HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process

The prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility patterns. Knowledge of human dynamics can aid in various ways like urban planning, managing traffic congestion, personalized recommendation etc. These dynamics are influenced by factors like social impact, periodicity in mobility, spatial proximity, influence among users and semantic categories etc., which makes location modelling a critical task. However, categories which act as semantic characterization of the location, might be missing for some check-ins and can adversely affect modelling the mobility dynamics of users. At the same time, mobility patterns provide a cue on the missing semantic category. In this paper, we simultaneously address the problem of semantic annotation of locations and location adoption dynamics of users. We propose our model HAP-SAP, a latent spatio-temporal multivariate Hawkes process, which considers latent semantic category influences, and temporal and spatial mobility patterns of users. The model parameters and latent semantic categories are inferred using expectation-maximization algorithm, which uses Gibbs sampling to obtain posterior distribution over latent semantic categories. The inferred semantic categories can supplement our model on predicting the next check-in events by users. Our experiments on real datasets demonstrate the effectiveness of the proposed model for the semantic annotation and location adoption modelling tasks.Comment: 11 page

Preference relations based unsupervised rank aggregation for metasearch

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithms for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above. We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch

Heuristic search strategies for multiobjective state space search

The multiobjective search model is a framework for solving multicriteria optimization problems using heuristic search techniques, where the different dimensions of a multiobjective search problem are mapped into a vector valued cost structure and partial order search is employed to determine the set of non-inferior solutions. This new framework for solving multicriteria optimization problems has been introduced by Stewart and White, who presented a generalization of the well known algorithmA* in this model. This paper presents several results on multiobjective state space search which helps in refining the scheme proposed by them. In particular, the following results have been presented. • The concept of pathmax has been generalized to the multiobjective framework. It has been established that unlike in the conventional model, multidimensional pathmax (in the multiobjective model) is useful for nonpathological tree search instances as well. We investigate the utility of an induced total order on the partial order search mechanism. The results presented are as follows: - If an induced total order is used in the selection process, then in general it is not necessary to compute the entire set of heuristic vectors at a node. - In memory-bounded search, a multiobjective search strategy that uses an induced total order for selection can back up a single cost vector while backtracking and yet guarantee admissibility though multiple noninferior candidate back-up vectors may be present in the space pruned while backtracking. • In this paper we study multiobjective state space search using inadmissible heuristics. We show that if heuristics are allowed to overestimate, then no algorithm is guaranteed to find all non-inferior solutions unless it expands dominated nodes also. The paper also addresses the task of multiobjective search under memory bounds, which is important in order to make the search scheme viable for practical purposes. The paper presents a linear space multiobjective search strategy called MOR*0 and suggests several variants of the strategy which may be useful under different situations

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Basal ganglia dysfunction in OCD: subthalamic neuronal activity correlates with symptoms severity and predicts high-frequency stimulation efficacy

Functional and connectivity changes in corticostriatal systems have been reported in the brains of patients with obsessive–compulsive disorder (OCD); however, the relationship between basal ganglia activity and OCD severity has never been adequately established. We recently showed that deep brain stimulation of the subthalamic nucleus (STN), a central basal ganglia nucleus, improves OCD. Here, single-unit subthalamic neuronal activity was analysed in 12 OCD patients, in relation to the severity of obsessions and compulsions and response to STN stimulation, and compared with that obtained in 12 patients with Parkinson's disease (PD). STN neurons in OCD patients had lower discharge frequency than those in PD patients, with a similar proportion of burst-type activity (69 vs 67%). Oscillatory activity was present in 46 and 68% of neurons in OCD and PD patients, respectively, predominantly in the low-frequency band (1–8 Hz). In OCD patients, the bursty and oscillatory subthalamic neuronal activity was mainly located in the associative–limbic part. Both OCD severity and clinical improvement following STN stimulation were related to the STN neuronal activity. In patients with the most severe OCD, STN neurons exhibited bursts with shorter duration and interburst interval, but higher intraburst frequency, and more oscillations in the low-frequency bands. In patients with best clinical outcome with STN stimulation, STN neurons displayed higher mean discharge, burst and intraburst frequencies, and lower interburst interval. These findings are consistent with the hypothesis of a dysfunction in the associative–limbic subdivision of the basal ganglia circuitry in OCD's pathophysiology

Efficacy and safety of aripiprazole in the treatment of bipolar disorder: a systematic review

Abstract BACKGROUND: The current article is a systematic review concerning the efficacy and safety of aripiprazole in the treatment of bipolar disorder. METHODS: A systematic Medline and repositories search concerning the usefulness of aripiprazole in bipolar disorder was performed, with the combination of the words 'aripiprazole' and 'bipolar'. RESULTS: The search returned 184 articles and was last updated on 15 April 2009. An additional search included repositories of clinical trials and previous systematic reviews specifically in order to trace unpublished trials. There were seven placebo-controlled randomised controlled trials (RCTs), six with comparator studies and one with add-on studies. They assessed the usefulness of aripiprazole in acute mania, acute bipolar depression and during the maintenance phase in comparison to placebo, lithium or haloperidol. CONCLUSION: Aripiprazole appears effective for the treatment and prophylaxis against mania. The data on bipolar depression are so far negative, however there is a need for further study at lower dosages. The most frequent adverse effects are extrapyramidal signs and symptoms, especially akathisia, without any significant weight gain, hyperprolactinaemia or laboratory test changes

Heuristic search through islands

A heuristic search strategy via islands is suggested to significantly decrease the number of nodes expanded. Algorithm I, which searches through a set of island nodes ("island set"), is presented assuming that the island set contains at least one node on an optimal cost path. This algorithm is shown to be admissible and expands no more nodes than A*. For cases where the island set does not contain an optimal cost path (or any path). Algorithm I', a modification of Algorithm I, is suggested. This algorithm ensures a suboptimal cost path (which may be optimal) and in extreme cases falls back to A*

Assessing for autism in adult psychiatry

This editorial discusses a study by Nyrenius and colleagues in which they investigated rates of co-occurring psychiatric conditions and functioning in a population of adults referred to a Swedish psychiatric out-patient clinic, comparing those meeting DSM-5 diagnostic criteria for autism with their non-autistic peers

Multiobjective heuristic search in AND/OR graphs

The multiobjective search model is a framework for solving multi-criteria optimization problems using heuristic search techniques. In this framework, the different non-commensurate optimization criteria are mapped into distinct dimensions of a vector valued cost structure and partial order search techniques are used to determine the set of non-inferior solutions. Multiobjective state space search has been studied and generalizations of algorithms such as A* to the multiobjective framework have been considered. In this paper we address the problem of multiobjective heuristic (best-first) search of acyclic additive AND/OR graphs. We establish two results which show that in the multiobjective framework, the task of identifying a non-dominated cost potential solution graph is NP-hard in general. This indicates that by extending popular AND/OR graph search algorithms such as AO* to the multiobjective framework we will not be able to preserve some of their desirable properties. Under such circumstances, we investigate the task of developing effective algorithms for the multiobjective problem and present a linear space AND/OR graph search algorithm calledMObj*. Upper bounds on time and space complexities of this algorithm have been presented. It has also been established that when applied to OR graphs, the proposed algorithm is superior to the algorithm proposed by Stewart and White (Multiobjective A*,J. Assoc. Comput. Mech.38, No. 4 (1991), 775-814) in terms of the worst case set of nodes expanded