Phase synchronization of access points (APs) in a distributed multiple-input multiple-output (D-MIMO) system is critical to leverage the performance benefits of D-MIMO. Existing over-the-air phase synchronization methods assume that APs can communicate directly to perform necessary measurements. However, this assumption might not hold in scenarios where inter-AP signaling is too weak for effective communication. To address this, in this paper, we propose a novel over-the-air calibration scheme that uses repeater nodes to facilitate phase synchronization when direct AP signaling is infeasible. We give the steps of the algorithm for phase calibration in closed form, and show how it enables coherent joint transmission (CJT) by the APs. The framework expands the applicability of D-MIMO systems to challenging environments, where existing over-the-air synchronization techniques fall short.

Federated learning enables collaborative training without sharing raw data, but struggles under client heterogeneity and streaming distribution shifts, where drift and novel data can impair convergence and cause forgetting. We propose a federated associative-memory framework that learns shared archetypes in heterogeneous, continual settings, where client data are independent but not necessarily balanced. Each client encodes its experience as a low-rank Hebbian operator, sent to a central server for aggregation and factorization into global archetypes. This approach preserves privacy, avoids centralized replay buffers, and is robust to small, noisy, or evolving datasets. We cast aggregation as a low-rank-plus-noise spectral inference problem, deriving theoretical thresholds for detectability and retrieval robustness. An entropy-based controller balances stability and plasticity in streaming regimes. Experiments with heterogeneous clients, drift, and novelty show improved global archetype reconstruction and associative retrieval, supporting the spectral view of federated consolidation.

A fundamental problem in quantum coding theory is to determine the maximum size of quantum codes of given block length and distance. A recent work introduced bounds based on semidefinite programming, strengthening the well-known quantum linear programming bounds. However, floating-point inaccuracies prevent the extraction of rigorous non-existence proofs from the numerical methods. Here, we address this by providing rational infeasibility certificates for a range of quantum codes. Using a clustered low-rank solver with heuristic rounding to algebraic expressions, we can improve upon $18$ upper bounds on the maximum size of $n$-qubit codes with $6 \leq n \leq 19$. Our work highlights the practicality and scalability of semidefinite programming for quantum coding bounds.

We derive upper and lower bounds on the determinant of an exponential matrix. They can be transformed into corresponding bounds for the determinant of a univariate Gaussian matrix.

In nuclear matter, for interparticle separations larger than the healing distance (a characteristic long-distance scale of finite-density fermionic systems), the in-medium two-body wave function is essentially a free wave function. In terms of the renormalization group (RG), this implies that the running of the effective field theory (EFT) couplings freezes for r-space cutoffs above this distance (or p-space cutoffs below the corresponding healing momentum scale). As a consequence the leading order EFT description of nuclear matter (understood here as the infrared limit of the RG) corresponds to the mean-field approximation and a set of tree-level (i.e. perturbative) leading contact-range couplings. Though the contacts do in principle inherit the power counting they had in the vacuum, their iteration is suppressed in the infrared, explaining why they become perturbative in nuclear matter. In addition to the contact-range potential, RG evolution requires the inclusion of density-dependent terms in the equation of state that can be represented by a pseudo-potential, which (unlike the genuine contacts) should not be iterated. The LO EFT description ends up being a subset of Skyrme forces previously identified by the Orsay group.

Recent progress in experimental facilities, together with larger data samples and more refined analysis strategies has enabled the observation of many exotic hadronic states, adding new members to the hadron spectrum. Each newly reported signal encourages further experimental searches and simultaneously motivates theoretical studies aimed at uncovering additional nonconventional states. Motivated by this perspective and by the increasing interest in systems containing multiple heavy quarks, we present a spectroscopic study of fully heavy pentaquark candidates with spin-parity quantum numbers $J^{P}=\frac{1}{2}^{-}$ and quark contents $QQQ'Q\bar{Q'}$, $QQQ'Q'\bar{Q}$, and $Q'Q'QQ\bar{Q}$, where $Q(Q')$ represents either $c(b)$ or $b(c)$ quarks. We employ the QCD sum rule approach with three different types of interpolating currents to obtain the corresponding masses and current coupling constants of the considered states. The following masses for the states containing three $c$ and two $b$ quarks are predicted: $m_{(3c2b)}=14479.30\pm75.06~\mathrm{MeV}$ using the current $J_1$, $\tilde{m}_{(3c2b)}=14276.80\pm76.29~\mathrm{MeV}$ using $J_2$, and $\bar{m}_{(3c2b)}=14276.80\pm76.29~\mathrm{MeV}$ using $J_3$. The corresponding predictions for the states containing three $b$ and two $c$ quarks are as $m_{(3b2c)}=17458.90\pm130.11~\mathrm{MeV}$ with $J_1$, $\tilde{m}_{(3b2c)}=17202.70\pm132.37~\mathrm{MeV}$ with $J_2$, and $\bar{m}_{(3b2c)}=17250.80\pm131.98~\mathrm{MeV}$ with $J_3$, respectively. In addition, we provide the corresponding current coupling constants, which can serve as useful inputs for analyses of decay properties and interaction mechanisms of these fully heavy pentaquark candidates.

Mission-critical applications often run "forever" and process large data volumes in real time while demanding low latency. To handle the large state of these applications, modern streaming engines rely on key-value stores and store state on local storage or remotely, but accessing such state inflates latency. As today's engines tightly couple the data path with state I/O, a tuple triggers state access only when it reaches a stateful operator, placing I/O on the critical path and stalling the CPU. However, the keys used to access the state are frequently known earlier in the query plan. Building on this insight, we propose Keyed Prefetching, which decouples the data path from state access by extracting future access keys at upstream operators and proactively staging the corresponding state in memory before tuples arrive. This overlaps I/O with ongoing computation and hides the latency of large-state accesses. We pair Keyed Prefetching with Timestamp-Aware Caching, a cache-eviction policy that jointly manages previously accessed and prefetched entries to use memory efficiently. Together, these techniques reduce latency for long-running, real-time queries without sacrificing throughput.

Thermal machines are physical systems that, when fueled by input energy, perform output tasks such as heat pumping or the production of work. Their performance is characterized with several, often competing quantities, such as power, efficiency, energy waste, and resilience to environmental noise. Multi-objective optimization provides a key tool to investigate the characterization of the best thermal machines operating in the irreversible linear-response regime. Here, we derive exact analytical parameterizations for the optimal (Pareto) fronts associated with any given choice of relative weights assigned to their mean extracted power $P$, efficiency $η$, entropy production $Σ$ and the amplitude of power fluctuations $σ^2_P$. The geometry of the front of endoreversible machines is universal: two-, three-, and four-objective trade-offs follow analytical formulae that do not depend on the value of any physical parameter of the machine. We show that such universal thermodynamic Pareto fronts also set quantitative fundamental limits for the performance of non-endoreversible machines. Furthermore, we demonstrate that our results apply to existing experimental data from different physical systems also beyond the linear regime, ranging from atomic to macroscopic scales, including single-atom engines, colloidal systems, macroscopic engines and power plants.

The Higgs mechanism is essential for the success of the Standard Model (SM) and can be experimentally verified with the determination of the Higgs self-coupling. As the simplest model of a Higgs potential, the SM provides a clear prediction of the Higgs self-coupling in terms of the Higgs boson mass and the vacuum expectation value. Any deviations would indicate physics beyond the SM and help guide extended Higgs models. At large enough centre-of-mass energies, double-Higgs production provides tree-level sensitivity to the trilinear Higgs self-coupling. At 550 GeV the leading production mode in $e^+e^-$ comes from di-Higgs strahlung with a small contribution from $WW$-fusion. The most up-to-date ILD projections are extrapolated based on a full simulation analysis from 2014 by incorporating expected improvements in flavour tagging and kinematic reconstruction for event selection, and are presented in this contribution together with the ongoing re-analysis using fast SGV (Simulation a Grande Vitesse) simulations of the ILD detector concept on a full SM background including the aforementioned state-of-the-art reconstruction and analysis tools.

We aim to determine the most effective approach for estimating uncertainties in quasar photo-$z$ and to evaluate the ability of different models to reconstruct the true redshift distribution under varying data quality. We use photometric magnitudes from the Kilo-Degree Survey Data Release 5 and spectroscopically confirmed quasars from the Dark Energy Spectroscopic Instrument Data Release 1. We compare artificial neural networks (ANNs), Mixture Density Networks (MDNs), and Bayesian Neural Networks (BNNs), both latter combined with Gaussian Mixture Model (GMM) outputs. To assess robustness to observational limitations, we construct four test sets covering all combinations of sources fainter than those in the training sample and missing photometric bands. ANNs show substantial deviations in reconstructing the redshift distribution. MDNs require at least two Gaussian components to achieve accurate reconstruction, with the three-component MDN providing the best performance in this class. BNNs improve results for sources fainter than the training range, yielding a negative log-likelihood (NLL) gain of $0.11$, but reduce performance for brighter data by $0.07$ NLL. Reconstruction remains feasible for either fainter data or missing magnitudes individually; however, their combination leads to pronounced deviations. Unsupervised clustering identifies two dominant degenerate solutions at redshift pairs of $(1.2, 2.3)$ and $(1.6, 2.5)$. Accurate uncertainty modeling is essential for reliable reconstruction of the redshift distribution directly from photo-$z$. BNNs are particularly beneficial for out-of-distribution inference, although at the expense of reduced accuracy for brighter sources. Our methodology enables the identification and removal of degenerate photo-$z$ estimates unsuitable for tomographic analyses.

Planning entails identifying sequences of actions to reach a goal, yet we still have incomplete knowledge of how problem constraints, such as difficulty and available time, influence the visual strategies supporting plan construction, both in terms of coverage of the to-be-executed plans and its temporal organization. To fill this gap, we recorded participants' cursor and eye movements in a multi-target problem solving task on a grid. We manipulated two orthogonal dimensions: problem difficulty, by introducing the novel construct of misleadingness, which measures how nodes' distances on the grid diverged from their relative position along the solution, and waiting time, by allowing participants either to act immediately or wait before moving. We found that difficulty significantly affected both performance and gaze: harder problems reduced success rates, required more corrections and pauses, elicited longer pre-movement inspection that provided higher coverage of the to-be-executed plan, and more re-fixations. When participants could start immediately, they did so without fully consolidating their plan. This led to more pauses and backtracks, but also to more precise gaze-cursor alignment during execution, suggesting improved online control compensating for incomplete planning. With increased planning time, greater difficulty led participants to achieve a better temporal alignment between pre-movement visual inspection and cursor movement during execution. Overall, our results suggest that problem difficulty increases the visual coverage of the upcoming plan, whereas time availability shapes the extent of replanning during execution and determines whether gaze-path coherence emerges before movement or only during execution in difficult problems.

A-theory realizes U-duality symmetry by extending the string worldsheet to a higher dimensional brane worldvolume, in which the worldvolume and the spacetime belong to different representations of the exceptional group. The closure of the brane Virasoro algebra requires the Gauss law constraint. The Gauss law constraint promotes spacetime coordinates to gauge fields and extends the string worldsheet into the brane worldvolume. While the Virasoro constraint is used to reduce the spacetime coordinate, the Gauss law constraint is used to reduce both the worldvolume and the spacetime coordinates. As in conventional gauge theories, the treatment of the Gauss law constraint is a technically important aspect of the quantization of A-theory. We show that the string solution is only consistent solution of the Gauss law dimensional reduction condition for D=3 and 4 cases. This result implies that the physical symmetry of the theory is two-dimensional conformal symmetry, suggesting that the theory admits a string-like quantization. We further construct a string solution that is covariant under the exceptional group symmetry. The relation between this solution and the constant charge parameter appearing in the exceptional σ-model is also discussed.

In this work, I consider scalar field theory with negative quartic self-interaction, corresponding to an upside-down classical potential. Despite not possessing a classically stable ground state, such potentials are known to behave properly when treated quantum mechanically, leading to stable and unitary time evolution. Using two different saddle-point expansions for the same theory, I discuss the phase diagram in terms of bare parameters in Euclidean dimensions one to four, as well as the generalization to finite temperature. Comparing to other methods where available, I find that negative coupling field theory is a promising candidate for an interacting scalar field theory in the continuum. In particular, in four dimensions it exploits a loophole in mathematical proofs of quantum triviality, suggesting that negative coupling scalar field theory could offer a UV-complete and interacting description of the Higgs.

We propose a framework to explore the internal charge distribution of mesoscopic quasiparticles by inelastic x-ray scattering, while also accounting for the conventional scattering from electrons. Specifically, we investigate a new contribution of intrinsic and optically pumped excitons (bound electron-hole pairs) to the x-ray scattering spectrum of transition metal dichalcogenides (TMDCs). The optical excitation leads to the creation of Wannier exciton populations, adding new quasi-elastic processes beyond the conventional electronic features to the x-ray scattering spectra. Differential spectra (with and without optical pumping) can be used to isolate and identify the internal charge distribution of the optically pumped excitons in the scattering response, potentially offering insights into many-body interactions and quasi-particle dynamics in 2D systems.

We define and study a continuous version of 2-frieze patterns, a combinatorial structure closely related with frieze patterns of Coxeter and Conway. We describe the relation of continuous 2-friezes with the moduli space of projective curves and relate the (pre)symplectic structure on the space of closed 2-friezes, considered as a cluster variety, with the Adler-Gelfand-Dikii bracket on the space of 3rd order differential operators.

Cecotti and Vafa introduced the topological anti-topological fusion (tt*)-equation, whose solutions describe massive deformations of supersymmetric conformal field theories. We provide a rigorous analytic formulation of the $ADE$ classification of tt*-structures. Under natural structural assumptions, a tt*-structure over $\mathbb{C}^*$ can be described via isomonodromic deformations with upper unitriangular real Stokes matrices. Two fundamental issues arise: the ambiguities of Stokes matrices, governed by an action of a group $\tilde{Br}_n$, which is generated by reordering operations, and the solvability of the associated Riemann-Hilbert problem. Our first main result shows that the classification reduces to admissible Stokes matrices modulo $\tilde{Br}_n$-action, and that the $\tilde{Br}_n$-orbit of a Stokes matrix determines a tt*-structure over $\mathbb{C}^*$. Our second main result establishes that upper unitriangular matrices whose symmetrizations coincide with Cartan matrices of type $A_n, D_n, E_6, E_7,$ or $E_8$ give rise to tt*-structures over $\mathbb{C}^*$. This provides a direct analytic realization of the $ADE$ classification and clarifies the interplay between Stokes phenomena, $\tilde{Br}_n$-symmetry, and positivity of Cartan-type matrices.

Hybrid bosonic codes combining bosonic codes with photon states offer a promising pathway for fault-tolerant quantum computation. However, the efficient generation of such states in optical setups remains technically challenging due to the requirement for complex non-Gaussian resources. In this paper, we propose a novel scheme to efficiently generate hybrid entangled states between a GKP qubit and a photon-number state using small-amplitude cat states as the primary resource. We apply a breeding process using small-amplitude cat states to increase the non-Gaussianity of the input states. This method requires only linear optical elements and homodyne measurements. Furthermore, we demonstrate that this protocol can be extended to generate hybrid qudit states. This scheme has the potential to provide a resource-efficient and experimentally attractive route toward implementing hybrid quantum error correction.

In this paper, we consider the following fractional Schrödinger equation \begin{equation*} \left\{ \begin{array}{lcl} (-Δ)^{s}u+V(x)u=u^{{p_s}-ε}\ \ \ &\hbox{in}\ \mathbb{R}^N,\\ u>0\ \ \ &\hbox{in}\ \mathbb{R}^N, \end{array} \right. \end{equation*} where $0<s<1$, $ε>0$, $p_s=(N+2s)/(N-2s)$, $N>4s$ and $V(x)\in C^1(\mathbb{R}^N)\cap L^\infty (\mathbb{R}^N)$ is non-negative. We first use the Lyapunov-Schmidt reduction method to construct multi-peak solutions to the above equation provided that $V(x)$ possesses $k$ stable critical points. Then we prove the non-degeneracy and local uniqueness of the multi-peak solutions, for $\frac{1}{2}<s<1$, $N\geq 6s$, via the blow-up argument based on various local Pohozaev identities. Due to the nonlocal property of the fractional Laplacian, we need to make delicate analysis of the approximate solutions and establish the local Pohozaev identities for the corresponding harmonic extension instead of $u$. This approach not only requires to develop refined estimates for several integrals in the local Pohozaev identities, but also to apply Pohozaev identities through a markedly different way.

The extended Berkeley Packet Filter (eBPF) is useful for faster packet processing and network monitoring in softwarized deployments. Similarly, softwarized deployments of 5G core network services adopted eBPF to meet the stringent latency and bandwidth requirements of underlying applications. While the existing studies focused on network performance, security concerns over eBPF-enabled platforms are overlooked. In this paper, we study the vulnerability analysis of 5G core network deployments that use eBPF for packet processing and traffic monitoring. In particular, we consider the following aspects: a) tracing, b) denial-of-service (DoS), c) stealing information, and d) bash injection. We present the detailed attack scenarios with step-by-step implementation of containerized and eBPF-enabled 5G network functions using Open5GS. The experiment results show that the aforementioned vulnerabilities are present in eBPF-enabled 5G deployments and can be exploited by attackers. Finally, we present some mitigation techniques useful for addressing the vulnerabilities. The source code and implementation details are made available at https://github.com/chimms1/5G-eBPF-exploits.

We develop a framework for Matrix Product Quantum Channels (MPQCs), a one-dimensional tensor-network description of completely positive, trace-preserving maps. We focus on translation-invariant channels, generated by a single repeated tensor, that admit a local purification. We show that their purifying isometry can always be implemented by a constant-depth brickwork quantum circuit, implying that such channels generate only short-range correlations. In contrast to the unitary setting, where one-dimensional quantum cellular automata (in one-to-one correspondence with matrix product unitaries) carry a nontrivial index, we prove that all locally purified channels belong to a single phase, that is, they can be continuously deformed into one another. We then extend the framework to a broader class of translation-invariant channels capable of generating long-range entanglement and show that these remain deterministically implementable in constant depth using two rounds of measurements and feedforward.

This paper investigates the asymptotic behavior of the principal eigenvalue $λ(s)$, as $s\to+\infty$, for the following elliptic eigenvalue problem \begin{equation*}\label{E} -Δ_{M}u-s\langle \nabla_M f, \nabla_M u\rangle_g +c u=λ(s)u, \end{equation*} defined on an orientable and closed Riemannian manifold $(M,g)$. Assuming $f$ is a Morse function defined on $M$, we find that the limit $\lim\limits_{s\to+\infty} λ(s)$ is determined by the minimum value of the function $c$ over the set of the maximum points of $f$, a result that is independent of the curvature of manifold.

Understanding the complex anisotropic acoustic propagation in crystals is crucial for optimizing the performance of surface and bulk acoustic wave devices. Here, we investigate the anisotropy and coupling of GHz acoustic modes in (001)-cut gallium arsenide through theory and experiment. We first numerically calculate the angle-dependent phase velocities for surface and bulk modes, and we provide a code which can easily be adapted to different material systems. We validate our theoretical model experimentally by exciting surface modes with an interdigital transducer, and achieve omnidirectional acoustic propagation through random scattering of the acoustic waves. We measure the complex acoustic field with a scanning optical interferometer, and extract the angle-dependent velocities of surface and bulk modes using Fourier domain analysis. Our method could be used for the optimization of GHz-range classical and quantum acoustic devices, by studying losses of surface and bulk modes.

We introduce a stochastic SIR-type partial differential equation model incorporating random diffusion, reinfection, vital dynamics, and a randomly varying transmission rate. For the associated random dynamical system, we prove the existence of both random and exponential attractors. We construct a non-stationary, random disease-free global solution, which serves to localize the random attractor. Furthermore, we analyze the mean value of the random transmission coefficient to establish conditions under which the disease may either be eradicated or persist in an endemic state, depending on the system's parameters.

When the MLIR project was first introduced, it promised to address the issues that the HLS community had with the LLVM project. But is this really the case, and is MLIR the "right"/"best" compiler infrastructure for HLS? We here share our experiences based on the development of Dynamatic (github.com/EPFL-LAP/dynamatic).

Program comprehension is an essential activity in software engineering. Not only does it often challenge professionals, but it can also hinder novices from advancing their programming skills. Gaze, an emerging modality in developer tools, has so far primarily been utilized to improve our understanding of programmers' visual attention and as a means to reason about programmers' cognitive processes. There has been limited exploration of integrating gaze-based assistance into development environments to support programmers, despite the tight links between attention and gaze. We also know that joint attention is important in collaboration, further suggesting that there is value in exploring collective gaze. In this paper, we investigate the effect of visualizing gaze patterns gathered from experts to novice programmers to assist them with program comprehension in a new codebase. To this end, we present GazePrinter, designed to provide gaze-orienting visual cues informed by experts to aid novices with program comprehension. We present the results of a mixed-methods study conducted with 40 novices to study the effects of using GazePrinter for program comprehension tasks. The study included a survey, a controlled experiment, and interviews. We found that visualization of expert gaze can have a significant effect on novice programmers' behavior in terms of which path they take through the code base; with GazePrinter, novices took a path closer to the path taken by experts. We also found indications of reduced time and cognitive load among novices using GazePrinter.

Operads were originally defined by May to have right actions of the symmetric groups, but later formulations have also used no groups actions at all or group actions by such families as the braid groups. We call such families action operads, as they are the algebraic objects that encode parametrized group actions on operads. In Part I of this paper, we study the basic algebra of action operads $Λ$ and the $Λ$-operads they act upon. In Part II, we study $Λ$-operads in the 2-category of small categories.

We investigate some chemostat models incorporating wall growth, competition, random fluctuations on the dilution rate, and different consumption functions (Monod and Haldane). We analyze the asymptotic behavior of the solutions of the corresponding random differential systems to establish conditions on the model parameters under which the microbes persist in the gut or disappear. Moreover, several numerical simulations are presented to support the theoretical results and illustrate their biological interpretation.

A Runtime Verification (RV) framework that supports online, at-speed verification of properties that can change dynamically (during in-field operations) will benefit a large variety of applications. Several state-of-the-art RV frameworks propose to implement monitors on FPGAs. While this approach can support changes to the property being monitored during in-field operations, they struggle to keep pace with the system under verification which use high-performance processors. In this work, we propose a novel, reprogrammable monitor that is implemented using standard cells instead of FPGAs. This allows the monitor to be co-located with the system under verification (on the same die), and hence is amenable to at-speed monitoring of properties. Our proposed design consists of a programmable unit that implements five basic operations and a set of queue-update rules. We show that a composition of such programmable units faithfully implements discrete time, bounded MTL. We demonstrate through simulations that our proposed monitor can be reprogrammed (through its I/O pins) post deployment. A fairly large monitor which can support MTL formulae upto 16 atomic propositions occupies only 0.55 mm^2, while operating at a frequency of 1.25 GHz.

Cellular metabolic networks exhibit scale-free topologies with power-law degree distributions across diverse organisms. Although such topologies are often linked to mutational robustness and evolutionary advantage, their role in metabolic dynamics remains unclear. Using dynamical mean-field theory, we derive an exact solution for an intracellular catalytic reaction model on dense random networks with arbitrary degree distributions. We show that the metabolic-starvation transition observed under nutrient-poor conditions for homogeneous degree distributions disappears when the out-degree distribution is scale-free. We also show that the power-law distribution of biomolecular abundances observed in real cells reflects the power-law in-degree distribution of the underlying catalytic reaction network. Large-scale numerical simulations validate these predictions. Our results provide a theoretical framework linking network topology and metabolic dynamics, and identify a dynamical advantage of scale-free topology under nutrient limitation.

A graph is called a $k$-planar unit distance graph if it can be drawn in the plane such that every edge is a unit line segment and is involved in at most $k$ crossings. We investigate $u_k(n)$, the maximum number of edges of such graphs on $n$ vertices. For $k=1$, we improve the best known upper bound, by showing that $u_1(n) \leq 3n - c\sqrt{n}$ for some constant $c>0$. This bound is tight up to the value of the constant $c$. For $k=2$, we establish the first non-trivial upper bound by proving that $u_2(n) \leq 4n - 8$. Regarding lower bounds we give a construction for $k=2$ that shows $u_2(n) \geq u_0(n) + c\sqrt{n}$ if $n$ is sufficiently large.