Although several accounts of scientific understanding exist, the concept of understanding in relation to technology remains underexplored. This paper addresses this gap by proposing a philosophical account of technological understanding: the type of understanding that is required for and reflected by successfully designing and using technological artefacts. We develop this notion by building on the concept of scientific understanding. Drawing on parallels between science and technology, and specifically between scientific theories and technological artefacts, we extend the idea of scientific understanding into the realm of technology. We argue that, just as scientific understanding involves the ability to explain a phenomenon using a theory, technological understanding involves the ability to use a technological artefact to realise a practical aim. Both theories and artefacts are tools, and using them successfully requires the cognitive skill of understanding. Technological understanding is thus conceived as the ability to recognise how a practical aim can be achieved by using a technological artefact. In a context of design, this general notion of technological understanding is specified as the ability to design an artefact that, by producing a phenomenon through its physical structure, achieves the intended aim. By analogy with De Regt's criterion of the intelligibility of theories, we give, as a precondition for technological understanding, a criterion for the intelligibility of a technological artefact. We illustrate our concept of technological understanding through two running examples: magnetic resonance imaging (MRI) and superconducting quantum computers. Our account highlights the epistemic dimension of engaging with technology and, by allowing for context-dependent specifications, provides guidance for testing and improving technological understanding in specific contexts.

In this paper we analyze operators H = a^{ij}(x,t) X_i X_j - d/dt (having adopted Einstein's convention on repeated indexes), where the X_i's are Hörmander vector fields generating a Carnot group and A = [a_{ij}] is a symmetric and uniformly positive-definite matrix whose entries satisfy double Dini continuity, a strictly weaker condition than Hölder continuity. For these operators, we build a fundamental solution and show a two-sided Gaussian estimate for the latter, as well as upper Gaussian estimates for its derivatives up to weight 2. As a consequence of the whole procedure, we prove an existence result for the related Cauchy problem, under a Dini-type condition on the source.

We introduce partial Markov categories as a synthetic framework for synthetic probabilistic inference, blending the work of Cho and Jacobs, Fritz, and Golubtsov on Markov categories with the work of Cockett and Lack on cartesian restriction categories. We describe observations, Bayes' theorem, normalisation, and both Pearl's and Jeffrey's updates in purely categorical terms.

In statistical physics, phase transitions are arguably among the most extensively studied phenomena. In the computational approach to this field, the development of algorithms capable of estimating entropy across the entire energy spectrum in a single execution has highlighted the efficacy of microcanonical inflection point analysis, while Fisher's zeros technique has re-emerged as a powerful methodology for investigating these phenomena. This paper presents an alternative protocol for analyzing phase transitions using a parametrization of the entropy function in the microcanonical ensemble. We also provide a clear demonstration of the relation of the linear pattern of the Fisher's zeros on the complex inverse temperature map (a circle in the complex $x=e^{-β\varepsilon}$ map) with the order of the transition, showing that the latent heat is inversely related to the distance between the zeros. We study various model systems, including the Lennard-Jones cluster, the Ising, the XY, and the Zeeman models. By examining the behavior of thermodynamic quantities such as entropy and its derivatives in the microcanonical ensemble, we identify key features-such as loops and discontinuities in parametric curves-which signal phase transitions' presence and nature. This approach can facilitate the classification of phase transitions across various physical systems.

Going beyond short-range interactions, we explore the role of long-range interactions in the extended XY model for transferring quantum states through evolution. In particular, employing a spin-1/2 chain with interactions decaying as a power law, we demonstrate that long-range (LR) interactions significantly enhance the efficiency of a quantum state transfer (QST) protocol, improving the achievable fidelity, mitigating its slow decline as compared with the nearest-neighbor setting, associated with increasing system-size. Our study identifies the LR regime as providing an optimal balance between interaction range and transfer efficiency, outperforming the protocol with the short-range interacting model. Our detailed analysis reveals the impact of system parameters, such as anisotropy, magnetic field strength, and coordination number, on QST dynamics. Specifically, we find that intermediate coordination numbers lead to a faster and more reliable state transfer, while extreme values diminish performance. Furthermore, we exhibit that the presence of LR interactions considerably reduces the minimum time required to achieve fidelity beyond the classical limit.

Accurate RF propagation modeling in urban environments is critical for developing digital spectrum twins and optimizing wireless communication systems. We introduce OpenGERT, an open-source automated Geometry Extraction tool for Ray Tracing, which collects and processes terrain and building data from OpenStreetMap, Microsoft Global ML Building Footprints, and USGS elevation data. Using the Blender Python API, it creates detailed urban models for high-fidelity simulations with NVIDIA Sionna RT. We perform sensitivity analyses to examine how variations in building height, position, and electromagnetic material properties affect ray-tracing accuracy. Specifically, we present pairwise dispersion plots of channel statistics (path gain, mean excess delay, delay spread, link outage, and Rician K-factor) and investigate how their sensitivities change with distance from transmitters. We also visualize the variance of these statistics for selected transmitter locations to gain deeper insights. Our study covers Munich and Etoile scenes, each with 10 transmitter locations. For each location, we apply five types of perturbations: material, position, height, height-position, and all combined, with 50 perturbations each. Results show that small changes in permittivity and conductivity minimally affect channel statistics, whereas variations in building height and position significantly alter all statistics, even with noise standard deviations of 1 meter in height and 0.4 meters in position. These findings highlight the importance of precise environmental modeling for accurate propagation predictions, essential for digital spectrum twins and advanced communication networks. The code for geometry extraction and sensitivity analyses is available at github.com/serhatadik/OpenGERT/.

The increasing sophistication of modern cyber threats, particularly file-less malware relying on living-off-the-land techniques, poses significant challenges to traditional detection mechanisms. Memory forensics has emerged as a crucial method for uncovering such threats by analysing dynamic changes in memory. This research introduces SPECTRE (Snapshot Processing, Emulation, Comparison, and Threat Reporting Engine), a modular Cyber Incident Response System designed to enhance threat detection, investigation, and visualization. By adopting Volatility JSON format as an intermediate output, SPECTRE ensures compatibility with widely used DFIR tools, minimizing manual data transformations and enabling seamless integration into established workflows. Its emulation capabilities safely replicate realistic attack scenarios, such as credential dumping and malicious process injections, for controlled experimentation and validation. The anomaly detection module addresses critical attack vectors, including RunDLL32 abuse and malicious IP detection, while the IP forensics module enhances threat intelligence by integrating tools like Virus Total and geolocation APIs. SPECTRE advanced visualization techniques transform raw memory data into actionable insights, aiding Red, Blue and Purple teams in refining strategies and responding effectively to threats. Bridging gaps between memory and network forensics, SPECTRE offers a scalable, robust platform for advancing threat detection, team training, and forensic research in combating sophisticated cyber threats.

Non-split Real Tambara-Yamagami categories are a family of fusion categories over the real numbers that were recently introduced and classified by Plavnik, Sanford, and Sconce. We consider which of these categories admit braidings, and classify the resulting braided equivalence classes. We also prove some new results about the split real and split complex Tambara-Yamagami Categories. V2: Final Section removed, to appear in Transformation Groups.

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here we report scanning nitrogen-vacancy (NV) quantum sensing of local magnetic noise environment of an on- chip superconducting resonator. We find that quasiparticle-induced fluctuating magnetic fields can drive NV spin relaxation, which shows a peak value around the superconducting transition point of niobium at the thermal equilibrium state. External microwave driving at the resonator mode frequency significantly increases the quasiparticle density, leading to enhancement of magnetic noise. We further perform optically detected magnetic resonance measurements to demonstrate quasiparticle magnetic noise mediated off-resonant dipole coupling between the NV center and niobium resonator. Our work reports experimental observation of the Hebel-Slichter peak signature by an external sensor outside of a superconductor. The presented study also highlights the advantages of quantum sensors in investigating miniaturized superconducting devices, providing insights into their future performance improvements.

We revisit the convergence analysis of two approximation hierarchies for polynomial optimization on the unit sphere. The first one is based on the moment-sos approach and gives semidefinite bounds for which Fang and Fawzi (2021) showed an analysis in $O(1/r^2)$ for the r-th level bound, using the polynomial kernel method. The second hierarchy was recently proposed by Lovitz and Johnston (2023) and gives spectral bounds for which they show a convergence rate in $O(1/r)$, using a quantum de Finetti theorem of Christandl et al. (2007) that applies to complex Hermitian matrices with a "double" symmetry. We investigate links between these approaches, in particular, via duality of moments and sums of squares. Our main results include showing that the spectral bounds cannot have a convergence rate better than $O(1/r^2)$ and that they do not enjoy generic finite convergence. In addition, we propose alternative performance analyses that involve explicit constants depending on intrinsic parameters of the optimization problem. For this we develop a novel "banded" real de Finetti theorem that applies to real matrices with "double" symmetry. We also show how to use the polynomial kernel method to obtain a de Finetti type result in $O(1/r^2)$ for real maximally symmetric matrices, improving an earlier result in $O(1/r)$ of Doherty and Wehner (2012).

In this paper, we explore the higher- and lower-order connectivity aspects of the visibility graph representation of sandpile models, focusing on the Bak-Tang-Wiesenfeld (BTW) model. By converting the time series of avalanche events into visibility graphs, we investigate the structural properties that emerge at different scales of connectivity. Specifically, we utilize persistent homology and simplicial complex theory to identify higher-order topological features. Analyzing the statistics of degree and betweenness centralities reveal that the resulting graph is a scale-free network with the degree exponent of $γ_k=2.50\pm 0.02$ and betweenness exponent of $γ_b=1.585\pm 0.008$. The analysis of the simplexes show that the distribution function of one, two and three dimensional simplexes are power-law with the exponents of $γ_{σ_1}=0.795\pm 0.006$, $γ_{σ_2}=0.602\pm 0.009$ and $γ_{σ_3}=0.422\pm 0.008$ respectively. This power-law behavior is also seen in the homology group analysis, for the Betti numbers, the exponents of which are reported in the paper. The persistent entropy of life time of $d$ dimensional holes is analyzed indicating that it increases logarithmically in terms of the network size $N$ for $d=0,1$ and $2$.

Time-integrated state observables, which quantify the fraction of time spent by the system in a specific pool of states, are important in many fields, such as chemical sensing or the theory of fluorescence spectroscopy. We derive exact identities, called Fluctuation-Response Relations (FRRs), that connect the fluctuations of such observables to their response to external perturbations in nonequilibrium steady state of Markov jump processes. Using these results, we derive a first known upper bound on fluctuations of state observables, as well as some new lower bounds. We further demonstrate how our identities provide a deeper understanding of the mechanistic origin of fluctuations and reveal their properties dependent only on system topology, which may be relevant for model inference using measured data.

Observable convolutional codes defined over Zpr with the Predictable Degree Property admit minimal input/state/output representations that preserve structural properties under scalar restriction. We make use of this fact to present Rosenthal's decoding algorithm for these convolutional codes. When combined with the Greferath-Vellbinger algorithm and a modified version of the Torrecillas-Lobillo-Navarro algorithm, the decoding problem of convolutional codes over Zpr reduces to selecting two decoding algorithms for linear block codes over a field. Finally, we analyze both the theoretical and practical error-correction capabilities of the combined algorithm as well as its time complexity.

A probabilistic approach to the stable matching problem has been identified as an important research area with several important open problems. When considering random matchings, ex-post stability is a fundamental stability concept. A prominent open problem is characterizing ex-post stability and establishing its computational complexity. We investigate the computational complexity of testing ex-post stability. Our central result is that when either side has ties in the preferences/priorities, testing ex-post stability is NP-complete. The result even holds if both sides have dichotomous preferences. On the positive side, we give an algorithm using an integer programming approach, that can determine a decomposition with a maximum probability of being weakly stable. We also consider stronger versions of ex-post stability (in particular robust ex-post stability and ex-post strong stability) and prove that they can be tested in polynomial time.

Measurement of gravitational waves can provide precision tests of the nature of black holes and compact objects. In this work, we test Giddings' nonviolent nonlocality proposal, which posits that quantum information is transferred via a nonlocal interaction that generates metric perturbations around black holes. In contrast to firewalls, these quantum fluctuations would be spread out over a larger distance range -- up to a Schwarzschild radius away. In this letter, we model the modification to the gravitational waveform from nonviolent nonlocality. We modify the nonspinning EOBNRv2 effective one body waveform to include metric perturbations that are due to a random Gaussian process. We find that the waveform exhibits random deviations which are particularly important in the late inspiral-plunge phase. We find an optimal dephasing parameter for detecting this effect with a principal component analysis. This is particularly intriguing because it predicts random phase deviations across different gravitational wave events, providing theoretical support for hierarchical tests of general relativity. We estimate the constraint on the perturbations in nonviolent nonlocality with events for the LIGO-Virgo network and for a third-generation network.

Ideally, all analyses of normally distributed data should include the full covariance information between all data points. In practice, the full covariance matrix between all data points is not always available. Either because a result was published without a covariance matrix, or because one tries to combine multiple results from separate publications. For simple hypothesis tests, it is possible to define robust test statistics that will behave conservatively in the presence on unknown correlations. For model parameter fits, one can inflate the variance by a factor to ensure that things remain conservative at least up to a chosen confidence level. This paper describes a class of robust test statistics for simple hypothesis tests, as well as an algorithm to determine the necessary inflation factor for model parameter fits and Goodness of Fit tests and composite hypothesis tests. It then presents some example applications of the methods to real neutrino interaction data and model comparisons.

In the near future, the Jefferson Lab $b_1$ experiment will provide the second measurement of tensor polarized asymmetries in inclusive DIS on the deuteron. In this asymmetry, 4 independent tensor polarized structure functions contribute. This necessitates systematic approximations in the extraction of the leading twist structure function $b_1$ from a single tensor asymmetry measurement. Contamination from higher twist structure functions and kinematic effects is discussed here. Using a deuteron convolution model, we quantify the systematic errors from these approximations for two different choices for the target polarization direction (momentum transfer, electron beam direction). For Jefferson Lab 12 GeV kinematics, the systematic error turns out to be comparable between the two polarization options, while at higher $Q^2$ values the momentum transfer direction is preferred.

In this paper, we examine some shape functionals, introduced by Pólya and Makai, involving the torsional rigidity and the first Dirichlet-Laplacian eigenvalue for bounded, open and convex sets of $\mathbb{R}^n$. We establish new quantitative bounds, which give us key properties and information on the behavior of the optimizing sequences. In particular, we consider two kinds of reminder terms that provide information about the structure of these minimizing sequences, such as information about the thickness.

Off-lattice agent-based models (or cell-based models) of multicellular systems are increasingly used to create in-silico models of in-vitro and in-vivo experimental setups of cells and tissues, such as cancer spheroids, neural crest cell migration, and liver lobules. These applications, which simulate thousands to millions of cells, require robust and efficient numerical methods. At their core, these models necessitate the solution of a large friction-dominated equation of motion, resulting in a sparse, symmetric, and positive definite matrix equation. The conjugate gradient method is employed to solve this problem, but this requires a good preconditioner for optimal performance. In this study, we develop a graph-based preconditioning strategy that can be easily implemented in such agent-based models. Our approach centers on extending support graph preconditioners to block-structured matrices. We prove asymptotic bounds on the condition number of these preconditioned friction matrices. We then benchmark the conjugate gradient method with our support graph preconditioners and compare its performance to other common preconditioning strategies.

We study the evolution of stellar kinematics of a sample of 952 massive quiescent galaxies with $M_*>10^{10.5}M_\odot$ at $0.6<z<1$. Utilizing spatially integrated spectroscopy from the LEGA-C survey, we focus on the relationship between the observed integrated stellar velocity dispersion ($σ^\prime_{star}$) and the morphological axial ratio ($q$), and its variation with the stellar age and mass of quiescent galaxies. For the youngest quiescent galaxies, regardless of stellar mass, $σ^\prime_{star}$ decreases with increasing $q$, a trend that is consistent with a system having significant rotation and hence suggests that massive galaxies still retain significant amount of angular momentum in the aftermath of quenching. As they continue to evolve, the variation of the $σ^\prime_{star}$-$q$ relationship depends on stellar mass. For quiescent galaxies with $M_*<10^{11.3}M_\odot$, $σ^\prime_{star}$ decreases with $q$ in all stellar-age bins, suggesting that the quiescent populations of this mass regime retain significant rotation even long time after quenching. In contrast, for more massive quiescent galaxies with $M_*>10^{11.3}M_\odot$, the relationship between $σ^\prime_{star}$ and $q$ becomes significantly flattened with increasing stellar age. This indicates that, as the very massive galaxy populations continue to evolve after quenching, angular momentum gradually reduces, which eventually transforms them into velocity-dispersion supported systems. We suggest that incoherent, continuous merging and accretion events onto the galaxies are the main drivers of the observed mass-dependent, posting-quenching dynamical evolution, because more massive galaxies are more likely to undergo such interactions. We are witnessing the early formation epoch of fast and slow rotators at $z \sim 0.8$, when the Universe was only half of its age nowadays.

Latching techniques are widely used to enhance readout of qubits. These methods require precise tuning of multiple tunnel rates, which can be challenging to achieve under realistic experimental conditions, such as when a qubit is coupled to a single reservoir. Here, we present a method for single-shot measurement of a quantum dot qubit with a single reservoir using a latched-readout scheme. Our approach involves pulsing a barrier gate to dynamically control qubit-to-reservoir tunnel rates, a method that is readily applicable to the latched readout of various spin-based qubits. We use this method to enable qubit state latching and to reduce the qubit reset time in measurements of coherent Larmor oscillations of a Si/SiGe quantum dot hybrid qubit.

In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms.

We unravel the Rodeo Algorithm to determine the eigenstates and eigenvalues spectrum for a general Hamiltonian considering arbitrary initial states. By presenting a novel methodology, we detail the original method and show how to define all properties without having prior knowledge regarding the eigenstates. To this end, we exploit Pennylane and Qiskit platforms resources to analyze scenarios where the Hamiltonians are described by the Zeeman model for one and two spins. We also introduce strategies and techniques to improve the algorithm's performance by adjusting its intrinsic parameters and reducing the fluctuations inherent to data distribution. First, we explore the dynamics of a single qubit on Xanadu simulators to set the parameters that optimize the method performance and select the best strategies to execute the algorithm. On the sequence, we extend the methodology for bipartite systems to discuss how the algorithm works when degeneracy and entanglement are taken into account. Finally, we compare the predictions with the results obtained on a real superconducting device provided by the IBM Q Experience program, establishing the conditions to increase the protocol efficiency for multi-qubit systems.

Economists are often interested in the mechanisms by which a treatment affects an outcome. We develop tests for the "sharp null of full mediation" that a treatment $D$ affects an outcome $Y$ only through a particular mechanism (or set of mechanisms) $M$. Our approach exploits connections between mediation analysis and the econometric literature on testing instrument validity. We also provide tools for quantifying the magnitude of alternative mechanisms when the sharp null is rejected: we derive sharp lower bounds on the fraction of individuals whose outcome is affected by the treatment despite having the same value of $M$ under both treatments (``always-takers''), as well as sharp bounds on the average effect of the treatment for such always-takers. An advantage of our approach relative to existing tools for mediation analysis is that it does not require stringent assumptions about how $M$ is assigned. We illustrate our methodology in two empirical applications.

In this paper we introduce a new type of preferential attachment network, the growth of which is based on the eigenvalue centrality. In this network, the agents attach most probably to the nodes with larger eigenvalue centrality which represents that the agent has stronger connections. A new network is presented, namely a dandelion network, which shares some properties of star-like structure and also a hierarchical network. We show that this network, having hub-and-spoke topology is not generally scale free, and shows essential differences with respect to the Barab{á}si-Albert preferential attachment model. Most importantly, there is a super hub agent in the system (identified by a pronounced peak in the spectrum), and the other agents are classified in terms of the distance to this super-hub. We explore a plenty of statistical centralities like the nodes degree, the betweenness and the eigenvalue centrality, along with various measures of structure like the community and hierarchical structures, and the clustering coefficient. Global measures like the shortest path statistics and the self-similarity are also examined.

In this paper, we consider singular systems of linear forms over global function fields of class number one and give an upper bound for the Hausdorff dimension of the set of singular systems of linear forms by constructing an appropriate Margulis height function on the space of lattices over global function fields.

Unimolecular current rectifiers are fundamental building blocks in organic electronics. Rectifying behavior has been identified in numerous organic systems due to electron-hole asymmetries of orbital levels interfaced by a metal electrode. As a consequence, the rectifying ratio (RR) determining the diode efficiency remains fixed for a chosen molecule-metal interface. Here, we present a mechanically tunable molecular diode exhibiting an exceptionally large rectification ratio (>10^5) and reversible direction. The molecular system comprises a 7-armchair graphene nanoribbon (GNR) doped with a single unit of substitutional diboron within its structure, synthesized with atomic precision on a gold substrate by on-surface synthesis. The diboron unit creates half-populated in-gap bound states and splits the GNR frontier bands into two segments, localizing the bound state in a double barrier configuration. By suspending these GNRs freely between the tip of a low-temperature scanning tunneling microscope and the substrate, we demonstrate unipolar hole transport through the boron in-gap state's resonance. Strong current rectification is observed, associated with the varying widths of the two barriers, which can be tuned by altering the distance between tip and substrate. This study introduces an innovative approach for the precise manipulation of molecular electronic functionalities, opening new avenues for advanced applications in organic electronics.

Using the weak convergence approach, we prove the large deviation principle (LDP) for solutions to quasilinear stochastic evolution equations with small Gaussian noise in the critical variational setting, a recently developed general variational framework. No additional assumptions are made apart from those required for well-posedness. In particular, no monotonicity is required, nor a compact embedding in the Gelfand triple. Moreover, we allow for flexible growth of the diffusion coefficient, including gradient noise. This leads to numerous applications for which the LDP was not established yet, in particular equations on unbounded domains with gradient noise. Since our framework includes the 2D Navier-Stokes and Boussinesq equations with gradient noise and unbounded domains, our results resolve an open problem that has remained unsolved for over 15 years.

Dark matter (DM) with masses of order an electronvolt or below can have a non-zero coupling to electromagnetism while being compatible with cosmological observations. In these models, the ambient DM behaves as a new classical source in Maxwell's equations, which can excite potentially detectable electromagnetic (EM) fields in the laboratory. We propose a new integrated photonics-based approach to search for DM candidates in the 0.1 - few eV mass range. This approach offers a wide range of wavelength-scale devices like resonators and waveguides that are readily fabricated in large quantities, enabling a scalable and novel search. In particular, we demonstrate that refractive index-modulated resonators, such as etched/grooved microrings, or patterned slabs, support EM modes with efficient coupling to DM. When excited by DM, these modes are read out by coupling the resonators to a waveguide that terminates on a micron-scale-sized single photon detector, such as a single pixel of a low-noise charge-coupled device or a superconducting nanowire. We then estimate the sensitivity of this experimental concept in the context of axion-like particle and dark photon models of DM, demonstrating that nanophotonic confinement and scalability can extend dark matter sensitivity into previously unexplored parameter space.

By using the Three-lines theorem for a certain analytic function defined in terms of the trace and a duality argument method, we prove Audenaert-Kittaneh's conjecture related to $p$-Schatten classes. This generalizes the main result obtained by McCarthy in [Israel J. Math. 5 (1967)].