Ionic channel current burst analysis by a machine learning based approach

A new method to analyze single ionic channel current conduction is presented. It is based on an automatic classification by K-means algorithm and on the concept of information entropy. This method is used to study the conductance of multistate ion current jumps induced by tetanus toxin in planar lipid bilayers. A comparison is presented with the widely used Gaussian best fit approach, whose main drawback is the fact that it is based on the manual choice of the base line and of meaningful fragments of current signal. On the contrary, the proposed method is able to automatically process a great amount of information and to remove spurious transitions and multichannels. The number of levels and their amplitudes do not have to be known a priori. In this way the presented method is able to produce a reliable evaluation of the conductance levels and their characteristic parameters in a short time.     

Cache-aware design of general-purpose Single-Producer–Single-Consumer queues

Data processing pipelines normally use lockless Single-Producer–Single-Consumer (SPSC) queues to efficiently decouple their processing threads and achieve high throughput, minimizing the cost of synchronization. SPSC queues have been widely studied, mostly for applications such as streaming data or network monitoring, where the main goal is maximizing throughput. There are now many applications, such as virtual-machine–virtual-machine communication, software-defined networking, and message-based kernels, where low latency is also important, and the tradeoffs between high-throughput and low-latency algorithms have not been studied equally well. Furthermore, at high or variable transaction rates, the effect of memory hierarchies and cache coherence subsystems may be dominant and yield surprising results. In this paper, we make two contributions. First, we provide a comprehensive study of the two main families of SPSC queues, namely, “Lamport” and “FastForward” queues, with a detailed analytical and experimental characterization of their behavior in terms of operating regimes, throughput, latency, and cache misses. Second, we propose two new queue variants, namely, improved FastForward and batched improved FastForward, which have better worst-case behavior than other variants in terms of cache misses, which is an important feature for a number of applications. Together, these two contributions provide practical guidelines to choose the best solution depending on the application requirements.     

A framework for Resource-Aware Data Accumulation in sparse wireless sensor networks

Wireless sensor networks (WSNs) have become an enabling technology for a wide range of applications. In contrast with traditional scenarios where static sensor nodes are densely deployed, a sparse WSN architecture can also be used in many cases. In a sparse WSN, special mobile data collectors (MDCs) are used to gather data from ordinary sensor nodes. In general, sensor nodes do not know when they will be in contact with the MDC, hence they need to discover its presence in their communication range. To this end, discovery mechanisms based on periodic listening and a duty-cycle have shown to be effective in reducing the energy consumption of sensor nodes. However, if not properly tuned, such mechanisms can hinder the data collection process. In this paper, we introduce a Resource-Aware Data Accumulation (RADA), a novel and lightweight framework which allows nodes to learn the MDC arrival pattern, and tune the discovery duty-cycle accordingly. Furthermore, RADA is able to adapt to changes in the operating conditions, and is capable of effectively supporting a number of different MDC mobility patterns. Simulation results show that our solution obtains a higher discovery efficiency and a lower energy consumption than comparable schemes.     

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow‐up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.     

Person identification through entropy oriented mean shift clustering of human gaze patterns

Multimedia Tools and Applications - The paper describes a system aimed at improving the human machine interaction that is able to identify users according how she looks at the monitor. The proposed...     

Vertex angle and crossing angle resolution of leveled tree drawings

We study the problem of computing leveled tree drawings, i.e., straight-line drawings of trees where the vertices have distinct preassigned y-coordinates. Our optimization goal is to maximize the crossing angle resolution (i.e., the minimum angle formed by any two crossing edges) and/or the vertex angle resolution (i.e., the minimum angle formed by two edges incident to the same vertex) of the drawing. We provide tight and almost tight worst case bounds for the crossing angle resolution and for the total angle resolution (i.e., the minimum of crossing and vertex angle resolution), respectively.     

The power of verification for one-parameter agents

We initiate the study of mechanisms with verification for one-parameter agents. We give an algorithmic characterization of such mechanisms and show that they are provably better than mechanisms without verification, i.e., those previously considered in the literature. These results are obtained for a number of optimization problems motivated by the Internet and recently studied in the algorithmic mechanism design literature. The characterization can be regarded as an alternative approach to existing techniques to design truthful mechanisms. The construction of such mechanisms reduces to the construction of an algorithm satisfying certain “monotonicity” conditions which, for the case of verification, are much less stringent. In other words, verification makes the construction easier and the algorithm more efficient (both computationally and in terms of approximability).     

Exploiting spatial relations for grammar-based specification of multidimensional languages

Knowledge and Information Systems - As opposed to textual programming languages, multidimensional languages compiler construction paradigms lack standardization. To this aim, in this paper we...     

Further constructive results on interconnection and damping assignment control of mechanical systems: the Acrobot example

Interconnection and damping assignment passivity‐based control is a controller design methodology that achieves (asymptotic) stabilization of mechanical systems endowing the closed‐loop system with a Hamiltonian structure with a desired energy function—that qualifies as Lyapunov function for the desired equilibrium. The assignable energy functions are characterized by a set of partial differential equations that must be solved to determine the control law. A class of underactuation degree one systems for which the partial differential equations can be explicitly solved—making the procedure truly constructive—was recently reported by the authors. In this brief note, largely motivated by the interesting Acrobot example, we pursue this investigation for two degrees‐of‐freedom systems where a constant inertia matrix can be assigned. We concentrate then our attention on potential energy shaping and give conditions under which an explicit solution of the associated partial differential equation can be obtained. Using these results we show that it is possible to swing‐up the Acrobot from some configuration positions in the lower half plane, provided some conditions on the robot parameters are satisfied. Copyright © 2006 John Wiley & Sons, Ltd.     

Resilience Against Replay Attacks: A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems

In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use of predictive arguments with a twofold aim:1)Promptly detect malicious agent behaviors affecting normal system operations;2)Apply specific control actions,based on predictive ideas,for mitigating as much as possible undesirable domino effects resulting from adversary operations.Specifically,the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent.Finally,numerical simulations are carried out to show benefits and effectiveness of the proposed approach.