Use of exopolysaccharide-synthesizing lactic acid bacteria and fat replacers for manufacturing reduced-fat burrata cheese: Microbiological aspects and sensory evaluation

This study aimed to set-up a biotechnological protocol for manufacturing a reduced-fat Burrata cheese using semi-skimmed milk and reduced-fat cream, in different combinations with exopolysaccharides-synthesizing bacterial starters (Streptococcus thermophilus, E1, or Lactococcus lactis subsp. lactis and Lc. lactis subsp. cremoris, E2) and carrageenan or xanthan. Eight variants of reduced-fat cheese (fat concentration 34-51% lower than traditional full-fat Burrata cheese, used as the control) were obtained using: (i) semi-skimmed milk and reduced-fat cream alone (RC) or in combination with (ii) xanthan (RCX), (iii) carrageenan (RCC), (iv) starter E1 (RCE1), (v) starter E2 (RCE2), (vi) both starters (RCE1-2), (vii) E1 and xanthan (RCXE1), or E1 and carrageenan (RCCE1). Post-acidification occurred for the RCC, RCX, and RCE2 Burrata cheeses, due to the higher number of mesophilic cocci found in these cheeses after 16 days of storage. Overall, mesophilic and thermophilic cocci, although showing cheese variant-depending dynamics, were dominant microbial groups, flanked by Pseudomonas sp. during storage. Lactobacilli, increasing during storage, represented another dominant microbial group. The panel test gave highest scores to RCE1-2 and RCXE1 cheeses, even after 16 days of storage. The 16S-targeted metagenomic analysis revealed that a core microbiota (S. thermophilus, Streptococcus lutetiensis, Lc. lactis, Lactococcus sp., Leuconostoc lactis, Lactobacillus delbrueckii, and Pseudomonas sp.), characterized the Burrata cheeses. A consumer test, based on 105 people, showed that more than 50% of consumers did not distinguish the traditional full-fat from the RCXE1 reduced-fat Burrata cheese

Configurable DC current leads, with Peltier elements

There is interest in decreasing the thermal load to the cryogenic environment from the current leads. The cryogenic load is challenging both at the design current, as well as at part load operation, when the current is reduced or zero. In this paper we explore the combination of a Peltier elements and a novel concept of configurable current lead. The use of Peltier element reduces the cryogenic load by about 25%. The configurable concept is based on the use of multiple heat exchangers that allows the optimization of current leads when operating at various currents. When used together, Peltier/configurable current lead allows the reduction of the cryogenic load by a factor of 4 in low current/idle conditions. We also explore the transient operation of the current leads, as well as overload capacity.

Backpropagating through Markov Logic Networks

We integrate Markov Logic networks with deep learning architectures operating on high-dimensional and noisy feature inputs. Instead of relaxing the discrete components into smooth functions, we propose an approach that allows us to backpropagate through standard statistical relational learning components using perturbation-based differentiation. The resulting hybrid models are shown to outperform models solely relying on deep learning based function fitting. We find that using noise perturbations is required to allow the proposed hybrid models to robustly learn from the training data

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.

Although reinforcement learning has been successfully applied in many domains in recent years, we still lack agents that can systematically generalize. While relational inductive biases that fit a task can improve generalization of RL agents, these biases are commonly hard-coded directly in the agent's neural architecture. In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through a Relational Graph Convolution Network (R-GCN). We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging procedurally generated environments and MinAtar. Furthermore, we show that GTG produces agents that can jointly reason over observations and environment dynamics encoded in knowledge bases

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.

Although reinforcement learning has been successfully applied in many domains in recent years, we still lack agents that can systematically generalize. While relational inductive biases that fit a task can improve generalization of RL agents, these biases are commonly hard-coded directly in the agent's neural architecture. In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through a Relational Graph Convolution Network (R-GCN). We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging procedurally generated environments and MinAtar. Furthermore, we show that GTG produces agents that can jointly reason over observations and environment dynamics encoded in knowledge bases

Coopetitive business models in future mobile broadband with licensed shared access (LSA)

6siopenSpectrum scarcity forces mobile network operators (MNOs) providing mobile broadband services to develop new business models that address spectrum sharing. It engages MNOs into coopetitive relationship with incumbents. Licensed Shared Access (LSA) concept complements traditional licensing and helps MNOs to access new spectrum bands on a shared basis. This paper discusses spectrum sharing with LSA from business perspective. It describes how coopetition and business model are linked conceptually, and identifies the influence of coopetition on future business models in LSA. We develop business models for dominant and challenger MNOs in traditional licensing and future with LSA. The results indicate that coopetition and business model concepts are linked via value co-creation and value co-capture. LSA offers different business opportunities to dominant and challenger MNOs. Offering, value proposition, customer segments and differentiation in business models become critical in mobile broadband.openP. Ahokangas; M. Matinmikko; I. Atkova; L.F. Minervini; S. Yrjölä; M. MustonenP., Ahokangas; M., Matinmikko; I., Atkova; Minervini, LEO FULVIO; S., Yrjölä; M., Mustone