Tootfinder

This https://arxiv.org/abs/2401.15355 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Improved bounds on the interactive capacity via error pattern analysis
Any interactive protocol between a pair of parties can be reliably simulated in the presence of noise with a multiplicative overhead on the number of rounds (Schulman 1996). The reciprocal of the best (least) overhead is called the interactive capacity of the noisy channel. In this work, we present lower bounds on the interactive capacity of the binary erasure channel. Our lower bound improves the best known bound due to Ben-Yishai et al. 2021 by roughly a factor of 1.75. The improvement is d…

Persistent Homology generalizations for Social Media Network Analysis
Isabela Rocha
https://arxiv.org/abs/2404.19257 https://arxiv.org/pdf/2404.19257
arXiv:2404.19257v1 Announce Type: new
Abstract: This study details an approach for the analysis of social media collected political data through the lens of Topological Data Analysis, with a specific focus on Persistent Homology and the political processes they represent by proposing a set of mathematical generalizations using Gaussian functions to define and analyze these Persistent Homology categories. Three distinct types of Persistent Homologies were recurrent across datasets that had been plotted through retweeting patterns and analyzed through the k-Nearest-Neighbor filtrations. As these Persistent Homologies continued to appear, they were then categorized and dubbed Nuclear, Bipolar, and Multipolar Constellations. Upon investigating the content of these plotted tweets, specific patterns of interaction and political information dissemination were identified, namely Political Personalism and Political Polarization. Through clustering and application of Gaussian density functions, I have mathematically characterized each category, encapsulating their distinctive topological features. The mathematical generalizations of Bipolar, Nuclear, and Multipolar Constellations developed in this study are designed to inspire other political science digital media researchers to utilize these categories as to identify Persistent Homology in datasets derived from various social media platforms, suggesting the broader hypothesis that such structures are bound to be present on political scraped data regardless of the social media it's derived from. This method aims to offer a new perspective in Network Analysis as it allows for an exploration of the underlying shape of the networks formed by retweeting patterns, enhancing the understanding of digital interactions within the sphere of Computational Social Sciences.

Arrays in Practice: An Empirical Study of Array Access Patterns on the JVM
Beatrice {\AA}kerblomStockholm University, Sweden, Elias CastegrenUppsala University, Sweden
https://arxiv.org/abs/2403.02416

Arrays in Practice: An Empirical Study of Array Access Patterns on the JVM
The array is a data structure used in a wide range of programs. Its compact storage and constant time random access makes it highly efficient, but arbitrary indexing complicates the analysis of code containing array accesses. Such analyses are important for compiler optimisations such as bounds check elimination. The aim of this work is to gain a better understanding of how arrays are used in real-world programs. While previous work has applied static analyses to understand how arrays are acces…

Code Generation and Performance Engineering for Matrix-Free Finite Element Methods on Hybrid Tetrahedral Grids
Fabian B\"ohm, Daniel Bauer, Nils Kohl, Christie Alappat, Dominik Th\"onnes, Marcus Mohr, Harald K\"ostler, Ulrich R\"ude
https://arxiv.org/abs/2404.08371

Code Generation and Performance Engineering for Matrix-Free Finite Element Methods on Hybrid Tetrahedral Grids
This paper introduces a code generator designed for node-level optimized, extreme-scalable, matrix-free finite element operators on hybrid tetrahedral grids. It optimizes the local evaluation of bilinear forms through various techniques including tabulation, relocation of loop invariants, and inter-element vectorization - implemented as transformations of an abstract syntax tree. A key contribution is the development, analysis, and generation of efficient loop patterns that leverage the local s…

This https://arxiv.org/abs/2403.19612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handle…

Characterising Payload Entropy in Packet Flows
Anthony Kenyon, Lipika Deka, David Elizondo
https://arxiv.org/abs/2404.19121 https://arxiv.org/pdf/2404.19121
arXiv:2404.19121v1 Announce Type: new
Abstract: Accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity - such as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge there are no published baselines for payload entropy across common network services. We offer two contributions: 1) We analyse several large packet datasets to establish baseline payload information entropy values for common network services, 2) We describe an efficient method for engineering entropy metrics when performing flow recovery from live or offline packet data, which can be expressed within feature subsets for subsequent analysis and machine learning applications.

SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery
Kristina Mach, Hessam Roodaki, Michael Sommersperger, Nassir Navab
https://arxiv.org/abs/2404.19481 https://arxiv.org/pdf/2404.19481
arXiv:2404.19481v1 Announce Type: new
Abstract: This paper presents an innovative approach to intraoperative Optical Coherence Tomography (iOCT) image segmentation in ophthalmic surgery, leveraging statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools, facilitating the segmentation of previously unseen data without the necessity for manual labeling. The research involves fitting various statistical distributions to iOCT data, enabling the differentiation of different ocular structures and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding to leverage statistical and biological knowledge. Incorporating statistical parameters, physical analysis of light-tissue interaction, and deep learning informed by biological structures enhance segmentation accuracy, offering potential benefits to real-time applications in ophthalmic surgical procedures. The study demonstrates the adaptability and precision of using Gamma distribution parameters and the derived binary maps as sole inputs for segmentation, notably enhancing the model's inference performance on unseen data.

This https://arxiv.org/abs/2309.06833 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Agnostic detection of large-scale weather patterns in the northern hemisphere: from blockings to teleconnections
Detecting recurrent weather patterns and understanding the transitions between such regimes are key to advancing our knowledge on the low-frequency variability of the atmosphere and have important implications in terms of weather and climate-related risks. We adapt an analysis pipeline inspired by Markov State Modelling and detect in an unsupervised manner the dominant winter mid-latitude Northern Hemisphere weather patterns in the Atlantic and Pacific sectors. The daily 500 hPa geopotential he…

Estimation of migrate histories of the Japanese sardine in the Sea of Japan by combining the microscale stable isotope analysis of otoliths and a data assimilation model
Tomoya Aono, Tatsuya Sakamoto, Toyoho Ishimura, Motomitsu Takahashi, Tohya Yasuda, Satoshi Kitajima, Kozue Nishida, Takayoshi Matsuura, Akito Ikari, Shin-ichi Ito
https://

Estimation of migrate histories of the Japanese sardine in the Sea of Japan by combining the microscale stable isotope analysis of otoliths and a data assimilation model
The Japanese sardine (Sardinops melanostictus) is a small pelagic fish found in the Sea of Japan, the marginal sea of the western North Pacific. It is an important species for regional fisheries, but their transportation and migration patterns during early life stages remain unclear. In this study, we analyzed the stable oxygen isotope ratios of otoliths of young-of-the-year (age 0) Japanese sardines collected from the northern offshore and southern coastal areas of the Sea of Japan in 2015 and…

This https://arxiv.org/abs/2403.19612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handle…