Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum gravity corrections to the fall of an apple

We consider the motion of a massive particle in a static, weakly-curved spacetime where the gravitational field is taken to be quantized. We find that Newton's law of free-fall is modified by quantum-gravitational corrections, in addition to the known special-relativistic and post-Newtonian modifications. The quantum-gravitational corrections take the form of stochastic noise in the particle trajectory, where the statistical properties of the noise depend on the quantum state of the gravitational field.
On some Foundational Aspects of Human-Centered Artificial Intelligence

The burgeoning of AI has prompted recommendations that AI techniques should be"human-centered". However, there is no clear definition of what is meant by Human Centered Artificial Intelligence, or for short, HCAI. This paper aims to improve this situation by addressing some foundational aspects of HCAI. To do so, we introduce the term HCAI agent to refer to any physical or software computational agent equipped with AI components and that interacts and/or collaborates with humans. This article identifies five main conceptual components that participate in an HCAI agent: Observations, Requirements, Actions, Explanations and Models. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI. In this paper, we focus our analysis on scenarios consisting of a single agent operating in dynamic environments in presence of humans.
Active Learning of Quantum System Hamiltonians yields Query Advantage

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers. Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources. Standard techniques for Hamiltonian learning require careful design of queries and $O(\epsilon^{-2})$ queries in achieving learning error $\epsilon$ due to the standard quantum limit. With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error $\epsilon$ through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. We formally specify and experimentally assess the performance of this Hamiltonian active learning (HAL) algorithm for learning the six parameters of a two-qubit cross-resonance Hamiltonian on four different superconducting IBM Quantum devices. Compared with standard techniques for the same problem and a specified learning error, HAL achieves up to a $99.8\%$ reduction in queries required, and a $99.1\%$ reduction over the comparable non-adaptive learning algorithm. Moreover, with access to prior information on a subset of Hamiltonian parameters and given the ability to select queries with linearly (or exponentially) longer system interaction times during learning, HAL can exceed the standard quantum limit and achieve Heisenberg (or super-Heisenberg) limited convergence rates during learning.
Optimal minimax random designs for weighted least squares estimators

This work studies an experimental design problem where the values of a predictor variable, denoted by x, are to be determined with the goal of estimating a function m(x), which is observed with noise. A linear model is fitted to m(x) but it is not assumed that the model is correctly specified. It follows that the quantity of interest is the best linear approximation of m(x), which is denoted by ℓ(x). It is shown that in this framework the ordinary least squares estimator typically leads to an inconsistent estimation of ℓ(x), and rather weighted least squares should be considered. An asymptotic minimax criterion is formulated for this estimator, and a design that minimizes the criterion is constructed. An important feature of this problem is that the xs should be random, rather than fixed. Otherwise, the minimax risk is infinite. It is shown that the optimal random minimax design is different from its deterministic counterpart, which was studied previously, and a simulation study indicates that it generally performs better when m(x) is a quadratic or a cubic function. Another finding is that when the variance of the noise goes to infinity, the random and deterministic minimax designs coincide. The results are illustrated for polynomial regression models and a generalization is given in the Supplementary Material.
Uniformizing Lee-Yang singularities

Motivated by the search for the QCD critical point, we discuss how to obtain the singular behavior of a thermodynamic system near a critical point, namely the Lee-Yang singularities, from a limited amount of local data generated in a different region of the phase diagram. We show that by using a limited number of Taylor series coefficients, it is possible to reconstruct the equation of state past the radius of convergence, in particular in the critical region. Furthermore we also show that it is possible to extend this reconstruction to go from a crossover region to the first-order transition region in the phase diagram, using a uniformizing map to pass between Riemann sheets. We illustrate these ideas via the Chiral Random Matrix Model and the Ising Model.
Supercritical Carbon Dioxide Brayton Power Cycle Development Overview

Bechtel Marine Propulsion Corporation (BMPC) is testing a supercritical carbon dioxide (S-CO2) Brayton system at the Bettis Atomic Power Laboratory. The 100 kWe Integrated System Test (IST) is a two shaft recuperated closed Brayton cycle with a variable speed turbine driven compressor and a constant speed turbine driven generator using S-CO2 as the working fluid. The IST was designed to demonstrate operational, control and performance characteristics of an S-CO2 Brayton power cycle over a wide range of conditions. The IST design includes a comprehensive instrumentation and control system to facilitate precise control of loop operations and to allow detailed evaluation of component and system performance. A detailed dynamic performance model is being used to predict IST performance, support test procedure development and to evaluate test results.An overview of IST testing progress and plans is provided. Testing in the IST was initiated in 2012. Initial test operations included successful system startup, initial transition to electrical power generation and increased power operations using independent speed control of the turbomachinery. Results of testing completed to date and future testing plans will be summarized.Copyright © 2013 by ASME
On Local Convergence of Iterative Hard Thresholding for Matrix Completion

—Iterative hard thresholding (IHT) has gained in popularity over the past decades in large-scale optimization. However, convergence properties of this method have only been explored recently in non-convex settings. In matrix completion, existing works often focus on the guarantee of global convergence of IHT via standard assumptions such as incoherence property and uniform sampling. While such analysis provides a global upper bound on the linear convergence rate, it does not describe the actual performance of IHT in practice. In this paper, we provide a novel insight into the local convergence of a speciﬁc variant of IHT for matrix completion. We uncover the exact linear rate of IHT in a closed-form expression and identify the region of convergence in which the algorithm is guaranteed to converge. Furthermore, we utilize random matrix theory to study the linear rate of convergence of IHTSVD for large-scale matrix completion. We ﬁnd that asymptotically, the rate can be expressed in closed form in terms of the relative rank and the sampling rate. Finally, we present various numerical results to verify the aforementioned theoretical analysis.
Generalised Uncertainty Relations and the Problem of Dark Energy

We outline a new model in which generalised uncertainty relations, that govern the behaviour of microscopic world, and dark energy, that determines the large-scale evolution of the Universe, are intrinsically linked via the quantum properties of space-time. In this approach the background is treated as a genuinely quantum object, with an associated state vector, and additional ﬂuctuations of the geometry naturally give rise to the extended generalised uncertainty principle (EGUP). An eﬀective dark energy density then emerges from the ﬁeld that minimises the modiﬁed uncertainty relations. These results are obtained via modiﬁcations of the canonical quantum operators, but without modiﬁcations of the canonical Heisenberg algebra, allowing many well known problems associated with existing GUP models to be circumvented.
Capacity to trust? Institutional capacity, conflict, and political trust in Africa, 2000–2005

Civil conflict and state failure has often been linked to breakdowns in regime legitimacy. Trust in government is a critical element of regime legitimacy and the state’s ability to mediate between the demands of competing groups within society. We contend that government capability is a primary factor in shaping individuals’ ascription of legitimacy to the state. Capable governments foster perceptions of legitimacy while poor institutional performance decreases the degree to which individuals trust their government. While some tests of this relationship exist in extant literature, much of the work fails to integrate both micro- and macro-level factors, is confined to regions with established state performance, or is based on single-country studies. Our approach avoids many of these deficiencies by using 32 Afrobarometer surveys collected across 16 different countries from 2000 to 2005 and employing hierarchical linear models to estimate the effects of temporal-specific, state-level variables on levels of individual trust. We find that higher institutional capacity is associated with increased levels of individual trust in government across African countries. Furthermore, we demonstrate that this effect on political trust is independent of other individual-level attitudes, socio-economic characteristics, and a state’s prior internal conflicts.
Mixed-Integer Programming Using a Bosonic Quantum Computer

We propose a scheme for solving mixed-integer programming problems in which the optimization problem is translated to a ground-state preparation problem on a set of bosonic quantum field modes (qumodes). We perform numerical demonstrations by simulating a circuit-based optical quantum computer with each individual qumode prepared in a Gaussian state. We simulate an adiabatic evolution from an initial mixing Hamiltonian, written in terms of the momentum operators of the qumodes, to a final Hamiltonian which is a polynomial of the position and boson number operators. In these demonstrations, we solve a variety of small non-convex optimization problems in integer programming, continuous non-convex optimization, and mixed-integer programming.
Electric field and voltage fluctuations in the Casimir effect

The effects of reflecting boundaries on vacuum electric field fluctuations are treated. The presence of the boundaries can enhance these fluctuations and possibly lead to observable effects. The electric field fluctuations lead to voltage fluctuations along the worldline of a charged particle moving perpendicularly to a pair of reflecting plates, These voltage fluctuations in turn lead to fluctuations in the kinetic energy of the particle, which may enhance the probability of quantum barrier penetration by the particle. A recent experiment by Moddel {\it et al} is discussed as a possible example of this enhanced barrier penetration probability.
Partial Factorizations of Generalized Binomial Products

product of the elements of the n-th row of Pascal’s triangle. It is known that Gn = ∏ p≤n p νp(Gn) with νp(Gn) being computable from the base p expansions of integers up to n. These radix statistics make sense for all bases b ≥ 2, and we define the generalized binomial product Gn = ∏ 2≤b≤n b ν(n,b) and show it is an integer. The statistic bν(n,b) is not the same as the maximal power of b dividing Gn. This paper studies the partial products G(n, x) = ∏ 2≤b≤x b ν(n,b), which are also integers, and
Neural Network Approach for Safety Climate Prediction

AbstractSafety climate is a snapshot of the safety culture of any organization and it is dynamic in nature. It reflects the employees’ perceptions and attitude towards the existing safety practices in construction projects. The main goal of this paper is to evaluate and differentiate construction projects based on their safety climate and to determine the significant constructs of a safety climate. This paper develops a model to predict safety climate in a construction project based on an artificial neural network (ANN). The important constructs for a safety climate have been determined through literature review. The constructs of safety climate are used as inputs and the safety climate of the project is used as an output for the ANN algorithm. This study collected a total of 250 responses through a questionnaire survey across the country. A three-layer feed-forward back-propagation neural network (10-18-1) has been utilized for the analysis. The developed model predicts the safety climate of a constructi.
Fast Changeset-based Bug Localization with BERT

Automatically localizing software bugs to the changesets that induced them has the potential to improve software developer efficiency and to positively affect software quality. To facilitate this automation, a bug report has to be effectively matched with source code changes, even when a significant lexical gap exists between natural language used to describe the bug and identifier naming practices used by developers. To bridge this gap, we need techniques that are able to capture software engineering-specific and project-specific semantics in order to detect relatedness between the two types of documents that goes beyond exact term matching. Popular transformer-based deep learning architectures, such as BERT, excel at leveraging contextual information, hence appear to be a suitable candidate for the task. However, BERT-like models are computationally expensive, which precludes them from being used in an environment where response time is important. In this paper, we describe how BERT can be made fast enough to be applicable to changeset-based bug localization. We also explore several design decisions in using BERT for this purpose, including how best to encode changesets and how to match bug reports to individual changes for improved accuracy. We compare the accuracy and performance of our model to a non-contextual baseline (i.e., vector space model) and BERT-based architectures previously used in software engineering. Our evaluation results demonstrate advantages in using the proposed BERT model compared to the baselines, especially for bug reports that lack any hints about related code elements.
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models. Despite its importance, there is limited work on how to sample clients effectively. In this paper, we cast client sampling as an online learning task with bandit feedback, which we solve with an online stochastic mirror descent (OSMD) algorithm designed to minimize the sampling variance. We then theoretically show how our sampling method can improve the convergence speed of optimization algorithms. To handle the tuning parameters in OSMD that depend on the unknown problem parameters, we use the online ensemble method and doubling trick. We prove a dynamic regret bound relative to any sampling sequence. The regret bound depends on the total variation of the comparator sequence, which naturally captures the intrinsic difficulty of the problem. To the best of our knowledge, these theoretical contributions are new and the proof technique is of independent interest. Through both synthetic and real data experiments, we illustrate advantages of the proposed client sampling algorithm over the widely used uniform sampling and existing online learning based sampling strategies. The proposed adaptive sampling procedure is applicable beyond the FL problem studied here and can be used to improve the performance of stochastic optimization procedures such as stochastic gradient descent and stochastic coordinate descent.
Initial Development and Validation of a Mechanistic Spray Drift Model for Ground Boom Sprayers

This article summarizes the initial development of a mechanistic spray drift model for ground boom sprayers, built upon the same analytical Lagrangian approach used in the aerial spray model AGDISP. For ground boom sprayers the wake and turbulence of the aircraft are removed and more detailed modeling of the nozzle jets (and jet entrainment) is included. The model predicts the spray deposition downwind from the application area for any set of initial conditions, and recovers results compared with Spray Drift Task Force and Canadian field datasets for two ground sprayer nozzle types.
Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data

Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However, existing models struggle to capture complex relationships between physical, sociodemographic, psychological and mental health factors that effect cognition. Using data from an observational, cohort study, Midlife in the United States (MIDUS), we modeled a large number of variables to predict executive function and episodic memory measures. We used cross-sectional and longitudinal outcomes with varying sparsity, or amount of missing data. Deep neural network (DNN) models consistently ranked highest in all of the cognitive performance prediction tasks, as assessed with root mean squared error (RMSE) on out-of-sample data. RMSE differences between DNN and other model types were statistically significant (T(8) = -3.70; p<0.05). The interaction effect between model type and sparsity was significant (F(9)=59.20; p<0.01), indicating the success of DNNs can partly be attributed to their robustness and ability to model hierarchical relationships between health-related factors. Our findings underscore the potential of neural networks to model clinical datasets and allow better understanding of factors that lead to cognitive decline.
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing

Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have thus relied on domain-specific heuristics and manually crafted resources, such as dictionaries, in order to apply mixup in NLP. In this paper, we instead propose an unsupervised learning approach to text interpolation for the purpose of data augmentation, to which we refer as"Learning to INterpolate for Data Augmentation"(LINDA), that does not require any heuristics nor manually crafted resources but learns to interpolate between any pair of natural language sentences over a natural language manifold. After empirically demonstrating the LINDA's interpolation capability, we show that LINDA indeed allows us to seamlessly apply mixup in NLP and leads to better generalization in text classification both in-domain and out-of-domain.
Wafer-Scale Characterization of a Superconductor Integrated Circuit Fabrication Process, Using a Cryogenic Wafer Prober

Using a fully automated cryogenic wafer prober, we measured superconductor fabrication process control monitors and simple integrated circuits on 200-mm wafers at 4.4 K, including SQIF-based magnetic field sensors, SQUID-based circuits for measuring inductors, Nb/Al-AlOx/Nb Josephson junctions, test structures for measuring critical current of superconducting wires and vias, resistors, etc., to demonstrate the feasibility of using the system for characterizing niobium superconducting devices and integrated circuits on a wafer scale. Data on the wafer-scale distributions of the residual magnetic field, junction tunnel resistance, energy gap, inductance of multiple Nb layers, and critical currents of interlayer vias are presented. A comparison with existing models is made. The wafers were fabricated in the SFQ5ee process, the fully planarized process with eight niobium layers and a layer of kinetic inductors, developed for superconductor electronics at MIT Lincoln Laboratory, Lexington, MA, USA. The cryogenic wafer prober was developed at HPD/FormFactor, Inc., Boulder, CO, USA.
Analysis of blockwork coastal structures

Blockwork coastal structures often suffer damages from breaking wave action. The type of damage observed suggests that wave impact induced pressure propagation into water filled cracks and the subsequent build-up of internal bursting pressures is the probable cause. A series of model tests was conducted to investigate structural aspects of this problem. The mechanism of seaward block removal was demonstrated for the first time, the internal stress distribution inside of blockwork structures was analysed and the effect of wall inclination on internal pressures investigated. It was found that the structural characteristics and the geometry of blockwork walls affect their load resistance significantly.
Automatic quantum circuit encoding of a given arbitrary quantum state

We propose a quantum-classical hybrid algorithm to encode a given arbitrarily quantum state $\vert \Psi \rangle$ onto an optimal quantum circuit $\hat{\mathcal{C}}$ with a finite number of single- and two-qubit quantum gates. The proposed algorithm employs as an objective function the absolute value of fidelity $F = \langle 0 \vert \hat{\mathcal{C}}^{\dagger} \vert \Psi \rangle$, which is maximized iteratively to construct an optimal quantum circuit $\hat{\mathcal{C}}$ with controlled accuracy. The key ingredient of the algorithm is the sequential determination of a set of optimal two-qubit unitary operators one by one via the singular value decomposition of the fidelity tensor. Once the optimal unitary operators are determined, including the location of qubits on which each unitary operator acts, elementary quantum gates are assigned algebraically. With noiseless numerical simulations, we demonstrate the algorithm to encode a ground state of quantum many-body systems, including the spin-1/2 antiferromagnetic Heisenberg model and the spin-1/2 XY model. The results are also compared with the quantum circuit encoding of the same quantum state onto a quantum circuit in a given circuit structure. Moreover, we demonstrate that the algorithm can also be applied to construct an optimal quantum circuit for classical data such as a classical image that is represented as a quantum state by the amplitude encoding. Finally, we also experimentally demonstrate that a quantum circuit generated by the AQCE algorithm can indeed represent the original quantum state reasonably on a noisy real quantum device.