Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft Contextual Data Augmentation for Neural Machine Translation

While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In this paper, we present a novel data augmentation method for neural machine translation.Different from previous augmentation methods that randomly drop, swap or replace words with other words in a sentence, we softly augment a randomly chosen word in a sentence by its contextual mixture of multiple related words. More accurately, we replace the one-hot representation of a word by a distribution (provided by a language model) over the vocabulary, i.e., replacing the embedding of this word by a weighted combination of multiple semantically similar words. Since the weights of those words depend on the contextual information of the word to be replaced,the newly generated sentences capture much richer information than previous augmentation methods. Experimental results on both small scale and large scale machine translation data sets demonstrate the superiority of our method over strong baselines.
SQIL: Imitation Learning via Regularized Behavioral Cloning

Learning to imitate expert behavior given action demonstrations containing high-dimensional, continuous observations and unknown dynamics is a difficult problem in robotic control. Simple approaches based on behavioral cloning (BC) suffer from state distribution shift, while more complex methods that generalize to out-of-distribution states can be difficult to use, since they typically involve adversarial optimization. We propose an alternative that combines the simplicity of BC with the robustness of adversarial imitation learning. The key insight is that under the maximum entropy model of expert behavior, BC corresponds to fitting a soft Q function that maximizes the likelihood of observed actions. This perspective suggests a way to regularize BC so that it generalizes to out-of-distribution states: combine the standard maximum-likelihood objective with a penalty on the soft Bellman error of the soft Q function. We show that this penalty term gives the agent an incentive to take actions that lead it back to demonstrated states when it encounters new states. Experiments show that our method outperforms BC and GAIL on a variety of image-based and low-dimensional environments in Box2D, Atari, and MuJoCo.
What is the relationship between face alignment and facial expression recognition?

Face expression recognition is still a complex task, particularly due to the presence of head pose variations. Although face alignment approaches are becoming increasingly accurate for characterizing facial regions, it is important to consider the impact of these approaches when they are used for other related tasks such as head pose registration or facial expression recognition. In this paper, we compare the performance of recent face alignment approaches to highlight the most appropriate techniques for preserving facial geometry when correcting the head pose variation. Also, we highlight the most suitable techniques that locate facial landmarks in the presence of head pose variations and facial expressions.
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation

Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject. Such face with very low-resolution is totally out of detail information of the face identity compared to normal resolution in a gallery and hard to find corresponding faces therein. To this end, we propose a Resolution Invariant Model (RIM) for addressing such cross-resolution face recognition problems, with three distinct novelties. First, RIM is a novel and unified deep architecture, containing a Face Hallucination sub-Net (FHN) and a Heterogeneous Recognition sub-Net (HRN), which are jointly learned end to end. Second, FHN is a well-designed tri-path Generative Adversarial Network (GAN) which simultaneously perceives facial structure and geometry prior information, i.e. landmark heatmaps and parsing maps, incorporated with an unsupervised cross-domain adversarial training strategy to super-resolve very low-resolution query image to its 8x larger ones without requiring them to be well aligned. Third, HRN is a generic Convolutional Neural Network (CNN) for heterogeneous face recognition with our proposed residual knowledge distillation strategy for learning discriminative yet generalized feature representation. Quantitative and qualitative experiments on several benchmarks demonstrate the superiority of the proposed model over the state-of-the-arts. Codes and models will be released upon acceptance.
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems

Many recommendation algorithms are available to digital library recommender system operators. The effectiveness of algorithms is largely unreported by way of online evaluation. We compare a standard term-based recommendation approach to two promising approaches for related-article recommendation in digital libraries: document embeddings, and keyphrases. We evaluate the consistency of their performance across multiple scenarios. Through our recommender-as-a-service Mr. DLib, we delivered 33.5M recommendations to users of Sowiport and Jabref over the course of 19 months, from March 2017 to October 2018. The effectiveness of the algorithms differs significantly between Sowiport and Jabref (Wilcoxon rank-sum test; p < 0.05). There is a ~400% difference in effectiveness between the best and worst algorithm in both scenarios separately. The best performing algorithm in Sowiport (terms) is the worst performing in Jabref. The best performing algorithm in Jabref (keyphrases) is 70% worse in Sowiport, than Sowiport`s best algorithm (click-through rate; 0.1% terms, 0.03% keyphrases).
Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting when Learning Cumulatively

In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this paper, we present a new connectionist model, the Sequential Neural Coding Network, and its learning procedure, grounded in the neurocognitive theory of predictive coding. The architecture experiences significantly less forgetting as compared to standard neural models and outperforms a variety of previously proposed remedies and methods when trained across multiple task datasets in a stream-like fashion. The promising performance demonstrated in our experiments offers motivation that directly incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, can create viable pathways for tackling the challenge of lifelong machine learning.
Indicating Asynchronous Multipliers

Multiplication is a basic arithmetic operation that is encountered in almost all general-purpose microprocessing and digital signal processing applications, and multiplication is physically realized using a multiplier. This paper discusses the physical implementation of indicating asynchronous multipliers, which are inherently elastic and are robust to timing, process, and parametric variations, and are modular. We consider the physical implementation of many weak-indication asynchronous multipliers using a 32/28-nm CMOS technology by adopting the array multiplier architecture. The multipliers are synthesized in a semi-custom ASIC-design style. The 4-phase return-to-zero (RTZ) and the 4-phase return-to-one (RTO) handshake protocols are considered for the data communication. The multipliers are realized using strong-indication or weak-indication full adders. Strong-indication 2-input AND function is used to generate the partial products in the case of both RTZ and RTO handshaking. The full adders considered are derived from different indicating asynchronous logic design methods. Among the multipliers considered, a weak-indication asynchronous multiplier utilizing the biased weak-indication full adder is found to be efficient in terms of the cycle time and the power-cycle time product with respect to both RTZ and RTO handshaking. Also, the 4-phase RTO handshake protocol is found to be preferable than the 4-phase RTZ handshake protocol for achieving enhanced optimizations in the design metrics.
Do Both U.S. and Foreign Macro Surprises Matter for the Intraday Exchange Rate? Evidence from Japan

We investigate the effects of both U.S. and Japanese news surprises, measured as the difference between macroeconomic announcements and preceding survey expectations, on the intraday JPY/USD exchange rate. No previous study has considered the intraday JPY/USD exchange rate responses to a broad set of comparable news surprises from both the U.S. and Japan. We show that news surprises from Japan are as influential as those from the U.S. in moving 5-minute JPY/USD exchange rate returns and, therefore, focusing on U.S. news while disregarding foreign news misses half the story. Our results also show that distinguishing between positive and negative news surprises and the state of the Japanese business cycle is important in understanding the link between exchange rates and news.
Interbank lending rates and monetary policy in China ∗

This paper explores the relationship between the Interbank lending rate CHIBOR and monetary policy in China. In addition to standard macroeconomic variables, a second model controls for the influence of stock prices. The empirical evidence suggests based on monthly data in between April 1999 and November 2013 that positive shocks to the Interbank lending rate lead to an increase of domestic credit in the short-run. Shocks to domestic credit have a persistent negative impact on the Interbank lending rate in China. Controlling for stock prices in a second model, yields comparable results for shocks to both CHIBOR and domestic credit. ∗I would like to thank Professor Kent Kimbrough, for his suggestions and guidance, particularly with regard to this paper. Additionally, I would like to thank Professor Edward Tower and Professor Charles Becker for their suggestions in preparing this paper for submission. †Department of Economics, Duke University, 213 Social Sciences Building, 27708 Durham, USA. Email: johannes.maus@duke.edu.
Social–Cognitive Predictors of Low-Income Parents’ Restriction of Screen Time Among Preschool-Aged Children

Parents’ rules regarding child television, DVD, video game, and computer use (screen time) have been associated with lower screen use in children. This study aimed to identify modifiable correlates of this behavior by examining social–cognitive predictors of parents’ restriction of child screen time. Low-income parents (N = 147) of preschool-aged children (2-6 years) completed self-administered questionnaires examining parent and child screen time, parent restriction of screen time, self-efficacy to restrict screen time, and beliefs about screen time. Structural equation modeling results indicated that greater self-efficacy to restrict screen time (β = .29, p = .016) and greater perceived importance of restricting child screen use (β = .55, p < .001) were associated with greater restriction of child screen use, after controlling for parent screen time. Family-based interventions that consider broader attitudinal factors around child screen time may be necessary to engage parents in restricting screen use.
Electrical Impedance Tomography

I. BROWN R. A~: A stereotaxic head frame for the use with CT body scanners. Invest. Radiol. 14 (1979), 300. 2. FREDRICK R. , BROWN T, H. , MILLER M. H. , BAHR M. L. & TAYLOR K. H. : A light-guidance system to be used for CTguided biopsy. Radiology 154 (1985), 535. 3. LEWANDER R. , BERGSTROM M. , BOETIDUS J. et al.: Stereotactic computer tomography for biopsy of glioma. Acta Radiol. Diagnosis 19 (1978), 867. 4. MAGNUSSON A. : CT-guided biopsies of thoracic, abdominal and pelvic lesions. (In Swedish.) Liikartidningen 85 (1988), 1886. 5. ONIK G. , COSMAN E. R. , WELLS T. H. et al.: CT guided aspirations for the body. Comparison of hand guidance with stereotaxis. Radiology 166 (1988), 389. 6. ONIK G. , COSTELLO P., COSMAN E. et al.: CT body stereotaxis. An aid for CT-guided biopsies. AJR 146 (1986), 163. 7. PALESTRANT A. M. : Comprehensive approach to CT-guided procedures with a hand-held guidance device. Radiology 174 (1990),270. 8. PARKER S. H. , HOPPER K. D. , YAKES W. F. , GIBSON M. D. , OWNBEY J. L. & CARTER T. E. : Image-directed percutaneous biopsies with a biopty gun. Radiology 171 (1989),663. 9. WUNSCIDK F. , GEORGI M. & PASTYR 0. : Stereotactic biopsy using computed tomography. J. Comput. Assist. Tomogr. 8 (1984), 32.
Freight Demand Modeling and Data Improvement

Understanding freight flows and forecasting them is critical to determining the need for future transportation capacity on the nation’s highways or other modal infrastructure. However, current freight demand forecasting models and data sources are based on methods and tools developed for passenger transportation, and are inadequate for forecasting freight movements or volumes. The overall objective of SHRP2 C-20 is to “foster fresh ideas and new approaches to designing and implementing freight demand modeling,” instead of addressing freight movement planning through short-term measures that represent improvements to the current state of practice but still contain many inherent weaknesses. This research report documents the current state of the practice for freight demand modeling and the data required for effective freight modeling, data collection, and decision-making. The information presented is derived from outreach to practitioners and decision-makers at all levels, in both the public and private sectors. The report also provides a strategic plan to advance a broad new direction for improving freight planning; promoting continuous innovation for breakthrough solutions to freight analytical and data needs; and fostering a collaborative approach for private, public, and academic stakeholders.
Graph Grammar Based Product Family Modeling

Many industries are shifting from mass production to mass customization, which demands quick response to the needs of individual customers with high quality and low costs. The development of product families has received an increasing interest in recent years because, by sharing components across products, a family of products can be derived to cater variety while maintaining the economy of scale. Aiming at the computerization, and eventual automation, of product family design, this paper tackles the formal representation issue surrounding this economically important class of engineering design problem. Breaking free from conventional understanding of product families, which is limited as shared components, the paper defines a product family as a structured system to create variety of products with shared core product technologies. It not only involves the shared base product, but also encompasses customization modules, standard designs, and primary patterns of variety to generate custom designs. The paper introduces graph grammar formalisms to the modeling of such a product family. Based on Programmed Attributed Graph Grammars (PAGG), the graph language is developed to specify the design space of the product family. The process of customizing the base product through manipulating particular modules is modeled by rewriting the starting graph using a series of productions according to the control diagram. Configuration constraints are dealt with by defining application conditions for production rules. Control diagrams are constructed to capture complex relationships among modules and used to control the application sequence of production rules. A case study of power supplies is presented to demonstrate the potential of the graph grammar based modeling approach.
Extraction de code fonctionnel certifié à partir de spécifications inductives. (Extraction of Certified Functional Code from Inductive Specifications)

Les outils d’aide a la preuve bases sur la theorie des types permettent a l’utilisateur d’adopter soit un style fonctionnel, soit un style relationnel (c’est-a-dire en utilisant des types inductifs). Chacun des deux styles a des avantages et des inconvenients. Le style relationnel peut etre prefere parce qu’il permet a l’utilisateur de decrire seulement ce qui est vrai, de s’abstraire temporairement de la question de la terminaison, et de s’en tenir a une description utilisant des regles. Cependant, une specification relationnelle n’est pas executable.Nous proposons un cadre general pour transformer une specification inductive en une specification fonctionnelle, en extrayant a partir de la premiere une fonction et en produisant eventuellement la preuve de correction de la fonction extraite par rapport a sa specification inductive. De plus, a partir de modes definis par l’utilisateur, qui permettent de considerer les arguments de la relation comme des entrees ou des sorties (de fonction), nous pouvons extraire plusieurs comportements calculatoires a partir d’un seul type inductif.Nous fournissons egalement deux implantations de notre approche, l’une dans l’outil d’aide a la preuve Coq et l’autre dans l’environnement Focalize. Les deux sont actuellement distribuees avec leurs outils respectifs.
Weakly Supervised Object Boundaries Supplementary material

In this work we propose to train boundary detectors using weakly supervised annotations. We propose and evaluate multiple strategies to generate annotations fusing different sources, such as unsupervised image segmentation [2], object proposal methods [10, 5], and object detectors [3, 6] (trained on bounding boxes). Figure 5 illustrates the examples of the proposed weakly supervised boundary annotations, these extend the example in Figure 4 of the main paper. See Section 5 of the main paper for more details.
Nucleotide Sequence and Polymorphism of the Pig Acyl Coenzyme A Synthetase Long-Chain 1 (ACSL1) Gene

We have sequenced 3,013 bp of the pig acyl coenzyme A long-chain synthetase 1 (ACSL1) gene. Sequence analysis allowed us to identify three conserved elements in the predicted amino acid sequence, two of them related to the ATP/AMP signature motif and the third involved in enzyme catalysis and fatty acid substrate specificity. In addition, we have identified five C → T and one G → A transition polymorphisms located in exon 16 (SNPe16), exon 17 (SNPe17) and the 3′ UTR (SNPa to d), which have been genotyped in 143 pigs from the Landrace, Large White, Piétrain, Iberian, and Duroc breeds.
Cysts of a Toxoplasma-like organism in an Atlantic bottlenose dolphin.

Reports of toxoplasmosis in marine mammals are limited to three captive pinnipeds [two California sea lions Zalophus californianus (1,2), one harbor seal, Phoca vitulina (3)], and a free ranging West Indian manatee, Trichechus manatus (4). Our finding of an organism morphologically and immunohistochemically indistinguishable from Toxoplasma gondii in the liver and adrenal glands of an Atlantic bottlenose dolphin (Tursiops truncatus) appears to be the first reported in a free-ranging cetacean. On February 1, 1988, an immature male bottlenose dolphin (188 cm total length) was observed close to shore near Ocean Beach, Florida. Attempts to guide the animal back to sea were unsuccessful, and it was transported to Marineland of Florida, St. Augustine, for rehabilitation. Despite two days of intensive care along with antibiotic and corticosteroid therapy, its condition deteriorated. It was euthanized February 3, 1989. Samples collected during routine postmortem examination were preserved in 10% neutral buffered formalin, processed according to standard histological techniques, and stained with hematoxylin and eosin (H&E).
Why Do We Share? The Impact of Viral Videos Dramatized to Sell

ABSTRACT The decline of traditional mass-media advertising impels marketers to find new ways of engaging consumers. One innovative marketing technique of branded entertainment is the minifilm—otherwise known as microfilm advertising (MFA)—a short streaming advertisement designed to go viral. Applying media and advertising theories, this study investigated the format and the persuasive effects of MFA. Structural-equation modeling results demonstrated that “transportation” (the extent of a message being absorbed into the narrative flow of a story) and enjoyment significantly influenced attitude toward the advertisement and forwarding intention, but did not directly influence attitude toward the brand. The persuasive mechanism discovered by this study has academic and practical implications for branded entertainment advocates.
Development of a tree shrew-specific interferon-gamma assay

ABSTRACT Tree shrews (Tupaia belangeri) are small squirrel-like mammals closely related to primates. Due to their susceptibility to several human viruses, tree shrews have been proposed as potential animal models for the study of human viral infections. However, there are no standardized assays currently available for the detection of tree shrew-specific interferon (IFN)-γ, a major cytokine secreted during the antiviral immune response. Herein, we developed a novel enzyme-linked immunosorbent assay (ELISA) for the quantification of IFN-γ in tree shrew serum samples. Tree shrew-specific IFN-γ was expressed in Escherichia coli via fusion with glutathione S-transferase (GST-TS-IFN-γ) to obtain recombinant IFN-γ. To generate anti-IFN-γ monoclonal antibodies, mice were immunized with the GST-TS-IFN-γ recombinant fusion protein, and hybridoma cell lines were established. Similarly, anti-IFN-γ polyclonal antibodies were obtained from immunized rabbits, purified, and conjugated to horseradish peroxidase (HRP). Based on the results obtained from the antibody matching test, we optimized the monoclonal antibody (1:2000) and the HRP-conjugated polyclonal antibody (1:8000) as coating and detection antibodies, respectively. Titration curves were generated with recombinant IFN-γ to develop a sensitive sandwich ELISA; the lowest detection limit of the assay was 20 ng/mL. We also tested mitogen-stimulated tree shrew blood samples in this ELISA, and found significantly higher levels of IFN-γ in the stimulated versus the unstimulated samples. Most importantly, our ELISA system detected native IFN-γ in serum samples from 50 healthy tree shrews. We have thus developed a novel ELISA, and have demonstrated the first ELISA-based measurement of IFN-γ in tree shrew serum samples.
Electro-optic dual-comb interferometry over 40  nm bandwidth.

Dual-comb interferometry is a measurement technique that uses two laser frequency combs to retrieve complex spectra in a line-by-line basis. This technique can be implemented with electro-optic frequency combs, offering intrinsic mutual coherence, high acquisition speed and flexible repetition-rate operation. A challenge with the operation of this kind of frequency comb in dual-comb interferometry is its limited optical bandwidth. Here, we use coherent spectral broadening and demonstrate electro-optic dual-comb interferometry over the entire telecommunications C band (200 lines covering ∼40 nm, measured within 10 μs at 100 signal-to-noise ratio per spectral line). These results offer new prospects for electro-optic dual-comb interferometry as a suitable technology for high-speed broadband metrology, for example in optical coherence tomography or coherent Raman microscopy.