Scaling forecasting algorithms using clustered modeling

Cataloged from PDF version of article.Research on forecasting has traditionally focused on building more accurate statistical models for a given time series. The models are mostly applied to limited data due to efficiency and scalability problems. However, many enterprise applications require scalable forecasting on large number of data series. For example, telecommunication companies need to forecast each of their customers' traffic load to understand their usage behavior and to tailor targeted campaigns. Forecasting models are typically applied on aggregate data to estimate the total traffic volume for revenue estimation and resource planning. However, they cannot be easily applied to each user individually as building accurate models for large number of users would be time consuming. The problem is exacerbated when the forecasting process is continuous and the models need to be updated periodically. This paper addresses the problem of building and updating forecasting models continuously for multiple data series. We propose dynamic clustered modeling for forecasting by utilizing representative models as an analogy to cluster centers. We apply the models to each individual series through iterative nonlinear optimization. We develop two approaches: The Integrated Clustered Modeling integrates clustering and modeling simultaneously, and the Sequential Clustered Modeling applies them sequentially. Our findings indicate that modeling an individual's behavior using its segment can be more scalable and accurate than the individual model itself. The grouped models avoid overfits and capture common motifs even on noisy data. Experimental results from a telco CRM application show the method is efficient and scalable, and also more accurate than having separate individual models

Striatal intrinsic reinforcement signals during recognition memory: relationship to response bias and dysregulation in schizophrenia

Ventral striatum (VS) is a critical brain region for reinforcement learning and motivation, and VS hypofunction is implicated in psychiatric disorders including schizophrenia. Providing rewards or performance feedback has been shown to activate VS. Intrinsically motivated subjects performing challenging cognitive tasks are likely to engage reinforcement circuitry even in the absence of external feedback or incentives. However, such intrinsic reinforcement responses have received little attention, have not been examined in relation to behavioral performance, and have not been evaluated for impairment in neuropsychiatric disorders such as schizophrenia. Here we used fMRI to examine a challenging “old” vs. “new” visual recognition task in healthy subjects and patients with schizophrenia. Targets were unique fractal stimuli previously presented as salient distractors in a visual oddball task, producing incidental memory encoding. Based on the prediction error theory of reinforcement learning, we hypothesized that correct target recognition would activate VS in controls, and that this activation would be greater in subjects with lower expectation of responding correctly as indexed by a more conservative response bias. We also predicted these effects would be reduced in patients with schizophrenia. Consistent with these predictions, controls activated VS and other reinforcement processing regions during correct recognition, with greater VS activation in those with a more conservative response bias. Patients did not show either effect, with significant group differences suggesting hyporesponsivity in patients to internally generated feedback. These findings highlight the importance of accounting for intrinsic motivation and reward when studying cognitive tasks, and add to growing evidence of reward circuit dysfunction in schizophrenia that may impact cognition and function

Memory-delineated subtypes of schizophrenia: Relationship to clinical, neuroanatomical, and neurophysiological measures.

Memory performance was examined in patients with schizophrenia to determine whether subgroups conforming to cortical and subcortical dementias could be identified and, if so, whether subgroups differed on clinical, neuroanatomical, and neurophysiological measures. A cluster analysis of California Verbal Learning Test performance classified patients into 3 subgroups. Two groups exhibited memory deficits consistent with the cortical–subcortical distinction, whereas 1 group was unimpaired. Cortical patients tended to be male, and they had earlier illness onset, reduced temporal lobe gray matter, and hypometabolism. Subcortical patients had ventricular enlargement and more negative symptoms. Unimpaired patients had fewer negative symptoms and dorsal medial prefrontal hypermetabolism. The authors con-clude that categorizing patients on the basis of memory deficits may yield neurobiologically meaningful disease subtypes. There is increasing consensus that Kraepelin’s conceptu-alization of schizophrenia as a disorder characterized by disturbed cognition rather than psychotic symptomatology was fundamentally correct (see Sharma & Harvey, 2000, fo

Analysis of the distribution and structure of integrated banana streak virus DNA in a range of Musa cultivars

The cDNA encoding the glycoprotein α (GPα) subunit of tilapia (Oreochromis mossambicus) was partially cloned using RACE-polymerase chain reaction (PCR) technique. The amplified cDNA was found to be 583 bases long, and to consist of a portion of the signal peptide, the full sequence encoding the mature peptide (94 amino acids) and the 3′ untranslated region. Northern blot analysis revealed a single band of approximately 600 bp. Alignment of the deduced amino acids of the mature protein showed that the tilapia GPα subunit shares more than 80% identity with that of other perciform fish (i.e. striped bass, sea bream and yellowfin porgy) and less than 70% with that of more taxonomically remote fish and other vertebrates. Exposure of dispersed tilapia pituitary cells to salmon gonadotropin-releasing hormone (sGnRH) elevated GPα mRNA levels via both PKC and cAMP-protein kinase A (PKA) pathways. The transcript levels were also regulated by pituitary adenylate cyclase activating polypeptide (PACAP) and neuropeptide Y (NPY), both acting through PKC and PKA pathways. Moreover, a combined treatment of PACAP or NPY with GnRH seems to have an additive effect on the GPα subunit gene transcription. These results suggest that in tilapia the expression of GPα subunit is regulated by GnRH mainly via PKC and PKA pathways. Furthermore, PACAP and NPY can elevate the GnRH-stimulated GPα subunit transcription and can directly affect the subunit mRNA levels, via the same transduction pathways

“It's Not What You Say, But How You Say it”: A Reciprocal Temporo-frontal Network for Affective Prosody

Humans communicate emotion vocally by modulating acoustic cues such as pitch, intensity and voice quality. Research has documented how the relative presence or absence of such cues alters the likelihood of perceiving an emotion, but the neural underpinnings of acoustic cue-dependent emotion perception remain obscure. Using functional magnetic resonance imaging in 20 subjects we examined a reciprocal circuit consisting of superior temporal cortex, amygdala and inferior frontal gyrus that may underlie affective prosodic comprehension. Results showed that increased saliency of emotion-specific acoustic cues was associated with increased activation in superior temporal cortex [planum temporale (PT), posterior superior temporal gyrus (pSTG), and posterior superior middle gyrus (pMTG)] and amygdala, whereas decreased saliency of acoustic cues was associated with increased inferior frontal activity and temporo-frontal connectivity. These results suggest that sensory-integrative processing is facilitated when the acoustic signal is rich in affective information, yielding increased activation in temporal cortex and amygdala. Conversely, when the acoustic signal is ambiguous, greater evaluative processes are recruited, increasing activation in inferior frontal gyrus (IFG) and IFG STG connectivity. Auditory regions may thus integrate acoustic information with amygdala input to form emotion-specific representations, which are evaluated within inferior frontal regions

Emotional Graphic Cigarette Warning Labels Reduce the Electrophysiological Brain Response to Smoking Cues

There is an ongoing public debate about the new graphic warning labels (GWLs) that the Food and Drug Administration (FDA) proposes to place on cigarette packs. Tobacco companies argued that the strongly emotional images FDA proposed to include in the GWLs encroached on their constitutional rights. The court ruled that FDA did not provide sufficient scientific evidence of compelling public interest in such encroachment. This study\u27s objectives were to examine the effects of the GWLs on the electrophysiological and behavioral correlates of smoking addiction and to determine whether labels rated higher on the emotional reaction (ER) scale are associated with greater effects. We studied 25 non-treatment-seeking smokers. Event-related potentials (ERPs) were recorded while participants viewed a random sequence of paired images, in which visual smoking (Cues) or non-smoking (non-Cues) images were preceded by GWLs or neutral images. Participants reported their cigarette craving after viewing each pair. Dependent variables were magnitude of P300 ERPs and self-reported cigarette craving in response to Cues. We found that subjective craving response to Cues was significantly reduced by preceding GWLs, whereas the P300 amplitude response to Cues was reduced only by preceding GWLs rated high on the ER scale. In conclusion, our study provides experimental neuroscience evidence that weighs in on the ongoing public and legal debate about how to balance the constitutional and public health aspects of the FDA-proposed GWLs. The high toll of smoking-related illness and death adds urgency to the debate and prompts consideration of our findings while longitudinal studies of GWLs are underway

Telling the truth from lie in individual subjects with fast event-related fMRI

Deception is a clinically important behavior with poorly understood neurobiological correlates. Published functional MRI (fMRI) data on the brain activity during deception indicates that, on a multisubject group level, lie is distinguished from truth by increased prefrontal and parietal activity. These findings are theoretically important; however, their applied value will be determined by the accuracy of the discrimination between single deceptive and truthful responses in individual subjects. This study presents the first quantitative estimate of the accuracy of fMRI in conjunction with a formal forced-choice paradigm in detecting deception in individual subjects. We used a paradigm balancing the salience of the target cues to elicit deceptive and truthful responses and determined the accuracy of this model in the classification of single lie and truth events. The relative salience of the task cues affected the net activation associated with lie in the superior medial and inferolateral prefrontal cortices. Lie was discriminated from truth on a single-event level with an accuracy of 78%, while the predictive ability expressed as the area under the curve (AUC) of the receiver operator characteristic curve (ROC) was 85%. Our findings confirm that fMRI, in conjunction with a carefully controlled query procedure, could be used to detect deception in individual subjects. Salience of the task cues is a potential confounding factor in the fMRI pattern attributed to deception in forced choice deception paradigms

Semantic argument frequency-based Multi-Document Summarization

Semantic Role Labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates. In this paper, we introduce and assess the idea of using SRL on generic Multi-Document Summarization (MDS). We score sentences according to their inclusion of frequent semantic phrases and form the summary using the top-scored sentences. We compare this method with a term-based sentence scoring approach to investigate the effects of using semantic units instead of single words for sentence scoring. We also integrate our scoring metric as an auxiliary feature to a cutting edge summarizer with the intention of examining its effects on the performance. The experiments using datasets from the Document Understanding Conference (DUC) 2004 show that the SRL-based summarization outperforms the term-based approach as well as most of the DUC participants. © 2009 IEEE