Miracle-dz/UniFAR-FTUs huggingface dataset:
Tightening the tripartite quantum-memory-assisted entropic uncertainty relation

The uncertainty principle determines the distinction between the classical and quantum worlds. This principle states that it is not possible to measure two incompatible observables with the desired accuracy simultaneously. In quantum information theory, Shannon entropy has been used as an appropriate measure to express the uncertainty relation. According to the applications of entropic uncertainty relation, studying and trying to improve the bound of this relation is of great importance. Uncertainty bound can be altered by considering an extra quantum system as the quantum memory $B$ which is correlated with the measured quantum system $A$. One can extend the bipartite quantum memory assisted entropic uncertainty relation to tripartite quantum memory assisted entropic uncertainty relation in which the memory is split into two parts. In this work, we obtain a lower bound for the tripartite quantum memory assisted entropic uncertainty relation. Our lower bound has two additional terms compared to the lower bound in [Phys. Rev. Lett. 103, 020402 (2009)] which depending on the conditional von Neumann entropy, the Holevo quantity and mutual information. It is shown that the bound obtained in this work is more tighter than other bounds. In addition, using our lower bound, a lower bound for the quantum secret key rate has been obtained. The lower bound is also used to obtain the states for which the strong subadditivity inequality and Koashi-Winter inequality is satisfied with equality.
Solving holographic defects

Defect conformal field theories (dCFTs) have been attracting increased attention recently, mainly because they enable us to bridge the gap between idealistic, highly symmetric models of our world (such as the particle/string duality) and real-world systems. This talk is about the AdS/defect CFT correspondence, an exciting new proposal that joins the forces of holography, integrability, supersymmetric localization and the conformal bootstrap program in a framework that is appropriate for the study of defects in real-world systems. After introducing dCFTs and some of their holographic realizations, we will present some recent results for the one-point functions of the integrable dCFTs that are the holographic duals of the D3-probe-D5 and the D3-probe-D7 systems of intersecting branes.
Accumulation of Essential and Nonessential Metals by Chironomid Larvae in Relation to Physical and Chemical Properties of the Elements

Molar concentration factors for nine metals, calculated as quotients of concentrations in chironomids divided by concentrations in sediment, were systematically related to fundamental atomic properties using multiple regressions and canonical analyses. Ionization and oxidation potentials, as well as the ionization potential differential and atomic radius, were useful predictors of metal concentration factors. Their relative importance varied with lake pH. Concentration factors were higher for the essential elements Zn, Ca, and Cu than for Ni and the nonessential elements Pb, Cd, and Al. The low concentration factors for Mn and Fe were driven by high sediment concentrations and the tendency for these elements to form insoluble hydroxides.
Detecting Electric Devices in 3D Images of Bags

The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband in the minimum time possible. Automation and semi-automation of this procedure offers the potential to increase security by detecting more threats and improve the customer experience by speeding up the process. Traditional 2D x-ray images are often extremely difficult to examine due to the fact that they are tightly packed and contain a wide variety of cluttered and occluded objects. Because of these limitations, major airports are introducing 3D x-ray Computed Tomography (CT) baggage scanning. We investigate whether we can automate the process of detecting electric devices in these 3D images of luggage. Detecting electrical devices is of particular concern as they can be used to conceal explosives. Given the massive volume of luggage that needs to be screened for this threat, the best way to automate the detection is to first filter whether a bag contains an electric device or not, and if it does, to identify the number of devices and their location. We present an algorithm, Unpack, Predict, eXtract, Repack (UXPR), which involves unpacking through segmenting the data at a range of scales using an algorithm known as the Sieve, predicting whether a segment is electrical or not based on the histogram of voxel intensities, then repacking the bag by ensembling the segments and predictions to identify the devices in bags. Through a range of experiments using data provided by ALERT (Awareness and Localization of Explosives-Related Threats) we show that this system can find a high proportion of devices with unsupervised segmentation if a similar device has been seen before, and shows promising results for detecting devices not seen at all based on the properties of its constituent parts.
Error Bounds for Dynamical Spectral Estimation

Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called “the variational approach to conformational dynamics” (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC’s convergence properties and suggests new strategies for tuning VAC to improve accuracy.
Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models

Inertial algorithms for minimizing nonsmooth and nonconvex functions as the inertial proximal alternating linearized minimization algorithm (iPALM) have demonstrated their superiority with respect to computation time over their non inertial variants. In many problems in imaging and machine learning, the objective functions have a special form involving huge data which encourage the application of stochastic algorithms. While the stochastic gradient descent algorithm is still used in the majority of applications, recently also stochastic algorithms for minimizing nonsmooth and nonconvex functions were proposed. In this paper, we derive an inertial variant of the SPRING algorithm, called iSPRING, and prove linear convergence of the algorithm under certain assumptions. Numerical experiments show that our new algorithm performs better than SPRING or its deterministic counterparts, although the improvement for the inertial stochastic approach is not as large as those for the inertial deterministic one. The second aim of the paper is to demonstrate that (inertial) PALM both in the deterministic and stochastic form can be used for learning the parameters of Student-$t$ mixture models. We prove that the objective function of such models fulfills all convergence assumptions of the algorithms and demonstrate their performance by numerical examples.
Astrochemical relevance of VUV ionization of large PAH cations.

Context As a part of interstellar dust, polycyclic aromatic hydrocarbons (PAHs) are processed by the interaction with vacuum ultra-violet (VUV) photons that are emitted by hot young stars. This interaction leads to the emission of the well-known aromatic infrared bands but also of electrons, which can significantly contribute to the heating of the interstellar gas. Aims Our aim is to investigate the impact of molecular size on the photoionization properties of cationic PAHs. Methods Trapped PAH cations of sizes between 30 and 48 carbon atoms were submitted to VUV photons in the range of 9 to 20 eV from the DESIRS beamline at the synchrotron SOLEIL. All resulting photoproducts including dications and fragment cations were mass-analyzed and recorded as a function of photon energy. Results Photoionization is found to be predominant over dissociation at all energies, which differs from an earlier study on smaller PAHs. The photoionization branching ratio reaches 0.98 at 20 eV for the largest studied PAH. The photoionization threshold is observed to be between 9.1 and 10.2 eV, in agreement with the evolution of the ionization potential with size. Ionization cross sections were indirectly obtained and photoionization yields extracted from their ratio with theoretical photoabsorption cross sections, which were calculated using time-dependent density functional theory. An analytical function was derived to calculate this yield for a given molecular size. Conclusions Large PAH cations could be efficiently ionized in H i regions and provide a contribution to the heating of the gas by photoelectric effect. Also, at the border of or in H ii regions, PAHs could be exposed to photons of energy higher than 13.6 eV. Our work provides recipes to be used in astronomical models to quantify these points.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r. t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
Superconducting proximity effect in a transparent van der Waals superconductor-metal junction

We report on Andreev reflections at clean NbSe2-bilayer graphene junctions. The high transparency of the junction, which manifests as a large conductance enhancement of up to 1.8, enables us to see clear evidence of a proximity-induced superconducting gap in bilayer graphene and two Andreev reflections through a vertical NbSe2-graphene and a lateral graphene-graphene junction respectively. Quantum transport simulations capture the complexity of the experimental data and illuminate the impact of various microscopic parameters on the transmission of the junction. Our work establishes the practice and understanding of an all-van-der-Waals, high-performance superconducting junction. The realization of a highly transparent proximized graphene-graphene junction opens up possibilities to engineer emergent quantum phenomena.
On the role of features in vertex nomination: Content and context together are better (sometimes)

Vertex nomination is a lightly-supervised network information retrieval (IR) task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other IR tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both theoretically and practically, the dual roles of content (i.e., edge and vertex attributes) and context (i.e., network topology) in vertex nomination. We provide necessary and sufficient conditions under which vertex nomination schemes that leverage both content and context outperform schemes that leverage only content or context separately. While the joint utility of both content and context has been demonstrated empirically in the literature, the framework presented in this paper provides a novel theoretical basis for understanding the potential complementary roles of network features and topology.
Nonlinear response properties in the inferior colliculus: Common sound-frequency dependence for linear and nonlinear responses

Neurons in the main center of convergence in the auditory midbrain, the central nucleus of the inferior colliculus (ICC) have been shown to display either linear significant receptive fields, or both, linear and nonlinear significant receptive fields. In this study, we used reverse correlation to probe linear and nonlinear response properties of single neurons in the cat ICC. The receptive fields display areas of stimulus parameters leading to enhanced or inhibited spiking activity, and thus allow investigating the interplay to process complex sounds. Spiking responses were obtained from neural recordings of anesthetized cats in response to dynamic moving ripple (DMR) stimuli. The DMR sound contains amplitude and frequency modulations and allows systematically mapping neural preferences. Correlations of the stimulus envelope that preferably excite neurons can be mapped with the spike-triggered covariance. The spike-triggered average and -covariance were computed for the envelope of the DMR, separately for each frequency carrier (spanning a range of 0-5.5 octaves). This enables studying processing of the sound envelope, and to investigate whether nonlinearities are more pronounced at the neurons' preferred frequencies rather than at other frequencies. We find that more than half of the neurons (n=120) display significant nonlinear response properties at least at one frequency carrier. Nonlinearities are dominant at the neuron's best frequency. The nonlinear preferences can have either the same or opposite temporal receptive field pattern (e.g. on-off) as the linear preferences. No relationship to other neural properties such as feature-selectivity, phase-locking, or the like has been found. Thus, these nonlinearities do not seem to be linked to a specific type of neuron but to be inherent to ICC neurons indicating a diverse range of filtering characteristics.
Detection of Human Contact by Thermal Exchange Observer of Robot Skin

Physical human-robot interaction (pHRI) enables to achieve various assistive tasks for people by robots such as collaborative robotics or motion assistance. It pertains both planning-control and hardware considerations. We propose a new soft robot skin that can detect human contact through thermal features. An electrically conductive pipe is interposed between graphite sheet that has high heat conductivity and soft material for cover compliance. Then the temperature of the water pipe is controlled by a heat source and a small water pump. Besides, an observer for estimating heat flow is implemented; it can detect human interaction by measuring the temperature change of the water pipe. Hence, there is no need to attach sensors to the robot skin's surface, which can be kept soft and eventually textured with a desired pattern. We assessed the validity of the prototype in experiments of contact detection by human fingers.
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.
The formation of neutron star systems through accretion-induced collapse in white-dwarf binaries

The accretion-induced collapse (AIC) scenario was proposed 40 years ago as an evolutionary end state of oxygen-neon white dwarfs (ONe WDs), linking them to the formation of neutron star (NS) systems. However, there has been no direct detection of any AIC event so far, even though there exists a lot of indirect observational evidence. Meanwhile, the evolutionary pathways resulting in NS formation through AIC are still not thoroughly investigated. In this article, we review recent studies on the two classic progenitor models of AIC events, i.e., the single-degenerate model (including the ONe WD+MS/RG/He star channels and the CO WD+He star channel) and the double-degenerate model (including the double CO WD channel, the double ONe WD channel and the ONe WD+CO WD channel). Recent progress on these progenitor models is reviewed, including the evolutionary scenarios leading to AIC events, the initial parameter space for producing AIC events and the related objects (e.g., the pre-AIC systems and the post-AIC systems). For the single-degenerate model, the pre-AIC systems (i.e., the progenitor systems of AIC events) could potentially be identified as supersoft X-ray sources, symbiotics and cataclysmic variables (such as classical novae, recurrent novae, Ne novae and He novae) in the observations, whereas the post-AIC systems (i.e., NS systems) could potentially be identified as low-/intermediate-mass X-ray binaries, and the resulting low-/intermediate-mass binary pulsars, most notably millisecond pulsars. For the double-degenerate model, the pre-AIC systems are close double WDs with short orbital periods, whereas the post-AIC systems are single isolated NSs that may correspond to a specific kind of NS with peculiar properties. We also review the predicted rates of AIC events, the mass distribution of NSs produced via AIC and the gravitational wave (GW) signals from double WDs that are potential GW sources in the Galaxy in the context of future space-based GW detectors, such as LISA, TianQin, Taiji, etc. Recent theoretical and observational constraints on the detection of AIC events are summarized. In order to confirm the existence of the AIC process, and resolve this long-term issue presented by current stellar evolution theories, more numerical simulations and observational identifications are required.
A simple test for causality in complex systems

We provide a new solution to the long-standing problem of inferring causality from observations without modeling the unknown mechanisms. We show that the evolution of any dynamical system is related to a predictive asymmetry that quantifies causal connections from limited observations. A built-in significance criterion obviates surrogate testing and drastically improves computational efficiency. We validate our test on numerous synthetic systems exhibiting behavior commonly occurring in nature, from linear and nonlinear stochastic processes to systems exhibiting nonlinear deterministic chaos, and on real-world data with known ground truths. Applied to the controversial problem of glacial-interglacial sea level and CO$_{2}$ evolving in lock-step, our test uncovers empirical evidence for CO$_{2}$ as a driver of sea level over the last 800 thousand years. Our findings are relevant to any discipline where time series are used to study natural systems.
Edge-Weighted Online Bipartite Matching

Online bipartite matching is one of the most fundamental problems in the online algorithms literature. Karp, Vazirani, and Vazirani (STOC 1990) introduced an elegant algorithm for the unweighted bipartite matching that achieves an optimal competitive ratio of $1-{{}^{1}}/_{e}$. Aggarwal et al. (SODA 2011) later generalized their algorithm and analysis to the vertex-weighted case. Little is known, however, about the most general edge-weighted problem aside from the trivial 1/2-competitive greedy algorithm. In this paper, we present the first online algorithm that breaks the long-standing 1/2 barrier and achieves a competitive ratio of at least 0.5086. In light of the hardness result of Kapralov, Post, and Vondrák (SODA 2013) that restricts beating a 1/2 competitive ratio for the more general problem of monotone submodular welfare maximization, our result can be seen as strong evidence that edge-weighted bipartite matching is strictly easier than submodular welfare maximization in the online setting. The main ingredient in our online matching algorithm is a novel subroutine called online correlated selection (OCS), which takes a sequence of pairs of vertices as input and selects one vertex from each pair. Instead of using a fresh random bit to choose a vertex from each pair, the OCS negatively correlates decisions across different pairs and provides a quantitative measure on the level of correlation. We believe our OCS technique is of independent interest and will find further applications in other online optimization problems.
Abstract Mathematical morphology based on structuring element: Application to morpho-logic

A general definition of mathematical morphology has been defined within the algebraic framework of complete lattice theory. In this framework, dealing with deterministic and increasing operators, a dilation (respectively an erosion) is an operation which is distributive over supremum (respectively infimum). From this simple definition of dilation and erosion, we cannot say much about the properties of them. However, when they form an adjunction, many important properties can be derived such as monotonicity, idempotence, and extensivity or anti-extensivity of their composition, preservation of infimum and supremum, etc. Mathematical morphology has been first developed in the setting of sets, and then extended to other algebraic structures such as graphs, hypergraphs or simplicial complexes. For all these algebraic structures, erosion and dilation are usually based on structuring elements. The goal is then to match these structuring elements on given objects either to dilate or erode them. One of the advantages of defining erosion and dilation based on structuring elements is that these operations are adjoint. Based on this observation, this paper proposes to define, at the abstract level of category theory, erosion and dilation based on structuring elements. We then define the notion of morpho-category on which erosion and dilation are defined. We then show that topos and more precisely topos of presheaves are good candidates to generate morpho-categories. However, topos do not allow taking into account the notion of inclusion between substructures but rather are defined by monics up to domain isomorphism. Therefore we define the notion of morpholizable category which allows generating morpho-categories where substructures are defined along inclusion morphisms. {A direct application of this framework is to generalize modal morpho-logic to other algebraic structures than simple sets.