Why is Argentina’s Fiscal Federalism so Inefficient? Entering the Labyrinth

A long-standing concern in political economy is whether outcomes are efficient in political equilibrium. Recent contributions have examined the efficiency/inefficiency of policy choices from a theoretical point of view. The aim of this paper is to examine such issue empirically. Building on existing "economic" diagnoses that highlight the deficient incentives present in Argentina’s Federal Tax-Sharing Agreement the paper will attempt to understand the politics behind its adoption and persistence. We suggest an explanation based on the transaction costs of Argentina’s political market. Although potentially Pareto-improving policies could have been adopted, they were not introduced because of the uncertainty over the future status of today’s bargains, and given the lack of institutions to enforce bargains among the political actors. The paper concludes offering some preliminary ideas for institutional engineering: what governance structures could help reduce these transaction costs? The purpose is to create an institutional framework in which political actors could negotiate among themselves, ensuring the enforceability of agreements, in order to achieve more efficient outcomes.

The orbifold cohomology of moduli of genus 3 curves

In this work we study the additive orbifold cohomology of the moduli stack of smooth genus g curves. We show that this problem reduces to investigating the rational cohomology of moduli spaces of cyclic covers of curves where the genus of the covering curve is g. Then we work out the case of genus g=3. Furthermore, we determine the part of the orbifold cohomology of the Deligne-Mumford compactification of the moduli space of genus 3 curves that comes from the Zariski closure of the inertia stack of M_3.Comment: 29 pages, 2 figures. Minor changes, to appear in Manuscripta Mat

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks

Positive-unlabeled learning for open set domain adaptation

Open Set Domain Adaptation (OSDA) focuses on bridging the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source as unknown. The challenges of this task are closely related to those of Positive-Unlabeled (PU) learning where it is essential to discriminate between positive (known) and negative (unknown) class samples in the unlabeled target data. With this newly discovered connection, we leverage the theoretical framework of PU learning for OSDA and, at the same time, we extend PU learning to tackle uneven data distributions. Our method combines domain adversarial learning with a new non-negative risk estimator for PU learning based on self-supervised sample reconstruction. With experiments on digit recognition and object classification, we validate our risk estimator and demonstrate that our approach allows reducing the domain gap without suffering from negative transfer

O-MOORE-NICE!: new methodologies and algorithms for design and simulation of analog integrated circuits

No abstract

ISO-LWS grating spectroscopy: the case of R CrA star forming region

We present the far infrared spectra of the R CrA star forming region obtained with ISO-LWS. We collected a pointed observation on the Herbig Ae star R CrA and a raster scan covering the surrounding region, where HH100 (with its exciting source) and the pre-Main Sequence star T CrA are located. The OI 63μm and the CII 158μm lines have been detected in all the pointed positions, with a ratio consistent with PDR excitation. CO rotational lines (between Jup=14 and Jup=19) are detected on R CrA; from their intensities we derived, using a LVG model, the density and temperature of the emitting region. Other molecular transitions (OH and H2O) have been detected on the investigated objects; the derived cooling of all the molecular species is in agreement with C-shock as the likely excitation mechanism. The continuum emission of R CrA peaks around 100μm (as expected for a Herbig star) while the other sources (T CrA, HH100) show increasing continua up to ~200μm, indicating that they are probably less evolved sources

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

Shedding Light on Diatom Photonics by means of Digital Holography

Diatoms are among the dominant phytoplankters in the worl's ocean, and their external silica investments, resembling artificial photonics crystal, are expected to play an active role in light manipulation. Digital holography allowed studying the interaction with light of Coscinodiscus wailesii cell wall reconstructing the light confinement inside the cell cytoplasm, condition that is hardly accessible via standard microscopy. The full characterization of the propagated beam, in terms of quantitative phase and intensity, removed a long-standing ambiguity about the origin of the light. The data were discussed in the light of living cell behavior in response to their environment