Query: Quantum-Inspired Immune Clonal Algorithm for Global Optimization

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A Fast Quantum Mechanical Algorithm for Database Search <=SEP=> were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ----------------------------------------------

Quantum theory , the Church-Turing principle and the universal quantum computer <=SEP=> It is argued that underlying the Church-Turing hypothesis there is an implicit physical assertion. Here, this assertion is presented explicitly as a physical principle: ‘every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means’. Classical physics and the universal Turing machine, because the former is continuous and the latter discrete, do not obey the principle, at least in the strong form above. A class of model computing machines that is the quantum generalization of the class of Turing machines is described, and it is shown that quantum theory and the ‘universal quantum computer’ are compatible with the principle. Computing machines resembling the universal quantum computer could, in principle, be built and would have many remarkable properties not reproducible by any Turing machine. These do not include the computation of non-recursive functions, but they do include ‘quantum parallelism’, a method by which certain probabilistic tasks can be performed faster by a universal quantum computer than by any classical restriction of it. The intuitive explanation of these properties places an intolerable strain on all interpretations of quantum theory other than Everett’s. Some of the numerous connections between the quantum theory of computation and the rest of physics are explored. Quantum complexity theory allows a physically more reasonable definition of the ‘complexity’ or ‘knowledge’ in a physical system than does classical complexity theory. Current address: Centre for Quantum Computation, Clarendon Laboratory, Department of Physics, Parks Road, OX1 3PU Oxford, United Kingdom. Email: david.deutsch@qubit.org This version (Summer 1999) was edited and converted to L TEX by Wim van Dam at the Centre for Quantum Computation. Email:wimvdam@qubit.org 1 Computing machines and the Church-Turing principle The theory of computing machines has been extensively developed during the last few decades. Intuitively, a computing machine is any physical system whose dynamical evolution takes it from one of a set of ‘input’ states to one of a set of ‘output’ states. The states are labelled in some canonical way, the machine is prepared in a state with a given input label and then, following some motion, the output state is measured. For a classical deterministic system the measured output label is a definite functionf of the prepared input label; moreover the value of that label can in principle be measured by an outside observer (the ‘user’) and the machine is said to ‘compute’the functionf . Two classical deterministic computing machines are ‘computationally equivalent’ under given labellings of their input and output states if they compute the same function under those labellings. But quantum computing machines, and indeed classical stochastic computing machines, do not ‘compute functions’ in the above sense: the output state of a stochastic machine is random with only the probability distribution function for the possible outputs depending on the input state. The output state of a quantum machine, although fully determined by the input state is not an observable and so the user cannot in general discover its label. Nevertheless, the notion of computational equivalence can be generalized to apply to such machines also. Again we define computational equivalence under given labellings,but it is now necessary to specify more precisely what is to be labelled. As far as the input is concerned, labels must be given for each of the possible ways of preparing the machine, which correspond, by definition, to all the possible input states. This is identical with the classical deterministic case. However, there is an asymmetry between input and output because there is an asymmetry between preparation and measurement: whereas a quantum system can be prepared in any desired permitted input state, measurement cannot in general determine its output state; instead one must measure the value of some observable. (Throughout this paper I shall be using the Schr ̈ odinger picture, in which the quantum state is a function of time but observables are constant operators.) Thus what must be labelled is the set of ordered pairs consisting of an output observable and a possible measured value of that observable (in quantum theory, a Hermitian operator and one of its eigenvalues). Such an ordered pair contains, in effect, the specification of a possible experiment that could be made on the output, together with a possible result of that experiment. Two computing machines are computationally equivalent under given labellings if in any possible experiment or sequence of experiments in which their inputs were prepared equivalently under the input labellings, and observables corresponding to each other under the output labellings were measured, the measured values of these observables for the two machines would be statistically indistinguishable. That is, the probability distribution functions for the outputs of the two machines would be identical. In the sense just described, a given computing machine M computes at most one function. However, there ought to be no fundamental difference between altering the input state in which M is prepared, and altering systematically the constitution of M so that it becomes a different machine M0 computing a different function. To formalize such operations, it is often useful to consider machines with two inputs, the preparation of one constituting a ‘program’ determining which function of the other is to be computed. To each such machine M there corresponds a set C (M) of ‘M-computable functions’. A functionf isM-computable ifM can computef when prepared with some program. The set C(M) can be enlarged by enlarging the set of changes in the constitution of M that are labelled as possible M-programs. Given two machines M andM0 it is possible to construct a composite machine whose set of computable functions contains the union of C (M) and C(M0). There is no purely logical reason why one could not go on ad infinitumbuilding more powerful

Quantum Inspired Genetic Algorithms <=SEP=> |A novel evolutionary computing method | quantum inspired genetic algorithms | is introduced, where concepts and principles of quantum mechanics are used to inform and inspire more eecient evolutionary computing methods. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain. The paper concludes with some speculative comments concerning the relationship between quantum inspired genetic algorithms and various complexity classes.

Algorithms for Quantum Computation: Discrete Log and Factoring Extended Abstract <=SEP=> This paper gives algorithms for the discrete log and the factoring problems that take random polynomial time on a quantum computer (thus giving the rst examples of quantum cryptanalysis).

A Method for Obtaining Digital Signatures and Public-Key Cryptosystems (Reprint) <=SEP=> An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

A Path-Tracking Criterion for an LHD Articulated Vehicle <=SEP=> A path-tracking criterion for the so-called LHD (load-haul-dump) truck used in underground mining is proposed in this paper. It exploits the particular configuration of this vehicle, composed of two units connected by an actuated articulation. The task is to follow the path represented by the middle of the tunnel, maintaining the whole vehicle at a reduced distance from the path itself, to decrease the risk of crashes against the walls of the tunnel. This is accomplished via feedback through the synthesis of an appropriate path-tracking criterion. The criterion is based on monitoring the distances of the midpoints of both axles of the vehicle from their orthogonal projections on the path, using two different moving frames simultaneously. Local asymptotic stability to paths of constant curvature is achieved by means of linear-state feedback. KEY WORDS—mobile robots, articulated vehicles, mining trucks, path tracking, off-tracking, feedback stabilization

A Multilingual Multimedia Indian Sign Language Dictionary Tool <=SEP=> This paper presents a cross platform multilingual multimedia Indian Sign Language (ISL) dictionary building tool. ISL is a linguistically under-investigated language with no source of well documented electronic data. Research on ISL linguistics also gets hindered due to a lack of ISL knowledge and the unavailability of any educational tools. Our system can be used to associate signs corresponding to a given text. The current system also facilitates the phonological annotation of Indian signs in the form of HamNoSys structure. The generated HamNoSys string can be given as input to an avatar module to produce an animated sign representation.

Detection and Recognition of Malaysian Special License Plate Based On SIFT Features <=SEP=> Automated car license plate recognition systems are developed and applied for purpose of facilitating the surveillance, law enforcement, access control and intelligent transportation monitoring with least human intervention. In this paper, an algorithm based on SIFT feature points clustering and matching is proposed to address the issue of recognizing Malaysian special plates. These special plates do not follow the normal standard car plates’ format as they may contain italic, cursive, connected and small letters. The algorithm is tested with 150 Malaysian special plate images under different environment and the promising experimental results demonstrate that the proposed algorithm is relatively robust. Keywords-license plate recognition, scale invariant feature transform (SIFT), feature extraction, homography, special license plate.

The quality of online social relationships <=SEP=> Online relationships are less valuable than offline ones. Indeed, their net benefit depends on whether they supplement or substitute for offline social relationships.

Low power and low voltage VT extractor circuit and MOSFET radiation dosimeter <=SEP=> This work discusses two fundamental blocks of an in vivo MOS dosimeter, namely the radiation sensor and the VT-extractor circuit. It is shown that threshold extractor circuits based on an all-region MOSFET model are very appropriate for low power design. The accuracy of the extractor circuits allows using the PMOS transistors of the integrated circuit CD4007 as the radiation sensor in a dosimeter for radiotherapy applications.

Control of hybrid AC/DC microgrid involving energy storage, renewable energy and pulsed loads <=SEP=> This paper proposes the coordinated control of a hybrid AC/DC power system with renewable energy source, energy storages and critical loads. The hybrid microgrid consists of both AC and DC sides. A synchronous generator and a PV farm supply power to the system's AC and DC sides, respectively. A bidirectional fully controlled AC/DC converter with active and reactive power decoupling technique is used to link the AC bus with the DC bus while regulating the system voltage and frequency. A DC/DC boost converter with a maximum power point tracking (MPPT) function is implemented to maximize the energy generation from the PV farm. Current controlled bidirectional DC/DC converters are applied to connect each lithium-ion battery bank to the DC bus. Lithium-ion battery banks act as energy storage devices that serve to increase the system stability by absorbing or injecting power to the grid as ancillary services. The proposed system can function in both grid-connected mode and islanding mode. Power electronic converters with different control strategies are analyzed in both modes in detail. Simulation results in MATLAB Simulink verify that the proposed topology is coordinated for power management in both AC and DC sides under critical loads with high efficiency, reliability, and robustness under both grid-connected and islanding modes.

Does the Tyndall effect describe the blue hue periodically observed in subdermal hyaluronic acid gel placement? <=SEP=> BACKGROUND
The blue hue of skin overlying injected hyaluronic acid (HA) fillers in certain cases has been hypothesized in the literature as related to the Tyndall effect. This investigation aims to understand the relevant optical concepts and to discuss the plausibility of this assertion.


METHODS
Theoretic and physical aspects of relevant optical theories including the Tyndall effect, the Raleigh criterion and the Mie Solution are discussed, with simple examples. The physical properties of the system (both HA and subcutaneous tissue) are explored. Alternate concepts of dermal hue generation are discussed.


RESULTS
The Tyndall effect (and Rayleigh criterion) describe optical phenomenon that occur as light passes through colloidal solutions containing uniform spherical particles of sizes less than the length of a wavelength of visible light. HA fillers are complex, large, non-spherical, cross-linked hydrogels, and thus are not well characterized by these theories.Skin is a complex optical surface in which shorter wavelengths of light are selectively filtered at superficial depths. Light passing through to subdermal HA would have low blue light amplitude, minimizing what light could be preferentially scattered. Further, should blue hues be 'generated' subdermally, the same skin filters work in reverse, making the blue light poorly detectable by an external observer.


CONCLUSIONS
The Tyndall effect is unlikely to cause dermal hue changes in HA filler instillation. Optical and perceptual processes explaining superficial vein coloration may better describe subdermal HA hue changes. Vein coloration is thought to be related to three processes: the reflective properties of the skin, the absorptive properties of blood and the perceptive properties of an observer's eyes. Subdermal HA may simulate these phenomena by a number of undetermined, yet plausible mechanisms.

Character-Based Text Classification using Top Down Semantic Model for Sentence Representation <=SEP=> Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.

Treating the Banana Fold with the Dermotuberal Anchorage Technique: Case Report <=SEP=> The banana fold, or the infragluteal fold, is a fat deposit on the posterior thigh close to the gluteal crease and parallel to it. A banana fold may form for different reasons, among which an iatrogenic cause is recurrent. Although banana fold is a common problem, unrelished by most women, few procedures are targeted specifically to fight it. This report presents a severe case of iatrogenic banana fold corrected by a modification of the dermotuberal anchorage buttock-lifting technique, as reported by the author for gluteal ptosis. The operation is performed by tucking in part of the banana fold tissue caudal to the gluteal crease, sliding that tissue, after depithelization, under the buttock, and pulling it up toward the ischial tuberosity until the redundant skin on the posterior thigh is tight and the banana fold is reduced. Assessment of the results 1 year after surgery showed that the technique provided a good scar kept within the subgluteal crease, and that it satisfactorily corrected the patient’s major complaint: the banana-shaped fold.

Sliding spotlight SAR processing for TerraSAR-X using a new formulation of the extended chirp scaling algorithm <=SEP=> This paper describes the sliding spotlight algorithm and the processing strategy to be applied for TerraSAR-X. The steering spotlight geometry is analysed. Analysis of scene size and resolution demonstrates the particularities of this mode. The Doppler frequencies for individual targets and for a whole sliding spotlight scene are analyzed and the result shows the applicability of azimuth subaperture processing to sliding spotlight data. A description of the Extended Chirp Scaling Algorithm for sliding spotlight is presented.

Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling <=SEP=> Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE [1], TransH [2], and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate corrupted triplets during negative sampling, which significantly improves the training time and performance of TransR. The proposed approach can be applied to other translationbased models.

A survey of wireless sensor technologies applied to precision agriculture <=SEP=> This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture (PA). The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are (i) scalar wireless sensor networks, (ii) wireless multimedia sensor networks, (iii) Mobility of nodes, (iv) tag-based systems, and (v) smart-phone applications.

Neural Clinical Paraphrase Generation with Attention <=SEP=> Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoderdecoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.

ViBe: A Disruptive Method for Background Subtraction <=SEP=> The proliferation of the video surveillance cameras, which accounts for the vast majority of cameras worldwide, has resulted in the need to nd methods and algorithms for dealing with the huge amount of information that is gathered every second. This encompasses processing tasks, such as raising an alarm or detouring moving objects, as well as some semantic tasks like event monitoring, trajectory or ow analysis, counting people, etc.

A Large Scale Ranker-Based System for Search Query Spelling Correction <=SEP=> This paper makes three significant extensions to a noisy channel speller designed for standard written text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. Second, a distributed infrastructure is proposed for training and applying Web scale n-gram language models. Third, a new phrase-based error model is presented. This model places a probability distribution over transformations between multi-word phrases, and is estimated using large amounts of query-correction pairs derived from search logs. Experiments show that each of these extensions leads to significant improvements over the stateof-the-art baseline methods.

Unifying Logical and Statistical AI <=SEP=> Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to a wide variety of problems in natural language understanding, vision, computational biology, social networks and others, and is the basis of the open-source Alchemy system.

The role of autophagy in cancer development and response to therapy <=SEP=> Autophagy is a process in which subcellular membranes undergo dynamic morphological changes that lead to the degradation of cellular proteins and cytoplasmic organelles. This process is an important cellular response to stress or starvation. Many studies have shed light on the importance of autophagy in cancer, but it is still unclear whether autophagy suppresses tumorigenesis or provides cancer cells with a rescue mechanism under unfavourable conditions. What is the present state of our knowledge about the role of autophagy in cancer development, and in response to therapy? And how can the autophagic process be manipulated to improve anticancer therapeutics?

Optical Character Recognition for Cursive Handwriting <=SEP=> ÐIn this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, Hidden Markov Model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in HMM training stage together with the estimation of the HMM model parameters. Finally, the lexicon information and HMM ranks are combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments in dicate higher recognition rates compared to the available methods reported in the literature. Index TermsÐHandwritten word recognition, preprocessing, segmentation, optical character recognition, cursive handwriting, hidden Markov model, search, graph, lexicon matching.

IGLOO: Slicing the Features Space to Represent Long Sequences <=SEP=> Up until recently Recurrent neural networks (RNNs) have been the standard go-to component when processing sequential data with neural networks. Issues relative to vanishing gradient have been partly addressed by Long short-term memory (LSTM) and gated recurrent unit (GRU), but in practice experiments show that very long terms dependencies (beyond 1000 time steps) are difficult to learn. We introduce IGLOO, a new neural network architecture which aims at being faster than both LSTM and GRU but also their respective CuDNN optimized versions when convergence happens and provide an alternative in the case when there is no convergence at all. IGLOO’s core idea is to use the relationships between patches sliced out of the features maps of convolution layers at different levels of granularity to build a representation for the sequence. We show that the model can deal with dependencies of more than 25,000 steps in a reasonable time frame. Beyond the well bench-marked copy-memory and addition problems which show good results, we also achieve best recorded accuracy on permuted MNIST (98.4%). IGLOO is also applied on the IMDB set and on the TUH Abnormal EEG Corpus.

Wafer level fabrication method of hemispherical reflector coupled micro-led array stimulator for optogenetics <=SEP=> We report a monolithic wafer-level fabrication method for a hemispherical reflector coupled light-emitting-diode (LED) array using isotropic etching of silicon. These neural stimulators collect the backside as well as the front side emission of the μ-LEDs and thus provide higher intensity, which is imperative for opsin expressions in optogenetics experiments. Aluminum was used as the reflective layer and the planarization of polymer on the reflector cavity was done using polydimethylsiloxane (PDMS). The lateral and vertical profiles of silicon etching were measured and the light intensity increase due to the reflector was investigated. It was found that the intensity increases by a minimum of 49% and maximum of 65% when coupling a reflector with the μ-LEDs.

Improved extraction of vegetable oils under high-intensity ultrasound and/or microwaves. <=SEP=> Ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) techniques have been employed as complementary techniques to extract oils from vegetable sources, viz, soybean germ and a cultivated marine microalga rich in docosahexaenoic acid (DHA). Ultrasound (US) devices developed by ourselves, working at several frequencies (19, 25, 40 and 300 kHz), were used for US-based protocols, while a multimode microwave (MW) oven (operating with both open and closed vessels) was used for MAE. Combined treatments were also studied, such as simultaneous double sonication (at 19 and 25 kHz) and simultaneous US/MW irradiation, achieved by inserting a non-metallic horn in a MW oven. Extraction times and yields were compared with those resulting from conventional procedures. With soybean germ the best yield was obtained with a 'cavitating tube' prototype (19 kHz, 80 W), featuring a thin titanium cylinder instead of a conventional horn. Double sonication, carried out by inserting an immersion horn (25 kHz) in the same tube, improved the yield only slightly but halved the extraction time. Almost comparable yields were achieved by closed-vessel MAE and simultaneous US/MW irradiation. Compared with conventional methods, extraction times were reduced by up to 10-fold and yields increased by 50-500%. In the case of marine microalgae, UAE worked best, as the disruption by US of the tough algal cell wall considerably improved the extraction yield from 4.8% in soxhlet to 25.9%. Our results indicate that US and MW, either alone or combined, can greatly improve the extraction of bioactive substances, achieving higher efficiency and shorter reaction times at low or moderate costs, with minimal added toxicity.

The Complexity of Temporal Logic Model Checking <=SEP=> Temporal logic. Logical formalisms for reasoning about time and the timing of events appear in several fields: physics, philosophy, linguistics, etc. Not surprisingly, they also appear in computer science, a field where logic is ubiquitous. Here temporal logics are used in automated reasoning, in planning, in semantics of programming languages, in artificial intelligence, etc. There is one area of computer science where temporal logic has been unusually successful: the specification and verification of programs and systems, an area we shall just call “programming” for simplicity. In today’s curricula, thousands of programmers first learn about temporal logic in a course on model checking! Temporal logic and programming. Twenty five years ago, Pnueli identified temporal logic as a very convenient formal language in which to state, and reason about, the behavioral properties of parallel programs and more generally reactive systems [Pnu77, Pnu81]. Indeed, correctness for these systems typically involves reasoning upon related events at different moments of a system execution [OL82]. Furthermore, when it comes to liveness properties, the expected behavior of reactive systems cannot be stated as a static property, or as an invariant one. Finally, temporal logic is well suited to expressing the whole variety of fairness properties that play such a prominent role in distributed systems [Fra86]. For these applications, one usually restricts oneself to propositional temporal logic: on the one hand, this does not appear to be a severe limitation in practice, and on the other hand, this restriction allows decision procedures for validity and entailment, so that, at least in principle, the above-mentioned reasoning can be automated. Model checking. Generally speaking, model checking is the algorithmic verification that a given logic formula holds in a given structure (the model

Stereo-Based Autonomous Navigation and Obstacle Avoidance* <=SEP=> This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.

Practical electrical parameter aware methodology for analog designers with emphasis on LDE aware for devices <=SEP=> The device layout structure has proven to have profound effects to its electrical characteristics for advanced technology nodes, which, if not taken into account during the design cycle, will have devastating impact to the circuit functionality. A new design methodology is presented in this paper, which can help circuit designers identify early in the design stage the performance implication due to shift of critical device instance parameters from its layout.

Understanding Mutual Information and its Use in InfoGAN <=SEP=> Interpretable variables are useful in generative models. Generative Adversarial Networks (GANs) are generative models that are flexible in their input. The Information Maximizing GAN (InfoGAN) ties the output of the generator to a component of its input called the latent codes. By forcing the output to be tied to this input component, we can control some properties of the output representation. It is notoriously difficult to find the Nash equilibrium when jointly training the discriminator and generator in a GAN. We uncover some successful and unsuccessful configurations for generating images using InfoGAN.