Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection

The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (Local Outlier Factor and One Class Support Vector Machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use ROC curves to quantify and compare the performance of the two ML techniques.Comment: Submitted to AAS Journals, 30 pages, 14 figure

Identifying the Group-Theoretic Structure of Machine-Learned Symmetries

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building.Comment: 10 pages, 8 figures, 2 table

Discovering Sparse Representations of Lie Groups with Machine Learning

Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter, we extend this technique to derive sparse representations of arbitrary Lie algebras. We show that our method reproduces the canonical (sparse) representations of the generators of the Lorentz group, as well as the $U(n)$ and $SU(n)$ families of Lie groups. This approach is completely general and can be used to find the infinitesimal generators for any Lie group.Comment: 14 pages, 6 figure

Amenorrhea and pituitary human chorionic gonadotrophin production in a 38-year-old presenting as pregnancy of unknown location: case report and review of literature

Background: Amenorrhea and extraplacental production of serum human chorionic gonadotropin (hCG), particularly in young women, can mimic a pregnancy of unknown location. Elevated serum hCG in the absence of pregnancy can pose a diagnostic dilemma and has led to potentially harmful and unwarranted interventions including chemotherapeutic agents like methotrexate or have led to delay in necessary medical interventions in women. We report a case to demonstrate that amenorrhea and extraplacental human chorionic gonadotropin (hCG) production in young women can mimic a pregnancy of unknown location. Furthermore, we performed a critical review of literature on pituitary hCG production. Case: A 38-year-old woman with a diagnosis of Silver-Russell syndrome, a unicornuate uterus, history of right oophorectomy for a benign serous cystadenoma and a desire for pregnancy presenting with a provisional diagnosis of pregnancy of unknown location.After performing a thorough review of history, physical examination, ultrasound exams, and a review of hormone analysis [including hCG, Tumor markers, Follicle-stimulating hormone (FSH), Luteinizing hormone (LH), Anti-Mullerian Hormone (AMH), Estradiol (E2) levels], we confirmed the diagnosis of premature ovarian insufficiency and pituitary hCG production. Conclusions: In women, serum levels of hCG may increase with age, and are not always an indicator of pregnancy. Therefore, it is imperative to interpret false-positive test results and rule out the extraplacental production of hCG. This will help prevent unnecessary surgical procedures and treatment, including chemotherapy

A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, greatly motivate the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, one can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their AUC scores, the quantum networks were shown to outperform the classical networks. However, seeing the computational advantage of the quantum networks in practice may have to wait for the further development of quantum technology and its associated APIs.Comment: 15 pages, 7 figures, 3 appendice

Being tolerated and being discriminated against:Links to psychological well-being through threatened social identity needs

We investigated whether and how the experience of being tolerated and of being discriminated against are associated with psychological well‐being in three correlational studies among three stigmatized groups in Turkey (LGBTI group members, people with disabilities, and ethnic Kurds, total N = 862). Perceived threat to social identity needs (esteem, meaning, belonging, efficacy, and continuity) was examined as a mediator in these associations. Structural equation models showed evidence for the detrimental role of both toleration and discrimination experiences on positive and negative psychological well‐being through higher levels of threatened social identity needs. A mini‐meta analysis showed small to moderate effect sizes and toleration was associated with lower positive well‐being through threatened needs among all three stigmatized groups

Sex-related differences in aging rate are associated with sex chromosome system in amphibians

Sex-related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture-recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex-specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the "unguarded X/Z effect") or repeat-rich Y/W chromosome (the "toxic Y/W effect") could accelerate aging in the heterogametic sex in some vertebrate clades.Peer reviewe

Finite element and experimental investigation of temperature changes on a twist drill in sequential dry drilling

The drilling process is highly non-linear. Coupled with a thermo-mechanical machining, localized heating and temperature increases in the workpiece are caused by the rapid plastic deformation of the workpiece and by the friction along the drill-chip interface. The cutting temperature at the tool-chip interface is an important factor which directly affects workpiece surface integrity, tool wear, and hole diameter and cylindricity in the drilling process. In this study, the effects of sequential dry drilling operations on the drill bit temperature were investigated both experimentally and numerically. Drill temperatures were measured by inserting standard thermocouples into the coolant (oil) hole of TiN/TiAlN-coated carbide drills. Experimental studies were conducted using two different workpiece materials, AISI 1040 steel and Al 7075-T651. The drill bit temperature was predicted using a numerical computation with Third Wave AdvantEdge finite element method (FEM) software, which is based on Lagrangian explicit. The results obtained from the experimental study and finite element analyses (FEA) were compared. Reasonable agreement between the measured and calculated drill bit temperature results were found for sequential dry drilling