Tootfinder

The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
Nathan Grieser, Eduardo Rodrigues, Niladri Sahoo, Shuqi Sheng, Nicole Skidmore, Mark Smith
https://arxiv.org/abs/2509.05294

The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
The LHCb Stripping project is a pivotal component of the experiment's data processing framework, designed to refine vast volumes of collision data into manageable samples for offline analysis. It ensures the re-analysis of Runs 1 and 2 legacy data, maintains the software stack, and executes (re-)Stripping campaigns. As the focus shifts toward newer data sets, the project continues to optimize infrastructure for both legacy and live data processing. This paper provides a comprehensive overview o…

Aus unserem Kiez der Woche. Etwas versteckt am Pankeweg auf Höhe Schönwalder Straße steht ein Gedenkstein für die Weddinger Partnerstadt Bottrop.
https://weddingweiser.de/zwei-glanzlose-partnerstaedte-des-wedding/

Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison
Niladri Halder, Chandra R. Murthy
https://arxiv.org/abs/2508.21373

Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison
We present a unified receiver processing framework for communication over delay-scale (DS)-spread channels that arise in underwater acoustic (UWA) communications that addresses both channel estimation (CE) and data detection for different modulation waveforms, namely OFDM, OTFS, OCDM, and ODSS, through a common input--output relation. Using this framework, we conduct a fair and comprehensive comparative study of these waveforms under DS-spread UWA channels and similar receiver complexities. W…

On random bipartite graphs evolving by degrees
Neeladri Maitra
https://arxiv.org/abs/2509.23943 https://arxiv.org/pdf/2509.23943

On random bipartite graphs evolving by degrees
In this paper, we study a bipartite analogue of the `random graphs evolving by degrees' process. We are given a bipartitioned set of vertices $V$ into two disjoint parts ${L}$ and ${R}$ and possibly unequal positive constants $α$ and $β$. The graph evolves starting from $B_0$, the empty graph (with only isolated vertices). Given $B_t$, a non-adjacent vertex pair $u \in {L}, v \in {R}$ is sampled with probability proportional to $(d_u(t)+α)(d_v(t)+β)$, and the edge $\{u,v\}$ is included to $…

Sharp Online Hardness for Large Balanced Independent Sets
Abhishek Dhawan, Eren C. K{\i}z{\i}lda\u{g}, Neeladri Maitra
https://arxiv.org/abs/2508.20785 https://

Sharp Online Hardness for Large Balanced Independent Sets
We study the algorithmic problem of finding large $γ$-balanced independent sets in dense random bipartite graphs; an independent set is $γ$-balanced if a $γ$ proportion of its vertices lie on one side of the bipartition. In the sparse regime, Perkins and Wang established tight bounds within the low-degree polynomial (LDP) framework, showing a factor-$1/(1-γ)$ statistical-computational gap via the Overlap Gap Property (OGP) framework tailored for stable algorithms. However, these techniques …

Crosslisted article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new
[1/1]:
- Sharp Online Hardness for Large Balanced Independent Sets
Abhishek Dhawan, Eren C. K{\i}z{\i}lda\u{g}, Neeladri Maitra

Complexity Analysis of the Regular Simplicial Search Method with Reflection and Shrinking Steps for Derivative-Free Optimization
Liyuan Cao, Wei Hu, Jinxin Wang
https://arxiv.org/abs/2508.16561

Complexity Analysis of the Regular Simplicial Search Method with Reflection and Shrinking Steps for Derivative-Free Optimization
Simplex-type methods, such as the well-known Nelder-Mead algorithm, are widely used in derivative-free optimization (DFO), particularly in practice. Despite their popularity, the theoretical understanding of their convergence properties has been limited, and until very recently essentially no worst-case complexity bounds were available. Recently, Cao et al. provided a sharp error bound for linear interpolation and extrapolation and derived a worst-case complexity result for a basic simplex-type…

Adaptive Divide and Conquer with Two Rounds of Communication
Niladri Kal, Botond Szab\'o, Rajarshi Guhaniyogi, Natesh Pillai, Debdeep Pati
https://arxiv.org/abs/2508.17073 h…

Adaptive Divide and Conquer with Two Rounds of Communication
We introduce a two-round adaptive communication strategy that enables rate-optimal estimation in the white noise model without requiring prior knowledge of the underlying smoothness. In the first round, local machines send summary statistics using $(\log_2(n))^2$ bits to enable the central machine to select the tuning parameters of the procedure. In the second round, another set of statistics are transmitted using optimal number of bits, enabling the central machine to aggregate and produce a f…

Descent problem for certificate of non-negativity on semi-algebraic sets
Manoj K. Keshari, Debapriya Ojha, Niladri Sekhar Patra
https://arxiv.org/abs/2508.07060 https://

Descent problem for certificate of non-negativity on semi-algebraic sets
Let $F$ be a subfield of $\mathbb R$ and let $K$ be a basic closed semi-algebraic set in $\mathbb R$ with $\partial K\subset F$. Let $\mathcal N$ be the natural choice of generators of $K$. We show that if $f\in F[x]$ is $\geq 0$ on $K$, then $f$ can be written as $$f=\sum_{e\in\{0,1\}^s } a_eσ_e g^e $$ where $a_e\in F_{\geq 0}$, $σ_e\in \sum F[x]^2$ and $g^{e}=g_1^{e_1} \cdots g_s^{e_s}$. In other words, the preordering $T_{\mathcal N}$ of $F[x]$ is saturated. In case $F=\mathbb R$, this res…

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
Kevin Pack, Shai Machnes, Frank K. Wilhelm
https://arxiv.org/abs/2509.08555 https://

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices. As part of our ongoing efforts to automate bring-up, tune-up, and system identification procedures, we investigate a broad range of optimizers within a simulated environment designed to closely mimic the challenges of real-world experimental conditions. Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution …