High performance SIMD modular arithmetic for polynomial evaluation

Two essential problems in Computer Algebra, namely polynomial factorization and polynomial greatest common divisor computation, can be efficiently solved thanks to multiple polynomial evaluations in two variables using modular arithmetic. In this article, we focus on the efficient computation of such polynomial evaluations on one single CPU core. We first show how to leverage SIMD computing for modular arithmetic on AVX2 and AVX-512 units, using both intrinsics and OpenMP compiler directives. Then we manage to increase the operational intensity and to exploit instruction-level parallelism in order to increase the compute efficiency of these polynomial evaluations. All this results in the end to performance gains up to about 5x on AVX2 and 10x on AVX-512

We Are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod $N$ with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as \textsf{Cado-NFS}, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation

Gene selection with multiple ordering criteria

BACKGROUND: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. RESULTS: We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. CONCLUSION: The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

We Are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod N with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as Cado-NFS, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation

We Are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod N with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as Cado-NFS, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation

We Are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod N with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as Cado-NFS, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation

We Are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod N with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as Cado-NFS, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation

Growth, Duplication and Lateral Mutual Compressive Deformation of Akouemma hemisphaeria on the Seafloor of Okondja Basin at 2.2 Ga (Gabon)

International audienc