Maximum likelihood analysis of algorithms and data structures

We present a new approach for an average-case analysis of algorithms and data structures that supports a non-uniform distribution of the inputs and is based on the maximum likelihood training of stochastic grammars. The approach is exemplified by an analysis of the expected size of binary tries as well as by three sorting algorithms and it is compared to the known results that were obtained by traditional techniques. Investigating traditional settings like the random permutation model, we rediscover well-known results formerly derived by pure analytic methods; changing to biased data yields original results. All but one step of our analysis can be automated on top of a computer-algebra system. Thus our new approach can reduce the effort required for an average-case analysis, allowing for the consideration of realistic input distributions with unknown distribution functions at the same time. As a by-product, our approach yields an easy way to generate random combinatorial objects according to various probability distributions.     

Influence of the measurement conditions on the sensitivity of SnO2 gas sensors operated thermo-cyclically

The conductance of several tin oxide gas sensitive layers was simultaneously measured in thermo-cyclic and isothermal operation mode at various concentrations of CO and propene in air, respectively. Different measurement conditions were set by the cycle time, the gas flow rate, the humidity and the thickness of the sensitive layer. The sensor response is expressed by the conductance-over-time profiles (CTPs), the gas sensitivity or by the sum of the CTP sampling points. The resultant sensitivities from the CTPs were found to be higher than those of the isothermal measurements. The CTPs considerably change with the kind of gas as well as with the variation of the measurement conditions. This is discussed for both types of target gases with respect to the temperature, the adsorbates assumed and the reaction kinetics in relation to the gas transport conditions in the porous gas sensitive layer.     

Detecting zero-day attacks using context-aware anomaly detection at the application-layer

Anomaly detection allows for the identification of unknown and novel attacks in network traffic. However, current approaches for anomaly detection of network packet payloads are limited to the analysis of plain byte sequences. Experiments have shown that application-layer attacks become difficult to detect in the presence of attack obfuscation using payload customization. The ability to incorporate syntactic context into anomaly detection provides valuable information and increases detection accuracy. In this contribution, we address the issue of incorporating protocol context into payload-based anomaly detection. We present a new data representation, called \({c}_n\)-grams, that allows to integrate syntactic and sequential features of payloads in an unified feature space and provides the basis for context-aware detection of network intrusions. We conduct experiments on both text-based and binary application-layer protocols which demonstrate superior accuracy on the detection of various types of attacks over regular anomaly detection methods. Furthermore, we show how \({c}_n\)-grams can be used to interpret detected anomalies and thus, provide explainable decisions in practice.     

The Asynchronous Bounded-Cycle model

This paper shows how synchrony conditions can be added to the purely asynchronous model in a way that avoids any reference to message delays and computing step times, as well as system-wide constraints on execution patterns and network topology. Our Asynchronous Bounded-Cycle (ABC) model just bounds the ratio of the number of forward- and backward-oriented messages in certain (“relevant”) cycles in the space-time diagram of an asynchronous execution. We show that clock synchronization and lock-step rounds can be implemented and proved correct in the ABC model, even in the presence of Byzantine failures. Furthermore, we prove that any algorithm working correctly in the partially synchronous Θ-Model also works correctly in the ABC model. In our proof, we first apply a novel method for assigning certain message delays to asynchronous executions, which is based on a variant of Farkas’ theorem of linear inequalities and a non-standard cycle space of graphs. Using methods from point-set topology, we then prove that the existence of this delay assignment implies model indistinguishability for time-free safety and liveness properties. We also introduce several weaker variants of the ABC model, and relate our model to the existing partially synchronous system models, in particular, the classic models of Dwork, Lynch and Stockmayer and the query-response model by Mostefaoui, Mourgaya, and Raynal. Finally, we discuss some aspects of the ABC model’s applicability in real systems, in particular, in the context of VLSI Systems-on-Chip.     

Solvatochromic betaine dyes as optochemical sensor materials: detection of polar and non-polar vapors

Solvatochromic effects in the UV/VIS spectra of dyes can be applied to the monitoring of solvents in the gaseous phase. Within the class of solvatochromic dyes Reichhardt's betaine reveals an outstanding hypsochromic effect on protic solvents with a band shift of up to 350 nm. For vapor detection, the sensor behavior was optimized according to the linearity of the response signal, the sensitivity and a minimum influence of humidity by hydrophobizing siloxanes as additives. Furthermore, the dyes were immobilized under mild conditions by a sol–gel process which generates a high porosity for easy analyte access. The detection principle could be further extended to aprotic, polar solvents by embedding the dyes in protic matrices that form hydrogen bonds to the betaine oxygen. Then, the analyte disturbs the hydrogen bonds resulting in a bathochromic band shift resembling a back titration. Finally, the betaine phenol blue even allows the detection of halogenated hydrocarbons that lack a pronounced functionality but nonetheless a hyperchromic effect occurs during analyte exposition. Mechanistic aspects of these solvent absorption processes were traced by mass-sensitive measurements.     

Coinduction for Exact Real Number Computation

This paper studies coinductive representations of real numbers by signed digit streams and fast Cauchy sequences. It is shown how the associated coinductive principle can be used to give straightforward and easily implementable proofs of the equivalence of the two representations as well as the correctness of various corecursive exact real number algorithms. The basic framework is the classical theory of coinductive sets as greatest fixed points of monotone operators and hence is different from (though related to) the type theoretic approach by Ciaffaglione and Gianantonio.     

Convergence analysis of geometrical multigrid methods for solving data-sparse boundary element equations

The convergence analysis of multigrid methods for boundary element equations arising from negative-order pseudo-differential operators is quite different from the usual finite element multigrid analysis for elliptic partial differential equations. In this paper, we study the convergence of geometrical multigrid methods for solving large-scale, data-sparse boundary element equations. In particular, we investigate multigrid methods for \(\mathcal{H}\)-matrices arising from the adaptive cross approximation to the single layer potential operator.     

Analysis of the human immune response to vaccinia by use of a novel protein microarray suggests that antibodies recognize less than 10% of the total viral proteome

Control of smallpox by mass vaccination was one of the most effective public health measures ever employed for eradicating a devastating infectious disease. However, new methods are needed for monitoring smallpox immunity within current vulnerable populations, and for the development of replacement vaccines for use by immunocompromized or low-responding individuals. As a measure for achieving this goal, we developed a protein microarray of the vaccinia virus proteome by using high-throughput baculovirus expression and purification of individual elements. The array was validated with therapeutic-grade, human hyperimmune sera, and these data were compared to results obtained from individuals vaccinated against smallpox using Dryvax. A high level of reproducibility with a very low background were apparent in repetitive assays that confirmed previously reported antigens and identified new proteins that may be important for neutralizing viral infection. Our results suggest that proteins recognized by antibodies from all vaccinees constituted <10% of the total vaccinia proteome.