We explore the potential for detecting rotational instabilities in the post-merger phase of binary neutron star mergers using different network configurations of upgraded and next-generation gravitational wave detectors. Our study employs numerically generated post-merger waveforms, which reveal the re-excitation of the $l=m=2$ $f$-mode at a time of $O(10{\rm})$ms after merger. We evaluate the detectability of these signals by injecting them into colored Gaussian noise and performing a reconstruction as a sum of wavelets using Bayesian inference. Computing the overlap between the reconstructed and injected signal, restricted to the instability part of the post-merger phase, we find that one could infer the presence of rotational instabilities with a network of planned 3rd-generation detectors, depending on the total mass and distance to the source. For a recently suggested high-frequency detector design, we find that the instability part would be detectable even at 200 Mpc, significantly increasing the anticipated detection rate. For a network consisting of the existing HLV detectors, but upgraded to twice the A+ sensitivity, we confirm that the peak frequency of the whole post-merger gravitational-wave emission could be detectable with a network signal-to-noise ratio of 8 at a distance of 40Mpc.

Differential privacy (DP) enables private data analysis. In a typical DP deployment, controllers manage individuals' sensitive data and are responsible for answering analysts' queries while protecting individuals' privacy. They do so by choosing the privacy parameter $ε$, which controls the degree of privacy for all individuals in all possible datasets. However, it is challenging for controllers to choose $ε$ because of the difficulty of interpreting the privacy implications of such a choice on the within-dataset individuals. To address this challenge, we first derive a relative disclosure risk indicator (RDR) that indicates the impact of choosing $ε$ on the within-dataset individuals' disclosure risk. We then design an algorithm to find $ε$ based on controllers' privacy preferences expressed as a function of the within-dataset individuals' RDRs, and an alternative algorithm that finds and releases $ε$ while satisfying DP. Lastly, we propose a solution that bounds the total privacy leakage when using the algorithm to answer multiple queries without requiring controllers to set the total privacy budget. We evaluate our contributions through an IRB-approved user study that shows the RDR is useful for helping controllers choose $ε$, and experimental evaluations showing our algorithms are efficient and scalable.

This work presents a new approach for simulating the HHL linear systems of equations solver algorithm with tensor networks. First, a novel HHL in the qudits formalism, the generalization of qubits, is developed, and then its operations are transformed into an equivalent classical HHL, taking advantage of the non-unitary operations that they can apply. The main novelty of this proposal is to perform a classical simulation of the HHL as efficiently as possible to benchmark the algorithm steps according to its input parameters and the input matrix. The algorithm is applied to three classical simple simulation problems, comparing it with an exact inversion algorithm, and its performance is compared against an implementation of the original HHL simulated in the Qiskit framework, providing both codes. It is also applied to study the sensitivity of the HHL algorithm with respect to its hyperparameter values, reporting the existence of saturation points and maximal performance values. The results show that this approach can achieve a promising performance in computational efficiency to simulate the HHL process without quantum noise, providing a higher bound for its performance.

Let $S\subset \text{SL}_2(\mathbb Z)\times \text{SL}_2(\mathbb Z)$ or $\text{SL}_2(\mathbb Z)\ltimes \mathbb Z^2$ be finite symmetric and assume $S$ generates a group $G$ which is a Zariski-dense subgroup $\text{SL}_2(\mathbb Z)\times \text{SL}_2(\mathbb Z)$ or $\text{SL}_2(\mathbb Z)\ltimes \mathbb Z^2$. We prove that the Cayley graphs $$\{\mathcal Cay(G(\text{mod } q), S (\text{mod } q))\}_{q\in \mathbb Z}$$ form a family of expanders.

In the case of a quantum-classical hybrid system with a finite number of degrees of freedom, the problem of characterizing the most general dynamical semigroup is solved, under the restriction of being quasi-free. This is a generalization of a Gaussian dynamics, and it is defined by the property of sending (hybrid) Weyl operators into Weyl operators in the Heisenberg description. The result is a quantum generalization of the Lévy-Khintchine formula; Gaussian and jump contributions are included. As a byproduct, the most general quasi-free quantum-dynamical semigroup is obtained; on the classical side the Liouville equation and the Kolmogorov-Fokker-Planck equation are included. As a classical subsystem can be observed, in principle, without perturbing it, information can be extracted from the quantum system, even in continuous time; indeed, the whole construction is related to the theory of quantum measurements in continuous time. While the dynamics is formulated to give the hybrid state at a generic time $t$, we show how to extract multi-time probabilities and how to connect them to the quantum notions of positive operator valued measure and instrument. The structure of the generator of the dynamical semigroup is analized, in order to understand how to go on to non quasi-free cases and to understand the possible classical-quantum interactions; in particular, all the interaction terms which allow to extract information from the quantum system necessarily vanish if no dissipation is present in the dynamics of the quantum component. A concrete example is given, showing how a classical component can input noise into a quantum one and how the classical system can extract information on the behaviour of the quantum one.

Changepoint detection typically relies on a grid-search strategy for optimal data segmentation. When model fitting itself is expensive, repeatedly fitting a model on every candidate segment dominates the computation. Existing approaches mitigate this by pruning the grid, thus reducing the number of segments (and model fits). We propose Reliever, which instead cuts the number of model fits directly and nests seamlessly within standard grid-search routines. Reliever fits a small, deterministic collection of proxy models and reuses them wherever they apply, making it compatible with a wide range of existing algorithms. For high-dimensional regression with changepoints, coupling Reliever with an optimal grid-search method yields changepoint and coefficient estimators that are rate-optimal up to a logarithmic factor. Extensive numerical experiments demonstrate that Reliever rapidly and accurately detects changepoints across a wide range of high-dimensional and nonparametric models.

Optical cavities, resonant with vibrational or electronic transitions of material within the cavity, enable control of light-matter interaction. Previous studies have reported cavity-induced modifications of chemical reactivity, fluorescence, phase behavior, and charge transport. Here, we explore the effect of resonant cavity-phonon coupling on the transient photoconductivity in a hybrid organic-inorganic perovskite. To this end, we measure the ultrafast photoconductivity response of perovskite in a tunable Fabry-Perot terahertz cavity, designed to be transparent for optical excitation. The terahertz-cavity field-phonon interaction causes apparent Rabi splitting between the perovskite phonon mode and the cavity mode. We explore whether the cavity-phonon interaction affects the material electron-phonon interaction by determining the charge carrier mobility through the photoconductivity. Despite the apparent hybridization of cavity and phonon modes, we show that the perovskite properties, in both ground (phonon response) and excited (photoconductive response) states, remain unaffected by the tunable light-matter interaction. Yet the response of the integral perovskite-terahertz optical cavity system depends critically on the interaction strength of the cavity with the phonon: the transient terahertz response to optical excitation can be increased up to 3-fold by tuning the cavity-perovskite interaction strength. These results enable tunable switches and frequency-controlled induced transparency devices.

In this series of lectures, we discuss the basic theoretical concepts of magnonics and spintronics. We first briefly recall the relevant topics from quantum mechanics, electrodynamics of continuous media, and basic theory of magnetism. We then discuss the classical theory of magnetic dynamics: ferromagnetic and antiferromagnetic resonance, dynamic susceptibilities, and spin waves. We open the main discussion with phenomena of spin and exchange spin currents, spin torques, the spin Hall effect, and the spin Hall and Hanle magnetoresistance. Special emphasis is given to the effects of spin transfer torque and spin pumping, where we follow the celebrated derivation utilizing Landauer quantum multi-channel scattering matrix approach. Finally, we outline the most important features distinguishing antiferromagnetic dynamics from ferromagnetic one, which make antiferromagnets particularly promising material candidates for spintronics and magnonics.

We propose a definition of graph subshifts of finite type that can be seen as extending both the notions of subshifts of finite type from classical symbolic dynamics and finitely presented groups from combinatorial group theory. These are sets of graphs that are defined by forbidding finitely many local patterns. In this paper, we focus on the question whether such local conditions can enforce a specific support graph, and thus relate the model to classical symbolic dynamics. We prove that the subshifts that contain only infinite graphs are either aperiodic, or feature no residual finiteness of their period group, yielding non-trivial examples as well as two natural undecidability theorems.

Mean field spin glass models have undergone substantial mathematical development, but finite dimensional short range spin glasses remain much less understood. This paper proves several rigorous zero temperature signatures of glassy behavior for the Edwards-Anderson model with Gaussian couplings, in finite boxes in arbitrary dimension. First, the ground state is sensitive to small perturbations of the disorder: after a perturbation of size $p$, the new ground state is nearly orthogonal to the original one in site overlap once $p$ is sufficiently larger than the inverse system size. Second, the droplets generated by such perturbations have large interfaces; in the macroscopic-droplet regime, their boundaries satisfy lower bounds consistent with a fractal dimension strictly greater than $d-1$. Third, there exist macroscopic spin excitations whose energy cost is negligible compared with the size of their interface, in sharp contrast with ferromagnetic behavior. Fourth, the expected size of the critical droplet associated with a typical bond grows at least as a power of the volume. Finally, a natural boundary condition sensitivity for nearest-neighbor spin products cannot decay faster than order the inverse distance to the boundary, contrasting with recent exponential decay results for the two-dimensional random field Ising model. Taken together, these results provide rigorous evidence -- and, in the senses made precise below, proof -- of zero temperature glassy behavior in a short range spin glass model.

Principal stratification is a framework for making sense of causal effects conditioned on variables that may themselves have been affected by the treatment. For instance, in an evaluation of an educational intervention, some subjects in the treatment group may not fully utilize the intervention, and researchers may be interested in how this subgroup is affected. Most principal stratification estimators rely on strong structural or modeling assumptions and often require advanced statistical training to fit and evaluate, making them inaccessible to many applied researchers. In this paper, we introduce a new principal effect estimator for one-way noncompliance based on a binary indicator. Estimates may be computed using conventional regression methods (though the standard errors require a specialized sandwich estimator) and do not rely on distributional assumptions. We present a simulation study that demonstrates the novel method's greater robustness compared to popular alternatives and illustrate the method through a real-data analysis.

Matsubara dynamics is the classical dynamics which results when imaginary-time path-integrals are smoothed; it conserves the quantum Boltzmann distribution and appears in drastically approximated form in path-integral dynamics methods such as (thermostatted) ring-polymer molecular dynamics (T)RPMD and centroid molecular dynamics (CMD). However, it has never been compared directly with exact quantum dynamics for non-linear operators, because the difficulty of treating the phase has limited the number of Matsubara modes to fewer than 10. Here, we treat up to $\sim$200 Matsubara modes in simulations of a Morse oscillator coupled to a dissipative bath of harmonic oscillators. This is done by expressing the Matsubara equations of motion in the form of a generalised Langevin equation, approximating the noise to be real, and analytically continuing the momenta to convert the Matsubara phase into ring-polymer springs. The resulting equations of motion are stable up to a maximum value of modes which increases with bath coupling strength and decreases with system anharmonicity. The dynamics of the tail of highly oscillatory Matsubara modes is found to be harmonic, and can thus be computed efficiently. For a moderately anharmonic oscillator with a strong but subcritical coupling to the bath, the Matsubara simulations yield non-linear $\large\langle{\hat q^2\hat q^2(t)}\large\rangle$ time-correlation functions in almost perfect agreement with the exact quantum results. Reasonable agreement is also obtained for weaker coupling strengths, where errors arise because of the real-noise approximation. These results give strong evidence that Matsubara dynamics correctly explains how classical dynamics arises in quantum systems which are in thermal equilibrium.

For every given $p\in [1,+\infty)$ and $n\in\mathbb{N}$ with $n\ge 1$, the authors identify the strong $L^p$-closure $L_{\mathbb{Z}}^p(D)$ of the class of vector fields having finitely many integer topological singularities on a domain $D$ which is either bi-Lipschitz equivalent to the open unit $n$-dimensional cube or to the boundary of the unit $(n+1)$-dimensional cube. Moreover, for every $n\in\mathbb{N}$ with $n\ge 2$ the authors prove that $L_{\mathbb{Z}}^p(D)$ is weakly sequentially closed for every $p\in (1,+\infty)$ whenever $D$ is an open domain in $\mathbb{R}^n$ which is bi-Lipschitz equivalent to the open unit cube. As a byproduct of the previous analysis, a useful characterisation of such class of objects is obtained in terms of existence of a (minimal) connection for their singular set.

We study exact recovery for community detection in a Gaussian mixture model with dependent and heterogeneous Gaussian noise. The noise covariance matrix $Σ$ may be non-diagonal and, in the general formulation, singular. In the singular case, we write the Gaussian likelihood on the support of the induced measure and show that the maximum likelihood estimator (MLE) is a constrained quadratic optimization problem involving the Moore--Penrose inverse. For general covariance structures, we obtain sufficient conditions for exact recovery of the MLE when the community sizes are unknown and when they are known. These conditions are driven by the $Σ$-whitened separation $L_Σ(x,y)$ together with local one-step comparison inequalities in the near-truth regime. Under the additional assumption that $Σ$ is invertible, we derive converse results showing failure of exact recovery when a large family of local perturbations has sufficiently nondegenerate Gaussian comparison statistics. We then analyze a full-rank non-diagonal block-covariance model, prove a sharp exact-recovery threshold in the unknown-size setting, and identify a general no-gap mechanism under which the sufficient and necessary conditions coincide asymptotically.

The incremental singular value decomposition (SVD) updates a truncated SVD as new columns arrive, replacing a single large SVD with a sequence of small ones. In floating-point arithmetic, each update multiplies the running singular basis by a small orthogonal factor, and the accumulated product loses orthogonality unless the basis is reorthogonalized periodically. How often this reorthogonalization is needed has been an open question; we answer it by restructuring the algorithm so that rank-preserving updates are accumulated implicitly and applied in batches, reducing the number of large orthogonal multiplications from $n$, the stream length, to $r$, the numerical rank. We prove that this restructuring preserves the exact-arithmetic output of the original algorithm and establish two forward-error bounds. First, we sharpen the existing operator-norm truncation bound from $n\,\texttt{tol}$ to $\sqrt{n}\,\texttt{tol}$, and show the new rate is attained on a constructive example. Second, under a standard probabilistic rounding-error model, we prove that the loss of orthogonality of the computed left factor is independent of the stream length $n$ and depends on $m$, the length of each incoming column, only through a single $\sqrt{m}$ factor. Numerical experiments confirm both bounds and demonstrate that the proposed algorithm runs $4.5\times$ to $34\times$ faster than its closest competitors.

This paper considers polynomial optimization with unbounded sets. We give a homogenization formulation and propose a hierarchy of Moment-SOS relaxations to solve it. Under the assumptions that the feasible set is closed at infinity and the ideal of homogenized equality constraining polynomials is real radical, we show that this hierarchy of Moment-SOS relaxations has finite convergence, if some optimality conditions (i.e., the linear independence constraint qualification, strict complementarity and second order sufficiency conditions) hold at every minimizer, including the one at infinity. Moreover, we prove extended versions of Putinar-Vasilescu type Positivstellensatz for polynomials that are nonnegative on unbounded sets. The classical Moment-SOS hierarchy with denominators is also studied. In particular, we give a positive answer to a conjecture of Mai, Lasserre and Magron in their recent work.

Dantzig's vertex pivot simplex method has been published for more than seven decades. Amazingly, it remains one of the most efficient methods to solve linear programming (LP) problem after numerous efforts trying to find some better methods. In this paper, we propose a facet pivot simplex method and demonstrate by numerical testing that the new method is very promising compared to the vertex pivot method. Since there is no polynomial pivot simplex algorithm for linear programming problems after many decades of effort, we hope that this new type of pivot algorithm will give us some hope to find a polynomial pivot simplex method for linear programming problems. A Matlab implementation of the facet pivot algorithm and Netlib benchmark test problems are available in Matlab file exchange website.

We study the geometry of the Quot scheme $\mathrm{Quot}^l_{S}(\mathcal{E})$ of length $l$ coherent sheaf quotients of a locally free sheaf $\mathcal{E}$ on a smooth projective surface $\mathrm{S}$. In particular, we investigate the nature of its singularities, its intersection theory, and the cohomology of sheaves on $\mathrm{Quot}^l_{S}(\mathcal{E})$.

In this paper we extend a decision procedure for the Boolean algebra of finite sets with cardinality constraints ($\mathcal{L}_{\lvert\cdot\rvert}$) to a decision procedure for $\mathcal{L}_{\lvert\cdot\rvert}$ extended with set terms denoting finite integer intervals ($\mathcal{L}_{[\,]}$). In $\mathcal{L}_{[\,]}$ interval limits can be integer linear terms including \emph{unbounded variables}. These intervals are a useful extension because they allow to express non-trivial set operators such as the minimum and maximum of a set, still in a quantifier-free logic. Hence, by providing a decision procedure for $\mathcal{L}_{[\,]}$ it is possible to automatically reason about a new class of quantifier-free formulas. The decision procedure is implemented as part of the $\{log\}$ tool. The paper includes a case study based on the elevator algorithm showing that $\{log\}$ can automatically discharge all its invariance lemmas some of which involve intervals.

We study the distribution of zeroes of power series with infinite radius of convergence. The coefficients of the series have the form $ξ(n)a(n)$, where $a$ is a smooth sequence of positive numbers, and $ξ$ is a sequence of complex-valued multipliers having binary correlations and no gaps in the spectrum. We show that under certain assumptions on the smoothness of the sequence $a$ and on the binary correlations of the multipliers $ξ$, the zeroes of the power series are equidistributed with respect to a radial measure defined by the sequence $a$. We apply our approach to several examples of the sequence $ξ$: (i) IID sequences, (ii) sequences $e(αn^2)$ with Diophantine $α$, (iii) random multiplicative sequences, (iv) the Golay--Rudin--Shapiro sequence, (v) the indicator function of the square-free integers, (vi) the Thue--Morse sequence.

We show that the variance of the number of connected components of the zero set of the two-dimensional Gaussian ensemble of random spherical harmonics of degree n grows as a positive power of n. The proof uses no special properties of spherical harmonics and works for any sufficiently regular ensemble of Gaussian random functions on the two-dimensional sphere with distribution invariant with respect to isometries of the sphere. Our argument connects the fluctuations in the number of nodal lines with those in a random loop ensemble on planar graphs of degree four, which can be viewed as a step towards justification of the Bogomolny-Schmit heuristics.

The degree sequence $(n_{i,j}(k), 1\leq i,j\leq d, k\geq 0)$ of a multitype forest with $d$ types encodes the number of individuals of type $i$ with $k$ children of type $j$. In this paper, we introduce a simple algorithm to sample a multitype forest uniformly from the set of all forests with a given degree sequence (MFGDS). This generalizes the single-type construction of Broutin and Marckert (2014). To achieve this, we extend the Vervaat transform (1979) to multidimensional discrete exchangeable increment processes. We demonstrate that MFGDS extend multitype Bienaymé--Galton--Watson (MBGW) forests. Specifically, mixing MFGDS laws recovers MBGW forests conditioned on a fixed size for each type (CMBGW). Under general assumptions, we derive the law of the total population by types in an MBGW forest and relate it to a multidimensional first-hitting time. This result, which is of independent interest, generalizes the Otter--Dwass (1949,1969) and Kemperman (1950) formulas. By combining this relation with our MFGDS construction, we provide an efficient algorithm to simulate CMBGW forests, generalizing the work of Devroye (2012). When the variance is finite, the expected simulation time outperforms standard naïve methods. For the proof we derive a generalized local limit theorem for multidimensional first-hitting times. Finally, we apply our results to enumerate plane, labeled, and binary multitype forests with fixed sizes, generalizing results of Pitman (1998).

Given a sequence $ξ\colon \mathbb Z_+ \to \mathbb C$, we find a simple spectral condition which guarantees the angular equidistribution of the zeroes of the Taylor series \[ F_ξ(z) = \sum_{n\ge 0} ξ(n) \frac{z^n}{n!}\,. \] This condition yields practically all known instances of random and pseudo-random sequences $ξ$ with this property (due to Nassif, Littlewood, Chen-Littlewood, Levin, Eremenko-Ostrovskii, Kabluchko-Zaporozhets, Borichev-Nishry-Sodin), and provides several new ones. Among them are Besicovitch almost periodic sequences and multiplicative random sequences. It also conditionally yields that the Möbius function $μ$ has this property assuming "the binary Chowla conjecture".

The projective unitary group PU(n) is the quotient of the unitary group U(n) by its center. We compute the integral cohomology ring of PU(n) using explicit constructed generators.

Under steady flow conditions, the topological complexity inherent to all random 3D porous media imparts complicated flow and transport dynamics. It has been established that this complexity generates persistent chaotic advection via a three-dimensional (3D) fluid mechanical analogue of the baker's map which rapidly accelerates scalar mixing in the presence of molecular diffusion. Hence pore-scale fluid mixing is governed by the interplay between chaotic advection, molecular diffusion and the broad (power-law) distribution of fluid particle travel times which arise from the non-slip condition at pore walls. To understand and quantify mixing in 3D porous media, we consider these processes in a model 3D open porous network and develop a novel stretching continuous time random walk (CTRW) which provides analytic estimates of pore-scale mixing which compare well with direct numerical simulations. We find that chaotic advection inherent to 3D porous media imparts scalar mixing which scales exponentially with longitudinal advection, whereas the topological constraints associated with 2D porous media limits mixing to scale algebraically. These results decipher the role of wide transit time distributions and complex topologies on porous media mixing dynamics, and provide the building blocks for macroscopic models of dilution and mixing which resolve these mechanisms.

Classification is a common statistical task in many areas. In order to ameliorate the performance of the existing methods, there are always some new classification procedures proposed. These procedures, especially those raised in the machine learning and data-mining literature, are usually complicated, and therefore extra effort is required to understand them and the impacts of individual variables in these procedures. However, in some applications, for example, pharmaceutical and medical related research, future developments and/or research plans will rely on the interpretation of the classification rule, such as the role of individual variables in a diagnostic rule/model. Hence, in these kinds of research, despite the optimal performance of the complicated models, the model with the balanced ease of interpretability and satisfactory performance is preferred. The complication of a classification rule might diminish its advantage in performance and become an obstacle to be used in those applications. In this paper, we study how to improve the classification performance, in terms of area under the receiver operating characteristic curve of a conventional logistic model, while retaining its ease of interpretation. The proposed method increases the sensitivity at the whole range of specificity and hence is especially useful when the performance in the high-specificity range of a receiver operating characteristic curve is of interest. Theoretical justification is presented, and numerical results using both simulated data and two real data sets are reported.