RegTech and Predictive Lawmaking: Closing the RegLag Between Prospective Regulated Activity and Regulation

Regulation chronically suffers significant delay starting at the detectable initiation of a “regulable activity” and culminating at effective regulatory response. Regulator reaction is impeded by various obstacles: (i) confusion in optimal level, form and choice of regulatory agency, (ii) political resistance to creating new regulatory agencies, (iii) lack of statutory authorization to address particular novel problems, (iv) jurisdictional competition among regulators, (v) Congressional disinclination to regulate given political conditions, and (vi) a lack of expertise, both substantive and procedural, to deploy successful counter-measures. Delay is rooted in several stubborn institutions, including libertarian ideals permeating both the U.S. legal system and the polity, constitutional constraints on exercise of governmental powers, chronic resource constraints including underfunding, and agency technical incapacities. Therefore, regulatory prospecting to identify regulable activity often lags the suspicion of future regulable activity or its first discernable appearance. This Article develops the regulatory lag theory (RegLag), argues that regulatory technologies (RegTech), including those from the blockchain technology space, can help narrow the RegLag gap, and proposes programs to improve regulatory agency clairvoyance to more aggressively adapt to changing regulable activities, such as by using promising anticipatory approaches

Alexithymia, emotion processing and social anxiety in adults with ADHD

<p>Abstract</p> <p>Objective</p> <p>Given sparse research on the issue, this study sought to shed light upon the interactions of alexithymia, emotion processing, and social anxiety in adults with attention-deficit hyperactivity disorder (ADHD).</p> <p>Subjects and methods</p> <p>73 German adults with ADHD according to DSM-IV diagnostic criteria participated. We used the Toronto Alexithymia Scale (TAS-20) to assess alexithymia, the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) to assess different features of social anxiety, and we applied the German 'Experience of Emotions Scale' (SEE) to measure emotion processing.</p> <p>Results</p> <p>40% of the sample were found to meet the DSM-IV criteria of social anxiety disorder, and about 22% were highly alexithymic according to a TAS-20 total score ≥ 61; however, the mean TAS-20 total score of 50.94 ± 9.3 was not much higher than in community samples. Alexithymic traits emerged to be closely linked to emotion processing problems, particularly 'difficulty accepting own emotions', and to social anxiety features.</p> <p>Discussion/conclusion</p> <p>Our findings suggest interactions of alexithymia, emotion processing dysfunction, and social anxiety in adults with ADHD, which may entail the therapeutic implication to thoroughly instruct these patients to identify, accept, communicate, and regulate their emotions to aid reducing interaction anxiety.</p

The MINERν\nuA Data Acquisition System and Infrastructure

MINER$\nu$A (Main INjector ExpeRiment $\nu$-A) is a new few-GeV neutrino cross section experiment that began taking data in the FNAL NuMI (Fermi National Accelerator Laboratory Neutrinos at the Main Injector) beam-line in March of 2010. MINER$\nu$A employs a fine-grained scintillator detector capable of complete kinematic characterization of neutrino interactions. This paper describes the MINER$\nu$A data acquisition system (DAQ) including the read-out electronics, software, and computing architecture.Comment: 34 pages, 16 figure

Evaluating Depressive Symptoms in Schizophrenia: A Psychometric Comparison of the Calgary Depression Scale for Schizophrenia and the Hamilton Depression Rating Scale

Background: The aim of this study was to compare two measures of depression in patients with schizophrenia and schizophrenia spectrum disorder, including patients with delusional and schizoaffective disorder, to conclude implications for their application. Sampling and Methods: A total of 278 patients were assessed using the Calgary Depression Scale for Schizophrenia (CDSS) and the Hamilton Depression Rating Scale (HAMD-17). The Positive and Negative Syndrome Scale (PANSS) was also applied. At admission and discharge, a principal component analysis was performed with each depression scale. The two depression rating scales were furthermore compared using correlation and regression analyses. Results: Three factors were revealed for the CDSS and HAMD-17 factor component analysis. A very similar item loading was found for the CDSS at admission and discharge, whereas results of the loadings of the HAMD-17 items were less stable. The first two factors of the CDSS revealed correlations with positive, negative and general psychopathology. In contrast, multiple significant correlations were found for the HAMD-17 factors and the PANSS sub-scores. Multiple regression analyses demonstrated that the HAMD-17 accounted more for the positive and negative symptom domains than the CDSS. Conclusions:The present results suggest that compared to the HAMD-17, the CDSS is a more specific instrument to measure depressive symptoms in schizophrenia and schizophrenia spectrum disorder, especially in acutely ill patients. Copyright (c) 2012 S. Karger AG, Base

MINERvA neutrino detector response measured with test beam data

The MINERvA collaboration operated a scaled-down replica of the solid scintillator tracking and sampling calorimeter regions of the MINERvA detector in a hadron test beam at the Fermilab Test Beam Facility. This article reports measurements with samples of protons, pions, and electrons from 0.35 to 2.0 GeV/c momentum. The calorimetric response to protons, pions, and electrons are obtained from these data. A measurement of the parameter in Birks' law and an estimate of the tracking efficiency are extracted from the proton sample. Overall the data are well described by a Geant4-based Monte Carlo simulation of the detector and particle interactions with agreements better than 4%, though some features of the data are not precisely modeled. These measurements are used to tune the MINERvA detector simulation and evaluate systematic uncertainties in support of the MINERvA neutrino cross section measurement program.Comment: as accepted by NIM

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure

Measurement of cosmic-ray reconstruction efficiencies in the MicroBooNE LArTPC using a small external cosmic-ray counter

The MicroBooNE detector is a liquid argon time projection chamber at Fermilab designed to study short-baseline neutrino oscillations and neutrino-argon interaction cross-section. Due to its location near the surface, a good understanding of cosmic muons as a source of backgrounds is of fundamental importance for the experiment. We present a method of using an external 0.5 m (L) x 0.5 m (W) muon counter stack, installed above the main detector, to determine the cosmic-ray reconstruction efficiency in MicroBooNE. Data are acquired with this external muon counter stack placed in three different positions, corresponding to cosmic rays intersecting different parts of the detector. The data reconstruction efficiency of tracks in the detector is found to be $\epsilon_{\mathrm{data}}=(97.1\pm0.1~(\mathrm{stat}) \pm 1.4~(\mathrm{sys}))\%$, in good agreement with the Monte Carlo reconstruction efficiency $\epsilon_{\mathrm{MC}} = (97.4\pm0.1)\%$. This analysis represents a small-scale demonstration of the method that can be used with future data coming from a recently installed cosmic-ray tagger system, which will be able to tag $\approx80\%$ of the cosmic rays passing through the MicroBooNE detector.Comment: 19 pages, 12 figure