@article{14739, abstract = {Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods.}, author = {Ali, Dashti and Asaad, Aras and Jimenez, Maria-Jose and Nanda, Vidit and Paluzo-Hidalgo, Eduardo and Soriano Trigueros, Manuel}, issn = {1939-3539}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {Applied Mathematics, Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Software}, number = {12}, pages = {14069--14080}, publisher = {IEEE}, title = {{A survey of vectorization methods in topological data analysis}}, doi = {10.1109/tpami.2023.3308391}, volume = {45}, year = {2023}, } @article{14778, abstract = {We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some components to be negative. This standard feature of non-probabilistic termination proofs was hitherto not known to be sound in the probabilistic setting, as the soundness proof requires a careful analysis of the underlying stochastic process. Second, we present polynomial-time algorithms using our generalized LexRSMs for proving a.s. termination in broad classes of linear-arithmetic programs.}, author = {Chatterjee, Krishnendu and Kafshdar Goharshady, Ehsan and Novotný, Petr and Zárevúcky, Jiří and Zikelic, Dorde}, issn = {1433-299X}, journal = {Formal Aspects of Computing}, keywords = {Theoretical Computer Science, Software}, number = {2}, publisher = {Association for Computing Machinery}, title = {{On lexicographic proof rules for probabilistic termination}}, doi = {10.1145/3585391}, volume = {35}, year = {2023}, } @article{10602, abstract = {Transforming ω-automata into parity automata is traditionally done using appearance records. We present an efficient variant of this idea, tailored to Rabin automata, and several optimizations applicable to all appearance records. We compare the methods experimentally and show that our method produces significantly smaller automata than previous approaches.}, author = {Kretinsky, Jan and Meggendorfer, Tobias and Waldmann, Clara and Weininger, Maximilian}, issn = {1432-0525}, journal = {Acta Informatica}, keywords = {computer networks and communications, information systems, software}, pages = {585--618}, publisher = {Springer Nature}, title = {{Index appearance record with preorders}}, doi = {10.1007/s00236-021-00412-y}, volume = {59}, year = {2022}, } @article{12128, abstract = {We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, in addition to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness.}, author = {Poelking, Carl and Faber, Felix A and Cheng, Bingqing}, issn = {2632-2153}, journal = {Machine Learning: Science and Technology}, keywords = {Artificial Intelligence, Human-Computer Interaction, Software}, number = {4}, publisher = {IOP Publishing}, title = {{BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale}}, doi = {10.1088/2632-2153/ac4d11}, volume = {3}, year = {2022}, } @article{12147, abstract = {Continuous-time neural networks are a class of machine learning systems that can tackle representation learning on spatiotemporal decision-making tasks. These models are typically represented by continuous differential equations. However, their expressive power when they are deployed on computers is bottlenecked by numerical differential equation solvers. This limitation has notably slowed down the scaling and understanding of numerous natural physical phenomena such as the dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving the given dynamical system in closed form. This is known to be intractable in general. Here, we show that it is possible to closely approximate the interaction between neurons and synapses—the building blocks of natural and artificial neural networks—constructed by liquid time-constant networks efficiently in closed form. To this end, we compute a tightly bounded approximation of the solution of an integral appearing in liquid time-constant dynamics that has had no known closed-form solution so far. This closed-form solution impacts the design of continuous-time and continuous-depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for complex numerical solvers. Consequently, we obtain models that are between one and five orders of magnitude faster in training and inference compared with differential equation-based counterparts. More importantly, in contrast to ordinary differential equation-based continuous networks, closed-form networks can scale remarkably well compared with other deep learning instances. Lastly, as these models are derived from liquid networks, they show good performance in time-series modelling compared with advanced recurrent neural network models.}, author = {Hasani, Ramin and Lechner, Mathias and Amini, Alexander and Liebenwein, Lucas and Ray, Aaron and Tschaikowski, Max and Teschl, Gerald and Rus, Daniela}, issn = {2522-5839}, journal = {Nature Machine Intelligence}, keywords = {Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Human-Computer Interaction, Software}, number = {11}, pages = {992--1003}, publisher = {Springer Nature}, title = {{Closed-form continuous-time neural networks}}, doi = {10.1038/s42256-022-00556-7}, volume = {4}, year = {2022}, } @article{9234, abstract = {In this paper, we present two new inertial projection-type methods for solving multivalued variational inequality problems in finite-dimensional spaces. We establish the convergence of the sequence generated by these methods when the multivalued mapping associated with the problem is only required to be locally bounded without any monotonicity assumption. Furthermore, the inertial techniques that we employ in this paper are quite different from the ones used in most papers. Moreover, based on the weaker assumptions on the inertial factor in our methods, we derive several special cases of our methods. Finally, we present some experimental results to illustrate the profits that we gain by introducing the inertial extrapolation steps.}, author = {Izuchukwu, Chinedu and Shehu, Yekini}, issn = {1572-9427}, journal = {Networks and Spatial Economics}, keywords = {Computer Networks and Communications, Software, Artificial Intelligence}, number = {2}, pages = {291--323}, publisher = {Springer Nature}, title = {{New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity}}, doi = {10.1007/s11067-021-09517-w}, volume = {21}, year = {2021}, } @inproceedings{10108, abstract = {We argue that the time is ripe to investigate differential monitoring, in which the specification of a program's behavior is implicitly given by a second program implementing the same informal specification. Similar ideas have been proposed before, and are currently implemented in restricted form for testing and specialized run-time analyses, aspects of which we combine. We discuss the challenges of implementing differential monitoring as a general-purpose, black-box run-time monitoring framework, and present promising results of a preliminary implementation, showing low monitoring overheads for diverse programs.}, author = {Mühlböck, Fabian and Henzinger, Thomas A}, booktitle = {International Conference on Runtime Verification}, isbn = {978-3-030-88493-2}, issn = {1611-3349}, keywords = {run-time verification, software engineering, implicit specification}, location = {Virtual}, pages = {231--243}, publisher = {Springer Nature}, title = {{Differential monitoring}}, doi = {10.1007/978-3-030-88494-9_12}, volume = {12974}, year = {2021}, } @article{10191, abstract = {In this work we solve the algorithmic problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf, respectively. For an execution of n events over k threads and d variables, we establish novel bounds that scale as nk+1 for TSO and as nk+1· min(nk2, 2k· d) for PSO. Moreover, based on our solution to these problems, we develop an SMC algorithm under TSO and PSO that uses the RF equivalence. The algorithm is exploration-optimal, in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when k is bounded. Finally, we implement all our algorithms in the SMC tool Nidhugg, and perform a large number of experiments over benchmarks from existing literature. Our experimental results show that our algorithms for VTSO-rf and VPSO-rf provide significant scalability improvements over standard alternatives. Moreover, when used for SMC, the RF partitioning is often much coarser than the standard Shasha-Snir partitioning for TSO/PSO, which yields a significant speedup in the model checking task. }, author = {Bui, Truc Lam and Chatterjee, Krishnendu and Gautam, Tushar and Pavlogiannis, Andreas and Toman, Viktor}, issn = {2475-1421}, journal = {Proceedings of the ACM on Programming Languages}, keywords = {safety, risk, reliability and quality, software}, number = {OOPSLA}, publisher = {Association for Computing Machinery}, title = {{The reads-from equivalence for the TSO and PSO memory models}}, doi = {10.1145/3485541}, volume = {5}, year = {2021}, } @misc{9946, abstract = {We argue that the time is ripe to investigate differential monitoring, in which the specification of a program's behavior is implicitly given by a second program implementing the same informal specification. Similar ideas have been proposed before, and are currently implemented in restricted form for testing and specialized run-time analyses, aspects of which we combine. We discuss the challenges of implementing differential monitoring as a general-purpose, black-box run-time monitoring framework, and present promising results of a preliminary implementation, showing low monitoring overheads for diverse programs.}, author = {Mühlböck, Fabian and Henzinger, Thomas A}, issn = {2664-1690}, keywords = {run-time verification, software engineering, implicit specification}, pages = {17}, publisher = {IST Austria}, title = {{Differential monitoring}}, doi = {10.15479/AT:ISTA:9946}, year = {2021}, } @article{10861, abstract = {We introduce in this paper AMT2.0, a tool for qualitative and quantitative analysis of hybrid continuous and Boolean signals that combine numerical values and discrete events. The evaluation of the signals is based on rich temporal specifications expressed in extended signal temporal logic, which integrates timed regular expressions within signal temporal logic. The tool features qualitative monitoring (property satisfaction checking), trace diagnostics for explaining and justifying property violations and specification-driven measurement of quantitative features of the signal. We demonstrate the tool functionality on several running examples and case studies, and evaluate its performance.}, author = {Nickovic, Dejan and Lebeltel, Olivier and Maler, Oded and Ferrere, Thomas and Ulus, Dogan}, issn = {1433-2787}, journal = {International Journal on Software Tools for Technology Transfer}, keywords = {Information Systems, Software}, number = {6}, pages = {741--758}, publisher = {Springer Nature}, title = {{AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic}}, doi = {10.1007/s10009-020-00582-z}, volume = {22}, year = {2020}, } @inproceedings{10190, abstract = {The verification of concurrent programs remains an open challenge, as thread interaction has to be accounted for, which leads to state-space explosion. Stateless model checking battles this problem by exploring traces rather than states of the program. As there are exponentially many traces, dynamic partial-order reduction (DPOR) techniques are used to partition the trace space into equivalence classes, and explore a few representatives from each class. The standard equivalence that underlies most DPOR techniques is the happens-before equivalence, however recent works have spawned a vivid interest towards coarser equivalences. The efficiency of such approaches is a product of two parameters: (i) the size of the partitioning induced by the equivalence, and (ii) the time spent by the exploration algorithm in each class of the partitioning. In this work, we present a new equivalence, called value-happens-before and show that it has two appealing features. First, value-happens-before is always at least as coarse as the happens-before equivalence, and can be even exponentially coarser. Second, the value-happens-before partitioning is efficiently explorable when the number of threads is bounded. We present an algorithm called value-centric DPOR (VCDPOR), which explores the underlying partitioning using polynomial time per class. Finally, we perform an experimental evaluation of VCDPOR on various benchmarks, and compare it against other state-of-the-art approaches. Our results show that value-happens-before typically induces a significant reduction in the size of the underlying partitioning, which leads to a considerable reduction in the running time for exploring the whole partitioning.}, author = {Chatterjee, Krishnendu and Pavlogiannis, Andreas and Toman, Viktor}, booktitle = {Proceedings of the 34th ACM International Conference on Object-Oriented Programming, Systems, Languages, and Applications}, issn = {2475-1421}, keywords = {safety, risk, reliability and quality, software}, location = {Athens, Greece}, publisher = {ACM}, title = {{Value-centric dynamic partial order reduction}}, doi = {10.1145/3360550}, volume = {3}, year = {2019}, } @article{10396, abstract = {Stimfit is a free cross-platform software package for viewing and analyzing electrophysiological data. It supports most standard file types for cellular neurophysiology and other biomedical formats. Its analysis algorithms have been used and validated in several experimental laboratories. Its embedded Python scripting interface makes Stimfit highly extensible and customizable.}, author = {Schlögl, Alois and Jonas, Peter M and Schmidt-Hieber, C. and Guzman, S. J.}, issn = {1862-278X}, journal = {Biomedical Engineering / Biomedizinische Technik}, keywords = {biomedical engineering, data analysis, free software}, location = {Graz, Austria}, number = {SI-1-Track-G}, publisher = {De Gruyter}, title = {{Stimfit: A fast visualization and analysis environment for cellular neurophysiology}}, doi = {10.1515/bmt-2013-4181}, volume = {58}, year = {2013}, }