Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning

Reinforcement learning can effectively learn amortised design policies for designing sequences of experiments. However, current methods rely on contrastive estimators of expected information gain, which require an exponential number of contrastive samples to achieve an unbiased estimation. We propose an alternative lower bound estimator, based on the cross-entropy of the joint model distribution and a flexible proposal distribution. This proposal distribution approximates the true posterior of the model parameters given the experimental history and the design policy. Our estimator requires no contrastive samples, can achieve more accurate estimates of high information gains, allows learning of superior design policies, and is compatible with implicit probabilistic models. We assess our algorithm's performance in various tasks, including continuous and discrete designs and explicit and implicit likelihoods

Large language model for Bible sentiment analysis: Sermon on the Mount

The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specifically, comparative religion. Sentiment analysis provides a mechanism to study the emotions expressed in text. Recently, sentiment analysis has been used to study and compare translations of the Bhagavad Gita, which is a fundamental and sacred Hindu text. In this study, we use sentiment analysis for studying selected chapters of the Bible. These chapters are known as the Sermon on the Mount. We utilize a pre-trained language model for sentiment analysis by reviewing five translations of the Sermon on the Mount, which include the King James version, the New International Version, the New Revised Standard Version, the Lamsa Version, and the Basic English Version. We provide a chapter-by-chapter and verse-by-verse comparison using sentiment and semantic analysis and review the major sentiments expressed. Our results highlight the varying sentiments across the chapters and verses. We found that the vocabulary of the respective translations is significantly different. We detected different levels of humour, optimism, and empathy in the respective chapters that were used by Jesus to deliver his message

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Due to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from different dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for a particular variant (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We conclude that the proposed framework can effectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future

On d=4,5,6 Vacua with 8 Supercharges

We show how all known N=2, d=4,5,6 maximally supersymmetric vacua (Hpp-waves and aDSxS solutions) are related through dimensional reduction/oxidation preserving all the unbroken supersymmetries. In particular we show how the N=2, d=5 family of vacua (which are the near-horizon geometry of supersymmetric rotating black holes) interpolates between aDS_2xS^3 and aDS_3xS^2 in parameter space and how it can be dimensionally reduced to an N=2, d=4 dyonic Robinson-Bertotti solution with geometry aDS_2xS^2 and oxidized to an N=2, d=6 solution with aDS_3xS^3 geometry (which is the near-horizon of the self-dual string).Comment: Latex2e, 19 pages, 1 figure. v2: typos corrected, refs. added. v3: very minor corrections, more refs. added, version to be published in Classical and Quantum Gravit

Geometric Construction of Killing Spinors and Supersymmetry Algebras in Homogeneous Spacetimes

We show how the Killing spinors of some maximally supersymmetric supergravity solutions whose metrics describe symmetric spacetimes (including $AdS,AdS\times S$ and H$pp$-waves) can be easily constructed using purely geometrical and group-theoretical methods. The calculation of the supersymmetry algebras is extremely simple in this formalism.Comment: misprints corrected and references added. Version to appear in Classical and Quantum Gravit

Topology Change and theta-Vacua in 2D Yang-Mills Theory

We discuss the existence of $\theta$-vacua in pure Yang-Mills theory in two space-time dimensions. More precisely, a procedure is given which allows one to classify the distinct quantum theories possessing the same classical limit for an arbitrary connected gauge group G and compact space-time manifold M (possibly with boundary) possessing a special basepoint. For any such G and M it is shown that the above quantizations are in one-to-one correspondence with the irreducible unitary representations (IUR's) of $\pi_1(G)$ if M is orientable, and with the IUR's of $\pi_1(G)/2\pi_1(G)$ if M is nonorientable.Comment: 16 pages, 4 figures, uses harvmac and psbo

An E box comprises a positional sensor for regional differences in skeletal muscle gene expression and methylation

AbstractTo dissect the molecular mechanisms conferring positional information in skeletal muscles, we characterized the control elements responsible for the positionally restricted expression patterns of a muscle-specific transgene reporter, driven by regulatory sequences from the MLC1/3 locus. These sequences have previously been shown to generate graded transgene expression in the segmented axial muscles and their myotomal precursors, fortuitously marking their positional address. An evolutionarily conserved E box in the MLC enhancer core, not recognized by MyoD, is a target for a nuclear protein complex, present in a variety of tissues, which includes Hox proteins and Zbu1, a DNA-binding member of the SW12/SNF2 gene family. Mutation of this E box in the MLC enhancer has only a modest positive effect on linked CAT gene expression in transfected muscle cells, but when introduced into transgenic mice the same mutation elevates CAT transgene expression in skeletal muscles, specifically releasing the rostral restriction on MLC-CAT transgene expression in the segmented axial musculature. Increased transgene activity resulting from the E box mutation in the MLC enhancer correlates with reduced DNA methylation of the distal transgenic MLC1 promoter as well as in the enhancer itself. These results identify an E box and the proteins that bind to it as a positional sensor responsible for regional differences in axial skeletal muscle gene expression and accessibility

Whole body composition analysis by the BodPod air-displacement plethysmography method in children with phenylketonuria shows a higher body fat percentage

BACKGROUND: Phenylketonuria (PKU) causes irreversible central nervous system damage unless a phenylalanine (PHE) restricted diet with amino acid supplementation is maintained. To prevent growth retardation, a protein/amino acid intake beyond the recommended dietary protein allowance is mandatory. However, data regarding disease and/or diet related changes in body composition are inconclusive and retarded growth and/or adiposity is still reported. The BodPod whole body air-displacement plethysmography method is a fast, safe and accurate technique to measure body composition. AIM: To gain more insight into the body composition of children with PKU. METHODS: Patients diagnosed with PKU born between 1991 and 2001 were included. Patients were identified by neonatal screening and treated in our centre. Body composition was measured using the BodPod system (Life Measurement Incorporation©). Blood PHE values determined every 1–3 months in the year preceding BodPod analysis were collected. Patients were matched for gender and age with data of healthy control subjects. Independent samples t tests, Mann–Whitney and linear regression were used for statistical analysis. RESULTS: The mean body fat percentage in patients with PKU (n = 20) was significantly higher compared to healthy controls (n = 20) (25.2% vs 18.4%; p = 0.002), especially in girls above 11 years of age (30.1% vs 21.5%; p = 0.027). Body fat percentage increased with rising body weight in patients with PKU only (R = 0.693, p = 0.001), but did not correlate with mean blood PHE level (R = 0.079, p = 0.740). CONCLUSION: Our data show a higher body fat percentage in patients with PKU, especially in girls above 11 years of age