Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

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This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals. The performance of the existing compressed sensing reconstruction algorithms have not been investigated yet for the discrete multi-coset sampling. We compare the following algorithms-orthogonal matching pursuit, multiple signal classification, subspace-augmented multiple signal classification, focal under-determined system solver and basis pursuit denoising. The comparison is performed via numerical simulations for different sampling conditions. According to the simulations, focal under-determined system solver outperforms all other algorithms for signals with low signal-to-noise ratio. In other cases, the multiple signal classification algorithm is more beneficial.

We present a new design method for robust low-delay coding of auto-regressive (AR) sources for transmission across erasure channels. The method is based on Linear Predictive Coding (LPC) with Kalman estimation at the decoder. The method designs the encoder and decoder offline through an iterative algorithm based on minimization of the trace of the decoder state error covariance. The design method applies to stationary AR sources of any order. Simulation results show considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of Signal-to-Noise Ratio (SNR) compared to the same coding framework optimized for no loss. We furthermore investigate the impact on decoding performance when channel losses are correlated. We find that the method still provides substantial improvements in this case despite being designed for i.i.d. losses.