Let B be a set of n unit balls in R-3. We present a linear-size data structure for storing B that can determine in O*(root n) time whether a query line intersects any ball of B and report all k such balls in additiona...
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Let B be a set of n unit balls in R-3. We present a linear-size data structure for storing B that can determine in O*(root n) time whether a query line intersects any ball of B and report all k such balls in additional O(k) time. The data structure can be constructed in O(n log n) time. (The O*(center dot) notation hides subpolynomial factors, e.g., of the form O(n(epsilon)), for arbitrarily small epsilon > 0, and their coefficients which depend on epsilon.) We also consider the dual problem: Let L be a set of n lines in R-3. We preprocess L, in O*(n(2)) time, into a data structure of size O*(n(2)) that can determine in O(log n) time whether a query unit ball intersects any line of L, or report all k such lines in additional O(k) time.
Editor’s notes: This article protects deep learning models by leveraging hardware device-specific model fingerprinting and trusted execution environment. —Gang Qu, University of Maryland, USA
Editor’s notes: This article protects deep learning models by leveraging hardware device-specific model fingerprinting and trusted execution environment. —Gang Qu, University of Maryland, USA
Linear computation broadcast (LCBC) refers to a setting with d dimensional data stored at a central server, where K users, each with some prior linear side-information, wish to compute various linear combinations of t...
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Linear computation broadcast (LCBC) refers to a setting with d dimensional data stored at a central server, where K users, each with some prior linear side-information, wish to compute various linear combinations of the data. For each computation instance, the data is represented as ad-dimensional vector with elements in a finite field F-pn where p(n) is a power of a prime. The computation is to be performed many times, and the goal is to determine the minimum amount of information per computation instance that must be broadcast to satisfy all the users. The reciprocal of the optimal broadcast cost per computation instance is the capacity of LCBC. The capacity is known for up to K = 3users. Since LCBC includes index coding as a special case, large K settings of LCBC are at least as hard as the index coding problem. As such the general LCBC problem is beyond our reach and we do not pursue it. Instead of the general setting (all cases), by focusing on the generic setting(almost all cases) this work shows that the generic capacity of the symmetric LCBC (where every user has m ' dimensions of side-information and m dimensions of demand) for large number of users (K >= d suffices) is C-g = 1/triangle(g), where triangle(g)= min {max{0, d-m '}, dm/m+m '}, is the broadcast cost that is both achievable and unbeatable asymptotically almost surely for largen, among all LCBC instances with the given parameter sp, K, d, m, m '. Relative to baseline schemes of random coding or separate transmissions, C-g shows an extremal gain by a factor of K as a function of number of users, and by a factor of approximate to d/4 as a function of data dimensions, when optimized over remaining parameters. For arbitrary number of users, the generic capacity of the symmetric LCBC is characterized with in a factor of 2.
The collective statistics of voting on judicial courts present hints about their inner workings. Many approaches for studying these statistics, however, assume that judges' decisions are conditionally independent:...
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The collective statistics of voting on judicial courts present hints about their inner workings. Many approaches for studying these statistics, however, assume that judges' decisions are conditionally independent: a judge reaches a decision based on the case at hand and his or her personal views. In reality, judges interact. We develop a minimal model that accounts for judge bias, depending on the context of the case, and peer interaction. We apply the model to voting data from the US Supreme Court. We find strong evidence that interaction is an important factor across natural courts from 1946 to 2021. We also find that, after accounting for interaction, the recovered biases differ from highly cited ideological scores. Our method exemplifies how physics and complexity-inspired modelling can drive the development of theoretical models and improved measures for political *** article is part of the theme issue 'A complexity science approach to law and governance'.
Background Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing ...
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Background Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen *** To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative *** In conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, offers a promising solution for efficient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale.
Achieving consistent and predictable gene expression from plasmids remains challenging. While much attention has focused on intra-genetic elements like promoters and ribosomal binding sites, the spatial arrangement of...
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Achieving consistent and predictable gene expression from plasmids remains challenging. While much attention has focused on intra-genetic elements like promoters and ribosomal binding sites, the spatial arrangement of genes within plasmids-referred to as gene syntax-also plays a crucial role in shaping gene expression dynamics. This study addresses the largely overlooked impact of gene syntaxes on gene expression variability and accuracy. Utilizing a dual-fluorescent protein system, we systematically investigated how different gene orientations and orders affect expression profiles including mean levels, relative expression ratios, and cell-to-cell variations. We found that arbitrary gene placement on a plasmid can cause significantly different expression means and ratios. Genes aligned in the same direction as a plasmid's origin of replication (Ori) typically exhibit higher expression levels;adjacent genes in the divergent orientation tend to suppress each other's expression;altering gene order without changing orientation can yield varied expression. Despite unchanged total cell-to-cell variation across different syntaxes, gene syntaxes can also influence intrinsic and extrinsic noise. Interestingly, cell-to-cell variation appears to depend on the reporter proteins, with RFP consistently showing higher variation than GFP. Moreover, the effects of gene syntax can propagate to downstream circuits, strongly affecting the performance of incoherent feedforward loops and contributing to unpredictable outcomes in genetic networks. Our findings reveal that gene syntaxes on plasmids modulate gene expression and circuit behavior, providing valuable insights for the rational design of plasmids and genetic circuits.
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spu...
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.
The global health crisis triggered by the SARS-CoV-2 virus has highlighted the urgent need for effective treatments. As existing drugs are not specifically targeted at this virus, there is a growing interest in explor...
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The global health crisis triggered by the SARS-CoV-2 virus has highlighted the urgent need for effective treatments. As existing drugs are not specifically targeted at this virus, there is a growing interest in exploring natural antimicrobial peptides such as defensin as potential therapeutic options. Human beta defensin type 2 (hBD-2), which is a cationic cysteine-rich peptide, serves as the initial barrier against bacterial and fungal invaders in mammals. It can bind with Spike-RBD and occupy the same site as the ACE2 receptor, thereby hindering viral entry into cells expressing ACE2. To explore the effect of different point mutations on the binding of hBD-2 with RBD, the binding dynamics and interactions between hBD-2 point mutants with RBD were studied and compared with that of RBD&hBD-2 wild-type complex. In total, 247 hBD-2 point mutants were built with the mutation sites at the binding region of hBD-2 (RES18-30) with the RBD of CoV-2. All-atom molecular dynamics simulations were carried out on RBD binding with hBD-2 point mutants. Analysis based on root-mean-square deviation (RMSD), hydrogen bonds analysis, and binding free energy using the MM/PBSA method revealed that many point mutants of hBD-2 exhibit weaker binding with RBD compared to the wild type;however, a subset of mutants, including C20I, C20K, R22W, R23H, R23L, Y24L, K25F, K25H, G28Y, T29R, and C30K, displayed enhanced binding with RBD. The findings can offer insights designing hBD-2-based novel drugs to combat SARS-CoV-2 in the long term.
A standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. This manuscript provides algorithms for computing the number of connected co...
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A standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. This manuscript provides algorithms for computing the number of connected components, the Euler characteristic, and deciding the connectivity between two points for a smooth manifold arising as the complement of a real hypersurface of a real algebraic variety. When considering the complement of the set of singular points of a real algebraic variety, this yields an approach for determining smooth connectivity in a real algebraic variety. The method is based upon gradient ascent/descent paths on the real algebraic variety inspired by a method proposed by Hong, Rohal, Safey El Din, and Schost for complements of real hypersurfaces. Several examples are included to demonstrate the approach.
Developing an algorithm for computing the Betti numbers of semi-algebraic sets with singly exponential complexity has been a holy grail in algorithmic semi-algebraic geometry and only partial results are known. In thi...
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Developing an algorithm for computing the Betti numbers of semi-algebraic sets with singly exponential complexity has been a holy grail in algorithmic semi-algebraic geometry and only partial results are known. In this paper we consider the more general problem of computing the image under the homology functor of a continuous semi-algebraic map f:X -> Y\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f:X \rightarrow Y$$\end{document} between closed and bounded semi-algebraic sets. For every fixed l >= 0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell \ge 0$$\end{document} we give an algorithm with singly exponential complexity that computes bases of the homology groups Hi(X),Hi(Y)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ H}_i(X), \text{ H}_i(Y)$$\end{document} (with rational coefficients) and a matrix with respect to these bases of the induced linear maps Hi(f):Hi(X)-> Hi(Y),0 <= i <= l\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ H}_i(f):\text{ H}_i(X) \rightarrow \text{ H}_i(Y), 0 \le i \le \ell $$\end{document}. We generalize this algorithm to more general (zigzag) diagrams of continuous semi-algebraic maps between closed and bounded semi-algebraic sets and give a singly exponential algorithm for computing the homology funct
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