A 3 dimensional diagnostic diagram for Seyfert 2s: probing X-ray absorption and Compton thickness

We present and discuss a "3-dimensional" diagnostic diagram for Seyfert2 galaxies obtained by means of X-ray and [OIII] data on a large sample of objects (reported in the Appendix). The diagram shows the Kalpha iron line equivalent width as a function of both the column density derived from the photoelectric cutoff and the 2-10 keV flux normalized to the [OIII] optical line flux (the latter corrected for extinction and assumed to be a true indicator of the source intrinsic luminosity). We find that the hard X-ray properties of type 2 objects depend on a single parameter, the absorbing column density along the line of sight,in accordance with the unified model. The diagram can be used to identify Compton thick sources and to isolate and study peculiar objects. From this analysis we have obtained a column density distribution of Seyfert 2 galaxies which is thought to be a good approximation of the real distribution. A large population of heavily absorbed objects is discovered, including many Compton thick candidates. Our results indicate that the mean Log Nh/cm^(-2)in type 2 Seyferts is 23.5 and that as much as 23-30% of sources have Nh > 10^24 cm^(-2).Comment: 33 pages, 3 figures, to be published in ApJ Sup

Does trading volume really explain stock returns volatility?

Assuming that the variance of daily price changes and trading volume are both driven by the same latent variable measuring the number of price-relevant information arriving on the market, the Mixture of Distribution Hypothesis (MDH) represents an intuitive and appealing explanation for the empirically observed correlation between volume and volatility of speculative assets. This paper investigates to which extent the temporal dependence of volatility and volume is compatible with a MDH model through a systematic analysis of the long memory properties of power transformations of both series. It is found that the fractional differencing parameter of the volatility series reaches its maximum for a power transformation around and then decreases for other order moments while the differencing parameter of the trading volume remains remarkably unchanged. The volatility process thus exhibits a high degree of intermittence whereas the volume dynamic appears much smoother. The results suggest that volatility and volume may share common short-term movements but that their long-run behavior is fundamentally different.Volatility Persistence, Long Memory, Trading Volume

Responsibility Perspective Transfer for Italian Femicide News

Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gender-based violence (GBV) influence the reader's perception of who is to blame for the violence, possibly reinforcing stereotypes which see the victim as partly responsible, too. As a contribution to raise awareness on perspective-based writing, and to facilitate access to alternative perspectives, we introduce the novel task of automatically rewriting GBV descriptions as a means to alter the perceived level of responsibility on the perpetrator. We present a quasi-parallel dataset of sentences with low and high perceived responsibility levels for the perpetrator, and experiment with unsupervised (mBART-based), zero-shot and few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models using a questionnaire study and a suite of automatic metrics.Comment: Accepted for publication in Findings of ACL 202

Parameterized Linear Temporal Logics Meet Costs: Still not Costlier than LTL

We continue the investigation of parameterized extensions of Linear Temporal Logic (LTL) that retain the attractive algorithmic properties of LTL: a polynomial space model checking algorithm and a doubly-exponential time algorithm for solving games. Alur et al. and Kupferman et al. showed that this is the case for Parametric LTL (PLTL) and PROMPT-LTL respectively, which have temporal operators equipped with variables that bound their scope in time. Later, this was also shown to be true for Parametric LDL (PLDL), which extends PLTL to be able to express all omega-regular properties. Here, we generalize PLTL to systems with costs, i.e., we do not bound the scope of operators in time, but bound the scope in terms of the cost accumulated during time. Again, we show that model checking and solving games for specifications in PLTL with costs is not harder than the corresponding problems for LTL. Finally, we discuss PLDL with costs and extensions to multiple cost functions.Comment: In Proceedings GandALF 2015, arXiv:1509.0685

Responsibility Perspective Transfer for Italian Femicide News

Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gender-based violence (GBV) influence the reader's perception of who is to blame for the violence, possibly reinforcing stereotypes which see the victim as partly responsible, too. As a contribution to raise awareness on perspective-based writing, and to facilitate access to alternative perspectives, we introduce the novel task of automatically rewriting GBV descriptions as a means to alter the perceived level of responsibility on the perpetrator. We present a quasi-parallel dataset of sentences with low and high perceived responsibility levels for the perpetrator, and experiment with unsupervised (mBART-based), zero-shot and few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models using a questionnaire study and a suite of automatic metrics.</p

A long N-terminal-extended nested set of abundant and antigenic major histocompatibility complex class I natural ligands from HIV envelope protein

Viral antigens complexed with major histocompatibility complex (MHC) class I molecules are recognized by cytotoxic T lymphocytes on infected cells. Assays with synthetic peptides identify optimal MHC class I ligands often used for vaccines. However, when natural peptides are analyzed, more complex mixtures including long peptides bulging in the middle of the binding site or with carboxyl extensions are found, reflecting lack of exposure to carboxypeptidases in the antigen processing pathway. In contrast, precursor peptides are exposed to extensive cytosolic aminopeptidase activity, and fewer than 1% survive, only to be further trimmed in the endoplasmic reticulum. We show here a striking example of a nested set of at least three highly antigenic and similarly abundant natural MHC class I ligands, 15, 10, and 9 amino acids in length, derived from a single human immunodeficiency virus gp160 epitope. Antigen processing, thus, gives rise to a rich pool of possible ligands from which MHC class I molecules can choose. The natural peptide set includes a 15-residue-long peptide with unprecedented 6 N-terminal residues that most likely extend out of the MHC class I binding groove. This 15-mer is the longest natural peptide known recognized by cytotoxic T lymphocytes and is surprisingly protected from aminopeptidase trimming in living cells.This work was supported by grants from European Union, Ministerio de Educación y Ciencia, Comunidad de Madrid, Instituto de Salud Carlos III, Red Temática de Investigación Cooperativa en Sindrome de Inmunodeficiencia Adquirida (SIDA) del Fondo de Investigaciones Sanitarias (to M. D. V.), Comunidad de Madrid, Instituto de Salud Carlos III, Fundación para la Investigación y la Prevención del Sindrome de Inmunodeficiencia Adquirida en España (to D. L.), and by European Commission Grant QLK2-CT-2001-01167 (to P. M. V. E.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.S

Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag