Resolving The ISM Surrounding GRBs with Afterglow Spectroscopy

We review current research related to spectroscopy of gamma-ray burst (GRB) afterglows with particular emphasis on the interstellar medium (ISM) of the galaxies hosting these high redshift events. These studies reveal the physical conditions of star-forming galaxies and yield clues to the nature of the GRB progenitor. We offer a pedagogical review of the experimental design and review current results. The majority of sightlines are characterized by large HI column densities, negligible molecular fraction, the ubiquitous detection of UV pumped fine-structure transitions, and metallicities ranging from 1/100 to nearly solar abundance.Comment: Conference procedings for Gamma Ray Bursts 2007 November 5-9, 2007 Santa Fe, New Mexico (8 pages, 4 figures

Blue-Light-Emitting Color Centers in High-Quality Hexagonal Boron Nitride

Light emitters in wide band gap semiconductors are of great fundamental interest and have potential as optically addressable qubits. Here we describe the discovery of a new color center in high-quality hexagonal boron nitride (h-BN) with a sharp emission line at 435 nm. The emitters are activated and deactivated by electron beam irradiation and have spectral and temporal characteristics consistent with atomic color centers weakly coupled to lattice vibrations. The emitters are conspicuously absent from commercially available h-BN and are only present in ultra-high-quality h-BN grown using a high-pressure, high-temperature Ba-B-N flux/solvent, suggesting that these emitters originate from impurities or related defects specific to this unique synthetic route. Our results imply that the light emission is activated and deactivated by electron beam manipulation of the charge state of an impurity-defect complex

Generalized exclusion and Hopf algebras

We propose a generalized oscillator algebra at the roots of unity with generalized exclusion and we investigate the braided Hopf structure. We find that there are two solutions: these are the generalized exclusions of the bosonic and fermionic types. We also discuss the covariance properties of these oscillatorsComment: 10 pages, to appear in J. Phys.

Mesoscopic, Non-equilibrium Fluctuations of Inhomogeneous Electronic States in Manganites

By using the dark-field real-space imaging technique of transmission electron microscopy (TEM), we have observed slow 200 A-scale fluctuations of charge-ordered (CO) phase in mixed-valent manganites under a strong electron beam irradiation. In addition to these unusual fluctuations of the CO phase, we observed the switching-type fluctuations of electrical resistivity in the same sample, which were found to be as large as several percents. Systematic analysis indicates that these two different types of fluctuations with a similar time scale of the order of seconds are interconnected through a meta-stable insulating charge-disordered state. Current dependence of the fluctuations suggests a non-equilibrium nature of this slow dynamics.Comment: To appear in Europhysics Letter

Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development

Dark-adapted red flash ERGs in healthy adults

Purpose: The x-wave of the dark-adapted (DA) ERG to a red flash reflects DA cone function. This exploratory study of healthy adults aimed to investigate changes in the DA red ERG with flash strength and during dark adaptation to optimise visualisation and therefore quantification of the x-wave. Methods: The effect of altering red flash strength was investigated in four subjects by recording ERGs after 20 minutes dark adaptation to red flashes (0.2–2.0 cd s m-2) using skin electrodes and natural pupils. The effect of dark adaptation duration was investigated in 16 subjects during 20 minutes in the dark, by recording DA 1.5 red ERGs at 1, 2, 3, 4, 5, 10, 15 and 20 minutes. Results: For a dark adaption period of 20 minutes, the x-wave was more clearly visualised to weaker (< 0.6 cd s m-2) red flash strengths: to stronger flashes it became obscured by the b-wave. For red flashes of 1.5 cd s m-2, the x-wave was most prominent in ERGs recorded after 1–5 minutes of dark adaptation: with longer dark-adaptation, it was subsumed into the b-wave’s rising edge. Conclusions: This small study suggests that x-wave visibility in healthy subjects after 20 minutes dark adaptation is improved by using flashes weaker than around 0.6 cd s m-2; for flash strengths of 1.5 cd s m-2, x-wave visibility is enhanced by recording after only around 5 minutes of dark adaptation. No evidence was found that interim red flash ERGs affecting the dark-adapted state of the normal retina

On Feedback Vertex Set: New Measure and New Structures

We present a new parameterized algorithm for the {feedback vertex set} problem ({\sc fvs}) on undirected graphs. We approach the problem by considering a variation of it, the {disjoint feedback vertex set} problem ({\sc disjoint-fvs}), which finds a feedback vertex set of size $k$ that has no overlap with a given feedback vertex set $F$ of the graph $G$. We develop an improved kernelization algorithm for {\sc disjoint-fvs} and show that {\sc disjoint-fvs} can be solved in polynomial time when all vertices in $G \setminus F$ have degrees upper bounded by three. We then propose a new branch-and-search process on {\sc disjoint-fvs}, and introduce a new branch-and-search measure. The process effectively reduces a given graph to a graph on which {\sc disjoint-fvs} becomes polynomial-time solvable, and the new measure more accurately evaluates the efficiency of the process. These algorithmic and combinatorial studies enable us to develop an $O^*(3.83^k)$-time parameterized algorithm for the general {\sc fvs} problem, improving all previous algorithms for the problem.Comment: Final version, to appear in Algorithmic

Bayesian modelling of high-throughput sequencing assays with malacoda.

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell\u27s output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations

The Measurement Calculus

Measurement-based quantum computation has emerged from the physics community as a new approach to quantum computation where the notion of measurement is the main driving force of computation. This is in contrast with the more traditional circuit model which is based on unitary operations. Among measurement-based quantum computation methods, the recently introduced one-way quantum computer stands out as fundamental. We develop a rigorous mathematical model underlying the one-way quantum computer and present a concrete syntax and operational semantics for programs, which we call patterns, and an algebra of these patterns derived from a denotational semantics. More importantly, we present a calculus for reasoning locally and compositionally about these patterns. We present a rewrite theory and prove a general standardization theorem which allows all patterns to be put in a semantically equivalent standard form. Standardization has far-reaching consequences: a new physical architecture based on performing all the entanglement in the beginning, parallelization by exposing the dependency structure of measurements and expressiveness theorems. Furthermore we formalize several other measurement-based models: Teleportation, Phase and Pauli models and present compositional embeddings of them into and from the one-way model. This allows us to transfer all the theory we develop for the one-way model to these models. This shows that the framework we have developed has a general impact on measurement-based computation and is not just particular to the one-way quantum computer.Comment: 46 pages, 2 figures, Replacement of quant-ph/0412135v1, the new version also include formalization of several other measurement-based models: Teleportation, Phase and Pauli models and present compositional embeddings of them into and from the one-way model. To appear in Journal of AC