Unbinned Profiled Unfolding

Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters. Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables. Recently, there have been a number of proposals to perform unbinned unfolding with machine learning. However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters. We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters. The machine learning loss function is the full likelihood function, based on binned inputs at detector-level. We first demonstrate the method with simple Gaussian examples and then show the impact on a simulated Higgs boson cross section measurement.Comment: Fixed a referenc

Go-getters and procrastinators: Investigating individual differences in visual cognition across university semesters

University-based psychological research typically relies on the participation of undergraduate students for data collection. Using this particular sample brings with it several possible issues, including the self-scheduling done by the participants. Research on performance between students who sign up early versus late in the semester has been inconsistent. Some research report benefits for early participant semesters, while others find no differences between the two groups. Anecdotally, it seems that the former holds true, as many researchers worry about the data collected late in the semester, sometimes opting for more motivated earlier participants in the next semester. The purpose of our study was to examine for the effect of time of semester across a well-known set of visual cognition tasks. To do so, participants completed canonical versions of a rapid serial visual presentation task, a flanker task, an additional singleton paradigm task, a multiple object tracking task and a visual working memory task. These tasks were chosen as typical measures of executive control, temporal selectivity, visual working memory capacity, resistance to distraction, and attentional capacity. Crucially, we correlated task performance with time of semester students chose to participate. Our results demonstrate that there were no significant differences in any of the tasks across semester timing. Furthermore, our findings support the validity of cognitive research relying on the system of recruiting undergraduate students from volunteer pools where students can self-select the time of the semester they undertake the experiments

Functional and Biochemical Characterization of KCNQ1/KCNE1 Subunit Interactions in the Cardiac IKs Potassium Channel

The IKs potassium channel, critical to control of heart electrical activity, requires assembly of pore-forming alpha subunits (KCNQ1) and accessory beta (KCNE1) subunits. IKs is the slowly activating component of delayed rectifier K+ current in the heart and is a major contributor to the timing of repolarization of the cardiomyocyte membrane potential. Inherited mutations in either IKs channel subunit are associated with cardiac arrhythmia syndromes, including long QT syndrome (LQTS), short QT syndrome (SQTS) and familial atrial fibrillation (FAF). The biophysical properties of IKs channel current are dramatically altered when KCNE1 associates with the KCNQ1 channel. Functional tetrameric channels can be formed by KCNQ1 alone, but co-assembly with KCNE1 is required for the unique kinetics necessary to regulate human cardiac electrical activity as well as for the channel's functional response to the sympathetic nervous system. Specifically, KCNE1 co-assembly results in a depolarizing shift in the voltage dependence of activation, an increase in the single channel conductance, and an increase in current density. IKs channel current is also characterized by slow activation and deactivation kinetics, with little or no inactivation, in contrast to the KCNQ1 homomeric channel, which is characterized by fast activation and deactivation kinetics and clear inactivation. We wanted to understand how KCNE1 modulates the KCNQ1 channel functionally and investigate the structural determinants of this modulation. In Chapter II, we explore the role(s) of KCNE1 in the context of two KCNQ1 atrial fibrillation associated mutations, S140G and V141M. In contrast to published results, we find distinct dependence on the KCNE1 subunit for V141M, but not for S140G. Having determined the importance of KCNE1 for V141M functionally, we continued to explore the role of KCNE1 structurally for this mutation. Using cysteine substitution in both KCNQ1 and KCNE1 subunits, we monitored spontaneous disulfide bond formation and find that V141C crosslinks to KCNE1, while S140C does not. Having established the functional and structural importance of KCNE1 for V141M, we proposed that there could be mutations in KCNE1 that could reverse the consequences of slow deactivation in the V141M mutation. In Chapter III, we engineer amino terminal KCNE1 mutations and demonstrate that this domain is important for controlling deactivation, but not activation, kinetics of the KCNQ1 channel. We find two KCNE1 mutations, L45F and Y46W, which when co-expressed with either V141M or S140G mutations in KCNQ1, help restore the mutant channel back towards a wild-type IKs channel. From these results, we propose that the amino-terminal domain could play an important role in mediating the rate of deactivation in KCNQ1/KCNE1 channels. After testing mutations on KCNE1 that could affect normal channel function, we continued with a project to study mutations on KCNQ1 that would have similar dramatic effects on the channel. In Chapter IV, we mutated KCNQ1 residue S140 to Threonine and found that S140T co-assembled with KCNE1 produced a channel having functional characteristics opposite to that of S140G/KCNE1 channels. In contrast to S140G/KCNE1 channels, where channels tend to stay open due to very slow deactivation kinetics, S140T/KCNE1 channels tend to be stabilized in the closed state and require more depolarized pulses to open channels. In addition, we find that a mutation at position Y46 in KCNE1, when co-expressed with the S140T mutation in KCNQ1, helps restore the mutant channel back towards a wild-type channel. Again, here we provide evidence that the amino terminal end of KCNE1 could play a role in controlling deactivation. In Chapter V, we investigated the importance of where KCNE1 is located in the channel and also how KCNQ1/KCNE1 subunits assemble using a tandem construct, with 1 KCNE1 subunit tethered to 2 KCNQ1 subunits (EQQ). To investigate the significance of KCNE1 location, we explored the functional consequences of having the S140G or V141M mutations in the proximal (closest to KCNE1) or distal (farthest from KCNE1) KCNQ1 subunit. We find that having a mutation in the proximal subunit is subject to modulation by KCNE1, but not the distal subunit. Using crosslinking, we want to confirm proper assembly of the heterotetrameric channel to verify that KCNE1 assembles between S1 from one KCNQ1 subunit and the S6 domain of an opposing KCNQ1 subunit. Taken together, we demonstrate that the proximity between the N-terminus of KCNE1 and the S1 domain of KCNQ1 could play a role in modulating deactivation kinetics of KCNQ1. These findings will be of great importance in understanding normal IKs channel function, which will be essential for maintaining proper heart function

Dolomitization and Dolomite Neomorphism: Trenton and Black River Limestones (Middle Ordovician) Northern Indiana, U.S.A.

The Trenton and Black River Limestones are dolomitized extensively along the axis of the Kankakee Arch in Indiana, with the proportion of dolomite decreasing to the south and southeast of the arch. Planar and nonplanar dolomite replacement textures and rhombic (type 1) and saddle (type 2) void-filling dolomite cements are present. Three stages of dolomitization, involving different fluids, are inferred on the basis of petrographic and geochemical characteristics of the dolomites. Nonferroan planar dolomite has relatively high δ18O values (-1.8 to -6.1‰ PDB) and has 87Sr/86Sr ratios (0.70833 to 0.70856) that overlap those of Middle Ordovician seawater. Petrography, geochemistry, and the geometry of the dolomitized body suggest that the planar dolomite was formed in Middle and Late Ordovician seawater during the deposition of the overlying Maquoketa Shale. Ferroan planar and nonplanar dolomite occurs in the upper few meters of the Trenton Limestone, confined to areas underlain by planar dolomite. This dolomite contains patches of nonferroan dolomite with cathodoluminescence (CL) characteristics similar to underlying planar dolomite. Ferroan dolomite has relatively low δ18O values (-5.1 to -7.3‰ PDB) and has slightly radiogenic 87Sr/86Sr ratios (0.70915 to 0.70969) similar to those obtained for the overlying Maquoketa Shale. These data indicate that ferroan dolomite formed by neomorphism of nonferroan planar dolomite as fluids were expelled from the overlying Maquoketa Shale during burial. The absence of ferroan dolomite at the Trenton-Maquoketa contact, in areas where the earlier-formed nonferroan planar dolomite also is absent, indicates that the fluid expelled from the overlying shale did not contain enough Mg2+ to dolomitize limestone. Type 1 dolomite cement has isotopic compositions similar to those of the ferroan dolomite, suggesting that it also formed from shale-derived burial fluids. CL growth zoning patterns in these cements suggest that diagenetic fluids moved stratigraphically downward and toward the southeast along the axis of the Kankakee Arch. Type 2 saddle dolomite cements precipitated late; their low δ18O values (-6.0 to -7.0‰ PDB) are similar to those of the type 1 dolomite cement. However, fluid-inclusion data indicate that the saddle dolomite was precipitated from more saline, basinal fluids and at higher temperatures (94° to 143°C) than the type 1 cements (80° to 104°C). A trend of decreasing fluid-inclusion homogenization temperatures and salinities from the Michigan Basin to the axis of Kankakee Arch suggests that these fluids emerged from the Michigan Basin after precipitation of type 1 cement

How Well Do Executives Trust Their Intuition

In this age of Big Data and analytics, knowledge gained through experiential learning and intuition may be taking a back seat to analytics. However, the use of intuition should not be underestimated and should play an important role in the decision process. How Well Do Executives Trust Their Intuition covers the Fulbright research study conducted by this international team of editors. The main question of their investigation is: How well do executives trust their intuition? In other words, do they typically prefer intuition over analysis and analytics. And equally importantly, what types of intuition may be most favorable looking at different variables? The research utilizes survey and biometrics approaches with C-level executives from Canada, U.S., Poland, and Italy. In addition, the book contains chapters from leading executives in industry, academia, and government. Their insights provide examples of how their intuition enabled key decisions that they made. This book covers such topics as: Using intuition How gender, experience, role, industry, and country affect intuition Trust and intuition in management Trusting intuition It’s a matter of heart Leadership intuition and the future of work Creating an intuitive awareness for executives Improvisation and instinct. The book explores how executives can use intuition to guide decision making. It also explains how to trust intuition-based decisions. How Well Do Executives Trust Their Intuition is a timely and prescient reminder in this age of data-driven analytics that human insight, instinct, and intuition should also play key roles

Fitting a Deep Generative Hadronization Model

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods. However, these approaches will not work with data, where we do not have a matching between observed hadrons and partons. In this paper, we develop a protocol for fitting a deep generative hadronization model in a realistic setting, where we only have access to a set of hadrons in data. Our approach uses a variation of a Generative Adversarial Network with a permutation invariant discriminator. We find that this setup is able to match the hadronization model in Herwig with multiple sets of parameters. This work represents a significant step forward in a longer term program to develop, train, and integrate machine learning-based hadronization models into parton shower Monte Carlo programs.Comment: 14 pages, 4 figure