Ukazatelʹ k pisʹmam Gogoli︠a︡, zakli︠u︡chai︠u︡shchīĭ v sebi︠e︡ obʺi︠a︡snenīe init︠s︡īalov i drugikh sokrashchenīĭ v izdanīi Kulisha (romanized title) : s prilozhenīem neizdannykh otryvkov iz pisem materi N. V. i ego sobstvennykh /

Photocopy. Ann Arbor, Mich. : University Microfilms, 1978. -- 24 cm.

Grammatică practică romano-franțozeaseă compusă dupre autorii clasicii cei mai noi ...

T.p. and text in the Cyrillic alphabet.

Augusti Pfeiferi D. Critica sacra, : de sacri codicis partitione, editionibus variis, lingvis originalibus & illibatâ puritate fontium, interpretatione scripturae legitima, nec non ejusdem : Translatione in lingvas totius universi de Masora & Kabbala, Talmude deniq; & Alcorano. : Accessere, suisq locis inserti, Tractatus qvatuor, I. De antiqvis ritibus Ebraeorum. II. De natura, usu & subsidiis lingvarum orientalium omnium. III. De compendiaria ratione legendi scripta rabbinico-talmudica. IV. De accentuatione tàm prosaica qvàm metrica facilè discenda.

Includes index.

Statistical physics of low density parity check error correcting codes

We study the performance of Low Density Parity Check (LDPC) error-correcting codes using the methods of statistical physics. LDPC codes are based on the generation of codewords using Boolean sums of the original message bits by employing two randomly-constructed sparse matrices. These codes can be mapped onto Ising spin models and studied using common methods of statistical physics. We examine various regular constructions and obtain insight into their theoretical and practical limitations. We also briefly report on results obtained for irregular code constructions, for codes with non-binary alphabet, and on how a finite system size effects the error probability.

On the utility of alternative amino acid scripts

In this work we propose the hypothesis that replacing the current system of representing the chemical entities known as amino acids using Latin letters with one of several possible alternative symbolic representations will bring significant benefits to the human construction, modification, and analysis of multiple protein sequence alignments. We propose ways in which this might be done without prescribing the choice of actual scripts used. Specifically we propose and explore three ways to encode amino acid texts using novel symbolic alphabets free from precedents. Primary orthographic encoding is the direct substitution of a new alphabet for the standard, Latin-based amino acid code. Secondary encoding imposes static residue groupings onto the orthography of the alphabet by manipulating the shape and/or orientation of amino acid symbols. Tertiary encoding renders each residue as a composite symbol; each such symbol thus representing several alternative amino acid groupings simultaneously. We also propose that the use of a new group-focussed alphabet will free the colouring of amino acid residues often used as a tool to facilitate the representation or construction of multiple alignments for other purposes, possibly to indicate dynamic properties of an alignment such as position-wise residue conservation.

Chapter 1 : nucleobases with designed patterns of hydrogen bonding

The chemistry used in key bond-forming steps to prepare nucleobases with designed patterns of hydrogen bonding is surveyed. Incorporation of the nucleobases into DNA and RNA oligomers is achieved either chemically using building blocks such as nucleoside phosphoramidites or enzymatically using nucleotide triphosphates. By varying the hydrogen bonding pattern within nucleobases, and by incorporating additional substituents, new structures have been designed that "reach over" so that contacts with both strands in targeted duplex DNA can be made in antigene strategies to control gene expression. Various new base-pairing systems have been evaluated that expand the genetic alphabet beyond Watson-Crick base pairs A.T and G.C. For example, benzo-homologated analogs of the natural DNA bases represent a new genetic set of orthogonal, size-expanded derivatives that have been shown to encode amino acids of a protein in a living organism.

Cortical mechanisms of attention in time:neural correlates of the Lag-1-sparing phenomenon

If humans monitor streams of rapidly presented (approximately 100-ms intervals) visual stimuli, which are typically specific single letters of the alphabet, for two targets (T1 and T2), they often miss T2 if it follows T1 within an interval of 200-500 ms. If T2 follows T1 directly (within 100 ms; described as occurring at 'Lag 1'), however, performance is often excellent: the so-called 'Lag-1 sparing' phenomenon. Lag-1 sparing might result from the integration of the two targets into the same 'event representation', which fits with the observation that sparing is often accompanied by a loss of T1-T2 order information. Alternatively, this might point to competition between the two targets (implying a trade-off between performance on T1 and T2) and Lag-1 sparing might solely emerge from conditional data analysis (i.e. T2 performance given T1 correct). We investigated the neural correlates of Lag-1 sparing by carrying out magnetoencephalography (MEG) recordings during an attentional blink (AB) task, by presenting two targets with a temporal lag of either 1 or 2 and, in the case of Lag 2, with a nontarget or a blank intervening between T1 and T2. In contrast to Lag 2, where two distinct neural responses were observed, at Lag 1 the two targets produced one common neural response in the left temporo-parieto-frontal (TPF) area but not in the right TPF or prefrontal areas. We discuss the implications of this result with respect to competition and integration hypotheses, and with respect to the different functional roles of the cortical areas considered. We suggest that more than one target can be identified in parallel in left TPF, at least in the absence of intervening nontarget information (i.e. masks), yet identified targets are processed and consolidated as two separate events by other cortical areas (right TPF and PFC, respectively).

Optimization of cascaded regenerative links based on phase sensitive amplifiers

We develop an analytical method for optimizing phase sensitive amplifiers for regeneration in multilevel phase encoded transmission systems. The model accurately predicts the optimum transfer function characteristics and identifies operating tolerances for different signal constellations and transmission scenarios. The results demonstrate the scalability of the scheme and show the significance of having simultaneous optimization of the transfer function and the signal alphabet. The model is general and can be applied to any regenerative system. © 2013 Optical Society of America.

Optimization method for PSA-based multi-level regenerators

We develop an analytical methodology for optimizing phase regeneration based on phase sensitive amplification. The results demonstrate the scalability of the scheme and show the significance of simultaneous optimization of transfer function and the signal alphabet.

Optimizing amino acid groupings for GPCR classification

MOTIVATION: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. RESULTS: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

Secret key exchange in ultra-long lasers by radio-frequency spectrum coding

We propose a new approach to the generation of an alphabet for secret key exchange relying on small variations in the cavity length of an ultra-long fiber laser. This new concept is supported by experimental results showing how the radio-frequency spectrum of the laser can be exploited as a carrier to exchange information. The test bench for our proof of principle is a 50 km-long fiber laser linking two users, Alice and Bob, where each user can randomly add an extra 1 km-long segment of fiber. The choice of laser length is driven by two independent random binary values, which makes such length become itself a random variable. The security of key exchange is ensured whenever the two independent random choices lead to the same laser length and, hence, to the same free spectral range.

Method for computing the optimal signal distribution and channel capacity

An iterative method for computing the channel capacity of both discrete and continuous input, continuous output channels is proposed. The efficiency of new method is demonstrated in comparison with the classical Blahut - Arimoto algorithm for several known channels. Moreover, we also present a hybrid method combining advantages of both the Blahut - Arimoto algorithm and our iterative approach. The new method is especially efficient for the channels with a priory unknown discrete input alphabet.

Algebraic theory of minimal nondeterministic finite automata with applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA’s behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.

Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.

Algebraic Theory of Minimal Nondeterministic Finite Automata with Applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA's behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.