A Perspective on the Potential Role of Neuroscience in the Court

This Article presents some lessons learned while offering expert testimony on neuroscience in courts. As a biomedical investigator participating in cutting-edge research with clinical and mentoring responsibilities, Dr. Ruben Gur, Ph.D., became involved in court proceedings rather late in his career. Based on the success of Dr. Gur and other research investigators of his generation, who developed and validated advanced methods for linking brain structure and function to behavior, neuroscience findings and procedures became relevant to multiple legal issues, especially related to culpability and mitigation. Dr. Gur found himself being asked to opine in cases where he could contribute expertise on neuropsychological testing and structural and functional neuroimaging. Most of his medical-legal consulting experience has been in capital cases because of the elevated legal requirement for thorough mitigation investigations in such cases, and his limited availability due to his busy schedule as a full-time professor and research investigator who runs the Brain and Behavior Lab at the University of Pennsylvania (“Penn”). Courtroom testimony, however, has not been a topic of his research and so he has not published extensively on the issues in peer-reviewed literature

Default, foreclosure, and strategic renegotiation / 1542

Includes bibliographical references (p. 21-24)

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

Moždana vazomotorna reaktivnost i okluzivna bolest karotidnih arterija

Cerebral autoregulation is a mechanism that enables relatively constant cerebral blood flow during variations of cerebral perfusion pressure. The differences between cerebral blood flow at rest and after administration of a potent vasodilatory stimulus test such as hypercapnia reflect cerebral vasomotor reactivity defined as the vasodilation capacity of cerebral arterioles to external stimuli, providing important information about the cerebral hemodynamic status. Cerebral vasomotor reactivity provides important information about the cerebral hemodynamic status. In this article, cerebral vasomotor reactivity assessment tests are presented, with emphasis on transcranial Doppler, as well as the use of transcranial Doppler in assessing cerebral vasomotor reactivity in carotid stenosis, occlusion, and the importance of cerebral vasomotor reactivity for carotid surgery.Moždana autoregulacija je mehanizam koji omogućava relativno ustaljeni moždani protok krvi za vrijeme promjena tlaka prokrvljenosti mozga. Razlike između moždanog protoka krvi u mirovanju i nakon testa snažne vazodilatacijske stimulacije poput hiperkapnije odražavaju moždanu vazomotornu reaktivnost definiranu kao vazodilatacijski kapacitet moždanih arteriola za vanjske poticaje, pružajući važne podatke o moždanom hemodinamskom statusu. Moždana vazomotorna reaktivnost daje važne informacije o statusu moždane hemodinamike. U članku se prikazuju testovi za procjenu moždane vazomotorne reaktivnosti s naglaskom na transkranijski Doppler, te primjena transkranijskog Dopplera u procjeni moždane vazomotorne reaktivnosti kod karotidne stenoze, okluzije, kao i važnost moždane vazomotorne reaktivnosti za kirurgiju karotidnih arterija

Deciphering M-T diagram of shape memory Heusler alloys: reentrance, plateau and beyond

We present our recent results on temperature behaviour of magnetization observed in Ni_47Mn_39In_14 Heusler alloys. Three regions can be distinguished in the M-T diagram: (I) low temperature martensitic phase (with the Curie temperature T_CM = 140 K), (II) intermediate mixed phase (with the critical temperature T_MS = 230 K) exhibiting a reentrant like behavior (between T_CM and T_MS) and (III) high temperature austenitic phase (with the Curie temperature T_CA = 320 K) exhibiting a rather wide plateau region (between T_MS and T_CA). By arguing that powerful structural transformations, causing drastic modifications of the domain structure in alloys, would also trigger strong fluctuations of the order parameters throughout the entire M-T diagram, we were able to successfully fit all the data by incorporating Gaussian fluctuations (both above and below the above three critical temperatures) into the Ginzburg-Landau scenario