The classical simulation of quantum algorithms is a crucial tool for circuit development, testing, and validation. Although acceleration using GPUs significantly reduces simulation time, most high-performance simulators rely on vendor-specific frameworks that target data-center hardware. To broaden access to quantum simulation, this work proposes a vendor-agnostic approach targeting the integrated GPUs commonly found in consumer-grade laptops. A primary challenge in state-vector simulation is its inherently poor spatial locality, which creates a memory bandwidth bottleneck. Consequently, baseline implementations experience a severe degradation in relative GPU speedup as the number of simulated qubits increases. To address this limitation, we introduce a state partitioning optimization that reorganizes the quantum state vector to maximize the last-level cache locality and minimize costly main memory fetches. We evaluate this strategy using a Quantum Phase Estimation algorithm across diverse architectures from Intel, AMD, and Apple. The experimental results demonstrate that the proposed optimization successfully mitigates performance degradation at larger qubit scales. In particular, for a 28-qubit simulation, the optimization reversed a performance deficit on an Intel Core i5, improving the GPU speedup over the CPU from 0.95x to 1.89x, and increased the Apple M1 Pro speedup from 3.71x to 5.88x. Overall, this approach yields consistent execution time improvements, demonstrating the viability of integrated GPUs for efficient quantum circuit simulation.

Detecting memory corruption vulnerabilities in stripped binaries requires recovering object semantics, interprocedural propagation, and feasible triggers from low-level, lossy representations. Recent LLM-based approaches improve code understanding, but reliable detection still requires grounding in memory-relevant semantics and runtime feasibility evidence. We present Veritas, a semantically grounded framework for binary memory corruption vulnerability detection. Veritas combines a static slicer over RetDec-lifted LLVM IR, a dual-view LLM detector that reasons step by step over grounded flows using decompiled C and selective LLVM IR, and a multi-agent validator that checks hypotheses against debugger-visible artifacts and runtime evidence. The slicer reconstructs value-flow relations from LLVM-IR facts, including def-use, calls, returns, globals, and pointer operations, and emits compact witness-backed flow objects. The detector uses these artifacts to reason about control flow, bounds, and object correspondence without rediscovering whole-binary propagation. The validator confirms or rejects candidates through guided debugging, breakpoint inspection, and memory-checking oracles. We implement Veritas as a modular pipeline and evaluate it on a curated benchmark of real-world binary vulnerability cases. Veritas achieves 90\% recall. For false-positive assessment, we exhaustively validate and manually verify 623 detector candidates and audit additional candidates from larger cases. The exhaustive subset produces no false positives, while the additional audit identifies two confirmed false positives. In a real-world application, Veritas discovered a previously unknown Apple vulnerability that was confirmed and assigned a CVE. These results support semantic grounding as an operational design principle for practical binary vulnerability detection.

We use the Heegaard Floer homology cobordism maps to obstruct the existence of a symplectic structure on the Akbulut-Kirby Mazur manifolds whose boundary is a Brieskorn sphere $Y$ among $Σ(2,3,13),$ $Σ(2,5,7)$ and $Σ(3,4,5)$. Furthermore, we describe how our results imply the existence of exotic pairs of simply connected 4-manifolds, with definite intersection form, whose boundary is $Y$.

Linear-constraint loops are programs whose transition relation is specified by a system of linear inequalities. The termination problem asks, given a loop, whether it admits an infinite computation. Decidability of termination remains open for linear-constraint loops over integers, rationals, and reals. We focus on loops over integers and show that they are tightly connected to generalized Collatz sequences - integer sequences generated by maps that are linear on each residue class modulo a fixed natural number. We prove that termination of one-variable linear-constraint loops is decidable in polynomial time, provided a long-standing conjecture about generalized Collatz sequences holds. Conversely, we show that any decision procedure for one-variable loops would prove or refute specific instances of this conjecture, which remain open. Moreover, we show that if a one-variable loop has a cyclic trace, then it also has a cyclic trace of length at most two.

News media coverage of monetary policy is not a passive transcript of central-bank communication: it filters announcements, macroeconomic news, and editorial choices into narratives that move expectations and policy decisions. We embed media sentiment into a behavioral New-Keynesian model in which the central bank reacts to sentiment and sentiment follows an explicit law of motion. We construct monetary-policy sentiment indicators from more than 50,000 Canadian newspaper articles using dictionary methods, transformer models, and a generative-AI framework. Media sentiment shifts household inflation and wage expectations, improves out-of-sample forecasts of GDP growth and inflation, and loads positively on the Bank of Canada's estimated Taylor rule once treated as endogenous. A Bayesian SVAR identifies anticipated and unanticipated monetary-policy shocks together with a narrative shock; the narrative shock contributes a non-trivial share of medium-horizon macroeconomic variance, and a counterfactual that shuts down the dynamic feedback from media sentiment attenuates the propagation of monetary policy to output and prices. %The results suggest that media narratives are an integral part of monetary-policy transmission, not merely an additional source of information.

This paper investigates the inverse problem of determining a general Signorini obstacle using boundary measurements. We demonstrate that both the shape of the obstacle and the obstacle function can be uniquely determined from solution measurements taken on an arbitrary open subset of the boundary. This result applies to both the scalar and elasticity versions of the Signorini problem.

How much energy does a quantum computer consume? Are they more efficient than their classical counterparts? In this work, we make a step towards answering these questions. We define the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time. We analyze the most representative physical platforms currently envisioned to be used as building blocks of quantum computers: superconducting qubits, silicon spin qubits, trapped ions, neutral atoms and photonic qubits. Including insights from experts in all these technologies and taking into account algorithm compilation constraints, we discuss the advantages and inconveniences of each platform from an energy standpoint. Beyond providing concrete values of the energy consumption of current quantum computers, we lay the foundation of a framework to benchmark the energy efficiency of any future quantum computing architecture.

Nondestructive detection of single-electron motion is crucial for quantum information processing with electrons trapped in Paul traps. The standard approach in Penning traps is to detect the image current induced on the trap electrodes by the electron's oscillatory motion. However, applying this approach in Paul traps for single electrons is currently hindered by motional frequency fluctuations arising from trap anharmonicities and instabilities in the rf trapping field. In this work, we propose a robust detection scheme exploiting the transient dynamics of parametric driving to overcome these limitations. Distinct from traditional steady-state approaches, our method focuses on the transient regime to break the temporal constraints imposed by steady-state assumptions, thereby enabling fast readout. We show that a controlled ramp of the parametric drive effectively locks the frequency of the electron motion in the transient regime, rendering the signal highly resilient to realistic experimental noise and inherent micromotion. This work paves the way for the experimental realization of nondestructive detection of single-electron motion in Paul traps.

Data-dependent secondary transforms, which aim to decorrelate coefficients of a separable primary transform, can improve residual coding efficiency; however, their deployment is often constrained by computational complexity. Recent video codecs use variants of the low-frequency non-separable transform (LFNST), which discards some high-frequency secondary transform coefficients, limiting achievable coding gains. Moreover, existing data-dependent secondary transforms lack explicit rate-distortion (RD) optimal design criteria. In this work, we propose a framework for designing low-complexity data-dependent secondary transforms, termed Fast Sparsifying Secondary Transforms (FaSSTs). Our approach approximates data-driven sparse orthonormal transforms (SOTs) by factorizing them into a sequence of Givens rotations. The rotations are efficiently determined using an alternating minimization strategy combined with an approximate Givens factorization procedure. Our method adapts the number of rotations based on the prediction mode, further reducing computational complexity. We design mode-dependent secondary transforms for intra-prediction residuals in AV2 using FaSST. Experimental results show that mode-adaptive FaSST matches the RD performance of LFNST while reducing the number of computations by 83.67%. Moreover, by avoiding fixed-coefficient truncation, FaSST achieves up to 1.80% BD-rate savings relative to LFNST while operating at 66.24% lower complexity.

Python's native serialization protocol, pickle, is a powerful but insecure format for transferring untrusted data. It is frequently used, especially for saving machine learning models, despite known security challenges. While developers sometimes mitigate this risk by restricting imports during unpickling or using static and dynamic analysis tools, these approaches are error-prone and depend heavily on accurate interpretations of the Pickle Virtual Machine (PVM) opcodes. Discrepancies across Python's three native PVM modules can lead to incorrect detection of malicious payloads and undermine existing defenses. To efficiently and scalably identify discrepancies, we present PickleFuzzer, a custom generation-based fuzzer that identifies inconsistencies across pickle implementations. PickleFuzzer generates pickle objects, passes them to each implementation, and detects differences in thrown exceptions or changes to key internal states. It generates pickle objects using a grammar, which we developed to account for the missing pickle specification. It determines discrepancies by comparing the execution behaviors of each test implementation, rather than requiring a specification-derived oracle. PickleFuzzer detected 14 new discrepancies between the pickle implementations. Four discrepancies are critical and can be used to bypass security-critical scanning tools like those deployed on the popular model hosting platform, Hugging Face. We disclosed all findings to the Python Software Foundation for remediation, and additionally disclosed the security issues to a bug bounty platform and were awarded a $750 bounty. We demonstrate that differential testing is a viable approach for identifying security-relevant discrepancies in important pickle implementations, and our work can lead to promising future directions for finding deeper pickle bugs with more directed fuzzing.

Cubical type theories are designed around an abstract unit interval from which types of paths, used to represent equalities, are defined. Varying the operations available on this interval yields different type theories. A reversal is an involutive operator on the interval that swaps its two endpoints. We show that for cubical type theories with self-dual interval theories, such as the minimal theory of two endpoints or the theory of a bounded distributive lattice, the extension of the theory with a reversal that internalizes the duality is a conservative extension. The key tool is a "twist construction": the product of an interval and its dual is again an interval with a reversal given by swapping coordinates. Our conservativity result applies to "opaque" cubical type theories, without strict equations reducing the filling operator at concrete type formers or eliminators from higher inductive types at path constructors. Using the same twist construction, we also construct models of strict cubical type theory with reversals in categories of cubical sets without reversals. We thereby give the first model of a theory with reversals whose homotopy theory corresponds to that of topological spaces.

Tomographic redshift distributions from photometric data are crucial ingredients in cosmic shear analysis, since they are required for the theoretical calculation of the signal based on the redshift distribution of the galaxies where the shear field is sampled. In this paper, we develop as a proof of concept Leistedt et al.'s template-based Bayesian Hierarchical Model framework into an application to weak lensing data by sampling the redshift distributions of the galaxies in the KiDS+VIKING-450 survey. We also use a principal component analysis to provide a set of representative templates drawn from a large superset. For computational tractability, subsets of $10^5$ galaxies are chosen to determine the redshift distributions, and we test the sensitivity of the cosmological inference to the subset chosen, finding it to be subdominant compared to the statistical error. We marginalise over the inferred redshift distributions and find that the Bayesian method increases the clustering parameter compared with previous studies, alleviating the $S_8$ tension with Planck, where $S_{8}\equivσ_{8}\sqrt{Ω_{\tm{m}}/0.3}=0.756\pm 0.039$, assuming flat $Λ$CDM. The tension with Planck for this survey is reduced from $2.3σ$ to $1.9σ$. We also infer a value for the matter density, $Ω_{\tm{m}}=0.31\pm 0.10$.

Quantifying quantum resources for simulating the fundamental forces of Nature is sensitive to the mapping of gauge fields onto finite quantum computational architectures. When locally truncating lattice gauge theories in the irreducible representation basis, it has been proposed to further deform the theory via quantum groups. The purpose of this deformation is (1) to provide an infinite tower of finite-dimensional ($d = k+1$) groups systematically approximating the infinite-dimensional gauge links and (2) to restore the physical unitarity of a plaquette operator diagonalization procedure analytically derived from the field continuum by recontracting vertex pairs. For the SU(2)$_k$ Yang-Mills pure-gauge theory, we provide a constructive strategy of gauge-variant completions to extend this unitarity to the entire computational Hilbert space, leading to well-defined time evolution unitaries as targets for optimized circuit synthesis. Leveraging basic circuit decompositions and symmetries of the diagonalized plaquette operator, we report resource upper-bounds on the generalized-controlled-X two-qudit gates for arbitrary local truncation $d$, reducing estimates and scaling relative to the non-deformed theory by three polynomial powers from $O(d^8)$ to $O(d^5)$. Examining the stronger q-deformed gauge constraint, which softens the total flux at vertices, we show that the physical Hilbert space dimension of the deformed plaquette operator scales equivalently to its non-deformed counterpart with a constant factor $0.2563(5)$. Thus, despite affecting interactions at all scales as exemplified by the observed flux hierarchy inversion symmetry, q-deformation continues to pass scrutiny as a reliable truncation offering advantages in quantum circuit synthesis.

In this work we revisit classical systems of integers inside the real normed division algebras from the point of view of finite norm shells and root systems. Building on the icosian framework of Moody--Patera and on the integral root-system viewpoint of Chen--Moody--Patera and of Johnson, we isolate the precise axiomatic ingredients of the non-crystallographic analogue: an order over the golden ring \(\Zphi\) together with a distinguished finite root shell whose Cartan coefficients lie in \(\Zphi\). We show that the usual Gaussian, Eisenstein, Hamilton, Hurwitz and Coxeter--Dickson examples are recovered by separating the order, its units, and its distinguished finite shells; once the lattice requirement is replaced by a finite root-shell requirement, the golden integer ring becomes the natural coefficient ring for the non-crystallographic cases \(H_2\) and \(H_4\). We then construct a weak golden octonion order by Cayley--Dickson doubling of the icosian ring; the resulting free rank-\(8\) \(\Zphi\)-order has a \(240\)-element finite shell of type \(H_4\oplus H_4\) and its multiplication is genuinely octonionic. Finally, we prove (i) that this weak double is self-dual with respect to the polar norm pairing, hence has no strict norm-integral overorder, and (ii) that the first trace-integral discriminant tower over it contains no octonion-stable nonzero isotropic gluing.

The Guarded Fragment (GF) is a well-established decidable fragment of first-order logic. We study an extension of GF with nested equivalence relations, namely a family of distinguished binary predicates $E_1, E_2, \dots$ interpreted as equivalence relations such that $E_{k+1}$ is coarser than $E_k$ for every $k$. We show that the equality-free GF with nested equivalence relations enjoys the finite model property and has a decidable satisfiability problem. Moreover, we establish tight complexity bounds for satisfiability: TOWER-completeness in general, and $(K{+}2)$-ExpTime-completeness when the number of distinguished predicates is fixed to $K$. Finally, we show that satisfiability becomes undecidable if either the nesting condition is dropped (already with two equivalence relations) or equality is admitted (already with a single equivalence relation).

This paper is focused on necessary conditions for hypoellipticity of an operator $L$ of the form $L=L_1(x)+g(x)L_2(y)$, where the operator $L_1$ is either elliptic or parabolic, $L_2$ is degenerately elliptic and $g(x)$ may itself vanish adding further degeneracy. First, we establish a logarithmic criterion: if the operator $L$ above is hypoelliptic and $L_1$ has a family of spectral solutions we define in the paper, then the remaining part $L_2$ must gain a power of a logarithm of a derivative. Such a property can be thought of as a restriction on degeneracy of the operator $L_2$. We then use this criterion to examine degenerate elliptic and parabolic operators closing gaps between sufficiency and necessity that have been open since 1980s in three and higher dimensions.

The dependence of the stellar initial mass function (IMF) on star-formation environment, particularly at low metallicities and high redshifts, remains poorly constrained. Ultra-faint dwarf galaxies (UFDs) are local fossils of high-redshift galaxies hosting old, metal-poor populations, and their resolved stellar populations provide unique pathways to constrain the sub-solar IMF. We investigate the low-mass IMF in the Boötes I (Boo I) UFD with JWST/NIRCam, leveraging its capability to resolve over 10,000 stars reaching $\lesssim0.15 M_{\odot}$, obtaining one of the largest, deepest resolved stellar samples for UFDs. We explore three different functional forms of the IMF with machine learning and statistical techniques, combining forward modeling of synthetic color-magnitude diagrams with simulation-based inference. We find that a single power-law IMF fails to reproduce the observed luminosity function and also deviates from the canonical Salpeter IMF. Our best-fit broken power-law and lognormal IMF parameters are consistent with the Milky Way within 68% confidence level, providing evidence that star formation at metallicities as low as [Fe/H]$\approx-2.4$ follows a similar IMF as in the Milky Way. By treating Boo I as a local relic analogous to a high-redshift galaxy with a stellar mass of $\lesssim10^5 M_{\odot}$ at $z\gtrsim6$, our results provide evidence for the universality of the IMF across both local and high-redshift environments.

During near-Sun crossings of the heliospheric current sheet (HCS), Parker Solar Probe (PSP) observed populations of high-energy protons and heavier ions indicating possible energization by magnetic reconnection up to 10s -- 100s keV nucleon$^{-1}$. Here we study ion acceleration by magnetic reconnection at the HCS. To estimate ion energization, we solve the Parker transport equation coupled to a large-scale 2D MHD reconnection simulation. We find that multiple ion species develop power-law distributions with both spectral index and high-energy cutoff $E_{\text{max}}$ consistent with in-situ data. By accounting for the injection physics determined by kinetic simulations, we confirm that the charge-to-mass ratio scales as $E_{\text{max}} \propto (Q/M)^α$ with $α\sim 0.8-1.1$, approximately consistent with PSP measurements in the broader range $α\sim 0.6-1.7$. In the limit where ions are injected at the same energy per nucleon, $α$ can be as low as $\sim 0.3$. These findings further support the role of magnetic reconnection in producing high-energy heavy ions at the HCS.

In 2000, Vu proved that the number of solutions of $x_1^k + \cdots + x_s^k = N$ in an arbitrary box satisfies the expected Hardy--Littlewood upper bound with a power-saving error term, for $s \geq O(8^k k^3)$. We show that one may take $s\geq k^2 - k + O(\sqrt{k})$.

In graph bootstrap percolation, edges of an Erdős-Rényi random graph ${\mathcal G}_{n,p}$ are initially active. Activation spreads to other edges of the complete graph $K_n$ by an iterative process governed by a fixed graph $H$, whereby an edge becomes active whenever it is the only inactive edge in a copy of $H$. If all edges of $K_n$ are eventually activated, we say the process $H$-percolates. The case $H=K_3$ corresponds to the classical sharp threshold for connectivity in ${\mathcal G}_{n,p}$. When $H=K_4$, there are close connections with $2$-neighbor bootstrap percolation from statistical physics. Varying $H$ produces a wide range of behaviors. In this work, for every graph $H$, we locate the critical $H$-percolation threshold $p_c(n,H)$, answering a question of Balogh, Bollobás, and Morris. Our general methods recover and improve several previous results. The location of $p_c(n,H)$ is related to a critical limiting density $ρ(H)$ of graphs that most efficiently activate a given edge. Introducing the parameter $ρ(H)$ raises several questions. For instance, it remains open whether $ρ(H)$ is computable in general, and its expression appears to indicate when the $H$-percolation threshold is sharp.

Actively tunable nanophotonic platforms that control light-matter interactions enable reconfigurable optical systems and programmable photonic integrated circuits. Hydrostatic pressure provides a noninvasive and material-agnostic mechanism for modulating the refractive index and resonance conditions without introducing free carriers or structural damage. Here, we demonstrate multiple pressure-dependent functionalities in silicon nitride nanostructures, including resonance tuning, refractive index modulation, and polarization state conversion. Applying a pressure of up to 5 GPa, we observe a Fabry-Pérot resonance shift of up to 30 nm and a relative refractive index decrease of up to 4%. Based on the results, we design and examine, to the best of our knowledge, the first extreme-pressure-tunable, polarization-converting metasurface, which tunes the ellipticity and orientation angle of the output light. These findings establish pressure-controllable silicon nitride as a viable platform for reconfigurable photonics and extreme-environment nanophotonic systems, including deep-ocean exploration, planetary interiors, and space applications.

As AI systems take on greater autonomy, a quiet anxiety has settled over the HCI community: human agency is eroding. Users no longer control execution, interfaces recede, and machines decide. We argue that this anxiety, while understandable, reflects a framing problem rather than an empirical finding. Agency has not diminished but has relocated. As interaction has shifted from command- and feature-based paradigms toward conversational, generative, and agentic AI, human agency migrates from interface affordances to interaction itself: articulating goals, evaluating outputs, and negotiating outcomes. To make this relocation visible, we revisit control as a diagnostic lens, distinguish process control and outcome control, and map different systems across this space to show that what looks like agency's disappearance is actually its redistribution. We take seriously the objection that outcome-based agency may be illusory in systems that produce plausible but unverifiable outputs, and argue that this concern reveals what agency in human-AI interaction truly requires. This paper invites the CUI community to reconsider what agency means, where it lives, and what it demands, including who gets to have it and who holds responsibility when it fails, before the consequences become impossible to overlook.

We study the distribution of the order of a random permutation of $[n]$ through the lens of Rényi entropy. In particular, we obtain an asymptotic for the Rényi $q$-entropy of the order in the full range $1 \leq q \leq \infty$. For $q > 1$, our results are quantitatively optimal and reveal a tight connection between the asymptotic behaviour of the Rényi $q$-entropy and arithmetic properties of $n$. Of particular interest are the cases $q = \infty$ and $q = 2$, which correspond to the maximum probability of achieving a particular order and the probability that two independent random permutations have equal orders, respectively. In the former case, we show that the probability in question is asymptotic to $1/n$ and additionally characterise the maximiser for sufficiently large $n$. In the latter case, we determine a minimal and maximal order for the probability as a function of $n$, of respective forms $c/n^2$ and $\log^*n/n^2$. Our results provide an essentially complete answer to a set of questions raised by Acan, Burnette, Eberhard, Schmutz and Thomas, some of which go back to work of Erdős and Turán from the 1960s.

We derive a graded character formula for the action of any finite group on the Artinian reduction of the Stanley--Reisner ring of any complete simplicial fan, which is given by an equivariant version of the classical h-polynomial. This gives the graded character formula for the representation of the group on the cohomology of the associated toric variety when the fan is rational. As an application, we use a navel tool, which we called hybrid fan, to compute the Poincaré polynomial of the invariants of the Artinian reduction of the Stanley--Reisner ring of any complete simplicial fan under a finite reflection group action. This implies that the Poincaré polynomial of the quotient of a compact toric orbifold by any finite reflection group is equal to the Poincaré polynomial of the compact toric orbifold associated to the hybrid fan.

We study dynamical scaling in the quantum-critical fan of the pseudogap-metal to Fermi-liquid transition of the two-dimensional Hubbard model. Using a four-patch dynamical cluster approximation with the numerical renormalization group as a cluster impurity solver, we access real-frequency dynamics over several decades at arbitrary temperatures. Close to the critical doping, the local spin and cluster-current susceptibility spectra exhibit $x=ω/T$ scaling of the form $χ''(ω,T)\sim \tanh(x/2)$, and the cluster contribution to the optical conductivity obeys $Tσ'_{\mathrm{cl}}(ω,T) \sim \tanh(x/2)/x$, implying a $1/T$ cluster dc conductivity. In the scaling regime, the vertex contribution to the cluster optical response is much larger than the bubble contribution. We further find evidence for a marginal-Fermi-liquid nodal self-energy. This, together with the $1/T$ vertex contribution to the conductivity, implies strange-metal optical transport in the quantum critical region. Our results describe several qualitative aspects of several experimental observations.

We study the normal-state, doping-driven phase diagram of the square-lattice Hubbard model using the dynamical cluster approximation combined with the numerical renormalization group as a cluster solver, which gives direct access to real-frequency dynamics at essentially zero temperature. In a parameter regime relevant for cuprates, $U=7t$ and $t'=-0.3t$, we find a critical doping $p^{\ast}$ that marks a continuous quantum phase transition between a pseudogap metal and a normal Fermi liquid. The transition is identified by a continuous collapse, from both sides, of the Fermi-liquid scale extracted from charge, spin, and $d_{x^2-y^2}$-wave pairing susceptibilities. This collapse produces a non-Fermi-liquid regime at intermediate energy scales, which appears to extend to arbitrarily low scales at $p^{\ast}$. As $p^{\ast}$ is crossed from the normal Fermi liquid at $p>p^{\ast}$ into the pseudogap metal at $p<p^{\ast}$, the coherent low-energy spectral weight in the antinodal region is lost and replaced by a narrow, metallic pseudogap, while the nodal region evolves smoothly and remains comparatively coherent. This gives rise to Fermi arcs in the pseudogap metal at $p<p^{\ast}$, since the zero-frequency spectral weight remains large in the nodal region but is strongly suppressed in the antinodal region.

We investigate numerically the time evolution of velocity and magnetic field fluctuations in a coronal loop, focusing on the dynamics due to both phase mixing and turbulent cascade. The intensity, doppler velocity and non-thermal broadening are synthesized from numerical results in order to establish if the upcoming Multi-slit Solar Explorer (MUSE) mission could reveal the presence of those phenomena in the solar corona through its unprecedented high-resolution spectroscopic observations. The loop is represented by a cylindrical pressure-balanced magnetic structure with a transverse density and magnetic field inhomogeneity. The initial perturbation is a superposition of a torsional Alfvén wave and a transverse turbulent component with different tunable weights. In order to reconstruct plasma emission features we calculate moments of the Fe IX 171 Å spectral line. 2D maps obtained by integrating the emission along the assumed line of sight are calculated for the emission intensity $I_0$, the Doppler shift $I_1$ and the non-thermal broadening $I_2$, for several values of the model parameters. Finally, we simulate MUSE spectrograph by considering a resolution of $312$ km $\times$ $312$ km. We observe how intensity maps show the formation of longitudinal threads. The generation of small-scale fluctuations mainly takes place in the inhomogeneity region at the loop boundary, where the effects of phase mixing and non-thermal broadening are stronger. 1D power spectra of intensity and Doppler shift maps are calculated and compared with the corresponding spectra of density and line-of-sight velocity component. The agreement observed between the spectral indexes of the intensity power spectra at MUSE resolution and the one computed from the full 3D density field indicates that spectra of $I_0$ can be used to infer information on the spectrum of density inside a loop.

While companies increasingly rely on data, especially when it comes to targeted advertising, adapting content to users, selling data and training machine learning models, the collection of data raises privacy concerns. One way of collecting data is by using HTTP cookies when interacting with a website. Legal regulations require service providers to collect consent for some forms of cookie collection, which is often acquired through \emph{cookie consent banners}, but their effectiveness has been debated. This study aims to understand what influences users' experience and behaviour when managing their cookie consent, by investigating the gap between their stated privacy preferences and their actual actions. A mixed methods approach was used, collecting data from a usability test and a survey (N=20). The results showed that although participants generally want to reject cookie collection, they often end up accepting because of deceptive patterns in the cookie consent banner design. It also showed that they were more willing to consent to websites they trusted and if they expected it would improve their user experience. Although the current EU legislation states that withdrawing consent must be as easy as giving it, withdrawing consent took on average more than 20 times longer than giving it. This suggests that cookie consent banners in their current form are not ideal with respect to user autonomy, often leading users to \emph{consent by design}.

The class of quasi-Polish spaces admits several equivalent representations, including UF spaces, NP spaces, $\mathbfΠ_2^0$ subspaces of $\mathcal{P}(\mathbb{N})$, and sober spaces of countably presented frames. In this paper, we formalize these structures within second-order arithmetic and conduct a systematic reverse mathematical analysis of the transitions between them.

Online video games have become major online social spaces where users interact, compete, and create together. These spaces, however, expose users to a wide spectrum of online harms, including harassment, discrimination, inappropriate content, privacy breach, cheating, and more. The shape and severity of such harms vary across game design, mechanics, and community context. To mitigate these harms, game companies issue Codes of Conduct (CoCs) that articulate online safety rules and direct players to safety resources. However, it remains unclear how prevalent CoCs are, what safety, security and privacy violations they govern, and whether they meet growing regulatory and industry expectations. We develop and leverage CONDUCTIFY, a pipeline for identifying and analyzing CoCs at scale. Applied to Steam, the largest PC game marketplace, it located the available CoCs for 350 of the 9,586 multiplayer titles on Steam. We found that CoCs are more available among popular, adult-oriented, and community-driven games, while most multiplayer games operate without CoCs despite regulatory and industry recommendations. Although over 80% of the games with CoCs available consistently address traditional security and safety violations, their governance approaches vary substantially across types of violations. A further asymmetry emerges in specificity. Compared with harms related to gameplay mechanics, the articulations of interpersonal harm and the underage player safety are often less specific, despite their relevance to many game communities. Together, these results inform the improvement of online safety governance and CoC enforcement practices, and building better safety infrastructure for the community of players and developers.