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1. Learning energy-based spatial-temporal generative ConvNets for dynamic patterns

   https://doi.org/10.1109/TPAMI.2019.2934852  

   Xie, J; Zhu, S. C.; Wu, Y. N. (January 2021, IEEE transactions on pattern analysis and machine intelligence)

   Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales. The model can be learned from the training video sequences by an “analysis by synthesis” learning algorithm that iterates the following two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters based on the difference between the synthesized video sequences and the observed training sequences. We show that the learning algorithm can synthesize realistic dynamic patterns. We also show that it is possible to learn the model from incomplete training sequences with either occluded pixels or missing frames, so that model learning and pattern completion can be accomplished simultaneously. 

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2. The owl sensor: a ‘fragile’ DNA nanostructure for the analysis of single nucleotide variations

   https://doi.org/10.1039/c8nr01107a  

   Karadeema, Rebekah J.; Stancescu, Maria; Steidl, Tyler P.; Bertot, Sophia C.; Kolpashchikov, Dmitry M. (January 2018, Nanoscale)

   Analysis of single nucleotide variations (SNVs) in DNA and RNA sequences is instrumental in healthcare for the detection of genetic and infectious diseases and drug-resistant pathogens. Here we took advantage of the developments in DNA nanotechnology to design a hybridization sensor, named the ‘owl sensor’, which produces a fluorescence signal only when it complexes with fully complementary DNA or RNA analytes. The novelty of the owl sensor operation is that the selectivity of analyte recognition is, at least in part, determined by the structural rigidity and stability of the entire DNA nanostructure rather than exclusively by the stability of the analyte–probe duplex, as is the case for conventional hybridization probes. Using two DNA and two RNA analytes we demonstrated that owl sensors differentiate SNVs in a wide temperature range of 5 °C–32 °C, a performance unachievable by conventional hybridization probes including the molecular beacon probe. The owl sensor reliably detects cognate analytes even in the presence of 100 times excess of single base mismatched sequences. The approach, therefore, promises to add to the toolbox for the diagnosis of SNVs at ambient temperatures. 

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3. Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation

   https://doi.org/10.3390/biom15081079  

   Wu, Xuantai; Wang, Kui; Hu, Gang; Kurgan, Lukasz (August 2025, Biomolecules)

   Phase separation processes facilitate the formation of membrane-less organelles and involve interactions within structured domains and intrinsically disordered regions (IDRs) in protein sequences. The literature suggests that the involvement of proteins in phase separation can be predicted from their sequences, leading to the development of over 30 computational predictors. We focused on intrinsic disorder due to its fundamental role in related diseases, and because recent analysis has shown that phase separation can be accurately predicted for structured proteins. We evaluated eight representative amino acid-level predictors of phase separation, capable of identifying phase-separating IDRs, using a well-annotated, low-similarity test dataset under two complementary evaluation scenarios. Several methods generate accurate predictions in the easier scenario that includes both structured and disordered sequences. However, we demonstrate that modern disorder predictors perform equally well in this scenario by effectively differentiating phase-separating IDRs from structured regions. In the second, more challenging scenario—considering only predictions in disordered regions—disorder predictors underperform, and most phase separation predictors produce only modestly accurate results. Moreover, some predictors are broadly biased to classify disordered residues as phase-separating, which results in low predictive performance in this scenario. Finally, we recommend PSPHunter as the most accurate tool for identifying phase-separating IDRs in both scenarios. 

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4. Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution

   Kashgarani, Haniye; Kotthoff, Lars (January 2021, Proceedings of Machine Learning Research)

   null (Ed.)

   For many practical problems, there is more than one algorithm or approach to solve them. Such algorithms often have complementary performance – where one fails, another performs well, and vice versa. Per-instance algorithm selection leverages this by employing portfolios of complementary algorithms to solve sets of difficult problems, choosing the most appropriate algorithm for each problem instance. However, this requires complex models to effect this selection and introduces overhead to compute the data needed for those models. On the other hand, even basic hardware is more than capable of running several algorithms in parallel. We investigate the tradeoff between selecting a single algorithm and running multiple in parallel and incurring a slowdown because of contention for shared resources. Our results indicate that algorithm selection is worth it, especially for large portfolios. 

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5. Extrapolating the Arnoldi Algorithm to Improve Eigenvector Convergence

   Pollock, S.; Scott, L. R. (August 2021, International journal of numerical analysis and modeling)

   We consider extrapolation of the Arnoldi algorithm to accelerate computation of the dominant eigenvalue/eigenvector pair. The basic algorithm uses sequences of Krylov vectors to form a small eigenproblem which is solved exactly. The two dominant eigenvectors output from consecutive Arnoldi steps are then recombined to form an extrapolated iterate, and this accelerated iterate is used to restart the next Arnoldi process. We present numerical results testing the algorithm on a variety of cases and find on most examples it substantially improves the performance of restarted Arnoldi. The extrapolation is a simple post-processing step which has minimal computational cost. 

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