Regional differences in the coupling between resting cerebral blood flow and metabolism may indicate action preparedness as a default state.

Although most functional neuroimaging studies examine task effects, interest intensifies in the "default" resting brain. Resting conditions show consistent regional activity, yet oxygen extraction fraction constancy across regions. We compared resting cerebral metabolic rates of glucose (CMRgl) measured with 18F-labeled 2-fluoro-2-deoxy-D-glucose to cerebral blood flow (CBF) 15O-H2O measures, using the same positron emission tomography scanner in 2 samples (n = 60 and 30) of healthy right-handed adults. Region to whole-brain ratios were calculated for 35 standard regions of interest, and compared between CBF and CMRgl to determine perfusion relative to metabolism. Primary visual and auditory areas showed coupling between CBF and CMRgl, limbic and subcortical regions--basal ganglia, thalamus and posterior fossa structures--were hyperperfused, whereas association cortices were hypoperfused. Hyperperfusion was higher in left than right hemisphere for most cortical and subcallosal limbic regions, but symmetric in cingulate, basal ganglia and somatomotor regions. Hyperperfused regions are perhaps those where activation is anticipated at short notice, whereas downstream cortical modulatory regions have longer "lead times" for deployment. The novel observation of systematic uncoupling of CBF and CMRgl may help elucidate the potential biological significance of the "default" resting state. Whether greater left hemispheric hyperperfusion reflects lateral dominance needs further examination

How high frequency trading affects a market index

The relationship between a market index and its constituent stocks is complicated. While an index is a weighted average of its constituent stocks, when the investigated time scale is one day or longer the index has been found to have a stronger effect on the stocks than vice versa. We explore how this interaction changes in short time scales using high frequency data. Using a correlation-based analysis approach, we find that in short time scales stocks have a stronger influence on the index. These findings have implications for high frequency trading and suggest that the price of an index should be published on shorter time scales, as close as possible to those of the actual transaction time scale.We would like to thank Yoash Shapira, Idan Michaeli and Dustin Plotnick for all of their help. DYK and EBJ acknowledge support in part by the Tauber Family Foundation and the Maguy-Glass Chair in Physics of Complex Systems at Tel Aviv University. HES and DYK thank the support of the Office of Naval Research (ONR, Grant N00014-09-1-0380, Grant N00014-12-1-0548), Keck Foundation and the NSF (Grant CMMI 1125290) for support. This work was also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government. (Tauber Family Foundation; Maguy-Glass Chair in Physics of Complex Systems at Tel Aviv University; N00014-09-1-0380 - Office of Naval Research (ONR); N00014-12-1-0548 - Office of Naval Research (ONR); Keck Foundation; CMMI 1125290 - NSF; D12PC00285 - Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC))Published versio

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

Black Holes and Random Matrices

We argue that the late time behavior of horizon fluctuations in large anti-de Sitter (AdS) black holes is governed by the random matrix dynamics characteristic of quantum chaotic systems. Our main tool is the Sachdev-Ye-Kitaev (SYK) model, which we use as a simple model of a black hole. We use an analytically continued partition function $|Z(\beta +it)|^2$ as well as correlation functions as diagnostics. Using numerical techniques we establish random matrix behavior at late times. We determine the early time behavior exactly in a double scaling limit, giving us a plausible estimate for the crossover time to random matrix behavior. We use these ideas to formulate a conjecture about general large AdS black holes, like those dual to 4D super-Yang-Mills theory, giving a provisional estimate of the crossover time. We make some preliminary comments about challenges to understanding the late time dynamics from a bulk point of view.Comment: 73 pages, 15 figures, minor errors correcte

An Improved Interactive Streaming Algorithm for the Distinct Elements Problem

The exact computation of the number of distinct elements (frequency moment $F_0$) is a fundamental problem in the study of data streaming algorithms. We denote the length of the stream by $n$ where each symbol is drawn from a universe of size $m$. While it is well known that the moments $F_0,F_1,F_2$ can be approximated by efficient streaming algorithms, it is easy to see that exact computation of $F_0,F_2$ requires space $\Omega(m)$. In previous work, Cormode et al. therefore considered a model where the data stream is also processed by a powerful helper, who provides an interactive proof of the result. They gave such protocols with a polylogarithmic number of rounds of communication between helper and verifier for all functions in NC. This number of rounds $\left(O(\log^2 m) \;\text{in the case of} \;F_0 \right)$ can quickly make such protocols impractical. Cormode et al. also gave a protocol with $\log m +1$ rounds for the exact computation of $F_0$ where the space complexity is $O\left(\log m \log n+\log^2 m\right)$ but the total communication $O\left(\sqrt{n}\log m\left(\log n+ \log m \right)\right)$. They managed to give $\log m$ round protocols with $\operatorname{polylog}(m,n)$ complexity for many other interesting problems including $F_2$, Inner product, and Range-sum, but computing $F_0$ exactly with polylogarithmic space and communication and $O(\log m)$ rounds remained open. In this work, we give a streaming interactive protocol with $\log m$ rounds for exact computation of $F_0$ using $O\left(\log m \left(\,\log n + \log m \log\log m\,\right)\right)$ bits of space and the communication is $O\left( \log m \left(\,\log n +\log^3 m (\log\log m)^2 \,\right)\right)$. The update time of the verifier per symbol received is $O(\log^2 m)$.Comment: Submitted to ICALP 201

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