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We initiate the study of relativistic zero-knowledge quantum proof of knowledge systems with classical communication, formally defining a number of useful concepts and constructing appropriate knowledge extractors for all the existing protocols in the relativistic setting which satisfy a weaker variant of the special soundness property due to Unruh (EUROCRYPT 2012). We show that there exists quantum proofs of knowledge with knowledge error 1/2 + negl({\eta}) for all relations in NP via a construction of such a system for the Hamiltonian cycle relation using a general relativistic commitment scheme exhibiting the fairly-binding property due to Fehr and Fillinger (EUROCRYPT 2016). We further show that one can construct quantum proof of knowledge extractors for proof systems which do not exhibit special soundness, and therefore require an extractor to rewind multiple times. We develop a new multi-prover quantum rewinding technique by combining ideas from monogamy of entanglement and gentle measurement lemmas that can break the quantum rewinding barrier. Finally, we prove a new bound on the impact of consecutive measurements and use it to significantly improve the soundness bound of some existing relativistic zero knowledge proof systems, such as the one due to Chailloux and Leverrier (EUROCRYPT 2017)., Comment: 37 pages

In recent years, discrete-modulated continuous-variable quantum key distribution (DM-CV-QKD) has gained traction due to its practical advantages: cost-effectiveness, simple state preparation, and compatibility with existing communication technologies. This work presents a security analysis of DM-CV-QKD against general sequential attacks, including finite-size effects. Remarkably, our proof considers attacks that are neither independent nor identical, and makes no assumptions about the Hilbert space dimension of the receiver. To analyse the security, we leverage the recent generalised entropy accumulation theorem and the numerical methods based on quasi-relative entropy. We also develop a novel dimension reduction technique which is compatible with the entropy accumulation framework. While our analysis reveals significant finite-size corrections to the key rate, the protocol might still offer advantages in specific scenarios due to its practical merits. Our work also offers some insights on how future security proofs can improve the security bounds derived in this work.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support., Comment: Accepted to the Insightful Ideas and Emerging Results Track of the 38th Brazilian Symposium on Software Engineering (SBES 2024)

A metric probability space $(\Omega,d)$ obeys the ${\it concentration\; of\; measure\; phenomenon}$ if subsets of measure $1/2$ enlarge to subsets of measure close to 1 as a transition parameter $\epsilon$ approaches a limit. In this paper we consider the concentration of the space itself, namely the concentration of the metric $d(x,y)$ for a fixed $y\in \Omega$. For any $y\in \Omega$, the concentration of $d(x,y)$ is guaranteed for product distributions in high dimensions $n$, as $d(x,y)$ is a Lipschitz function in $x$. In fact, in the product setting, the rate at which the metric concentrates is of the same order in $n$ for any fixed $y\in \Omega$. The same thing, however, cannot be said for certain dependent (non-product) distributions. For the Boolean cube $I_n$ (a widely analyzed simple model), we show that, for any dependent distribution, the rate of concentration of the Hamming distance $d_H(x,y)$, for a fixed $y$, depends on the choice of $y\in I_n$, and on the variance of the conditional distributions $\mu(x_k \mid x_1,\dots, x_{k-1})$, $2\leq k\leq n$. We give an inductive bound which holds for all probability distributions on the Boolean cube, and characterize the quality of concentration by a certain positive (negative) correlation condition. Our method of proof is advantageous in that it is both simple and comprehensive. We consider uniform bounding techniques when the variance of the conditional distributions is negligible, and show how this basic technique applies to the concentration of the entire class of Lipschitz functions on the Boolean cube.

Particle-in-Cell (PIC) method is a powerful plasma simulation tool for investigating high-intensity femtosecond laser-matter interaction. However, its simulation capability at high-density plasmas around the Fermi temperature is considered to be inadequate due, among others, to the necessity of implementing atomic-scale collisions. Here, we performed a one-dimensional with three-velocity space (1D3V) PIC simulation that features the realistic collision frequency around the Fermi temperature and atomic-scale cell size. The results are compared with state-of-the-art experimental results as well as with hydrodynamic simulation. We found that the PIC simulation is capable of simulating the nanoscale dynamics of solid-density plasmas around the Fermi temperature up to $\sim$2~ps driven by a laser pulse at the moderate intensity of $10^{14-15}$~$\mathrm{W/cm^{2}}$, by comparing with the state-of-the-art experimental results. The reliability of the simulation can be further improved in the future by implementing multi-dimensional kinetics and radiation transport.

Antiferromagnetic (AF) order of U$_{2}$Pt$_{6}$Ga$_{15}$ with the ordering temperature $T_{\rm N}$ = 26 K was investigated by resonant X-ray scattering and neutron diffraction on single crystals. This compound possesses a unique crystal structure in which uranium ions form honeycomb layers and then stacks along the $c$-axis with slight offset, which gives rise to a stacking disorder. The AF order can be described with the propagation vector of $q = (1/6, 1/6, 0)$ in the hexagonal notation. The ordered magnetic moments orient perpendicular to the honeycomb layers, indicating a collinear spin structure consistent with Ising-like anisotropy. The magnetic reflections are found to be broadened along $c^*$ indicating that the stacking disorder results in anisotropic correlation lengths. The semi-quantitative analysis of neutron diffraction intensity, combined with group theory considerations based on the crystallographic symmetry, suggests a zig-zag type magnetic structure for the AF ground state, in which the AF coupling runs perpendicular to the stacking offset, characterized as $q = (1, 0, 0)_{\rm orth}$. The realization of the zig-zag magnetic structure implies the presence of frustrating intralayer exchange interactions involving both ferromagnetic (FM) first-neighbor and AF second and third-neighbor interactions in this compound., Comment: 17 pages, 10 figures, accepted for Phys. Rev. B

Optical tweezer arrays have had a transformative impact on atomic and molecular physics over the past years, and they now form the backbone for a wide range of leading experiments in quantum computing, simulation, and metrology. Underlying this development is the simplicity of single particle control and detection inherent to the technique. Typical experiments trap tens to hundreds of atomic qubits, and very recently systems with around one thousand atoms were realized without defining qubits or demonstrating coherent control. However, scaling to thousands of atomic qubits with long coherence times and low-loss, high-fidelity imaging is an outstanding challenge and critical for progress in quantum computing, simulation, and metrology, in particular, towards applications with quantum error correction. Here, we experimentally realize an array of optical tweezers trapping over 6,100 neutral atoms in around 12,000 sites while simultaneously surpassing state-of-the-art performance for several key metrics associated with fundamental limitations of the platform. Specifically, while scaling to such a large number of atoms, we also demonstrate a coherence time of 12.6(1) seconds, a record for hyperfine qubits in an optical tweezer array. Further, we show trapping lifetimes close to 23 minutes in a room-temperature apparatus, enabling record-high imaging survival of 99.98952(1)% in combination with an imaging fidelity of over 99.99%. Our results, together with other recent developments, indicate that universal quantum computing with ten thousand atomic qubits could be a near-term prospect. Furthermore, our work could pave the way for quantum simulation and metrology experiments with inherent single particle readout and positioning capabilities at a similar scale., Comment: H.J.M., G.N., and E.B. contributed equally to this work

Research interest in autonomous agents is on the rise as an emerging topic. The notable achievements of Large Language Models (LLMs) have demonstrated the considerable potential to attain human-like intelligence in autonomous agents. However, the challenge lies in enabling these agents to learn, reason, and navigate uncertainties in dynamic environments. Context awareness emerges as a pivotal element in fortifying multi-agent systems when dealing with dynamic situations. Despite existing research focusing on both context-aware systems and multi-agent systems, there is a lack of comprehensive surveys outlining techniques for integrating context-aware systems with multi-agent systems. To address this gap, this survey provides a comprehensive overview of state-of-the-art context-aware multi-agent systems. First, we outline the properties of both context-aware systems and multi-agent systems that facilitate integration between these systems. Subsequently, we propose a general process for context-aware systems, with each phase of the process encompassing diverse approaches drawn from various application domains such as collision avoidance in autonomous driving, disaster relief management, utility management, supply chain management, human-AI interaction, and others. Finally, we discuss the existing challenges of context-aware multi-agent systems and provide future research directions in this field., Comment: 11 pages, 1 figure

Recent advancements in protein docking site prediction have highlighted the limitations of traditional rigid docking algorithms, like PIPER, which often neglect critical stochastic elements such as solvent-induced fluctuations. These oversights can lead to inaccuracies in identifying viable docking sites due to the complexity of high-dimensional, stochastic energy manifolds with low regularity. To address this issue, our research introduces a novel model where the molecular shapes of ligands and receptors are represented using multi-variate Karhunen-Lo `eve (KL) expansions. This method effectively captures the stochastic nature of energy manifolds, allowing for a more accurate representation of molecular interactions.Developed as a plugin for PIPER, our scientific computing software enhances the platform, delivering robust uncertainty measures for the energy manifolds of ranked binding sites. Our results demonstrate that top-ranked binding sites, characterized by lower uncertainty in the stochastic energy manifold, align closely with actual docking sites. Conversely, sites with higher uncertainty correlate with less optimal docking positions. This distinction not only validates our approach but also sets a new standard in protein docking predictions, offering substantial implications for future molecular interaction research and drug development.

In Polaris, we introduced a cloud-native distributed query processor to perform analytics at scale. In this paper, we extend the underlying Polaris distributed computation framework, which can be thought of as a read-only transaction engine, to execute general transactions (including updates, deletes, inserts and bulk loads, in addition to queries) for Tier 1 warehousing workloads in a highly performant and predictable manner. We take advantage of the immutability of data files in log-structured data stores and build on SQL Server transaction management to deliver full transactional support with Snapshot Isolation semantics, including multi-table and multi-statement transactions. With the enhancements described in this paper, Polaris supports both query processing and transactions for T-SQL in Microsoft Fabric., Comment: 12 pages, 12 Figures

Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a position-varying mass-damper-spring system., Comment: Accepted for presentation at the European Control Conference, Stockholm, 2024

A machine learning (ML) feature network is a graph that connects ML features in learning tasks based on their similarity. This network representation allows us to view feature vectors as functions on the network. By leveraging function operations from Fourier analysis and from functional analysis, one can easily generate new and novel features, making use of the graph structure imposed on the feature vectors. Such network structures have previously been studied implicitly in image processing and computational biology. We thus describe feature networks as graph structures imposed on feature vectors, and provide applications in machine learning. One application involves graph-based generalizations of convolutional neural networks, involving structured deep learning with hierarchical representations of features that have varying depth or complexity. This extends also to learning algorithms that are able to generate useful new multilevel features. Additionally, we discuss the use of feature networks to engineer new features, which can enhance the expressiveness of the model. We give a specific example of a deep tree-structured feature network, where hierarchical connections are formed through feature clustering and feed-forward learning. This results in low learning complexity and computational efficiency. Unlike "standard" neural features which are limited to modulated (thresholded) linear combinations of adjacent ones, feature networks offer more general feedforward dependencies among features. For example, radial basis functions or graph structure-based dependencies between features can be utilized.

Given a directed graph, the Minimal Feedback Arc Set (FAS) problem asks for a minimal set of arcs which, when removed, results in an acyclic graph. Equivalently, the FAS problem asks to find an ordering of the vertices that minimizes the number of feedback arcs. The FAS problem is considered an algorithmic problem of central importance in discrete mathematics. Our purpose in this paper is to consider the problem in the context of Erd\H{o}s-R\'{e}nyi random directed graphs, denoted $D(n,p)$, in which each possible directed arc is included with a fixed probability $p>0$. Our interest is the typical ratio of the number of feedforward arcs to the number of feedback arcs that are removed in the FAS problem. We show that as the number $n$ of vertices goes to infinity the probability that this ratio is greater than $1+\epsilon$ for any fixed $\epsilon > 0$ approaches zero. Similarly, letting $p$ go to zero as $n\rightarrow \infty$ this result remains true if $p>C\log{n}/n$ where $C$ depends on $\epsilon$.

We investigated the magnetic structure of the antiferromagnetic (AFM) ordered state ($T_{\rm N} \sim$ 34 K) in tetragonal UPt$_{2}$Si$_{2}$ using polarized and unpolarized neutron diffraction. Previous neutron scattering studies reported that this system possesses a simple AFM structure with a propagation vector, $Q = 0$, and the ordered magnetic moments aligned along the $c$-axis. By contrast, our latest resonant X-ray scattering (RXS) experiments have revealed that the magnetic structure is modulated by the charge-density-wave (CDW) order, which emerges in one of the two Pt atomic layers in the unit cell below $\sim$ 320 K. The modulation is characterized by a transverse wave in the $c$-plane, with the propagation vector of the CDW order, $q_{\rm CDW } =$ ($\sim$0.42, 0, 0). In the present study using neutron scattering, we observed that the superlattice reflections specified by $q_{\rm CDW }$ develop below $T_{\rm N}$, in addition to the magnetic reflections with $Q = 0$, thereby further confirming the presence of modulation in the AFM structure of this system. From detailed analyses, we revealed that the amplitude of the transverse-wave magnetic modulation to be 0.72(2) $\mu_{\rm B}$/U, which is a piece of quantitative information that could not be obtained through the RXS experiments. This implies that the CDW drives the ordered magnetic moments to be tilted up to 20$^\circ$ in the AFM state. The observations strongly suggest that the magnetism of UPt$_{2}$Si$_{2}$ is heavily influenced by the hybridization effects between 5f electrons of U and 5d electrons of Pt., Comment: 11 pages, 13 figures

Smart technologies are significant in supporting ageing in place for elderly. Leveraging Artificial Intelligence (AI) and Machine Learning (ML), it provides peace of mind, enabling the elderly to continue living independently. Elderly use smart technologies for entertainment and social interactions, this can be extended to provide safety and monitor health and environmental conditions, detect emergencies and notify informal and formal caregivers when care is needed. This paper provides an overview of the smart home technologies commercially available to support ageing in place, the advantages and challenges of smart home technologies, and their usability from elderlys perspective. Synthesizing prior knowledge, we created a structured Smart Home Goal Feature Model (SHGFM) to resolve heuristic approaches used by the Subject Matter Experts (SMEs) at aged care facilities and healthcare researchers in adapting smart homes. The SHGFM provides SMEs the ability to (i) establish goals and (ii) identify features to set up strategies to design, develop and deploy smart homes for the elderly based on personalised needs. Our model provides guidance to healthcare researchers and aged care industries to set up smart homes based on the needs of elderly, by defining a set of goals at different levels mapped to a different set of features., Comment: Preprint 9 pages

Document-based Question-Answering (QA) tasks are crucial for precise information retrieval. While some existing work focus on evaluating large language models performance on retrieving and answering questions from documents, assessing the LLMs performance on QA types that require exact answer selection from predefined options and numerical extraction is yet to be fully assessed. In this paper, we specifically focus on this underexplored context and conduct empirical analysis of LLMs (GPT-4 and GPT-3.5) on question types, including single-choice, yes-no, multiple-choice, and number extraction questions from documents in zero-shot setting. We use the CogTale dataset for evaluation, which provide human expert-tagged responses, offering a robust benchmark for precision and factual grounding. We found that LLMs, particularly GPT-4, can precisely answer many single-choice and yes-no questions given relevant context, demonstrating their efficacy in information retrieval tasks. However, their performance diminishes when confronted with multiple-choice and number extraction formats, lowering the overall performance of the model on this task, indicating that these models may not yet be sufficiently reliable for the task. This limits the applications of LLMs on applications demanding precise information extraction from documents, such as meta-analysis tasks. These findings hinge on the assumption that the retrievers furnish pertinent context necessary for accurate responses, emphasizing the need for further research. Our work offers a framework for ongoing dataset evaluation, ensuring that LLM applications for information retrieval and document analysis continue to meet evolving standards., Comment: 10 pages, 1 figure

We consider the task of filtering a dynamic parameter evolving as a diffusion process, given data collected at discrete times from a likelihood which is conjugate to the marginal law of the diffusion, when a generic dual process on a discrete state space is available. Recently, it was shown that duality with respect to a death-like process implies that the filtering distributions are finite mixtures, making exact filtering and smoothing feasible through recursive algorithms with polynomial complexity in the number of observations. Here we provide general results for the case of duality between the diffusion and a regular jump continuous-time Markov chain on a discrete state space, which typically leads to filtering distribution given by countable mixtures indexed by the dual process state space. We investigate the performance of several approximation strategies on two hidden Markov models driven by Cox-Ingersoll-Ross and Wright-Fisher diffusions, which admit duals of birth-and-death type, and compare them with the available exact strategies based on death-type duals and with bootstrap particle filtering on the diffusion state space as a general benchmark.

The nonlinear Poisson-Boltzmann equation (NPBE) is an elliptic partial differential equation used in applications such as protein interactions and biophysical chemistry (among many others). It describes the nonlinear electrostatic potential of charged bodies submerged in an ionic solution. The kinetic presence of the solvent molecules introduces randomness to the shape of a protein, and thus a more accurate model that incorporates these random perturbations of the domain is analyzed to compute the statistics of quantities of interest of the solution. When the parameterization of the random perturbations is high-dimensional, this calculation is intractable as it is subject to the curse of dimensionality. However, if the solution of the NPBE varies analytically with respect to the random parameters, the problem becomes amenable to techniques such as sparse grids and deep neural networks. In this paper, we show analyticity of the solution of the NPBE with respect to analytic perturbations of the domain by using the analytic implicit function theorem and the domain mapping method. Previous works have shown analyticity of solutions to linear elliptic equations but not for nonlinear problems. We further show how to derive \emph{a priori} bounds on the size of the region of analyticity. This method is applied to the trypsin molecule to demonstrate that the convergence rates of the quantity of interest are consistent with the analyticity result. Furthermore, the approach developed here is sufficiently general enough to be applied to other nonlinear problems in uncertainty quantification., Comment: 28 pages,4 figures

Semi-linear elliptic Partial Differential Equations (PDEs) such as the non-linear Poisson Boltzmann Equation (nPBE) is highly relevant for non-linear electrostatics in computational biology and chemistry. It is of particular importance for modeling potential fields from molecules in solvents or plasmas with stochastic fluctuations. The extensive applications include ones in condensed matter and solid state physics, chemical physics, electrochemistry, biochemistry, thermodynamics, statistical mechanics, and materials science, among others. In this paper we study the complex analytic properties of semi-linear elliptic Partial Differential Equations with respect to random fluctuations on the domain. We first prove the existence and uniqueness of the nPBE on a bounded domain in $\mathbb{R}^3$. This proof relies on the application of a contraction mapping reasoning, as the standard convex optimization argument for the deterministic nPBE no longer applies. Using the existence and uniqueness result we subsequently show that solution to the nPBE admits an analytic extension onto a well defined region in the complex hyperplane with respect to the number of stochastic variables. Due to the analytic extension, stochastic collocation theory for sparse grids predict algebraic to sub-exponential convergence rates with respect to the number of knots. A series of numerical experiments with sparse grids is consistent with this prediction and the analyticity result. Finally, this approach readily extends to a wide class of semi-linear elliptic PDEs., Comment: arXiv admin note: substantial text overlap with arXiv:2106.05811

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance., Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 2023

Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs.

Randomness extraction is an essential post-processing step in practical quantum cryptography systems. When statistical fluctuations are taken into consideration, the requirement of large input data size could heavily penalise the speed and resource consumption of the randomness extraction process, thereby limiting the overall system performance. In this work, we propose a sampled sub-block hashing approach to circumvent this problem by randomly dividing the large input block into multiple sub-blocks and processing them individually. Through simulations and experiments, we demonstrate that our method achieves an order-of-magnitude improvement in system throughput while keeping the resource utilisation low. Furthermore, our proposed approach is applicable to a generic class of quantum cryptographic protocols that satisfy the generalised entropy accumulation framework, presenting a highly promising and general solution for high-speed post-processing in quantum cryptographic applications such as quantum key distribution and quantum random number generation., Comment: 15 pages, 8 figures. All comments are welcome

We performed magnetization, resistivity, and neutron diffraction measurements under uniaxial stress applied along [1-10] direction on the tetragonal magnet CeRh$_2$Si$_2$ with commensurate magnetic orders. CeRh$_2$Si$_2$ has two successive antiferromagnetic (AF) orders in zero magnetic field. The high temperature phase (AF1 phase) has the magnetic modulation wave vector of $q = (\frac{1}{2}, \frac{1}{2}, 0)$, and the low temperature phase (AF2 phase) is characterized by the four $q$-vectors of $q = (\frac{1}{2}, \frac{1}{2}, 0), (\frac{1}{2}, -\frac{1}{2}, 0), (\frac{1}{2}, \frac{1}{2}, \frac{1}{2})$, and $(\frac{1}{2}, -\frac{1}{2}, \frac{1}{2})$. By measuring the uniaxial stress dependence of the magnetization, resistivity and the intensities of magnetic Bragg reflections, we confirmed that the AF1 phase has the single-$q$ magnetic order with two-fold rotational symmetry and the AF2 phase has the multi-$q$ magnetic order with four-fold rotational symmetry. In order to understand the origin of multi-$q$ order of CeRh$_2$Si$_2$, we also performed inelastic neutron scattering measurement on the single crystal samples. We found a magnetic excitation at the transfer energy $\hbar \omega \sim$ 8 meV. By applying the linear spin-wave theory, we found that the nearest and the next-nearest neighbor exchange interactions on the $ab$-plane, $J_1$ and $J_2$, are dominant in the AF2 phase. However, the $J_1$-$J_2$ model cannot lift the degeneracy between the single-$q$ (AF1) and multi-$q$ (AF2) phases. We suggest that it can be lifted by taking into account the biquadratic interaction derived from the perturbative expansion for the Kondo lattice Hamiltonian. [S. Hayami et al., Phys. Rev. B 95, 224424 (2017)., Comment: 7 pages, 7 figures

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, we propose a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eleimates the demanding requirements for fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechano-logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is successfully implemented through experiments by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. Our research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials could have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond., Comment: 24 pages, 7 figures

We consider the sets of negatively associated (NA) and negatively correlated (NC) distributions as subsets of the space $\mathcal{M}$ of all probability distributions on $\mathbb{R}^n$, in terms of their relative topological structures within the topological space of all measures on a given measurable space. We prove that the class of NA distributions has a non-empty interior with respect to the topology of the total variation metric on $\mathcal{M}$. We show however that this is not the case in the weak topology (i.e. the topology of convergence in distribution), unless the underlying probability space is finite. We consider both the convexity and the connectedness of these classes of probability measures, and also consider the two classes on their (widely studied) restrictions to the Boolean cube in $\mathbb{R}^n$.

Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction. In this paper, these nonlinear dynamics are compensated using a physics-guided neural network (PGNN), consisting of a physical model, embedding prior knowledge of the dynamics, in parallel with a neural network to learn hard-to-model dynamics. To ensure that the neural network learns only unmodelled effects, the neural network output in the subspace spanned by the physical model is regularized via an orthogonal projection-based approach, resulting in complementary physical model and neural network contributions. The PGNN feedforward controller reduces the tracking error of an interventional X-ray system by a factor of 5 compared to an optimally tuned physical model, successfully compensating the unmodeled parasitic dynamics., Comment: Included conference information in banner of paper

DevOps responds the growing need of companies to streamline the software development process and, thus, has experienced widespread adoption in the past years. However, the successful adoption of DevOps requires companies to address significant cultural and organizational changes. Understanding the organizational structure and characteristics of teams adopting DevOps is key, and comprehending the existing theories and representations of team taxonomies is critical to guide companies in a more systematic and structured DevOps adoption process. As there was no unified theory to explain the different topologies of DevOps teams, in previous work, we built a theory to represent the organizational structure and characteristics of teams adopting DevOps, harmonizing the existing knowledge. In this paper, we expand the theory-building in the context of DevOps Team Taxonomies. Our main contributions are presenting and executing the Operationalization and Testing phases for a continuously evolving theory on DevOps team structures. We operationalize the constructs and propositions that make up our theory to generate empirically testable hypotheses to confirm or disconfirm the theory. Specifically, we focus on the research operation side of the theory-research cycle: identifying propositions, deriving empirical indicators from constructs, establishing testable hypotheses, and testing them. We performed the operationalization and testing of the DevOps Team Taxonomies Theory, which resulted in an empirically verified and trustworthy theory. Our theory has 28 propositions representing this model that map properties to the constructs of our theory. The operationalization generated 34 testable hypotheses, and we thoroughly tested 11 of them. The testing has proved the effectiveness of the theoretical framework, while the operationalization of the constructs has enhanced the initial framework., Comment: Submitted to Empirical Software Engineering

Bayesian nonparametric mixture models are common for modeling complex data. While these models are well-suited for density estimation, recent results proved posterior inconsistency of the number of clusters when the true number of components is finite, for the Dirichlet process and Pitman--Yor process mixture models. We extend these results to additional Bayesian nonparametric priors such as Gibbs-type processes and finite-dimensional representations thereof. The latter include the Dirichlet multinomial process, the recently proposed Pitman-Yor, and normalized generalized gamma multinomial processes. We show that mixture models based on these processes are also inconsistent in the number of clusters and discuss possible solutions. Notably, we show that a post-processing algorithm introduced for the Dirichlet process can be extended to more general models and provides a consistent method to estimate the number of components., Comment: Added a real-data analysis (Section 6) Revised version (changes in Section 4.1)

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physics-based model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models., Comment: Accepted for presentation at the 2022 Conference on Decision and Control (CDC)

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example., Comment: To appear in Conference on Decision and Control 2022

Massive vector field datasets are common in multi-spectral optical and radar sensors, among many other emerging areas of application. In this paper we develop a novel stochastic functional (data) analysis approach for detecting anomalies based on the covariance structure of nominal stochastic behavior across a domain. An optimal vector field Karhunen-Loeve expansion is applied to such random field data. A series of multilevel orthogonal functional subspaces is constructed from the geometry of the domain, adapted from the KL expansion. Detection is achieved by examining the projection of the random field on the multilevel basis. In addition, reliable hypothesis tests are formed that do not require prior assumptions on probability distributions of the data. The method is applied to the important problem of deforestation and degradation in the Amazon forest. This is a complex non-monotonic process, as forests can degrade and recover. Using multi-spectral satellite data from Sentinel-2, the multilevel filter is constructed and anomalies are treated as deviations from the initial state of the forest. Forest anomalies are quantified with robust hypothesis tests. Our approach shows the advantage of using multiple bands of data in a vectorized complex, leading to better anomaly detection beyond the capabilities of scalar-based methods.

High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in non-linear mixed-effects models where data are collected repeatedly on several individuals. In this work, variable selection is approached from a Bayesian perspective and a selection procedure is proposed, combining the use of a spike-and-slab prior and the Stochastic Approximation version of the Expectation Maximisation (SAEM) algorithm. Similarly to Lasso regression, the set of relevant covariates is selected by exploring a grid of values for the penalisation parameter. The SAEM approach is much faster than a classical MCMC (Markov chain Monte Carlo) algorithm and our method shows very good selection performances on simulated data. Its flexibility is demonstrated by implementing it for a variety of nonlinear mixed effects models. The usefulness of the proposed method is illustrated on a problem of genetic markers identification, relevant for genomic-assisted selection in plant breeding.

Operating Systems are built upon a set of abstractions to provide resource management and programming APIs for common functionality, such as synchronization, communication, protection, and I/O. The process abstraction is the bridge across these two aspects; unsurprisingly, research efforts pay particular attention to the process abstraction, aiming at enhancing security, improving performance, and supporting hardware innovations. However, given the intrinsic difficulties to implement modifications at the OS level, recent endeavors have not yet been widely adopted in production-oriented OSes. Still, we believe the current hardware evolution and new application requirements provide favorable conditions to change this trend. This paper evaluates recent research on OS process features identifying potential evolution paths. We derive a set of relevant process characteristics, and propose how to extend them as to benefit OSes and applications.

Massive Open Online Courseware (MOOCs) appeared in 2008 and grew considerably in the past decade, now reaching millions of students and professionals all over the world. MOOCs do not replace other educational forms. Instead, they complement them by offering a powerful educational tool that can reach students that, otherwise, would not have access to that information. Nevertheless, designing and implementing a successful MOOC is not straightforward. Simply recording traditional classes is an approach that does not work, since the conditions in which a MOOC student learns are very different from the conventional classroom. In particular, dropout rates in MOOCs are, normally, at least an order of magnitude higher than in conventional courses. In this paper, we analyze data from 7 successful MOOCs that have attracted over 150,000 students in the past years. The analysis led to the proposal of a set of guidelines to help instructors in designing more effective MOOCs. These results contribute to the existing body of knowledge in the field, bring new insights, and pose new questions for future research.

This Snowmass21 Contributed Paper encourages the Particle Physics community in fostering R&D in Superconducting Nb3Sn coated Copper RF Cavities instead of costly bulk Niobium. It describes the pressing need to devote effort in this direction, which would deliver higher gradient and higher temperature of operation and reduce the overall capital and operational costs of any future collider. It is unlikely that an ILC will be built in the next ten years with Nb as one of the main cost drivers of SRFs. This paper provides strong arguments on the benefits of using this time for R&D on producing Nb3Sn on inexpensive and thermally efficient metals such as Cu or bronze, while pursuing in parallel the novel U.S. concept of parallel-feed RF accelerator structures. A technology that synergistically uses both of these advanced tools would make an ILC or equivalent machines more affordable and more likely to be built. Such a successful enterprise would readily apply to other HEP accelerators, for instance a Muon Collider, and to accelerators beyond HEP. We present and assess current efforts in the U.S. on the novel concept of parallel-feed RF accelerator structures, and in the U.S. and abroad in producing Nb3Sn films on either Cu or bronze despite minimal funding., Comment: Contribution to Snowmass 2021

We have carried out exclusive measurements for the photoproduction of an $\eta$ meson from a proton target with an egg-shaped calorimeter made of BGO crystals (BGOegg) and forward charged-particle detectors at the SPring-8 LEPS2 beamline. The differential cross sections and photon beam asymmetries of the $\gamma p \to \eta p$ reaction are measured in a center-of-mass energy ($W$) range of $1.82$-$2.32$ GeV and a polar angle range of $-1.0 < \cos{\theta^{\eta}_{\mathrm{c.m.}}} < 0.6$. The reaction is identified by selecting a proton and two $\gamma$'s produced by an $\eta$-meson decay. The kinematic fit method was employed to select the reaction candidate with the confidence level larger than $1$\%. A bump structure at $W$ = $2.0$-$2.3$ GeV in the differential cross section is confirmed at extremely backward $\eta$ polar angles, where the existing data are inconsistent with each other. This bump structure is likely associated with high-spin resonances that couple with $s\bar{s}$ quarks. The results of the photon beam asymmetries in a wide $\eta$ polar angle range are new for the photon beam energies exceeding $2.1$ GeV. These results are not reproduced by the existing partial wave analyses. They provide an additional constraint to nucleon resonance studies at high energies., Comment: 16 pages, 11 figures

An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degrades control performance, and to provide a solution. The proposed method employs instrumental variables to ensure consistent parameter estimates. A nonlinear system example reveals that the developed instrumental variable neural network (IVNN) approach asymptotically recovers the optimal solution, while pre-existing approaches are shown to lead to inconsistent estimates.

Transitions of multistability in structures have been exploited for various functions and applications, such as spectral gap tuning, impact energy trapping, and wave steering. However, a fundamental and comprehensive understanding of the transitions, either quasi-static or dynamic transitions, has not yet been acquired, especially in terms of the sequence predictability and tailoring mechanisms. This research, utilizing the stacked Miura-ori-variant (SMOV) structure that has exceptional multistability and shape reconfigurability as a platform, uncovers the deep knowledge of quasi-static and dynamic transitions, and pioneers the corresponding versatile formation and tuning of mechanical logic gates. Through theoretical, numerical, and experimental means, discriminative and deterministic quasi-static transition sequences, including reversible and irreversible ones, are uncovered, where they constitute a transition map that is editable upon adjusting the design parameters. Via applying dynamic excitations and tailoring the excitation conditions, reversible transitions between all stable configurations become attainable, generating a fully-connected transition map. Benefiting from the nonlinearity of the quasi-static and dynamic transitions, basic and compound mechanical logic gates are achieved. The versatility of the scheme is demonstrated by employing a single SMOV structure to realize different complex logic operations without increasing structural complexity, showing its superior computing power and inspiring the avenue for efficient physical intelligence., Comment: 26 pages, 5 figures, 8 supplementary figures, 2 supplementary tables

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in the subspace of the model is penalized through orthogonal projection. This results in uniquely identifiable model coefficients, enabling both increased performance and good generalization. The feedforward control framework is validated on a representative system with performance limiting nonlinear friction characteristics., Comment: Submitted for presentation at the 2022 American Control Conference (ACC)

The performance increase up to the sensor resolution in repetitive control (RC) invalidates the standard assumption in RC that data is available at equidistant time instances, e.g., in systems with package loss or when exploiting timestamped data from optical encoders. The aim of this paper is to develop an intermittent sampling RC framework for non-equidistant measurements. Sufficient stability conditions are derived that can be verified using non-parametric frequency response function data. This results in a frequency domain design procedure to explicitly address uncertainty. The RC framework is validated on an industrial printbelt setup for which exact non-equidistant measurement data is available., Comment: Accepted for presentation at the 60th Conference on Decision and Control 2021 (CDC2021)

In this paper, we present Zero-data Based Repeated bit flip Attack (ZeBRA) that precisely destroys deep neural networks (DNNs) by synthesizing its own attack datasets. Many prior works on adversarial weight attack require not only the weight parameters, but also the training or test dataset in searching vulnerable bits to be attacked. We propose to synthesize the attack dataset, named distilled target data, by utilizing the statistics of batch normalization layers in the victim DNN model. Equipped with the distilled target data, our ZeBRA algorithm can search vulnerable bits in the model without accessing training or test dataset. Thus, our approach makes the adversarial weight attack more fatal to the security of DNNs. Our experimental results show that 2.0x (CIFAR-10) and 1.6x (ImageNet) less number of bit flips are required on average to destroy DNNs compared to the previous attack method. Our code is available at https://github. com/pdh930105/ZeBRA., Comment: 14 pages, 3 figures, 5 tables, Accepted at British Machine Vision Conference (BMVC) 2021

Robust statistical data modelling under potential model mis-specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite dimensional objects such as functions, probability distributions or infinite vectors. In the Bayesian nonparametric approach, prior distributions are designed for these parameters, which provide a handle to manage the complexity of nonparametric models in practice. However, most modern Bayesian nonparametric models seem often out of reach to practitioners, as inference algorithms need careful design to deal with the infinite number of parameters. The aim of this work is to facilitate the journey by providing computational tools for Bayesian nonparametric inference. The article describes a set of functions available in the \R package BNPdensity in order to carry out density estimation with an infinite mixture model, including all types of censored data. The package provides access to a large class of such models based on normalized random measures, which represent a generalization of the popular Dirichlet process mixture. One striking advantage of this generalization is that it offers much more robust priors on the number of clusters than the Dirichlet. Another crucial advantage is the complete flexibility in specifying the prior for the scale and location parameters of the clusters, because conjugacy is not required. Inference is performed using a theoretically grounded approximate sampling methodology known as the Ferguson & Klass algorithm. The package also offers several goodness of fit diagnostics such as QQ-plots, including a cross-validation criterion, the conditional predictive ordinate. The proposed methodology is illustrated on a classical ecological risk assessment method called the Species Sensitivity Distribution (SSD) problem, showcasing the benefits of the Bayesian nonparametric framework.

Let $M$ be a real hypersurface of a complex space form $M^n(c)$, $c\neq 0$. Suppose that the structure vector field $\xi$ of $M$ is an eigen vector field of the Ricci tensor $S$, $S\xi=\beta\xi$, $\beta$ being a function. We study on $M$, a gradient pseudo-Ricci soliton as an extended concept of Ricci soliton, closely related to pseudo-Einstein real hypersurfaces. We show that a $3$-dimensional ruled real hypersurface of $M^2(c), c<0$ admits a non-trivial gradient pseudo-Ricci soliton.

It has long been a recognized problem that many datasets contain significant levels of missing numerical data. A potentially critical predicate for application of machine learning methods to datasets involves addressing this problem. However, this is a challenging task. In this paper, we apply a recently developed multi-level stochastic optimization approach to the problem of imputation in massive medical records. The approach is based on computational applied mathematics techniques and is highly accurate. In particular, for the Best Linear Unbiased Predictor (BLUP) this multi-level formulation is exact, and is significantly faster and more numerically stable. This permits practical application of Kriging methods to data imputation problems for massive datasets. We test this approach on data from the National Inpatient Sample (NIS) data records, Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. Numerical results show that the multi-level method significantly outperforms current approaches and is numerically robust. It has superior accuracy as compared with methods recommended in the recent report from HCUP. Benchmark tests show up to 75% reductions in error. Furthermore, the results are also superior to recent state of the art methods such as discriminative deep learning., Comment: 11 pages, 4 figures

In our approach, we consider the data as instances of a random field within a relevant Bochner space. Our key observation is that the classes can predominantly reside in two distinct subspaces. To uncover the separation between these classes, we employ the Karhunen-Loeve expansion and construct the appropriate subspaces. This allows us to effectively reveal the distinction between the classes. The novel features forming the above bases are constructed by applying a coordinate transformation based on the recent Functional Data Analysis theory for anomaly detection. The associated signal decomposition is an exact hierarchical tensor product expansion with known optimality properties for approximating stochastic processes (random fields) with finite dimensional function spaces. Using a hierarchical finite dimensional expansion of the nominal class, a series of orthogonal nested subspaces is constructed for detecting anomalous signal components. Projection coefficients of input data in these subspaces are then used to train a Machine Learning (ML classifier. However, due to the split of the signal into nominal and anomalous projection components, clearer separation surfaces for the classes arise. In fact we show that with a sufficiently accurate estimation of the covariance structure of the nominal class, a sharp classification can be obtained. This is particularly advantageous for large unbalanced datasets. We demonstrate it on a number of high-dimensional datasets. This approach yields significant increases in accuracy of ML methods compared to using the same ML algorithm with the original feature data. Our tests on the Alzheimer's Disease ADNI dataset shows a dramatic increase in accuracy (from 48% to 89% accuracy). Furthermore, tests using unbalanced semi-synthetic datasets created from the benchmark GCM dataset confirm increased accuracy as the dataset becomes more unbalanced.

Secure information retrieval is an essential task in today's highly digitised society. In some applications, it may be necessary that user query's privacy and database content's security are enforced. For these settings, symmetric private information retrieval (SPIR) could be employed, but its implementation is known to be demanding, requiring a private key-exchange network as the base layer. Here, we report for the first time a realisation of provably-secure SPIR supported by a quantum-secure key-exchange network. The SPIR scheme looks at biometric security, offering secure retrieval of 582-byte fingerprint files from a database with 800 entries. Our experimental results clearly demonstrate the feasibility of SPIR with quantum secure communications, thereby opening up new possibilities in secure distributed data storage and cloud computing over the future Quantum Internet.

Much theoretical effort and automatization are required to confront new physics models with experimental data for many types of particle reactions at future colliders. In this context, we extend GRACE, an automatic calculation system for invariant amplitudes, to incorporate particles and interactions in the Georgi-Machacek model. With the extended GRACE system, we study fermiophobic Higgs boson production processes at $e^+ e^-$ and $e^- e^-$ colliders in the model. The results show some advantages of $e^- e^-$ colliders over $e^+ e^-$ colliders for new physics search and thus its complementary role., Comment: 22 pages, 7 figures; v2: 1 figure added, other minor additions and modifications, version accepted in EPJC

We prove the existence and uniqueness of the complexified Nonlinear Poisson-Boltzmann Equation (nPBE) in a bounded domain in $\mathbb{R}^3$. The nPBE is a model equation in nonlinear electrostatics. The standard convex optimization argument to the complexified nPBE no longer applies, but instead, a contraction mapping argument is developed. Furthermore, we show that uniqueness can be lost if the hypotheses given are not satisfied. The complixified nPBE is highly relevant to regularity analysis of the solution of the real nPBE with respect to the dielectric (diffusion) and Debye-H\"uckel coefficients. This approach is also well-suited to investigate the existence and uniqueness problem for a wide class of semi-linear elliptic Partial Differential Equations (PDEs).

Understanding students' programming misconceptions is critical. Doing so depends on identifying the reasons why students make errors when learning a new programming language. Knowing the misconceptions can help students to improve their reflection about their mistakes and also help instructors to design better teaching strategies. In this technical report, we propose catalogs of antipatterns for two programming languages: C and Python. To accomplish this, we analyzed the codes of 166 CS1 engineering students when they were coding solutions to programming exercises. In our results, we catalog 41 CS1 antipatterns from 95 cataloged misconceptions in C and Python. These antipatterns were separated into three catalogs: C, Python, and antipatterns found in code using both programming languages. For each antipattern, we present code examples, students' solutions (if they are present), a possible solution to avoid the antipattern, among other information., Comment: 114 pages, 3 figures, 1 table, 5 appendices

We present a precise measurement of the cross section, proton and $\rm ^3He$ analyzing powers, and spin correlation coefficient $C_{y,y}$ for $p$-$\rm ^3He$ elastic scattering near 65 MeV, and a comparison with rigorous four-nucleon scattering calculations based on realistic nuclear potentials and a model with $\Delta$-isobar excitation. Clear discrepancies are seen in some of the measured observables in the regime around the cross section minimum. Theoretical predictions using scaling relations between the calculated cross section and the $\rm ^3 He$ binding energy are not successful in reproducing the data. Large sensitivity to the $NN$ potentials and rather small $\Delta$-isobar effects in the calculated cross section are noticed as different features from those in the deuteron-proton elastic scattering. The results obtained above indicate that $p$-$\rm ^3He$ scattering at intermediate energies is an excellent tool to explore nuclear interactions not accessible by three-nucleon scattering., Comment: 22 pages, 6 figures