We study the Rectified Linear Unit (ReLU) dual, an existing dual formulation for stochastic programs that reformulates non-anticipativity constraints using ReLU functions to generate tight, non-convex, and mixed-integer representable cuts. While this dual reformulation guarantees convergence with mixed-integer state variables, it admits multiple optimal solutions that can yield weak cuts. To address this issue, we propose normalizing the dual in the extended space to identify solutions that yield stronger cuts. We prove that the resulting normalized cuts are tight and Pareto-optimal in the original state space. We further compare normalization with existing regularization-based approaches for handling dual degeneracy and explain why normalization offers key advantages. In particular, we show that normalization can recover any cut obtained via regularization, whereas the converse does not hold. Computational experiments demonstrate that the proposed approach outperforms existing methods by consistently yielding stronger cuts and reducing solution times on harder instances.

We explore the consequences of allowing non-Hermitian structures in quantum cosmology by extending the Wheeler DeWitt framework beyond strictly Hermitian dynamics. Using a controlled semiclassical reduction, we show how anti Hermitian contributions propagate into both early universe primordial fluctuations and late-time structure growth as effective damping or gain terms. Confronting this framework with inflationary observables, growth of structure and the observed near flatness of the universe, we derive strong infrared constraints that suppress non Hermiticity across cosmic history. We demonstrate that these bounds are mutually consistent between early and late-time probes and can be partially relaxed in theories beyond General Relativity. Our results establish cosmology as a novel arena for testing foundational aspects of quantum mechanics and suggest that Hermiticity may emerge dynamically along the semiclassical branch describing our universe.

Despite considerable developments in the literature of the past decades, a standing open problem in the analysis of continuum mechanics appears to consist of determining how far the prototypical model for small-strain thermoviscoelastic evolution in Kelvin-Voigt materials with inertia, as given by \[ u_{tt} = μΔu_t + (λ+μ)\nabla\nabla\cdot u_t + \hatμ Δu + (\hatλ+\hatμ) \nabla\nabla\cdot u - B\nablaΘ, \qquad \qquad κΘ_t = DΔΘ+ μ|\nabla u_t|^2 + (λ+μ) |{\rm div} \, u_t|^2 - BΘ{\rm div} \, u_t, \qquad \qquad \qquad (\star) \] is globally solvable in multi-dimensional settings and for initial data of arbitrary size. The present manuscript addresses this in the context of an initial value problem in smoothly bounded $n$-dimensional domains with $n\ge 2$, posed under homogeneous boundary conditions of Dirichlet type for the displacement variable $u$, and of Neumann type for the temperature $Θ$. Within suitably generalized concepts of solvability, global existence of solutions is shown without any size restrictions on the data, and for a system actually more general than ($\star$) by, inter alia, allowing the heat capacity $κ$ to depend on $Θ$. Apart from that, results on large time behavior are derived which particularly assert stabilization of $Θ$ toward a spatially homogeneous limit. Besides on standard features related to energy conservation and entropy production, in its core parts the analysis relies on an evolution property of certain logarithmic refinements of classical entropy functionals, to the best of our knowledge undiscovered in precedent literature and possibly of independent interest.

In an open bounded interval $Ω$, the problem \[ u_{tt} = u_{xx} - \big(f(Θ)\big)_x, Θ_t = Θ_{xx} - f(Θ) u_{xt}, \] is considered under the boundary conditions $u|_{\partialΩ}=Θ_x|_{\partialΩ}=0$, and for $f\in C^2([0,\infty))$ satisfying $f(0)=0$, $f'>0$ on $[0,\infty)$ and $f'\in W^{1,\infty}((0,\infty))$. In the sense of unconditional global existence, uniqueness and continuous dependence, this problem is shown to be well-posed within ranges of initial data merely satisfying \[ u_0\in W_0^{1,2}(Ω), \quad u_{0t} \in L^2(Ω) \quad \mbox{and} \quad Θ_0 \in L^2(Ω) \mbox{ with $Θ\ge 0$ a.e.~in $Ω$,} \] and in classes of solutions fulfilling \[ u\in C^0([0,\infty);W_0^{1,2}(Ω)), \qquad u_t \in C^0([0,\infty);L^2(Ω)) \qquad \mbox{and} \qquad Θ\in C^0([0,\infty);L^2(Ω)) \cap L^2_{loc}([0,\infty);W^{1,2}(Ω)). \]

In an open bounded real interval $Ω$, the model for one-dimensional thermoelasticity given by \[ u_{tt} = u_{xx} - \big(f(Θ)\big)_x, \qquad Θ_t = Θ_{xx} - f(Θ) u_{xt}, \] is considered along with homogeneous boundary conditions of Dirichlet type for $u$ and of Neumann type for $Θ$, under the assumption that $f\in C^1([0,\infty))$ satisfies $f(0)=0$, $f'\in L^\infty((0,\infty))$ and $f'>0$ on $[0,\infty)$. The focus is on initial data which are merely required to be consistent with the fundamental principles of energy conservation and entropy nondecrease, by satisfying \[ u_0\in W_0^{1,2}(Ω), u_{0t} \in L^2(Ω), 0 \le Θ_0\in L^1(Ω), Θ_0 \not\equiv 0. \] Despite an apparent lack of favorable compactness properties that have underlain previous related studies on more regular settings, it is shown that corresponding weak solutions stabilize in the sense that \[ \lim_{t\to\infty} \|u(\cdot,t)\|_{L^\infty(Ω)}=0 \] and \[ {\rm ess} \lim_{\!\!\!\! t\to\infty} \|Θ(\cdot,t)-Θ_\infty\|_{L^\infty(Ω)}=0 \] with some $Θ_\infty>0$.

We report on the realization of a $^{133}$Cs optical tweezer array in a cryogenic blackbody radiation (BBR) environment. By enclosing the array within a 4K radiation shield, we measure long Rydberg lifetimes, up to $406 (36)\,μ$s for the $55 P_{3/2}$ Rydberg state, a factor of 3.3(3) longer than the room-temperature value. We employ single-photon coupling for coherent manipulation of the ground-Rydberg qubit. We measure a small differential dynamic polarizability of the transition, beneficial for reducing dephasing due to light intensity fluctuations. Our results pave the path for advancing neutral-atom two-qubit gate fidelities as their error budgets become increasingly dominated by $T_1$ relaxation of the ground-Rydberg qubit.

We study non-equilibrium bacterial colony growth using a geometry-first, time-resolved analysis of morphology. From time-lapse microscopy data, we track the coupled evolution of area, perimeter, and boundary-sensitive shape descriptors along the full growth history. We find that non-equilibrium growth can exhibit extended intervals of compact area--perimeter scaling with exponent $α\approx 2$, consistent with growth governed by a single effective geometric length scale, as well as time-localized breakdowns of this scaling during ongoing growth. These breakdowns coincide with transient boundary reorganization while bulk area growth remains sustained. Our results demonstrate that visually distinct morphologies can arise within the same geometric growth regime, and that departures from single-scale behavior reflect intrinsic dynamical restructuring rather than growth arrest. More broadly, this work establishes time-resolved geometry as a coarse-grained framework for identifying when non-equilibrium growth departs from single-scale geometric constraints in living systems.

Winds launched at the scale of the accretion disc or dusty torus in Active Galactic Nuclei (AGN) are thought to drive energy-conserving outflows that shape galaxy evolution. The key signature of such outflows, the presence of a hot ($T \gtrsim 10^9 \, \rm K$), shocked wind component, is hard to detect directly. Observations of AGN outflows typically probe a separate outflow phase: cool/warm gas with $T \lesssim 10^5 \, \rm K$. Here, we show that the density of cool outflowing gas scales with AGN luminosity, serving as an indirect diagnostic of the elusive hot, shocked wind. We use hydrodynamic simulations with the moving-mesh code AREPO to target the interaction between a small-scale AGN wind of speed $\approx 10^4 \, \rm km \, s^{-1}$ and galactic discs containing an idealised, clumpy interstellar medium (ISM). Through a new refinement scheme targeting rapidly-cooling, fast-moving gas, our simulations reach a resolution of $\lesssim 0.1 \, \rm pc$ in the cool, outflowing phase. We extract an ensemble of cool clouds from the AGN-driven outflows produced in our simulations, finding that their densities increase systematically with AGN wind power and AGN luminosity. Moreover, the mass distribution and internal properties of these cloudlets appear to be insensitive to the initial properties of the ISM, and shaped mainly by the dynamics of radiative, turbulent mixing layers. The increase in cool outflow density with kinetic wind power and AGN luminosity has profound implications for observational estimates of outflow rates and their scaling with AGN luminosity. Depending on the available outflow and density tracers, observationally-derived outflow rates may be overestimated by orders of magnitude.

We study the \emph{Online Facility Assignment} (OFA) problem on a discrete $r\times c$ grid graph under the standard model of Ahmed, Rahman, and Kobourov: a fixed set of facilities is given, each with limited capacity, and an online sequence of unit-demand requests must be irrevocably assigned upon arrival to an available facility, incurring Manhattan ($L_1$) distance cost. We investigate how the discrete geometry of grids interacts with capacity depletion by analyzing two natural baselines and one capacity-aware heuristic. First, we give explicit adversarial sequences on grid instances showing that purely local rules can be forced into large competitive ratios: (i) a capacity-sensitive weighted-Voronoi heuristic (\textsc{CS-Voronoi}) can suffer cascading \emph{region-collapse} effects when nearby capacity is exhausted; and (ii) nearest-available \textsc{Greedy} (with randomized tie-breaking) can be driven into repeated long reassignments via an \emph{oscillation} construction. These results formalize geometric failure modes that are specific to discrete $L_1$ metrics with hard capacities. Motivated by these lower bounds, we then discuss a semi-online extension in which the algorithm may delay assignment for up to $τ$ time steps and solve each batch optimally via a min-cost flow computation. We present this batching framework as a remediation strategy and delineate the parameters that govern its performance, while leaving sharp competitive guarantees for this semi-online variant as an open direction.

We derive eigenvalue bounds for symmetric block-tridiagonal multiple saddle-point systems preconditioned with block-diagonal Schur complement matrices. This analysis applies to an arbitrary number of blocks and accounts for the case where the Schur complements are approximated, generalizing the findings in [Bergamaschi et al., Linear Algebra and its Applications, 2026]. Numerical experiments are carried out to validate the proposed estimates.

We present a new Monte-Carlo generator for the simulation of di-jet production in polarized proton-proton collisions at the next-to-leading order in QCD matched to parton showers using the framework of the POWHEG BOX. With this program we compute a variety of observables of immediate relevance for the spin program of the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. While parton-shower effects are generally small, we find that in some search regions their inclusion improves agreement of predictions with data. Moreover, we provide a critical assessment of selection criteria applied in experiment in the light of perturbative stability.

Ensuring data quality in machine learning (ML) systems has become increasingly complex as regulatory requirements expand. In the European Union (EU), frameworks such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act) articulate data quality requirements that closely parallel technical concerns in ML practice, while also extending to legal obligations related to accountability, risk management, and human rights protection. This paper presents a qualitative interview study with EU-based data practitioners working on ML systems in regulated contexts. Through semi-structured interviews, we investigate how practitioners interpret regulatory-aligned data quality, the challenges they encounter, and the supports they identify as necessary. Our findings reveal persistent gaps between legal principles and engineering workflows, fragmentation across data pipelines, limitations of existing tools, unclear responsibility boundaries between technical and legal teams, and a tendency toward reactive, audit-driven quality practices. We also identify practitioners' needs for compliance-aware tooling, clearer governance structures, and cultural shifts toward proactive data governance.

We prove that the Schrödinger operator describing four particles in two dimensions, interacting solely through short-range three-body forces, can possess infinitely many bound states. This holds under the assumption that each three-body subsystem has a virtual level at zero energy. Our result establishes an analog of the Efimov effect for such four-particle systems in two dimensions.

We present deep, medium-resolution $λ=1-5\,μ$m JWST/NIRSpec spectroscopy for 14 quiescent galaxies at $3<z<5$ with $\log_{10}(M_*/\mathrm{M_\odot}){\,>\,}10$, obtained as part of the EXCELS survey. We perform a complete re-reduction of these data, including a custom optimal-extraction approach to combat the spectral "wiggles" that result from undersampling of the NIRSpec spatial PSF. We constrain the star-formation histories and stellar metallicities of these objects via full-spectral fitting, finding a clear stellar age vs stellar mass correlation, in which more massive galaxies assembled their stellar mass at earlier times. This confirms spectroscopically that the archaeological "downsizing" trend was already in place by $z\simeq4$. The slope of our measured relation ($\simeq1.5$ Gyr per dex in stellar mass) is consistent with literature results at $0 < z < 3$. We do not observe objects with $\log_{10}(M_*/\mathrm{M_\odot})\lesssim10.5$ and ages of more than a few hundred Myr at this epoch, suggesting that recently reported examples of higher-redshift quiescent galaxies at these masses are likely to soon rejuvenate. We measure relatively high stellar metallicities for the majority of our sample, consistent with similar objects at $0 < z < 3$. Finally, we explore evidence for $α$-enhancement in six older and more luminous galaxies within our sample, finding considerable disagreements in the chemical abundances measured using different stellar population models, different fitted rest-frame wavelength ranges, star-formation history models and fitting codes. We therefore conclude that inferring detailed stellar chemical abundances for the earliest quiescent galaxies remains challenging, and higher signal-to-noise spectra are required (SNR per resolution element $>100$ for $R\simeq1000$).

Physical Reservoir Computing (PRC) offers an efficient paradigm for processing temporal data, yet most physical implementations are static, limiting their performance to a narrow range of tasks. In this work, we demonstrate in silico that a canonical Molecular Communication (MC) channel can function as a highly versatile and task-adaptive PRC whose computational properties are reconfigurable. Using a dual-simulation approach -- a computationally efficient deterministic mean-field model and a high-fidelity particle-based stochastic model (Smoldyn) -- we show that tuning the channel's underlying biophysical parameters, such as ligand-receptor kinetics and diffusion dynamics, allows the reservoir to be optimized for distinct classes of computation. We employ Bayesian optimization to efficiently navigate this high-dimensional parameter space, identifying discrete operational regimes. Our results reveal a clear trade-off: parameter sets rich in channel memory excel at chaotic time-series forecasting tasks (e.g., Mackey Glass), while regimes that promote strong receptor nonlinearity are superior for nonlinear data transformation. We further demonstrate that post-processing methods improve the performance of the stochastic reservoir by mitigating intrinsic molecular noise. These findings establish the MC channel not merely as a computational substrate, but as a design blueprint for tunable, bioinspired computing systems, providing a clear optimization framework for future wetware AI implementations.

Geosocial reachability queries (\textsc{RangeReach}) determine whether a given vertex in a geosocial network can reach any spatial vertex within a query region. The state-of-the-art 3DReach method answers such queries by encoding graph reachability through interval labelling and indexing spatial vertices in a 3D R-tree. We present 2DReach, a simpler approach that avoids interval labelling entirely. Like 3DReach, 2DReach collapses strongly connected components (SCCs) into a DAG, but instead of computing interval labels, it directly stores a 2D R-tree per component over all reachable spatial vertices. A query then reduces to a single 2D R-tree lookup. We further propose compressed variants that reduce storage by excluding spatial sinks and sharing R-trees between components with identical reachable sets. Experiments on four real-world datasets show that 2DReach achieves faster index construction than 3DReach, with the compressed variant yielding the smallest index size among all methods. 2DReach delivers competitive or superior query performance with more stable response times across varying query parameters.

This document summarizes talks and discussions from the workshop "deci-Hz Gravitational Wave Observations on the Moon and Beyond" that took place at Johns Hopkins University between September 1 and September 3, 2025. The workshop focused on experimental proposals to observe gravitational waves in the deci-Hz band, including lunar detectors, laser interferometers in space, and atom interferometry; gravitational wave sources in the deci-Hz frequency band; and the multi-messenger and multi-band astronomy that would be enabled by these observations.

Recent results from combined cosmological probes indicate that the Dark Energy component of the Universe could be dynamical. The simplest explanation envisages the presence of a quintessence field rolling into a potential, where the Dark Energy energy density parameter $Ω_{DE}=Ω_Λ+Ω_{x}$ results from the contribution of the ground state energy $Ω_Λ$ and the scalar field energy $Ω_{x}$. Provided that $Ω_{DE}\approx 0.7$, negative values of $Ω_Λ$ can be consistent with current measurements from cosmological probes, and could help in explaining the large abundance of bright galaxies observed by JWST at $z> 10$, largely exceeding the pre-JWST expectations in a $ΛCDM$ Universe. Here we explore to what extent such a scenario can account also for the early presence of massive Black Holes (BHs) with masses $M_{BH}\gtrsim 10^7\,M_{\odot}$ observed at $z\gtrsim 8$, and for the large over-abundance of AGN with respect to pre-JWST expectations. Our aim is not to provide a detailed description of BH growth, but rather to compute the maximal BH growth that can occur in cosmological models with negative $Ω_Λ$ under the simple assumption of Eddington-limited accretion onto initial light Black Hole seeds with mass $M_{seed}\sim 10^2\,M_{\odot}$ originated from PopIII stars. To this aim we develop a simple analytic framework to connect the growth of dark matter halos to the maximal growth of BHs within the above assumptions. We show such models can account for present observations assuming values of $Ω_Λ\approx -1$, simultaneously boosting both galaxy and AGN number counts without invoking any additional physics. This would allow us to trace the observed excess of bright and massive galaxies and the early formation of massive Black Holes and the abundance of AGN to the same cosmological origin.

Contraction algebras are noncommutative algebras introduced by Donovan and Wemyss to classify of 3-dimensional flops. Wemyss conjectures that contraction algebras can be deformed to a single semisimple algebra. This gives an intrinsic method of calculating Gopakumar-Vafa invariants of the flop. The main result is a proof of Wemyss' conjecture for types A and D. In the course of the proof, we recall and introduce new techniques for constructing flat deformations of associative algebras and compare various notions of deformations. We also put forward two conjectures which hint towards a deeper theory.

Understanding the physical computing mechanisms of individual network nodes is essential for scaling neuromorphic photonic architectures. This work proposes a compact passive nonlinear photonic core based on a Side-Coupled Integrated Spaced Sequence of Resonators (SCISSOR) made of three nominally equal microrings and investigate its computing capabilities. Its nonlinearities and internal feedback enable analogue, spiking, and bistable responses that are accessed by tuning the injection power and wavelength. Implemented as a single nonlinear node in a time-multiplexed reservoir computing, the SCISSOR achieves error-free classification on the Iris dataset and accuracies above 97% on the Sonar task, using both analogue and digital reservoir representations with 150 virtual nodes. In the digital scheme, spiking dynamics naturally generate sparse reservoir states, enabling efficient classification even with a single spike. Intriguingly, optimal operating points are at the boundaries where sharp transitions in dynamical complexity and/or output power occur. In these points, the SCISSOR supports high task-performance, opening novel strategies for future on-chip training. Spiking and thermal bistabilities also participate to enhance the computational performance at low injected powers below 4 mW. These results suggest optical coupled microring resonators as effective building blocks for future edge computing and neuromorphic photonic systems.

The realization of fault-tolerant quantum computers remains a challenging endeavor, forcing state-of-the-art quantum hardware to rely heavily on noise mitigation techniques. Standard quantum error mitigation is typically based on post-processing strategies. In contrast, the present work explores a pre-processing approach, in which the effects of noise are mitigated before performing a measurement on the output state. The main idea is to find an observable $Y$ such that its expectation value on a noisy quantum state $\mathcal{E(ρ)}$ matches the expectation value of a target observable $X$ on the noiseless quantum state $ρ$. Our method requires the execution of a noisy quantum circuit, followed by the measurement of the surrogate observable $Y$. The main enablers of our method in practical scenarios are Tensor Networks. The proposed method improves over Tensor Error Mitigation (TEM) in terms of average error, circuit depth, and complexity, attaining a measurement overhead that approaches the theoretical lower bound. The improvement in terms of classical computation complexity is in the order of $\sim 10^6$ times when compared to the post-processing computational cost of TEM in practical scenarios. Such gain comes from eliminating the need to perform the set of informationally complete positive operator-valued measurements (IC-POVM) required by TEM, as well as any other tomographic strategy.

When exposed to a contagion phenomenon, individuals may respond to the perceived risk of infection by adopting behavioral changes, aiming to reduce their exposure or their risk of infecting others. The social cost of such adaptive behaviors and their impact on the contagion dynamics have been investigated in pairwise networks, with binary interactions driving both contagion and risk perception. However, contagion and adaptive mechanisms can also be driven by group (higher-order) interactions. Here, we consider several adaptive behaviors triggered by awareness of risk perceived through higher-order and pairwise interactions, and we compare their impact on pairwise and higher-order contagion processes. By numerical simulations and a mean-field analytic approach, we show that adaptive behaviors driven by higher-order information are more effective in limiting the spread of a contagion, than similar mechanisms based on pairwise information. Meanwhile, they also entail a lower social cost, measured as the reduction of the intensity of interactions in the population. Indeed, adaptive mechanisms based on higher-order information lead to a heterogeneous risk perception within the population, producing a higher alert on nodes with large hyperdegree (i.e., participating in many groups), on their neighborhoods, and on large groups. This in turn prevents the spreading process to exploit the properties of these nodes and groups, which tend to drive and sustain the dynamics in the absence of adaptive behaviors.

Walking droplets are millimetric fluid drops that propel themselves across a vibrated liquid bath through interaction with their self-generated waves. They constitute classical active wave-particle entities and exhibit a range of hydrodynamic quantum analogs. We investigate an \emph{active soft-impact oscillator} as a minimal model for a walking droplet moving within a piecewise-smooth external potential, analogous to classical mass-spring soft-impact oscillators and recently explored quantum soft-impact oscillators. Our active soft-impact oscillator model couples a non-smooth soft-impact force to the Lorenz-like dynamics arising from the wave-particle entity. Theoretical and numerical exploration of the full parameter space reveals a wide variety of nonlinear behaviors and bifurcations driven by impact and grazing events. These include grazing-induced and impact-induced transitions between periodic and chaotic motion, as well as grazing-mediated attractor switching and impact-free (invisible) attractor switching. The active soft-impact oscillator thus provides a versatile platform for probing nonlinear impact dynamics in active systems and exploring hydrodynamic quantum analogs in non-smooth potentials.

This is the third article in a series of three dealing with the exploitation of speckle for imaging purposes. In complex media, a fundamental limit is the multiple scattering phenomenon that completely blurs the imaging process in depth. Matrix imaging can provide a relevant framework for solving this problem. As it proved to be an adequate tool for probing reverberations in speckle [E. Giraudat et al., Part I], we will show how it can be used to tailor complex spatio-temporal focusing laws to monitor the interference between the multiply-reflected paths and the ballistic component of the wave-field. To do so, we extend the distortion matrix concept to the frequency domain. An iterative phase reversal process operated from the space-time Fourier space is then used to compensate for reverberations and optimize both the axial and transverse resolution of the confocal image. Here, we first present an experimental proof-of-concept consisting in imaging a tissue-mimicking phantom through a reverberating plate before outlining the potential and the limits of this strategy for transcranial ultrasound and beyond.

This is the first article in a series of three dealing with the exploitation of speckle for imaging purposes. Speckle is the complex interference wave-field produced by a random distribution of un-resolved scatterers. In this paper, we show how these scatterers can be used as virtual microphones to monitor the spatio-temporal propagation of a wave-packet inside the medium. To do so, the concept of matrix imaging is particularly useful. It consists in decoupling the location of the transmitted and received focal spots in a standard beamforming process. By scanning the wave-field with the output focal spot that then acts as a virtual transducer, one can image the spatio-temporal evolution of the wave-packet inside the medium. This unique observable will allow us to highlight the imperfections of the focusing process, in particular the defocus and reverberations induced by a strong aberrating layer. As a proof-of-concept, we will consider ultrasound experiments on tissue-mimicking phantoms. In the next two papers, we will show how this observable can be leveraged to compensate for these phenomena that hamper wave focusing and imaging in all fields of wave physics. Our method is indeed broadly applicable to different types of waves beyond ultrasound for which multi-element technology allows a reflection matrix to be measured.

This paper develops an explicit spectral representation for solutions of a one-dimensional linear wave equation with a constant time delay. The model is considered on a bounded interval with non-homogeneous Dirichlet boundary data and a prescribed history function. To accommodate the loss of global smoothness in time caused by delay terms, solutions are understood in a \textit{stepwise classical sense}, allowing jump discontinuities in the second time derivative at multiples of the delay while maintaining continuity of the solution and its first time derivative. By combining separation of variables with Sturm-Liouville expansions, the delayed PDE is reduced to a family of scalar second-order delay differential equations. Using delay-dependent fundamental solutions, we derive closed-form representation formulas for the modal dynamics and reconstruct the PDE solution as a Fourier series. Convergence conditions guaranteeing uniform convergence and admissibility of termwise differentiation in space are established. A numerical example demonstrates the practical computation of truncated series solutions and their visualization.

We present a polynomial-time algorithm that colors any 3-colorable $n$-vertex graph using $O(n^{0.19539})$ colors, improving upon the previous best bound of $\widetilde{O}(n^{0.19747})$ by Kawarabayashi, Thorup, and Yoneda [STOC 2024]. Our result constitutes the first progress in nearly two decades on SDP-based approaches to this problem. The earlier SDP-based algorithms of Arora, Chlamtáč, and Charikar [STOC 2006] and Chlamtáč [FOCS 2007] rely on extracting a large independent set from a suitably "random-looking" second-level neighborhood, under the assumption that the KMS algorithm [Karger, Motwani, and Sudan, JACM 1998] fails to find one globally. We extend their analysis to third-level neighborhoods. We then come up with a new vector $5/2$-coloring, which allows us to extract a large independent set from some third-level neighborhood. The new vector coloring construction may be of independent interest.

Frustration on the triangular lattice has long been a source of intriguing and often debated phases in many-body systems. Although symmetry analysis has been employed, the role of the seemingly trivial parity symmetry has received little attention. In this work, we show that phases induced by frustration are systematically shaped by an implicit rule of thumb associated with spontaneous parity breaking. This principle enables us to anticipate and rationalize the regimes and conditions under which nontrivial phases emerge. For the spin-$S$ antiferromagnetic XXZ model, we demonstrate that a controversial parity-broken phase appears only at intermediate values of $S$. In bilayer systems, enhanced frustration leads to additional phases, such as supersolids, whose properties can be classified by their characteristic parity features. Benefiting from our improved tensor network contraction techniques, we confirm these results through large-scale tensor-network calculations. This study offers an alternative viewpoint and a systematic approach for examining the interplay between spin, symmetry, and frustration in many-body systems.

Improving the computational efficiency of quantum many-body calculations from a hardware perspective remains a critical challenge. Although field-programmable gate arrays (FPGAs) have recently been exploited to improve the computational scaling of algorithms such as Monte Carlo methods, their application to tensor network algorithms is still at an early stage. In this work, we propose a fine-grained parallel tensor network design based on FPGAs to substantially enhance the computational efficiency of two representative tensor network algorithms: the infinite time-evolving block decimation (iTEBD) and the higher-order tensor renormalization group (HOTRG). By employing a quad-tile partitioning strategy to decompose tensor elements and map them onto hardware circuits, our approach effectively translates algorithmic computational complexity into scalable hardware resource utilization, enabling an extremely high degree of parallelism on FPGAs. Compared with conventional CPU-based implementations, our scheme exhibits superior scalability in computation time, reducing the bond-dimension scaling of the computational cost from $O(D_b^3)$ to $O(D_b)$ for iTEBD and from $O(D_b^6)$ to $O(D_b^2)$ for HOTRG. This work provides a theoretical foundation for future hardware implementations of large-scale tensor network computations.

We propose an Adagrad-like algorithm for multi-objective unconstrained optimization that relies on the computation of a common descent direction only. Unlike classical local algorithms for multi-objective optimization, our approach does not rely on the dominance property to accept new iterates, which allows for a flexible and function-free optimization framework. New points are obtained using an adaptive stepsize that does not require neither knowledge of Lipschitz constants nor the use of line search procedures. The rate of convergence is analyzed and is shown to be $\mathcal{O}(1 / \sqrt{ k+1})$ with respect to the norm of the common descent direction. The method is extensively validated on a broad class of unconstrained multi-objective problems and simple multi-task learning instances, and compared against a first-order line search algorithm. Additionally, we present a preliminary study of the behavior under noisy multi-objective settings, highlighting the robustness of the method.