130,648 research outputs found
Secure Distributed Network Optimization Against Eavesdroppers
No full textWe present a new algorithmic framework for distributed network optimization in the presence of eavesdropper adversaries, also known as passive wiretappers. In this setting, the adversary is listening to the traffic exchanged over a fixed set of edges in the graph, trying to extract information on the private input and output of the vertices. A distributed algorithm is denoted as f-secure, if it guarantees that the adversary learns nothing on the input and output for the vertices, provided that it controls at most f graph edges.
Recent work has presented general simulation results for f-secure algorithms, with a round overhead of D^Θ(f), where D is the diameter of the graph. In this paper, we present a completely different white-box, and yet quite general, approach for obtaining f-secure algorithms for fundamental network optimization tasks. Specifically, for n-vertex D-diameter graphs with (unweighted) edge-connectivity Ω(f), there are f-secure congest algorithms for computing MST, partwise aggregation, and (1+ε) (weighted) minimum cut approximation, within Õ(D+f √n) congest rounds, hence nearly tight for f = Õ(1).
Our algorithms are based on designing a secure algorithmic-toolkit that leverages the special structure of congest algorithms for global optimization graph problems. One of these tools is a general secure compiler that simulates light-weight distributed algorithms in a congestion-sensitive manner. We believe that these tools set the ground for designing additional secure solutions in the congest model and beyond
Broadcast CONGEST Algorithms Against Eavesdroppers
No full textAn eavesdropper is a passive adversary that aims at extracting private information on the input and output values of the network’s participants, by listening to the traffic exchanged over a subset of edges in the graph. We consider secure congest algorithms for the basic broadcast task, in the presence of eavesdropper (edge) adversaries.
For D-diameter n-vertex graphs with edge connectivity Θ(f), we present f-secure broadcast algorithms that run in Õ(D+√{f n}) rounds. These algorithms transmit some broadcast message m^* to all the vertices in the graph, in a way that is information-theoretically secure against an eavesdropper controlling any subset of at most f edges in the graph. While our algorithms are heavily based on network coding (secret sharing), we also show that this is essential. For the basic problem of secure unicast we demonstrate a network coding gap of Ω(n) rounds.
In the presence of vertex adversaries, known as semi-honest, we introduce the Forbidden-Set Broadcast problem: In this problem, the vertices of the graph are partitioned into two sets, trusted and untrusted, denoted as R, F ⊆ V, respectively, such that G[R] is connected. It is then desired to exchange a secret message m^* between all the trusted vertices while leaking no information to the untrusted set F. Our algorithm works in Õ(D+√|R|) rounds and its security guarantees hold even when all the untrusted vertices F are controlled by a (centralized) adversary
Phase and antigenic variation in mycoplasmas
No full textWith their reduced genome bound by a single membrane, bacteria of the Mycoplasma species represent some of the simplest autonomous life forms. Yet, these minute prokaryotes are able to establish persistent infection in a wide range of hosts, even in the presence of a specific immune response. Clues to their success in host adaptation and survival reside, in part, in a number of gene families that are affected by frequent, stochastic genotypic hanges. These genetic events alter the expression, the size and the antigenic structure of abundant surface proteins, thereby creating highly versatile and dynamic surfaces within a clonal population. This phenomenon provides these wall-less pathogens with a means to escape the host immune response and to modulate surface accessibility by masking and unmasking stably expressed components that are essential in host interaction and survival
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
Biomolecular implementation of computing devices with unbounded memory
No full textWe propose a new way to implement (general) computing devices with unbounded memory. In particular, we show a procedure to implement automata with unbounded stack memory, push-down automata, using circular DNA molecules and a class IIs restriction enzyme. The proposed ideas are inspired by the results from [1]. The same ideas are extended to show a way to implement push-down automata with two stacks (i.e, universal computing devices) using two circular molecules glued with a DX molecule and a class IIs restriction enzyme. In this case each computational molecule also contains a DX portion. These devices can potentially be incorporated in an array of TX molecules. © Springer-Verlag Berlin Heidelberg 2005
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