Abstract
A novel micro-organism designated AS10(T) was isolated from dry salt collected from Aveiro saltern in the north of Portugal. Cells were Gram-stain-positive, non-motile, non-endospore-forming, rod-shaped and aerobic. Strain AS10(T) was characterized by long filaments of rod-shaped cells, presenting also coccoid cellular forms at the end of the filaments, unveiling some pleomorphism. Rod-shaped cells varied from 0.3 to 0.6 mu m wide and from 0.6 to 2 mu m long. Growth of AS10(T) occurred at 15-40 degrees C (optimum, 20-30 degrees C), 0-10% (w/v) NaCl (optimum, 2%) and pH 4.5-11.0 (optimum, pH 8.0-11.0). The peptidoglycan type was A1 Upsilon-type with 3-hydroxy-diaminopimelic acid. The major fatty acids were C-16(:0), iso-C-1(4:)0, C-17(:0) and C-1(4:)0. The major respiratory quinone was MK-9(H-4). Phylogenetic analysis based on 16S rRNA gene sequences showed that strain AS10(T) was similar to actinobacterial members of the class Nitriliruptoria. with Nitriliruptor alkaliphilus ANL-isor being the closest relative the species with a sequence pairwise similarity of 91.21%. Average nucleotide identity, average amino acid identity and in silico DNA-DNA hybridization values between strain AS10(T) and N. alkaliphilus ANL-iso2(T) were 71.34, 53.57 and 18.90%, respectively. The genome DNA G+C content of AS10(T) was 71.8 mol%. Based on genomic, phylogenetic, phenotypic and chemotaxonomic studies, we describe a new species of a novel genus represented by strain AS10(T) (=LMG 31937(T)=CECT 30148(T)) for which we propose the name Salsipaludibacter albus gen. nov., sp. nov. We also propose that this organism represents a new family named Salsipaludibacteraceae fam. nov. of a novel order named Salsipaludibacterales ord. nov.

Abstract
This paper investigates duopoly competition when horizontally differentiated firms are able to make personalized product-price offers to returning customers, within a behavior-based discrimination model. In the second period, firms can profile old customers according to their preferences, selling them targeted products at personalized prices. Product-price personalization (PP) allows firms to retain all old customers, eliminating second-period customer poaching. The overall profit effects of PP are shown to be ambiguous. In the second period, PP improves the matching between customers??? preferences and firms??? offers, but firms do not make any revenues in the rival???s turf. In the Bertrand outcome, second-period profits only increase for both firms if the size of their old turfs are not too different or initial products are not too differentiated. However, the additional secondperiod profits may be offset by lower first-period profits. PP is likely to increase firms??? overall discounted profits when consumers??? (firms???) discount factor is low (high) and firms??? initial products are exogenous and sufficiently different. When the location of initial products is endogenous, profits are hurt because of an additional location (strategic) effect aggravating head-to-head competition in the first period. Likewise, when a fraction of active consumers conceals their identity, PP increases second-period profits at the cost of aggressive first-period price competition. Finally, we show that the room for profitable PP enlarges considerably if firms rely on PP as an effective device to sustain tacit collusive outcomes, with firms credibly threatening to respond to first-period price deviations with

Abstract

Abstract
The infrared low order sensor (IRLOS) upgrade project was recently launched to increase the sky coverage of GALACSI narrow-field mode (NFM). While the baseline is to perform low-order wavefront sensing with a 2x2 Shack-Hartmann wavefront sensor (SHWFS) operating in the J+H band, a full-pupil mode was proposed to address the faintest end of the magnitude range by concentrating the photons from the full aperture in a single point spread function (PSF). In this context, we have investigated the wavefront sensing approach called the linearized focal-plane technique (LIFT). It enables the retrieval of low-order modes such as tip/tilt, defocus, astigmatism (and possibly more) from a single focal-plane PSF of a very faint natural guide star (NGS) target. LIFT is a phase diversity technique based on introducing a known amount of astigmatism into the optical path. The morphological change induced by the astigmatic shift allows encoding information about the phase aberrations into the PSF morphology. In this work, we discuss the linearity and flux sensitivity of the method and present experimental on-sky results obtained at the VLT. We discuss the applicability of this method in realistic conditions and the limitations that this method can face while operated on-sky.

Abstract
Secure multiparty computation (SMC) allows for complex computations over encrypted data. Privacy concerns for cloud applications makes this a highly desired technology and recent performance improvements show that it is practical. To make SMC accessible to non-experts and empower its use in varied applications, many domain-specific compilers are being proposed.We review the role of these compilers and provide a formal treatment of the core steps that they perform to bridge the abstraction gap between high-level ideal specifications and efficient SMC protocols. Our abstract framework bridges this secure compilation problem across two dimensions: 1) language-based source- to target-level semantic and efficiency gaps, and 2) cryptographic ideal- to real-world security gaps. We link the former to the setting of certified compilation, paving the way to leverage long-run efforts such as CompCert in future SMC compilers. Security is framed in the standard cryptographic sense. Our results are supported by a machine-checked formalisation carried out in EasyCrypt.

Abstract
Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For that reason, many losses have been proposed in the literature, which model the output probabilities as following a unimodal distribution. This manuscript reviews many of these losses on three different datasets and suggests a potential improvement that focuses the unimodal constraint on the neighborhood around the true class, allowing for a more flexible distribution, aptly called quasi-unimodal loss. For this purpose, two constraints are proposed: A first constraint concerns the relative order of the top-three probabilities, and a second constraint ensures that the remaining output probabilities are not higher than the top three. Therefore, gradient descent focuses on improving the decision boundary around the true class in detriment to the more distant classes. The proposed loss is found to be competitive in several cases.