data-hungry and low-resource languages, by their nature, have limited parallel data. Many low-resource languages are morphologically rich, which complicates matters further by increasing data sparsity. However, a good linguist is capable of building a morphological analyzer in far fewer hours than it would take to collect and translate the amount of parallel data needed for conventional NMT. We combine the benefits of both NMT and linguistic information in our work. We use morphological analyzer to automatically generate interlinear glosses with dictionary or parallel data, and translate the source text to interlinear gloss as an interlingua representation, and finally translate into the target text using NMT trained on the ODIN dataset that includes a large collection of interlinear glosses and their corresponding target translations. Our result for translating from the interlinear gloss to the target text using the entire ODIN dataset achieves a BLEU score of 35.07. And our qualitative results show positive findings in a low-resource scenario of Turkish-English translation using 865 lines of training data. Our translation system yield better results than training NMT directly from the source language to the target language in a constrained-data setting, and is helpful to produce translation with sufficiently good content and fluency when data is scarce.
On the well-posedness of the Hall-magnetohydrodynamics system in critical spaces

Abstract We investigate the existence and uniqueness issues of the 3D incompressible Hall-magnetohydrodynamic system supplemented with initial velocity u0 and magnetic field B0 in critical regularity spaces. In the case where u0, B0 and the current belong to the homogeneous Besov space and are small enough, we establish a global result and the conservation of higher regularity. If the viscosity is equal to the magnetic resistivity, then we obtain the global well-posedness provided u0, B0 and J0 are small enough in the larger Besov space If then we also establish the local existence for large data, and exhibit continuation criteria for solutions with critical regularity. Our results rely on an extended formulation of the Hall-MHD system that has some similarities with the incompressible Navier–Stokes equations.
Schwarzschild geometry counterpart in semiclassical gravity

We investigate the effects of vacuum polarization on vacuum static spherically symmetric spacetimes. We start from the Polyakov approximation to the renormalized stress-energy tensor (RSET) of a minimally coupled massless scalar field. This RSET is not regular at $r=0$, so we define a regularized version of the Polyakov RSET. Using this regularized RSET, and under the previous symmetry assumptions, we find all the solutions to the semiclassical field equations in vacuum. The resulting counterpart to the Schwarzschild classical geometry substitutes the presence of an event horizon by a wormhole throat that connects an external asymptotically flat region with an internal asymptotic region possessing a naked singularity: there are no semiclassical vacuum solutions with well-defined Cauchy surfaces. We also show that the regularized Polyakov RSET allows for wormhole geometries of arbitrarily small throat radius. This analysis paves the way to future investigations of proper stellar configurations with an internal nonvacuum region.
Why Do Masked Neural Language Models Still Need Common Sense Knowledge?

Currently, contextualized word representations are learned by intricate neural network models, such as masked neural language models (MNLMs). The new representations significantly enhanced the performance in automated question answering by reading paragraphs. However, identifying the detailed knowledge trained in the MNLMs is difficult owing to numerous and intermingled parameters. This paper provides empirical but insightful analyses on the pretrained MNLMs with respect to common sense knowledge. First, we propose a test that measures what types of common sense knowledge do pretrained MNLMs understand. From the test, we observed that MNLMs partially understand various types of common sense knowledge but do not accurately understand the semantic meaning of relations. In addition, based on the difficulty of the question-answering task problems, we observed that pretrained MLM-based models are still vulnerable to problems that require common sense knowledge. We also experimentally demonstrated that we can elevate existing MNLM-based models by combining knowledge from an external common sense repository.