Miracle-dz/UniFAR-FTUs huggingface dataset:
Modifying Non-local Variations Across Multiple Views

We present an algorithm for modifying small non-local variations between repeating structures and patterns in multiple images of the same scene. The modification is consistent across views, even-though the images could have been photographed from different view points and under different lighting conditions. We show that when modifying each image independently the correspondence between them breaks and the geometric structure of the scene gets distorted. Our approach modifies the views while maintaining correspondence, hence, we succeed in modifying appearance and structure variations consistently. We demonstrate our methods on a number of challenging examples, photographed in different lighting, scales and view points.
Roll‐to‐Roll Fabrication of Solution Processed Electronics

The production of electronic devices using solution based (“wet”) deposition technologies has some decisive technical and commercial advantages compared to competing approaches like vacuum based (“dry”) manufacturing. Particularly, the potential to scale up production processes to large areas and high volumes by introducing continuous roll‐to‐roll (R2R) methods on flexible substrates has been the topic of intense studies from both applied research institutes and industry already for some years. Decisive steps forward have been achieved during that time, resulting in the dawn of commercial applications for a number of processes, while additional development work is still needed in some other fields. This review summarizes the work published during the last few years on the R2R printing and wet coating of electronic devices. An overview is presented of the basic operational principles for the most commonly used R2R printing and coating methods and techniques for proper web handling in R2R lines. Then, the most commonly used types of flexible substrate materials are introduced, followed by a review of the work published in the application areas of transparent conductor materials, printed electric connections, light emitting devices, photovoltaic energy generation, printed logic, and sensing.
Skewness map: estimating object orientation for high speed 3D object retrieval system

3D object retrieval system is a system where a similar or the same object in the database should be retrieved given a 2D query image (sketches or photographs). Unfortunately, as the appearance of 3D object might vary depending on the viewing directions, a vast amount of 2D rendered images must be processed (matched) to solve this problem. In this paper, we present a novel method called skewness map to relieve this problem. Skewness map can estimate the orientation of the object and select a few representative images accurately from the database; therefore matching every image in the database can be avoided. Experimental results show the retrieval system becomes much faster (14 times faster in matching time) and accurate in estimating the object orientation (less than one-degree error in average).
What You Submit Is Who You Are: A Multimodal Approach for Deanonymizing Scientific Publications

The peer-review system of most academic conferences relies on the anonymity of both the authors and reviewers of submissions. In particular, with respect to the authors, the anonymity requirement is heavily disputed and pros and cons are discussed exclusively on a qualitative level. In this paper, we contribute a quantitative argument to this discussion by showing that it is possible for a machine to reveal the identity of authors of scientific publications with high accuracy. We attack the anonymity of authors using statistical analysis of multiple heterogeneous aspects of a paper, such as its citations, its writing style, and its content. We apply several multilabel, multiclass machine learning methods to model the patterns exhibited in each feature category for individual authors and combine them to a single ensemble classifier to deanonymize authors with high accuracy. To the best of our knowledge, this is the first approach that exploits multiple categories of discriminative features and uses multiple, partially complementing classifiers in a single, focused attack on the anonymity of the authors of an academic publication. We evaluate our author identification framework, deAnon, based on a real-world data set of 3894 papers. From these papers, we target 1405 productive authors that each have at least three publications in our data set. Our approach returns a ranking of probable authors for anonymous papers, an ordering for guessing the authors of a paper. In our experiments, following this ranking, the first guess corresponds to one of the authors of a paper in 39.7% of the cases, and at least one of the authors is among the top 10 guesses in 65.6% of all cases. Thus, deAnon significantly outperforms current state-of-the-art techniques for automatic deanonymization.
Amplified insert assembly : an optimized approach to standard assembly of BioBrickTM genetic circuits

A modified BioBrickTM assembly method was developed with higher fidelity than current protocols. The method utilizes a PCR reaction with a standard primer set to amplify the inserted part. Background colonies are reduced by a combination of dephosphorylation and digestion with DpnI restriction endonuclease to reduce vector and insert background respectively. The molar ratio of the insert to vector in the ligation was also optimized, with the accuracy of the transformed construct approaching 100%.
Endocrinological actions of Unkei-to, a herbal medicine, and its clinical usefulness in anovulatory and/or infertile women

Kampo medicine (Chinese herbal medicine) has been reported to be effective in the treatment of pituitary-ovarian dysfunction in young women and in the treatment of undefined symptoms in perimenopausal women. It has been considered that both the diagnosis and treatment of ovulatory failure in adolescent girls or young women are necessary for the sake of future fertile capacity. However, treatment for the above patients is not easy as steroid hormones and strong stimulators of ovulation should not be recommended because of the several side-effects it causes. Furthermore, young women, especially teenagers, seem to be reluctant to visit gynecology clinics. A number of herbal medicines have been used for many centuries in China and Japan for the treatment of menstrual disorders and infertility. In general, the traditional Chinese herbal prescriptions are rather inexpensive and safe with little side-effects, and have properties for normalizing biological balances. Unkei-to is known to stimulate the synthesis and release of gonadotropins in the rat pituitary. In humans, Unkei-to is known to be effective in various menstrual disorders, abnormal uterine bleeding, and infertility. In the treatment of ovulatory failure, recent dynamic studies have revealed the mechanism by which Unkei-to stimulates the diencephalon-pituitary-ovarian axis in humans. In this paper, we critically review the clinical data available (to date) in order to assess the efficacy of Unkei-to in inducing ovulation in several types of ovulatory disturbances.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource – a probabilistic knowledge base acquired in the news domain – by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987–2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.
Construction of Management System for Coordination Degree of Regional Economic Development Based on the Fusion of Spatial Econometric Model

In view of the situation that the traditional management system for the regional economy is influenced by excessively large number of entities and the huge dynamic changes of the economic structure, a kind of management system for coordination degree of regional economic development (hereinafter referred to as MSCDRED for short) based on the fusion of the spatial econometric model is put forward in this paper. The spatial econometric model is used to optimize the nonlinear characteristic indexes that have the influence on the regional economic development, so as to further obtain the initial data for the management of the regional economic development. And then the multi objective theory of decision making is applied to conduct the trade mediation on the long-term conflict of the regional economy. Finally, the experimental results are analyzed, and the following can be known from the analysis: Compared with the other different regional economic management systems, the efficiency of management in the system designed in this paper is very high. And it can predict the regional economic development trend accurately, so as to provide a reference frame for the management of the regional economic development.
The Soybean GmNARK Affects ABA and Salt Responses in Transgenic Arabidopsis thaliana

GmNARK (Glycine max nodule autoregulation receptor kinase) is the homolog of Arabidopsis thaliana CLAVATA1 (CLV1) and one of the most important regulators in the process of AON (Autoregulation of Nodulation), a process that restricts excessive nodule numbers in soybean. However, except for the function in AON, little is known about this gene. Here, we report that GmNARK plays important roles in process of plant response to abiotic stresses. Bioinformatic analysis and subcellular localization experiment results showed that GmNARK was a putative receptor like kinase and located at membrane. The promoter of GmNARK contains manifold cis regulatory elements that are responsive to hormone and stresses. Gene transcript expression pattern analysis in soybean revealed GmNARK was induced by ABA and NaCl treatment in both shoot and root. Overexpression of GmNARK in Arabidopsis resulted in higher sensitivity to ABA and salt treatment during seed germination and greening stages. We also checked the expression levels of some ABA response genes in the transgenic lines; the results showed that the transcript level of all the ABA response genes were much higher than that of wild type under ABA treatment. Our results revealed a novel role of GmNARK in response to abiotic stresses during plant growth and development.
The Effect of Sanggua Drink Extract on Insulin Resistance through the PI3K/AKT Signaling Pathway

Treating type 2 diabetes mellitus (T2DM) using thiazolidinediones and biguanides can present several challenges for patients. Sanggua Drink (SGD) is a commonly used agent in traditional Chinese medicine, and it consists of Folium Mori, Fructus Momordicae Charantiae, Radix Puerariae Lobatae, and Rhizoma Dioscorea. The hypoglycemic effects and mechanisms of SGD extracts on insulin resistance in diabetic rats were investigated. SGD (1.24 g/kg orally) was verified in T2DM rats induced by a high-fat diet and streptozotocin. The results showed that SGD treatment was observed to reduce fasting blood glucose, water and food intake, total cholesterol triglycerides, and LDL, OGTT, FINS, HOMA-IR, GHb, and MDA and increase hepatic glycogen, HDL, SOD, CAT, and GSH-Px in diabetic rats. Simultaneously, SGD treatment by T2DM showed significantly ameliorated pathological changes and reduced inflammation in the pancreas. Treatment was also observed to increase gene and protein expressions of InsR, IRS-2, PI3K, AKT, and Glut4 in the livers of diabetic treated rats. These results suggest that SGD extracts have hypoglycemic properties and may alleviate insulin resistance in T2DM rats through the PI3K/AKT pathway. Therefore, SGD appears to be a promising insulin sensitizer.
Generating Structured Data by Nontechnical Experts in Research Settings

Abstract Semantic technologies provide meaning to information resources in the form of machine-accessible structured data. Research over the past two decades has commonly focused on tools and interfaces for technical experts, leading to various usability problems regarding users unfamiliar with the underlying technologies – so-called nontechnical experts. Existing approaches to semantic technologies consider mostly consumers of structured data and leave out the creation perspective. In this work, we focus on the usability of creating structured data from textual resources, especially the creation of relations between entities. The research was conducted in collaboration with scholars from the humanities. We review existing research on the usability of semantic technologies and the state of the art of annotation tools to identify shortcomings. Subsequently we use the knowledge gained to propose a new interaction design for the creation of relations between entities to create structured data in the subject-predicate-object form. We implemented our interaction design and conducted a user study which showed that the proposal performed well, making it a contribution to enhance the overall usability in this field. However, this research provides an example of how technically sophisticated technology needs to be “translated” to make it usable for nontechnical experts. We need to extend this perspective in the future by providing more insight into the internal functioning of semantic technologies.
Spectroscopic perspective on the interplay between electronic and magnetic properties of magnetically doped topological insulators

The work at PSI was supported by the Swiss National Science Foundation (SNF-Grant No. 200021_165910). We acknowledge support from DFG through priority program SPP1666 (Topological Insulators), University of the Basque Country (Grants No. GIC07IT36607 and No. IT-756-13), Spanish Ministry of Science and Innovation (Grants No. FIS2013-48286-C02-02-P, No. FIS2013-48286-C02-01-P, and No. FIS2016-75862-P) and Tomsk State University competitiveness improvement programme (Project No. 8.1.01.2017). Partial support by the Saint Petersburg State University Project No. 15.61. 202.2015 is also acknowledged. At MIT, C.Z. C. and J.S. M. acknowledge support from the STC Center for Integrated Quantum Materials under NSF grant DMR-1231319 as well as grants NSF (DMR-1207469, DMR1700137), ONR (N00014-13-1-0301 and N00014-16-1-2657). C.Z. C also acknowledges the support from the startup grant provided by Penn State University.
A new development in quantum field equations of dyons

In this study, we describe a novel approach to quantum phenomena of the generalized electromagnetic fields of dyons with quaternionic analysis. Starting with quaternionic quantum wave equations, we have established a quantized condition for time coordinate that transforms microscopic to macroscopic fields. In view of the classical electromagnetic field equations, we propose a new set of quantized Proca–Maxwell’s equations for dyons. Furthermore, a quantized form of four-current densities and the quantized Lorentz gauge conditions for electric and magnetic potentials, respectively, of dyons are obtained. We have established the new quantized continuity equations for electric and magnetic densities of dyons, which are associated with a torque density result from the two spin states. The quantized Klein–Gordon-like field equations and the unified quaternionic electromagnetic potential wave equations for massive dyons are demonstrated. Moreover, we investigate the quaternionic quantized relativistic Dirac field equations for massive dyons, which indicate that the antiparticle of dyons will exist, called antidyons.
A Univariate Bound of Area Under ROC

Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they require pairwise comparisons among training data, which leads to slow running time and increasing local storage for online learning. In this work, we describe a new surrogate loss based on a reformulation of the AUC risk, which does not require pairwise comparison but rankings of the predictions. We further show that the ranking operation can be avoided, and the learning objective obtained based on this surrogate enjoys linear complexity in time and storage. We perform experiments to demonstrate the effectiveness of the online and batch algorithms for AUC optimization based on the proposed surrogate loss.
Ahmaxsat: Description and Evaluation of a Branch and Bound Max-SAT Solver

Branch and bound (BnB) solvers for Max-SAT count at each node of the search tree the number of disjoint inconsistent subsets to compute the lower bound. In the last ten years, important advances have been made regarding the lower bound computation. Unit propagation based methods have been introduced to detect inconsistent subsets and a resolution-like inference rules have been proposed which allow a more incremental lower bound computation. We present in this paper our solver Ahmaxsat, which uses these new methods and improves them in several ways. The main contributions of our solver over the state of the art are: a new unit propagation scheme which considers all the variable propagation sources; extended sets of patterns which increase the amount of learning performed by the solver; a heuristic which modifies the order of application of the max-resolution rule when transforming inconsistent subset; and a new inconsistent subset treatment method which improves the lower bound estimation. We describe these four main contributions and we give a general overview of \ahmaxsat, with additional implementation details. Finally, we present the result of the experimental study we have conducted. We first evaluate the impact of each contribution on \ahmaxsat performances before comparing our solver to the current best performing BnB solvers. The obtained results confirm the ones of the Max-SAT Evaluation 2014 where \ahmaxsat was ranked first in three of the nine categories.
Modeling Storylines in Lyrics

This paper addresses the issue of modeling the discourse nature of lyrics and presented the first study aiming at capturing the two common discourse-related notions: storylines and themes. We assume that a storyline is a chain of transitions over topics of segments and a song has at least one entire theme. We then hypothesize that transitions over topics of lyric segments can be captured by a probabilistic topic model which incorporates a distribution over transitions of latent topics and that such a distribution of topic transitions is affected by the theme of lyrics. Aiming to test those hypotheses, this study conducts experiments on the word prediction and segment order prediction tasks exploiting a large-scale corpus of popular music lyrics for both English and Japanese (around 100 thousand songs). The findings we gained from these experiments can be summarized into two respects. First, the models with topic transitions significantly outperformed the model without topic transitions in word prediction. This result indicates that typical storylines included in our lyrics datasets were effectively captured as a probabilistic distribution of transitions over latent topics of segments. Second, the model incorporating a latent theme variable on top of topic transitions outperformed the models without such variables in both word prediction and segment order prediction. From this result, we can conclude that considering the notion of theme does contribute to the modeling of storylines of lyrics. key words: bayesian model, generative model, lyrics structure, lyrics understanding, natural language processing
On f-Divergences: Integral Representations, Local Behavior, and Inequalities

This paper is focused on f-divergences, consisting of three main contributions. The first one introduces integral representations of a general f-divergence by means of the relative information spectrum. The second part provides a new approach for the derivation of f-divergence inequalities, and it exemplifies their utility in the setup of Bayesian binary hypothesis testing. The last part of this paper further studies the local behavior of f-divergences.
Plasma and erythrocyte biomarkers of dairy fat intake and risk of ischemic heart disease 1 3

Background: The relation between dairy product intake and the risk of ischemic heart disease (IHD) remains controversial. Objective: We aimed to explore biomarkers of dairy fat intake in plasma and erythrocytes and to assess the hypothesis that higher concentrations of these biomarkers are associated with a greater risk of IHD in US women. Design: Among 32 826 participants in the Nurses’ Health Study who provided blood samples in 1989–1990, 166 incident cases of IHD were ascertained between baseline and 1996. These cases were matched with 327 controls for age, smoking, fasting status, and date of blood drawing. Results: Among controls, correlation coefficients between average dairy fat intake in 1986–1990 and 15:0 and trans 16:1n 7 content were 0.36 and 0.30 for plasma and 0.30 and 0.32 for erythrocytes, respectively. In multivariate analyses, with control for age, smoking, and other risk factors of IHD, women with higher plasma concentrations of 15:0 had a significantly higher risk of IHD. The multivariate-adjusted relative risks (95% CI) from the lowest to highest tertile of 15:0 concentrations in plasma were 1.0 (reference), 2.18 (1.20, 3.98), and 2.36 (1.16, 4.78) (P for trend 0.03). Associations for other biomarkers were not significant. Conclusions: Plasma and erythrocyte contents of 15:0 and trans 16:1n 7 can be used as biomarkers of dairy fat intake. These data suggest that a high intake of dairy fat is associated with a greater risk of IHD. Am J Clin Nutr 2007;86:929–37.