justin

Today I read a paper titled “How Push-To-Talk Makes Talk Less Pushy”

The abstract is:
This paper presents an exploratory study of college-age students using two-way, push-to-talk cellular radios.

We describe the observed and reported use of cellular radio by the participants.

We discuss how the half-duplex, lightweight cellular radio communication was associated with reduced interactional commitment, which meant the cellular radios could be used for a wide range of conversation styles.

One such style, intermittent conversation, is characterized by response delays.

Intermittent conversation is surprising in an audio medium, since it is typically associated with textual media such as instant messaging.

We present design implications of our findings.

Today I read a paper titled “A Bayesian Reflection on Surfaces”

The abstract is:
The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework.

Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology.

The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter.

The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale.

An application example instance, the inference of continuous surfaces from measurements (for example, camera image data), is presented.

Today I read a paper titled “Collusion in Unrepeated, First-Price Auctions with an Uncertain Number of Participants”

The abstract is:
We consider the question of whether collusion among bidders (a “bidding ring”) can be supported in equilibrium of unrepeated first-price auctions.

Unlike previous work on the topic such as that by McAfee and McMillan [1992] and Marshall and Marx [2007], we do not assume that non-colluding agents have perfect knowledge about the number of colluding agents whose bids are suppressed by the bidding ring, and indeed even allow for the existence of multiple cartels.

Furthermore, while we treat the association of bidders with bidding rings as exogenous, we allow bidders to make strategic decisions about whether to join bidding rings when invited.

We identify a bidding ring protocol that results in an efficient allocation in Bayes{Nash equilibrium, under which non-colluding agents bid straightforwardly, and colluding agents join bidding rings when invited and truthfully declare their valuations to the ring center.

We show that bidding rings benefit ring centers and all agents, both members and non-members of bidding rings, at the auctioneer’s expense.

The techniques we introduce in this paper may also be useful for reasoning about other problems in which agents have asymmetric information about a setting..

Today I read a paper titled “Circle and sphere blending with conformal geometric algebra”

The abstract is:
Blending schemes based on circles provide smooth `fair’ interpolations between series of points.

Here we demonstrate a simple, robust set of algorithms for performing circle blends for a range of cases.

An arbitrary level of G-continuity can be achieved by simple alterations to the underlying parameterisation.

Our method exploits the computational framework provided by conformal geometric algebra.

This employs a five-dimensional representation of points in space, in contrast to the four-dimensional representation typically used in projective geometry.

The advantage of the conformal scheme is that straight lines and circles are treated in a single, unified framework.

As a further illustration of the power of the conformal framework, the basic idea is extended to the case of sphere blending to interpolate over a surface..

Today I read a paper titled “Fault-tolerant routing in peer-to-peer systems”

The abstract is:
We consider the problem of designing an overlay network and routing mechanism that permits finding resources efficiently in a peer-to-peer system.

We argue that many existing approaches to this problem can be modeled as the construction of a random graph embedded in a metric space whose points represent resource identifiers, where the probability of a connection between two nodes depends only on the distance between them in the metric space.

We study the performance of a peer-to-peer system where nodes are embedded at grid points in a simple metric space: a one-dimensional real line.

We prove upper and lower bounds on the message complexity of locating particular resources in such a system, under a variety of assumptions about failures of either nodes or the connections between them.

Our lower bounds in particular show that the use of inverse power-law distributions in routing, as suggested by Kleinberg (1999), is close to optimal.

We also give efficient heuristics to dynamically maintain such a system as new nodes arrive and old nodes depart.

Finally, we give experimental results that suggest promising directions for future work.

Today I read a paper titled “Bayesian Grammar Induction for Language Modeling”

The abstract is:
We describe a corpus-based induction algorithm for probabilistic context-free grammars.

The algorithm employs a greedy heuristic search within a Bayesian framework, and a post-pass using the Inside-Outside algorithm.

We compare the performance of our algorithm to n-gram models and the Inside-Outside algorithm in three language modeling tasks.

In two of the tasks, the training data is generated by a probabilistic context-free grammar and in both tasks our algorithm outperforms the other techniques.

The third task involves naturally-occurring data, and in this task our algorithm does not perform as well as n-gram models but vastly outperforms the Inside-Outside algorithm..

Today I read a paper titled “The Mysterious Optimality of Naive Bayes: Estimation of the Probability in the System of “Classifiers””

The abstract is:
Bayes Classifiers are widely used currently for recognition, identification and knowledge discovery.

The fields of application are, for example, image processing, medicine, chemistry (QSAR).

But by mysterious way the Naive Bayes Classifier usually gives a very nice and good presentation of a recognition.

It can not be improved considerably by more complex models of Bayes Classifier.

We demonstrate here a very nice and simple proof of the Naive Bayes Classifier optimality, that can explain this interesting fact.The derivation in the current paper is based on arXiv:cs/0202020v1 .

Today I read a paper titled “Synthesis of Low-Power Digital Circuits Derived from Binary Decision Diagrams”

The abstract is:
This paper introduces a novel method for synthesizing digital circuits derived from Binary Decision Diagrams (BDDs) that can yield to reduction in power dissipation.

The power reduction is achieved by decreasing the switching activity in a circuit while paying close attention to information measures as an optimization criterion.

We first present the technique of efficient BDD-based computation of information measures which are used to guide the power optimization procedures.

Using this technique, we have developed an algorithm of BDD reordering which leads to reducing the power consumption of the circuits derived from BDDs.

Results produced by the synthesis on the ISCAS benchmark circuits are very encouraging.

Today I read a paper titled “Playing Games with Algorithms: Algorithmic Combinatorial Game Theory”

The abstract is:
Combinatorial games lead to several interesting, clean problems in algorithms and complexity theory, many of which remain open.

The purpose of this paper is to provide an overview of the area to encourage further research.

In particular, we begin with general background in Combinatorial Game Theory, which analyzes ideal play in perfect-information games, and Constraint Logic, which provides a framework for showing hardness.

Then we survey results about the complexity of determining ideal play in these games, and the related problems of solving puzzles, in terms of both polynomial-time algorithms and computational intractability results.

Our review of background and survey of algorithmic results are by no means complete, but should serve as a useful primer.

Today I read a paper titled “Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited”

The abstract is:
This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar-based classification, on the Word Sense Disambiguation (WSD) problem.

The aim of the work is twofold.

Firstly, it attempts to contribute to clarify some confusing information about the comparison between both methods appearing in the related literature.

In doing so, several directions have been explored, including: testing several modifications of the basic learning algorithms and varying the feature space.

Secondly, an improvement of both algorithms is proposed, in order to deal with large attribute sets.

This modification, which basically consists in using only the positive information appearing in the examples, allows to improve greatly the efficiency of the methods, with no loss in accuracy.

The experiments have been performed on the largest sense-tagged corpus available containing the most frequent and ambiguous English words.

Results show that the Exemplar-based approach to WSD is generally superior to the Bayesian approach, especially when a specific metric for dealing with symbolic attributes is used.

Today I read a paper titled “Visualization of variations in human brain morphology using differentiating reflection functions”

The abstract is:
Conventional visualization media such as MRI prints and computer screens are inherently two dimensional, making them incapable of displaying true 3D volume data sets.

By applying only transparency or intensity projection, and ignoring light-matter interaction, results will likely fail to give optimal results.

Little research has been done on using reflectance functions to visually separate the various segments of a MRI volume.

We will explore if applying specific reflectance functions to individual anatomical structures can help in building an intuitive 2D image from a 3D dataset.

We will test our hypothesis by visualizing a statistical analysis of the genetic influences on variations in human brain morphology because it inherently contains complex and many different types of data making it a good candidate for our approach.

Today I read a paper titled “A neural network and iterative optimization hybrid for Dempster-Shafer clustering”

The abstract is:
In this paper we extend an earlier result within Dempster-Shafer theory [“Fast Dempster-Shafer Clustering Using a Neural Network Structure,” in Proc.

Seventh Int.

Conf.

Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence are clustered into subsets by a neural network structure.

The clustering is done by minimizing a metaconflict function.

Previously we developed a method based on iterative optimization.

While the neural method had a much lower computation time than iterative optimization its average clustering performance was not as good.

Here, we develop a hybrid of the two methods.

We let the neural structure do the initial clustering in order to achieve a high computational performance.

Its solution is fed as the initial state to the iterative optimization in order to improve the clustering performance.

Today I read a paper titled “Agent Programming with Declarative Goals”

The abstract is:
A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks.

The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a `declarative goal’.

Instead, such frameworks have focused mainly on plans or `goals-to-do’ instead of the end goals to be realised which are also called `goals-to-be’.

In this paper, a new programming language called GOAL is introduced which incorporates such declarative goals.

The notion of a `commitment strategy’ – one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals – is used to construct a computational semantics for GOAL.

Finally, a proof theory for proving properties of GOAL agents is introduced.

Thus, we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents.

An example program is proven correct by using this programming logic.

Today I read a paper titled “A steady state model for graph power laws”

The abstract is:
Power law distribution seems to be an important characteristic of web graphs.

Several existing web graph models generate power law graphs by adding new vertices and non-uniform edge connectivities to existing graphs.

Researchers have conjectured that preferential connectivity and incremental growth are both required for the power law distribution.

In this paper, we propose a different web graph model with power law distribution that does not require incremental growth.

We also provide a comparison of our model with several others in their ability to predict web graph clustering behavior.

Today I read a paper titled “The Design and Experimental Analysis of Algorithms for Temporal Reasoning”

The abstract is:
Many applications — from planning and scheduling to problems in molecular biology — rely heavily on a temporal reasoning component.

In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen’s influential interval-based framework for representing temporal information.

At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information.

Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm.

For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation.

For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm.

As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search.

Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.