Long short-term cognitive networks

Neural Computing and Applications - In this paper, we present a recurrent neural system named long short-term cognitive networks (LSTCNs) as a generalization of the short-term cognitive network...     

Mild Binding of Protein to C2N Monolayer Reveals Its Suitable Biocompatibility

The development of biocompatible nanomaterials for smart drug delivery and bioimaging has attracted great interest in recent years in biomedical fields. Here, the interaction between the recently reported nitrogenated graphene (C₂N) and a prototypical protein (villin headpiece HP35) utilizing atomistic molecular dynamics simulations is studied. The simulations reveal that HP35 can form a stable binding with the C₂N monolayer. Although the C₂N–HP35 attractive interactions are constantly preserved, the binding strength between C₂N and the protein is mild and does not cause significant distortion in the protein's structural integrity. This intrinsic biofriendly property of native C₂N is distinct from several widely studied nanomaterials, such as graphene, carbon nanotubes, and MoS₂, which can induce severe protein denaturation. Interestingly, once the protein is adsorbed onto C₂N surface, its transverse migration is highly restricted at the binding sites. This restriction is orchestrated by C₂N's periodic porous structure with negatively charged “holes,” where the basic residues—such as lysine—can form stable interactions, thus functioning as “anchor points” in confining the protein displacement. It is suggested that the mild, immobilized protein attraction and biofriendly aspects of C₂N would make it a prospective candidate in bio‐ and medical‐related applications.     

Desflurane-mediated sympathetic activation occurs in humans despite preventing hypotension and baroreceptor unloading

BACKGROUND: Increasing concentrations of desflurane result in progressive decreases in blood pressure (BP) and, unlike other currently marketed, potent volatile anesthetics, heightened sympathetic nervous system activity. This study aimed to determine whether baroreflex mechanisms are involved in desflurane-mediated sympathetic excitation. METHODS: Healthy volunteers were anesthetized with desflurane (n = 8) or isoflurane (n = 9). Heart rate (HR; measured by electrocardiograph), blood pressure (BP; measured by arterial catheter), and efferent sympathetic nerve activity (SNA; obtained from percutaneous recordings from the peroneal nerve) were monitored. Baroreflex sensitivity was evaluated at baseline while volunteers were conscious and during 0.5, 1, and 1.5 minimum alveolar concentration (MAC) anesthesia via bolus injections of nitroprusside (100 microg) and phenylephrine (150 microg) to decrease and increase BP. To prevent the BP decline with increasing depths of anesthesia, phenylephrine was infused to maintain mean BP at the 0.5 MAC level. RESULTS: The HR, BP, and SNA were similar between the groups at the conscious baseline measurement. Efferent SNA did not change during higher MAC of isoflurane, but it increased progressively as desflurane concentrations were increased beyond 0.5 MAC, despite maintaining BP at the 0.5 MAC value with phenylephrine infusions (P < 0.05). Cardiac baroslopes (based on changes in HR) were progressively and similarly decreased with increasing concentrations of isoflurane and desflurane (P < 0.05). Sympathetic baroslopes (based on SNA) decreased with increasing isoflurane concentrations but were maintained with increasing concentrations of desflurane; the response was significantly different between groups. CONCLUSIONS: The increase in basal levels of SNA with increasing concentrations of desflurane persisted despite "fixing" BP and thus is probably not due to hypotension and unloading of the baroreceptors. Further, the preservation of reflex increases in SNA to nitroprusside during desflurane indicates that desflurane preserves one component of the baroreflex in humans when BP is "fixed."     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.     

Combining Support Vector Machines and simulated annealing for stereovision matching with fish eye lenses in forest environments

We present a novel strategy for computing disparity maps from omni-directional stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. Two of them are identified by applying the powerful Support Vector Machines approach. At a second stage, a stereovision matching process is designed based on the application of four stereovision matching constraints: epipolarity, similarity, uniqueness and smoothness. The epipolarity guides the process. The similarity and uniqueness are mapped once again through the Support Vector Machines, but under a different way to the previous case; after this an initial disparity map is obtained. This map is later filtered by applying the Discrete Simulated Annealing framework where the smoothness constraint is conveniently mapped. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.     

Hybrid genetic optimization and statistical model based approachfor the classification of shadow shapes in sonar imagery

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results     

Building the IBM 4758 secure coprocessor

Meeting the challenge of building a user-configurable secure coprocessor provided several lessons in hardware and software development and continues to spur further research. In developing the 4758, we met our major research security goals and provided the following features: (1) a lifetime-secure tamper-responding device, rather than one that is secure only between resets that deployment-specific security officers perform; (2) a secure booting process in which each layer progressively validates the next less-trusted layer, with hardware restricting access to its secrets before passing control to that layer; (3) an actual manufacturable product - a nontrivial accomplishment considering that we designed the device so that it does not have a personality until configured in the field; (4) the first FIPS 140-1 Level 4 validation, arguably the only general-purpose computational platform validated at this level so far; and (5) a multipurpose programmable device based on a 99-MHz 486 CPU internal environment, with a real operating system, a C language development environment and relatively high-speed cryptography     

Stronger Lempel-Ziv Based Compressed Text Indexing

Given a text T[1..u] over an alphabet of size σ, the full-text search problem consists in finding the occ occurrences of a given pattern P[1..m] in T. In indexed text searching we build an index on T to improve the search time, yet increasing the space requirement. The current trend in indexed text searching is that of compressed full-text self-indices, which replace the text with a more space-efficient representation of it, at the same time providing indexed access to the text. Thus, we can provide efficient access within compressed space. The Lempel-Ziv index (LZ-index) of Navarro is a compressed full-text self-index able to represent T using 4uH _k (T)+o(ulog?σ) bits of space, where H _k (T) denotes the k-th order empirical entropy of T, for any k=o(log? _σ u). This space is about four times the compressed text size. The index can locate all the occ occurrences of a pattern P in T in O(m ³log?σ+(m+occ)log?u) worst-case time. Although this index has proven very competitive in practice, the O(m ³log?σ) term can be excessive for long patterns. Also, the factor 4 in its space complexity makes it larger than other state-of-the-art alternatives. In this paper we present stronger Lempel-Ziv based indices (LZ-indices), improving the overall performance of the original LZ-index. We achieve indices requiring (2+ε)uH _k (T)+o(ulog?σ) bits of space, for any constant ε>0, which makes them the smallest existing LZ-indices. We simultaneously improve the search time to O(m ²+(m+occ)log?u), which makes our indices very competitive with state-of-the-art alternatives. Our indices support displaying any text substring of length ? in optimal O(?/log? _σ u) time. In addition, we show how the space can be squeezed to (1+ε)uH _k (T)+o(ulog?σ) to obtain a structure with O(m ²) average search time for m≥2log? _σ u. Alternatively, the search time of LZ-indices can be improved to O((m+occ)log?u) with (3+ε)uH _k (T)+o(ulog?σ) bits of space, which is much less than the space needed by other Lempel-Ziv-based indices achieving the same search time. Overall our indices stand out as a very attractive alternative for space-efficient indexed text searching.