Probing the Evolution of the Dark Energy Density with Future Supernova Surveys

The time dependence of the dark energy density can be an important clue to the nature of dark energy in the universe. We show that future supernova data from dedicated telescopes (such as SNAP), when combined with data of nearby supernovae, can be used to determine how the dark energy density $\rho_X(z)$ depends on redshift, if $\rho_X(z)$ is not too close to a constant. For quantitative comparison, we have done an extensive study of a number of dark energy models. Based on these models we have simulated data sets in order to show that we can indeed reconstruct the correct sign of the time dependence of the dark energy density, outside of a degeneracy region centered on $1+w_0 = -w_1 z_{max}/3$ (where $z_{max}$ is the maximum redshift of the survey, e.g., $z_{max}=1.7$ for SNAP). We emphasize that, given the same data, one can obtain much more information about the dark energy density directly (and its time dependence) than about its equation of state.Comment: submitted to PR

Future Type Ia Supernova Data as Tests of Dark Energy from Modified Friedmann Equations

In the Cardassian model, dark energy density arises from modifications to the Friedmann equation, which becomes H^2 = g(\rhom), where g(\rhom) is a new function of the energy density. The universe is flat, matter dominated, and accelerating. The distance redshift relation predictions of generalized Cardassian models can be very different from generic quintessence models, and can be differentiated with data from upcoming pencil beam surveys of Type Ia Supernovae such as SNAP. We have found the interesting result that, once $\Omega_m$ is known to 10% accuracy, SNAP will be able to determine the sign of the time dependence of the dark energy density. Knowledge of this sign (which is related to the weak energy condition) will provide a first discrimination between various cosmological models that fit the current observational data (cosmological constant, quintessence, Cardassian expansion). Further, we have performed Monte Carlo simulations to illustrate how well one can reproduce the form of the dark energy density with SNAP. To be concrete we study a class of two parameter ($n$,$q$) generalized Cardassian models that includes the original Cardassian model (parametrized by $n$ only) as a special case. Examples are given of MP Cardassian models that fit current supernovae and CMB data, and prospects for differentiating between MP Cardassian and other models in future data are discussed. We also note that some Cardassian models can satisfy the weak energy condition $w>-1$ even with a dark energy component that has an effective equation of state $w_X < -1$.Comment: revised version accepted by Ap

Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation

Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfortunately, most estimators are not scalable enough to handle the sizes of modern high-dimensional data sets (often on the order of terabytes), and assume Gaussian samples. To address these deficiencies, we introduce HP-CONCORD, a highly scalable optimization method for estimating a sparse inverse covariance matrix based on a regularized pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal gradient method uses a novel communication-avoiding linear algebra algorithm and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving parallel scalability on problems with up to ~819 billion parameters (1.28 million dimensions); even on a single node, HP-CONCORD demonstrates scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to estimate the underlying dependency structure of the brain from fMRI data, and use the result to identify functional regions automatically. The results show good agreement with a clustering from the neuroscience literature.Comment: Main paper: 15 pages, appendix: 24 page

Integration of real-time speech recognition and action in humanoid robots

Human speech and visual data are two crucial sources of communication that aid people in interacting with their surrounding environment. Thus, both speech and visual inputs are essential and should contribute to the robot’s action to promote the use of the robot as a cognitive tool. Speech recognition and face recognition are two demanding areas of research: they represent two means by which intelligence behaviors can be expressed. In this thesis, we are interested in investigating whether a robot is able to integrate visual and speech information to make decisions and perform actions accordingly. The iCub robot will listen to real-time human speech from the user and point its finger at a person’s face in an image as dictated by the user. In the following sections, our methods, experimental results, and future work will be further discussed.Ope

Irreversible aggregation of silica colloidal particles in binary solvent of 2,6-lutidine and water

Small silica colloidal particles suspended in a binary solvent, such as water and 2,6-lutidine, have attracted increasing attention in the past several decades as model systems to study critical adsorption, critical Casimir force, and colloidal glass transitions because the preferential solvent adsorption and the effective interaction between these colloidal particles can be tuned by controlling the temperature and solvent concentrations. In these early studies, the aggregation or clustering of particles driven by the solvent fluctuation is believed to be stable and thermally reversible. However, we demonstrate here conclusively that irreversible aggregates and gels can occur for silica nanoparticles in the binary solvent 2,6-lutidine and water when either the lutidine concentration or particle volume fraction is high enough. Hence, the interpretation of the experiment results needs to be taken into consideration when using such systems as model thermally reversible colloidal systems

Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System

Neural language models are increasingly deployed into APIs and websites that allow a user to pass in a prompt and receive generated text. Many of these systems do not reveal generation parameters. In this paper, we present methods to reverse-engineer the decoding method used to generate text (i.e., top-$k$ or nucleus sampling). Our ability to discover which decoding strategy was used has implications for detecting generated text. Additionally, the process of discovering the decoding strategy can reveal biases caused by selecting decoding settings which severely truncate a model's predicted distributions. We perform our attack on several families of open-source language models, as well as on production systems (e.g., ChatGPT).Comment: 6 pages, 4 figures, 3 tables. Also, 5 page appendix. Accepted to INLG 202

First Principles Calculations of the pK_a Values and Tautomers of Isoguanine and Xanthine

The accurate replication of DNA requires the formation of complementary hydrogen bonds between a template base and the base moiety of an incoming deoxynucleotide-5‘-triphosphate. Recent structural studies suggest that some DNA polymerases contribute additional constraints by interrogating the minor groove face of the incoming and template bases. Therefore, the hydrogen bond-donating or -accepting properties of the base pairing as well as minor groove faces of the bases could be important determinants of correct base selection. In this paper, we investigate two purines that could arise by endogenous damage of the normal DNA bases:  isoguanine (which can be generated by the oxidation of adenine) and xanthine (which can be generated by the deamination of guanine). In both cases, the potential exists for the placement of a proton in the N3 position, converting the N3 position from a hydrogen bond acceptor to a donor. In this paper, we use first principles quantum mechanical methods (density functional theory using the B3LYP functional and the 6-31G++G**basis set) to predict the ionization and tautomeric equilibria of both isoguanine and xanthine in the gas phase and aqueous solution. For isoguanine, we find that the N1H and N3H neutral tautomeric forms are about equally populated in aqueous solution, while the enol tauotomers are predominant in the gas phase. In contrast, we find that xanthine displays essentially no tautomeric shifts in aqueous solution but is nearly equally populated by both an anionic and a neutral form at physiological pH. To obtain these results, we carried out an extensive examination of the tautomeric and ionic configurations for both xanthine and isoguanine in solution and in the gas phase. The potential hydrogen-bonding characteristics of these damaged purines may be used to test predictions of the important components of base selection by different DNA polymerases during DNA replication

Dislocation and Relocation: Women in the Federal Prison System and Repurposing FCI Danbury for Men

(Excerpt) This Report tracks the lack of progress in keeping federal prison space in the Northeast available for women and the impact of the absence of bed-spaces for women on the implementation of federal policies committed to reducing over-incarceration. The problems began in the summer of 2013, when the federal Bureau of Prisons (BOP) announced plans to transform its only prison for women in the Northeast—FCI Danbury—into a facility for men. The BOP explained that this self-described “mission change” was a response to the need to provide more low-security beds for male prisoners