Linear Memory Networks

Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models

No go for a flow

We prove that a very large class of 15502 general Argyres-Douglas theories can- not admit a UV lagrangian which ows to them via the Maruyoshi-Song supersymmetry enhancement mechanism. We do so by developing a computer program which brute-force lists, for any given 4d N = 2 superconformal theory TIR, all possible UV candidate su- perconformal lagrangians TUV satisfying some necessary criteria for the supersymmetry enhancement to happen. We argue that this is enough evidence to conjecture that it is impossible, in general, to find new examples of Maruyoshi-Song lagrangians for generalized Argyres-Douglas theories. All lagrangians already known are, on the other hand, recovered and confirmed in our scan. Finally, we also develop another program to compute efficiently Coulomb branch spectrum, masses, couplings and central charges for (G, G’) Argyres-Douglas theories of arbitrarily high rankThe work of FC is supported by the ERC Consolidator Grant STRINGFLATION under the HORIZON 2020 grant agreement no. 647995. AM would like to thank Florent Baume, Emilio Ambite and Marcos Ramírez for the support with the HYDRA cluster in the IFT. AM received funding from “La Caixa” Foundation (ID 100010434) with fellowship code LCF/BQ/IN18/11660045 and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No. 71367

Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory

The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.Comment: accepted @ ECML 2020. arXiv admin note: substantial text overlap with arXiv:2001.1177

Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization

Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We introduce an initialization schema that pretrains the weights of a recurrent neural network to approximate the linear autoencoder of the input sequences and we show how such pretraining can better support solving hard classification tasks with long sequences. We test our approach on sequential and permuted MNIST. We show that the proposed approach achieves a much lower reconstruction error for long sequences and a better gradient propagation during the finetuning phase.Comment: Accepted at NeurIPS 2020 workshop "Beyond Backpropagation: Novel Ideas for Training Neural Architectures

A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting

World economies, and especially European ones, have become strongly interconnected in the last decades and a joint modelling is required. We propose here the use of Copulas to build flexible multivariate distributions, since they allow for a rich dependence structure and more flexible marginal distributions that better fit the features of empirical data, such as leptokurtosis. We use our approach to forecast industrial production series in the core EMU countries and we provide evidence that the copula-VAR model outperforms or at worst compares similarly to normal VAR models, keeping the same computational tractability of the latter approach.Forecasting, Industrial Production, Copulas, VAR models.

Studio sulla sostanza e sulla relazione nella teologia trinitaria di Sant'Agostino

The thesis is concerned with use of the philosophical categories of substance and relation in the trinitary theology of St. Augustine of Hippo. The starting point is two passages: Conf. 4,16; De Trin. 5,2,3. In Conf. 4,16, St. Augustine explains that God is not included in the category of substance as God is simple and unchangeable. In De Trin. 5,2,3 on the other hand, St. Augustine states that God is with- out doubt substance or essence, ousia. Because God is not included in the category of substance; yet God certainly is substance. How can the two statements be reconciled? Is there an aporia here? We will attempt to clear up this somewhat paradoxical circumstance. In Chap. 1 we see how it relates to the possibility of talking about God, as God has to be ineffable at the same time. We identify a number of rules St. Augustine applied to theological discourse. An affirmative theology emerges which is nonetheless capable of ensuring that God cannot be mentioned and cannot be understood. Chap. 2 is dedicated to the theme of essence (substance). We note two definitions: 1) quod est; 2) qua est. We see that both coincide in God, because ʻGod is that which He hasʼ (quod habet hoc est). In Chap. 3 we reflect on the fact that God is a Trinity. This circumstances significantly complicates the theological picture. In Chap. 4 we attempt a final reflection and discuss ʻaporiaʼ in St. Augustine

Design, synthesis, molecular modeling and anti-HIV 1 integrase activity of a series of photoactivable diketo acid-containing inhibitors as affinity probes

Photoaffinity Labelling (PL) is a powerful method in the chemical proteomic approach of protein functions. This method is especially useful for the identification of ligand-binding sites of target proteins and for the investigation of ligand–receptor interactions. The use of affinity-labeled inhibitors to covalently modify the site of interaction and subsequent analysis of the protein have been very effective in providing useful informations about inhibitor binding for a multitude of therapeutic target proteins. Therefore, it could reasonably be applied in drug discovery and development processes. For example, such approach can be used to obtain structural information detailing the association between the enzyme HIV-1 integrase (IN) and inhibitors under development

Tobacco Smoking Is a Strong Predictor of Failure of Conservative Treatment in Hinchey IIa and IIb Acute Diverticulitis-A Retrospective Single-Center Cohort Study

Background and Objectives: Therapeutic management of patients with complicated acute diverticulitis remains debatable. The primary objective of this study is to identify predictive factors for the failure of conservative treatment of Hinchey IIa and IIb diverticular abscesses. Materials and Methods: This is a retrospective cohort study that included patients hospitalized from 1 January 2014 to 31 December 2022 at the Emergency Surgery Department of the Cagliari University Hospital (Italy), diagnosed with acute diverticulitis complicated by Hinchey grade IIa and IIb abscesses. The collected variables included the patient's baseline characteristics, clinical variables on hospital admission, abscess characteristics at the contrast-enhanced CT scan, clinical outcomes of the conservative therapy, and follow-up results. Univariable and multivariable logistic regression models were used to identify prognostic factors of conservative treatment failure and success. Results: Two hundred and fifty-two patients diagnosed with acute diverticulitis were identified from the database search, and once the selection criteria were applied, 71 patients were considered eligible. Conservative treatment failed in 25 cases (35.2%). Univariable analysis showed that tobacco smoking was the most significant predictor of failure of conservative treatment (p = 0.007, OR 7.33, 95%CI 1.55; 34.70). Age (p = 0.056, MD 6.96, 95%CI -0.18; 0.99), alcohol drinking (p = 0.071, OR 4.770, 95%CI 0.79; 28.70), platelets level (p = 0.087, MD -32.11, 95%CI -0.93; 0.06), Hinchey stage IIa/IIb (p = 0.081, OR 0.376, 95%CI 0.12; 1.11), the presence of retroperitoneal air bubbles (p = 0.025, OR 13.300, 95%CI 1.61; 291.0), and the presence of extraluminal free air at a distance (p = 0.043, OR 4.480, 95%CI 1.96; 20.91) were the other variables possibly associated with the risk of failure. In the multivariable logistic regression analysis, only tobacco smoking was confirmed to be an independent predictor of conservative treatment failure (p = 0.006; adjusted OR 32.693; 95%CI 2.69; 397.27). Conclusion: The role of tobacco smoking as a predictor of failure of conservative therapy of diverticular abscess scenarios highlights the importance of prevention and the necessity to reduce exposure to modifiable risk factors