Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies. © 2012 Springer-Verlag.

Decoding semantics across fMRI sessions with different stimulus modalities:a practical MVPA study

Both embodied and symbolic accounts of conceptual organization would predict partial sharing and partial differentiation between the neural activations seen for concepts activated via different stimulus modalities. But cross-participant and cross-session variability in BOLD activity patterns makes analyses of such patterns with MVPA methods challenging. Here, we examine the effect of cross-modal and individual variation on the machine learning analysis of fMRI data recorded during a word property generation task. We present the same set of living and non-living concepts (land-mammals, or work tools) to a cohort of Japanese participants in two sessions: the first using auditory presentation of spoken words; the second using visual presentation of words written in Japanese characters. Classification accuracies confirmed that these semantic categories could be detected in single trials, with within-session predictive accuracies of 80-90%. However cross-session prediction (learning from auditory-task data to classify data from the written-word-task, or vice versa) suffered from a performance penalty, achieving 65-75% (still individually significant at p « 0.05). We carried out several follow-on analyses to investigate the reason for this shortfall, concluding that distributional differences in neither time nor space alone could account for it. Rather, combined spatio-temporal patterns of activity need to be identified for successful cross-session learning, and this suggests that feature selection strategies could be modified to take advantage of this.

Enabling Complexity-Performance Trade-Offs for Successive Cancellation Decoding of Polar Codes

Polar codes are one of the most recent advancements in coding theory and they have attracted significant interest. While they are provably capacity achieving over various channels, they have seen limited practical applications. Unfortunately, the successive nature of successive cancellation based decoders hinders fine-grained adaptation of the decoding complexity to design constraints and operating conditions. In this paper, we propose a systematic method for enabling complexity-performance trade-offs by constructing polar codes based on an optimization problem which minimizes the complexity under a suitably defined mutual information based performance constraint. Moreover, a low-complexity greedy algorithm is proposed in order to solve the optimization problem efficiently for very large code lengths.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Wireless data encoding and decoding using OAM modes

A means of encoding and decoding data using wireless orbital angular momentum (OAM) modes is proposed and analysed. Source data symbols are used to select an OAM mode, which is generated using an 8-element circular array. A 2-element array is used to detect the mode by estimating the phase gradient of the received signal, and hence identifying the transmitted data symbol. The results are presented in terms of mode estimation error.

Development of new approaches for the synthesis and decoding of one-bead one-compound cyclic peptide libraries

La plupart des processus cellulaires et biologiques reposent, à un certain niveau, sur des interactions protéine-protéine (IPP). Leur manipulation avec des composés chimiques démontre un grand potentiel pour la découverte de nouveaux médicaments. Malgré la demande toujours croissante en molécules capables d’interrompre sélectivement des IPP, le développement d’inhibiteurs d’IPP est fortement limité par la grande taille de la surface d’interaction. En considérant la nature de cette surface, la capacité à mimer des structures secondaires de protéines est très importante pour lier une protéine et inhiber une IPP. Avec leurs grandes capacités peptidomimétiques et leurs propriétés pharmacologiques intéressan-tes, les peptides cycliques sont des prototypes moléculaires de choix pour découvrir des ligands de protéines et développer de nouveaux inhibiteurs d’IPP. Afin d’exploiter pleinement la grande diversité accessible avec les peptides cycliques, l’approche combinatoire «one-bead-one-compound» (OBOC) est l’approche la plus accessible et puissante. Cependant, l’utilisation des peptides cycliques dans les chimiothèques OBOC est limitée par les difficultés à séquencer les composés actifs après le criblage. Sans amine libre en N-terminal, la dégradation d’Edman et la spectrométrie de masse en tandem (MS/MS) ne peuvent pas être utilisées. À cet égard, nous avons développé de nouvelles approches par ouverture de cycle pour préparer et décoder des chimiothèques OBOC de peptides cycliques. Notre stratégie était d’introduire un résidu sensible dans le macrocycle et comme ancrage pour permettre la linéarisation des peptides et leur largage des billes pour le séquençage par MS/MS. Tout d’abord, des résidus sensibles aux nucléophiles, aux ultraviolets ou au bromure de cyanogène ont été introduits dans un peptide cyclique et leurs rendements de clivage évalués. Ensuite, les résidus les plus prometteurs ont été utilisés dans la conception et le développement d’approches en tandem ouverture de cycle / clivage pour le décodage de chimiothèques OBOC de peptides cycliques. Dans la première approche, une méthionine a été introduite dans le macrocycle comme ancrage pour simultanément permettre l’ouverture du cycle et le clivage des billes par traitement au bromure de cyanogène. Dans la seconde approche, un résidu photosensible a été utilisé dans le macrocycle comme ancrage pour permettre l’ouverture du cycle et le clivage suite à une irradiation aux ultraviolets. Le peptide linéaire généré par ces approches peut alors être efficacement séquencé par MS/MS. Enfin, une chimiothèque OBOC a été préparée et criblée la protéine HIV-1 Nef pour identifier des ligands sélectifs. Le développement de ces méthodologies permttra l’utilisation de composés macrocycliques dans les chimiothèques OBOC et constitue une contribution importante en chimie médicinale pour la découverte de ligands de protéines et le développement d’inhibiteurs d’IPP.

The modified Max-Log-MAP turbo decoding algorithm by extrinsic information scaling for wireless applications

The iterative nature of turbo-decoding algorithms increases their complexity compare to conventional FEC decoding algorithms. Two iterative decoding algorithms, Soft-Output-Viterbi Algorithm (SOVA) and Maximum A posteriori Probability (MAP) Algorithm require complex decoding operations over several iteration cycles. So, for real-time implementation of turbo codes, reducing the decoder complexity while preserving bit-error-rate (BER) performance is an important design consideration. In this chapter, a modification to the Max-Log-MAP algorithm is presented. This modification is to scale the extrinsic information exchange between the constituent decoders. The remainder of this chapter is organized as follows: An overview of the turbo encoding and decoding processes, the MAP algorithm and its simplified versions the Log-MAP and Max-Log-MAP algorithms are presented in section 1. The extrinsic information scaling is introduced, simulation results are presented, and the performance of different methods to choose the best scaling factor is discussed in Section 2. Section 3 discusses trends and applications of turbo coding from the perspective of wireless applications.

Parallel decoding of turbo codes using multi-point trellis termination and collision-free interleavers

The UMTS turbo encoder is composed of parallel concatenation of two Recursive Systematic Convolutional (RSC) encoders which start and end at a known state. This trellis termination directly affects the performance of turbo codes. This paper presents performance analysis of multi-point trellis termination of turbo codes which is to terminate RSC encoders at more than one point of the current frame while keeping the interleaver length the same. For long interleaver lengths, this approach provides dividing a data frame into sub-frames which can be treated as independent blocks. A novel decoding architecture using multi-point trellis termination and collision-free interleavers is presented. Collision-free interleavers are used to solve memory collision problems encountered by parallel decoding of turbo codes. The proposed parallel decoding architecture reduces the decoding delay caused by the iterative nature and forward-backward metric computations of turbo decoding algorithms. Our simulations verified that this turbo encoding and decoding scheme shows Bit Error Rate (BER) performance very close to that of the UMTS turbo coding while providing almost %50 time saving for the 2-point termination and %80 time saving for the 5-point termination.

Increasing the speed of parallel decoding of turbo codes

Turbo codes experience a significant decoding delay because of the iterative nature of the decoding algorithms, the high number of metric computations and the complexity added by the (de)interleaver. The extrinsic information is exchanged sequentially between two Soft-Input Soft-Output (SISO) decoders. Instead of this sequential process, a received frame can be divided into smaller windows to be processed in parallel. In this paper, a novel parallel processing methodology is proposed based on the previous parallel decoding techniques. A novel Contention-Free (CF) interleaver is proposed as part of the decoding architecture which allows using extrinsic Log-Likelihood Ratios (LLRs) immediately as a-priori LLRs to start the second half of the iterative turbo decoding. The simulation case studies performed in this paper show that our parallel decoding method can provide %80 time saving compared to the standard decoding and %30 time saving compared to the previous parallel decoding methods at the expense of 0.3 dB Bit Error Rate (BER) performance degradation.

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

Decoding the canada's foreing and security policy: tracking the strategic and ethicliberal approaches of a middle power's behavior

The article attempts to explain the main paradox faced by Canada at formulating its foreign policy on international security. Explained in economic and political terms, this paradox consists in the contradiction between the Canadian ability to achieve its strategic goals, serving to its own national interest and its dependence on the United States. The first section outlines three representative examples to evaluate this paradox: the Canada’s position in North American security regime, the US-Canada economic security relations, and the universe of possibilities for action of Canada as a middle power. The second section suggests that liberal agenda, especially concerning to ethical issues, has been established by this country to minimize this paradox. By pursing this agenda, Canada is able to reaffirm its national identity and therefore its independence on the United States. The third section evaluates both the explained paradox and the reaffirmation of Canadian identity during the Jean Chrétien (1993-2003), Paul Martin (2003-2006) and Stephen Harper’s (2006) governments.

Decoding emotional prosody in Parkinson's disease and its potential neuropsychological basis

Parkinson's disease patients may have difficulty decoding prosodic emotion cues. These data suggest that the basal ganglia are involved, but may reflect dorsolateral prefrontal cortex dysfunction. An auditory emotional n-back task and cognitive n-back task were administered to 33 patients and 33 older adult controls, as were an auditory emotional Stroop task and cognitive Stroop task. No deficit was observed on the emotion decoding tasks; this did not alter with increased frontal lobe load. However, on the cognitive tasks, patients performed worse than older adult controls, suggesting that cognitive deficits may be more prominent. The impact of frontal lobe dysfunction on prosodic emotion cue decoding may only become apparent once frontal lobe pathology rises above a threshold.