GKW representation theorem and linear BSDEs under restricted information. An application to risk-minimization

In this paper we provide Galtchouk-Kunita-Watanabe representation results in the case where there are restrictions on the available information. This allows to prove existence and uniqueness for linear backward stochastic differential equations driven by a general c\`adl\`ag martingale under partial information. Furthermore, we discuss an application to risk-minimization where we extend the results of F\"ollmer and Sondermann (1986) to the partial information framework and we show how our result fits in the approach of Schweizer (1994).Comment: 22 page

Closed sequential pattern mining for sitemap generation

A sitemap represents an explicit specification of the design concept and knowledge organization of a website and is therefore considered as the website’s basic ontology. It not only presents the main usage flows for users, but also hierarchically organizes concepts of the website. Typically, sitemaps are defined by webmasters in the very early stages of the website design. However, during their life websites significantly change their structure, their content and their possible navigation paths. Even if this is not the case, webmasters can fail to either define sitemaps that reflect the actual website content or, vice versa, to define the actual organization of pages and links which do not reflect the intended organization of the content coded in the sitemaps. In this paper we propose an approach which automatically generates sitemaps. Contrary to other approaches proposed in the literature, which mainly generate sitemaps from the textual content of the pages, in this work sitemaps are generated by analyzing the Web graph of a website. This allows us to: i) automatically generate a sitemap on the basis of possible navigation paths, ii) compare the generated sitemaps with either the sitemap provided by the Web designer or with the intended sitemap of the website and, consequently, iii) plan possible website re-organization. The solution we propose is based on closed frequent sequence extraction and only concentrates on hyperlinks organized in “Web lists”, which are logical lists embedded in the pages. These “Web lists” are typically used for supporting users in Web site navigation and they include menus, navbars and content tables. Experiments performed on three real datasets show that the extracted sitemaps are much more similar to those defined by website curators than those obtained by competitor algorithms

The detailed mechanism of the eta production in pp scattering up to the Tlab = 4.5 GeV

Contrary to very early beliefs, the experimental cross section data for the eta production in proton-proton scattering are well described if pi and only eta meson exchange diagrams are used to calculate the Born term. The inclusion of initial and final state interactions is done in the factorization approximation by using the inverse square of the Jost function. The two body Jost functions are obtained from the S matrices in the low energy effective range approximation. The danger of double counting in the p-eta final state interaction is discussed. It is shown that higher partial waves in meson-nucleon amplitudes do not contribute significantly bellow excess energy of Q=100 MeV. Known difficulties of reducing the multi resonance model to a single resonance one are illustrated.Comment: 10 pages, 5 figures, corrected typos in relation (3), changed content (added section with differential cross sections

Targeting tumor-associated macrophages to increase the efficacy of immune checkpoint inhibitors: a glimpse into novel therapeutic approaches for metastatic melanoma

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic intervention for a variety of advanced/metastatic solid tumors, including melanoma, but in a large number of cases, patients fail to establish a sustained anti-tumor immunity and to achieve a long-lasting clinical benefit. Cells of the tumor micro-environment such as tumor-associated M2 macrophages (M2-TAMs) have been reported to limit the efficacy of immunotherapy, promoting tumor immune evasion and progression. Thus, strategies targeting M2-TAMs have been suggested to synergize with immune checkpoint blockade. This review recapitulates the molecular mechanisms by which M2-TAMs promote cancer immune evasion, with focus on the potential cross-talk between pharmacological interventions targeting M2-TAMs and ICIs for melanoma treatment

Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data

The huge amount of data generated by sensor networks enables many potential analyses. However, one important limiting factor for the analyses of sensor data is the possible presence of anomalies, which may affect the validity of any conclusion we could draw. This aspect motivates the adoption of a preliminary anomaly detection method. Existing methods usually do not consider the spatial nature of data generated by sensor networks. Properly modeling the spatial nature of the data, by explicitly considering spatial autocorrelation phenomena, has the potential to highlight the degree of agreement or disagreement of multiple sensor measurements located in different geographical positions. The intuition is that one could improve anomaly detection performance by considering the spatial context. In this paper, we propose a spatially-aware anomaly detection method based on a stacked auto-encoder architecture. Specifically, the proposed architecture includes a specific encoding stage that models the spatial autocorrelation in data observed at different locations. Finally, a distance-based approach leverages the embedding features returned by the auto-encoder to identify possible anomalies. Our experimental evaluation on real-world geo-distributed data collected from renewable energy plants shows the effectiveness of the proposed method, also when compared to state-of-the-art anomaly detection methods

ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections

Direct numerical simulations of turbulent pipe flow at high Reynolds number

This paper is associated with a video winner of a 2021 American Physical Society’s Division of Fluid Dynamics (DFD) Gallery of Fluid Motion Award for work presented at the DFD Gallery of Fluid Motion. The original video is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2021.GFM.V005

Stability of the Zagreb Carnegie-Mellon-Berkeley model

In ref. [1] we have used the Zagreb realization of Carnegie-Melon-Berkeley coupled-channel, unitary model as a tool for extracting pole positions from the world collection of partial wave data, with the aim of eliminating model dependence in pole-search procedures. In order that the method is sensible, we in this paper discuss the stability of the method with respect to the strong variation of different model ingredients. We show that the Zagreb CMB procedure is very stable with strong variation of the model assumptions, and that it can reliably predict the pole positions of the fitted partial wave amplitudes.Comment: 25 pages, 12 figures, 19 table

CADMIUM ACCUMULATION IN HORSE MUSCLE, LIVER AND KIDNEY SLAUGHTERED IN BARI

The occurrence of Cadmium was measured in the muscle, liver and kidneys of 220 horses, 120 Polish, 55 Spanish and 45 Italian, subdivided into age-group. The kidneys were found to be the most contaminated and the concentration reveals a situation which should be under investigation

Phenomenology of pp->pp eta reaction close to threshold

The recent high statistics measurement of the pp -> pp eta reaction at an excess energy Q=15.5 MeV has been analysed by means of partial wave decomposition of the cross section. Guided by the dominance of the final state 1S0 pp interaction (FSI), we keep only terms involving the FSI enhancement factor. The measured p-p and p-eta effective mass spectra can be well reproduced by lifting the standard on-shell approximation in the enhancement factor and by allowing for a linear energy dependence in the leading 3P0->1S0,s partial wave amplitude. Higher partial waves seem to play only a marginal role