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Sanghyuk Lee

Publications and source records attributed to Sanghyuk Lee.

At least 19 recordsLinked to original sources

On the endpoint estimate for discrete spherical average over sparse sequences

Let $d\geq5$. For a strictly increasing sequence $(μ_k)$ of positive integers, set $λ_k=μ_k!$ and consider the lacunary discrete spherical maximal operator $A_\star f:=\sup_k |A_{λ_k}f|$ associated with the discrete spherical averages \[ A_λf(x):=\frac1{s_λ}\sum_{\substack{n\in\mathbb{Z}^d,\\ |n|^2=λ}} f(x-n), \] where $s_λ:=\#\{n\in\mathbb{Z}^d:|n|^2=λ\}$. Kesler, Lacey and Mena proved that $A_\star$ is bounded on $\ell^p(\mathbb{Z}^d)$ for every $p>1$ if $\logμ_k/\log k\longrightarrow\infty$, and asked about its endpoint behavior at $\ell\log\ell$. We resolve this endpoint question by characterizing all factorial sequences for which the $\ell\log\ell$ estimate holds. Define \[ C_{\log}=\sup_{N\geq2}\frac{\#\left\{k\geq 1:μ_k\leq N\right\}}{1+\log N}. \] We prove that the $\ell\log\ell$ endpoint estimate holds if and only if $C_{\log}<\infty$. More precisely, if $C_{\log}<\infty$, then for every $α>0$ and every finitely supported $f:\mathbb{Z}^d\to\mathbb C$, \begin{align*} \#\{x\in\mathbb{Z}^d:A_\star f(x)>α\} \leq C_d(1+C_{\log})\sum_x \frac{|f(x)|}α \left(1+\log^+\frac{|f(x)|}α\right), \end{align*} where $C_d$ depends only on $d$. Conversely, if the above inequality holds with a finite constant $C_0$ in place of $C_d(1+C_{\log})$, then $C_{\log}\leq C_d(1+C_0)$.

math.CA

Two-parameter variational estimates for averages over tori

One-parameter variational inequalities are well developed, whereas their multi-parameter counterparts remain much less understood. We explore two-parameter variational inequalities for averages over tori in $\mathbb{R}^3$. To capture the underlying two-parameter structure, we introduce a local two-parameter $r$-variation norm that combines rectangular increments with variations along the boundary. The resulting variation operator pointwise dominates the corresponding two-parameter local maximal function and, unlike the maximal function, also captures oscillation across the two parameters. We establish sharp $L^p$--$L^q$ bounds for this variation operator up to endpoints. For comparison, we also obtain sharp $L^p$ bounds up to endpoints for the corresponding local one-parameter variation operator, revealing a genuine difference between the one- and two-parameter boundedness regions. The proof combines square function estimates for two-parameter propagators with local smoothing estimates through mixed-norm interpolation.

math.CA

The Endpoint Eigenfunction Bound for the Hermite Operator in Two Dimensions

In this paper, we establish the optimal \(L^2(\mathbb{R}^2)\to L^{10/3}(\mathbb{R}^2)\) endpoint estimate for the spectral projection operator associated with the Hermite operator on \(\mathbb{R}^2\). This completes a long-standing line of inquiry into sharp eigenfunction bounds for the Hermite operator, developed through the works of Thangavelu, Karadzhov, Koch--Tataru, and others. In higher dimensions \(d\ge 3\), the corresponding endpoint estimates \[ L^2(\mathbb{R}^d)\to L^{\frac{2(d+3)}{d+1}}(\mathbb{R}^d) \] were recently established by the present authors. Together with these earlier results, the present work fully resolves the problem of optimal \(L^2\to L^q\) eigenfunction bounds for Hermite spectral projections in all dimensions. Although our approach builds on our previous method, we overcome its limitations through a multiscale decomposition in space and time relative to the degeneracy set, combined with an asymmetric refinement on the input side and almost orthogonality.

math.CA

Sharpness of convolution bounds for measures

In this paper, we determine the optimal universal \(L^p\)-\(L^q\) type sets for convolution operators \(f\mapsto μ*f\) associated with fractal measures $μ\in \mathcal P_{α,β}(\mathbb R^d)$, which denotes the class of compactly supported Borel probability measures satisfying the \(α\)-Frostman condition \[ μ(B(x,ρ)) \lesssim ρ^α, \qquad x\in\mathbb R^d,\quad 0<ρ<1, \] and the \(β/2\)-Fourier decay condition \[ |\widehatμ(ξ)| \lesssim |ξ|^{-β/2}, \qquad ξ\in\mathbb R^d. \] More precisely, we characterize the largest \(L^p\)-\(L^q\) region that is forced solely by the Frostman and Fourier decay assumptions throughout the full admissible range of \((α,β)\), with distinct optimal regions in the geometric and nongeometric regimes. We prove optimality in the worst-case sense over \(\mathcal P_{α,β}(\mathbb R^d)\) by constructing, for each admissible pair \((α,β)\), a single extremal measure whose support has the smallest Hausdorff dimension allowed by the hypotheses. Moreover, variants of the same constructions also yield a single-measure sharpness theorem for the \(L^2\) Fourier restriction theorem of Mockenhaupt--Mitsis--Bak--Seeger: in every dimension and in both the geometric and nongeometric regimes, we construct a measure in \(\mathcal P_{α,β}(\mathbb R^d)\) for which the Mockenhaupt--Mitsis--Bak--Seeger threshold exponent is sharp.

math.CA

MeMo: Memory as a Model

Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications require timely, domain-specific information, motivating the need for efficient mechanisms to incorporate new knowledge. In this paper, we introduce MeMo (Memory as a Model), a modular framework that encodes new knowledge into a dedicated memory model while keeping the LLM parameters unchanged. Compared to existing methods, MeMo offers several advantages: (a) it captures complex cross-document relationships, (b) it is robust to retrieval noise, (c) it avoids catastrophic forgetting in the LLM, (d) it does not require access to the LLM's weights or output logits, enabling plug-and-play integration with both open and proprietary closed-source LLMs, and (e) its retrieval cost is independent of corpus size at inference time. Our experimental results on three benchmarks, BrowseComp-Plus, NarrativeQA, and MuSiQue, show that MeMo achieves strong performance compared to existing methods across diverse settings.

cs.CL

On Rubio de Francia's maximal theorem

In his influential 1986 paper, Rubio de Francia established $L^p$ bounds for the maximal function generated by dilations of measures $μ$ whose Fourier transforms $\widehatμ$ satisfy specific decay condition. In the present work, we obtain results that complement his work in several directions. In particular, we obtain restricted weak-type endpoint bound on the maximal function and $L^p$--$L^q$ bounds on its local variant. We also investigate how Frostman's growth condition on the measure influences those maximal bounds. While a key feature of Rubio de Francia's result is that $L^p$ boundedness is determined solely by the decay order of $\widehatμ$, we show that the Frostman condition plays a significant role when the growth order exceeds $d-1$ or when $L^p$--$L^q$ estimates are considered.

math.CA

Endpoint estimates for maximal operators associated to the wave equation

We consider the $H^{s}$--$L^q$ maximal estimates associated to the wave operator \begin{equation*} e^{ it\sqrt{-Δ}}f(x) = \frac{1}{(2π)^d}\int_{\mathbb{R}^d} e^{i(x \cdot ξ\, + t|ξ|)} \widehat{f}(ξ\,) dξ. \end{equation*} Rogers--Villarroya proved $H^{s}$--$L^q$ estimates for the maximal operator $f\mapsto$ $\sup_{t} |e^{ it\sqrt{-Δ}}f|$ up to the critical Sobolev exponents $s_c(q,d)$. However, the endpoint case estimates for the critical exponent $s=s_c(q,d)$ have remained open so far. We obtain the endpoint $H^{s_c(q,d)}$--$L^q$ bounds on the maximal operator $f\mapsto \sup_{t} |e^{ it\sqrt{-Δ}}f|$. We also prove that several different forms of the maximal estimates considered by Rogers--Villarroya are basically equivalent to each other.

math.CA

Strichartz and local smoothing estimates for the fractional Schrödinger equations over fractal time

We obtain Strichartz-type estimates for the fractional Schrödinger operator $f \mapsto e^{it(-Δ)^{γ/2}} f$ over a time set $E$ of fractal dimension. To obtain those estimates capturing fractal nature of $E$, we employ the notions in the spirit of the Assouad dimension, such as, bounded Assouad characteristic and Assouad specturm. We also prove the estimate $$ \| e^{it(-Δ)^{γ/2}} f \|_{L_t^q(\mathrm{d}μ; L_x^r(\mathbb{R}^d))} \le C \|f\|_{H^s}, $$ where $μ$ is a measure satisfying an $α$-dimensional growth condition. In addition, we establish related inhomogeneous estimates and $L^2$ local smoothing estimates. A surprising feature of our work is that, despite dealing with rough fractal sets, we extend the known estimates for the fractional Schrödinger operators in a natural way, precisely consistent with the associated fractal dimensions.

math.AP

Maximal estimates for orthonormal systems of wave equations with sharp regularity

We study maximal estimates for the wave equation with orthonormal initial data. In dimension $d=3$, we establish optimal results with the sharp regularity exponent up to the endpoint. In higher dimensions $d \ge 4$ and also in $d=2$, we obtain sharp bounds for the Schatten exponent (summability index) $β\in [2, \infty]$ when $d\ge4$, and $β\in[1, 2]$ when $d=2$, improving upon the previous estimates due to Kinoshita--Ko--Shiraki. Our approach is based on a novel analysis of a key integral arising in the case $β=2$, which allows us to refine existing techniques and achieve the optimal estimates.

math.AP

Endpoint estimates for the fractal circular maximal function and related local smoothing

Sharp $L^p$--$L^q$ estimates for the spherical maximal function over dilation sets of fractal dimensions, including the endpoint estimates, were recently proved by Anderson--Hughes--Roos--Seeger. More intricate $L^p$--$L^q$ estimates for the fractal circular maximal function were later established in the sharp range by Roos--Seeger, but the endpoint estimates have been left open, particularly when the fractal dimension of the dilation set lies in $[1/2, 1)$. In this work, we prove these missing endpoint estimates for the circular maximal function. We also study the closely related $L^p$--$L^q$ local smoothing estimates for the wave operator over fractal dilation sets, which were recently investigated by Beltran--Roos--Rutar--Seeger and Wheeler. Making use of a bilinear approach, we also extend the range of $p,q$, for which the optimal estimate holds.

math.CA

Damping oscillatory Integrals of convex analytic functions

Let $H\subset \R^{d+1}$ be a compact, convex, analytic hypersurface of finite type with a smooth measure $σ$ on $H$. Let $κ$ denote the Gaussian curvature on $H$. We consider the oscillatory integral $(κ^{1/2} σ)^\wedge$ with the damping factor $κ^{1/2}$ and prove the optimal decay estimate \[ |(κ^{1/2} σ)^\wedge(ξ)|\le C|ξ|^{-d/2}\] for $d=2,3,$ and with an extra logarithmic factor for $d=4$. Our result provides an essentially complete answer, since such decay estimates generally fail for $d \ge 5$, even for convex analytic hypersurfaces, as shown by Cowling--Disney--Mauceri--Müller. Furthermore, we prove the same estimates for $(κ^{1/2+it} σ)^\wedge$ with $C$ growing polynomially in $|t|$. As consequences, we obtain the best possible estimates for the convolution, maximal, and adjoint restriction operators associated with $H$, incorporating the mitigating factors of optimal orders. In particular, for $d=2, 3$, we prove the $L^2$--$L^{2(d+2)/(d+4)}$ restriction estimate with respect to the affine surface measure $κ^{1/(d+2)} σ$. This work was inspired by the stationary set method due to Basu--Guo--Zhang--Zorin-Kranich.

math.CA

The elliptic maximal function

We study the elliptic maximal functions defined by averages over ellipses and rotated ellipses which are multi-parametric variants of the circular maximal function. We prove that those maximal functions are bounded on $L^p$ for some $p\neq \infty$. For this purpose, we obtain some sharp multi-parameter local smoothing estimates.

math.CA

Endpoint eigenfunction bounds for the Hermite operator

We establish the optimal $L^p$, $p=2(d+3)/(d+1),$ eigenfunction bound for the Hermite operator $\mathcal H=-Δ+|x|^2$ on $\mathbb R^d$. Let $Π_λ$ denote the projection operator to the vector space spanned by the eigenfunctions of $\mathcal H$ with eigenvalue $λ$. The optimal $L^2$--$L^p$ bounds on $Π_λ$, $2\le p\le \infty$, have been known by the works of Karadzhov and Koch-Tataru except $p=2(d+3)/(d+1)$. For $d\ge 3$, we prove the optimal bound for the missing endpoint case. Our result is built on a new phenomenon: improvement of the bound due to asymmetric localization near the sphere $\sqrtλ\mathbb S^{d-1}$.

math.CA

Bochner-Riesz mean for the twisted Laplacian in $\mathbb R^2$

We study the Bochner-Riesz problem for the twisted Laplacian $\mathcal L$ on $\mathbb R^2$. For $p\in [1, \infty]\setminus\{2\}$, it has been conjectured that the Bochner-Riesz means $S_λ^δ(\mathcal L) f$ of order $δ$ converges in $L^p$ for every $f\in L^p$ if and only if $δ> \max(0,|(p-2)/p|-1/2)$. We prove the conjecture by obtaining uniform $L^p$ bounds on $S_λ^δ(\mathcal L)$ up to the sharp summability indices.

math.CA

FP8 versus INT8 for efficient deep learning inference

Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted with entire networks in FP32, or sometimes FP16 with mixed-precision, the step to having some parts of a network run in FP8 with 8-bit weights is an appealing potential speed-up for the generally costly and time-intensive training procedures in deep learning. A natural question arises regarding what this development means for efficient inference on edge devices. In the efficient inference device world, workloads are frequently executed in INT8. Sometimes going even as low as INT4 when efficiency calls for it. In this whitepaper, we compare the performance for both the FP8 and INT formats for efficient on-device inference. We theoretically show the difference between the INT and FP formats for neural networks and present a plethora of post-training quantization and quantization-aware-training results to show how this theory translates to practice. We also provide a hardware analysis showing that the FP formats are somewhere between 50-180% less efficient in terms of compute in dedicated hardware than the INT format. Based on our research and a read of the research field, we conclude that although the proposed FP8 format could be good for training, the results for inference do not warrant a dedicated implementation of FP8 in favor of INT8 for efficient inference. We show that our results are mostly consistent with previous findings but that important comparisons between the formats have thus far been lacking. Finally, we discuss what happens when FP8-trained networks are converted to INT8 and conclude with a brief discussion on the most efficient way for on-device deployment and an extensive suite of INT8 results for many models.

cs.LG

Sharp Sobolev regularity of restricted X-ray transforms

We study $L^p$-Sobolev regularity estimate for the restricted X-ray transforms generated by nondegenerate curves. Making use of the inductive strategy in the recent work by the authors, we establish the sharp $L^p$-regularity estimates for the restricted X-ray transforms in $\mathbb R^{d+1}$, $d\ge 3$. This extends the result due to Pramanik and Seeger in $\mathbb R^3$ to every dimension.

math.CA

A Fusion Model: Towards a Virtual, Physical and Cognitive Integration and its Principles

Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years. These new emerging technologies are changing the world significantly. This research introduces a fusion model, i.e. Fusion Universe (FU), where the virtual, physical, and cognitive worlds are merged together. Therefore, it is crucial to establish a set of principles for the fusion model that is compatible with our physical universe laws and principles. This paper investigates several aspects that could affect immersive and interactive experience; and proposes the fundamental principles for Fusion Universe that can integrate physical and virtual world seamlessly.

cs.AI