Absorption of fermionic dark matter by nuclear targets

Absorption of fermionic dark matter leads to a range of distinct and novel signatures at dark matter direct detection and neutrino experiments. We study the possible signals from fermionic absorption by nuclear targets, which we divide into two classes of four Fermi operators: neutral and charged current. In the neutral current signal, dark matter is absorbed by a target nucleus and a neutrino is emitted. This results in a characteristically different nuclear recoil energy spectrum from that of elastic scattering. The charged current channel leads to induced β decays in isotopes which are stable in vacuum as well as shifts of the kinematic endpoint of β spectra in unstable isotopes. To confirm the possibility of observing these signals in light of other constraints, we introduce UV completions of example higher dimensional operators that lead to fermionic absorption signals and study their phenomenology. Most prominently, dark matter which exhibits fermionic absorption signals is necessarily unstable leading to stringent bounds from indirect detection searches. Nevertheless, we find a large viable parameter space in which dark matter is sufficiently long lived and detectable in current and future experiments

Decision making under time pressure: an independent test of sequential sampling models

Choice probability and choice response time data from a risk-taking decision-making task were compared with predictions made by a sequential sampling model. The behavioral data, consistent with the model, showed that participants were less likely to take an action as risk levels increased, and that time pressure did not have a uniform effect on choice probability. Under time pressure, participants were more conservative at the lower risk levels but were more prone to take risks at the higher levels of risk. This crossover interaction reflected a reduction of the threshold within a single decision strategy rather than a switching of decision strategies. Response time data, as predicted by the model, showed that participants took more time to make decisions at the moderate risk levels and that time pressure reduced response time across all risk levels, but particularly at the those risk levels that took longer time with no pressure. Finally, response time data were used to rule out the hypothesis that time pressure effects could be explained by a fast-guess strategy

Society, Law, and Culture in the Middle East. “Modernities” in the Making

Society, Law, and Culture in the Middle East: “Modernities” in the Making is an edited volume that seeks to deepen and broaden our understanding of various forms of change in Middle Eastern and North African societies during the Ottoman period. It offers an in-depth analysis of reforms and gradual change in the longue durée, challenging the current discourse on the relationship between society, culture, and law. The focus of the discussion shifts from an external to an internal perspective, as agency transitions from “the West” to local actors in the region. Highlighting the ongoing interaction between internal processes and external stimuli, and using primary sources in Arabic and Ottoman Turkish, the authors and editors bring out the variety of modernities that shaped south-eastern Mediterranean history. The first part of the volume interrogates the urban elite household, the main social, political, and economic unit of networking in Ottoman societies. The second part addresses the complex relationship between law and culture, looking at how the legal system, conceptually and practically, undergirded the socio-cultural aspects of life in the Middle East. Society, Law, and Culture in the Middle East consists of eleven chapters, written by well-established and younger scholars working in the field of Middle East and Islamic Studies. The editors, Dror Ze'evi and Ehud R. Toledano, are both leading historians, who have published extensively on Middle Eastern societies in the Ottoman and post-Ottoman periods

Using a neural network approach for muon reconstruction and triggering

The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.Comment: A talk given at ACAT03, KEK, Japan, November 2003. Submitted to Nuclear Instruments and Methods in Physics Research, Section

Twenty Years of Timing SS433

We present observations of the optical ``moving lines'' in spectra of the Galactic relativistic jet source SS433 spread over a twenty year baseline from 1979 to 1999. The red/blue-shifts of the lines reveal the apparent precession of the jet axis in SS433, and we present a new determination of the precession parameters based on these data. We investigate the amplitude and nature of time- and phase-dependent deviations from the kinematic model for the jet precession, including an upper limit on any precessional period derivative of $\dot P < 5 \times 10^{-5}$. We also dicuss the implications of these results for the origins of the relativistic jets in SS433.Comment: 21 pages, including 9 figures. To appear in the Astrophysical Journa

Twist instability in strongly correlated carbon nanotubes

We show that strong Luttinger correlations of the electron liquid in armchair carbon nanotubes lead to a significant enhancement of the onset temperature of the putative twist Peierls instability. The instability results in a spontaneous uniform twist deformation of the lattice at low temperatures, and a gapped ground state. Depending on values of the coupling constants the umklapp electron scattering processes can assist or compete with the twist instability. In case of the competition the umklapp processes win in wide tubes. In narrow tubes the outcome of the competition depends on the relative strength of the e-e and e-ph backscattering. Our estimates show that the twist instability may be realized in free standing (5,5) tubes.Comment: 4 pages, 1 figur

Gi- and Gs-coupled GPCRs show different modes of G-protein binding.

More than two decades ago, the activation mechanism for the membrane-bound photoreceptor and prototypical G protein-coupled receptor (GPCR) rhodopsin was uncovered. Upon light-induced changes in ligand-receptor interaction, movement of specific transmembrane helices within the receptor opens a crevice at the cytoplasmic surface, allowing for coupling of heterotrimeric guanine nucleotide-binding proteins (G proteins). The general features of this activation mechanism are conserved across the GPCR superfamily. Nevertheless, GPCRs have selectivity for distinct G-protein family members, but the mechanism of selectivity remains elusive. Structures of GPCRs in complex with the stimulatory G protein, Gs, and an accessory nanobody to stabilize the complex have been reported, providing information on the intermolecular interactions. However, to reveal the structural selectivity filters, it will be necessary to determine GPCR-G protein structures involving other G-protein subtypes. In addition, it is important to obtain structures in the absence of a nanobody that may influence the structure. Here, we present a model for a rhodopsin-G protein complex derived from intermolecular distance constraints between the activated receptor and the inhibitory G protein, Gi, using electron paramagnetic resonance spectroscopy and spin-labeling methodologies. Molecular dynamics simulations demonstrated the overall stability of the modeled complex. In the rhodopsin-Gi complex, Gi engages rhodopsin in a manner distinct from previous GPCR-Gs structures, providing insight into specificity determinants

Globally and Locally Minimal Weight Spanning Tree Networks

The competition between local and global driving forces is significant in a wide variety of naturally occurring branched networks. We have investigated the impact of a global minimization criterion versus a local one on the structure of spanning trees. To do so, we consider two spanning tree structures - the generalized minimal spanning tree (GMST) defined by Dror et al. [1] and an analogous structure based on the invasion percolation network, which we term the generalized invasive spanning tree or GIST. In general, these two structures represent extremes of global and local optimality, respectively. Structural characteristics are compared between the GMST and GIST for a fixed lattice. In addition, we demonstrate a method for creating a series of structures which enable one to span the range between these two extremes. Two structural characterizations, the occupied edge density (i.e., the fraction of edges in the graph that are included in the tree) and the tortuosity of the arcs in the trees, are shown to correlate well with the degree to which an intermediate structure resembles the GMST or GIST. Both characterizations are straightforward to determine from an image and are potentially useful tools in the analysis of the formation of network structures.Comment: 23 pages, 5 figures, 2 tables, typographical error correcte

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1% improvement in RMSE for the Netflix dataset and an 1.8% for the Yahoo music dataset