Miracle-dz/UniFAR-FTUs huggingface dataset:
A Shiny Application for Conducting Electronic Surveys Using Randomized Response Techniques

Randomized response techniques (RRT) are useful for collecting information on sensitive or confidential attributes in sample surveys. However, such RRTs are rarely used except for pure academic research, as they are deemed to be confusing for respondents and difficult to be administered. Switching to an electronic medium is a solution for this. Standard electronic survey mediums lack the option of effectively incorporating a randomization device into it. This paper introduces a Shiny application that implements two popular Randomized Response Techniques, namely, Warner (1965) and Simmons (1967), to collect information on sensitive issues via electronic surveys, and save them using local or remote storage. Code and its detailed explanation has been provided for suitable use by survey statisticians and practitioners. The method suggested here can be extended to implement any Randomized Response Technique. Thus, this paper lays the groundwork of implementing RRTs as electronic surveys for widespread use by the polling industry.
Paving the Way for Consensus: Convergence of Block Gossip Algorithms

Gossip protocols are popular methods for average consensus problems in distributed computing. We prove new convergence guarantees for a variety of such protocols, including path, clique, and synchronous pairwise gossip. These arise by exploiting the connection between these protocols and the block randomized Kaczmarz method for solving linear systems. Moreover, we extend existing convergence results for block randomized Kaczmarz to allow for a more general choice of blocks, rank-deficient systems, and provide a tighter convergence rate guarantee. Previous results for block randomized Kaczmarz assumed blocks formed a paving of the systems; our analysis, contrary to the title of our paper, generalizes to a broader class of blocks that we call coverings. We furthermore apply this analysis to inconsistent consensus models and obtain similar guarantees. An extensive empirical analysis of these methods is provided for a variety of synthetic and real networks.
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

We study the problem of inferring heterogeneous treatment effects from time-to-event data. While both the related problems of (i) estimating treatment effects for binary or continuous outcomes and (ii) predicting survival outcomes have been well studied in the recent machine learning literature, their combination -- albeit of high practical relevance -- has received considerably less attention. With the ultimate goal of reliably estimating the effects of treatments on instantaneous risk and survival probabilities, we focus on the problem of learning (discrete-time) treatment-specific conditional hazard functions. We find that unique challenges arise in this context due to a variety of covariate shift issues that go beyond a mere combination of well-studied confounding and censoring biases. We theoretically analyse their effects by adapting recent generalization bounds from domain adaptation and treatment effect estimation to our setting and discuss implications for model design. We use the resulting insights to propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations. We investigate performance across a range of experimental settings and empirically confirm that our method outperforms baselines by addressing covariate shifts from various sources.
Large deviations for irreducible random walks on relatively hyperbolic groups

We show existence of the weak large deviation principle, with a convex rate function, for the renormalized distance from the starting point of irreducible random walks on relatively hyperbolic groups. Under the assumption of finiteness of exponential moments, the full large deviation principle holds, and the rate function governing it can be expressed as the Fenchel-Legendre transform of the limiting logarithmic moment generating function of the sequence of renormalized distances.
Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration

In this paper, we present an accelerometer-based kinematic calibration algorithm to accurately estimate the pose of multiple sensor units distributed along a robot body. Our approach is self-contained, can be used on any robot provided with a Denavit-Hartenberg kinematic model, and on any skin equipped with Inertial Measurement Units (IMUs). To validate the proposed method, we first conduct extensive experimentation in simulation and demonstrate a sub-cm positional error from ground truth data—an improvement of six times with respect to prior work; subsequently, we then perform a real-world evaluation on a seven degrees-of-freedom collaborative platform. For this purpose, we additionally introduce a novel design for a stand-alone artificial skin equipped with an IMU for use with the proposed algorithm and a proximity sensor for sensing distance to nearby objects. In conclusion, in this work, we demonstrate seamless integration between a novel hardware design, an accurate calibration method, and preliminary work on applications: the high positional accuracy effectively enables to locate distributed proximity data and allows for a distributed avoidance controller to safely avoid obstacles and people without the need of additional sensing.
Electron Preacceleration at Weak Quasi-perpendicular Intracluster Shocks: Effects of Preexisting Nonthermal Electrons

Radio relics in the outskirts of galaxy clusters imply the diffusive shock acceleration (DSA) of electrons at merger-driven shocks with Mach number M s ≲ 3–4 in the intracluster medium (ICM). Recent studies have suggested that electron preacceleration and injection, prerequisite steps for DSA, could occur at supercritical shocks with M s ≳ 2.3 in the ICM, thanks to the generation of multiscale waves by microinstabilities such as the Alfvén ion cyclotron (AIC) instability, the electron firehose instability (EFI), and the whistler instability (WI). On the other hand, some relics are observed to have subcritical shocks with M s ≲ 2.3, leaving DSA at such weak shocks as an outstanding problem. Reacceleration of preexisting nonthermal electrons has been contemplated as one of possible solutions for that puzzle. To explore this idea, we perform particle-in-cell simulations for weak quasi-perpendicular shocks in high-β (β = P gas/P B ) plasmas with power-law suprathermal electrons in addition to Maxwellian thermal electrons. We find that suprathermal electrons enhance the excitation of electron-scale waves via the EFI and WI. However, they do not affect the ion reflection and the ensuing generation of ion-scale waves via the AIC instability. The presence of ion-scale waves is the key for the preacceleration of electrons up to the injection momentum; thus, the shock criticality condition for electron injection to DSA is preserved. Based on the results, we conclude that preexisting nonthermal electrons in the preshock region alone would not resolve the issue of electron preacceleration at subcritical ICM shocks.
The USS Trustee's risky strategy

How much risk, and what types of risk, is the Universities Superannuation Scheme (USS) taking? This is a critical question for universities across the UK and many of their employees. Will the fund have enough money to pay for all our pensions? Will it run out? Or is there a significant risk that we are collectively overpaying? You might have thought that these questions would also be of interest to the USS Trustee, that oversees the fund for members’, or The Pensions Regulator, that regulates it. However, curiously, while the USS has published a plethora of incoherent statistics and opaque analyses, neither it or TPR have bothered to publish estimates of the risk of the scheme defaulting. In September 2021, David Miles and James Sefton, Professors of Financial Economics at Imperial College Business School, stepped into this vacuum, publishing ‘How much risk is the USS taking? ’ [2]. The paper presents important, accessible and highly readable analysis which estimates how likely the USS is to default over time. Their work is particularly relevant to the current UCU dispute with 69 employers over the benefit cuts that Universities UK (UUK) is planning to implement on the basis of the 2020 USS valuation. In this Brief, we assess the assumptions, replicate the results, explore further their model and consider potential extensions. We demonstrate that for a cautious model with reasonable assumptions for assets and asset growth, the fund has a less than 7% chance of defaulting for the duration that pensions promises are due, but a greater than 80% chance of being over funded by at least £100bn, and nearly 50% chance of having over £400bn. We offer warm thanks to David Miles and James Sefton for sharing their code and data, for their helpful conversations and clarification. Their analysis is infinitely clearer, better and more credible than anything the USS has produced. We hope this Brief will be the beginning of more work in this area. All errors are our own.
Dual-Mode Synchronization Predictive Control of Robotic Manipulator

To reduce the contour error of the end-effector of a robotic manipulator during trajectory tracking, a dual-mode synchronization predictive control is proposed. Firstly, the dynamic model of n-DoF robotic manipulator is discretized by using the Taylor expansion method, and the mapping relationship between the joint error in the joint space and the contour error in task space is constructed. Secondly, the synchronization error and the tracking error in the joint space are defined, and the coupling error of joints is derived through the coupling coefficient λ. Thirdly, adual-mode synchronization predictive control is proposed, and the stability of the proposed control system is guaranteed using constraint set conditions. Finally, numerical simulation and experimental results are shown to prove the effectiveness of the proposed control strategy.
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning

The paper concerns the stochastic approximation recursion, \[ \theta_{n+1}= \theta_n + \alpha_{n + 1} f(\theta_n, \Phi_{n+1}) \,,\quad n\ge 0, \] where the {\em estimates} $\theta_n\in\Re^d$ and $ \{ \Phi_n \}$ is a Markov chain on a general state space. In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain. The main results are established under additional conditions on the mean flow and a version of the Donsker-Varadhan Lyapunov drift condition known as (DV3) for the chain: (i) An appropriate Lyapunov function is constructed that implies convergence of the estimates in $L_4$. (ii) A functional CLT is established, as well as the usual one-dimensional CLT for the normalized error. Moment bounds combined with the CLT imply convergence of the normalized covariance $\text{ E} [ z_n z_n^T ]$ to the asymptotic covariance $\Sigma^\Theta$ in the CLT, where $z_n= (\theta_n-\theta^*)/\sqrt{\alpha_n}$. (iii) The CLT holds for the normalized version $z^{\text{ PR}}_n$ of the averaged parameters $\theta^{\text{ PR}}_n$, subject to standard assumptions on the step-size. Moreover, the normalized covariance of both $\theta^{\text{ PR}}_n$ and $z^{\text{ PR}}_n$ converge to $\Sigma^{\text{ PR}}$, the minimal covariance of Polyak and Ruppert. (iv)} An example is given where $f$ and $\bar{f}$ are linear in $\theta$, and the Markov chain is geometrically ergodic but does not satisfy (DV3). While the algorithm is convergent, the second moment of $\theta_n$ is unbounded and in fact diverges.
Entropy-based adaptive Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used to implement HMC in practice, but its performance can be sensitive to the choice of mass matrix used therein. We develop a gradient-based algorithm that allows for the adaptation of the mass matrix by encouraging the leapfrog integrator to have high acceptance rates while also exploring all dimensions jointly. In contrast to previous work that adapt the hyperparameters of HMC using some form of expected squared jumping distance, the adaptation strategy suggested here aims to increase sampling efficiency by maximizing an approximation of the proposal entropy. We illustrate that using multiple gradients in the HMC proposal can be beneficial compared to a single gradient-step in Metropolis-adjusted Langevin proposals. Empirical evidence suggests that the adaptation method can outperform different versions of HMC schemes by adjusting the mass matrix to the geometry of the target distribution and by providing some control on the integration time.
Scalable Bayesian Network Structure Learning with Splines

The graph structure of a Bayesian network (BN) can be learned from data using the well-known score-and-search approach. Previous work has shown that incorporating structured representations of the conditional probability distributions (CPDs) into the score-and-search approach can improve the accuracy of the learned graph. In this paper, we present a novel approach capable of learning the graph of a BN and simultaneously modelling linear and non-linear local probabilistic relationships between variables. We achieve this by a combination of feature selection to reduce the search space for local relationships and extending the score-and-search approach to incorporate modelling the CPDs over variables as Multivariate Adaptive Regression Splines (MARS). MARS are polynomial regression models represented as piecewise spline functions. We show on a set of discrete and continuous benchmark instances that our proposed approach can improve the accuracy of the learned graph while scaling to instances with a large number of variables.
Application of Time Separation Technique to Enhance C-arm CT Dynamic Liver Perfusion Imaging

Perfusion imaging is an interesting new modality for evaluation and assessment of the liver cancer treatment. C-Arm CT provides a possibility to perform perfusion imaging scans intra-operatively for even faster evaluation. The slow speed of the C-Arm CT rotation and the presence of the noise, however, have an impact on the reconstruction and therefore model based approaches have to be applied. In this work we apply the Time separation technique (TST), to denoise data, speed up reconstruction and improve resulting perfusion images. We show on animal experiment data that Dynamic C-Arm CT Liver Perfusion Imaging together with the processing of the data based on the TST provides comparable results to standard CT liver perfusion imaging.
Complex cobordism, Hamiltonian loops and global Kuranishi charts

Let (X,ω) be a closed symplectic manifold. A loop φ : S → Diff(X) of diffeomorphisms of X defines a fibration π : Pφ → S . By applying Gromov-Witten theory to moduli spaces of holomorphic sections of π, Lalonde, McDuff and Polterovich proved that if φ lifts to the Hamiltonian group Ham(X,ω), then the rational cohomology of Pφ splits additively. We prove, with the same assumptions, that the E-generalised cohomology of Pφ splits additively for any complex-oriented cohomology theory E, in particular the integral cohomology splits. This class of examples includes all complex projective varieties equipped with a smooth morphism to CP, in which case the analogous rational result was proved by Deligne using Hodge theory. The argument employs virtual fundamental cycles of moduli spaces of sections of π in Morava K-theory and results from chromatic homotopy theory. Our proof involves a construction of independent interest: we build global Kuranishi charts for moduli spaces of pseudo-holomorphic spheres in X in a class β ∈ H2(X;Z), depending on a choice of integral symplectic form Ω on X and ample Hermitian line bundle over the moduli space of one-pointed degree d = 〈Ω, β〉 stable genus zero curves in CP.
Fairer LP-based Online Allocation

In this paper, we consider a Linear Program (LP)-based online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected revenue given limited resources. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward. We consider a stochastic setting where all the orders are i.i. d. sampled from an unknown distribution. Such formulation contains many classic applications such as the canonical (quantity-based) network revenue management problem and the Adwords problem. Speciﬁcally, we study the objective of providing fairness guarantees while maintaining low regret. Our deﬁnition of fairness is that a fair online algorithm should treat similar agents/customers similarly and the decision made for similar individuals should be consistent over time. We deﬁne a fair oﬄine solution as the analytic center of the oﬄine optimal solution set, and propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending. Our algorithm can control cumulative unfairness (the cumulative deviation from the online solutions to the oﬄine fair solution) on the scale of order O (log( T )) , while maintaining the regret to be bounded with dependency on T . Our approach do not formulate the fairness requirement as a constrain in the optimization instance, and instead we address the problem from the perspective of algorithm design. We get the desirable fairness guarantee without imposing any fairness constraint, and our regret result is strong for the reason that we evaluate the regret by comparing to the original objective value.
Enhancement of Complete Active Space Self-Consistent Field (CASSCF) Calculation Accuracy on Noisy Quantum Device: Use of Spin-Restricted Ansatz and Constrained Optimizer for Photo-Excitation Calculations of Ethylene and Phenol Blue Dye

Quantum chemical calculations have contributed to a better understanding of the mechanisms of chemical phenomena and rational material design. However, the calculations are still difficult for materials such as polynuclear metal complexes and photofunctional molecules because of their quasi-degenerate wavefunctions. Quantum chemical calculations on quantum computers are expected to solve this problem. Because quantum computers currently have noisiness and a short decoherence time, algorithms that are applicable to noisy intermediate-scale quantum (NISQ) devices have attracted attention. This study examined a strategy that combined a chemistry-inspired spin-restricted ansatz and a new excited state calculation method called the variational quantum eigensolver under automatically-adjusted constraint (VQE/AC). Unlike the conventional excited state calculation method, called the variational quantum deflation (VQD), the VQE/AC does not require the predetermination of hyperparameters, i.e. , the weights of constraint, which indicates that the VQE/AC could have a potential to describe the smooth potential energy surfaces and to achieve the geometry optimization. To validate this strategy, the singlet ground and excited states (S 0 and S 1 ) of ethylene and phenol blue were computed at the complete active space self-consistent field (CASSCF) level of theory using a simulator with a realistic first the basic idea of VQE, VQD, VQE/AC, and the spin-restricted ansatz. We apply our approach the ground and excited states of ethylene CI geometries, and compare it with other methods. We also feasibility of our approach by the excited state calculation of the phenol blue dye real
A comparison of particle and fluid models for positive streamer discharges in air

Both fluid and particle models are commonly used to simulate streamer discharges. In this paper, we quantitatively study the agreement between these approaches for axisymmetric and 3D simulations of positive streamers in air. We use a drift–diffusion–reaction fluid model with the local field approximation and a particle-in-cell, Monte Carlo collision particle model. The simulations are performed at 300 K and 1 bar in a 10 mm plate–plate gap with a 2 mm needle electrode. Applied voltages between 11.7 and 15.6 kV are used, which correspond to background fields of about 15–20 kV cm−1. Streamer properties like maximal electric field, head position and velocity are compared as a function of time or space. Our results show good agreement between the particle and fluid simulations, in contrast to some earlier comparisons that were carried out in 1D or for negative streamers. To quantify discrepancies between the models, we mainly look at streamer velocities as a function of streamer length. For the test cases considered here, the mean deviation in streamer velocity between the particle and fluid simulations is less than 4%. We study the effect of different types of transport data for the fluid model, and find that flux coefficients lead to good agreement whereas bulk coefficients do not. Furthermore, we find that with a two-term Boltzmann solver, data should be computed using a temporal growth model for the best agreement. The numerical convergence of the particle and fluid models is also studied. In fluid simulations the streamer velocity increases somewhat using finer grids, whereas the particle simulations are less sensitive to the grid. Photoionization is the dominant source of stochastic fluctuations in our simulations. When the same stochastic photoionization model is used, particle and fluid simulations exhibit similar fluctuations.
The Cosmic Ultraviolet Baryon Survey (CUBS) IV: The Complex Multiphase Circumgalactic Medium as Revealed by Partial Lyman Limit Systems

We present a detailed study of two partial Lyman limit systems (pLLSs) of neutral hydrogen column density NH I ≈ (1 − 3) × 1016 cm−2 discovered at z = 0.5 in the Cosmic Ultraviolet Baryon Survey (CUBS). Available far-ultraviolet spectra from the Hubble Space Telescope Cosmic Origins Spectrograph and optical echelle spectra from MIKE on the Magellan Telescopes enable a comprehensive ionization analysis of diffuse circumgalactic gas based on resolved kinematics and abundance ratios of atomic species spanning five different ionization stages. These data provide unambiguous evidence of kinematically aligned multi-phase gas that masquerades as a single-phase structure and can only be resolved by simultaneous accounting of the full range of observed ionic species. Both systems are resolved into multiple components with inferred α-element abundance varying from [α/H] ≈ − 0.8 to near solar and densities spanning over two decades from log nH/cm−3 ≈ −2.2 to <−4.3. Available deep galaxy survey data from the CUBS program taken with VLT/MUSE, Magellan/LDSS3-C and Magellan/IMACS reveal that the z = 0.47 system is located 55 kpc from a star-forming galaxy with prominent Balmer absorption of stellar mass ${{M_{\rm star}}}\approx 2\times 10^{10}\, {{\rm M_{\odot }}}$, while the z = 0.54 system resides in an over-dense environment of 11 galaxies within 750 kpc in projected distance, with the most massive being a luminous red galaxy of ${{M_{\rm star}}}\approx 2\times 10^{11}\, {\rm {M_{\odot }}}$ at 375 kpc. The study of these two pLLSs adds to an emerging picture of the complex, multiphase circumgalactic gas that varies in chemical abundances and density on small spatial scales in diverse galaxy environments. The inhomogeneous nature of metal enrichment and density revealed in observations must be taken into account in theoretical models of diffuse halo gas.