Policy-Adaptive Estimator Selection for Off-Policy Evaluation

Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of actions, and noise level. Thus, the data-driven estimator selection problem is becoming increasingly important and can have a significant impact on the accuracy of OPE. However, identifying the most accurate estimator using only the logged data is quite challenging because the ground-truth estimation accuracy of estimators is generally unavailable. This paper studies this challenging problem of estimator selection for OPE for the first time. In particular, we enable an estimator selection that is adaptive to a given OPE task, by appropriately subsampling available logged data and constructing pseudo policies useful for the underlying estimator selection task. Comprehensive experiments on both synthetic and real-world company data demonstrate that the proposed procedure substantially improves the estimator selection compared to a non-adaptive heuristic.Comment: accepted at AAAI'2

Off-Policy Evaluation of Ranking Policies under Diverse User Behavior

Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluation of ranking policies using logged data. A de-facto approach for OPE is Inverse Propensity Scoring (IPS), which provides an unbiased and consistent value estimate. However, it becomes extremely inaccurate in the ranking setup due to its high variance under large action spaces. To deal with this problem, previous studies assume either independent or cascade user behavior, resulting in some ranking versions of IPS. While these estimators are somewhat effective in reducing the variance, all existing estimators apply a single universal assumption to every user, causing excessive bias and variance. Therefore, this work explores a far more general formulation where user behavior is diverse and can vary depending on the user context. We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior. Moreover, AIPS achieves the minimum variance among all unbiased estimators based on IPS. We further develop a procedure to identify the appropriate user behavior model to minimize the mean squared error (MSE) of AIPS in a data-driven fashion. Extensive experiments demonstrate that the empirical accuracy improvement can be significant, enabling effective OPE of ranking systems even under diverse user behavior.Comment: KDD2023 Research trac

EMPRESS. XI. SDSS and JWST Search for Local and z~4-5 Extremely Metal-Poor Galaxies (EMPGs): Clustering and Chemical Properties of Local EMPGs

We search for local extremely metal-poor galaxies (EMPGs), selecting photometric candidates by broadband color excess and machine-learning techniques with the SDSS photometric data. After removing stellar contaminants by shallow spectroscopy with Seimei and Nayuta telescopes, we confirm that three candidates are EMPGs with 0.05--0.1 $Z_\odot$ by deep Magellan/MagE spectroscopy for faint {\sc[Oiii]}$\lambda$4363 lines. Using a statistical sample consisting of 105 spectroscopically-confirmed EMPGs taken from our study and the literature, we calculate cross-correlation function (CCF) of the EMPGs and all SDSS galaxies to quantify environments of EMPGs. Comparing another CCF of all SDSS galaxies and comparison SDSS galaxies in the same stellar mass range ($10^{7.0}-10^{8.4} M_\odot$), we find no significant ($>1\sigma$) difference between these two CCFs. We also compare mass-metallicity relations (MZRs) of the EMPGs and those of galaxies at $z\sim$ 0--4 with a steady chemical evolution model and find that the EMPG MZR is comparable with the model prediction on average. These clustering and chemical properties of EMPGs are explained by a scenario of stochastic metal-poor gas accretion on metal-rich galaxies showing metal-poor star formation. Extending the broadband color-excess technique to a high-$z$ EMPG search, we select 17 candidates of $z\sim$ 4--5 EMPGs with the deep ($\simeq30$ mag) near-infrared JWST/NIRCam images obtained by ERO and ERS programs. We find galaxy candidates with negligible {\sc[Oiii]}$\lambda\lambda$4959,5007 emission weaker than the local EMPGs and known high-$z$ galaxies, suggesting that some of these candidates may fall in 0--0.01 $Z_\odot$, which potentially break the lowest metallicity limit known to date

Supporting data

 Argonaute proteins play a central role in RNA silencing by forming protein-small RNA complexes responsible for the silencing process. While most Argonaute proteins have a short N-terminal region, Argonaute2 in Drosophila melanogaster (DmAgo2) harbors a long and unique N-terminal region. Previous in vitro biochemical studies have shown that the loss of this region does not impair the RNA silencing activity of the complex. However, an N-terminal mutant of Drosophila melanogaster has demonstrated abnormal RNA silencing activity. To explore the causes of this discrepancy between in vitro and in vivo studies, we investigated the biophysical properties of the region. Because the N-terminal region is highly rich in glutamine and glycine residues, which is a well-known property for prion-like domains (PrLD), the possibility of the N-terminal region functioning as a PrLD was tested. Our biochemical assays demonstrated that the N-terminal region can form aggregates that are not dissociated even in the presence of SDS. Also, the aggregates enhanced the fluorescence intensity of thioflavin-T, an amyloid detection reagent. The kinetics of the aggregation followed that of typical amyloid formation exhibiting the self-propagating activity. Further, we directly visualized the aggregation process of the N-terminal region under fluorescence microscopy and found that the aggregations took fractal or fibril shapes. Together, the results indicate that the N-terminal region is a PrLD. Many other PrLDs have been reported to modulate the function of proteins through their aggregation. Therefore, our results raise the possibility that aggregation of the N-terminal region regulates the RNA silencing activity of DmAgo2. </p

Cloning and functional characterization of Chondrichthyes, cloudy catshark, Scyliorhinus torazame and whale shark, Rhincodon typus estrogen receptors

Sex-steroid hormones are essential for normal reproductive activity in both sexes in all vertebrates. Estrogens are required for ovarian differentiation during a critical developmental stage and promote the growth and differentiation of the female reproductive system following puberty. Recent studies have shown that environmental estrogens influence the developing reproductive system as well as gametogenesis, especially in males. To understand the molecular mechanisms of estrogen actions and to evaluate estrogen receptor ligand interactions in Elasmobranchii, we cloned a single estrogen receptor (ESR) from two shark species, the cloudy catshark (Scyliorhinus torazame) and whale shark (Rhincodon typus) and used an ERE-luciferase reporter assay system to characterize the interaction of these receptors with steroidal and other environmental estrogens. In the transient transfection ERE-luciferase reporter assay system, both shark ESR proteins displayed estrogen-dependent activation of transcription, and shark ESRs were more sensitive to 17β-estradiol compared with other natural and synthetic estrogens. Further, the environmental chemicals, bisphenol A, nonylphenol, octylphenol and DDT could activate both shark ESRs. The assay system provides a tool for future studies examining the receptor-ligand interactions and estrogen disrupting mechanisms in Elasmobranchii

Oxytocin Is a Positive Allosteric Modulator of κ-Opioid Receptors but Not δ-Opioid Receptors in the G Protein Signaling Pathway

Oxytocin (OT) influences various physiological functions such as uterine contractions, maternal/social behavior, and analgesia. Opioid signaling pathways are involved in one of the analgesic mechanisms of OT. We previously showed that OT acts as a positive allosteric modulator (PAM) and enhances μ-opioid receptor (MOR) activity. In this study, which focused on other opioid receptor (OR) subtypes, we investigated whether OT influences opioid signaling pathways as a PAM for δ-OR (DOR) or κ-OR (KOR) using human embryonic kidney-293 cells expressing human DOR or KOR, respectively. The CellKeyTM results showed that OT enhanced impedance induced by endogenous/exogenous KOR agonists on KOR-expressing cells. OT did not affect DOR activity induced by endogenous/exogenous DOR agonists. OT potentiated the KOR agonist-induced Gi/o protein-mediated decrease in intracellular cAMP, but did not affect the increase in KOR internalization caused by the KOR agonists dynorphin A and (-)-U-50488 hydrochloride (U50488). OT did not bind to KOR orthosteric binding sites and did not affect the binding affinities of dynorphin A and U50488 for KOR. These results suggest that OT is a PAM of KOR and MOR and enhances G protein signaling without affecting β-arrestin signaling. Thus, OT has potential as a specific signaling-biased PAM of KOR

Wearing a face mask during controlled‐intensity exercise is not a risk factor for exertional heatstroke: A pilot study

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