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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