1,501 research outputs found
Unary Positional Computing
Faculty advisor: Marc RiedelThis research was supported by the Undergraduate Research Opportunities Program (UROP)
Quantitative games with interval objectives
Traditionally quantitative games such as mean-payoff games and discount sum
games have two players -- one trying to maximize the payoff, the other trying
to minimize it. The associated decision problem, "Can Eve (the maximizer)
achieve, for example, a positive payoff?" can be thought of as one player
trying to attain a payoff in the interval . In this paper we
consider the more general problem of determining if a player can attain a
payoff in a finite union of arbitrary intervals for various payoff functions
(liminf, mean-payoff, discount sum, total sum). In particular this includes the
interesting exact-value problem, "Can Eve achieve a payoff of exactly (e.g.)
0?"Comment: Full version of CONCUR submissio
Probabilistic thread algebra
We add probabilistic features to basic thread algebra and its extensions with
thread-service interaction and strategic interleaving. Here, threads represent
the behaviours produced by instruction sequences under execution and services
represent the behaviours exhibited by the components of execution environments
of instruction sequences. In a paper concerned with probabilistic instruction
sequences, we proposed several kinds of probabilistic instructions and gave an
informal explanation for each of them. The probabilistic features added to the
extension of basic thread algebra with thread-service interaction make it
possible to give a formal explanation in terms of non-probabilistic
instructions and probabilistic services. The probabilistic features added to
the extensions of basic thread algebra with strategic interleaving make it
possible to cover strategies corresponding to probabilistic scheduling
algorithms.Comment: 25 pages (arXiv admin note: text overlap with arXiv:1408.2955,
arXiv:1402.4950); some simplifications made; substantially revise
On inverted index compression for search engine efficiency
Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to users’ queries . While the performance of an information retrieval (IR) system can be enhanced through the compression of its posting lists, there is little recent work in the literature that thoroughly compares and analyses the performance of modern integer compression schemes across different types of posting information (document ids, frequencies, positions). In this paper, we experiment with different modern integer compression algorithms, integrating these into a modern IR system. Through comprehensive experiments conducted on two large, widely used document corpora and large query sets, our results show the benefit of compression for different types of posting information to the space- and time-efficiency of the search engine. Overall, we find that the simple Frame of Reference compression scheme results in the best query response times for all types of posting information. Moreover, we observe that the frequency and position posting information in Web corpora that have large volumes of anchor text are more challenging to compress, yet compression is beneficial in reducing average query response times
uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing
Hyperdimensional computing (HDC) is a novel computational paradigm that
operates on long-dimensional vectors known as hypervectors. The hypervectors
are constructed as long bit-streams and form the basic building blocks of HDC
systems. In HDC, hypervectors are generated from scalar values without taking
their bit significance into consideration. HDC has been shown to be efficient
and robust in various data processing applications, including computer vision
tasks. To construct HDC models for vision applications, the current
state-of-the-art practice utilizes two parameters for data encoding: pixel
intensity and pixel position. However, the intensity and position information
embedded in high-dimensional vectors are generally not generated dynamically in
the HDC models. Consequently, the optimal design of hypervectors with high
model accuracy requires powerful computing platforms for training. A more
efficient approach to generating hypervectors is to create them dynamically
during the training phase, which results in accurate, low-cost, and highly
performable vectors. To this aim, we use low-discrepancy sequences to generate
intensity hypervectors only, while avoiding position hypervectors. By doing so,
the multiplication step in vector encoding is eliminated, resulting in a
power-efficient HDC system. For the first time in the literature, our proposed
approach employs lightweight vector generators utilizing unary bit-streams for
efficient encoding of data instead of using conventional comparator-based
generators.Comment: 7 pages, 6 figures, Accepted to the Design, Automation and Test in
Europe (DATE) Conference 202
The difficulty of prime factorization is a consequence of the positional numeral system
The importance of the prime factorization problem is very well known
(e.g., many security protocols are based on the impossibility of a fast factorization
of integers on traditional computers). It is necessary from a number k
to establish two primes a and b giving k = a · b. Usually, k is written in a positional
numeral system. However, there exists a variety of numeral systems
that can be used to represent numbers. Is it true that the prime factorization is
difficult in any numeral system? In this paper, a numeral system with partial
carrying is described. It is shown that this system contains numerals allowing
one to reduce the problem of prime factorization to solving [K/2] − 1
systems of equations, where K is the number of digits in k (the concept of
digit in this system is more complex than the traditional one) and [u] is the
integer part of u. Thus, it is shown that the difficulty of prime factorization is
not in the problem itself but in the fact that the positional numeral system is
used traditionally to represent numbers participating in the prime factorization.
Obviously, this does not mean that P=NP since it is not known whether
it is possible to re-write a number given in the traditional positional numeral
system to the new one in a polynomial time
Fair Knapsack
We study the following multiagent variant of the knapsack problem. We are
given a set of items, a set of voters, and a value of the budget; each item is
endowed with a cost and each voter assigns to each item a certain value. The
goal is to select a subset of items with the total cost not exceeding the
budget, in a way that is consistent with the voters' preferences. Since the
preferences of the voters over the items can vary significantly, we need a way
of aggregating these preferences, in order to select the socially best valid
knapsack. We study three approaches to aggregating voters' preferences, which
are motivated by the literature on multiwinner elections and fair allocation.
This way we introduce the concepts of individually best, diverse, and fair
knapsack. We study the computational complexity (including parameterized
complexity, and complexity under restricted domains) of the aforementioned
multiagent variants of knapsack.Comment: Extended abstract will appear in Proc. of 33rd AAAI 201
Modeling Graph Languages with Grammars Extracted via Tree Decompositions
Work on probabilistic models of natural language tends to focus on strings and trees, but there is increasing interest in more general graph-shaped structures since they seem to be better suited for representing natural language semantics, ontologies, or other varieties of knowledge structures. However, while there are relatively simple approaches to defining generative models over strings and trees, it has proven more challenging for more general graphs. This paper describes a natural generalization of the n-gram to graphs, making use of Hyperedge Replacement Grammars to define generative models of graph languages.9 page(s
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