Examining the Effects of Sketch and Speak Intervention on Expository Discourse Utilizing SALT’s ESS for Adolescents with Language-Related Learning Disabilities

Purpose: The purpose of the current study was twofold: (a) to determine the distal effects of Sketch and Speak intervention on the use of taught note-taking strategies on a distal standardized expository task for adolescents with LLD when compared to pre-treatment; and (b) to determine the effect of Sketch and Speak intervention on the quality of oral performance on that distal expository task for adolescents with LLD when compared to pre-treatment. Oral rehearsal was not a formal research question, but independent use of this taught strategy was also examined. Method: Planning notes and oral explanations were obtained as part of a larger study. In the larger project, four students in junior high with LLD learned two types of note-taking strategies along with oral sentence formulation and rehearsal strategies to compose oral reports from novel informational texts, and then present those oral reports without use of notes. Pre- and posttest sessions included the standardized expository task in the current investigation. The standardized task involved making planning notes on a formatted notesheet similar to the notesheet used in treatment, and then using those notes to explain a familiar sport/game. The planning notes were scored using the investigator-designed notes scoring measure that involved four components: quantity, open/close/topic, format, and simplicity. The oral explanations were transcribed and scored utilizing the standardized holistic trait measure called the Expository Scoring Scheme (ESS). The ESS involved ten components: eight matching the planning notes categories (e.g. object of the game, rules, duration), plus terminology and coherence. Both the notes and explanation measures were independently scored by two researchers blind to pre/post status. Point-to-point inter-rater agreements for notes and report components were found to be satisfactory. The notes and explanation data were descriptively examined to determine changes from pretest to posttest which could logically be caused by transfer of taught note-taking and oral practice strategies from the intervention. Results: One participant chose to use pictography notes in the transfer task at posttest. Three of the four participants demonstrated gains in simplicity of notes taken at posttest. None of the participants used the open/close/topic or bulleted note features. One participant implemented the taught note-taking strategy of pictography. No quantitative or descriptive gains were shown in the expository rubric scores for the oral explanations at posttest. Conclusion: Results of this study indicate that the taught note-taking strategy feature of simplicity emphasized in Sketch and Speak can transfer to a distal note-taking task involving familiar information. The pictography may also transfer. However, participation in Sketch and Speak does not appear to improve oral expository discourse as measured by the ESS while referring to planning notes. This study showed limited distal generalization of the taught strategies and raises questions about what tasks are suitable to show generalization of strategies involving learning, recalling, and using new information in expository discourse

Neuronal activity in the human lateral temporal lobe. I. Responses to speech.

Single and multiple unit neuronal activity was recorded from the cortex of the lateral temporal lobe in conscious humans during open brain surgery for the treatment of epilepsy. Recordings were obtained from the right and left superior, middle and inferior temporal gyrus of 34 patients (41 recording sites). Recordings were restricted to regions to be resected during subsequent surgery. This excluded recordings from language areas proper. Neuronal responses to words and sentences presented over a loudspeaker and during free conversation were recorded. No significant differences between the right and left hemisphere were obvious. All neurons in the superior temporal gyrus responded to various aspects of spoken language with temporally well defined activation/inhibition patterns, but not or only little to non-linguistic noises or tones. Excitatory responses were typically short or prolonged (up to several hundred ms) bursts of discharges at rates above 20/sec, reaching peak rates of 50–100/s. Such responses could be specifically related to certain combinations of consonants suggesting a function in categorization, they could depend on word length, could differentiate between polysyllabic and compound words of the same length or could be unspecifically related to language as such. No formant specific responses were found, but the prolonged excitations across syllables suggest that consonant/vowel combinations may play a role for some activation patterns. Responses of some neurons (or neuronal populations) depended on the attention paid to the words and sentences, or the task connected with them (repeat words, speech addressed to the patient demanding something). Neurons in the middle and inferior temporal gyrus were only little affected by listening to single words or sentences, but some were unspecifically activated by words or while listening to sentences. Excitatory responses varied within a limited range of discharge rates usually below 5–10/s. Phonetic distortion of spoken language could reduce responses in superior temporal gyrus neurons, but also the slight changes in discharge rate of middle temporal neurons could be absent during distorted and uncomprehensible speech sounds. We conclude that superior temporal gyrus neuron responses reflect some general phonetic but not semantic aspects of spoken language. Middle and inferior temporal gyrus neurons do not signal phonetic aspects of language, but may be involved in understanding language under certain conditions

Neuronal activity in the human lateral temporal lobe. II. Responses to the subjects own voice.

We have recorded neuronal responses in the lateral temporal lobe of man to overt speech during open brain surgery for epilepsy. Tests included overt naming of objects and reading words or short sentences shown on a projector screen, repetition of tape recorded words or sentences presented over a loudspeaker, and free conversation. Neuronal activity in the dominant and non-dominant temporal lobe were about equally affected by overt speech. As during listening to language (see Creutzfeldt et al. 1989), responses differed between recordings from sites in the superior and the middle or inferior temporal gyrus. In the superior temporal gyrus all neurons responded clearly and each in a characteristic manner. Activation could be related to phonemic aspects, to segmentation or to the length of spoken words or sentences. However, neurons were mostly differently affected by listening to words and language as compared to overt speaking. In neuronal populations recorded simultaneously with one or two microelectrodes, some neurons responded predominantly to one or the other type of speech. Excitatory responses during overt speaking were always auditory. In the middle temporal gyrus more neurons (about 2/3) responded to overt speaking than to listening alone. Activations elicited during overt speech were seen in about 1/3 of our sample, but they were more sluggish than those recorded in the superior gyrus. A prominent feature was suppression of on-going activity, which we found in about 1/3 of middle and in some superior temporal gyrus neurons. This suppression could preced vocalization by up to a few hundred ms, and could outlast it by up to 1 s. Evoked ECoG-potentials to words heard or spoken were different, and those to overt speech were more widespread

Manycore processing of repeated range queries over massive moving objects observations

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate query processing we devise a hybrid CPU/GPU pipeline that compresses data output and save query processing work. The devised system relies on an ad-hoc spatial index leading to a problem decomposition that results in a set of independent data-parallel tasks. The index is based on a point-region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non-trivial GPU data structures that avoid the need of locked memory accesses and favour coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, characterized by highly skewed spatial distributions. In comparison with state-of-the-art CPU-based implementations, our method highlights significant speedups in the order of 14x-20x, depending on the datasets, even when considering very cheap GPUs

Query-level Early Exit for Additive Learning-to-Rank Ensembles

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers to investigate algorithms to make faster the traversal of the additive ensemble or to early terminate the evaluation of documents that are unlikely to be ranked among the top-k. In this paper, we investigate the novel problem of query-level early exiting, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble. Besides the obvious advantage on query latency and throughput, we address the possible positive impact on ranking effectiveness. To this end, we study the actual contribution of incremental portions of the tree ensemble to the ranking of the top-k documents scored for a given query. Our main finding is that queries exhibit different behaviors as scores are accumulated during the traversal of the ensemble and that query-level early stopping can remarkably improve ranking quality. We present a reproducible and comprehensive experimental evaluation, conducted on two public datasets, showing that query-level early exiting achieves an overall gain of up to 7.5% in terms of NDCG@10 with a speedup of the scoring process of up to 2.2x

Parallel Traversal of Large Ensembles of Decision Tree

Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. The deployment of such models is computationally demanding: to compute the final prediction, the whole ensemble must be traversed by accumulating the contributions of all its trees. In particular, traversal cost impacts applications where the number of candidate items is large, the time budget available to apply the learnt model to them is limited, and the users' expectations in terms of quality-of-service is high. Document ranking in web search, where sub-optimal ranking models are deployed to find a proper trade-off between efficiency and effectiveness of query answering, is probably the most typical example of this challenging issue. This paper investigates multi/many-core parallelization strategies for speeding up the traversal of large ensembles of regression trees thus obtaining machine-learnt models that are, at the same time, effective, fast, and scalable. Our best results are obtained by the GPU-based parallelization of the state-of-the-art algorithm, with speedups of up to 102.6x. IEE

Education and Decision Making at the Time of Triptan Prescribing: Patient Expectations vs Actual Practice

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106941/1/head12308.pd

An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure

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