Miracle-dz/UniFAR-FTUs huggingface dataset:
Josephson photonics: Statistics of photons emitted by inelastic Cooper pair tunneling

This thesis contributes to the emerging field of Josephson photonics through the study of correlations between microwave photons emitted by inelastic Cooper pair tunneling across a voltage-biased Josephson junction. We show that the photon statistics can be strongly modified by embedding the junction into a carefully engineered electromagnetic environment. Doing so, we have elaborated and measured a bright on-demand radiation source, capable of emitting bunched and anti-bunched microwave photons depending only on a single in-situ tunable parameter.In order to conduct this experiment, we have implemented a Hanbury-Brown & Twiss setup for photon correlation measurements using linear amplifiers in a dilution refrigerator. Furthermore, we have designed microwave circuits presenting specific frequency-dependent impedances to the junction. To build these devices we have developed a nano-fabrication process for vertical Josephson junctions made from niobium nitride and using magnesium oxide as a tunnel barrier. Finally, we have contributed to the theoretical advances associated with the understanding of these devices, which extend the so-called P(E) theory of inelastic Cooper pair tunneling to include correlations between tunneling events.These results pave the way for further developments, notably with the possibility to extend the frequency range of these radiation sources to the THz domain but also in view of other devices based on the same physics, such as detectors and amplifiers close to the quantum limit.
Analytic crossed products and outer conjugacy classes of automorphisms of von Neumann algebras.

The analytic crossed product determined by M and e is defined to be the o--weakly closed subalgebra of M > ~ Z generated by {//~(x) lx ~ M} and the non-negative powers of L~; it is denoted M > ~ Z + . The terminology stems from the fact that when M is the field of scalars, ~, M > ~ Z + is isomorphic to the classical Hardy space H~fT) consisting of bounded analytic functions on the open unit disc. In general, we like to think of M > ~ Z + as a non-commutative version of H~ an operator theoretic analogue of a twisted polynomial ring. Let N be another yon Neumann algebra, and let f le Aut(N). Recall that e and are called outer conjugate if there is a *-isomorphism ? from M onto N and a unitary u in M such that ? o (Ad u) o ~ =/~ o ? , where, for x ~ M, Ad u(x) = uxu1. In this event, if we define r on generators by the formulas
Diffusion tensor imaging : brain pathway reconstruction

Diffusion tensor imaging (DTI) is a recently developed modality of magnetic resonance imaging (MRI), which allows producing in-vivo images of biological fibrous tissues such as the neural axons of white matter in the brain. The techniques for reconstructing connections between different brain areas using DTI are collectively known as fiber tracking or tractography. The brain connectivity map, derived from the tractography visualization and analysis, is an important tool to diagnose and analyze various brain diseases, and is of essential value in providing exquisite details on tissue microstructure and neural networks. Assuming that fiber pathways follow the most efficient diffusion propagation trajectories, we specifically develop a geodesic-based tractography technique for the reconstruction of fiber pathways. Results we obtain using our technique, based on finding multiple geodesics connecting two given points or regions are encouraging and give confidence that this method can be used for practical purposes in the near future.
End-To-End Machine Learning Data Feature Representation Trained / Tuned Model Predictions Model Evaluation / Inference Computational Environment Extract Train Test Evaluate

ABSTRACT Replication of machine learning experiments can be a useful tool to evaluate how both modeling and experimental design contribute to experimental results; however, existing replication efforts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM dropout prediction model. In our first experiment, we replicate the original authors’ methodology as precisely as possible in collaboration with the original authors. In a second experiment, we demonstrate that this initial experiment likely overestimates the generalization performance of the proposed model due to the design of its validation. In a third experiment, we attempt to achieve the previously-reported performance in a more difficult, but more relevant, hold-out set design by exploring a large space of model regularization configurations. We demonstrate that we can reduce overfitting and improve generalization performance of the model, but cannot achieve the previously-reported level of performance. This work demonstrates the importance of replication of predictive modeling experiments in education, and demonstrates how experimental design and modeling decisions can impact the extent to which model performance generalizes beyond the initial training data.
Microdeformation Processes in Soft Magnetic Compounds

Microdeformation Processes in Soft Magnetic Compounds R. Bidulskýa,∗, J. Bidulská, P. Petroušek, A. Fedoriková, E. Dudrová and M. Actis Grande Technical University of Košice, Faculty of Metallurgy, Institute of Materials, Letná 9, 042 00 Košice, Slovakia Institute of Materials Research, Slovak Academy of Sciences, Watsonova 45, 040 01 Košice, Slovakia Department of Applied Science and Technology, Politecnico di Torino, Viale Teresa Michel, 5, 15121, Alessandria, Italy
Kernelized Hashcode Representations for Relation Extraction

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r. t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
LG ] 4 M ay 2 01 1 Domain Adaptation : Overfitting and Small Sample Statistics

We study the prevalent problem when a test distribution differs from the training distribution. We consider a setting where our training set consists of a small number of sample domains, but where we have many samples in each domain. Our goal is to generalize to a new domain. For example, we may want to learn a similarity function using only certain classes of objects, but we desire that this similarity function be applicable to object classes not present in our training sample (e.g. we might seek to learn that " dogs are similar to dogs " even though images of dogs were absent from our training set). Our theoretical analysis shows that we can select many more features than domains while avoiding overfitting by utilizing data-dependent variance properties. We present a greedy feature selection algorithm based on using T-statistics. Our experiments validate this theory showing that our T-statistic based greedy feature selection is more robust at avoiding overfitting than the classical greedy procedure.
Image Saliency Detection Video Saliency Detection Co-saliency Detection Temporal RGBD Saliency Detection Motion

Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.
Detection Tracking Stance Verification Rumour vs Non-rumour Collection of sources ‣ Supporting ‣ Denying ‣ Questioning ‣

Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used. Title and Abstract in Russian Три в одном: Многозадачное Обучение для Верификации Слухов Автоматическая верификация слухов в Интернете это сложная задача, которая может быть разбита на подзадачи, включающие в себя обнаружение слухов, отслеживание слухов, классификацию отношения пользователей к правдивости этих слухов и, в итоге, разрешение основной задачи определение достоверности слуха. В предыдущих исследованиях модели для решения каждой из подзадач разрабатывались как отдельные компоненты, где вывод модели для задачи предыдущего этапа являлся вводдом для модели задачи следующего этапа. Мы предлагаем использование многозадачного обучения для совместного обучения основной и вспомогательных задач в одной модели, что позволяет улучшить качество модели верификации слухов. Также мы рассматриваем связь между свойствами набора данных и результатами использования моделей с многозадачным обучением.
Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections

Face authentication systems are becoming increasingly prevalent, especially with the rapid development of Deep Learning technologies. However, human facial information is easy to be captured and reproduced, which makes face authentication systems vulnerable to various attacks. Liveness detection is an important defense technique to prevent such attacks, but existing solutions did not provide clear and strong security guarantees, especially in terms of time. To overcome these limitations, we propose a new liveness detection protocol called Face Flashing that significantly increases the bar for launching successful attacks on face authentication systems. By randomly flashing well-designed pictures on a screen and analyzing the reflected light, our protocol has leveraged physical characteristics of human faces: reflection processing at the speed of light, unique textual features, and uneven 3D shapes. Cooperating with working mechanism of the screen and digital cameras, our protocol is able to detect subtle traces left by an attacking process. To demonstrate the effectiveness of Face Flashing, we implemented a prototype and performed thorough evaluations with large data set collected from real-world scenarios. The results show that our Timing Verification can effectively detect the time gap between legitimate authentications and malicious cases. Our Face Verification can also differentiate 2D plane from 3D objects accurately. The overall accuracy of our liveness detection system is 98.8\%, and its robustness was evaluated in different scenarios. In the worst case, our system's accuracy decreased to a still-high 97.3\%.
A HEMISPHERIC TWO - CHANNEL CODE ACCOUNTS FOR BINAURAL UNMASKING IN HUMANS .

The ability to localize sound sources relies on differences between the signals at the two ears. These differences are also the basis for binaural unmasking, an improvement in detecting or understanding a sound masked by sources from other locations. The neurocomputational operation that underlies binaural unmasking is still a matter of debate. Current models rely on the cross-correlation function of the signals at the two ears, the neuronal substrate of which has been observed in the barn owl but not in mammals. This disagreement lead to the formulation of an alternative coding mechanism where interaural differences are encoded using the neuronal activity within two hemispheric channels. This mechanism agrees with mammalian physiology but has not yet been shown to account for binaural unmasking in humans. This study introduces a new mathematical formulation for the two-channel model, which is then used to explain the outcome of an extensive library of psychoacoustic experiments.
Improved Mixed-Example Data Augmentation

In order to reduce overﬁtting, neural networks are typically trained with data augmentation, the practice of artiﬁcially generating additional training data via label-preserving transformations of existing training examples. Recent work has demonstrated a surprisingly effective type of non-label-preserving data augmentation, in which pairs of training examples are averaged together. In this work, we generalize this “mixed-example data augmentation”, which allows us to ﬁnd methods that improve upon previous work. This generalization also reveals that linearity is not necessary as an inductive bias in order for mixed-example data augmentation to be effective, providing evidence against the primary theoretical hypothesis from prior work
Detecting Adversarial Perturbations with Salieny

In this paper, we propose a novel method for detecting adversarial examples by training a binary classifier with both origin data and saliency data. Saliency In the case of image classification model, saliency simply explain how the model makes decisions by identifying significant pixels for prediction. A model shows wrong classification output always learns wrong features and shows wrong saliency as well. Our approach shows good performance on detecting adversarial perturbations. We quantitatively evaluate generalization ability of the detector, showing that detectors trained with strong adversaries perform well on weak adversaries.
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects

Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.
Deep-Energy: Unsupervised Training of Deep Neural Networks

The success of deep learning has been due, in no small part, to the availability of large annotated datasets. Thus, a major bottleneck in current learning pipelines is the time-consuming human annotation of data. In scenarios where such input-output pairs cannot be collected, simulation is often used instead, leading to a domain-shift between synthesized and real-world data. This work offers an unsupervised alternative that relies on the availability of task-specific energy functions, replacing the generic supervised loss. Such energy functions are assumed to lead to the desired label as their minimizer given the input. The proposed approach, termed "Deep Energy", trains a Deep Neural Network (DNN) to approximate this minimization for any chosen input. Once trained, a simple and fast feed-forward computation provides the inferred label. This approach allows us to perform unsupervised training of DNNs with real-world inputs only, and without the need for manually-annotated labels, nor synthetically created data. "Deep Energy" is demonstrated in this paper on three different tasks -- seeded segmentation, image matting and single image dehazing -- exposing its generality and wide applicability. Our experiments show that the solution provided by the network is often much better in quality than the one obtained by a direct minimization of the energy function, suggesting an added regularization property in our scheme.
On c-nilpotent multiplier and c-covers of a pair of Lie algebras

ABSTRACT In this paper, we characterize the structure of the c-nilpotent multiplier of a pair of Lie algebras concerning the c-covering pairs and discuss some results on the existence of c-covers of a pair of Lie algebras. Moreover, we show that every nilpotent pair of Lie algebras of class at most k with non-trivial c-nilpotent multiplier does not admit any c-covering pair, if c>k. Also, we obtain the structure of c-covers of a c-capable pair of Lie algebras.
The true significance of ‘high’ correlations between EQ-5D value sets

High correlation coefficients for EQ-5D value sets derived from different samples, e.g. across countries, are conventionally interpreted as evidence that the people in the respective samples have similar health-related quality of life preferences. However, EQ-5D value sets contain many inherent rankings of health state values by design. By calculating coefficients for value sets created from random data, we demonstrate that ‘high’ correlation coefficients are an artefact of these inherent rankings; e.g. median Pearson’s r = 0.783 for the EQ-5D-3L and 0.850 for the EQ-5D-5L instead of zero. Therefore, high correlation coefficients do not necessarily constitute evidence of ‘true’ associations. After calculating significance levels based on our simulations – available as a resource for other researchers – we find that many high coefficients are not as significant as conventionally interpreted, whereas other coefficients are not significant. These ‘high’ but insignificant correlations are in fact spurious.
Efficient estimation of age-specific social contact rates between men and women

Social contact patterns reveal with whom individuals tend to socialize, and therefore to whom they transmit respiratory infections. We infer highly detailed age-speciﬁc contact rates between the sexes using a hierarchical Bayesian model that smooths while simultaneously guaranteeing the inher-ent reciprocity of contact rates. Application of this approach to social contact data from a large prospective survey conﬁrms a tendency that people, especially children and adolescents, mostly contact other people of their own age and sex, and reveals that women have more contact with children than men. These ﬁndings imply different exposure patterns between the two sexes for speciﬁc age groups, which agrees with available observations.
Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-Parametric and Multi-Centre Study on Magnetic Resonance Images

One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.
Neutrino Astrophysics with JUNO

The Jiangmen Underground Neutrino Observatory will aim for the determination of the neutrino mass hierarchy with a medium baseline reactor νe oscillation experiment. With its 20 kt liquid scintillator detector, it will also be sensitive to astrophysical low-energy neutrinos. In case of a typical galactic core-collapse supernova, ∼ 5000 neutrino interactions will be detected via the inverse beta decay alone. Moreover, a 3σ evidence for the diffuse supernova neutrino background seems within reach after ten years of measurement. The performance of the experiment’s solar neutrino program will decisively depend on the achieved radiopurity and cosmogenic background rejection efficiency.
The Cross-Section of Expected Corporate Bond Returns: Betas or Characteristics?

This paper finds that default betas are significantly related to the cross-section of average bond returns even after controlling for characteristics such as duration, ratings, and yield-to-maturity. Among characteristics, only yield-to-maturity is significantly related to average bond returns after controlling for default and term betas. The default and term factors are able to price the returns of beta-sorted portfolios better than they do the returns of yield-sorted portfolios. The magnitude of the ex ante Sharpe ratio generated by yield-sorted portfolios suggests non-risk based explanations. Overall, given the elusive nature of systematic risk in empirical asset pricing, the central finding of our paper is that systematic risk matters for corporate bonds.