Efficient Doubling on Genus Two Curves over Binary Fields

In most algorithms involving elliptic and hyperelliptic curves, the costliest part consists in computing multiples of ideal classes. This paper investigates how to compute faster doubling over fields of characteristic two. We derive explicit doubling formulae making strong use of the defining equation of the curve. We analyze how many field operations are needed depending on the curve making clear how much generality one loses by the respective choices. Note, that none of the proposed types is known to be weak – one only could be suspicious because of the more special types. Our results allow to choose curves from a large enough variety which have extremely fast doubling needing only half the time of an addition. Combined with a sliding window method this leads to fast computation of scalar multiples. We also speed up the general case

Overcoming the Impasse in Modern Economics

This document is the Accepted Manuscript version of the following article: Francesca Gagliardi, and David Gindis, 'Overcoming the Impasse in Modern Economics', Competition and Change, Vol. 15 (4): 336-42, November 2011, doi: 10.1179/102452911X13135903675732. Published by SAGE.Peer reviewe

A low-memory algorithm for finding short product representations in finite groups

We describe a space-efficient algorithm for solving a generalization of the subset sum problem in a finite group G, using a Pollard-rho approach. Given an element z and a sequence of elements S, our algorithm attempts to find a subsequence of S whose product in G is equal to z. For a random sequence S of length d log_2 n, where n=#G and d >= 2 is a constant, we find that its expected running time is O(sqrt(n) log n) group operations (we give a rigorous proof for d > 4), and it only needs to store O(1) group elements. We consider applications to class groups of imaginary quadratic fields, and to finding isogenies between elliptic curves over a finite field.Comment: 12 page

The Political Economy of US Military Spending

The causes of the dramatic rise in military spending in the post-war era have been the subject of much political and academic controversy. No extant formulation seems to provide a compelling explanation of the dynamics involved in the levels of, and rates of change in, such spending. In light of this, the authors develop a new model, based mainly on a political-business cycle argument, to account for these dynamics. The basic proposition in this model is that variations in national defense spending arise from political considerations which are related to real and desired conditions within the national economy. Applying this model to the experience of the United States 1948-1976, the authors show that it has a large measure of empirical validity. If one removes the effects of war-time mobilization, it is clear that for the United States the principal driving forces in military spending dynamics were (1) the perceived utility of such spending in stabilizing aggregate demand, (2) the political or electoral value of the perceived economic effects arising out of such spending, and (3) the pressures of institutional-constituency demands.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68958/2/10.1177_002234337901600202.pd

On the Design and Implementation of an Efficient DAA Scheme

International audienceDirect Anonymous Attestation (DAA) is an anonymous digital signature scheme that aims to provide both signer authentication and privacy. One of the properties that makes DAA an attractive choice in practice is the split signer role. In short, a principal signer (a Trusted Platform Module (TPM)) signs messages in collaboration with an assistant signer (the Host, a standard computing platform into which the TPM is embedded). This split aims to harness the high level of security offered by the TPM, and augment it using the high level of computational and storage ability offered by the Host. Our contribution in this paper is a modification to an existing pairing-based DAA scheme that significantly improves efficiency, and a comparison with the original RSA-based DAA scheme via a concrete implementation

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy