Papers

Following previous results recently obtained by us, on Information Geometry versus Geometrical Thermodynamics and on the exact calculation of partition functions for extended Souriau Gibbs distributions on Calabi Vesentini manifolds, we study the differential geometry of the corresponding thermo-metrics. A general intriguing scheme is discovered and put into evidence. A small yet significant difference, distinguishes the even from the odd dimensional instance of the microscopic CV manifolds. Apart from that the complete thermo-space is flat when no constraint is introduced. Freezing the magnetic fields, which can be done according to complicated combinatorials, forces the thermo-system to evolve on curved submanifolds of the thermo–space that have a structure depending only on the length of the $n-1$ chain of frozen contiguous magnetic fields. The behavior of Riemann tensor components for such spaces is codified by a symmetric matrix with peculiar behavior along special symmetrically arranged submanifolds that, might be responsible for the generation of curvature walls and for the categorical partitioning of the thermo space. The embedding of this curved submanifold into $\mathbb{R}^{2n-1}$ can be traced back to the vanishing of magnetic fields and, in this case, the flat metric on $\mathbb{R}^{2n-1}$ is the $\mathfrak{a}_{2n-1}$ simple Lie algebra Cartan matrix. In another version the flat embedding reveals the geometric interpretation of the $n$-manifold as a generalized translation hypersurface. The boundary at infinity has a hypercube structure whose face central points and vertices appear, numerically, to be the end-points of all geodesics depending only on their angular slope at the start. This general feature is reminiscent of the causal structure at infinity of Lorentzian space-times and of Penrose diagrams.

The evaluation homomorphism from the super Yangian \( \mathrm{Y}(\mathfrak{gl}_{m|n}) \) to \( \mathrm{U}(\mathfrak{gl}_{m|n}) \) induces a \( \mathrm{Y}(\mathfrak{gl}_{m|n}) \)-module structure on any finite dimensional simple \( \mathrm{U}(\mathfrak{gl}_{m|n}) \)-module \( L(\lambda) \). In this paper, we give necessary and sufficient conditions for the tensor product of such evaluation \( \mathrm{Y}(\mathfrak{gl}_{m|n}) \)-modules, \( L_g(\lambda) \otimes L_h(\gamma) \), to be simple, provided each of \( \lambda \) and \( \gamma \) is either covariant tensor or essentially typical. Our proof is based on the existence of a Gelfand–Tsetlin basis for finite dimensional simple \( \mathrm{U}(\mathfrak{gl}_{m|n}) \)-modules with highest weights that belong to these two families: covariant tensor and essentially typical. The obtained result is a super analogue of the Molev's result for the classical Yangian \( \mathrm{Y}(\mathfrak{gl}_n) \). Combining this with the binary property of tensor products of covariant evaluation modules, we obtain an irreducibility criterion for arbitrary tensor products of covariant evaluation modules.

Phase separation phenomena occurring in complex fluids are known to be sensitive to hydrodynamic effects: when a binary fluid mixture undergoes spinodal decomposition, the emerging pattern of domains is no longer dictated by interfacial dynamics alone, but is reshaped by advection. In several applications of interest, advection effects may even cause the onset of turbulence, further affecting the segregation process on a wide range of spatial scales. Motivated by this, we propose a Cahn-Hilliard model driven by transport noise able to capture the intrinsic stochasticity of turbulence. The model is analyzed in its thermodynamically-relevant framework, namely employing a singular Flory-Huggins potential and a possibly degenerate mobility, and the noise is considered both in Ito and Stratonovich form. In this work, as a first-step investigation, we establish well-posedness of the system in two and three spatial dimensions and its thermodynamical consistency.

In this paper, we propose a derivative-based finite volume Hermite WENO (HWENO) scheme for hyperbolic conservation laws, where both the solution and its first-order derivatives are evolved in time and utilized in spatial reconstructions. The key challenge for solving hyperbolic conservation laws is the possible emergence of discontinuities in the numerical solutions. When facing discontinuities, the derivatives can become excessively large, which may compromise the robustness of HWENO schemes. In the first HWENO scheme, different sets of stencils were adopted for reconstructing the governing equation and the derivative equation, respectively, aiming to reduce the influence of the derivatives while preserving high-order accuracy. However, this approach not only substantially increases computational cost but also introduces considerable algorithmic complexity. To overcome these limitations, we exclude the information of the target cell's derivatives from spatial reconstructions, while employing the same reconstructed polynomial during temporal evolution to limit the derivatives. This strategy enhances the robustness of traditional HWENO schemes and allows unified stencils within the derivative-based HWENO framework. Furthermore, the proposed scheme supports arbitrary positive linear weights that sum to one and maintains a compact stencil. Numerical results demonstrate the high-order accuracy, efficiency, high resolution, and robustness of the proposed HWENO scheme.

Classical bootstrap methods can behave poorly in high-dimensional linear models: the pairs bootstrap often yields overly conservative inference, whereas the residual bootstrap can be anti-conservative, reflecting systematic failures in variance calibration. We propose a diffusion-based pairs bootstrap that replaces the empirical joint distribution with a learned generative law. We establish variance consistency under a score approximation assumption, using complementary SDE and PDE arguments. Counterexamples show that terminal $W_4$ convergence alone is insufficient for variance consistency. Experiments indicate that diffusion pairs bootstrap improves variance calibration and generally improves Type~I error calibration, including in settings not covered by our theory.

We prove that the expectation of the Euler characteristic X(H) of random level surface H in a given simplicial complex G is E[X(H)] =2-2K(G)-X(G), where K(G)=1-f_0/2+f_1/3- ... is the curvature functional of G and X(G)=f_0-f_1+f_2-... is the Euler characteristics. More generally, the expectation of the f-vector of a submanifold is explicitly linked to the f-vector of the host manifold.

We study the effect of compression in Coordinate Descent Full Configuration Interaction (CDFCI) within an unconstrained optimization formulation of the full configuration interaction ground-state problem. Under suitable local assumptions, we prove that the compressed iteration converges linearly to the solution of an associated restricted problem. We also characterize the convergence point of the compressed algorithm. Under an additional exponential decay assumption on the target eigenvector, we show that the resulting eigenvalue error is of order $\tau^2$, where $\tau$ denotes the compression threshold. Numerical results support the analysis.

We formulate a proof on non-Archimedean analogue of the Caffarelli-Silvestre extension for fractional Laplacian on the Bruhat-Tits tree $T_p$ in terms of energy relations after identifying $\partial T_p$ with $\mathbb{P}^1(\mathbb{Q}_p)$, which reproduces the generalised Vladimirov derivative of the rescaled boundary function.

We prove new bounds for bilinear forms with Kloosterman sums, valid for all moduli c. In the critical range where the summation length is the square root of the modulus, the saving over the trivial bound is c^{-1/32}, improving on all previous approaches even for prime moduli. This is based on a new connection to quadratic character sums. Applications to moments of twisted L-functions and to the large sieve for exceptional Maass forms are given.

Classical early-warning signals assume that recovery slows near a tipping point, but in a well-studied CESM simulation of AMOC collapse, autocorrelation and variance decrease instead. We develop a preregistered diagnostic that uses only past data to estimate the slowest mode of a memory operator fitted to the evolving overturning structure. The operator ensemble produces an exploratory alarm 77 years before collapse, compared with 29 years for a physics-based freshwater-transport indicator. A blind test on the recovery branch does not validate the same timing rule, supporting interpretation of the diagnostic as a proximity measure rather than a universal clock. In nine additional simulations, it warns before all four finite-rate forced transitions but not before three rapid noise-induced collapses available only as scalar AMOC records. Matched ablations show that the signal depends on long-timescale memory and vertical overturning structure rather than trend, forcing, or multivariate information alone.

Problem 5 in Five Open Problems in Quantum Information Theory [PRX Quantum 3, 010101 (2022)], asks whether the two-ququart Werner state $\varrho(4,-\tfrac12)$ is two-copy distillable, where $\varrho(d,\alpha)=(I+\alpha F)/(d^2+\alpha d)$. We answer it in the negative. To this end, we show the following stronger statement: for all $C\in M_{d_1d_2}(\mathbb{C})$ of rank at most $r \le d_1 d_2$, $\mathrm{tr}_1(C)\|_F^2+\|\mathrm{tr}_2(C)\|_F^2 \le r\|C\|_F^2+\frac{1}{r}|\mathrm{tr}(C)|^2$. A result by Costa Rico on the equivalence of this inequality with two-copy undistillability at $r = 2$ then settles Problem 5: $\varrho(4,-\tfrac{1}{2})$ is not two-copy distillable. Furthermore, we show that $\varrho(d,\alpha)$ is two-copy undistillable for every $d\ge2$, if and only if $\alpha\ge-\tfrac{1}{2}$. Thus, the one and two-copy distillability regions of $\varrho(d,\alpha)$ coincide. These results have been found and written up with AI tools, pointing towards a structural change affecting the field of quantum information and computation.

In this paper, which is a continuation of our recent paper [1], we give two new definitions of a weak solution of the one-dimensional, elliptic, nonlinear singular problem which is formally written as $$ \begin{cases} \displaystyle -\frac{d}{dx}\left(a(x) \frac{d u}{dx}\right) = - \frac{d \phi (u) }{dx}- \frac{d g(x) }{dx}& \text{in}\;(0,L),\\ \newline u(0)=u(L)=0\,, & \end{cases} $$ where $\phi:\mathbb{R}\mapsto\mathbb{R}\cup\{+\infty\}$ is a singular function whose model is given by $\displaystyle\phi(s)=\phi_\gamma (s)=\frac{1}{|s|^{\gamma}}$ with $\gamma>0$. In these two definitions the solutions belong respectively to the space $u\in W_0^{1,1}(0,L)$ with $\phi(u)\in L^1(0,L)$, and to the space $u\in W_0^{1,m}(0,L)$ with $\phi(u)\in L^m(0,L)$ for $m>1$. In these frameworks we state and prove results of non-existence, existence, and non-isolation of the solutions.

We formulate a statistical physics framework to model a networked stochastic dynamical system exhibiting bistability, driven by additive noise and social conformity. We apply this model to understand and mitigate AI-induced delusional spiraling-a phenomenon where algorithmic sycophancy from Large Language Models continuously reinforces inaccurate beliefs within a socially interacting society. By partitioning the network into a majority of regular agents and a minority of "aware" nodes (Teachers) placed at topological hubs, we use a degree-weighted mean-field approximation to reduce high-dimensional coupled Langevin equations into a single macroscopic drift equation. We provide a closed-form analytical derivation for the deterministic critical tipping time through a saddle-node bifurcation. We validate this analytical boundary using finite-size scaling and demonstrate a universal data collapse across diverse network topologies. Finally, we optimize an intervention strategy under a strict budget constraint that balances the topological footprint against driving velocity. We prove mathematically that under certain conditions, a highly concentrated, rapid intervention targeting massive hubs strictly outperforms a distributed, slow approach to rescue the network.

In coding theory, local recovery enables the efficient recovery of some part of (lost) coded data by accessing only a small number of other data entries. Locality was mostly but intensively studied for the recovery of individual symbols, that is, in the context of the Hamming metric. In this work, we propose a new definition of locality for general rank-metric codes. This definition differs from a previous work of Kadhe, El Rouayheb, Duursma and Sprintson [IEEE Trans. Inf. Theory 2019], by allowing to efficiently recover any element of the support, and without relying on any choice of bases of the underlying vector spaces. Our work firstly relies on a precise study of code puncturing and shortening for codes viewed as spaces of linear maps. We then provide examples and general constructions, showing the difference between our notion and that of Kadhe et al. We then derive a Singleton-like bound for rank locally recoverable codes, and we finally prove that a construction similar to classical Tamo-Barg codes is optimal with respect to this bound.

Fréchet regression is well developed for Euclidean predictors, but local linear methods remain limited for general manifold-valued predictors. We propose local constant and local linear estimators for predictors lying on a general Riemannian manifold and responses taking values in a general metric space. The proposed local linear estimator is the first local linear Fréchet regression method in this setting. Our construction uses geodesic neighborhoods, logarithmic-map coordinates, volume-density correction, and frame-invariant scalar equivalent weights. For both estimators, we establish not only pointwise consistency and convergence rates but also uniform consistency and convergence rates. Simulations and real data applications demonstrate the finite-sample performance and practical applicability of the proposed methods across diverse predictor and response geometries.

We prove that the normalized star, extreme, and periodic $L_1$-discrepancies with arbitrary nonnegative weights suffer from the curse of dimensionality. A single elementary lemma is used in all three cases: after normalizing the target and every one-dimensional kernel to probability densities, a uniform square-root-affinity gap tensorizes over the coordinates. For every $d\in \mathbb{N}$ and $0<\varepsilon<1$, the normalized information complexities have lower bounds of the form \[ \frac{(1-\varepsilon)^2}{1+\varepsilon}\,q^d, \] with \[ q_{\mathrm{star}}=\frac12+\frac1{\sqrt3},\qquad q_{\mathrm{ext}}=\frac{75(9+4\sqrt2)}{784},\qquad q_{\mathrm{per}}=\frac{25}{16}. \] All three constants are larger than one. The argument uses only nonnegativity, product integration, the elementary inequalities for the square root, and Cauchy–Schwarz.

Despite remarkable advances in artificial intelligence and cognitive neuroscience, no generally accepted first-principle explains why intelligent systems organize latent semantic states as they do. Existing frameworks such as information theory, the Information Bottleneck, the Degree of Information Abstraction, predictive coding and the Free Energy Principle provide powerful frameworks for understanding communication, learning, and prediction, but do not explicitly explain the emergence and organization of semantic intelligence. Here we propose the Semantic Least-Energy Principle (SLEP) as a hypothesis that intelligent systems evolve internal representations by maximizing semantic utility while progressively minimizing semantic, predictive, and computational energy. We formulate this hypothesis within a variational framework in which semantic cognition is governed by a Semantic Action Functional whose stationary solutions define efficient trajectories on a latent semantic manifold. This formulation emerges a series of theoretical predictions, including semantic geometry, semantic thermodynamics, and low-energy latent semantic states as complementary consequences of the same underlying optimization process. SLEP unifies semantic abstraction, reasoning, planning, and communication within a common mathematical framework while generating experimentally testable predictions for both artificial and biological intelligence. Although the hypothesis remains to be rigorously validated, it provides a principled foundation for investigating semantic intelligence from a first-principle perspective.

Channel knowledge map (CKM) is regarded as a promising technology for future sixth-generation (6G) networks, facilitating environmental-aware wireless communication, sensing, and localization. Research works on CKM construction can be classified as model-based methods and data-based approaches. Specifically, data-based CKM construction exploits the fundamental principle of spatial correlation to complete CKM based on limited measurement data, leading to the question of "where to perform channel measurements". In this paper, we study the spatial measurement strategy for efficient data-based CKM construction, and consider a specific type of CKM named channel gain map (CGM). The general objective is to select a subset of locations for channel measurements, so as to minimize the average mean-squared-error (AMSE) of the global CGM construction. In order to reduce the infinite measurement locations to a finite set, we discretize the underlying physical space into a finite number of cubic grid points, and formulate a combinatorial optimization problem to select measurement locations from them. In order to solve the proposed problem, we employ two representative algorithms, namely the greedy algorithm and the simulated annealing (SA), and discuss their respective advantages. To overcome the accuracy-complexity trade-off of traditional uniform discretization, we develop an adaptive discretization strategy from the viewpoint of Gaussian random field theory to minimize the information loss from the original continuous field to its approximated discrete representation in the mean-squared sense. Compared to uniform discretization, the proposed adaptive discretization strategy achieves a significant performance gain in terms of AMSE-reduction, establishing the theoretical framework of spatial measurement and providing practical guidance for implementation.

We prove a 2-valued analogue of Calabi's intrinsic harmonicity theorem and use it to introduce the Calabi surgery method, a surgery theory for $\mathbb{Z}/2$ harmonic $1$-forms. Once the ambient metric is allowed to vary, the construction of new $\mathbb{Z}/2$ harmonic forms can be reduced to cutting and pasting closed 2-valued 1-forms, matching local harmonic models, and controlling the transitivity of the resulting singular foliation. For these constructions, the Nash–Moser-type analytic deformation problem that arises in singular gluing is replaced by local model matching and a global dynamical condition on the foliation. The resulting procedure gives a flexible way to construct and modify $\mathbb{Z}/2$ harmonic 1-forms under weak regularity assumptions. As applications, we obtain connected-sum and local replacement theorems, blow up isolated ordinary zeros by prescribed Euclidean models, split smooth $\vec{k}$-nondegenerate branching components, and desingularize graphic singular sets in dimensions 3 and 4 with suitable resolution models.

Let $\widetilde{S}\to S$ be an unbranched regular $p$-fold cyclic cover of a closed orientable surface $S$ of genus $g$. Two natural groups are associated to this cover. The first is the centralizer $\mathrm{Mod}(\widetilde{S},\sigma)$ of a chosen generator $\sigma$ of the deck transformation group in $\mathrm{Mod}(\widetilde{S})$. The second is the finite-index subgroup $\mathrm{Mod}(S,[\beta])$ of $\mathrm{Mod}(S)$ consisting of mapping classes that fix the nonzero class $[\beta]\in H_1(S;\mathbb Z/p\mathbb Z)$ corresponding to the cover. For $p=2$, Sato computed the abelianizations of these groups. We compute their abelianizations for every odd prime $p$ and show that they exhibit a splitting phenomenon different from the case $p=2$. We also construct explicit abelianization maps using Morita's crossed homomorphism and the Prym representation.

Let $H \leq \mathbb{F}_p^*$ be a proper multiplicative subgroup, and suppose that $H = A+B$ for some $A,B \subseteq \mathbb{F}_p$. We prove that either one of the summands is a singleton, or $|A|=|B|=2$ and $|H|=4$. In particular, no proper multiplicative subgroup of $\mathbb{F}_p^*$ can be written as $A+B$ with $|A|,|B|>2$. Our proof builds on the Hanson-Petridis polynomial method and Kalmynin's subsequent resolution of Sárközy's conjecture for quadratic residues. Using Kalmynin's $|A|=|B|$ theorem as a structural input, we develop uniform combinatorial and arithmetic arguments which apply to multiplicative subgroups of arbitrary index.

We study the sign distribution of square-free Fourier coefficients of half-integral weight cusp forms. We prove that, for half-integral weight cusp forms \(f\) satisfying the eigenform conditions and having a nonzero cuspidal Shimura lift, the numbers of positive and negative square-free Fourier coefficients up to \(X\) are both \(\gg_{f,\varepsilon}X^{4/7-\varepsilon}\). As an application to the congruent-number problem, we consider the weight \(3/2\) cusp form whose Shimura lift is the weight \(2\) newform attached to $ E:y^2=x^3-x$. We prove that each each sign occurs among its square-free Fourier coefficients at \(\gg_{\varepsilon}X^{4/7-\varepsilon}\) odd square-free integers \(t\leq X\). In particular, the square-free Fourier coefficients change sign infinitely often.

Diagnosability is a critical parameter for evaluating the reliability and self-diagnostic capacity of multiprocessor systems. As an advanced extension of traditional diagnosability, cyclic diagnosability has been proposed to enable more comprehensive assessment of the diagnostic capabilities of interconnection networks. In this paper, we prove that under both the PMC and MM* models, the cyclic diagnosability of Cayley graphs generated by triangle-free unicyclic graphs is 5n-10 for n\ge11.

We investigate the extent to which the coefficients $(w_1,w_2,w_3,w_4,w_6)$ of the reduced minimal Weierstrass model of an elliptic curve $E/\mathbb{Q}$ are determined by the Dirichlet coefficients $a_n(E)$ of its $L$-function, whose values at primes of good reduction are the Frobenius traces of $E$. We prove that $w_1$, $w_2$ and $w_3$ are given by explicit formulae in $a_2(E)$, $a_3(E)$ and $a_4(E)$, that $w_4$ modulo $5$ is then determined by $a_5(E)$, and that $w_6$ modulo $7$ is determined by $a_7(E)$ together with $w_1,w_2,w_3,w_4$. These formulae, which appear to be new, were discovered by training decision tree models on the LMFDB; we report the accompanying experiments and explore applications to computing tables of elliptic curves.

We study the representation theory of the affine nilCoxeter algebra $A$ of type $\tilde A_{n-1}$, over a field $k$ of any characteristic. Our main theorem states that this is a Noetherian prime affine PI algebra of PI degree $n!$. As a consequence, the simple $A$-modules are all finite dimensional, and the maximum dimension of a simple module is $n!$ over a suitable finite extension of $k$. To achieve this, we investigate a large commutative subalgebra $C$ which is finitely generated as an algebra and over which $A$ is finitely generated as a module. We show that the associated primes of $C$ are minimal primes, and there are $n!$ of them, regularly permuted by $\mathfrak{S}_n$. The algebra $R=C^{\mathfrak{S}_n}$ is equal to the centre of $A$, and isomorphic to $C/\mathfrak{p}$ for each of the minimal primes $\mathfrak{p}$. We prove that the ring $R$ is isomorphic to $k[X_1,\dots,X_{n-1}]^{\mu_n}$, where $\mu_n$ is the finite group scheme of $n$th roots of unity, acting so that $X_i$ has degree $i$ modulo $n$. The ring $R$ is Cohen–Macaulay, and is Gorenstein if and only if $n$ is odd or $n=2$. It is a toric ring, with divisor class group $\mathsf{Cl}(R)\cong\mathbb{Z}/n$, and every projective $R$-module is free.

We study the partition function of Abelian $BF$ theory with an axial gauge fixing condition given by a Morse–Smale vector field, and we show that it recovers the Milnor metric on the determinant line on the twisted cohomology of the manifold. To do this we use the Batalin–Vilkovisky formalism and, in particular, a BV pushforward to cohomology in two steps. In this context, the Milnor metric serves as the generalisation of the Ruelle dynamical zeta function evaluated at zero for systems containing both closed orbits and critical points. This, together with a classic result due to Schwarz on the partition function of $BF$ theory in the Lorenz gauge, provides a field-theoretic realisation of Fried's conjecture, which is true for Morse–Smale flows.

Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence testing, among others. Their fastest estimators are known to converge at a parametric rate—$n^{-1/2}$—under mild conditions. While this rate is known to be minimax optimal on $\mathbb R^d$ under strict assumptions with bounded kernels, little is known about its optimality beyond the finite-dimensional Euclidean setting with unbounded kernels. In this work, we prove that the minimax lower bound of estimation of the most popular kernel discrepancies (maximum mean discrepancy, Hilbert-Schmidt independence criterion and kernel Stein discrepancy; MMD, HSIC, KSD) is $n^{-1/2}$ on general topological spaces, and under mild assumptions on the kernel; the same rates are shown (as corollaries) to hold for the estimation of the mean embedding and the centered cross-covariance operator. Our results settle the question of optimal estimation of these kernel discrepancies.

This paper studies the problem of longitudinal control, with safety and string stability guarantees, in vehicle platoons. A nonlinear adaptive cruise controller based on the Constant Time Headway (CTH) policy is designed for platoons of heterogeneous vehicles, that incorporates a speed-dependent nonlinear gain guaranteeing bounded vehicle speeds and a saturation-type nonlinearity to guarantee bounded (or non-aggressive) accelerations, while preserving collision-avoidance properties of time-headway-based spacing policies. By introducing a suitable spacing-error variable, explicit nonlinear error dynamics are obtained, allowing a direct analysis of the closed-loop system. It is shown that the collision-free region is positively invariant and that the proposed controller guarantees convergence of the spacing errors. Furthermore, nonlinear string-stability properties are established directly from the closed-loop dynamics without resorting to linearization or frequency-domain arguments. In particular, sufficient conditions are derived for $L_p$ string stability, $1\le p\le\infty$, with respect to disturbances, external inputs acting on the leading vehicle, and perturbations of the initial conditions.

A long-standing question by S. Lando asks whether the $\mathfrak{sl}(2)$-weight system extends to a unique 4-invariant of graphs. We show that, in full generality, the answer to this question is negative. However, for certain specializations of the weight system, extensions do exist. Explicit formulae for computing two such specializations of the weight system are already known. We construct recurrence relations for one additional such extension and discuss the last remaining specialization, which conjecturally admits an extension. We also study the polynomial coefficients of the $\mathfrak{sl}(2)$-weight system and resolve the questions concerning their extension.

In this work, we study a one-dimensional nonlocal and nonlinear eikonal equation without sign restriction on its spatial gradient. The equation is characterized by weak regularity assumptions on both the nonlocal velocity field and the initial data. We derive a periodic version of this model and propose a semi-explicit (IMEX) scheme for its numerical approximation. We prove that the scheme preserves a discrete gradient entropy estimate and establish its convergence in the viscosity sense. Finally, we present numerical results illustrating the behavior of the model and the performance of the proposed scheme.