Faster Exponential-Time Approximation Algorithms Using Approximate Monotone Local Search

We generalize the monotone local search approach of Fomin, Gaspers, Lokshtanov and Saurabh [J.ACM 2019], by establishing a connection between parameterized approximation and exponential-time approximation algorithms for monotone subset minimization problems. In a monotone subset minimization problem the input implicitly describes a non-empty set family over a universe of size n which is closed under taking supersets. The task is to find a minimum cardinality set in this family. Broadly speaking, we use approximate monotone local search to show that a parameterized ?-approximation algorithm that runs in c^k?n^?(1) time, where k is the solution size, can be used to derive an ?-approximation randomized algorithm that runs in d??n^?(1) time, where d is the unique value in (1, 1+{c-1}/?) such that ?(1/??{d-1}/{c-1}) = {ln c}/? and ?(a?b) is the Kullback-Leibler divergence. This running time matches that of Fomin et al. for ? = 1, and is strictly better when ? > 1, for any c > 1. Furthermore, we also show that this result can be derandomized at the expense of a sub-exponential multiplicative factor in the running time. We use an approximate variant of the exhaustive search as a benchmark for our algorithm. We show that the classic 2??n^?(1) exhaustive search can be adapted to an ?-approximate exhaustive search that runs in time (1+exp(-???(1/(?))))??n^?(1), where ? is the entropy function. Furthermore, we provide a lower bound stating that the running time of this ?-approximate exhaustive search is the best achievable running time in an oracle model. When compared to approximate exhaustive search, and to other techniques, the running times obtained by approximate monotone local search are strictly better for any ? ? 1, c > 1. We demonstrate the potential of approximate monotone local search by deriving new and faster exponential approximation algorithms for Vertex Cover, 3-Hitting Set, Directed Feedback Vertex Set, Directed Subset Feedback Vertex Set, Directed Odd Cycle Transversal and Undirected Multicut. For instance, we get a 1.1-approximation algorithm for Vertex Cover with running time 1.114??n^?(1), improving upon the previously best known 1.1-approximation running in time 1.127??n^?(1) by Bourgeois et al. [DAM 2011]

Faster Exponential-Time Approximation Algorithms Using Approximate Monotone Local Search

We generalize the monotone local search approach of Fomin, Gaspers,Lokshtanov and Saurabh [J.ACM 2019], by establishing a connection betweenparameterized approximation and exponential-time approximation algorithms formonotone subset minimization problems. In a monotone subset minimizationproblem the input implicitly describes a non-empty set family over a universeof size $n$ which is closed under taking supersets. The task is to find aminimum cardinality set in this family. Broadly speaking, we use approximatemonotone local search to show that a parameterized $\alpha$-approximationalgorithm that runs in $c^k \cdot n^{O(1)}$ time, where $k$ is the solutionsize, can be used to derive an $\alpha$-approximation randomized algorithm thatruns in $d^n \cdot n^{O(1)}$ time, where $d$ is the unique value in $d \in(1,1+\frac{c-1}{\alpha})$ such that$\mathcal{D}(\frac{1}{\alpha}\|\frac{d-1}{c-1})=\frac{\ln c}{\alpha}$ and$\mathcal{D}(a \|b)$ is the Kullback-Leibler divergence. This running timematches that of Fomin et al. for $\alpha=1$, and is strictly better when$\alpha >1$, for any $c > 1$. Furthermore, we also show that this result can bederandomized at the expense of a sub-exponential multiplicative factor in therunning time. We demonstrate the potential of approximate monotone local search by derivingnew and faster exponential approximation algorithms for Vertex Cover,$3$-Hitting Set, Directed Feedback Vertex Set, Directed Subset Feedback VertexSet, Directed Odd Cycle Transversal and Undirected Multicut. For instance, weget a $1.1$-approximation algorithm for Vertex Cover with running time $1.114^n\cdot n^{O(1)}$, improving upon the previously best known $1.1$-approximationrunning in time $1.127^n \cdot n^{O(1)}$ by Bourgeois et al. [DAM 2011].<br

Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling

In interactive information-seeking, a user often performs many interrelated queries and interactions covering multiple aspects of a broad topic of interest. Especially in difficult information-seeking tasks the user may need to find what is in common among such multiple aspects. Therefore, the user may need to compare and combine results across queries. While methods to combine queries or rankings have been proposed, little attention has been paid to interactive support for combining multiple queries in exploratory search. We introduce an interactive information retrieval system for exploratory search with multiple simultaneous search queries that can be combined. The user is able to direct search in the multiple queries, and combine queries by two operations: intersection and difference, which reveal what is relevant to the user intent of two queries, and what is relevant to one but not the other. Search is directed by relevance feedback on visualized user intent models of each query. Operations on queries act directly on the intent models inferring a combined user intent model. Each combination yields a new result (ranking) and acts as a new search that can be interactively directed and further combined. User experiments on difficult information-seeking tasks show that our novel system with query operations yields more relevant top-ranked documents in a shorter time than a baseline multiple-query system.Peer reviewe

Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

Cortex, countercurrent context, and dimensional integration of lifetime memory

The correlation between relative neocortex size and longevity in mammals encourages a search for a cortical function specifically related to the life-span. A candidate in the domain of permanent and cumulative memory storage is proposed and explored in relation to basic aspects of cortical organization. The pattern of cortico-cortical connectivity between functionally specialized areas and the laminar organization of that connectivity converges on a globally coherent representational space in which contextual embedding of information emerges as an obligatory feature of cortical function. This brings a powerful mode of inductive knowledge within reach of mammalian adaptations, a mode which combines item specificity with classificatory generality. Its neural implementation is proposed to depend on an obligatory interaction between the oppositely directed feedforward and feedback currents of cortical activity, in countercurrent fashion. Direct interaction of the two streams along their cortex-wide local interface supports a scheme of "contextual capture" for information storage responsible for the lifelong cumulative growth of a uniquely cortical form of memory termed "personal history." This approach to cortical function helps elucidate key features of cortical organization as well as cognitive aspects of mammalian life history strategies

Improving Visual Inspection Reliability in Aircraft Maintenance

Visual inspection is a fundamental safety critical task in the air transport industry. This study investigates how a visual search strategy with a specific eye scanning pattern can be used to improve the observation of aircraft defects during visual inspection tasks. N=100 aircraft maintenance technicians were recruited and N=48 were allocated to a control condition. This group conducted pre-flight visual inspections on aircraft, using their normal custom and practice. The remaining N=52 experimental group participants were trained to use a specific eye scanning pattern during their pre-flight inspection called systematic visual search. Prior to inspections, the number of observable defects on each aircraft has been ascertained by the researchers. The results demonstrated that the use of systematic visual search increased the mean number of defects observed from circa 36% to circa 56%. The experimental group were then tasked with further visual inspections using systematic visual search in order to investigate the effect of practice and feedback. This resulted in mean defect observation rates increasing to a plateau of circa 70%. The results clearly demonstrate that; by using a set eye scanning pattern as directed by the systematic visual search method, visual inspection reliability can be improved

Graph-Based Search Procedure for Vector Autoregressive Models

Vector Autoregressions (VARs) are a class of time series models commonly used in econometrics to study the dynamic effect of exogenous shocks to the economy. While the estimation of a VAR is straightforward, there is a problem of finding the transformation of the estimated model consistent with the causal relations among the contemporaneous variables. Such problem, which is a version of what is called in econometrics “the problem of identification,” is faced in this paper using a semi-automated search procedure. The unobserved causal relations of the structural form, to be identified, are represented by a directed graph. Discovery algorithms are developed to infer features of the causal graph from tests on vanishing partial correlations among the VAR residuals. Such tests cannot be based on the usual tests of conditional independence, because of sampling problems due to the time series nature of the data. This paper proposes consistent tests on vanishing partial correlations based on the asymptotic distribution of the estimated VAR residuals. Two different types of search algorithm are considered. A first algorithm restricts the analysis to direct causation among the contemporaneous variables, a second algorithm allows the possibility of cycles (feedback loops) and common shocks among contemporaneous variables. Recovering the causal structure allows a reliable transformation of the estimated vector autoregressive model which is very useful for macroeconomic empirical investigations, such as comparing the effects of different shocks (real vs. nominal) on the economy and finding a measure of the monetary policy shock.VARs, Problem of Identification, Causal Graphs, Structural Shocks

A directed mutation operator for real coded genetic algorithms

Copyright @ Springer-Verlag Berlin Heidelberg 2010.Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs to explore promising areas in the search space. In this DM method, the statistics information regarding the fitness and distribution of individuals over intervals of each dimension is calculated according to the current population and is used to guide the mutation of an individual toward the neighboring interval that has the best statistics result in each dimension. Experiments are carried out to compare the proposed DM technique with an existing directed variation on a set of benchmark test problems. The experimental results show that the proposed DM operator achieves a better performance than the directed variation on most test problems