Papers

Let $Hf(x)=x^{-1}\int_0^x f(t)\,dt$ be the Hardy operator on $\mathbb R_+$. Korenovskii proved that $$ \Vert Hf\Vert_{\mathrm{BMO}}\geq \frac{e\alpha_0}{4}\Vert f\Vert_{\mathrm{BMO}}, $$ for every nonincreasing and locally integrable $f$, where $\alpha_0$ is defined by the relation $\Vert H\chi_{(0,1)}\Vert_{\mathrm{BMO}} = \alpha_0 \Vert \chi_{(0,1)}\Vert_{\mathrm{BMO}}$, and conjectured that the factor $e/4$ could be removed. We prove this conjecture by showing that every nonincreasing locally integrable $f$ satisfies $$ \Vert Hf\Vert_{\mathrm{BMO}}\geq \alpha_0\Vert f\Vert_{\mathrm{BMO}}. $$ The constant $\alpha_0$ is optimal, with equality for the one-jump functions $\chi_{(0,a)}$.

Let $(R,\mathfrak m)$ be an unramified regular local ring of mixed characteristic $(0,p)$ and dimension $d$ and let $I\subseteq R$ be an ideal. We prove that $depth(R/I)\geq 3$ implies $cd(I)\leq d-3$, and if $R$ is essentially of finite type over a DVR, then $depth(R/I)\geq 4$ implies $cd(I)\leq d-4$. More generally, $H_I^j(R)$ is a $\mathbb{Q}$-vector space whenever $j>d-depth(R/I)$, thus vanishing of local cohomology in this range is determined completely by the characteristic zero fiber.

Dynamical systems model trajectory data generated by fixed underlying dynamics, with applications ranging from biology to physics. Especially in scientific settings, dynamical systems are not generic but often exhibit symmetries imposed by physical laws, formalized through equivariance with respect to group actions. The identification problem concerns recovering the parameters of a system from observed trajectories. In this work, we study adaptive symmetry discovery for dynamical system identification and address how a system can be identified from a single trajectory when it is equivariant with respect to an unknown symmetry group. To this end, we first show that for known symmetries, the system can be identified from a significantly shorter single trajectory than in the generic setting, and we precisely characterize this improvement. We then consider the automatic symmetry discovery setting, proposing a method to learn the symmetry group directly from a single trajectory and incorporate it into the identification procedure, achieving the same optimal trajectory length as in the known-symmetry case. Our analysis relies on tools from group representation theory and the expander properties of Cayley graphs, and may be of independent interest for the study of symmetries in dynamical systems.

In the classical normal means problem, independent train–test folds can be constructed by perturbing the data with normal randomization. Averaging over $K$ such folds yields a cross-validation estimator whose bias depends on the marginal distribution of the randomization variables, while its variance depends on their joint distribution. This raises the questions: which joint law is optimal, and how to construct the corresponding randomization scheme? We show that: (i) for smooth estimators, antithetic randomization with pairwise correlation $\rho=-1/(K-1)$ is necessary and sufficient for the reducible variance due to randomization to remain bounded as the bias vanishes; (ii) a general construction yields a class of antithetic schemes, within which the jointly normal scheme is minimax optimal; and (iii) for non-smooth estimators with finitely many jump discontinuities, antithetic randomization improves the asymptotic rate of the reducible variance, while a simple control variate restores bounded variance when the discontinuities are known.

This paper is concerned with the parabolic-elliptic chemotaxis system with weakly singular sensitivity and logistic source:~$ u_t=\Delta u-\chi\nabla\cdot(\frac{u}{v^\alpha}\nabla v) +ru-\mu u^2$, $0=\Delta v-v+u,$ under the homogeneous Neumann boundary in a smooth bounded convex domain $\Omega\subset\mathbb{R}^n$ for $n\ge 2$. where $\alpha\in(0,1)$ and $\chi,r,\mu>0$. If $\alpha\in(0,\frac{n+2}{2n})$ and $\mu>\mu_0$ with $\mu_0>0$ suitably large, we give the explicit expression of the upper bound for $u$ with respect to the coefficient $\mu$ after some time, without establishing the uniformly positive bound for $v$ from below. Furthermore, by dealing with the corresponding non-singular chemotaxis system via the transformation $z=v^{1-\alpha}$, it is proved that the solution $(u,v)$ converges to $(\frac{r}{\mu},\frac{r}{\mu})$ in $L^\infty$-norm as $t\rightarrow\infty$ if $\alpha\in(0,\frac{1}{2})$ and $\mu>\mu_\star$ sufficiently large, which is moreover enjoying exponential convergence when $\alpha\in(0,\frac{n+2}{n^2+4})$.

We study the monodromy action of the mixed braid group $B_{n,\mathcal{P}}$ on the first cohomology of cyclic branched covers of $\mathbb{P}^1$, which are mutually determined by a partition of branch points by equal ramification. The monodromy representation splits into irreducible representations on the $t$-eigenspaces of the deck transformation. For each, we construct an explicit spanning set using lifts of Pochhammer contours and figure-eight curves, and compute the Hermitian intersection form. The representation factors through a reduced mixed braid group by dropping $t$-invisible parts of the partition (those with trivial local monodromy). In this reduced representation, each generator acts by a complex reflection when the corresponding spanning class is non-isotropic, and by a unitary transvection when it is isotropic. Provided $\infty$ has non-trivial local monodromy, the factored representation is isomorphic to the reduced multivariate Burau representation evaluated at $t$.

We study the classifying map $c\colon BX\to K(\As(X),1)$ of a rack $X$ of finite type. Let $t = \Type(X)$. We prove that $t c_{n*}=0$ for every $n\ge2$ when $X$ is connected, and that $t^{n-1}c_{n*}=0$ on the torsion subgroup $\Tor H_n^\mathbb{R}(X)$ without any connectedness assumption. For a finite rack, under our sign conventions, the rationalized classifying map in degree $n$ is given by $(-1)^n$ times the canonical projection from the $n$-fold tensor power of the orbit module to its $n$-th exterior power. For an arbitrary rack of finite type, we determine $H_2^{\mathrm{gr}}(\As(X);\mathbb{Z}[1/t])$. We also derive low-dimensional applications to symplectic and Alexander structures.

This paper is devoted to the study of the holonomy properties of $(2n+s)$-dimensional $\mathcal{S}$-manifolds equipped with their characteristic connection. These structures generalize Sasakian geometry to higher CR-codimensions and, when viewed as geometries with parallel skew-torsion, share many holonomy features with the Sasakian case. We show that $\mathcal{S}$-manifolds of arbitrary CR-codimension $s\geq 1$ provide examples of geometries with parallel skew-torsion whose holonomy is reducible, indecomposable, and of special type. We also deduce that any $\mathcal{S}$-manifold admits a locally defined Riemannian submersion over a Kähler manifold. We describe the corresponding curvature relations and establish a bijective correspondence between the Kähler-Einstein condition on the base space and a generalized $\eta$-Einstein condition on the total space. As every $\mathcal{S}$-manifold comes with a characteristic foliation whose transverse geometry is Kähler, it is natural to relate the $\eta$-Einstein condition to the transverse metric, leading to a bijection between $\eta$-Einstein $\mathcal{S}$-manifolds and transverse Kähler-Einstein metrics, extending the classical Sasakian correspondence to arbitrary CR-codimensions. As an application to Ricci-flat metric connections with parallel skew-torsion, we prove that an $\mathcal{S}$-manifold is ${\rm Ric}^{\nabla}$-flat if and only if it is transverse Kähler-Einstein with Einstein constant $\lambda=4s$. An illustration of this characterization is presented by a Sasakian example.

Given a spherical finite tensor category C, not necessarily semisimple, and a two-sided modified trace on its projective ideal, we define an open-closed three-dimensional topological field theory with values in vector spaces. The bordism category has as morphisms three-dimensional bordisms with corners, whose boundary is partitioned into the gluing boundary, parametrised by source and target surface, and the unparametrised free boundary. The free boundary is equipped with an embedded C-coloured graph satisfying an admissibility condition. Our construction starts from a new three-manifold invariant based on the multi-handlebody invariant of [arXiv:1809.07991] and the chromatic maps of [arXiv:2302.04509]. The open-closed topological field theory is then obtained via the universal construction.

Let $p\neq 2$ be a prime. We determine the set of numbers that occur as the $u$-invariant of perfectoid subfields of $\mathbb{C}_p$. Given any number that is the $u$-invariant of a field of characteristic $p$, we give an explicit construction of a perfectoid field with the same $u$-invariant. In particular, if a number is the $u$-invariant of a field of characteristic $p$, it is also the $u$-invariant of a field of characteristic zero.

Level-constraints model one-bit-quantized signaling over real-alphabet continuous-time channels. New lower bounds on the capacity of bandlimited, additive white Gaussian noise channels with level-constrained inputs are derived by using probabilistic temporal shaping (PTS). The optimal shaping density is derived for signals with one data-dependent sign change per Nyquist-rate sample, which ensures they satisfy an invertibility requirement. Calculations show that PTS improves the best existing lower bound by at least 1.94 dB at high signal-to-noise ratio (SNR). A simpler sequential PTS scheme achieves a gain of 1.64 dB at high SNR.

We consider the heat equation on $(0,L)$ with homogeneous Dirichlet condition at one endpoint and a Dirichlet boundary control at the other. If \(C_{\mathrm H}(T,L)\) denotes the optimal \(L^2\) null-control cost for initial data in \(H^{-1}(0,L)\), we prove that \[ C_{\mathrm H}(T,L) = \exp\left(\frac{\kappa_*L^2+o(1)}{T}\right), \qquad \kappa_* = \frac{\Gamma(\frac14)^4}{8\pi^3} \simeq 0.696601964842838, \qquad T\to0^+. \] The constant \(\kappa_*\) coincides with the upper-bound constant obtained by Dardé and Ervedoza (2019, ANPDE), which was expressed there through a convergent series. This closes the gap between the lower bound obtained in by Lissy (2015, JDE) and the upper bound established by Dardé and Ervedoza.

We introduce conditional cylindrical neural networks for approximating functionals of conditional laws in McKean-Vlasov equations with common noise. Fourier moments of the initial law and truncated signatures of the time augmented common noise are mapped by a mixture density network to a Gaussian mixture approximation of the conditional law. A cylindrical neural network then evaluates the target functional through analytic integrals against this predicted measure. Rough path well posedness and stability provide a conditional law map that is continuous in the initial distribution and the rough driver and agrees almost surely with the classical conditional law at the Itô Brownian lift. Combining this continuity with Fourier separation, signature uniqueness, Wasserstein density of Gaussian mixtures, and neural universal approximation, we prove an $L^2$ universal approximation theorem for continuous square integrable functionals. The numerical study implements the resulting two stage procedure on six examples, including non Gaussian initial laws, nonlinear drift, multiplicative common noise, and a two dimensional state. Independent particle references are used when no closed form law is available. The learned conditional law and functional approximations consistently improve on the empirical particle plug in, and additional experiments examine feature sensitivity, training from one terminal observation per common noise scenario, and Itô–Stratonovich consistency.

The shape function of a pair of finite-valued random variables was introduced in arXiv:2606.23849, where it was used to derive a spectral bound on the entanglement of the pair, a quantity measuring the extent to which their mutual information can be extracted. In this article, we further develop the theory of shape functions for pairs of random variables. We prove that, when $(X,Y)$ is uniformly supported on the edges of a biregular bipartite graph, the value of the shape function $\mathcal{S}(X,Y)(\alpha,\beta)$ equals the logarithm of the operator norm of the graph's incidence matrix with respect to Lebesgue exponents determined by $(\alpha,\beta)$.This identification, in particular, enables the numerical approximation of the shape function and, by duality, of the extension profile, also known as the tension region, of the pair. We also establish a collection of relations and inequalities satisfied by shape functions, including convexity and monotonicity properties, composition inequalities, and relations describing their behavior under conditioning and the adjoining of variables.

We study feedback stabilization for the discrete-time system $x_{t+1}=f(x_t)+u_t+w_{t+1}$ in $\mathbb{R}^d$ with unknown $f$ and arbitrary bounded disturbances. For scalar plants, the sharp feedback capability threshold under generalized Lipschitz uncertainty is $3/2+\sqrt{2}$. We treat fully coupled vector-valued systems, where scalar order and interval recursion are unavailable and coupling precludes a coordinatewise reduction. We introduce a response-history escape game in which the adversary seeks a finite envelope and an unbounded state radius. Borel determinacy ensures that exactly one player has a winning strategy at each slope. We prove that the same player wins from every finite response history, and slope monotonicity gives an independently defined game value $\Gamma_d$. We prove that $\Gamma_d$ is finite and is the strict feedback capability threshold for the plant problem. If $L<\Gamma_d$, one causal feedback law stabilizes every plant in the uncertainty class against every bounded disturbance sequence. If $L>\Gamma_d$, for every causal feedback law there exist a plant in the same class and a bounded disturbance sequence such that the closed-loop state sequence is unbounded. The proof uses one controller for all subcritical slopes and a realization in a Hilbert space based on the Kirszbraun–Valentine extension theorem. An explicit nearest-neighbor law gives a lower bound above one in every finite dimension, including $\Gamma_2\ge 2/\sqrt{3}$. Dimension monotonicity gives $\Gamma_d\le\Gamma_1$, and comparison with the scalar theory yields $\Gamma_1=3/2+\sqrt{2}$.

On the occasion of George Andrews' and Bruce Berndt's combined 170th birthday, we prove two conjectures of Sir Frederick Pollock that are more than 170 years old. In 1843, Pollock conjectured that every positive integer is the sum of at most 13 icosahedral numbers, or at most 21 dodecahedral numbers. We establish refined versions of both conjectures.

In this paper, we focus on constructions of unique-decodable/list-decodable on the recently studied $(t,e)$-composite-asymmetric error-correcting codes ($(t,e)$-CAECCs). Let $X$ be an $m\times n$ binary matrix, in which each row has Hamming weight $w$. When at most $t$ rows of $X$ suffer from errors and in each of these erroneous rows, there are at most $e$ $1 \to 0$ errors, we say that a $(t,e)$-composite-asymmetric-error occurs in $X$. For general $m,n,w,t,e$, we propose new constructions of $(t,e)$-CAECCs with redundancy at most $(t-1)\log(m)+O(1)$, where $O(1)$ is a number independent of the code-length $m$. In particular, this gives a class of $(2,e)$-CAECCs that are optimal in terms of their redundancy. %(in terms of redundancy, regarded as a function of the number of rows $m$) s. When $m$ is a prime power, the redundancy can be further reduced to $(t-1)\log(m)-O(\log(m))$. To further increase the size of these codes, we introduce a combinatorial object called a weak $B_e$-sets. When $e=w$, we show an efficient way to encode/decode our codes. At last, we investigate how much we can gain if we relax the requirement of uniquely decoding to list-decoding. It is shown that when the list size is $t!$ or an exponential function of $t$, there are list-decodable $(t,e)$-CAECCs with constant redundancy. When the list size is two, we show that there are list-decodable $(3,2)$-CAECCs with redundancy $\log(m)+O(1)$.

In a finite recursive game in the sense of Everett, both players have stationary epsilon-optimal strategies for every epsilon>0. Frederiksen and Miltersen strengthened this result by showing that the strategies for all sufficiently small epsilon can be encoded by finitely many monomials: at every state, all but possibly one of the action probabilities are constants times integer powers of epsilon. The resulting finite symbolic object specifies a strategy for every sufficiently small accuracy. Their proof uses semialgebraic selection and Puiseux series. We give an alternative elementary proof of this regularity theorem for recursive games. We start with stationary strategies that guarantee vectors approaching the value through Everett's one-sided region. After fixing their support, we express, for each pure stationary reply, all absorption probabilities as quotients of directed-forest polynomials with nonnegative coefficients and a common positive denominator. Each payoff is a fixed signed linear combination of these quotients. We then compress the asymptotic orders of the finitely many forest monomials into one integer weight vector. This proof uses neither semialgebraic selection nor Puiseux series. Furthermore, for rational games with a fixed number N of active states, we present a deterministic polynomial-time algorithm that computes a monomial family exactly. It returns all algebraic coefficients in one ordered real univariate representation. The representation length and running time are at most L^{(N+1)^{O(N)}}, where L is the input length.

Motivated by the work of Ischebeck on the behaviour of algebraic $K$-theoretic functors under subintegral extensions, we investigate the analogous question in Hermitian $K$-theory. We prove that Grothendieck-Witt groups in degree zero are invariant under subintegral extensions. We also investigate the cancellation problem for quadratic spaces over geometric subrings of polynomial rings and establish a cancellation theorem for quadratic spaces of sufficiently large Witt index.

We give explicit examples of holomorphic submersions $p\colon Y\to X$ such that $Y$ and $X$ are complex manifolds, all the fibers of $p$ are biholomorphic to the unit disc in the complex plane, but the mapping $p$ is not holomorphically locally trivial. In our examples the complex manifolds $Y$ are neighborhoods of the zero section of a line bundle $L$ on $X$; the line bundle $L$ is not, in general, supposed to be positive or negative.

A convex polygon is called small if its diameter is at most one. Reinhardt proved the universal perimeter bound $\operatorname{perim}(P)\le U_n:=2n\sin(\pi/(2n))$, and the bound is attained whenever $n$ has a nontrivial odd divisor. The remaining power-of-two cases have resisted exact solution beyond $n=8$. We give computer-assisted proofs of the first three cases, $n=16,32,64$, and in each case prove uniqueness of the maximizing congruence class. The proof architecture is common to all three cases: pass to the difference body $P-P$; encode its reconstruction by a sign code; prove that every global maximizer is saturated, so all difference-body vertices lie on the unit circle; localize every competitive configuration near the regular angle vector; exhaustively screen the sign codes using exact arithmetic; eliminate all nonwinning dihedral orbits; and prove uniqueness inside the winning code by strong convexity and a quantitative KKT argument. The exact certificates cover $2^{15}$ normalized codes for $n=16$, $2^{31}$ normalized codes for $n=32$, and all $2^{64}$ half-codes for $n=64$, leaving respectively $16$, $96$, and $896$ survivors before orbit elimination. The accompanying source package contains the verifiers, recorded outputs, hashes, and separate computational cross-checks.

We establish quantitative $L^1$-stability estimates for the Bakry–Émery log-Sobolev and Talagrand inequalities with a universal exponent of 1/19 governing the corresponding deficits. Our approach relies on a Maurey-type argument combined with stability estimates for the Prékopa–Leindler inequality. In the radial setting, the exponent of both deficits can be improved to $1/2,$ which turns out to be optimal. As an application, we establish an estimate for the hypercontractivity deficit of the Hopf–Lax semigroup in the Bakry–Émery setting. In particular, these stability results provide elementary characterizations for the equality cases in the previously mentioned inequalities.

We study the achievable rate performance of dual-polarized passive reconfigurable intelligent surfaces (RISs) and dual-polarized network-controlled repeaters (NCRs) in a point-to-point wireless link. While RISs rely on passive beamforming with large array gains, NCRs exploit active amplification across polarization branches. We develop a unified analytical framework that captures the impact of amplification, noise propagation, and deployment geometry under different power-budget models. Closed-form rate expressions are derived, and the optimal one-stream and two-stream transmission strategies for NCRs are characterized. The results reveal that RISs are competitive in short-range scenarios and benefit from large array sizes, whereas NCRs become preferable in long-range deployments due to their ability to compensate for pathloss through amplification.

Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage. Combinatorial DNA encoding enhances this potential by increasing the logical density through combinations of DNA shortmers, where each sequence position is represented by a set of predefined short DNA fragments, allowing more data to be encoded using fewer synthesis cycles. However, this method introduces unique synthesis and sequencing errors. In this study, we characterize errors in combinatorial DNA-based storage systems. We reveal that asymmetric combinatorial erasure errors, defined as the omission of a single shortmer from the set defining the combinatorial letter, are a prevalent error type, particularly in large-scale systems where read coverage is limited. In two previously published datasets, we observed a high frequency of erasure errors, where missing sequences obstruct the reconstruction of combinatorial letters. We conducted a large-scale experimental proof-of-concept and confirmed that erasure errors become increasingly prominent with reduced sequencing depth: below 50 reads per sequence, their frequency sharply increased. We developed an asymmetric error-correcting code for these errors, utilizing tensor-product codes to integrate standard erasure and substitution-correcting codes (such as Reed-Solomon (RS) codes) with asymmetric Varshamov-Tenengolts codes. We validated its performance in simulations and in a second large-scale experiment directly comparing it with the more straightforward 2D RS scheme. Our method consistently outperformed 2D RS, particularly in erasure-dominated scenarios, and demonstrated superior decoding accuracy under low coverage conditions where 2D RS struggled to decode the data. Our findings demonstrate the importance of error correction schemes tailored to the asymmetric nature of errors in combinatorial DNA.

Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees—a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection—that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd–Max quantizers and a Bussgang decomposition-based achievable-rate bound.

We show that if $X$ is a projective variety of dimension $\geq 2$ that is not a rational surface, then the blow up of $X$ at sufficiently many very general points has no nontrivial automorphism. Similar results were known before for $\mathbb{P}^2$ and $\mathbb{P}^3.$

Two-way contingency tables are tested for independence throughout applied statistics (genomics, network and text co-occurrence, pharmacovigilance, ecology, survey cross-tabulation), and the routine test reads asymptotic Gaussian tail probabilities off the table cell by cell. On the tables people actually analyze this is badly miscalibrated: across 5,543 real public two-way tables, two-thirds have counts small enough or margins heterogeneous enough that the asymptotic maximum-cell independence scan false-positives at a mean 33% against a 0.05 target, while an exact margin-conditional anchor holds at about 1%. The failure is sharpest when the departure is sparse, the association concentrated in a few cells, where the omnibus chi-square is inefficient and the sharp object is the detection boundary. In three parts we answer where a sparse signal can be seen, which combiner attains that limit, and how to calibrate at small counts. Part I specializes the sparse-detection theory of Ingster (1997), Donoho and Jin (2004), and Chhor, Mukherjee and Sen (2024) to independence: the table is a heteroscedastic Gaussian sequence whose variance profile is the expected-count table, fixed by the margins via CVe^2 = (1 + CVr^2)(1 + CVc^2) - 1, giving an exact signal map and a closed-form separation radius valid under a moderate-deviation growing-count condition. Part II shows higher criticism, with its closed-form Jaeschke-Eicker null, attains that boundary adaptively over unknown sparsity. Part III measures the small-count calibration failure (size 0.3 to 0.6 even under uniform margins, so the driver is the per-cell tail), removes it with exact margin-conditional laws (size 0.003 to 0.009), and gives a routing rule sending large-count tables to the asymptotic gate and small-count tables to the exact anchor. Every claim is reproduced from openly deposited, deterministically seeded code.

We investigate a computable and empirically implementable framework for continuous-time mean–variance optimal portfolio selection with random market coefficients. The market model is built on a tractable multifactor stochastic volatility structure, which captures state-dependent risk premia, stochastic volatility, and dynamic cross-asset dependence. The optimal control is characterized by stochastic Riccati equations. On the computational side, we design an iterative BSDE-based procedure to approximate the stochastic Riccati equation from above and below, where the initial upper and lower bounds are obtained by solving two linear BSDEs. We then apply a logarithmic transformation to remove the singularity in the SRE, and solve the resulting transformed equation using both Deep BSDE and DBDP methods. The linear BSDE bounds also provide effective initial-value estimates, improving the convergence speed and training stability of the Deep BSDE solver. Empirical experiments based on sector ETF data show that the proposed multifactor mean–variance strategy produces smooth target-return wealth dynamics, with improved drawdown control and downside-risk protection relative to benchmark strategies. These results demonstrate the practical potential of combining stochastic Riccati equations, neural BSDE solvers, and multifactor market modeling for dynamic asset allocation.

In this work, we construct a generalization of the $G_n^k$-theory to the case of an arbitrary hypergraph. The case of spherical braids is considered separately, using the stratification of the moduli space $\mathcal{M}_n(S^2)$ and the hypergraph $\Gamma_n^{\mathrm{sph}}$ encoding projective constraints. In contrast to the original $G_{n}^{k}$ theory, where codimension-one properties are determined by exactly $k$ particles, the present work considers various cases corresponding to strata of codimension~$1$. These groups admit nice maps to free products of cyclic groups. Among unsolved problems, we emphasize the question how the above construction works for abelian varieties and, in particular, for elliptic curves.

Molecular motion within living cells provides a window into the physical principles underlying intracellular organization and transport. Yet a fundamental limitation remains: measured trajectories are often short, noisy, and randomly oriented, rendering spatial anisotropies inaccessible to conventional analyses. We show that turning-angle statistics provide a robust approach for uncovering anisotropies in anomalous diffusion. Using a two-dimensional anisotropic fractional Brownian motion model with random orientations, we show theoretically and through simulations that turning-angle distributions preserve signatures of anisotropy. We apply this approach to single-particle tracking data, including quantum dots in the cytoplasm of HeLa cells and membrane proteins in hippocampal neurons. Turning angles reveal hidden anisotropies in the motion of quantum dots and Kv1.4 channels, and in specific dynamical states of glycoprotein CD4. These results establish turning-angle analysis as a powerful strategy for detecting organization in complex environments and reveal that anisotropic anomalous diffusion is an {often} overlooked feature of intracellular dynamics.