Miracle-dz/UniFAR-FTUs huggingface dataset:
Semiclassical analysis for general weakly coupled nonlinear Schr\"{o}dinger systems

In this paper, we consider the following weakly coupled nonlinear Schr\"{o}dinger system \begin{equation*} \left\{ \begin{array}{ll} -\epsilon^{2}\Delta u_1 + V_1(x)u_1 = |u_1|^{2p - 2}u_1 + \beta|u_1|^{p - 2}|u_2|^pu_1, & x\in \mathbb{R}^N,\\ -\epsilon^{2}\Delta u_2 + V_2(x)u_2 = |u_2|^{2p - 2}u_2 + \beta|u_2|^{p - 2}|u_1|^pu_2, & x\in \mathbb{R}^N, \end{array} \right. \end{equation*} where $\epsilon>0$, $\beta\in\mathbb{R}$ is a coupling constant, $2p\in (2,2^*)$ with $2^* = \frac{2N}{N - 2}$ if $N\geq 3$ and $+\infty$ if $N = 1,2$, $V_1$ and $V_2$ belong to $C(\mathbb{R}^N,[0,\infty))$. This type of systems arise from models of nonlinear optics. Supposing that $V_1$ and $V_2$ have local minimum in an open set $\Lambda$, we construct a new penalized function to show the problem has a family of nonstandard solutions $\{w_{\epsilon} = (u^1_{\epsilon},u^2_{\epsilon}):0 0$ is suitably large and small. All decay rates of $V$ are admissible in this paper and our work is the first semiclassical study for the problem when $\beta>0$ is small. Moreover, we know the location of concentration points in the case $\beta>0$ small and $p\ge 2$ by local Pohozaev identities, see their applications in [23, 28] for example.
Lipid bilayer hydrodynamic drag

The hydrodynamic drag at a lipid bilayer surface determines in part the flow properties of suspensions of cells and liposomes. Given the fluidity of lipid bilayers, it is not obvious a priori whether solid-like no-slip, liquid-like no-stress, or intermediate boundary conditions apply at the water-bilayer interface. Though no-slip conditions have been widely assumed for many decades, this fundamental aspect of membrane rheology has, to our knowledge, never been directly measured for free bilayers. We applied light sheet fluorescence microscopy to image freely diffusing phospholipid vesicles and determined the hydrodynamic drag coefficient $C \pi \eta R$, where $\eta$ is the external fluid viscosity, $R$ is the vesicle radius, and the dimensionless $C$ characterizes the flow boundary condition. We find that $C = 5.92 \pm 0.13$ (stat.) $\pm 0.16$ (syst.), matching the theoretical value of $C=6$ for a no-slip boundary and far from the $C=4$ value for a zero shear stress boundary
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review.

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.
StackVault: Protection from Untrusted Functions

Data exfiltration attacks have led to huge data breaches. Recently, the Equifax attack affected 147M users and a third party library - Apache Struts - was alleged to be responsible for it. These attacks often exploit the fact that sensitive data are stored unencrypted in process memory and can be accessed by any function executing within the same process, including untrusted third party library functions.This paper presents StackVault, a kernel-based system to prevent sensitive stack-based data from being accessed in an unauthorized manner by intra-process functions. Stack-based data includes data on stack as well as data pointed to by pointer variables on stack. StackVault consists of three components: (1) a set of programming APIs to allow users to specify which data needs to be protected, (2) a kernel module which uses unforgeable function identities to reliably carry out the sensitive data protection, and (3) an LLVM compiler extension that enables transparent placement of stack protection operations. The StackVault system automatically enforces stack protection through spatial and temporal access monitoring and control over both sensitive stack data and untrusted functions. We implemented StackVault and evaluated it using a number of popular real world applications, including gRPC. The results show that StackVault is effective and efficient, incurring only up to 2.4% runtime overhead.
Privacy-Preserving Speaker Recognition with Cohort Score Normalisation

In many voice biometrics applications there is a requirement to preserve privacy, not least because of the recently enforced General Data Protection Regulation (GDPR). Though progress in bringing privacy preservation to voice biometrics is lagging behind developments in other biometrics communities, recent years have seen rapid progress, with secure computation mechanisms such as homomorphic encryption being applied successfully to speaker recognition. Even so, the computational overhead incurred by processing speech data in the encrypted domain is substantial. While still tolerable for single biometric comparisons, most state-of-the-art systems perform some form of cohort-based score normalisation, requiring many thousands of biometric comparisons. The computational overhead is then prohibitive, meaning that one must accept either degraded performance (no score normalisation) or potential for privacy violations. This paper proposes the first computationally feasible approach to privacy-preserving cohort score normalisation. Our solution is a cohort pruning scheme based on secure multi-party computation which enables privacy-preserving score normalisation using probabilistic linear discriminant analysis (PLDA) comparisons. The solution operates upon binary voice representations. While the binarisation is lossy in biometric rank-1 performance, it supports computationally-feasible biometric rank-n comparisons in the encrypted domain.
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings

We show by counterexample that policy-gradient algorithms have no guarantees of even local convergence to Nash equilibria in continuous action and state space multi-agent settings. To do so, we analyze gradient-play in $N$-player general-sum linear quadratic games. In such games the state and action spaces are continuous and the unique global Nash equilibrium can be found be solving coupled Ricatti equations. Further, gradient-play in LQ games is equivalent to multi-agent policy gradient. We first prove that the only critical point of the gradient dynamics in these games is the unique global Nash equilibrium. We then give sufficient conditions under which policy gradient will avoid the Nash equilibrium, and generate a large number of general-sum linear quadratic games that satisfy these conditions. The existence of such games indicates that one of the most popular approaches to solving reinforcement learning problems in the classic reinforcement learning setting has no guarantee of convergence in multi-agent settings. Further, the ease with which we can generate these counterexamples suggests that such situations are not mere edge cases and are in fact quite common.
Emergence of outcomes in quantum mechanics

A persistent focus on the concept of emergence as a core element of the scientific method allows a clean separation, insofar as this is possible, of the physical and philosophical aspects of the problem of outcomes in quantum mechanics. The philosophical part of the problem is to explain why a closed system has definite experimental outcomes. The physical part is to show mathematically that there exists a limit in which the contradiction between unitary Schroedinger dynamics and a reduction process leading to distinct outcomes becomes negligible according to an explicitly stated criterion, and to make this criterion as objective as possible. The physical problem is solved here by redefining the notion of a quantum state and finding a suitable measure for the change of state upon reduction. The appropriate definition of the quantum state is not as a ray or density operator in Hilbert space, but rather as an equivalence class consisting of all density operators in a given subspace, the members of which all describe the same experimental outcome. For systems containing only subsystems that are integrated with their environments, these equivalence classes can be represented mathematically by projection operators, and the resulting formalism is closely related to that used by von Neumann to study the increase of entropy predicted by the second law of thermodynamics. However, nearly isolated subsystems are reduced only indirectly, as a consequence of their interaction with integrated subsystems. The reduced states of isolated subsystems are the same conditional states used in the definition of quantum discord. The key concepts of decoherence theory can all be adapted to fit this definition of a quantum state, resulting in a unified theory capable of resolving, in principle, all aspects of the quantum measurement problem. The theory thus obtained is weakly objective but not strongly objective.
A Comparison of the X-Ray Properties of FU Ori-type Stars to Generic Young Stellar Objects

Like other young stellar objects (YSOs), FU Ori-type stars have been detected as strong X-ray emitters. However, little is known about how the outbursts of these stars affect their X-ray properties. We assemble available X-ray data from XMM-Newton and Chandra observations of 16 FU Ori stars, including a new XMM-Newton observation of Gaia 17bpi during its optical rise phase. Of these stars, six were detected at least once, while 10 were non-detections, for which we calculate upper limits on intrinsic X-ray luminosity (LX) as a function of plasma temperature (kT) and column density (NH). The detected FU Ori stars tend to be more X-ray luminous than is typical for non-outbursting YSOs, based on comparison to a sample of low-mass stars in the Orion Nebula Cluster. FU Ori stars with high LX have been observed both at the onset of their outbursts and decades later. We use the Kaplan–Meier estimator to investigate whether the higher X-ray luminosities for FU Ori stars are characteristic or a result of selection effects, and we find the difference to be statistically significant (p < 0.01) even when non-detections are taken into account. The additional X-ray luminosity of FU Ori stars relative to non-outbursting YSOs cannot be explained by accretion shocks, given the high observed plasma temperatures. This suggests that, for many FU Ori stars, either (1) the outburst leads to a restructuring of the magnetosphere in a way that enhances X-ray emission, or (2) FU Ori outbursts are more likely to occur among YSOs with the highest quiescent X-ray luminosity.
Artificial Intelligence Alter Egos:Who benefits from Robo-investing?

Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing. We have detailed investor characteristics and records of all trades. Our data set consists of investors typically targeted for robo-advising. We explore robo-investing strategies commonly used in the industry, including some involving advanced machine learning methods. The man versus machine comparison allows us to shed light on potential benefits the emerging robo-advising industry may provide to certain segments of the population, such as low income and/or high risk averse investors.
A Quantum Algorithm for Finding $k$-Minima

We propose a new finding $k$-minima algorithm and prove that its query complexity is $\mathcal{O}(\sqrt{kN})$, where $N$ is the number of data indices. Though the complexity is equivalent to that of an existing method, the proposed is simpler. The main idea of the proposed algorithm is to search a good threshold that is near the $k$-th smallest data. Then, by using the generalization of amplitude amplification, all $k$ data are found out of order and the query complexity is $\mathcal{O}(\sqrt{kN})$. This generalization of amplitude amplification is also not well discussed and we briefly prove the query complexity. Our algorithm can be directly adapted to distance-related problems like $k$-nearest neighbor search and clustering and classification. There are few quantum algorithms that return multiple answers and they are not well discussed.
QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also trained securely. The few prior works on secure DNN training have focused either on designing custom protocols for existing training algorithms, or on developing tailored training algorithms and then applying generic secure protocols. In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts. We present QUOTIENT, a new method for discretized training of DNNs, along with a customized secure two-party protocol for it. QUOTIENT incorporates key components of state-of-the-art DNN training such as layer normalization and adaptive gradient methods, and improves upon the state-of-the-art in DNN training in two-party computation. Compared to prior work, we obtain an improvement of 50X in WAN time and 6% in absolute accuracy.
Car following behavioral stochasticity analysis and modelling

This paper analyzes the car following behavioral stochasticity based on the experimental trajectory data by measuring the wave travel time Tn(t) of vehicle n. The analysis shows that the Tn(t) does not follow a consistent pattern. In particular, we found that (i) No matter the speed of leading vehicle oscillates significantly or slightly, Tn(t) might change significantly; (ii) Even if the leader is asked to move with the same speed, the oscillation pattern of Tn(t) might be different from run to run; (iii) Sometimes, even if the speed of leader severely fluctuates, Tn(t) can keep a nearly constant value. Further investigations show that the time series of the increment {\xi}n(t) = Tn(t) - Tn(t-dt) follows a mean reversion process, based on which a simple stochastic Newell model is proposed to reproduce the realistic wave travel time. Simulation results show that not only the concave growth pattern of oscillations but also the empirical congested traffic states can be successfully simulated by the new model, including the stable congested traffic states that cannot be simulated by recent stochastic car following models.
Dynamics of cluster-forming hub-filament systems: The case of the high-mass star-forming complex Monoceros R2.

Context High-mass stars and star clusters commonly form within hub-filament systems. Monoceros R2 (hereafter Mon R2), at a distance of 830 pc, harbors one of the closest such systems, making it an excellent target for case studies. Aims We investigate the morphology, stability and dynamical properties of the Mon R2 hub-filament system. Methods We employ observations of the 13CO and C18O 1→0 and 2→1 lines obtained with the IRAM-30m telescope. We also use H2 column density maps derived from Herschel dust emission observations. Results We identified the filamentary network in Mon R2 with the DisPerSE algorithm and characterized the individual filaments as either main (converging into the hub) or secondary (converging to a main filament) filaments. The main filaments have line masses of 30-100 M ⊙ pc-1 and show signs of fragmentation, while the secondary filaments have line masses of 12-60 M ⊙ pc-1 and show fragmentation only sporadically. In the context of Ostriker's hydrostatic filament model, the main filaments are thermally supercritical. If non-thermal motions are included, most of them are trans-critical. Most of the secondary filaments are roughly transcritical regardless of whether non-thermal motions are included or not. From the morphology and kinematics of the main filaments, we estimate a mass accretion rate of 10-4-10-3 M ⊙ yr-1 into the central hub. The secondary filaments accrete into the main filaments with a rate of 0.1-0.4×10-4 M ⊙ yr-1. The main filaments extend into the central hub. Their velocity gradients increase towards the hub, suggesting acceleration of the gas.We estimate that with the observed infall velocity, the mass-doubling time of the hub is ~ 2:5 Myr, ten times larger than the free-fall time, suggesting a dynamically old region. These timescales are comparable with the chemical age of the Hii region. Inside the hub, the main filaments show a ring- or a spiral-like morphology that exhibits rotation and infall motions. One possible explanation for the morphology is that gas is falling into the central cluster following a spiral-like pattern.
An Initial Attempt of Converged Machine-Learning Assisted Turbulence Modeling in RANS Simulations with Eddy-Viscosity Hypothesis

This work presents a converged framework of Machine-Learning Assisted Turbulence Modeling (MLATM). Our objective is to develop a turbulence model directly learning from high fidelity data (DNS/LES) with eddy-viscosity hypothesis induced. First, the target machine-learning quantity is discussed in order to avoid the ill-conditioning problem of RANS equations. Then, the novel framework to build the turbulence model using the prior estimation of traditional models is demonstrated. A close-loop computational chain is designed to ensure the convergence of result. Besides, reasonable non-dimensional variables are selected to predict the target learning variables and make the solver re-converge to DNS mean flow field. The MLATM is tested in incompressible turbulent channel flows, and it proved that the result converges well to DNS training data for both mean velocity and turbulence viscosity profiles.
A new amplification structure for time projection chambers based on electroluminescence

A simple hole-type secondary scintillation structure (2 mm-hole, 5 mm-pitch, 5 mm-thickness) is introduced and its operation demonstrated in pure xenon in the pressure range 2-10 bar. The new device, characteristically translucent, has been manufactured through a collaboration between IGFAE and the CERN workshop, and relies entirely on radiopure materials (acrylic and copper), being extremely rugged in the presence of sparks, mechanically robust, and easily scalable, yet made through a relatively simple process. With an overall figure (at 10 bar) characterized by an energy resolution of 18.9%(FWHM) for 55Fe x-rays, an optical gain of m γ = 500 ph/e, and a stable operation at reduced fields more than twice those of some of the presently running experiments (E EL = 3 kV/cm/bar), this family of structures seems to show great promise for electroluminescence readouts on large scale detectors. As argued below, further improvements have the potential of bringing the energy resolution close to the Fano factor and increasing the optical gain.
Study of methane hydrate formation and self-preservation using in-situ X-ray synchrotron tomography

This paper reports results of the synchrotron-based tomography experiments for studying the methane-hydrate formation/decomposition in sand samples. Fast scanning times (70 secs) provided successive images (every 2-15 min) for a detailed study of these processes. During the methane gas-hydrate formation we observed water flows that we connect to the effect of cryogenic suction due to the hydrate formation. These appeared to be multiple fast water flows in pore space followed by periods of the water position stability. Dynamic images allow identifying two types of gas-hydrate formation: into the gas pockets and inside water volumes. These two gas-hydrate types show different properties during the decomposition in the self-preservation mode - pressure drop at negative temperatures. Gas hydrate formed in water volumes remains more stable compared to hydrate formed as growing into the gas pocket. This means that the history of the gas-hydrate formation influences its behaviour at the decomposition stage (e.g gas-hydrate production).
Very high-energy constraints on the infrared extragalactic background light

Context. Measurements of the extragalactic background light (EBL) are a fundamental source of information on the collective emission of cosmic sources. Aims. At infrared wavelengths, however, these measurements are precluded by the overwhelming dominance from interplanetary dust emission and the Galactic infrared foreground. Only at λ > 300 μm, where the foregrounds are minimal, has the infrared EBL (IR EBL) been inferred from analysis of the COBE maps. The present paper aims to assess the possibility of evaluating the IR EBL from a few micrometers up to the peak of the emission at > 100 μm using an indirect method that avoids the foreground problem. Methods. To this purpose we exploit the effect of pair-production from gamma-gamma interaction by considering the highest-energy photons emitted by extragalactic sources and their interaction with the IR EBL photons. We simulate observations of a variety of low-redshift emitters with the forthcoming Imaging Atmospheric Cherenkov Telescope (IACT) arrays (CTA in particular) and water Cherenkov observatories (LHAASO, HAWC, SWGO) to assess their suitability to constrain the EBL at such long wavelengths. Results. We find that even under the most extremely favorable conditions of huge emission flares, extremely high-energy emitting blazars are not very useful for our purpose because they are much too distant (> 100 Mpc the nearest ones, MKN 501 and MKN 421). Observations of more local AGNs displaying very high-energy emission, like low-redshift radio galaxies (M 87, IC 310, Centaurus A), are better suited and will potentially allow us to constrain the EBL up to λ ≃ 100 μm.
Polymorphism and the free bicartesian closed category

We study two decidable fragments of System F, the polynomial and the Yoneda fragment, inducing two representations of the free bicartesian closed category. The first fragment is freely generated by the encoding of finite polynomial functors (generalizing the usual embedding of finite products and coproducts) and describes a class of well-behaved polymorphic terms: unlike those in full System F, the terms typable in this fragment can be interpreted as ordinary natural transformations and are equivalent, up to permutations, to terms typable using a strictly predicative type discipline. The second fragment is introduced to investigate the class of finite types, that is the types of System F which are isomorphic, modulo contextual equivalence, to a closed propositional type. The types of this fragment arise from a schema resembling the Yoneda isomorphism, and are shown to converge onto propositional types by a type rewriting approach.
Searching for Effective Neural Extractive Summarization: What Works and What’s Next

The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.