The Relativistic Hopfield network: rigorous results

The relativistic Hopfield model constitutes a generalization of the standard Hopfield model that is derived by the formal analogy between the statistical-mechanic framework embedding neural networks and the Lagrangian mechanics describing a fictitious single-particle motion in the space of the tuneable parameters of the network itself. In this analogy the cost-function of the Hopfield model plays as the standard kinetic-energy term and its related Mattis overlap (naturally bounded by one) plays as the velocity. The Hamiltonian of the relativisitc model, once Taylor-expanded, results in a P-spin series with alternate signs: the attractive contributions enhance the information-storage capabilities of the network, while the repulsive contributions allow for an easier unlearning of spurious states, conferring overall more robustness to the system as a whole. Here we do not deepen the information processing skills of this generalized Hopfield network, rather we focus on its statistical mechanical foundation. In particular, relying on Guerra's interpolation techniques, we prove the existence of the infinite volume limit for the model free-energy and we give its explicit expression in terms of the Mattis overlaps. By extremizing the free energy over the latter we get the generalized self-consistent equations for these overlaps, as well as a picture of criticality that is further corroborated by a fluctuation analysis. These findings are in full agreement with the available previous results.Comment: 11 pages, 1 figur

Morphology and miscibility of chitosan/soy protein blended membranes

A physico-chemical characterization of blended membranes composed by chitosan and soy protein has been carried out in order to probe the interactions that allow membranes to be formed from these biopolymer mixtures. These membranes are developed aiming at applications in wound healing and skin tissue engineering scaffolding. The structural features of chitosan/soy blended membranes were investigated by means of solid state carbon nuclear magnetic resonance (NMR), infrared spectroscopy (FTIR), contact angle, and atomic force microscopy. FTIR investigations suggested that chitosan and soy may have participated in a specific intermolecular interaction. The proton spin–lattice relaxation experiments in the rotating frame on blended membranes indicated that independently of the preparation conditions, the blend components are not completely miscible possibly due to a weak polymer–protein interaction. It was also shown that the blended systems showed a rougher surface morphology which was dependent of soy content in the blend system

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Deformation of lamellar γ-TiAl below the general yield stress

The occurrence of plasticity below the macroscopic yield stress during tensile monotonic loading of nearly lamellar Ti-45Al-2Nb-2Mn(at%)-0.8vol% TiB2 at both 25 °C and 700 °C, and in two conditions of lamellar thickness, was measured by digital image correlation strain mapping of a remodelled Au surface speckle pattern. Such initial plasticity, not necessarily related to the presence of common stress concentrators such as hard particles or cracks, could occur at applied stresses as low as 64 % of the general yield stress. For a same applied strain it was more prominent at room temperature, and located as slip and twinning parallel to, and near to or at (respect.) lamellar interfaces of all types in soft modeoriented colonies. These stretched the full colony width and the shear strain was most intense in the centre of the colonies. Further, the most highly operative microbands of plasticity at specimen fracture were not those most active prior to yielding. The strain mapping results from polycrystalline tensile loading were further compared to those from microcompression testing of soft-mode stacks of lamellae milled from single colonies performed at the same temperatures. Combined with post-mortem transmission electron microscopy of the pillars, the initial plasticity by longitudinal dislocation glide was found to locate within 30 – 50 nm of the lamellar interfaces, and not at the interfaces themselves. The highly localised plasticity that precedes high cycle fatigue failure is therefore inherently related to the lamellar structure, which predetermines the locations of plastic strain accumulation, even in a single loading cycle.The work was supported by the EPSRC / Rolls-Royce Strategic Partnership (EP/M005607/1). T.E.J.E. also acknowledges the kind support of the Worshipful Company of Armourers and Brasiers’ Gauntlet Trust

A computational study of stimulus driven epileptic seizure abatement

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Active brain stimulation to abate epileptic seizures has shown mixed success. In spike-wave (SW) seizures, where the seizure and background state were proposed to coexist, single-pulse stimulations have been suggested to be able to terminate the seizure prematurely. However, several factors can impact success in such a bistable setting. The factors contributing to this have not been fully investigated on a theoretical and mechanistic basis. Our aim is to elucidate mechanisms that influence the success of single-pulse stimulation in noise-induced SW seizures. In this work, we study a neural population model of SW seizures that allows the reconstruction of the basin of attraction of the background activity as a four dimensional geometric object. For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space. In the case of spontaneous noise-induced seizures, the basin becomes probabilistic introducing some degree of uncertainty to the stimulation outcome while maintaining qualitative features of the noise-free case. Additionally, due to the different time scales involved in SW generation, there is substantial variation between SW cycles, implying that there may not be a fixed set of optimal stimulation parameters for SW seizures. In contrast, the model suggests an adaptive approach to find optimal stimulation parameters patient-specifically, based on real-time estimation of the position in state space. We discuss how the modelling work can be exploited to rationally design a successful stimulation protocol for the abatement of SW seizures using real-time SW detection.This work was supported by the EPSRC (EP/K026992/1), the BBSRC, the DTC for Systems Biology (University of Manchester), and the Nanyang Technological University Singapore. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Identification of cell-surface mannans in a virulent Helicobacter pylori strain

With the intent of contributing to a carbohydrate-based vaccine against the gastroduodenal pathogen, Helicobacter pylori, we report here the structure of cell-surface mannans obtained from a virulent strain. Unlike other wild-type strains, this strain was found to express in good quantities this polysaccharide in vitro. Structural analysis revealed a branched mannan formed by a backbone of α-(1→6)-linked mannopyranosyl residues with approximately 80% branching at the O-2 position. The branches were composed of O-2-linked Man residues in both α- and β-configurations: (image) In addition, this strain also expressed cell-surface emblematic H. pylori lipopolysaccharides (LPS) containing partially fucosylated polyLacNAc O-chains. Affinity assays with polymyxin-B and concanavalin A revealed no association between the mannan and the LPS. The described mannans may be implicated in the mediation of host–microbial interactions and immunological modulation.This work was supported by Fundacao para a Ciencia e Tecnologia (FCT) through project Pylori E&LPS POCI/QUI/56393/2004, PhD grant SFRH/BD/19929/2004, by the Natural Sciences and Engineering Research Council of Canada (NSERC), and by the European Network of Research Infrastructures within the 6th Framework Programme of the EC (Contract # RII3-026145, EU-NMR). The authors further thank Dr. Adrien Favier (RALF-NMR facility, Grenoble - France) for conducting NMR experiments

Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure