Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Intraoperative neurophysiological monitoring in pediatric neurosurgery: why, when, how?

Abstract: Introduction. This review is primarily based on peer-reviewed scientific publications and on the authors' experience in the field of intraoperative neurophysiology. The purpose is a critical analysis of the role of intraoperative neurophysiological monitoring (INM) during various neurosurgical procedures, emphasizing the aspects that mainly concern the pediatric population. Original papers related to the field of intraoperative neurophysiology were collected using medline. INM consists in monitoring (continuous "on-line" assessment of the functional integrity of neural pathways) and mapping (functional identification and preservation of anatomically ambiguous nervous tissue) techniques. We attempted to delineate indications for intraoperative neurophysiological techniques according to their feasibility and reliability (specificity and sensitivity). Discussion and conclusions. In compiling this review, controversies about indications, methodologies and the usefulness of some INM techniques have surfaced. These discrepancies are often due to lack of familiarity with new techniques in groups from around the globe. Accordingly, internationally accepted guidelines for INM are still far from being established. Nevertheless, the studies reviewed provide sufficient evidence to enable us to make the following recommendations. (1) INM is mandatory whenever neurological complications are expected on the basis of a known pathophysiological mechanism. INM becomes optional when its role is limited to predicting postoperative outcome or it is used for purely research purposes. (2) INM should always be performed when any of the following are involved: supratentorial lesions in the central region and language-related cortex; brain stem tumors; intramedullary spinal cord tumors; conus-cauda equina tumors; rhizotomy for relief of spasticity; spina bifida with tethered cord. (3) Monitoring of motor evoked potentials (MEPs) is now a feasible and reliable technique that can be used under general anesthesia. MEP monitoring is the most appropriate technique to assess the functional integrity of descending motor pathways in the brain, the brain stem and, especially, the spinal cord. (4) Somatosensory evoked potential (SEP) monitoring is of value in assessment of the functional integrity of sensory pathways leading from the peripheral nerve, through the dorsal column and to the sensory cortex. SEPs cannot provide reliable information on the functional integrity of the motor system (for which MEPs should be used). (5) Monitoring of brain stem auditory evoked potentials remains a standard technique during surgery in the brain stem, the cerebellopontine angle, and the posterior fossa. (6) Mapping techniques (such as the phase reversal and the direct cortical/subcortical stimulation techniques) are invaluable and strongly recommended for brain surgery in eloquent cortex or along subcortical motor pathways. (7) Mapping of the motor nuclei of the VIIth, IXth–Xth and XIIth cranial nerves on the floor of the fourth ventricle is of great value in identification of "safe entry zones" into the brain stem. Techniques for mapping cranial nerves in the cerebellopontine angle and cauda equina have also been standardized. Other techniques, although safe and feasible, still lack a strong validation in terms of prognostic value and correlation with the postoperative neurological outcome. These techniques include monitoring of the bulbocavernosus reflex, monitoring of the corticobulbar tracts, and mapping of the dorsal columns. These techniques, however, are expected to open up new perspectives in the near future.

Hanging in, stepping up and stepping out: livelihood aspirations and strategies of the poor

Abstract: In recent years understanding of poverty and of ways in which people escape from or fall into poverty has become more holistic. This should improve the capabilities of policy analysts and others working to reduce poverty, but it also makes analysis more complex. This article describes a simple schema which integrates multi-dimensional, multi-level, and dynamic understandings of poverty, of poor people's livelihoods, and of changing roles of agricultural systems. The article suggests three broad types of strategy pursued by poor people: ‘hanging in’, ‘stepping up’, and ‘stepping out’. This simple schema explicitly recognises the dynamic aspirations of poor people, diversity among them, and livelihood diversification. It also brings together aspirations of poor people with wider sectoral, inter-sectoral, and macro-economic questions about policies necessary for the realisation of those aspirations.

Knows what it knows: a framework for self-aware learning

Abstract: We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcement-learning and active-learning problems. We catalog several KWIK-learnable classes as well as open problems, and demonstrate their applications in experience-efficient reinforcement learning.

It takes variety to make a world: diversification in recommender systems

Abstract: Recommendations in collaborative tagging sites such as del.icio.us and Yahoo! Movies, are becoming increasingly important, due to the proliferation of general queries on those sites and the ineffectiveness of the traditional search paradigm to address those queries. Regardless of the underlying recommendation strategy, item-based or user-based, one of the key concerns in producing recommendations, is over-specialization, which results in returning items that are too homogeneous. Traditional solutions rely on post-processing returned items to identify those which differ in their attribute values (e.g., genre and actors for movies). Such approaches are not always applicable when intrinsic attributes are not available (e.g., URLs in del.icio.us). In a recent paper [20], we introduced the notion of explanation-based diversity and formalized the diversification problem as a compromise between accuracy and diversity. In this paper, we develop efficient diversification algorithms built upon this notion. The algorithms explore compromises between accuracy and diversity. We demonstrate their efficiency and effectiveness in diversification on two real life data sets: del.icio.us and Yahoo! Movies.

Learning to Rank by Optimizing NDCG Measure

Abstract: Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels. The ranking algorithms are often evaluated using information retrieval measures, such as Normalized Discounted Cumulative Gain (NDCG) [1] and Mean Average Precision (MAP) [2]. Until recently, most learning to rank algorithms were not using a loss function related to the above mentioned evaluation measures. The main difficulty in direct optimization of these measures is that they depend on the ranks of documents, not the numerical values output by the ranking function. We propose a probabilistic framework that addresses this challenge by optimizing the expectation of NDCG over all the possible permutations of documents. A relaxation strategy is used to approximate the average of NDCG over the space of permutation, and a bound optimization approach is proposed to make the computation efficient. Extensive experiments show that the proposed algorithm outperforms state-of-the-art ranking algorithms on several benchmark data sets.