Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Optimization with Uncertain Preferences over Attributes

We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker whose preferences must be learned. Each feasible design is associated with a time-consuming-to-evaluate vector of attributes, each vector of attributes is assigned a utility by the decision-maker’s utility function, and this utility function may be learned approximately using preferences expressed by the decision-maker over pairs of attribute vectors. Past work has used this estimated utility function as if it were error-free within single-objective optimization. However, errors in utility estimation may yield a poor suggested decision. Furthermore, this approach produces a single suggested “best” design, whereas decision-makers often prefer to choose among a menu of designs. We pro-pose a novel Bayesian optimization algorithm that acknowledges the uncertainty in preference estimation and implicitly chooses designs to evaluate using the time-consuming function that are good not just for a single estimated utility function but a range of likely utility functions. Our algorithm then shows a menu of designs and evaluated attributes to the decision-maker who makes a ﬁnal selection. We demonstrate the value of our algo-rithm in a variety of numerical experiments.
A Penetration Metric for Deforming Tetrahedra using Object Norm

In this paper, we propose a novel penetration metric, called deformable penetration depth PDd, to define a measure of inter-penetration between two linearly deforming tetrahedra using the object norm [1]. First of all, we show that a distance metric for a tetrahedron deforming between two configurations can be found in closed form based on object norm. Then, we show that the PDd between an intersecting pair of static and deforming tetrahedra can be found by solving a quadratic programming (QP) problem in terms of the distance metric with non-penetration constraints. We also show that the PDd between two, intersected, deforming tetrahedra can be found by solving a similar QP problem under some assumption on penetrating directions, and it can be also accelerated by an order of magnitude using pre-calculated penetration direction. We have implemented our algorithm on a standard PC platform using an off-the-shelf QP optimizer, and experimentally show that both the static/deformable and deformable/deformable tetrahedra cases can be solvable in from a few to tens of milliseconds. Finally, we demonstrate that our penetration metric is three-times smaller (or tighter) than the classical, rigid penetration depth metric in our experiments.
Open topological defects and boundary RG flows

In the context of two-dimensional rational conformal field theories we consider topological junctions of topological defect lines with boundary conditions. We refer to such junctions as open topological defects. For a relevant boundary operator on a conformal boundary condition we consider a commutation relation with an open defect obtained by passing the junction point through the boundary operator. We show that when there is an open defect that commutes or anti-commutes with the boundary operator there are interesting implications for the boundary RG flows triggered by this operator. The end points of the flow must satisfy certain constraints which, in essence, require the end points to admit junctions with the same open defects. Furthermore, the open defects in the infrared must generate a subring under fusion that is isomorphic to the analogous subring of the original boundary condition. We illustrate these constraints by a number of explicit examples in Virasoro minimal models.
Election Control in Social Networks via Edge Addition or Removal

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).
Existence of local minima of a minimal 2D pose-graph SLAM problem

In this paper, we show that for a minimal pose-graph problem, even in the ideal case of perfect measurements and spherical covariance, using the so-called "wrap function" when comparing angles results in multiple suboptimal local minima. We numerically estimate regions of attraction to these local minima for some numerical examples, and give evidence to show that they are of nonzero measure. In contrast, under the same assumptions, we show that the \textit{chordal distance} representation of angle error has a unique minimum up to periodicity. For chordal cost, we also search for initial conditions that fail to converge to the global minimum, and find that this occurs with far fewer points than with geodesic cost.
SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model

In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning. This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components. The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner. Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity. Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.
Linear convergence of dual coordinate descent on non-polyhedral convex problems

This paper deals with constrained convex problems, where the objective function is smooth strongly convex and the feasible set is given as the intersection of a large number of closed convex (possibly non-polyhedral) sets. In order to deal efficiently with the complicated constraints we consider a dual formulation of this problem. We prove that the corresponding dual function satisfies a quadratic growth property on any sublevel set, provided that the objective function is smooth and strongly convex and the sets verify the Slater's condition. To the best of our knowledge, this work is the first deriving a quadratic growth condition for the dual under these general assumptions. Existing works derive similar quadratic growth conditions under more conservative assumptions, e.g., the sets need to be either polyhedral or compact. Then, for finding the minimum of the dual problem, due to its special composite structure, we propose random (accelerated) coordinate descent algorithms. However, with the existing theory one can prove that such methods converge only sublinearly. Based on our new quadratic growth property derived for the dual, we now show that such methods have faster convergence, that is the dual random (accelerated) coordinate descent algorithms converge linearly. Besides providing a general dual framework for the analysis of randomized coordinate descent schemes, our results resolve an open problem in the literature related to the convergence of Dykstra algorithm on the best feasibility problem for a collection of convex sets. That is, we establish linear convergence rate for the randomized Dykstra algorithm when the convex sets satisfy the Slater's condition and derive also a new accelerated variant for the Dykstra algorithm.
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.
Hybrid approach to treatment of atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. For years, there has been much interest and much is written with regard to the integrated or hybrid multidisciplinary approach toward cardiovascular care. The hybrid approach has been established in many centers, mostly with respect to ischemic heart disease. However, it is only a new and upcoming trend toward the use of a hybrid approach to treating cardiac arrhythmias. This hybrid approach to treating AF, while not the prevalent practice, is supported by medical literature. This review article examines the hybrid approach to treatment of AF, its benefits, disadvantages, and safety, as well as the long‐term results, based on current available literature. The hybrid approach to treatment of AF, combining surgical epicardial, and catheter endocardial ablation, showed a higher success rate compared to either technique alone. The increasing cooperation and professional intimacy between cardiologists and cardiothoracic surgeons are likely to continue and the hybrid approach, with its benefits while bearing in mind its limitations, may become the gold standard for treatment of drug‐resistant AF for the coming decade and beyond.
Study of Bending Losses in Optical Fibers using COMSOL

In FTTH, optical fibers are frequently bent at the corners of the walls causing the propagating light in the fiber to radiate away which results in transmission losses and limits reach of the fiber network. A large number of studies have been reported in the literature to compute bend-induced losses in fibers. However, most studies assume simple refractive index profiles such as step-index profile and include the effects of stress due to bending of fiber in an ad hoc manner by modifying the bend radius R to effective bend radius Reff = (1+δ)R, where δ is a number in the range {0.28, 0.31}. For simulations with arbitrary refractive index profiles, which are encountered commonly in practice, the refractive index of the bent fiber is modified into refractive index of an equivalent straight fiber using simple conformal mapping techniques. In this paper, we present a completely different approach for computing the modified refractive index profile of the bent fiber and compute bending losses. Specifically, we apply the geometrically exact beam theory (GEBT) and stress-optics law to account for stress effect of bent fibers, followed by conformal mapping to account for geometric effect of bending in order to obtain the modified refractive index profile. The modified index is imported to COMSOL using Interpolation function, which performs linear interpolation of the data. A 2D cross section of the fiber, with core and cladding is defined as the geometry of the model. We use Electromagnetic Waves, Frequency Domain physics, and Mode Analysis study from Wave Optics module for solving the wave equation to obtain the effective index of the propagating modes in the bent fiber. A Perfectly Matched Layer (PML) is included in the geometry to avoid the unwanted interference of mode fields confined in core and reflecting the radiation from cladding interface. We study the effect of PML thickness on effective index and find that the sensitivity is negligible for PML thickness of . A Physics-Controlled mesh with Fine Element size was applied to the geometry. Bend Loss was computed from the imaginary part of the effective index for bend diameters in the range {6, 20} mm at 1550 nm. Finally, by comparing our results with those of the literature, we find that our formulas agree well with experiments, especially at low bend diameters.
Conjectures, consequences, and numerical experiments for p-adic Artin L-functions

We conjecture that the p-adic L-function of a non-trivial irreducible even Artin character over a totally real field is non-zero at all non-zero integers. This implies that a conjecture formulated by Coates and Lichtenbaum at negative integers extends in a suitable way to all positive integers. We also state a conjecture that for certain characters the Iwasawa series underlying the p-adic L-series have no multiple roots except for those corresponding to the zero at s=0 of the p-adic L-function. We provide some theoretical evidence for our first conjecture, and prove both conjectures by means of computer calculations for a large set of characters (and integers where appropriate) over the rationals and over real quadratic fields, thus proving many instances of conjectures by Coates and Lichtenbaum and by Schneider. The calculations and the theoretical evidence also prove that certain p-adic regulators corresponding to 1-dimensional characters for the rational numbers are units in many cases. We also verify Gross' conjecture for the order of the zero of the p-adic L-function at s=0 in many cases. We gather substantial statistical data on the constant term of the underlying Iwasawa series, and propose a model for its behaviour for certain characters.
Kolmogorovian Active Turbulence of a Sparse Assembly of Interacting Marangoni Surfers

Active matter, composed of self-propelled entities, forms a wide class of out-of-equilibrium systems that display striking collective behaviors, among which, the so-called active turbulence where spatially and time-disordered flow patterns spontaneously arise in a variety of active systems. De facto, the active turbulence naming suggests a connection with a second seminal class of out-of-equilibrium systems, inertial turbulence, even though the latter is of very different nature with energy injected at global system scale rather than at the elementary scale of single constituents. Indeed, the existence of a possible strong tie between active and canonical turbulence remains an open question and a field of profuse research. Using an assembly of self-propelled interfacial particles, we show experimentally that the statistical properties of particles' velocities display a turbulentlike behavior, as described by the celebrated 1941 phenomenology of Kolmogorov. Moreover, the analogy between the dynamics of the self-propelled particles and inertial turbulence is observed to hold consistently both in the Eulerian and Lagrangian frameworks. Unlike the swimmers' velocities distribution, the subsurface fluid flow is found not turbulent, thus making Marangoni surfers' assemblies different from other active systems generating turbulence, such as living matter. Identifying an active system in the universality class of inertial turbulence not only benefits its future development but may also provide new insights into the long-standing description of turbulent flows, arguably one of the biggest remaining mysteries in classical physics.
Domain structures and quark potentials in SU(3) gauge theory

We analyze the static potentials for various representations in SU($3$) Yang-Mills theory within the framework of the domain model of center vortices. The influence of vortex interactions is investigated on the static potentials. We show that, by ad-hoc choosing the probability weights of the different vortex configurations contributing to the static potential, a phenomenologically satisfactory result for the different representations can be achieved. In particular including vacuum domains, a way to effectively parametrize vortex interactions, is crucial in obtaining an (almost) everywhere convex potential when interpolating between the short distances and the asymptotic regimes.
Galaxy formation with BECDM – II. Cosmic filaments and first galaxies

Bose–Einstein condensate dark matter (BECDM, also known as fuzzy dark matter) is motivated by fundamental physics and has recently received significant attention as a serious alternative to the established cold dark matter (CDM) model. We perform cosmological simulations of BECDM gravitationally coupled to baryons and investigate structure formation at high redshifts (z ≳ 5) for a boson mass m = 2.5 × 10−22 eV, exploring the dynamical effects of its wavelike nature on the cosmic web and the formation of first galaxies. Our BECDM simulations are directly compared to CDM as well as to simulations where the dynamical quantum potential is ignored and only the initial suppression of the power spectrum is considered – a warm dark matter-like (‘WDM’) model often used as a proxy for BECDM. Our simulations confirm that ‘WDM’ is a good approximation to BECDM on large cosmological scales even in the presence of the baryonic feedback. Similarities also exist on small scales, with primordial star formation happening both in isolated haloes and continuously along cosmic filaments; the latter effect is not present in CDM. Global star formation and metal enrichment in these first galaxies are delayed in BECDM/‘WDM’ compared to the CDM case: in BECDM/‘WDM’ first stars form at z ∼ 13/13.5, while in CDM star formation starts at z ∼ 35. The signature of BECDM interference, not present in ‘WDM’, is seen in the evolved dark matter power spectrum: although the small-scale structure is initially suppressed, power on kpc scales is added at lower redshifts. Our simulations lay the groundwork for realistic simulations of galaxy formation in BECDM.
Johann Sebastian Bach’s Music is Speeding Up: Fake News?

—This study in cultural informatics investigates the claim made in 2018 by several news media that performances of Johann Sebastian Bach’s Double Concerto in D Minor , his compositions in general, and of classical music as a whole are becoming ever faster. Data for a total number of 19,660 releases in ﬁve of Bach’s compositional categories from between 1950 and 2019 are retrieved from online database Discogs, of which 2,999 are analyzed historically for speed increase. For the Double Concerto, we do ﬁnd a slight speedup, but surprisingly enough, West European recordings speed up much more than non-West European recordings. And whereas the Brandenburg Concertos and several cantatas do speed up signiﬁcantly, the Trio Sonatas for Organ and Suites for Cello Solo actually tend to slow down . We review the original claims in light of this new evidence, and contextualize our ﬁndings by relating to studies on musical development, the pace of life in urbanized communities, and begin to construct a scientiﬁc hypothesis for explaining our ﬁndings.
NISQ computing for decision making under uncertainty

Noisy Intermediate-Scale Quantum computers are expected to be available this year. It is proposed to exploit such a device for decision making under uncertainty. The probabilistic character of quantum mechanics reflects this uncertainty. Concomitantly, the noise may add to it. The approach is standard in the sense that Bayes decision rule is used to decide on the basis of maximum expected reward. The novelty is to model the various action profiles and the development of ‘nature’ as unitary transformations on a set of qubits. Measurement eventually yields samples of classical binary random variables in which the reward function has to be expressed. In order to achieve sufficiently low variances for reliable decision making more runs of such a quantum algorithm are necessary. Some simple examples have been worked out to elucidate the idea. Here the calculations are still analytically feasible. Presently lacking an operating quantum device, the QX simulator of Quantum Inspire has been used to generate the necessary samples for comparison and demonstration. First obtained results are promising and point at a possible useful application for noisy intermediate-scale quantum computers.
By the user, for the user: A user-centric approach to quantifying the privacy of websites

Third-party tracking is common on almost all commercially operated websites. Prior work has studied in detail the extent of third-party tracking on the web, detection of third-party trackers, and defending against third-party tracking. Existing research and tools have also attempted to inform web users of trackers and the extent of their privacy violations. However, existing tools do not take into account users' perceptions of and understanding of the extent of trackers on the web. Taking these factors into account is important for the usability of such tools so that users can be aware and protect themselves to a reasonable and necessary extent that aligns with their overall comfort with trackers. In this paper, we elicit user perceptions and preferences about different trackers on various websites through an online survey of 43 users. We use this data to bootstrap a privacy scoring system. This scoring system weights the usage of trackers and the dispersion of user data within a page to third parties, with the type of website being visited. Our work presents a proof-of-concept methodology and tool to calculate a user-centric privacy score with preliminary bootstrap user data. We conclude with concrete future directions.
A shape optimization approach for electrical impedance tomography with point measurements

Working within the class of piecewise constant conductivities, the inverse problem of electrical impedance tomography can be recast as a shape optimization problem where the discontinuity interface is the unknown. Using Gröger’s Wp1 -estimates for mixed boundary value problems, the averaged adjoint method is extended to the case of Banach spaces, which allows one to compute the derivative of shape functionals involving point evaluations. We compute the corresponding distributed expression of the shape derivative and show that it may contain Dirac measures in addition to the usual domain integrals. We use this distributed shape derivative to devise a numerical algorithm, show various numerical results supporting the method, and based on these results we discuss the influence of the point measurements patterns on the quality of the reconstructions.