Homomorphism AutoEncoder — Learning Group Structured Representations from Observed Transitions

How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representation learning and group theory. We propose methods enabling an agent acting upon the world to learn internal representations of sensory information that are consistent with actions that modify it. We use an autoencoder equipped with a group representation acting on its latent space, trained using an equivariance-derived loss in order to enforce a suitable homomorphism property on the group representation. In contrast to existing work, our approach does not require prior knowledge of the group and does not restrict the set of actions the agent can perform. We motivate our method theoretically, and show empirically that it can learn a group representation of the actions, thereby capturing the structure of the set of transformations applied to the environment. We further show that this allows agents to predict the effect of sequences of future actions with improved accuracy

Homomorphism Autoencoder —- Learning Group Structured Representations from Interactions

It is crucial for agents, both biological and artificial, to acquire world models that veridically represent the external world and how it is modified by the agent's own actions. We consider the case where such modifications can be modelled as transformations from a group of symmetries structuring the world state space. We use tools from representation learning and group theory to learn latent representations that account for both sensory information and the actions that alters it during interactions. We introduce the Homomorphism AutoEncoder (HAE), an autoencoder equipped with a learned group representation linearly acting on its latent space trained on 2-step transitions to implicitly enforce the group homomorphism property on the action representation. Compared to existing work, our approach makes fewer assumptions on the group representation and on which transformations the agent can sample from. We motivate our method theoretically, and demonstrate empirically that it can learn the correct representation of the groups and the topology of the environment. We also compare its performance in trajectory prediction with previous methods

Analyzing the Local Electronic Structure of Co3_3O4_4 Using 2p3d Resonant Inelastic X-ray Scattering

We present the cobalt 2p3d resonant inelastic X-ray scattering (RIXS) spectra of Co$_3$O$_4$. Guided by multiplet simulation, the excited states at 0.5 and 1.3 eV can be identified as the $^4$T$_2$ excited state of the tetrahedral Co$^{2+}$ and the $^3$T$_{2g}$ excited state of the octahedral Co$^{3+}$, respectively. The ground states of Co$^{2+}$ and Co$^{3+}$ sites are determined to be high-spin $^4$A$_2$(T$_d$) and low-spin $^1$A$_{1g}$(O$_h$), respectively. It indicates that the high-spin Co$^{2+}$ is the magnetically active site in Co$_3$O$_4$. Additionally, the ligand-to-metal charge transfer analysis shows strong orbital hybridization between the cobalt and oxygen ions at the Co$^{3+}$ site, while the hybridization is weak at the Co$^{2+}$ site

Analyzing the Local Electronic Structure of Co3O4Using 2p3d Resonant Inelastic X-ray Scattering

We present the cobalt 2p3d resonant inelastic X-ray scattering (RIXS) spectra of Co3O4. Guided by multiplet simulation, the excited states at 0.5 and 1.3 eV can be identified as the 4T2excited state of the tetrahedral Co2+and the 3T2gexcited state of the octahedral Co3+, respectively. The ground states of Co2+and Co3+sites are determined to be high-spin 4A2(Td) and low-spin 1A1g(Oh), respectively. It indicates that the high-spin Co2+is the magnetically active site in Co3O4. Additionally, the ligand-to-metal charge transfer analysis shows strong orbital hybridization between the cobalt and oxygen ions at the Co3+site, while the hybridization is weak at the Co2+site

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Homomorphism AutoEncoder – Learning Group Structured Representations from Observed Transitions

ISSN:2640-349

Risk of gestational hypertension-preeclampsia in women with preceding endometriosis: A nationwide population-based study.

To investigate the association between preceding endometriosis and gestational hypertension-preeclampsia (GH-PE).In this nationwide population-based longitudinal study, data from 1998-2012 Taiwan National Health Insurance Research Database were used. We used ICD9-CM codes 617.X and 642.X respectively for the diagnoses of endometriosis and GH-PE, which were further confirmed by examining medical records of surgeries, blood pressure and urine protein to ensure the accuracy of the diagnoses. The study excluded women diagnosed with endometriosis at 45 years of age, chronic hypertension, and GH-PE prior to endometriosis. Each pregnant woman with a prior diagnosis of endometriosis was matched to 4 pregnant women without endometriosis by age. Logistic regression analysis was used to calculate odds ratios (ORs) for the risk of GH-PE with adjustment for age, occupation, urbanization, economic status and comorbidities.Among 6,300 women with a prior endometriosis diagnosis who were retrieved from a population of 1,000,000 residents, 2,578 (40.92%) had subsequent pregnancies that were eligible for further analysis and were compared with 10,312 pregnant women without previous endometriosis. GH-PE occurred more in women with prior endometriosis as compared to those without endometriosis (3.88% vs. 1.63%, p<0.0001). Further analysis revealed prior endometriosis was associated with GH-PE (adjusted OR = 2.27; 95% CI:1.76-2.93). For danazol-treated and non-danazol-treated subgroups, the incidences of GH-PE were 3.13% (15/480) and 4.05% (85/2,098), respectively. Although the risk for subsequent GH-PE was lower (adjusted OR = 1.49; 95% CI:0.86-2.56) after receiving danazol treatment than average (adjusted OR = 2.27; 95% CI:1.76-2.93) for women with preceding endometriosis, the reduction of risk was not statistically remarkable for danazol-treated (adjusted OR = 1.49) vs. non-danazol-treated (adjusted OR = 2.48) subgroups (p heterogeneity = 0.12).Preceding endometriosis is an independent and significant risk factor for the occurrence of GH-PE

Relationship between Polycystic Ovarian Syndrome and Subsequent Gestational Diabetes Mellitus: A Nationwide Population-Based Study.

This nationwide population-based study aims to explore the relationship between polycystic ovarian syndrome (PCOS) and subsequent gestational diabetes mellitus (GDM).Data from 1998-2012 Taiwan National Health Insurance Research Database were used for this study. ICD9-CM codes 256.4X and 648.X were used separately for the diagnoses of PCOS and GDM, which were further confirmed by records of blood tests or ultrasonography to ensure the accuracy of the diagnoses. Women diagnosed at 45 years of age, and those diagnosed with overt diabetes mellitus or GDM prior to PCOS were excluded. During pregnancy, each woman with a previous diagnosis of PCOS was age-matched to 10 women without PCOS. Odds ratios (ORs) for risk of GDM were calculated by logistic regression analysis with adjustment for economic status and co-morbidities.Among 7,629 eligible women with a valid PCOS diagnosis, 3,109 (42.87%) had subsequent pregnancies. GDM occurred frequently among women with a history of PCOS as compared to those without PCOS (20.46% vs. 10.54%, p0.05). If not used after conception, OHAs did not reduce the risk of GDM (adjusted OR = 1.20; 95% CI:0.88-1.62).A history of PCOS is a significant and independent risk factor for development of GDM. Medication for PCOS or pre-pregnancy use of OHAs does not reduce the risk of GDM. When at-risk women become pregnant, they require closer surveillance for maternal and fetal well-being, and should follow a strict diet and adhere to weight gain control to avoid obstetric complications due to GDM