Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learing based Recommendation System Survey Paper

:- With the proliferation of online information, recommender systems have shown to be an effective method of overcoming this information imbalance. The utility of recommendation systems cannot be overstated, as can their ability to ease several concerns associated with excessive choice. Deep learning has had a significant effect in recent years across a variety of research disciplines, including computer vision and natural language processing, contributing not only to astounding results but also to the alluring trait of learning feature representations from scratch. Deep learning's influence is equally ubiquitous, with research demonstrating its usefulness when utilised for recommender systems and information retrieval. The topic of deep learning in recommender systems appears to be growing. The purpose of this study is to provide an in-depth evaluation of recent research endeavours on deep-learning-based recommender systems. Simply put, we explain and categorise deep learning-based recommendation models, as well as provide a consistent appraisal of the research. Finally, we elaborate on current trends and give new perspectives on the industry's game-changing rise.
$\varepsilon $-approximability of harmonic functions in $L^p$ implies uniform rectifiability

Suppose that $\Omega \subset \mathbb{R}^{n+1}$, $n \ge 2$, is an open set satisfying the corkscrew condition with an $n$-dimensional ADR boundary, $\partial \Omega$. In this note, we show that if harmonic functions are $\varepsilon$-approximable in $L^p$ for any $p > n/(n-1)$, then $\partial \Omega$ is uniformly rectifiable. Combining our results with those in [HT] (Hofmann-Tapiola) gives us a new characterization of uniform rectifiability which complements the recent results in [HMM] (Hofmann-Martell-Mayboroda), [GMT] (Garnett-Mourgoglou-Tolsa) and [AGMT] (Azzam-Garnett-Mourgoglou-Tolsa).
Stable cohomology of the perfect cone toroidal compactification of 𝒜 g

We show that the cohomology of the perfect cone (also called first Voronoi) toroidal compactification {{{\mathcal{A}}_{g}^{\operatorname{Perf}}}} of the moduli space of complex principally polarized abelian varieties stabilizes in close to the top degree. Moreover, we show that this stable cohomology is purely algebraic, and we compute it in degree up to 13. Our explicit computations and stabilization results apply in greater generality to various toroidal compactifications and partial compactifications, and in particular we show that the cohomology of the matroidal partial compactification {{{\mathcal{A}}_{g}^{\operatorname{Matr}}}} stabilizes in fixed degree, and forms a polynomial algebra. For degree up to 8, we describe explicitly the generators of the cohomology, and discuss various approaches to computing all of the stable cohomology in general.
ELECTRON–POSITRON FLOWS AROUND MAGNETARS

The twisted magnetospheres of magnetars must sustain a persistent flow of electron–positron plasma. The flow dynamics is controlled by the radiation field around the hot neutron star. The problem of plasma motion in the self-consistent radiation field is solved using the method of virtual beams. The plasma and radiation exchange momentum via resonant scattering and self-organize into the “radiatively locked” outflow with a well-defined, decreasing Lorentz factor. There is an extended zone around the magnetar where the plasma flow is ultra-relativistic; its Lorentz factor is self-regulated so that it can marginally scatter thermal photons. The flow becomes slow and opaque in an outer equatorial zone, where the decelerated plasma accumulates and annihilates; this region serves as a reflector for the thermal photons emitted by the neutron star. The e± flow carries electric current, which is sustained by a moderate induced electric field. The electric field maintains a separation between the electron and positron velocities, against the will of the radiation field. The two-stream instability is then inevitable, and the induced turbulence can generate low-frequency emission. In particular, radio emission may escape around the magnetic dipole axis of the star. Most of the flow energy is converted to hard X-ray emission, which is examined in an accompanying paper.
CONSTANT NONLOCAL MEAN CURVATURES SURFACES AND RELATED PROBLEMS

The notion of Nonlocal Mean Curvature (NMC) appears recently in the mathematics literature. It is an extrinsic geometric quantity that is invariant under global reparameterization of a surface and provide a natural extension of the classical mean curvature. We describe some properties of the NMC and the quasilinear differential operators that are involved when it acts on graphs. We also survey recent results on surfaces having constant NMC and describe their intimate link with some problems arising in the study of overdetermined boundary value problems.
Precessing Warped Be Disk Triggering the Giant Outbursts in 2009 and 2011 in A 0535 + 262/V725 Tau

We carried out optical high-dispersion spectroscopic monitoring of the Be disk in a Be/X-ray binary, A 0535+262/V725 Tau, from 2009 to 2012, covering two giant outbursts and several normal ones. This monitoring was performed in order to investigate any variabilities of the Be disk due to interactions with the neutron star in the recent X-ray active phase from 2008 to 2011. Such variabilities give a clue to any uncleared detailed mechanism for very bright X-ray outbursts, which are unique to some Be/X-ray binaries with a relatively wide and eccentric orbit. In a previous letter (Moritani et al. 2011, PASJ, 63, L25), a brief overview of the line-profile variabilities around the 2009 giant outburst was given, and the possibility of a warped Be disk was discussed. In this paper, a full analysis of the H˛ line profiles as well as other line profiles is carried out. A bright blue component, or blue “shoulder,” showing up after periastron indicates the presence of a dense gas stream toward the neutron star, which is associated with observed outbursts. We re-analyze the H˛ line profiles before 2009 (down to 2005) in order to investigate the variability of the disk structure in the innermost region, which seems to have detached from the Be star surface by 2008. A redshifted enhanced component is remarkable in all emission lines observed around the 2009 giant outburst, occasionally forming a triple peak. These features indicate that the Be disk was warped in the X-ray active phase. We estimated the position of the warped region from fitting the radial velocity of the redshifted enhanced component of H˛, and found that it was very close to the periastron when two giant outbursts in 2009 and 2011 and a bright normal outburst in 2010 March occurred. These facts strongly suggest that the precessing warped Be disk triggered these giant outbursts.
A Posteriori Error Estimates in Maximum Norm for Interior Penalty Discontinuous Galerkin Approximation of the Obstacle Problem

Adaptive inite element method ̆AFEM ̄ is an efective numerical tool for solving linear and nonlinear PDEs. A proper local reinement plays a key role in AFEM and relies on proper a‚posteriori error estimators. In this contribution, we introduce a pointwise a posteriori error estimator for the symmetric interior penalty discontin‚ uous Galerkin ̆SIPG ̄ approximation of the elliptic obstacle problem. The elliptic obstacle problem is a prototype of the elliptic variational inequalities of the irst kind. This problem exhibits the free boundary and appears in various processes in engineering and physical sciences such as elasto‚plasticity, dam problem and math‚ ematical inance [3]. A‚posteriori error analysis in maximum norm for conforming approximation of obstacle problems is given in the seminal works [6, 7]. For dis‚ continuous Galerkin ̆DG ̄ approximation, the a‚posteriori error analysis in energy norm is contained in [4]. In the maximum norm, to the best of our knowledge, the results in [1] are the irst in this direction. Here, due to space limitation, we state the reliability result and focus on its numerical veriication and validation. Details on the analysis as well as further discussion can be found in [1].
Backward stochastic Volterra integral equations associated with a Levy process and applications

In this paper, we study a class of backward stochastic Volterra integral equations driven by Teugels martingales associated with an independent L\'{e}vy process and an independent Brownian motion (BSVIELs). We prove the existence and uniqueness as well as stability of the adapted M-solutions for those equations. Moreover, a duality principle and then a comparison theorem are established. As an application, we derive a class of dynamic risk measures by means of M-solutions of certain BSVIELs.
CHARACTERIZING AGENT BEHAVIOR UNDER META REINFORCEMENT LEARNING WITH GRIDWORLD

The capabilities of meta reinforcement learning agents tend to be heavily depend on the complexity and scope of the meta task over which they perform requiring different models, learning algorithms, and strategies to perform well. In this thesis, we show the fragility of agent design and limitations of agents across Gridworld-based meta tasks of increasing complexity. We begin by building a characterization of the complexity of meta tasks within a domain generalization context. We run experiments that demonstrate the ability of agents to perform effectively on meta tasks parameterized with different environmental states, but similar underlying rules. Next, we perform experiments that expose the limitations of those same agents over tasks with different underlying rules, but similar observational spaces. These experiments show that generalization-based strategies succeed with meta tasks that sample from a small scope of base tasks with similar underlying rules, but break beyond that complexity. We also infer from observed agent behaviors that the limitations of agents are attributable to the nature of the model architecture and the meta task design. Furthermore, we run experiments that identify the sensitivity of agent behavior to physical features by augmenting the agent observation size. These experiments show a resilience to limited environmental information, but a lack of spatial awareness to abundant environmental information. Overall, this work provides a baseline for meta reinforcement learning with the Gridworld task and exposes the necessary considerations of agent and environmental design.
5.1 AI-Based Vehicle Systems for Mobility-as-a-Service Application

Achieving sufﬁcient safety measures is among the major challenges in developing automated vehicles that can operate safely in an urban environment. Data fusion between an in-vehicle camera and a LiDAR sensor can be used for detection and tracking of other road users in an automated vehicle. In addition, simulated environments together with high-level deterministic, supervised and reinforcement learning-based autonomous control could provide trafﬁc safety beneﬁts in the future. These AI-based technologies have been studied in the AI4DI project to enable the Mobility as a Service (MaaS) operators ﬂeet management of automated vehicles. The development and testing of these methods are presented in this chapter with the ﬁrst promising results. The Camera - LiDAR fusion algorithm provided very good results with the accuracy evaluation using the KITTI dataset. The real-time applicability of the fusion algorithm was also successfully veriﬁed.
Early Childhood Teachers’ Perceptions of Environmental Sustainability: A Phenomenographic Investigation

Early childhood teachers’ environmental perceptions can influence young children’s learning about environmental issues. Yet, to date, there is minimal research focusing on the perceptions of environmental sustainability held by early childhood teachers. This qualitative phenomenographic study collected data via individual semi-structured interviews with five early childhood teachers and one head teacher, in Malta. Preliminary findings revealed that the participants perceived environmental sustainability in terms of environmental concerns; environmental responsibility; and environmental protection. A mismatch between the participants’ environmental concerns and their pro-environmental behaviour emerged. The data also show that any environmental activities at school were done so uncritically. These findings hold importance for the reorientation of in-service and pre-service early childhood teacher education programs towards sustainability to ensure that systems thinking around critical environmental issues are introduced in the early years by addressing gaps in knowledge and supporting educators' development at the earliest stage (i.e., pre-service).
A Central Limit Theorem for Fluctuations in Polyanalytic Ginibre Ensembles

We study fluctuations of linear statistics in Polyanalytic Ginibre ensembles, a family of point processes describing planar free fermions in a uniform magnetic field at higher Landau levels. Our main result is asymptotic normality of fluctuations, extending a result of Rider and Virag. As in the analytic case, the variance is composed of independent terms from the bulk and the boundary. Our methods rely on a structural formula for polyanalytic polynomial Bergman kernels which separates out the different pure $q$-analytic kernels corresponding to different Landau levels. The fluctuations with respect to these pure $q$-analytic Ginibre ensembles are also studied, and a central limit theorem is proved. The results suggest a stabilizing effect on the variance when the different Landau levels are combined together.
OPTIMISATION OF LI-ION BATTERY DESIGN PARAMETERS

Demand for energy storage facilities and non-fossil fuel powered vehicle has furthered the research into Li-ion batteries. In this work, an electrochemical Lithium-ion battery model was built taking into account the electrochemical reactions is presented. The mathematical model is validated against experimental data for LCO cell operating under 1 C electrical load at room temperature. Electrode thickness and the porosity of the positive electrode were varied to determine the influence on the battery capacity.
On stochastic optimization methods for Monte Carlo least-squares problems

This work presents stochastic optimization methods targeted at least-squares problems involving Monte Carlo integration. While the most common approach to solving these problems is to apply stochastic gradient descent (SGD) or similar methods such as AdaGrad and Adam, which involve estimating a stochastic gradient from a small number of Monte Carlo samples computed at each iteration, we show that for this category of problems it is possible to achieve faster asymptotic convergence rates using an increasing number of samples per iteration instead, a strategy we call increasing precision (IP). We then improve pre-asymptotic convergence by introducing a hybrid approach that combines the qualities of increasing precision and otherwise "constant" precision, resulting in methods such as the IP-SGD hybrid and IP-AdaGrad hybrid, essentially by modifying their gradient estimators to have an equivalent effect to increasing precision. Finally, we observe that, in some problems, incorporating a Gauss-Newton preconditioner to the IP-SGD hybrid method can provide much better convergence than employing a Quasi-Newton approach or covariance-preconditioning as in AdaGrad or Adam.
MaTCheD FIelD PROCessINg FOR sOURCe lOC alIzaTION BaseD ON aN aPPROaCh OF RIeMaNNIaN geOMe TRy

: The matched field processing (MFP) for source localization has long history, and remains a viable area of research as well as application of SONAR. Some methods such as empirical mode decomposition, adaptive MFP, compressive MFP and MFP using Riemannian geometry have been introduced recently in order to increase the performance of conventional MFP. In case of ocean environment variability, there are many modeled field replicas thus the number of degree of freedom is increased, consequently the true source selection becomes more complexity. In this paper, we presents a MFP using an approach of Riemannian geometry in which Riemannian distance is obtained in close-form from a new isometric mapping and 20 modeled field replicas that are received in simulation from variable sound speeds. On the basis of the proposed MFP and simulation results, the source localization could be found in a more realistic manner.
Hyperbolic Quasilinear Navier–Stokes Equations in R 2

We consider a hyperbolic quasilinear version of the Navier–Stokes equations in R 2 , arising from using a Cattaneo type law instead of a Fourier law. These equations, which depend on a parameter ε , are a way to avoid the inﬁnite speed of propagation which occurs in the classical Navier–Stokes equations. We ﬁrst prove the existence and uniqueness of solutions to these equations, and then exhibit smallness assumptions on the data, under which the solutions are global in time. In particular, these smallness assumptions disappear when ε vanishes, accordingly to the fact that the solutions of the 2D Navier–Stokes equations are global.
The Relationship between Corporate Governance Mechanisms and Earnings Management: An Empirical Study on the Listed Firms in China

: This paper empirically explores the relationship between corporate governance mechanisms and earnings management of the listed firms in China. It is found that ownership concentration and CEO duality show a significant positive effect on earnings management, while the number of directors and the proportion of independent directors have a significant negative effect on earnings management when all listed firms are taken as a whole group. When the sample is classified into two groups of state-owned and non-state-owned, both the number of directors and the proportion of independent directors remain as significant negative factors on earnings management for the state-owned firms, but their impact become not significant for the non-state-owned firms. Furthermore, both the ownership concentration and CEO duality remain as significant positive factors affecting earnings management for the state-owned firms, but their effects become not significant for the non-state-owned group. It is also found that Tobin‟s Q and the debt ratio have a very significant positive effect on earnings management for all three groups of firms, while ROA plays a significant negative role on earnings management in China. The implications of our findings are also discussed.
Fallback Disks, Magnetars and Other Neutron Stars

Abstract The presence of matter with angular momentum, in the form of a fallback disk around a young isolated neutron star will determine its evolution. This leads to an understanding of many properties of different classes of young neutron stars, in particular a natural explanation for the period clustering of AXPs, SGRs and XDINs. The spindown or spinup properties of a neutron star are determined by the dipole component of the magnetic field. The natural possibility that magnetars and other neutron stars may have different strengths of the dipole and higher multipole components of the magnetic field is now actually required by observations on the spindown rates of some magnetars. This talk gives a broad overview and some applications of the fallback disk model to particular neutron stars. Salient points are: (i) A fallback disk has already been observed around the AXP 4U 0142+61 some years ago. (ii) The low observed spindown rate of the SGR 0418+5729 provides direct evidence that the dipole component of the field is in the 1012G range. All properties of the SGR 0418+5729 at its present age can be explained by spindown under torques from a fallback disk. (iii) The anomalous braking index of PSR J1734-3333 can also be explained by the fallback disk model which gives the luminosity, period, period derivative and the period second derivative at the present age. (iv) These and all applications to a variety of other sources employ the same disk physics and evolution, differing only in the initial conditions of the disk.
The nature of manifolds of periodic points for higher dimensional integrable maps II

We study periodicity conditions of a rational map on $\bm{C}^d$ with $p$ invariants and show that a set of isolated periodic points and an algebraic variety of finife dimension do not exist in one map simultaneously if $p\ge d/2$. We also discuss in detail how the transition takes place between them.
RADIONUCLIDE IONIZATION IN PROTOPLANETARY DISKS: CALCULATIONS OF DECAY PRODUCT RADIATIVE TRANSFER

We present simple analytic solutions for the ionization rate ζSLR arising from the decay of short-lived radionuclides (SLRs) within protoplanetary disks. We solve the radiative transfer problem for the decay products within the disk, and thereby allow for the loss of radiation at low disk surface densities; energy loss becomes important outside R ≳ 30 AU for typical disk masses Mg = 0.04 M☉. Previous studies of chemistry/physics in these disks have neglected the impact of ionization by SLRs, and often consider only cosmic rays (CRs), because of the high CR-rate present in the interstellar medium. However, recent work suggests that the flux of CRs present in the circumstellar environment could be substantially reduced by relatively modest stellar winds, resulting in severely modulated CR ionization rates, ζCR, equal to or substantially below that of SLRs (ζSLR ≲ 10−18 s−1). We compute the net ionizing particle fluxes and corresponding ionization rates as a function of position within the disk for a variety of disk models. The resulting expressions are especially simple for the case of vertically Gaussian disks (frequently assumed in the literature). Finally, we provide a power-law fit to the ionization rate in the midplane as a function of gas disk surface density and time. Depending on location in the disk, the ionization rates by SLRs are typically in the range ζSLR ∼ (1–10) × 10−19 s−1.
A Comparative Study of Analysing and Clustering Crime Patterns Using Data Mining.

— Crime is the utmost potent and disquieting aspects in our day today life. Crime analysis is a sorted way of understanding and inspecting patterns and trends in crime. Analysis of crime is cardinal for providing uttermost immunity and protection society. Data mining can help us to discover and understand critical information which can further guide the law enforcement authority to detect crime and areas of importance. The main motive of the paper is to understand the crime which entails, robbery, suicides and various offences which also include suspicious activities, noise complaints. The different algorithms that can be used for clustering and analysing crime activities based on some previously defined cases are Agglomerative clustering algorithm, K-means clustering and Density based spatial clustering with noise (DBSCAN) algorithm. A correlative study of these algorithms is presented based on different crime patterns.
Gravitational-wave radiation from double compact objects with eLISA in the Galaxy

The phase of inspiral of double compact objects (DCOs: NS + WD, NS + NS, BH + NS, and BH + BH binaries) in the disk field population of the Galaxy provides a potential source in the frequency range from 10-4 to 0.1 Hz, which can be detected by the European New Gravitational Observatory (NGO: eLISA is derived from the previous LISA proposal) project. In this frequency range, much stronger gravitational wave (GW) radiation can be obtained from DCO sources because they possess more mass than other compact binaries (e.g., close double white dwarfs). In this study, we aim to calculate the gravitational wave signals from the resolvable DCO sources in the Galaxy using a binary population synthesis approach, and determine physical properties of these binaries using Monte Carlo simulations. Combining the sensitivity curve of the eLISA detector and a confusion-limited noise floor of close double white dwarfs, we find that only a handful of DCO sources can be detected by the eLISA detector. The detectable number of DCO sources reaches 160; in the context of low-frequency eLISA observations we find that the number of NS + WD, NS + NS, BH + NS, and BH + BH objects are 132, 16, 3, and 6, respectively.