The angular momentum-mass relation: a fundamental law from dwarf irregulars to massive spirals

In a $\Lambda$CDM Universe, the specific stellar angular momentum ($j_\ast$) and stellar mass ($M_\ast$) of a galaxy are correlated as a consequence of the scaling existing for dark matter haloes ($j_{\rm h}\propto M_{\rm h}^{2/3}$). The shape of this law is crucial to test galaxy formation models, which are currently discrepant especially at the lowest masses, allowing to constrain fundamental parameters, e.g. the retained fraction of angular momentum. In this study, we accurately determine the empirical $j_\ast-M_\ast$ relation (Fall relation) for 92 nearby spiral galaxies (from S0 to Irr) selected from the Spitzer Photometry and Accurate Rotation Curves (SPARC) sample in the unprecedented mass range $7 \lesssim \log M_\ast/M_\odot \lesssim 11.5$. We significantly improve all previous estimates of the Fall relation by determining $j_\ast$ profiles homogeneously for all galaxies, using extended HI rotation curves, and selecting only galaxies for which a robust $j_\ast$ could be measured (converged $j_\ast(<R)$ radial profile). We find the relation to be well described by a single, unbroken power-law $j_\ast\propto M_\ast^\alpha$ over the entire mass range, with $\alpha=0.55\pm 0.02$ and orthogonal intrinsic scatter of $0.17\pm 0.01$ dex. We finally discuss some implications for galaxy formation models of this fundamental scaling law and, in particular, the fact that it excludes models in which discs of all masses retain the same fraction of the halo angular momentum.Comment: A&A Letters, accepte

Multiscale causal structure learning

Causal structure learning methods are vital for unveiling causal relationships embedded into observed data. However, the state of the art suffers a major limitation: it assumes that causal interactions occur only at the frequency at which data is observed. To address this limitation, this paper proposes a method that allows structural learning of linear causal relationships occurring at different time scales. Specifically, we explicitly take into account instantaneous and lagged inter-relations between multiple time series, represented at different scales, hinging on wavelet transform. We cast the problem as the learning of a multiscale causal graph having sparse structure and dagness constraints, enforcing causality through directed and acyclic topology. To solve the resulting (non-convex) formulation, we propose an algorithm termed MS-CASTLE, which exhibits consistent performance across different noise distributions and wavelet choices. We also propose a single-scale version of our algorithm, SS-CASTLE, which outperforms existing methods in computational efficiency, performance, and robustness on synthetic data. Finally, we apply the proposed approach to learn the multiscale causal structure of the risk of 15 global equity markets, during covid-19 pandemic, illustrating the importance of multiscale analysis to reveal useful interactions at different time resolutions. Financial investors can leverage our approach to manage risk within equity portfolios from a causal perspective, tailored to their investment horizon

Unmet Needs in Understanding Sublingual Immunotherapy to Grass Pollen

The lack of medication for allergy symptoms at the end of the last millennium has been the promoter of the idea of treating allergies as if you were treating an infectious disease, by vaccination prophylaxis. Two forms of AIT 1) subcutaneous immunotherapy (SCIT) and 2) sublingual immunotherapy (SLIT) are used in the world. Considerable interest has emerged in SLIT both scientifically and especially financially. SLIT is not a new treatment modality. First description dates back to 1900 when H. Curtis. It was relatively widely used until the late 1970’s mainly in US by homeopathic therapists

Galaxy spin as a formation probe:the stellar-to-halo specific angular momentum relation

We derive the stellar-to-halo specific angular momentum relation (SHSAMR) of galaxies at $z=0$ by combining i) the standard $\Lambda$CDM tidal torque theory ii) the observed relation between stellar mass and specific angular momentum (Fall relation) and iii) various determinations of the stellar-to-halo mass relation (SHMR). We find that the ratio $f_j = j_\ast/j_{\rm h}$ of the specific angular momentum of stars to that of the dark matter i) varies with mass as a double power-law, ii) it always has a peak in the mass range explored and iii) it is $3-5$ times larger for spirals than for ellipticals. The results have some dependence on the adopted SHMR and we provide fitting formulae in each case. For any choice of the SHMR, the peak of $f_j$ occurs at the same mass where the stellar-to-halo mass ratio $f_\ast = M_\ast/M_{\rm h}$ has a maximum. This is mostly driven by the straightness and tightness of the Fall relation, which requires $f_j$ and $f_\ast$ to be correlated with each other roughly as $f_j\propto f_\ast^{2/3}$, as expected if the outer and more angular momentum rich parts of a halo failed to accrete onto the central galaxy and form stars (biased collapse). We also confirm that the difference in the angular momentum of spirals and ellipticals at a given mass is too large to be ascribed only to different spins of the parent dark-matter haloes (spin bias).Comment: matches MNRAS published versio

Learning Multi-Frequency Partial Correlation Graphs

Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands. Motivated by many applications in which this differentiation is pivotal, we overcome this limitation by learning a block-sparse, frequency-dependent, partial correlation graph, in which layers correspond to different frequency bands, and partial correlations can occur over just a few layers. To this aim, we formulate and solve two nonconvex learning problems: the first has a closed-form solution and is suitable when there is prior knowledge about the number of partial correlations; the second hinges on an iterative solution based on successive convex approximation, and is effective for the general case where no prior knowledge is available. Numerical results on synthetic data show that the proposed methods outperform the current state of the art. Finally, the analysis of financial time series confirms that partial correlations exist only within a few frequency bands, underscoring how our methods enable the gaining of valuable insights that would be undetected without discriminating along the frequency domain

Immunology of human rickettsial diseases.

Among human rickettsial diseases caused by micro-organisms of the genus Rickettsia (Order Rickettsiales; Family Rickettsiaceae), transmitted to human hosts through arthropod vectors, Mediterranean Spotted Fever, or Boutonneuse Fever, and Rocky Mountain Spotted Fever are considered to be important infectious diseases due to continued prevalence in the developed world, and potentially fatal outcome in severe cases. Proliferation of rickettsiae, at the site of the tick bite, results in focal epidermal and dermal necrosis (tache noire). Rickettsiae then spread via lymphatic vessels to the regional lymph nodes, and, via the bloodstream, to skin, brain, lungs, heart, liver, spleen and kidneys. The pathogen invades and proliferates in the endothelial cells of small vessels, target cells of rickettsial infection, destroying them, and spreading the infection to the endothelia of the vascular tree. The damage of the endothelium, and the subsequent endothelia dysfunction, is followed by the activation of acute phase responses, with alteration in the coagulation and in the cytokine network, together with a transient immune dysregulation, characterized by the reduction in peripheral CD4+ T lymphocytes

Securing Serverless Workflows on the Cloud Edge Continuum

Serverless Computing is an emergent solution that helps deploy applications in the Cloud and sometimes on the Edge, reducing the integration time and the maintenance cost of the data centers. The lack of a standard for functions and the impossibility of connecting them together in complex workflows is currently holding back the growth of Function-as-a-Service (FaaS) use. In this scenario, OpenWolf tries to overcome these issues by implementing a solution to spread functions over the Cloud-Edge Continuum and connecting them using a standardized Domain-Specific Language (DSL) to describe a serverless based workflow. In this work, we aim to enhance the OpenWolf project, solving many security threats the engine suffers, like the authenticated and authorized execution of workflows and the injection of malicious functions inside a workflow. We will validate this new version of OpenWolf in a Smart City surveillance scenario, providing validation and performance tests