Tensor Network Methods for Invariant Theory

Invariant theory is concerned with functions that do not change under the action of a given group. Here we communicate an approach based on tensor networks to represent polynomial local unitary invariants of quantum states. This graphical approach provides an alternative to the polynomial equations that describe invariants, which often contain a large number of terms with coefficients raised to high powers. This approach also enables one to use known methods from tensor network theory (such as the matrix product state factorization) when studying polynomial invariants. As our main example, we consider invariants of matrix product states. We generate a family of tensor contractions resulting in a complete set of local unitary invariants that can be used to express the R\'enyi entropies. We find that the graphical approach to representing invariants can provide structural insight into the invariants being contracted, as well as an alternative, and sometimes much simpler, means to study polynomial invariants of quantum states. In addition, many tensor network methods, such as matrix product states, contain excellent tools that can be applied in the study of invariants.Comment: 21 page

Infra-red fixed point structure of soft supersymmetry breaking mass terms

We show that the soft SUSY breaking mass terms may have infra-red stable fixed points at which they are related to the gaugino masses and argue that in a generic unification these masses should lie close to their fixed points. We consider the implications for the family dependence of squark and slepton masses and the related flavour changing neutral currents and determine conditions under which models with flavour changing couplings and masses at a high scale may lead to a family independent effective theory at low scales. The analysis is illustrated for a variety of models for which we compute both the fixed point structure and determine the rate of approach to the fixed point.Comment: 14 page

Effects of Contingent and Non-Contingent Signals During Delay Interval on Response Acquisition by Rats

Research on delay of reinforcement effects under temporally defined schedulesof reinforcement suggests delay effects are diluted under short cycle durations.This conclusion is tentative because attempts to replicate the seminalstudy conducted by Weil (1984) differed from the original study in a number ofways. The present study attempted a more direct replication of Weil`s studyand also to extended the original manipulation to encompass two different signaleddelay of reinforcement procedures. Thirty-six naive rats were exposedto a repetitive time cycle of 32-s. The cycle was divided into two portions, td and t delta. A response during td produced food at the end of the cycle; responsesemitted during t delta had no programmed consequences. For someexperimental groups td was signaled by a response-produced signal; in othergroups a non-contingent signal occurred during td ; in still other experimentalgroups td was unsignaled. The placement of td was varied to produce twodifferent response-reinforcer temporal relations; td duration was also variedto assess the generality of the results. Response rates were considerablylower when td was at the beginning of the cycle than when the opportunityto respond was at its end. Non-contingent signals produced low rates of responding;in contrast response produced signals were associated with highresponse rates. In general the results show that delay of reinforcement hasdetrimental effects on response acquisition even under short reinforcementcycles. Both non-contingent and contingent signals have facilitative effects onthe response acquisition process, but the former favors low rates of respondingand the later favors high response rates

Unification predictions

The unification of gauge couplings suggests that there is an underlying (supersymmetric) unification of the strong, electromagnetic and weak interactions. The prediction of the unification scale may be the first quantitative indication that this unification may extend to unification with gravity. We make a precise determination of these predictions for a class of models which extend the multiplet structure of the Minimal Supersymmetric Standard Model to include the heavy states expected in many Grand Unified and/or superstring theories. We show that there is a strong cancellation between the 2-loop and threshold effects. As a result the net effect is smaller than previously thought, giving a small increase in both the unification scale and the value of the strong coupling at low energies.Comment: 20 pages, Latex, 5 Postscipt figures; 2 references adde

Infra-red fixed points revisited

We reconsider how Yukawa couplings may be determined in terms of a gauge coupling through the infra-red fixed point structure paying particular regard to the rate of approach to the fixed point. Using this we determine whether the fixed point structure of an underlying unified theory may play a significant role in fixing the couplings at the gauge unification scale. We argue that, particularly in the case of compactified theories, this is likely to be the case and illustrate this by a consideration of phenomenologically interesting theories. We discuss in particular what the infra-red fixed point structure implies for the top quark mass.Comment: 13 pages, LaTeX, 1 figur

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained