Specialization of the rostral prefrontal cortex for distinct analogy processes

Analogical reasoning is central to learning and abstract thinking. It involves using a more familiar situation (source) to make inferences about a less familiar situation (target). According to the predominant cognitive models, analogical reasoning includes 1) generation of structured mental representations and 2) mapping based on structural similarities between them. This study used functional magnetic resonance imaging to specify the role of rostral prefrontal cortex (PFC) in these distinct processes. An experimental paradigm was designed that enabled differentiation between these processes, by temporal separation of the presentation of the source and the target. Within rostral PFC, a lateral subregion was activated by analogy task both during study of the source (before the source could be compared with a target) and when the target appeared. This may suggest that this subregion supports fundamental analogy processes such as generating structured representations of stimuli but is not specific to one particular processing stage. By contrast, a dorsomedial subregion of rostral PFC showed an interaction between task (analogy vs. control) and period (more activated when the target appeared). We propose that this region is involved in comparison or mapping processes. These results add to the growing evidence for functional differentiation between rostral PFC subregions

Information-Aware Guidance for Magnetic Anomaly based Navigation

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.Comment: 2022 International Conference on Intelligent Robots and Systems October 23 to 27, 2022 Kyoto, Japa

Estimation de la volatilité locale d'actifs financiers par une méthode d'inversion numérique

Nous nous intéressons à un problème de calibration rencontré en finance qui consiste à identifier la volatilité locale à partir de prix d'options observés sur le marché. Nous formulons le problème sous la forme d'un problème inverse. Le problème direct (pricing), qui consiste à calculer les prix d'options en résolvant l'équation aux dérivées partielles de Dupire, est résolu par différences finies. Le problème inverse (calibration) revient ensuite à résoudre un problème d'optimisation dans lequel il s'agit de déterminer la volatilité optimale telle que les prix calculés avec cette volatilité soit le plus proche possible des prix observés sur le marché. Ce problème inverse mal posé est régularisé en paramétrant la volatilité par des splines bicubiques et en adoptant une approche multiéchelle. De plus, nous utilisons l'algorithme de Quasi-Newton (avec ou sans bornes) pour résoudre le problème d'optimisation. Notre algorithme de calibration est testé sur des donnés synthétiques (simulées par notre modèle) et sur des données réelles du marché

DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data.Comment: 12 pages, 5 figures, 5 tables, Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 202

When some variational properties force convexity

Abstract The notion of adequate (resp. strongly adequate) function has been recently introduced to characterize the essentially strictly convex (resp. essentially firmly subdifferentiable) functions among the weakly lower semicontinuous (resp. lower semicontinuous) ones. In this paper we provide various necessary and sufficient conditions in order that the lower semicontinuous hull of an extended real-valued function on a reflexive Banach space is essentially strictly convex. Some new results on nearest (farthest) points are derived from this approach. keywords Convex duality, well posed optimization problem, essential strict convexity, essential smoothness, best approximation

Observation of the<i> B</i>⁺_c → <i>J/ψ</i>π⁺π⁰ decay

The frst observation of the B+c → J/ψπ+π0 decay is reported with high significance using proton-proton collision data, corresponding to an integrated luminosity of 9 fb−1, collected with the LHCb detector at centre-of-mass energies of 7, 8, and 13 TeV. The ratio ofits branching fraction relative to the B+c → J/ψπ+ channel is measured to beBB+c →J/ψπ+π0BB+c →J/ψπ+= 2.80 ± 0.15 ± 0.11 ± 0.16 ,where the first uncertainty is statistical, the second systematic and the third related to imprecise knowledge of the branching fractions for B+ → J/ψK∗+ and B+c → J/ψπ+ decays, which are used to determine the π0 detection efficiency. The π+π0 mass spectrum is found to be consistent with the dominance of an intermediate ρ+ contribution in accordance witha model based on QCD factorisation.<br/

Nuclear receptor coactivator/coregulator NCoA6(NRC) is a pleiotropic coregulator involved in transcription, cell survival, growth and development

NCoA6 (also referred to as NRC, ASC-2, TRBP, PRIP and RAP250) was originally isolated as a ligand-dependent nuclear receptor interacting protein. However, NCoA6 is a multifunctional coregulator or coactivator necessary for transcriptional activation of a wide spectrum of target genes. The NCoA6 gene is amplified and overexpressed in breast, colon and lung cancers. NCoA6 is a 250 kDa protein which harbors a potent N-terminal activation domain, AD1; and a second, centrally-located activation domain, AD2, which is necessary for nuclear receptor signaling. The intrinsic activation potential of NCoA6 is regulated by its C-terminal STL regulatory domain. Near AD2 is an LxxLL-1 motif which interacts with a wide spectrum of ligand-bound NRs with high-affinity. A second LxxLL motif (LxxLL-2) located towards the C-terminal region is more restricted in its NR specificity. The potential role of NCoA6 as a co-integrator is suggested by its ability to enhance transcriptional activation of a wide variety of transcription factors and from its in vivo association with a number of known cofactors including CBP/p300. NCoA6 has been shown to associate with at least three distinct coactivator complexes containing Set methyltransferases as core polypeptides. The composition of these complexes suggests that NCoA6 may play a fundamental role in transcriptional activation by modulating chromatin structure through histone methylation. Knockout studies in mice suggest that NCoA6 is an essential coactivator. NCoA6-/- embryos die between 8.5-12.5 dpc from general growth retardation coupled with developmental defects in the heart, liver, brain and placenta. NCoA6-/- MEFs grow at a reduced rate compared to WT MEFs and spontaneously undergo apoptosis, indicating the importance of NCoA6 as a prosurvival and anti-apoptotic gene. Studies with NCoA6+/- and conditional knockout mice suggest that NCoA6 is a pleiotropic coregulator involved in growth, development, wound healing and maintenance of energy homeostasis

A Functional Signature Ontology (FUSION) screen detects an AMPK inhibitor with selective toxicity toward human colon tumor cells

AMPK is a serine threonine kinase composed of a heterotrimer of a catalytic, kinase-containing α and regulatory β and γ subunits. Here we show that individual AMPK subunit expression and requirement for survival varies across colon cancer cell lines. While AMPKα1 expression is relatively consistent across colon cancer cell lines, AMPKα1 depletion does not induce cell death. Conversely, AMPKα2 is expressed at variable levels in colon cancer cells. In high expressing SW480 and moderate expressing HCT116 colon cancer cells, siRNA-mediated depletion induces cell death. These data suggest that AMPK kinase inhibition may be a useful component of future therapeutic strategies. We used Functional Signature Ontology (FUSION) to screen a natural product library to identify compounds that were inhibitors of AMPK to test its potential for detecting small molecules with preferential toxicity toward human colon tumor cells. FUSION identified 5′-hydroxy-staurosporine, which competitively inhibits AMPK. Human colon cancer cell lines are notably more sensitive to 5′-hydroxy-staurosporine than are non-transformed human colon epithelial cells. This study serves as proof-of-concept for unbiased FUSION-based detection of small molecule inhibitors of therapeutic targets and highlights its potential to identify novel compounds for cancer therapy development