Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Parallelism Opportunities with Deep Learning Frameworks

State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance profiling efforts and often relies on domain-specific knowledge. This article takes a deep dive into analyzing the performance impact of key design features in a machine learning framework and quantifies the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. Across a diverse set of real-world deep learning models, the evaluation results show that the proposed performance tuning guidelines outperform the Intel and TensorFlow recommended settings by 1.30× and 1.38×, respectively.
Catalog for the ESPRESSO blind radial velocity exoplanet survey

Aims. One of the main scientific drivers for ESPRESSO, Échelle SPectrograph, is the detection and characterization of Earth-class exoplanets. With this goal in mind, the ESPRESSO guaranteed time observations (GTO) Catalog identifies the best target stars for a blind search for the radial velocity (RV) signals caused by Earth-class exoplanets. Methods. Using the most complete stellar catalogs available, we screened for the most suitable G, K, and M dwarf stars for the detection of Earth-class exoplanets with ESPRESSO. For most of the stars, we then gathered high-resolution spectra from new observations or from archival data. We used these spectra to spectroscopically investigate the existence of any stellar binaries, both bound or background stars. We derived the activity level using chromospheric activity indexes using log (R′HK), as well as the projected rotational velocity v sin i. For the cases where planet companions are already known, we also looked at the possibility that additional planets may exist in the host’s habitable zone using dynamical arguments. Results. We estimated the spectroscopic contamination level, v sin i, activity, stellar parameters and chemical abundances for 249 of the most promising targets. Using these data, we selected 45 stars that match our criteria for detectability of a planet like Earth. The stars presented and discussed in this paper constitute the ESPRESSO GTO catalog for the RV blind search for Earth-class planets. They can also be used for any other work requiring a detailed spectroscopic characterization of stars in the solar neighborhood.
Does the quantum mechanical wave function exist

I address the question whether the wave function in quantum theory exists as a real (ontic) quantity or not. For this purpose, I discuss the essentials of the quantum formalism and emphasize the central role of the superposition principle. I then explain the measurement problem and discuss the process of decoherence. Finally, I address the special features that the quantization of gravity brings into the game. From all of this I conclude that the wave function really exists, that is, it is a real (ontic) feature of Nature.
Efficient Resource Allocation for Mobile-Edge Computing Networks With NOMA: Completion Time and Energy Minimization

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users’ tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closed-form solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.
Exploiting Multi-domain Visual Information for Fake News Detection

The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead readers for rapid dissemination, which makes visual content an important part of fake news. Fake-news images, images attached to fake news posts, include not only fake images that are maliciously tampered but also real images that are wrongly used to represent irrelevant events. Hence, how to fully exploit the inherent characteristics of fake-news images is an important but challenging problem for fake news detection. In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively. Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news. Specifically, we design a CNN-based network to automatically capture the complex patterns of fake-news images in the frequency domain; and utilize a multi-branch CNN-RNN model to extract visual features from different semantic levels in the pixel domain. An attention mechanism is utilized to fuse the feature representations of frequency and pixel domains dynamically. Extensive experiments conducted on a real world dataset demonstrate that MVNN outperforms existing methods with at least 9.2% in accuracy, and can help improve the performance of multi-modal fake news detection by over 5.2%.
On breadth‐first constructions of scaling limits of random graphs and random unicellular maps

We give alternate constructions of (i) the scaling limit of the uniform connected graphs with given fixed surplus, and (ii) the continuum random unicellular map of a given genus that start with a suitably tilted Brownian continuum random tree and make “horizontal” point identifications, at random heights, using the local time measures. Consequently, this can be seen as a continuum analogue of the breadth‐first construction of a finite connected graph. In particular, this yields a breadth‐first construction of the scaling limit of the critical Erdős–Rényi random graph which answers a question posed by Addario‐Berry, Broutin, and Goldschmidt. As a consequence of this breadth‐first construction, we obtain descriptions of the radii, the distance profiles, and the two point functions of these spaces in terms of functionals of tilted Brownian excursions.
Private Rank Aggregation under Local Differential Privacy

As a method for answer aggregation in crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
From non-interacting to interacting picture of thermodynamics and transport coefficients for quark gluon plasma

We have attempted to build first some simplified model to map the interaction of quarks and gluons, which can be contained by their thermodynamical quantity like entropy density, obtained from calculation of lattice quantum chromo dynamics (LQCD). With respect to entropy density of the standard non-interacting massless quark gluon plasma (QGP), its interacting values from LQCD simulation are reduced as we go from higher to lower temperature through the cross-over of quark-hadron phase transition. By parameterizing increasing degeneracy factor or increasing interaction-fugacity or decreasing thermal width of quarks and gluons with temperature, we have matched LQCD data.Using that interaction picture, shear viscosity and electrical conductivity are calculated. For getting nearly perfect fluid nature of QGP, interaction might have some role when we consider temperature dependent thermal width.
Droplets. II. Internal Velocity Structures and Potential Rotational Motions in Pressure-dominated Coherent Structures

We present an analysis of the internal velocity structures of the newly identified sub-0.1 pc coherent structures, droplets, in L1688 and B18. By fitting 2D linear velocity fields to the observed maps of velocity centroids, we determine the magnitudes of linear velocity gradients and examine the potential rotational motions that could lead to the observed velocity gradients. The results show that the droplets follow the same power-law relation between the velocity gradient and size found for larger-scale dense cores. Assuming that rotational motion giving rise to the observed velocity gradient in each core is a solid-body rotation of a rotating body with a uniform density, we derive the “net rotational motions” of the droplets. We find a ratio between rotational and gravitational energies, β, of ∼0.046 for the droplets, and when including both droplets and larger-scale dense cores, we find β ∼ 0.039. We then examine the alignment between the velocity gradient and the major axis of each droplet, using methods adapted from the histogram of relative orientations introduced by Soler et al. We find no definitive correlation between the directions of velocity gradients and the elongations of the cores. Lastly, we discuss physical processes other than rotation that may give rise to the observed velocity field.
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection

Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph. We simultaneously optimize graph-level classification loss and Infomax. We demonstrated that Infomax graph embedding improves classification performance as a regularization term. Furthermore, we found separable nodal representations of ASD and HC groups in prefrontal cortex, cingulate cortex, visual regions, and other social, emotional and execution related brain regions. In contrast with GNN with classification loss only, the proposed pipeline can facilitate training more robust ASD classification models. Moreover, the separable nodal representations can detect the functional differences between the two groups and contribute to revealing new ASD biomarkers.
Emphasis Regularisation by Gradient Rescaling for Training Deep Neural Networks Robustly

It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when noisy labels exist. Although great progress has been made, there is still one crucial research question which is not thoroughly explored yet: What training examples should be focused and how much more should they be emphasised when training DNNs under label noise? In this work, we study this question and propose gradient rescaling (GR) to solve it. GR modifies the magnitude of logit vector's gradient to emphasise on relatively easier training data points when severe noise exists, which functions as explicit emphasis regularisation to improve the generalisation performance of DNNs. Apart from regularisation, we also interpret GR from the perspectives of sample reweighting and designing robust loss functions. Therefore, our proposed GR helps connect these three approaches in the literature. We empirically demonstrate that GR is highly noise-robust and outperforms the state-of-the-art noise-tolerant algorithms by a large margin, e.g., increasing 7% on CIFAR-100 with 40% noisy labels. It is also significantly superior to standard regularisors. Furthermore, we present comprehensive ablation studies to explore the behaviours of GR under different cases, which is informative for applying GR in real-world scenarios.
V2X-Based Vehicular Positioning: Opportunities, Challenges, and Future Directions

Vehicle-to-Everything (V2X) creates many new opportunities in the area of wireless communications, while its feasibility on enabling vehicular positioning (VP) has not been fully explored despite its importance for autonomous driving. This article aims at investigating whether V2X can help VP from different perspectives. We first explain V2X's critical advantages over other technologies (e.g., GPS, RADAR, LIDAR, and camera) and suggest new scenarios of V2X-based VP. Then, we review the state-of-the-art positioning techniques discussed in different standardizations and point out their limitations. Lastly, some promising research directions for V2X-based VP are presented, which contribute to realizing fully autonomous driving by overcoming the current obstacles.
2 2 M ar 2 01 7 Effects of heavy-ion irradiation on FeSe

Yue Sun, Akiyoshi Park, Sunseng Pyon, Tsuyoshi Tamegai, Tadashi Kambara, Ataru Ichinose Department of Applied Physics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Nishina Center, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Central Research Institute of Electric Power Industry, Electric Power Engineering Research Laboratory, 2-6-1, Nagasaka, Yokosuka-shi, Kanagawa 240-0196, Japan (Dated: November 14, 2018)
The Politicization of the European Central Bank: What Is It, and How to Study It?

Abstract The politicization of the European Central Bank (ECB) is a recurrent theme in debates on the EU after the crisis, yet it is one that still suffers from a considerable degree of vagueness. This is unfortunate as it hinders the development of useful discussions on the place and legitimacy of the ECB within Europe's institutional order. To tackle this problem, this article presents a systematic analysis of the concept of ECB politicization and an agenda for future research on this phenomenon. After reviewing existing uses of the term in the form of three dichotomies – politicization versus independence, impartiality, and convention – the article proposes an alternative, preference‐based definition of politicization as a deviation from technocratic policy‐making in the ECB. Building on this definition, the article then indicates three avenues for the empirical study of politicization centred, respectively, on elite interviews and surveys, the analysis of central bankers’ networks and the study of ECB language.
Mergers and Acquisitions : Revisiting the Issue of Value Creation in the New Member States of European Union

During the last takeover wave in the period of 2001-2009 both the volume and the value of merger and acquisition transactions reached record heights. Nevertheless, the question of whether mergers and acquisitions create surplus value has never been more relevant than during the same period of 20012009 as well. It turned out that numerous transactions which were structured during the recent wave destroyed total combined shareholder value instead of increasing it. The general issue of value creation in merger and acquisition transactions is addressed in the study by analyzing a sample consisting of transactions that took place in the New Member States of European Union during the period of 2004-2011. Two important questions surrounding the issue are investigated: the paper examines whether value creation differs in domestic and cross-border transactions and what is the effect of timing of merger and acquisition transactions in terms of economic cycle. There is no evidence found that transactions concluded during crisis period create more value than those at the pre-crisis timeframe. The results of the paper support the conclusions of previous studies by academics that cross-border mergers and acquisitions create more value than domestic transactions.
UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS MÉDICAS: ENDOCRINOLOGIA ESTUDO DA ASSOCIAÇÃO DE POLIMORFISMOS NOS GENES DAS UCPS COM SUSCETIBILIDADE AO DIABETES MELLITUS TIPO 2 E DOENÇA RENAL DO DIABETES

15 INTRODUÇÃO. 18 PARTE I – Artigo original: “Associations between UCP1 -3826A/G, UCP2 -866G/A, Ala55Val and Ins/Del, and UCP3 -55C/T Polymorphisms and Susceptibility to Type 2 Diabetes Mellitus: Case-Control Study and Meta-Analysis”. 36 PARTE II – Artigo original: “Polymorphisms of the UCP2 gene are associated with diabetic kidney disease and glomerular filtration rate in type 2 diabetic patients and with decreased UCP2 gene expression in human kidney”. 56 CONCLUSÃO. 86
Synthetic CDOs of CDOs : Squaring the Delta-Hedged Equity Trade

Tight spreads in the credit markets have forced investors to turn to innovative structures in the search for yield. One such structure is the synthetic CDO of CDO tranches, also known as CDO2. In this article, we introduce this structure, present a framework for valuation, and highlight the risk-return profile of a delta-hedged equity super tranche referencing a portfolio of mezzanine CDO tranches. Prasun Baheti, Roy Mashal, Marco Naldi and Lutz Schloegl Fixed Income Quantitative Credit Research
Customer Lock-In With Long-Term Contracts

We consider a horizontally di¤erentiated industry with two . rms and two time periods. We allow for customers’ relative preferences towards . rms to change over time. Firms are allowed to o¤er shortterm and long-term contracts to customers. The main goal is to investigate the e¤ects of the presence of long-term contracts. On one hand, long-term contracts create social ine¢ciency, because as their preferences change, customers might be forced to purchase from a . rm, which they no longer like the most. The e¤ects on pro.ts and consumer welfare are not as clearcut. Although long-term contracts lock-in consumers, they must be o¤ered at a discount relative to short-term contracts. It depends on the parameter-con.guration, which of these e¤ects dominates. Sometimes, the ability of . rms to o¤er long-term contracts is bene.cial to consumers. Moreover, the bene.ts of long-term contracts are not equally distributed among consumers, so some of them might be better o¤, but at the same time, some of them might be worse o¤.
Biases in the Simulation and Analysis of Fractal Processes

Fractal processes have recently received a growing interest, especially in the domain of rehabilitation. More precisely, the evolution of fractality with aging and disease, suggesting a loss of complexity, has inspired a number of studies that tried, for example, to entrain patients with fractal rhythms. This kind of study requires relevant methods for generating fractal signals and for assessing the fractality of the series produced by participants. In the present work, we engaged a cross validation of three methods of generation and three methods of analysis. We generated exact fractal series with the Davies–Harte (DH) algorithm, the spectral synthesis method (SSM), and the ARFIMA simulation method. The series were analyzed by detrended fluctuation analysis (DFA), power spectral density (PSD) method, and ARFIMA modeling. Results show that some methods of generation present systematic biases: DH presented a strong bias toward white noise in fBm series close to the 1/f boundary and SSM produced series with a larger variability around the expected exponent, as compared with other methods. In contrast, ARFIMA simulations provided quite accurate series, without major bias. Concerning the methods of analysis, DFA tended to systematically underestimate fBm series. In contrast, PSD yielded overestimates for fBm series. With DFA, the variability of estimates tended to increase for fGn series as they approached the 1/f boundary and reached unacceptable levels for fBm series. The highest levels of variability were produced by PSD. Finally, ARFIMA methods generated the best series and provided the most accurate and less variable estimates.
in Computer Science and Software Engineering Analysing and Fortifying the AODV Protocol with New Rules for Handling Multiple RREQ Packets

— In the field of networking routing protocols play the crucial role, as the communicating nodes in the network always need a path to send their information. In this paper, we have studied and designed new rules for the existing AODV protocol for the Zig-Bee networks. The AODV protocol is reactive type of protocol and is called when the nodes necessitate path for data transmission. This protocol is invoked only when a new path is requisite. This research work aims to create new rules for handling multiple RREQ Packets. RREQ packets are basically broadcasted by nodes to get a path to destination. Zig-Bee networks are wireless radio networks created by Zig-Bee alliance.
The Effect of Estradiol Valerate Administration on Growth of Rainbow Trout , Oncorhynchus mykiss

The aim of the present study was to determine the effects of estradiol valerate (E2) applied with feed by the rate of 20 mg kg-1on the growth and survival rate of rainbow trout (Oncorhynchus mykiss). The 20 mg/kg-food rate of (E2) for 56 days was administrated to rainbow trout larvas, average 0.32±0.02 g started first feeding. The research was carried out for 355 days. In the result of experiment the average weight was found as 304.82±11.0 g for treatment group (E2) and 299.08±9.7 g for control group (C) (P>0.05). The condition factor and food conversion rate were calculated as 1.41±0.08 (E2), 1.34±0.11 (C) (P<0.05) and 0.89 (E2), 0.90 (C) respectively. The survival rates of groups were found as 84.5% (E2) and 98.0% (C). The applications of estradiol valerate applied with feed by the rate of 20 mg kgincreased the condition factor, had no effects on the weight increase and FCR, but decreased the survival rate.