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1. On Landis' conjecture in the plane when the potential has an exponentially decaying negative part

   Davey, B.; Kenig, C.; Wang, J.-N. (January 2019, Algebra i analiz)

   The authors establish an optimal lower bound for the decay of solutions of Schrodinger equations in the plane, when the potential has a negative part which decays exponentially. 

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2. A near-tight lower bound on the density of forward sampling schemes

   https://doi.org/10.1093/bioinformatics/btae736  

   Kille, Bryce; Groot_Koerkamp, Ragnar; McAdams, Drake; Liu, Alan; Treangen, Todd_J; Ponty, ed., Yann (December 2024, Bioinformatics)

   Abstract MotivationSampling k-mers is a ubiquitous task in sequence analysis algorithms. Sampling schemes such as the often-used random minimizer scheme are particularly appealing as they guarantee at least one k-mer is selected out of every w consecutive k-mers. Sampling fewer k-mers often leads to an increase in efficiency of downstream methods. Thus, developing schemes that have low density, i.e. have a small proportion of sampled k-mers, is an active area of research. After over a decade of consistent efforts in both decreasing the density of practical schemes and increasing the lower bound on the best possible density, there is still a large gap between the two. ResultsWe prove a near-tight lower bound on the density of forward sampling schemes, a class of schemes that generalizes minimizer schemes. For small w and k, we observe that our bound is tight when k≡1(mod w). For large w and k, the bound can be approximated by 1w+k⌈w+kw⌉. Importantly, our lower bound implies that existing schemes are much closer to achieving optimal density than previously known. For example, with the current default minimap2 HiFi settings w = 19 and k = 19, we show that the best known scheme for these parameters, the double decycling-set-based minimizer of Pellow et al. is at most 3% denser than optimal, compared to the previous gap of at most 50%. Furthermore, when k≡1(mod w) and the alphabet size σ goes to ∞, we show that mod-minimizers introduced by Groot Koerkamp and Pibiri achieve optimal density matching our lower bound. Availability and implementationMinimizer implementations: github.com/RagnarGrootKoerkamp/minimizers ILP and analysis: github.com/treangenlab/sampling-scheme-analysis. 

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3. Two lower-bounding algorithms for the p-center problem in an area

   https://doi.org/10.1007/s43762-021-00032-9  

   Liu, Yanchao (January 2022, Computational Urban Science)

   Abstract The p-center location problem in an area is an important yet very difficult problem in location science. The objective is to determine the location of p hubs within a service area so that the distance from any point in the area to its nearest hub is as small as possible. While effective heuristic methods exist for finding good feasible solutions, research work that probes the lower bound of the problem’s objective value is still limited. This paper presents an iterative solution framework along with two optimization-based heuristics for computing and improving the lower bound, which is at the core of the problem’s difficulty. One method obtains the lower bound via solving the discrete version of the Euclidean p-center problem, and the other via solving a relatively easier clustering problem. Both methods have been validated in various test cases, and their performances can serve as a benchmark for future methodological improvements. 

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4. Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions

   Asi, Hilal; Liu, Daogao; Tian, Kevin (June 2024, https://doi.org/10.48550/arXiv.2406.02789)

   This paper studies differentially private stochastic convex optimization (DP-SCO) in the presence of heavy-tailed gradients, where only a 𝑘 kth-moment bound on sample Lipschitz constants is assumed, instead of a uniform bound. The authors propose a reduction-based approach that achieves the first near-optimal error rates (up to logarithmic factors) in this setting. Specifically, under ( 𝜖 , 𝛿 ) (ϵ,δ)-approximate differential privacy, they achieve an error bound of 𝐺 2 𝑛 + 𝐺 𝑘 ⋅ ( 𝑑 𝑛 𝜖 ) 1 − 1 𝑘 , n ​ G 2 ​ ​ +G k ​ ⋅( nϵ d ​ ​ ) 1− k 1 ​ , up to a mild polylogarithmic factor in 1 𝛿 δ 1 ​ , where 𝐺 2 G 2 ​ and 𝐺 𝑘 G k ​ are the 2nd and 𝑘 kth moment bounds on sample Lipschitz constants. This nearly matches the lower bound established by Lowy and Razaviyayn (2023). Beyond the basic result, the authors introduce a suite of private algorithms that further improve performance under additional assumptions: an optimal algorithm under a known-Lipschitz constant, a near-linear time algorithm for smooth functions, and an optimal linear-time algorithm for smooth generalized linear models. 

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5. A Hypergraph Analog of Dirac's Theorem for Long Cycles in 2-Connected Graphs, II: Large Uniformities

   https://doi.org/10.37236/12486  

   Kostochka, Alexandr; Luo, Ruth; McCourt, Grace (January 2025, The Electronic Journal of Combinatorics)

   Dirac proved that each $$n$$-vertex $$2$$-connected graph with minimum degree $$k$$ contains a cycle of length at least $$\min\{2k, n\}$$. We obtain analogous results for Berge cycles in hypergraphs. Recently, the authors proved an exact lower bound on the minimum degree ensuring a Berge cycle of length at least $$\min\{2k, n\}$$ in $$n$$-vertex $$r$$-uniform $$2$$-connected hypergraphs when $$k \geq r+2$$. In this paper we address the case $$k \leq r+1$$ in which the bounds have a different behavior. We prove that each $$n$$-vertex $$r$$-uniform $$2$$-connected hypergraph $$H$$ with minimum degree $$k$$ contains a Berge cycle of length at least $$\min\{2k,n,|E(H)|\}$$. If $$|E(H)|\geq n$$, this bound coincides with the bound of the Dirac's Theorem for 2-connected graphs. 

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