Independence is elusive: set size effects on encoding precision in visual search.

Looking for a target in a visual scene becomes more difficult as the number of stimuli increases. In a signal detection theory view, this is due to the cumulative effect of noise in the encoding of the distractors, and potentially on top of that, to an increase of the noise (i.e., a decrease of precision) per stimulus with set size, reflecting divided attention. It has long been argued that human visual search behavior can be accounted for by the first factor alone. While such an account seems to be adequate for search tasks in which all distractors have the same, known feature value (i.e., are maximally predictable), we recently found a clear effect of set size on encoding precision when distractors are drawn from a uniform distribution (i.e., when they are maximally unpredictable). Here we interpolate between these two extreme cases to examine which of both conclusions holds more generally as distractor statistics are varied. In one experiment, we vary the level of distractor heterogeneity; in another we dissociate distractor homogeneity from predictability. In all conditions in both experiments, we found a strong decrease of precision with increasing set size, suggesting that precision being independent of set size is the exception rather than the rule.

A high-performance Cantonese keyword search system

We present a system for keyword search on Cantonese conversational telephony audio, collected for the IARPA Babel program, that achieves good performance by combining postings lists produced by diverse speech recognition systems from three different research groups. We describe the keyword search task, the data on which the work was done, four different speech recognition systems, and our approach to system combination for keyword search. We show that the combination of four systems outperforms the best single system by 7%, achieving an actual term-weighted value of 0.517. © 2013 IEEE.

The search for the IFN-gamma receptor in fish: Functional and expression analysis of putative binding and signalling chains in rainbow trout Oncorhynchus mykiss

Interferons (IFNs), consisting of three major subfamilies, type I, type II (gamma) and type III (lambda) IFN, activate vertebrate antiviral defences once bound to their receptors. The three IFN subfamilies bind to different receptors, IFNAR1 and IFNAR2 for type I IFNs, IFN gamma R1 and IFN gamma R2 for type II IFN, and IL-28R1 and IL-10R2 for type III IFNs. In fish, although many types I and II IFN genes have been cloned, little is known about their receptors. In this report, two putative IFN-gamma receptor chains were identified and sequenced in rainbow trout (Oncorhynchus mykiss), and found to have many common characteristics with mammalian type II IFN receptor family members. The presented gene synteny analysis, phylogenetic tree analysis and ligand binding analysis all suggest that these molecules are the authentic IFN gamma Rs in fish. They are widely expressed in tissues, with IFN gamma R1 typically more highly expressed than IFN gamma R2. Using the trout RTG-2 cell line it was possible to show that the individual chains could be differentially modulated, with rIFN-gamma and rIL-1 beta down regulating IFN gamma R1 expression but up regulating IFN gamma R2 expression. Overexpression of the two receptor chains in RTG-2 cells revealed that the level of IFN gamma R2 transcript was crucial for responsiveness to rIFN-gamma, in terms of inducing gamma IP expression. Transfection experiments showed that the two putative receptors specifically bound to rIFN-gamma. These findings are discussed in the context of how the IFN gamma R may bind IFN-gamma in fish and the importance of the individual receptor chains to signal transduction. (c) 2009 Elsevier Ltd. All rights reserved.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.

a backtracking search tool for constructing combinatorial test suites

Combinatorial testing is an important testing method. It requires the test cases to cover various combinations of parameters of the system under test. The test generation problem for combinatorial testing can be modeled as constructing a matrix which has certain properties. This paper first discusses two combinatorial testing criteria: covering array and orthogonal array, and then proposes a backtracking search algorithm to construct matrices satisfying them. Several search heuristics and symmetry breaking techniques are used to reduce the search time. This paper also introduces some techniques to generate large covering array instances from smaller ones. All the techniques have been implemented in a tool called EXACT (EXhaustive seArch of Combinatorial Test suites). A new optimal covering array is found by this tool.