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Impulsivity Relates to Relative Preservation of Mesolimbic Connectivity in Patients with Parkinson Disease.
IntroductionThe relationship between Parkinson Disease (PD) pathology, dopamine replacement therapy (DRT), and impulse control disorder (ICD) development is still incompletely understood. Given the sensorimotor-lateral substantia nigra (SN) selective degeneration associated with PD, we posit that a relative sparing of the limbic-medial SN in the context of DRT drives impulsive, reward-seeking behavior in PD patients with recent history of severe impulsivity.MethodsImpulsive and control participants were selected from a consecutive list of PD patients receiving pre-operative deep brain stimulation (DBS) planning scans including 3T structural MRI and 64 direction diffusion tensor imaging (DTI). Using previously identified substantia nigra (SN) subsegment network connectivity profiles to develop classification targets, split-hemisphere target-based SN segmentation with probabilistic tractography was performed. The relative subsegment volumes and strength of connectivity between the SN and the limbic, associative, and motor network targets were compared.ResultsOur results show that there is greater probability of connectivity between the SN and limbic network targets relative to motor and associative network targets in PD patients with recent history of severe impulsivity as compared to PD patients without impulsivity (P = 0.0075). We did not observe relative volumetric subsegment differences across groups.ConclusionFirstly, our results suggest that fine-grained, atlas-derived classification targets may be used in PD to parcellate and classify functionally distinct subsegments of the SN, with the apparent preservation of previously reported topographical limbic-medial SN, associative-ventral SN, and sensorimotor-lateral SN orientation. We suggest that relative, as opposed to absolute, degeneration amongst SN-associated dopaminergic networks relates to the impulsivity phenotype in PD
Multisensor data fusion for joint people tracking and identification with a service robot
Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution
Vote buying revisited: implications for receipt-freeness
In this paper, we analyse the concept of vote buying based
on examples that try to stretch the meaning of the concept. Which ex-
amples can still be called vote buying, and which cannot? We propose
several dimensions that are relevant to qualifying an action as vote buy-
ing or not. As a means of protection against vote buying and coercion,
the concept of receipt-freeness has been proposed. We argue that, in or-
der to protect against a larger set of vote buying activities, the concept
of receipt-freeness should be interpreted probabilistically. We propose a
general definition of probabilistic receipt-freeness by adapting existing
definitions of probabilistic anonymity to voting
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based
quasar target selection down to the faint limit of the Sloan Digital Sky Survey
(SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar
contamination is significant. We build models of the distributions of stars and
quasars in flux space down to the flux limit by applying the
extreme-deconvolution method to estimate the underlying density. We convolve
this density with the flux uncertainties when evaluating the probability that
an object is a quasar. This approach results in a targeting algorithm that is
more principled, more efficient, and faster than other similar methods. We
apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z
3.5) quasar probabilities for all 160,904,060
point sources with dereddened i-band magnitude between 17.75 and 22.45 mag in
the 14,555 deg^2 of imaging from SDSS Data Release 8. The catalog can be used
to define a uniformly selected and efficient low- or medium-redshift quasar
survey, such as that needed for the SDSS-III's Baryon Oscillation Spectroscopic
Survey project. We show that the XDQSO technique performs as well as the
current best photometric quasar-selection technique at low redshift, and
outperforms all other flux-based methods for selecting the medium-redshift
quasars of our primary interest. We make code to reproduce the XDQSO quasar
target selection publicly available
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