Domain Generalization with Small Data

In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the distribution over distributions (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods

Transition to turbulence of the Batchelor flow in a rotor/stator device

This experimental study is devoted to the transition to turbulence of the flow confined between a stationary and a rotating disk. Using visualization and video image analysis, we describe the different transitions occurring in the flow as the rotating velocity of the disk is varied. The space–time behavior of the wave patterns is analyzed using the Bi-Orthogonal Decomposition (BOD) technique. This decomposition of the experimental signals on proper modes permits to project the dynamics of the waves in a reduced embedding phase space. By this means, a torus doubling bifurcation is revealed before its complete destruction during the transition to a weak turbulence. Finally, a more classical 2D-Fourier analysis completes our description of the transition and shows for higher rotation rates, the appearance of a more developed turbulence issued from the former chaotic waves

Recovering coefficients of the complex Ginzburg-Landau equation from experimental spatio-temporal data: two examples from hydrodynamics

International audienceThere are many examples where the description of the complexity of flows can only be achieved by the use of simple models. These models, obtained usually from phenomenological arguments, need in general the knowledge of some parameters. The challenge is then to determine the values of these parameters from experiments. We will give two examples where we have been able to evaluate the coefficients of the complex Ginzburg-Landau equation (CGLE) from space-time chaotic data applied to first a row of coupled cylinder wakes and then to wave propagation in the Ekman layer of a rotating disk. In the first case, our analysis is based on a proper decomposition of experimental chaotic flow fields, followed by a projection of the CGLE onto the proper directions. We show that our method is able to recover the parameters of the model which permits to reconstruct the spatio-temporal chaos observed in the experiment. The second physical system under consideration is the flow above a rotating disk and its cross-flow instability. Our aim is to study the properties of the wavefield through a Volterra series equation. The kernels of the Volterra expansion, which contain relevant physical information about the system, are estimated by fitting two-point measurements via a nonlinear parametric model. We then consider describing the wavefield with the CGLE, and derive analytical relations which express the coefficients of the Ginzburg-Landau equation in terms of the kernels of the Volterra expansion

Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem

In many real world settings binary classification decisions are made based on limited data in near real-time, e.g. when assessing a loan application. We focus on a class of these problems that share a common feature: the true label is only observed when a data point is assigned a positive label by the principal, e.g. we only find out whether an applicant defaults if we accepted their loan application. As a consequence, the false rejections become self-reinforcing and cause the labelled training set, that is being continuously updated by the model decisions, to accumulate bias. Prior work mitigates this effect by injecting optimism into the model, however this comes at the cost of increased false acceptance rate. We introduce adversarial optimism (AdOpt) to directly address bias in the training set using adversarial domain adaptation. The goal of AdOpt is to learn an unbiased but informative representation of past data, by reducing the distributional shift between the set of accepted data points and all data points seen thus far. AdOpt significantly exceeds state-of-the-art performance on a set of challenging benchmark problems. Our experiments also provide initial evidence that the introduction of adversarial domain adaptation improves fairness in this setting

Inferring Ontological Categories of OWL Classes Using Foundational Rules

Several efforts that leverage the tools of formal ontology (such as OntoClean, OntoUML, and UFO) have demonstrated the fruitfulness of considering key metaproperties of classes in ontology engineering. These metaproperties include sortality, rigidity, and external dependence, and give rise to many fine-grained ontological categories for classes, including, among others, kinds, phases, roles, mixins, etc. Despite that, it is still common practice to apply representation schemes and approaches - such as OWL - that do not benefit from identifying these ontological categories, and simplistically treat all classes in the same manner. In this paper, we propose an approach to support the automated classification of classes into the ontological categories underlying the (g)UFO foundational ontology. We propose a set of inference rules derived from (g)UFO's axiomatization that, given an initial classification of the classes in an OWL ontology, can support the inference of the classification for the remaining classes in the ontology. We formalize these rules, implement them in a computational tool and assess them against a catalog of ontologies designed by a variety of users for a number of domains.</p

Models for the Type Ic Hypernova SN 2003lw associated with GRB 031203

The Gamma-Ray Burst 031203 at a redshift z=0.1055 revealed a highly reddened Type Ic Supernova, SN 2003lw, in its afterglow light. This is the third well established case of a link between a long-duration GRB and a type Ic SN. The SN light curve is obtained subtracting the galaxy contribution and is modelled together with two spectra at near-maximum epochs. A red VLT grism 150I spectrum of the SN near peak is used to extend the spectral coverage, and in particular to constrain the uncertain reddening, the most likely value for which is E_{G+H}(B-V) about 1.07 +/- 0.05. Accounting for reddening, SN 2003lw is about 0.3 mag brighter than the prototypical GRB-SN 1998bw. Light curve models yield a 56Ni mass of about 0.55 solar mass. The optimal explosion model is somewhat more massive (ejecta mass about 13 solar mass) and energetic (kinetic energy about 6 times 10^52 erg) than the model for SN 1998bw, implying a massive progenitor (40 - 50 solar mass). The mass at high velocity is not very large (1.4 solar mass above 30000 km/s, but only 0.1 solar mass above 60000 km/s), but is sufficient to cause the observed broad lines. The similarity of SNe 2003lw and 1998bw and the weakness of their related GRBs, GRB031203 and GRB980425, suggest that both GRBs may be normal events viewed slightly off-axis or a weaker but possibly more frequent type of GRB.Comment: 19 pages, 8 figures, accepted for publication in Ap

Immunohistochemical field parcellation of the human hippocampus along its antero-posterior axis

The primate hippocampus includes the dentate gyrus, cornu ammonis (CA), and subiculum. CA is subdivided into four felds (CA1-CA3, plus CA3h/hilus of the dentate gyrus) with specifc pyramidal cell morphology and connections. Work in non-human mammals has shown that hippocampal connectivity is precisely patterned both in the laminar and longitudinal axes. One of the main handicaps in the study of neuropathological semiology in the human hippocampus is the lack of clear laminar and longitudinal borders. The aim of this study was to explore a histochemical segmentation of the adult human hippocampus, integrating feld (medio-lateral), laminar, and anteroposterior longitudinal patterning. We provide criteria for head-body-tail feld and subfeld parcellation of the human hippocampus based on immunodetection of Rabphilin3a (Rph3a), Purkinje-cell protein 4 (PCP4), Chromogranin A and Regulation of G protein signaling-14 (RGS-14). Notably, Rph3a and PCP4 allow to identify the border between CA3 and CA2, while Chromogranin A and RGS-14 give specifc staining of CA2. We also provide novel histological data about the composition of human-specifc regions of the anterior and posterior hippocampus. The data are given with stereotaxic coordinates along the longitudinal axis. This study provides novel insights for a detailed region-specifc parcellation of the human hippocampus useful for human brain imaging and neuropathologyOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. EG-A, IP-S and CC were the recipients of grants from the Chair in Neuroscience UAM-Fundación Tatiana Pérez de Guzmán el Bueno (Spain), and from thePlan Propio de Investigaciónof the University of La Laguna. LMP was the recipient of grant PID2021-124829NB-I00 from the Ministry of Science and Innovation of Spai

"Orphan" afterglows in the Universal Structured Jet Model for gamma-ray bursts

The paucity of reliable achromatic breaks in Gamma-Ray Burst afterglow light curves motivates independent measurements of the jet aperture. Serendipitous searches of afterglows, especially at radio wavelengths, have long been the classic alternative. These survey data have been interpreted assuming a uniformly emitting jet with sharp edges (``top-hat'' jet), in which case the ratio of weakly relativistically beamed afterglows to GRBs scales with the jet solid angle. In this paper, we consider, instead, a very wide outflow with a luminosity that decreases across the emitting surface. In particular, we adopt the universal structured jet (USJ) model, that is an alternative to the top-hat model for the structure of the jet. However, the interpretation of the survey data is very different: in the USJ model we only observe the emission within the jet aperture and the observed ratio of prompt emission rate to afterglow rate should solely depend on selection effects. We compute the number and rate of afterglows expected in all-sky snapshot observations as a function of the survey sensitivity. We find that the current (negative) results for OA searches are in agreement with our expectations. In radio and X-ray bands this was mainly due to the low sensitivity of the surveys, while in the optical band the sky-coverage was not sufficient. In general we find that X-ray surveys are poor tools for OA searches, if the jet is structured. On the other hand, the FIRST radio survey and future instruments like the Allen Telescope Array (in the radio band) and especially GAIA, Pan-Starrs and LSST (in the optical band) will have chances to detect afterglows.Comment: 10 pages, 8 figures. MNRAS accepted. Moderate revision