Miracle-dz/UniFAR-FTUs huggingface dataset:
xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs

At online retail platforms, it is crucial to actively detect risks of fraudulent transactions to improve our customer experience, minimize loss, and prevent unauthorized chargebacks. Traditional rule-based methods and simple feature-based models are either inefficient or brittle and uninterpretable. The graph structure that exists among the heterogeneous typed entities of the transaction logs is informative and difficult to fake. To utilize the heterogeneous graph relationships and enrich the explainability, we present xFraud, an explainable Fraud transaction prediction system. xFraud is composed of a predictor which learns expressive representations for malicious transaction detection from the heterogeneous transaction graph via a self-attentive heterogeneous graph neural network, and an explainer that generates meaningful and human understandable explanations from graphs to facilitate further process in business unit. In our experiments with xFraud on two real transaction networks with up to ten millions transactions, we are able to achieve an area under a curve (AUC) score that outperforms baseline models and graph embedding methods. In addition, we show how the explainer could benefit the understanding towards model predictions and enhance model trustworthiness for real-world fraud transaction cases.
Effect of Cinnamon on Glucose Control and Lipid Parameters

OBJECTIVE —To perform a meta-analysis of randomized controlled trials of cinnamon to better characterize its impact on glucose and plasma lipids. RESEARCH DESIGN AND METHODS —A systematic literature search through July 2007 was conducted to identify randomized placebo-controlled trials of cinnamon that reported data on A1C, fasting blood glucose (FBG), or lipid parameters. The mean change in each study end point from baseline was treated as a continuous variable, and the weighted mean difference was calculated as the difference between the mean value in the treatment and control groups. A random-effects model was used. RESULTS —Five prospective randomized controlled trials ( n = 282) were identified. Upon meta-analysis, the use of cinnamon did not significantly alter A1C, FBG, or lipid parameters. Subgroup and sensitivity analyses did not significantly change the results. CONCLUSIONS —Cinnamon does not appear to improve A1C, FBG, or lipid parameters in patients with type 1 or type 2 diabetes.
Modelling quantum light-matter interactions in waveguide-QED with retardation and a time-delayed feedback: matrix product states versus a space-discretized waveguide model

We present two different methods for modelling non-Markovian quantum light-matter interactions in waveguide QED systems, using matrix product states (MPSs) and a space-discretized waveguide (SDW) model. After describing the general theory and implementation of both approaches, we compare and contrast these methods directly on three topical problems of interest in waveguide-QED, including (i) a two-level system (TLS) coupled to an infinite (one-dimensional) waveguide, (ii) a TLS coupled to a terminated waveguide with a time-delayed coherent feedback, and (iii) two spatially separated TLSs coupled within an infinite waveguide. Both approaches are shown to efficiently model multi-photon nonlinear dynamics in highly non-Markovian regimes, and we highlight the advantages and disadvantages of these methods for modelling waveguide QED interactions, including their implementation in Python, computational run times, and ease of conceptual understanding. We explore both vacuum dynamics as well as regimes of strong optical pumping, where a weak excitation approximation cannot be applied. The MPS approach scales better when modelling multi-photon dynamics and long delay times, and explicitly includes non-Markovian memory effects. In contrast, the SDW model accounts for non-Markovian effects through space discretization, and solves Markovian equations of motion, yet rigorously includes the effects of retardation. The SDW model, based on an extension of recent collisional pictures in quantum optics, is solved through quantum trajectory techniques, and can more easily add in additional dissipation processes, including off-chip decay and TLS pure dephasing. The impact of these processes is shown directly on feedback-induced population trapping and TLS entanglement between spatially separated TLSs.
GLGE: A New General Language Generation Evaluation Benchmark

Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models. In this paper, we present the General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. For each task, we continue to design three subtasks in terms of task difficulty (GLGE-Easy, GLGE-Medium, and GLGE-Hard). This introduces 24 subtasks to comprehensively compare model performance. To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet\footnote{The source code and dataset will be publicly available at this https URL.
Recent progress on graphs with fixed smallest eigenvalue.

We give a survey on graphs with fixed smallest eigenvalue, especially on graphs with large minimal valency and also on graphs with good structures. Our survey mainly consists of the following two parts: (i) Hoffman graphs, the basic theory related to Hoffman graphs and the applications of Hoffman graphs to graphs with fixed smallest eigenvalue and large minimal valency; (ii) recent results on distance-regular graphs and co-edge regular graphs with fixed smallest eigenvalue and the characterizations of certain families of distance-regular graphs. At the end of the survey, we also discuss signed graphs with fixed smallest eigenvalue and present some new findings.
Zero-Shot Audio Classification Via Semantic Embeddings

In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of recognizing audio instances of sound classes that have no available training samples, but only semantic side information. We employ a bilinear compatibility framework to learn an acoustic-semantic projection between intermediate-level representations of audio instances and sound classes, i.e., acoustic embeddings and semantic embeddings. We use VGGish to extract deep acoustic embeddings from audio clips, and pre-trained language models (Word2Vec, GloVe, BERT) to generate either label embeddings from textual labels or sentence embeddings from sentence descriptions of sound classes. Audio classification is performed by a linear compatibility function that measures how compatible an acoustic embedding and a semantic embedding are. We evaluate the proposed method on a small balanced dataset ESC-50 and a large-scale unbalanced audio subset of AudioSet. The experimental results show that classification performance is significantly improved by involving sound classes that are semantically close to the test classes in training. Meanwhile, we demonstrate that both label embeddings and sentence embeddings are useful for zero-shot learning. Classification performance is improved by concatenating label/sentence embeddings generated with different language models. With their hybrid concatenations, the results are improved further.
Rate of estimation for the stationary distribution of jump-processes over anisotropic Holder classes

We study the problem of the non-parametric estimation for the density of the stationary distribution of the multivariate stochastic differential equation with jumps (Xt) , when the dimension d is bigger than 3. From the continuous observation of the sampling path on [0, T ] we show that, under anisotropic Holder smoothness constraints, kernel based estimators can achieve fast convergence rates. In particular , they are as fast as the ones found by Dalalyan and Reiss [9] for the estimation of the invariant density in the case without jumps under isotropic Holder smoothness constraints. Moreover, they are faster than the ones found by Strauch [29] for the invariant density estimation of continuous stochastic differential equations, under anisotropic Holder smoothness constraints. Furthermore, we obtain a minimax lower bound on the L2-risk for pointwise estimation, with the same rate up to a log(T) term. It implies that, on a class of diffusions whose invariant density belongs to the anisotropic Holder class we are considering, it is impossible to find an estimator with a rate of estimation faster than the one we propose.
Linear instability of Sasaki Einstein and nearly parallel G2 manifolds

In this article we study the stability problem for the Einstein metrics on Sasaki Einstein and on complete nearly parallel ${\rm G}_2$ manifolds. In the Sasaki case we show linear instability if the second Betti number is positive. Similarly we prove that nearly parallel $\rm G_2$ manifolds with positive third Betti number are linearly unstable. Moreover, we prove linear instability for the Berger space ${\rm SO}(5)/{\rm SO}(3)_{irr} $ which is a $7$-dimensional homology sphere with a proper nearly parallel ${\rm G}_2$ structure.
Monte Carlo simulations of flexible polymeric chains in solution

MONTE CARLO SIMULATIONS OF FLEXIBLE POLYMERIC CHAINS IN SOLUTION MAY, 1990 SCOTT K. STARRY, B.A.A.S. (PHILOSOPHY), UNIVERSITY OF DELAWARE B.Ch.E. , UNIVERSITY OF DELAWARE M.S., UNIVERSITY OF MASSACHUSETTS Ph.D., UNIVERSITY OF MASSACHUSETTS Directed by: Professor M. Muthukumar Dilute and semidilute concentrations of polymeric solutions are often used to elucidate fundamental characteristics of the molecules and specific interactions with a solvent. However, the theories that describe these regions of concentration have not been able to predict the values and scaling relationships observed in such experiments. This thesis uses both lattice based and continuous space Monte Carlo computer simulations to investigate these regions of concentration in three ways: (l) Evaluate the lattice and continuous space models for their ability to predict experimental scaling relationships; (2) attempt to provide values from the equation of state that may be compared with experimental data, and (3) calculate the scattering function for an amorphous system to investigate local organization and to provide comparisons with solution X-ray and neutron scattering data. Results from the lattice simulations were found to follow the trends predicted by mean field lattice theories at dilute and semidilute concentrations. This means that the approximations made in obtaining numerical results from the theories are acceptable. However, as mean field approximations are known to be invalid at these concentrations, lattice models in general are inapplicable to dilute and semidilute concentrations.
Spectral Domain Spline Graph Filter Bank

In this letter, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in the choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.
Is a Green Screen Really Necessary for Real-Time Human Matting?

For human matting without the green screen, existing works either require auxiliary inputs that are costly to obtain or use multiple models that are computationally expensive. Consequently, they are unavailable in real-time applications. In contrast, we present a light-weight matting objective decomposition network (MODNet), which can process human matting from a single input image in real time. The design of MODNet benefits from optimizing a series of correlated sub-objectives simultaneously via explicit constraints. Moreover, since trimap-free methods usually suffer from the domain shift problem in practice, we introduce (1) a self-supervised strategy based on sub-objectives consistency to adapt MODNet to real-world data and (2) a one-frame delay trick to smooth the results when applying MODNet to video human matting. MODNet is easy to be trained in an end-to-end style. It is much faster than contemporaneous matting methods and runs at 63 frames per second. On a carefully designed human matting benchmark newly proposed in this work, MODNet greatly outperforms prior trimap-free methods. More importantly, our method achieves remarkable results in daily photos and videos. Now, do you really need a green screen for real-time human matting?
Spectral convergence in geometric quantization on $K3$ surfaces

We study the geometric quantization on $K3$ surfaces from the viewpoint of the spectral convergence. We take a special Lagrangian fibrations on the $K3$ surfaces and a family of hyper-Kahler structures tending to large complex structure limit, and show a spectral convergence of the $\bar{\partial}$-Laplacians on the prequantum line bundle to the spectral structure related to the set of Bohr-Sommerfeld fibers.
Formation of Massive Star Clusters by Fast H i Gas Collision

Young massive clusters (YMCs) are dense aggregates of young stars, which are essential to galaxy evolution, owing to their ultraviolet radiation, stellar winds, and supernovae. The typical mass and radius of YMCs are M ∼ 104 M⊙ and R ∼ 1 pc, respectively, indicating that many stars are located in a small region. The formation of YMC precursor clouds may be difficult because a very compact massive cloud should be formed before stellar feedback blows off the cloud. Recent observational studies suggest that YMCs can be formed as a consequence of the fast H i gas collision with a velocity of ∼100 km s−1, which is the typical velocity of the galaxy–galaxy interaction. In this study, we examine whether the fast H i gas collision triggers YMC formation using three-dimensional magnetohydrodynamics simulations, which includes the effects of self-gravity, radiative cooling/heating, and chemistry. We demonstrate that massive gravitationally bound gas clumps with M > 104 M⊙ and L ∼ 4 pc are formed in the shock compressed region induced by the fast H i gas collision, in which massive gas clumps can evolve into YMCs. Our results show that the YMC precursors are formed by the global gravitational collapse of molecular clouds, and YMCs can be formed even in low-metal environments, such as the Magellanic Clouds. Additionally, the very massive YMC precursor cloud, with M > 105 M⊙, can be created when we consider the fast collision of H i clouds, which may explain the origin of the very massive stellar cluster R136 system in the Large Magellanic Cloud.
Improving epidemic testing and containment strategies using machine learning

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these predictions, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
Dilation theorem for p-approximate Schauder frames for separable Banach spaces.

Famous Naimark-Han-Larson dilation theorem for frames in Hilbert spaces states that every frame for a separable Hilbert space $\mathcal{H}$ is image of a Riesz basis under an orthogonal projection from a separable Hilbert space $\mathcal{H}_1$ which contains $\mathcal{H}$ isometrically. In this paper, we derive dilation result for p-approximate Schauder frames for separable Banach spaces. Our result contains Naimark-Han-Larson dilation theorem as a particular case.
Stokes force on a cylinder in the presence of fluid confinement

In this note, we present Stokes' calculation of the force exerted by the fluid on an oscillating cylinder. While the calculation of the similar problem in the case of the sphere is treated in several textbooks, the case of the cylinder is absent from these textbooks. Because modified Bessel functions were not defined in 1851 when Stokes made this calculation, Stokes was not able to express his results in closed forms but he gave asymptotic formulas valid in the two limits $a\ll \delta$ and $a \gg \delta$, where $a$ is the cylinder radius and $\delta$ is the viscous penetration depth. The closed form results were given by Stuart in 1963. We recall this calculation and we compare Stokes' asymptotic formulas to these exact results. Using modified Bessel functions, it is possible to calculate the force when the fluid is confined by an external cylinder of radius $b$ sharing the same axis: we review previous publications which have treated this problem and we present an exact calculation of this force which is also developed in powers of $\delta/a$, with the expansion coefficients being functions of the ratio $\gamma= a/b$ of the cylinder radii.
New necessary and sufficient conditions in order that the real Jacobian conjecture in $\mathbb{R}^2$ holds.

This paper is devoted to investigate the two-dimensional real Jacobian conjecture. This conjecture claims that if $F=\left(f,g\right):\mathbb{R}^2\rightarrow \mathbb{R}^2$ is a polynomial map such that $\det DF\left(x,y\right)$ is nowhere zero and $F\left(0,0\right)=\left(0,0\right)$, then $F$ is a global injective. Firstly, we provide some new necessary and sufficient conditions such that the real Jacobian conjecture holds. By Bendixson compactification, an induced polynomial differential system can be obtained from the Hamiltonian system associated to polynomial map $F$. We prove that the following statements are equivalent: (A) $F$ is a global injective; (B) the origin of induced system is a center; (C) the origin of induced system is a monodromic singular point; (D) the origin of induced system has no hyperbolic sectors; (E) induced system has a $C^k$ first integral with an isolated minimun at the origin and $k\in\mathbb{N}^{+}\cup\{\infty\}$. The above conditions (B)-(D) are local dynamical conditions. Secondly, applying the above results we present a sufficient condition for the validity of the real Jacobian conjecture. By definition a criterion function, when its limit at the origin of induced system exists, then $F$ is a global injective. This analytical condition improves the main result of Braun et al [J. Differential Equations {\bf 260} (2016) 5250-5258]. Moreover, we use this analytical condition to give a new proof of the known algebraic sufficient condition. In this work, our all proofs are based on qualitative theory of dynamical systems.
The GALEX-PTF Experiment. II. Supernova Progenitor Radius and Energetics via Shock-cooling Modeling

The radius and surface composition of an exploding massive star, as well as the explosion energy per unit mass, can be measured using early ultraviolet (UV) observations of core-collapse supernovae (CC SNe). We present the results from a simultaneous Galaxy Evolution Explorer (GALEX) and Palomar Transient Factory (PTF) search for early UV emission from SNe. We analyze five CC SNe for which we obtained near-UV (NUV) measurements before the first ground-based R-band detection. We introduce SOPRANOS, a new maximum likelihood fitting tool for models with variable temporal validity windows, and use it to fit the Sapir & Waxman shock-cooling model to the data. We report four Type II SNe with progenitor radii in the range of R * ≈ 600–1100 R ⊙ and a shock velocity parameter in the range of v s* ≈ 2700–6000 km s−1 (E/M ≈ 2–8 × 1050 erg/M ⊙) and one Type IIb SN with R * ≈ 210 R ⊙ and v s* ≈ 11,000 km s−1 (E/M ≈ 1.8 × 1051 erg/M ⊙). Our pilot GALEX/PTF project thus suggests that a dedicated, systematic SN survey in the NUV band, such as the wide-field UV explorer ULTRASAT mission, is a compelling method to study the properties of SN progenitors and SN energetics.
Nudge Attacks on Point-Cloud DNNs

The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat. Existing adversarial attacks on point clouds achieve high success rates but modify a large number of points, which is usually difficult to do in real-life scenarios. In this paper, we explore a family of attacks that only perturb a few points of an input point cloud, and name them nudge attacks. We demonstrate that nudge attacks can successfully flip the results of modern point-cloud DNNs. We present two variants, gradient-based and decision-based, showing their effectiveness in white-box and grey-box scenarios. Our extensive experiments show nudge attacks are effective at generating both targeted and untargeted adversarial point clouds, by changing a few points or even a single point from the entire point-cloud input. We find that with a single point we can reliably thwart predictions in 12--80% of cases, whereas 10 points allow us to further increase this to 37--95%. Finally, we discuss the possible defenses against such attacks, and explore their limitations.