Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-Adaptive GAN based Reconstruction

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.
COMPUTER-BASED 3-DIMENSIONAL RECONSTRUCTION OF MAJOR ORGAN SYSTEMS OF A NEW AEOLID NUDIBRANCH SUBSPECIES, FLABELLINA ENGELI LUCIANAE, FROM BRAZIL (GASTROPODA: OPISTHOBRANCHIA)

Obtaining accurate and comprehensive anatomical information from small opisthobranch specimens has been a major problem. Computer-based 3-dimensional reconstruction from serial histological slides applying AMIRA (TGS Graphics) software is shown to be an efficient and fully reproducible way to analyse tiny and complex organ systems in their true relative positions and proportions; this method is herein applied to nudibranchs for the first time. We used Flabellina engeli lucianae n. subsp. , a small aeolid (up to 8 mm body preserved length) from subtropical southern Brazil, to illustrate all major organ systems including nervous systems, and discuss them comparatively. Flabellina engeli lucianae differs externally from congeners by having a translucent body with opaque white and iridescent blue spots, orange ceratal bands, and by the special branching of cerata forming distinct groups on common peduncles. External and internal differences from the apparently geographically and hydrographically separated Caribbean specimens of F. engeli engeli Marcus & Marcus, 1968 are discussed in detail.
GPflowOpt: A Bayesian Optimization Library using TensorFlow

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that is easy to extend with custom acquisition functions and models. The framework is thoroughly tested and well documented, and provides scalability. The current released version of GPflowOpt includes some standard single-objective acquisition functions, the state-of-the-art max-value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy use of custom modeling strategies implemented in GPflow.
Deep Two-path Semi-supervised Learning for Fake News Detection

News in social media such as Twitter has been generated in high volume and speed. However, very few of them can be labeled (as fake or true news) in a short time. In order to achieve timely detection of fake news in social media, a novel deep two-path semi-supervised learning model is proposed, where one path is for supervised learning and the other is for unsupervised learning. These two paths implemented with convolutional neural networks are jointly optimized to enhance detection performance. In addition, we build a shared convolutional neural networks between these two paths to share the low level features. Experimental results using Twitter datasets show that the proposed model can recognize fake news effectively with very few labeled data.
EMuRgency – A Basic Concept for an AI Driven Volunteer Notification System for Integrating Laypersons into Emergency Medical Services

In case of Sudden Cardiac Arrest, an untreated time interval of only a few minutes usually means the victims' death. Due to a variety of parameters, e.g., the current traffic situation and the mere traveling distance, emergency medical services often arrive too late in order to procure efficient cardiopulmonary. Given this premise, it is necessary to find alternative ways for providing immediate first aid measures. One promising approach is the implementation of a Volunteer Notification System (VNS) - integrating laypersons and medically trained volunteers into the professional medical services by notifying those potential helpers who are, at the time of incident, close to the victim. By tracking the users' location, the system is able to notify those volunteers who can arrive on scene fast enough to provide the urgently needed measures. In September 2011, the European research project "EMuRgency" started the development of such a system. Whereas a running prototype has already successfully been developed, a more sophisticated solution is required to determine the relevant volunteers. While the actual distance is an important parameter, it does not necessarily determine the time of arrival at the scene. Possible obstacles might be in the way and both physical performance and the type of movement have a direct influence on the traveling speed. Furthermore, secondary criteria apply; e.g., the current situation a volunteer is in, knowledge of the area, general engagement and his or her medical expertise. This publication will give you an overview of the "EMuRgency" project and discuss the current stage of development of the VNS; furthermore, this paper will introduce the main concept for determining the relevant volunteers within an ongoing emergency scenario and the advantages of an artificial intelligence approach to enable an efficient volunteer selection will be discussed.
Heartbeat Classification Using Normalized RR Intervals and Morphological Features

This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.
Robust Regression for Safe Exploration in Control

We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn to achieve a challenging control goal (e.g., an agile maneuver close to a boundary). A central challenge in this setting is how to quantify uncertainty in order to choose provably-safe actions that allow us to collect informative data and reduce uncertainty, thereby achieving both improved controller safety and optimality. To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration. We derive generalization bounds for learning, and connect them with safety and stability bounds in control. We demonstrate empirically that our robust regression approach can outperform the conventional Gaussian process (GP) based safe exploration in settings where it is difficult to specify a good GP prior.
Streaming Instability in Turbulent Protoplanetary Disks

The streaming instability for solid particles in protoplanetary disks is reexamined assuming the familiar alpha (α) model for isotropic turbulence. Turbulence always reduces the growth rates of the streaming instability relative to values calculated for globally laminar disks. While for small values of the turbulence parameter, α < 10−5, the wavelengths of the fastest growing disturbances are small fractions of the local gas vertical scale height H, we find that for moderate values of the turbulence parameter, i.e., α ∼ 10−5–10−3, the length scales of maximally growing disturbances shift toward larger scales, approaching H. At these moderate turbulent intensities and for local particle to gas mass density ratios ϵ < 0.5, the vertical scales of the most unstable modes begin to exceed the corresponding radial scales so that the instability appears in the form of vertically oriented sheets extending well beyond the particle scale height. We find that for hydrodynamical turbulent disk models reported in the literature, with α = 4 × 10−5–5 × 10−4, together with state-of-the-art global evolution models of particle growth, the streaming instability is predicted to be viable within a narrow triangular patch of α–τs parameter space centered on Stokes numbers, τs ∼ 0.01 and α ∼ 4 × 10−5, and further, exhibits growth rates on the order of several hundreds to thousands of orbit times for disks with 1% (Z = 0.01) cosmic solids abundance or metallicity. Our results are consistent with, and place in context, published numerical studies of streaming instabilities.
Why Independent Inventors Never Quit? In Search of Contribution of Inventive Outcomes on Subjective Success of Independent Inventors

Modern technological development of the modern society has been evaluated by the number of patents, commercialization and economic gains of technological innovations. Hence, the success of inventors has been purely measured by the objective measures of the invention process outcomes. Even though, this approach agreed with elite organizational inventors, independent inventors of a society are more humanitarian than the organizational inventors. Hence, the pure objective outcome measures were unable to address the question of why independent inventors continue in inventive activities even they are not objectively successful. Previous studies on the independent inventors has not focused on the social and psychological factors. Hence, the understanding of the subjective outcomes of inventive activities have remained unexplained. Along with the traditional objective outcome measures, the present study explains the inventive career satisfaction and sense of inventive community as two subjective outcomes of the inventive activities of independent inventors in Sri Lanka. It explores how the objective outcomes and subjective outcomes of the inventive life relates to the ultimate global happiness and satisfaction of the life of the independent inventors. DOI: http://dx.doi. org/10.4038/kjm.v1i1. 6444 Kelaniya Journal of Management Vol.1(1) 2012:1-25
The Impact of Management Information Systems on the Financial Performance of Islamic Banks in Jordan—Jordan Islamic Bank: A Case Study

This study aims to analyze the impact of management information systems on the financial performance of the Islamic banks and the Jordan Islamic Bank was selected as a case study for these banks. In order to achieve the objectives of this study, it was used the descriptive analytical method by using a questionnaire developed to identify and ensure its credibility and validity, and the study came up with some results including: that there is a statistically significant effect between information systems and dimensions represented in speed of achievement , and the simplification of procedures and information security and between financial performance and between financial performance represented in profitability and market share.
Hypercontractivity for global functions and sharp thresholds

The classical hypercontractive inequality for the noise operator on the discrete cube plays a crucial role in many of the fundamental results in the Analysis of Boolean functions, such as the KKL (Kahn-Kalai-Linial) theorem, Friedgut's junta theorem and the invariance principle of Mossel, O'Donnell and Oleszkiewicz. In these results the cube is equipped with the uniform ($1/2$-biased) measure, but it is desirable, particularly for applications to the theory of sharp thresholds, to also obtain such results for general $p$-biased measures. However, simple examples show that when $p$ is small there is no hypercontractive inequality that is strong enough for such applications. In this paper, we establish an effective hypercontractivity inequality for general $p$ that applies to `global functions', i.e. functions that are not significantly affected by a restriction of a small set of coordinates. This class of functions appears naturally, e.g. in Bourgain's sharp threshold theorem, which states that such functions exhibit a sharp threshold. We demonstrate the power of our tool by strengthening Bourgain's theorem, thereby making progress on a conjecture of Kahn and Kalai. An additional application of our hypercontractivity theorem, is a $p$-biased analog of the seminal invariance principle of Mossel, O'Donnell, and Oleszkiewicz. In a companion paper, we give applications to the solution of two open problems in Extremal Combinatorics.
Value hierarchy of future subject teachers in Serbia in the context of Schwartz theory

High concordance of value priorities and actual behaviour implies the importance of studying values, especially on teachers as important agents of socialization. The theoretical framework comprises Schwartz theory of individual values, a taxonomy of values which embraces ten value types with different motivational goals. We applied a questionnaire in the form of five-point Likert-scale with a com­prehensive list of values on the sample of 163 students, future teachers. The value hierarchy was defined as the set of the most important and less important values. For data analysis we used: descriptive statistics, paired-samples t-test; frequencies of ranks; and principal component analysis. We compared the value hierarchy of our student sample with the results obtained for the samples of students, teachers and representative samples in the cross-national study conducted by Schwartz and his associates. Generally speaking, our research results are in concordance with the results of the cross-cultural study, suggesting that the most important values can be found among the value types benevolence, universalism and self-direction, while less important values belong to the types stimulation, power and tradition. The main difference from the international study results is found in the treatment of the value type tradition, placed at the bottom of the hierarchy by our sample of students. [Projekat Ministarstva nauke Republike Srbije, br. 47008: From encouraging initiative, cooperation and creativity in education to new roles and identities in society]
Self-driving laboratory for accelerated discovery of thin-film materials

An autonomous laboratory for thin film discovery is used to optimize the doping and annealing of organic semiconductors. Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.
How to Promote Adoption of Complex E-Services Innovation? An Institutional Factor Perspective

By leveraging advanced information and communication technologies to provide innovative services through Internet, manufacturing service companies successfully integrate and coordinate the value chain activities to cost-effectively produce quality products for global customers. However, those complex electronic service (e-service) innovations usually are not necessarily adopted soon by customers as expected, which leads to slower investment returns and lower online service level. This study tried to understand the relationship between industry institutional factors and adoption intention of e-service innovation, and consider the moderating effect of e-service complexity. A survey included 263 respondent companies was conducted to collect empirical data from semiconductor industry. Research results shown that three institutional factors (technology standardization, propagating institution and institutional pressure) all have positive influence on the e-service innovation adoption intention. The e-service complexity has negative influence on the adoption intention and negatively moderates the effect of institutional pressure and propagating institution, while positively moderates technology standardization effect. Institutional factors can be useful strategic tools for promoting e-service innovation diffusion but need to consider innovation complexity. With the findings, this study contributes to academic understandings and provides several managerial implications for practitioners.
Ultimate merging at z ∼ 0.1

We present a study of 58 double-peaked emission line galaxies for which one of the components is suppressed in [OIII]λ5008 or is significantly weaker than the other one. Accordingly, the two components are classified differently in the BPT diagram. We show that the strong [OIII] component coincides with the stellar velocity and the suppressed component is off-centred in 66% of the galaxies, while in 12% of them it is the opposite. The analysis of their morphology reveals that about half of the sample is composed of S0, and the rest is composed of mergers and late-type galaxies in equal measure. We discuss the hypothesis that these characteristics exclude rotating discs and suggest different stages of merging. It is possible that the number of mergers is underestimated if the double nuclei are not resolved. Tidal features are detected in the outskirts of some S0 galaxies. This high fraction of S0 is surprising, as in addition most of the galaxies are isolated and the others are in small groups. All these galaxies that host an AGN component are massive, lie on the star forming sequence, and exhibit an enhanced star formation at their centre. While we cannot exclude outflows, these galaxies exhibit spectra that do not correspond to usual outflow observations characterised by high gas velocities, and the standard deviations of the two peaks are comparable. In parallel, these characteristics are compatible with ultimate stages of galaxy merging, where the two nuclei are either too close to be detected, or dynamical disturbances might be present in post-mergers like massive S0 galaxies.
THE NETWORK TOPOLOGY OF OPEN INNOVATION

eLancing is an emerging paradigm for outsourcing technical services wherein freelancers bid on projects posted in a large-scale online marketplace. When viewed in concert with other open innovation services such as kickstarter.com, a new networked innovation model is emerging: "very large scale innovation". "Very large scale innovation" networks supported by the Internet are a largescale form of innovation networks connecting producers and inventors to investors and retailers. In this paper, we study the statistical mechanics of the network structure of the projects and providers in eLancing to ascertain structural preconditions for their effective operation. The unipartite project and provider networks and the collaboration network exhibit the properties of a homogeneous network whereas the associated skill networks exhibit the properties of an inhomogeneous, scale-free network. All of the networks except the provider network are small-world networks. These results point to the lack of coordination and collaboration between providers, which could provide an opportunity for them to pursue more complex projects, and a need for systems integration services.
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. In particular, our dense-sparse phrase encoding effectively captures syntactic, semantic, and lexical information of the phrases and eliminates the pipeline filtering of context documents. Leveraging strategies for optimizing training and inference time, our model can be trained and deployed even in a single 4-GPU server. Moreover, by representing phrases as pointers to their start and end tokens, our model indexes phrases in the entire English Wikipedia (up to 60 billion phrases) using under 2TB. Our experiments on SQuAD-Open show that our model is on par with or more accurate than previous models with 6000x reduced computational cost, which translates into at least 68x faster end-to-end inference benchmark on CPUs. Code and demo are available at nlp.cs. washington.edu/denspi
On Feasibility and Flexibility Operating Regions of Virtual Power Plants and TSO/DSO Interfaces

Distributed energy resources are an ideal candidate for the provision of additional flexibility required by the power system to support the increasing penetration of renewable energy sources. The integrating large number of resources in the existing market structure, particularly in the light of providing flexibility services, is envisioned through the concept of virtual power plant (VPP). To this end, it is crucial to establish a clear methodology for VPP flexibility modelling. In this context, this paper first puts forward the need to clarify the difference between feasibility and flexibility potential of a VPP, and then propose a methodology for the evaluation of relevant operating regions. Similar concepts can also be used to modelling TSO/DSO interface operation. Several case studies are designed to reflect the distinct information conveyed by feasibility and flexibility operating regions in the presence of “slow” and “fast” responding resources for a VPP partaking in the provision of energy and grid support services. The results also highlight the impact of flexible load and importantly network topology on the VPP feasibility (FOR) and flexibility (FXOR) operating regions.