Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous settings have catered to a rich source of research problems. Robust convex optimization is a branch of optimization theory in which the variables or parameters involved have a certain level of uncertainty. In this work, we consider the online robust optimization meta-algorithm by Ben-Tal et al. and show that for a large range of stochastic subgradients, this algorithm has the same guarantee as the original non-stochastic version. We develop a hybrid quantum-classical version of this algorithm and show that an at most quadratic improvement in terms of the dimension can be achieved. The speedup is due to the use of quantum state preparation, quantum norm estimation, and quantum multi-sampling. We apply our quantum meta-algorithm to examples such as robust linear programs and robust semidefinite programs and give applications of these robust optimization problems in finance and engineering.

Spectrum of $^7$Li and elastic scattering reaction $^4$He($^3$H, $^3$H)$^4$He are studied using the unified description of the Gamow shell model in the coupled-channel formulation (GSMCC). The reaction channels are constructed using the cluster expansion with the two mass partitions [$^4$He + $^3$H], [$^6$Li + n].

The efficient simulation of complex quantum systems remains a central challenge due to the exponential growth of Hilbert space with system size. Tensor network methods have long been established as powerful approximation schemes, and their efficiency can be further enhanced by incorporating physics-informed priors. A prominent example is symmetry: recent progress on $U(1)$-symmetric tensor networks, accelerated on GPUs and scaled to supercomputers, shows how conserved charges induce block-sparse structures that reduce computational cost and enable larger simulations. The same principle extends to general symmetries, inspiring equivariant neural networks in machine learning and guiding symmetry-preserving ansatze in variational quantum algorithms. Beyond symmetry, physics-informed design also includes strategies such as hybrid tensor networks and parallel sequential circuits, which pursue efficiency from complementary principles. This Perspective argues that physics-informed tensor networks, grounded in both symmetry and beyond-symmetry insights, provide unifying strategies for scalable approaches in quantum simulation, computation, and machine learning.

Achieving high-precision light manipulation is crucial for delivering information through complex media with high fidelity. However, existing spatial light modulation devices face a fundamental tradeoff between speed and accuracy. Digital micromirror devices (DMDs) have emerged as a promising candidate as accessible high-speed wavefront shaping devices but at the cost of compromised fidelity, largely due to the limited control degrees of freedom and the challenge of numerically optimizing a binary amplitude mask. Here we leverage the sparse-to-random transformation through complex media to overcome the dimensionality limitation of spatial light modulation devices. We demonstrate that pattern compression in the form of sparsity-constrained wavefront optimization allows sparse and robust wavefront representations of generic patterns in the random basis provided by the complex media, and thus effectively addresses the dimensionality limitation of DMDs, which significantly improves the projection fidelity without sacrificing the full frame rate (22 kHz), hardware complexity, or optimization time (0.5 s for 1000 frames). Since the dimensionality limitation is intrinsic to spatial light modulation devices and sparse-to-random transformation to complex media, our methods can be generalized to different pattern types, complex media, and device settings, supporting consistent superior performance across different types of complex media with up to an 89% increase in projection accuracy and a 126% improvement in speckle suppression. The proposed optimization framework has the potential to enhance existing holographic setups without any change to the hardware, enable high-fidelity and high-speed wavefront shaping through different scattering media and platforms, and directly facilitate a wide range of physics and real-world applications.

Fargues and Scholze proved the geometric Satake equivalence over the Fargues-Fontaine curve. This can be transferred to the geometric Satake equivalence concerning a Witt vector affine Grassmannian via nearby cycle. On the other hand, Zhu proved the geometric Satake equivalence concerning a Witt vector affine Grassmannian. In this paper, we explain the coincidence of these two geometric Satake equivalences, including the coincidence of the two symmetric monoidal structures on the Satake category.

We show that the parabolic equation $u_t + (-Δ)^s u = q(x) |u|^{α-1} u$ posed in a time-space cylinder $(0,T) \times \mathbb{R}^N$ and coupled with zero initial condition and zero nonlocal Dirichlet condition in $(0,T) \times (\mathbb{R}^N \setminus Ω)$, where $Ω$ is a bounded domain, has at least one nontrivial nonnegative finite energy solution provided $α\in (0,1)$ and the nonnegative bounded weight function $q$ is separated from zero on an open subset of $Ω$. This fact contrasts with the (super)linear case $α\geq 1$ in which the only bounded finite energy solution is identically zero.

Sequential voting rules have played a crucial role in shaping decisions within parliamentary and legislative frameworks. After observing that the existing sequential rules fail several fundamental axioms, Horan and Sprumont [2022] proposed a sequential rule named two-stage majoritarian rule (TSMR). This paper examines this rule by investigating the complexity of {\sc{Agenda Control}}, {\sc{Coalition Manipulation}}, {\sc{Possible Winner}}, {\sc{Necessary Winner}}, and eight standard election control problems. Our study offers a comprehensive insight into the complexity landscape of these problems.

For a smooth bounded domain $Ω$ and $p \geq q \geq 2$, we establish quantified versions of the classical Friedrichs inequality $\|\nabla u\|_p^p - λ_1 \|u\|_q^p \geq 0$, $u \in W_0^{1,p}(Ω)$, where $λ_1$ is a generalized least frequency. We apply one of the obtained quantifications to show that the resonant equation $-Δ_p u = λ_1 \|u\|_q^{p-q} |u|^{q-2} u + f$ coupled with zero Dirichlet boundary conditions possesses a weak solution provided $f$ is orthogonal to the minimizer of $λ_1$.

The purpose of this paper is to present a general method for forcing on $ω_2$ and $ω_3$ with finite conditions, while preserving all cardinals and some fragments of $\mathrm{GCH}$. This method is based on the technique of forcing with finite symmetric systems of elementary submodels, and improves earlier versions of this forcing by including models of two types. We will present several applications of the pure side condition forcing and variants thereof, by adding a Kurepa tree on $ω_2$, a club subset of $ω_2$ that avoids infinite sets from the ground model, a function bounding every canonical function below $ω_3$ on a club, and a simplified $(ω_2,1)$-morass.

We investigate qualitative properties of weak solutions of the Dirichlet problem for the equation $-Δ_p u = λm(x)|u|^{p-2}u + ηa(x)|u|^{q-2}u + f(x)$ in a bounded domain $Ω\subset \mathbb{R}^N$, where $q<p$. Under certain regularity and qualitative assumptions on the weights $m, a$ and the source function $f$, we identify ranges of parameters $λ$ and $η$ for which solutions satisfy maximum and antimaximum principles in weak and strong forms. Some of our results, especially on the validity of the antimaximum principle under low regularity assumptions, are new for the unperturbed problem with $η=0$, and among them there are results providing new information even in the linear case $p=2$. In particular, we show that for any $p>1$ solutions of the unperturbed problem satisfy the antimaximum principle in a right neighborhood of the first eigenvalue of the $p$-Laplacian provided $m,f \in L^γ(Ω)$ with $γ>N$. For completeness, we also investigate the existence of solutions.

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.

As a free, intensive, weakly interacting, and well directional messenger, solar neutrinos have been driving both solar physics and neutrino physics developments for more than half a century. Since more extensive and advanced neutrino experiments are under construction, being planned or proposed, we are striving toward an era of precise and comprehensive measurement of solar neutrinos in the next decades. In this article, we review recent theoretical and experimental progress achieved in solar neutrino physics. We present not only an introduction to neutrinos from the standard solar model and the standard flavor evolution, but also a compilation of a variety of new physics that could affect and hence be probed by solar neutrinos. After reviewing the latest techniques and issues involved in the measurement of solar neutrino spectra and background reduction, we provide our anticipation on the physics gains from the new generation of neutrino experiments.

Soft Glassy Materials (SGM) consist in dense amorphous assemblies of colloidal particles of multiple shapes, elasticity, and interactions, which confer upon them solid-like properties at rest. They are ubiquitously encountered in modern engineering, including additive manufacturing, semi-solid flow cells, dip-coating, adhesive locomotion, where they are subjected to complex mechanical histories. Such processes often include a solid-to-liquid transition induced by large enough shear, which results in complex transient phenomena such as non-monotonic stress responses, i.e., stress overshoot, and spatially heterogeneous flows, e.g., shear-banding or brittle failure. In the present article, we propose a pedagogical introduction to a continuum model based on a spatially-resolved fluidity approach that we recently introduced to rationalize shear-induced yielding in SGMs. Our model, which relies upon non-local effects, quantitatively captures salient features associated with such complex flows, including the rate dependence of the stress overshoot, as well as transient shear-banded flows together with nontrivial scaling laws for fluidization times. This approach offers a versatile framework to account for subtle effects, such as avalanche-like phenomena, or the impact of boundary conditions, which we illustrate by including in our model the elasto-hydrodynamic slippage of soft particles compressed against solid surfaces.

We study a generalization of the classic Global Min-Cut problem, called Global Label Min-Cut (or sometimes Global Hedge Min-Cut): the edges of the input (multi)graph are labeled (or partitioned into color classes or hedges), and removing all edges of the same label (color or from the same hedge) costs one. The problem asks to disconnect the graph at minimum cost. While the $st$-cut version of the problem is known to be NP-hard, the above global cut version is known to admit a quasi-polynomial randomized $n^{O(\log \mathrm{OPT})}$-time algorithm due to Ghaffari, Karger, and Panigrahi [SODA 2017]. They consider this as ``strong evidence that this problem is in P''. We show that this is actually not the case. We complete the study of the complexity of the Global Label Min-Cut problem by showing that the quasi-polynomial running time is probably optimal: We show that the existence of an algorithm with running time $(np)^{o(\log n/ (\log \log n)^2)}$ would contradict the Exponential Time Hypothesis, where $n$ is the number of vertices, and $p$ is the number of labels in the input. The key step for the lower bound is a proof that Global Label Min-Cut is W[1]-hard when parameterized by the number of uncut labels. In other words, the problem is difficult in the regime where almost all labels need to be cut to disconnect the graph. To turn this lower bound into a quasi-polynomial-time lower bound, we also needed to revisit the framework due to Marx [Theory Comput. 2010] of proving lower bounds assuming Exponential Time Hypothesis through the Subgraph Isomorphism problem parameterized by the number of edges of the pattern. Here, we provide an alternative simplified proof of the hardness of this problem that is more versatile with respect to the choice of the regimes of the parameters.

Bell-Kochen-Specker theorem states that a non-contextual hidden-variable theory cannot completely reproduce the predictions of quantum mechanics. Asher Peres gave a remarkably simple proof of quantum contextuality in a four-dimensional Hilbert space of two spin-1/2 particles. Peres's argument is enormously simpler than that of Kochen and Specker. Peres contextuality demonstrates a logical contradiction between quantum mechanics and the noncontextual hidden variable models by showing an inconsistency when assigning noncontextual definite values to a certain set of quantum observables. In this work, we present a similar proof in time with a temporal version of the Peres-like argument. In analogy with the two-particle version of Peres's argument in the context of spin measurements at two different locations, we examine here single-particle spin measurements at two different times $t=t_1$ and $t=t_2$. We adopt three classical assumptions for time-separated measurements, which are demonstrated to conflict with quantum mechanical predictions. Consequently, we provide a non-probabilistic proof of certified quantumness in time, without relying on inequalities, demonstrating that our approach can certify the quantumness of a device through single-shot, time-separated measurements. Our results can be experimentally verified with the present quantum technology.

We investigate the basis properties of sequences of Fucik eigenfunctions of the one-dimensional Neumann Laplacian. We show that any such sequence is complete in $L^2(0,π)$ and a Riesz basis in the subspace of functions with zero mean. Moreover, we provide sufficient assumptions on Fucik eigenvalues which guarantee that the corresponding Fucik eigenfunctions form a Riesz basis in $L^2(0,π)$ and we explicitly describe the corresponding biorthogonal system.

Fargues and Scholze proved the geometric Satake equivalence over the Fargues--Fontaine curve. On the other hand, Zhu proved the geometric Satake equivalence using a Witt vector affine Grassmannian. In this paper, we explain the relation between the two version of the geometric Satake equivalence via nearby cycle.

We investigate the Kondo effect of a spin-3/2 Fermi gas and give a detailed calculation of the impurity resistance and ground state energy based on the s-d exchange model. It is found that the impurity resistance increases logarithmically with the decrease of temperature in the case of antiferromagnetic coupling similar to the spin-1/2 system but has a larger resistance minimum value due to the increase of spin scattering channels. In the case of antiferromagnetic interaction, the ground state is still the Kondo singlet state while the septuplet state has the lowest energy for ferromagnetic coupling. And with the same antiferromagnetic s-d coupling parameter, the energy of the Kondo singlet state is lower than spin-1/2, which indicates that the larger spin, the easier it is to enter the Kondo-screened phase. This provides some theoretical support for the realization of the Kondo effect with ultra-cold atoms.

We consider the single-conflict coloring problem, a graph coloring problem in which each edge of a graph receives a forbidden ordered color pair. The task is to find a vertex coloring such that no two adjacent vertices receive a pair of colors forbidden at an edge joining them. We show that for any assignment of forbidden color pairs to the edges of a $d$-degenerate graph $G$ on $n$ vertices of edge-multiplicity at most $\log \log n$, $O(\sqrt{ d } \log n)$ colors are always enough to color the vertices of $G$ in a way that avoids every forbidden color pair. This answers a question of Dvořák, Esperet, Kang, and Ozeki for simple graphs (Journal of Graph Theory 2021).

We provide improved sufficient assumptions on sequences of Fucik eigenvalues of the one-dimensional Dirichlet Laplacian which guarantee that the corresponding Fucik eigenfunctions form a Riesz basis in $L^2(0,π)$. For that purpose, we introduce a criterion for a sequence in a Hilbert space to be a Riesz basis.

Using symmetrization techniques, we show that, for every $N \geq 2$, any second eigenfunction of the fractional Laplacian in the $N$-dimensional unit ball with homogeneous Dirichlet conditions is nonradial, and hence its nodal set is an equatorial section of the ball.

The geometric Satake equivalence and the Springer correspondence are closely related when restricting to small representations of the Langlands dual group. We prove this result for étale sheaves, including the case of the mixed characteristic affine Grassmannian, assuming a sufficient ramification. In this process, we construct a monoidal structure on the restriction functor of Satake categories. We construct also a canonical isomorphism between a mixed characteristic affine Grassmannian under a sufficient ramification and an equal characteristic one.

We establish sufficient assumptions on sequences of Fucik eigenvalues of the one-dimensional Laplacian which guarantee that the corresponding Fucik eigenfunctions form a Riesz basis in $L^2(0,π)$.

Given two disjoint sets $W_1$ and $W_2$ of points in the plane, the Optimal Discretization problem asks for the minimum size of a family of horizontal and vertical lines that separate $W_1$ from $W_2$, that is, in every region into which the lines partition the plane there are either only points of $W_1$, or only points of $W_2$, or the region is empty. Equivalently, Optimal Discretization can be phrased as a task of discretizing continuous variables: we would like to discretize the range of $x$-coordinates and the range of $y$-coordinates into as few segments as possible, maintaining that no pair of points from $W_1 \times W_2$ are projected onto the same pair of segments under this discretization. We provide a fixed-parameter algorithm for the problem, parameterized by the number of lines in the solution. Our algorithm works in time $2^{O(k^2 \log k)} n^{O(1)}$, where $k$ is the bound on the number of lines to find and $n$ is the number of points in the input. Our result answers in positive a question of Bonnet, Giannopolous, and Lampis [IPEC 2017] and of Froese (PhD thesis, 2018) and is in contrast with the known intractability of two closely related generalizations: the Rectangle Stabbing problem and the generalization in which the selected lines are not required to be axis-parallel.

Shear thickening denotes the reversible increase in viscosity of a suspension of rigid particles under external shear. This ubiquitous phenomenon has been documented in a broad variety of multiphase particulate systems, while its microscopic origin has been successively attributed to hydrodynamic interactions and frictional contact between particles. The relative contribution of these two phenomena to the magnitude of shear thickening is still highly debated and we report here a discriminating experimental study using a model shear-thickening suspension that allows us to tune independently both the surface chemistry and the surface roughness of the particles. We show here that both properties matter when it comes to continuous shear thickening (CST) and that the presence of hydrogen bonds between the particles is essential to achieve discontinuous shear thickening (DST) by enhancing solid friction between closely contacting particles. Moreover, a simple argument allows us to predict the onset of CST, which for these very rough particles occurs at a critical volume fraction much lower than that previously reported in the literature. Finally, we demonstrate how mixtures of particles with opposing surface chemistry make it possible to finely tune the shear-thickening response of the suspension at a fixed volume fraction, paving the way for a fine control of the shear-thickening transition in engineering applications.

Until now, distributed algorithms for rational agents have assumed a-priori knowledge of $n$, the size of the network. This assumption is challenged here by proving how much a-priori knowledge is necessary for equilibrium in different distributed computing problems. Duplication - pretending to be more than one agent - is the main tool used by agents to deviate and increase their utility when not enough knowledge about $n$ is given. The a-priori knowledge of $n$ is formalized as a Bayesian setting where at the beginning of the algorithm agents only know a prior $σ$, a distribution from which they know $n$ originates. We begin by providing new algorithms for the Knowledge Sharing and Coloring problems when $n$ is a-priori known to all agents. We then prove that when agents have no a-priori knowledge of $n$, i.e., the support for $σ$ is infinite, equilibrium is impossible for the Knowledge Sharing problem. Finally, we consider priors with finite support and find bounds on the necessary interval $[α,β]$ that contains the support of $σ$, i.e., $α\leq n \leq β$, for which we have an equilibrium. When possible, we extend these bounds to hold for any possible protocol.

In this paper, we develop the method of circle of partitions and associated statistics. As an application we prove conditionally the binary Goldbach conjecture. We develop a series of steps to prove the binary Goldbach conjecture in full. We end the paper by proving the binary Goldbach conjecture for all sufficiently large even numbers.

Integrating a product of linear forms over the unit simplex can be done in polynomial time if the number of variables n is fixed (V. Baldoni et al., 2011). In this note, we highlight that this problem is equivalent to obtaining the normalizing constant of state probabilities for a popular class of Markov processes used in queueing network theory. In light of this equivalence, we survey existing computational algorithms developed in queueing theory that can be used for exact integration. For example, under some regularity conditions, queueing theory algorithms can exactly integrate a product of linear forms of total degree N by solving N systems of linear equations.