arXiv research

Optimizing over-the-air convex optimization, analog schemes are nearly optimal at low SNR.

Study on statistical estimation over Gaussian MAC, comparing analog and digital schemes.

Learnable multiclass hypothesis classes don't always have a sample compression scheme of fixed size.

We define and calculate signature and nullity invariants for complex schemes for curves in the real projective plane. We use an analog of the Murasugi-Tristram inequality to prohibit certain schemes from being realized by real algebraic curves. We give new formulas for Casson-Gordon invariants of graph manifolds, and s…

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral…

We study two quantization schemes for compact symplectic manifolds with almost complex structures. The first of these is the Spin-c quantization. We prove the analog of Kodaira vanishing for the Spin-c Dirac operator, which shows that the index space of this operator provides an honest (not virtual) vector space semicl…

This work proves a strong convergence result for a geometric EM scheme on Riemannian manifolds.

Geometric invariant theory introduces stability conditions mirroring abelian category theory.

Novel weak MLMC scheme for Lévy-driven SDEs, applied to financial derivatives pricing.

This is the second paper in a series of works devoted to nonholonomic Ricci flows. By imposing non-integrable (nonholonomic) constraints on the Ricci flows of Riemannian metrics we can model mutual transforms of generalized Finsler-Lagrange and Riemann geometries. We verify some assertions made in the first partner pap…

Many clustering schemes are defined by optimizing an objective function defined on the partitions of the underlying set of a finite metric space. In this paper, we construct a framework for studying what happens when we instead impose various structural conditions on the clustering schemes, under the general heading of…

Neural networks learn task-specific features, influenced by nonlinearity.

Compressed imitation learning uses simplicity priors for efficient expert behavior copying.

We construct a framework for studying clustering algorithms, which includes two key ideas: persistence and functoriality. The first encodes the idea that the output of a clustering scheme should carry a multiresolution structure, the second the idea that one should be able to compare the results of clustering algorithm…

Reinforcement learning has gained wide popularity as a technique for simulation-driven approximate dynamic programming. A less known aspect is that the very reasons that make it effective in dynamic programming can also be leveraged for using it for distributed schemes for certain matrix computations involving non-nega…

Paper generalizes Andreev's theorem with obtuse angles.

In Maslov (2003), a two level model of the occurrence of financial pyramid (bubbles) has been considered. We also considered the mathematical analogy of this model to Bose condensation. In the present paper, we explain why Ponzi schemes and bubbles result in a crisis in real economics. In Maslov (2005), the law of incr…

Study curvature and torsion from cross-ratios in discrete curves.

We propose here a new discretization method for a class continuum gauge theories which action functionnals are polynomials of the curvature. Based on the notion of holonomy, this discretization procedure appears gauge-invariant for discretized analogs of Yang-Mills theories, and hence gauge-fixing is fully rigorous for…

This paper proposes a new method to solve functional minimization problems in probability distributions using sliced-Wasserstein gradient flows.

Proposes a new model to price options considering market forces beyond Black-Scholes.

Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas $M$ is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal band…

Paper tackles federated linear bandit learning with AirComp for noisy channels.

We introduce a variant of Shepp's classical urn problem in which the optimal stopper does not know whether sampling from the urn is done with or without replacement. By considering the problem's continuous-time analog, we provide bounds on the value function and in the case of a balanced urn (with an equal number of ea…

The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects the components and their configuration, as well as their numerical …

A new method for CT-DCEGs simplifies inference for asymmetric processes.

A general, consistent and complete framework for geometrical formulation of mechanical systems is proposed, based on certain structures on affine bundles (affgebroids) that generalize Lie algebras and Lie algebroids. This scheme covers and unifies various geometrical approaches to mechanics in the Lagrangian and Hamilt…

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…

Improved performance of factorized neural layers through spectral initialization and Frobenius decay.

Authors calculate limits of curvatures on surfaces in sub-Riemannian manifolds.

Bayesian method for estimating functional graphical models from neuroimaging data.

Virtual invariants defined from sheaves on surfaces.

Analogator learns to make analogies by example.

Study proves existence and properties of shrinkers in area-preserving curve-shortening flow.

Analog forecasting uses local dynamics to predict chaotic systems.

This work analyzes discrete diffusion models using stochastic integrals, providing error bounds and insights.

In many applications, it is desirable to extract only the relevant aspects of data. A principled way to do this is the information bottleneck (IB) method, where one seeks a code that maximizes information about a 'relevance' variable, Y, while constraining the information encoded about the original data, X. Unfortunate…

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost-function. It is not well understood how learning occurs in these systems, in particular how they avo…

ADR helps LLMs find and use historical analogies for foresight analysis.

Analog methods improve forecast accuracy in complex models.

The paper generalizes Segre and Verlinde numbers for surfaces with holomorphic 2-forms.

The paper evaluates the probability distributions of analog-to-target distances for multiple analogs.

Based on the analogies between knot theory and number theory, we study a deformation theory for SL_2-representations of knot groups, following after Mazur's deformation theory of Galois representations. Firstly, by employing the pseudo-SL_2-representations, we prove the existence of the universal deformation of a given…

$α$-Dirac-harmonic maps are variations of Dirac-harmonic maps, analogous to $α$-harmonic maps that were introduced by Sacks-Uhlenbeck to attack the existence problem for harmonic maps from surfaces. For $α>1$, the latter are known to satisfy a Palais-Smale condtion, and so, the technique of Sacks-Uhlenbeck consists in …

Graph neural networks are explained through heat diffusion analogy.

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical propo…

Defines an odd analog of Plamenevskaya's invariant for transverse links.

DCT-SNN uses DCT to reduce inference latency in SNNs.