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Epithelial ovarian cancer (EOC) is the most frequent cause of death among women with gynecologic malignant tumors. Primary debulking surgery (PDS) with maximal surgical effort to reach completeness of cytoreduction, followed by chemotherapy, has become the standard of care; moreover, some experiences have shown that a comprehensive treatment approach of surgery combined with hyperthermic intraperitoneal chemotherapy (HIPEC) could improve the prognosis of ovarian cancer. We carried out a retrospective analysis of all consecutive sixty-six patients diagnosed with primary advanced or recurrent ovarian cancer who underwent debulking surgery plus HIPEC in a single center between September 2005 and October 2020. For 33 patients with primary EOC, with a median follow-up period of 70 months, the median overall survival was 56 months (range: 48.1–96.9); and the median disease-free survival (DFS) was 13 months (range: 19.9–53.7). In the recurrent population, the median follow-up period was 78 months, the median overall survival (OS) was 82 months (range: 48.1–96.9), and the median DFS was 17 months (range: 19.7–53.0). In our study, we have found that CRS plus HIPEC is feasible, with very low rates of major complications and good results in terms of overall survival. [ABSTRACT FROM AUTHOR]

Background: Low-grade serous ovarian cancer (LGSOC) is a rare entity with different behavior compared to high-grade serous (HGSOC). Because of its general low chemosensitivity, complete cytoreductive surgery with no residual disease is crucial in advanced stage LGSOC. We evaluated the impact of optimal cytoreduction on survival outcome both at first diagnosis and at recurrence. Methods: We retrospectively studied consecutive patients diagnosed with advanced LGSOCs who underwent cytoreductive surgery in two oncological centers from January 1994 to December 2018. Survival curves were estimated by the Kaplan–Meier method, and 95% confidence intervals (95% CI) were estimated using the Greenwood formula. Results: A total of 92 patients were included (median age was 47 years, IQR 35– 64). The median overall survival (OS) was 142.3 months in patients with no residual disease (RD), 86.4 months for RD 1–10 mm and 35.2 months for RD >10 mm (p = 0.002). Progression-free survival (PFS) was inversely related to RD after primary cytoreductive surgery (RD = 0 vs RD = 1–10 mm vs RD >10 mm, p = 0.002). On multivariate analysis, RD 1–10 mm (HR = 2.30, 95% CI 1.30– 4.06, p = 0.004), RD >10 mm (HR = 3.89, 95% CI 1.92–7.88, p = 0.0004), FIGO stage IV (p = 0.001), and neoadjuvant chemotherapy (NACT) (p = 0.010) were independent predictors of PFS. RD >10 mm (HR = 3.13, 95% CI 1.52–6.46, p = 0.004), FIGO stage IV (p <0.0001) and NACT (p = 0.030) were significantly associated with a lower OS. Conclusions: Optimal cytoreductive surgery improves survival outcomes in advanced stage LGSOCs. When complete debulking is impossible, a RD <10 mm confers better OS compared to an RD >10 mm in this setting of patients. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and recovery strategies for (distributed) adaptive learning of signals defined over graphs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model. The proposed methods incorporate prior information about the statistical dependency among observations, while at the same time processing data in real time and in a fully decentralized manner. A detailed mean-square analysis is carried out in order to prove stability and evaluate the steady-state performance of the proposed strategies. Finally, we also illustrate how the proposed techniques can be easily extended in order to incorporate thresholding operators for sparsity recovery applications. Numerical results show the potential advantages of using such techniques for distributed learning in adaptive networks deployed over GMRF. [ABSTRACT FROM PUBLISHER]

Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impulse to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization. [ABSTRACT FROM PUBLISHER]

The paper analyses the impact of tax evasion on government budgets, using a monetary circuit model. It is possible to show that, under specific parameter values, evading taxes on profit becomes an attractive proposition when government policies tax capital and employment in different ways and on different timescales. Without adequate tax enforcement, government is reduced to acting as a financial backer for companies. Furthermore, a lower tax rate on labour income enabling money to flow from households to companies through private consumption reduces incentives for tax evasion. These results hold true even if the government decides to implement a balanced budget fiscal policy. [ABSTRACT FROM AUTHOR]

We have used Kepler photometry to characterize variability in four radio-loud active galactic nuclei (AGN; three quasars and one object tentatively identified as a Seyfert 1.5 galaxy) on timescales from minutes to months, comparable to the light crossing time of the accretion disk around the central supermassive black hole or the base of the relativistic jet. Kepler’s almost continuous observations provide much better temporal coverage than is possible from ground-based observations. We report the first such data analyzed for quasars. We have constructed power spectral densities using eight Kepler quarters of long-cadence (30-minute) data for three AGN, six quarters for one AGN and two quarters of short-cadence (1-minute) data for all four AGN. On timescales longer than about 0.2–0.6 days, we find red noise with mean power-law slopes ranging from −1.8 to −1.2, consistent with the variability originating in turbulence either behind a shock or within an accretion disk. Each AGN has a range of red noise slopes which vary slightly by month and quarter of observation. No quasi-periodic oscillations of astrophysical origin were detected.We detected flares of several days long when brightness increased by 3%–7% in two objects. No flares on timescales of minutes to hours were detected. Our observations imply that the duty cycle for enhanced activity in these radio-loud AGN is small. These well-sampled AGN light curves provide an impetus to develop more detailed models of turbulence in jets and instabilities in accretion disks. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed resource assignment strategy for cognitive networks mimicking a swarm foraging mechanism, assuming that the communication among the cognitive nodes is impaired by random link failures and quantization noise. Using results from stochastic approximation theory, we propose a swarm mechanism that converges almost surely to a final allocation even in the presence of imperfect communication scenarios. The theoretical findings are corroborated by numerical results showing that the only effect of the random link failures is to decrease the convergence rate of the algorithm. We propose then a fast swarming approach, robust to random disturbances, that adapts its behavior with respect to the interference power perceived by every node, thus increasing the speed of convergence and improving the resource allocation capabilities. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively. [ABSTRACT FROM AUTHOR]

Background: Adulteration of diesel fuel by the addition of vegetable oil is a problem that touches several countries around the world and bypasses the complexity of the specifications regarding the automotive diesel fuel distinguishing between customs, fiscal, and commercial–environmental specifications. At an international level, the adoption of the same analysis methods is important for the harmonization processes and the fluidity of the market. In this context, we assist to the diffusion of the same fraud touching several countries or continents since the limit of the same methods are common to many specifications. For several European countries, the revenue lost as a result of this adulteration consists of billions of euros per year. This enormous amount of illicit money feeds organized criminal networks with huge social and environmental damages. Objective: This work presents a GC method to quantify vegetable oils in the range of 0.2–7% (w/w) in adulterated diesel fuel, intended for use as motor fuel, with or without extraneous heavy mineral oil. Methods: Our study was realized on 100 fraudulent samples collected by the Italian fiscal police in regular oil stations and by the Italian fiscal police and customs officers as a consequence of controls on trucks transporting, in suspicious circumstances, “oil” often declared “antistick and anticorrosion oil” or “protective oil.” Conclusions and Highlights: High reliability of results, external validity, ease of replication, simple instrumentation, and sample preparation make this method well suited for a new “smart” protocol for diesel fuels analysis for customs, fiscal, and antifraud purposes. [ABSTRACT FROM AUTHOR]

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN's weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.

A series of poly(aryleneethynylene)s functionalized with acetylated glucopyranosyl units were synthesized by the Pd-catalyzed reaction of trimethylsilylethynyl derivatives with aromatic halides in the presence of silver oxide.